From e5cfefbb108626abebab203ae33c54212533ba10 Mon Sep 17 00:00:00 2001 From: David Gable Date: Wed, 1 Jul 2026 19:32:26 -0700 Subject: [PATCH 01/19] Ran pre-commit. Updated pyproject.toml and Github Actions. --- .github/workflows/publish.yml | 89 +- .gitignore | 45 +- .pre-commit-config.yaml | 38 + .python-version | 1 + alternative_tag_selectors.py | 336 +- custom_handler.py | 1 + gallery/gallery.py | 253 +- implementation_recommendation.py | 81 +- improved_tag_selection.py | 77 +- plotly_examples/histogram_1.py | 901 +++-- plotly_examples/sensitivity_1.py | 829 +++-- pyproject.toml | 207 +- sample_hierarchical_data.py | 190 +- scripts/gen_ref_pages.py | 2 +- src/.DS_Store | Bin 6148 -> 0 bytes src/trendify/api/api.py | 30 +- src/trendify/api/base/data_product.py | 14 +- src/trendify/api/base/helpers.py | 12 +- src/trendify/api/base/pen.py | 13 +- src/trendify/api/formats/format2d.py | 22 +- src/trendify/api/formats/table.py | 6 +- .../api/generator/data_product_collection.py | 67 +- .../api/generator/data_product_generator.py | 3 +- src/trendify/api/generator/histogrammer.py | 11 +- src/trendify/api/generator/table_builder.py | 15 +- src/trendify/api/generator/xy_data_plotter.py | 25 +- src/trendify/api/plotting/axline.py | 8 +- src/trendify/api/plotting/histogram.py | 11 +- src/trendify/api/plotting/plotting.py | 20 +- src/trendify/api/plotting/point.py | 1 + src/trendify/api/plotting/trace.py | 17 +- src/trendify/api/styling/grid.py | 5 +- src/trendify/api/styling/legend.py | 4 +- src/trendify/api/styling/marker.py | 4 + src/trendify/{CLI.py => cli.py} | 0 src/trendify/examples.py | 6 +- src/trendify/gallery.py | 4 +- src/trendify/local_server.py | 9 +- src/trendify/plotly_dashboard.py | 20 +- src/trendify/production_server.py | 11 +- src/trendify/streamlit.py | 33 +- test.py | 39 + tests/unittests.py | 1 - uv.lock | 3211 +++++++++++++++++ 44 files changed, 5227 insertions(+), 1445 deletions(-) create mode 100644 .pre-commit-config.yaml create mode 100644 .python-version delete mode 100644 src/.DS_Store rename src/trendify/{CLI.py => cli.py} (100%) create mode 100644 test.py create mode 100644 uv.lock diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 45ad880..78f9c77 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -2,35 +2,82 @@ name: Publish Package on: push: - tags: - - 'v*' # Trigger on version tags (v0.1.0, v1.0.0, etc.) + branches: ["main"] # Run tests on every push to main + tags: ["v*"] # Run tests AND publish on tags + pull_request: + branches: ["main"] # Run tests on PRs -# Add this permissions block permissions: contents: read - id-token: write # This is needed for PyPI's trusted publishing + id-token: write jobs: - build-and-publish: + test: + name: "Test (Python ${{ matrix.python-version }})" runs-on: ubuntu-latest - environment: pypi + strategy: + matrix: + python-version: ["3.11", "3.12", "3.13", "3.14"] steps: - - uses: actions/checkout@v3 - - - name: Set up Python - uses: actions/setup-python@v4 + - name: Checkout + uses: actions/checkout@v4 + + - name: Install uv + uses: astral-sh/setup-uv@v5 + with: + enable-cache: true + + - name: Install Python ${{ matrix.python-version }} + run: uv python install ${{ matrix.python-version }} + + - name: Run unit tests + run: uv run --python ${{ matrix.python-version }} --group dev pytest + + publish: + name: "Build and Publish" + needs: [test] + # ONLY run this job if the push was actually a tag + if: startsWith(github.ref, 'refs/tags/v') + runs-on: ubuntu-latest + environment: + name: pypi + permissions: + id-token: write # Required for PyPI Trusted Publishing + contents: write # Required to create GitHub Release and upload assets + steps: + - name: Checkout + uses: actions/checkout@v6 with: - python-version: '3.11' - - - name: Install build dependencies + # Fetch all history so we can see other branches + fetch-depth: 0 + + - name: Verify Tag is on Main + # Only perform this check if we were triggered by a tag + if: startsWith(github.ref, 'refs/tags/v') run: | - python -m pip install --upgrade pip - pip install build - - - name: Build package - run: python -m build - + # Check if 'main' branch contains the current commit + if ! git branch -r --contains ${{ github.sha }} | grep -q "origin/main"; then + echo "::error::Tag ${{ github.ref_name }} was pushed on a non-main branch. Skipping workflow." + exit 1 + fi + + - name: Install uv + uses: astral-sh/setup-uv@v7 + + - name: Install Python 3.14 + run: uv python install 3.14 + + - name: Build + run: uv build + - name: Publish to PyPI - uses: pypa/gh-action-pypi-publish@release/v1 - # No need to specify token when using trusted publishing \ No newline at end of file + run: uv publish --check-url https://pypi.org/simple + + - name: Create GitHub Release + uses: softprops/action-gh-release@v2 + with: + generate_release_notes: true + files: | + dist/*.whl + dist/*.tar.gz diff --git a/.gitignore b/.gitignore index 636636b..01b08a8 100644 --- a/.gitignore +++ b/.gitignore @@ -1,19 +1,36 @@ -**/workdir/ -**/__pycache__/**/* -**/*.egg*/ -**/*.egg-info/** -**/*.pyc -**/*.pyo -**/*.pyd -**/*.pyz -**/*.code-workspace -dist/ +# Python-generated files +__pycache__/ +*.py[oc] build/ +dist/ +wheels/ +*.egg-info + +# Virtual environments +.venv + +mojo.log +mojo.log.* + +site +docs/reference/ +mojo-models +textures + +typings + +# TypeScript build artifacts (node_modules is dev-only; compiled .js files ARE committed) +node_modules/ +*.js.map + +.coverage + +**/*.code-workspace + trendify.log + # Test output files discriminator_tests.json discriminator_tests.log -.coverage -coverage.xml -# MkDocs build directory -site/ \ No newline at end of file + +/**/.DS_Store diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..afbe37a --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,38 @@ +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v6.0.0 + hooks: + - id: check-added-large-files + args: ["--maxkb=500"] + - repo: https://github.com/astral-sh/ruff-pre-commit + # Ruff version. + rev: v0.15.17 + hooks: + # Run the linter. + - id: ruff-check + args: [--fix] + # Run the formatter. + - id: ruff-format + - repo: https://github.com/astral-sh/uv-pre-commit + # uv version. + rev: 0.11.21 + hooks: + - id: uv-lock + - repo: https://github.com/astral-sh/uv-pre-commit + # uv version. + rev: 0.11.21 + hooks: + - id: uv-export + - repo: https://github.com/RobertCraigie/pyright-python + rev: v1.1.410 + hooks: + - id: pyright + - repo: local + hooks: + - id: act-test + name: Run GitHub Actions locally (act) + entry: act -j test + language: system + stages: [pre-push] + pass_filenames: false + files: ^(src/|tests/|pyproject\.toml|uv\.lock) diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..6324d40 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.14 diff --git a/alternative_tag_selectors.py b/alternative_tag_selectors.py index 012701b..8511def 100644 --- a/alternative_tag_selectors.py +++ b/alternative_tag_selectors.py @@ -2,251 +2,311 @@ Additional selection mechanisms for handling large numbers of tags in Trendify dashboards. These components can be integrated into the PlotlyDashboardGenerator class. """ -import dash + from dash import dcc, html from dash.dependencies import Input, Output, State -import re + class TagSelectionMethods: """Various tag selection UI components for Plotly Dash""" - + @staticmethod def create_searchable_dropdown(tag_options, default_value): """ Create a searchable dropdown for tag selection - + Args: tag_options (list): List of tag options in {'label': '...', 'value': '...'} format default_value (str): Default tag value to select - + Returns: html.Div: Dropdown component with search functionality + """ - return html.Div([ - html.Label("Search and select tag:"), - dcc.Dropdown( - id='tag-selector', - options=tag_options, - value=default_value, - clearable=False, - searchable=True, - style={'width': '100%'} - ) - ], style={'width': '40%', 'margin': '10px 0'}) - + return html.Div( + [ + html.Label("Search and select tag:"), + dcc.Dropdown( + id="tag-selector", + options=tag_options, + value=default_value, + clearable=False, + searchable=True, + style={"width": "100%"}, + ), + ], + style={"width": "40%", "margin": "10px 0"}, + ) + @staticmethod def create_radio_with_search_filter(tag_options, default_value): """ Create radio buttons with a search filter above - + Args: tag_options (list): List of tag options in {'label': '...', 'value': '...'} format default_value (str): Default tag value to select - + Returns: html.Div: Search filter and radio button component + """ - return html.Div([ - html.Label("Filter tags:"), - dcc.Input( - id='tag-search-input', - type='text', - placeholder='Type to filter tags...', - style={'width': '100%', 'marginBottom': '10px'} - ), - html.Div([ - dcc.RadioItems( - id='tag-selector', - options=tag_options, - value=default_value, - labelStyle={'display': 'block', 'margin': '5px 0'} - ) - ], id='filtered-radio-container', style={'maxHeight': '300px', 'overflowY': 'auto'}) - ], style={'width': '30%', 'margin': '10px 0'}) - + return html.Div( + [ + html.Label("Filter tags:"), + dcc.Input( + id="tag-search-input", + type="text", + placeholder="Type to filter tags...", + style={"width": "100%", "marginBottom": "10px"}, + ), + html.Div( + [ + dcc.RadioItems( + id="tag-selector", + options=tag_options, + value=default_value, + labelStyle={"display": "block", "margin": "5px 0"}, + ) + ], + id="filtered-radio-container", + style={"maxHeight": "300px", "overflowY": "auto"}, + ), + ], + style={"width": "30%", "margin": "10px 0"}, + ) + @staticmethod def create_category_based_selection(tag_options, default_value): """ Create a two-level selection for tags that can be organized by category Assumes tag names might follow a pattern like 'category/tag_name' - + Args: tag_options (list): List of tag options in {'label': '...', 'value': '...'} format default_value (str): Default tag value to select - + Returns: html.Div: Two-level selection component + """ # Try to extract categories from tag names (e.g., 'category/tag_name') categories = set() for opt in tag_options: - parts = opt['label'].split('/') + parts = opt["label"].split("/") if len(parts) > 1: categories.add(parts[0]) else: - categories.add('General') - + categories.add("General") + categories = sorted(list(categories)) - + # Default category - default_category = next((parts[0] for opt in tag_options - if opt['value'] == default_value - and len(parts := opt['label'].split('/')) > 1), - 'General') - - return html.Div([ - html.Div([ - html.Label("Category:"), - dcc.Dropdown( - id='category-selector', - options=[{'label': cat, 'value': cat} for cat in categories], - value=default_category, - clearable=False, - style={'width': '100%'} - ) - ], style={'marginBottom': '10px'}), - html.Div([ - html.Label("Tag:"), - dcc.Dropdown( - id='tag-selector', - # Options will be populated by callback - value=default_value, - clearable=False, - style={'width': '100%'} - ) - ]) - ], style={'width': '40%', 'margin': '10px 0'}) + default_category = next( + ( + parts[0] + for opt in tag_options + if opt["value"] == default_value + and len(parts := opt["label"].split("/")) > 1 + ), + "General", + ) + + return html.Div( + [ + html.Div( + [ + html.Label("Category:"), + dcc.Dropdown( + id="category-selector", + options=[ + {"label": cat, "value": cat} for cat in categories + ], + value=default_category, + clearable=False, + style={"width": "100%"}, + ), + ], + style={"marginBottom": "10px"}, + ), + html.Div( + [ + html.Label("Tag:"), + dcc.Dropdown( + id="tag-selector", + # Options will be populated by callback + value=default_value, + clearable=False, + style={"width": "100%"}, + ), + ] + ), + ], + style={"width": "40%", "margin": "10px 0"}, + ) @staticmethod def register_category_callbacks(app, tag_options): """Register callbacks for the category-based selection""" + @app.callback( - Output('tag-selector', 'options'), - [Input('category-selector', 'value')] + Output("tag-selector", "options"), [Input("category-selector", "value")] ) def update_tag_options(selected_category): filtered_options = [] for opt in tag_options: - parts = opt['label'].split('/') + parts = opt["label"].split("/") if len(parts) > 1 and parts[0] == selected_category: # Show only the tag part in the dropdown - filtered_options.append({ - 'label': parts[1], - 'value': opt['value'] - }) - elif len(parts) == 1 and selected_category == 'General': + filtered_options.append({"label": parts[1], "value": opt["value"]}) + elif len(parts) == 1 and selected_category == "General": filtered_options.append(opt) return filtered_options - + @staticmethod def register_search_filter_callbacks(app, tag_options): """Register callbacks for the search filter with radio buttons""" + @app.callback( - Output('filtered-radio-container', 'children'), - [Input('tag-search-input', 'value')], - [State('tag-selector', 'value')] + Output("filtered-radio-container", "children"), + [Input("tag-search-input", "value")], + [State("tag-selector", "value")], ) def filter_radio_options(search_text, current_value): if not search_text: return dcc.RadioItems( - id='tag-selector', + id="tag-selector", options=tag_options, value=current_value, - labelStyle={'display': 'block', 'margin': '5px 0'} + labelStyle={"display": "block", "margin": "5px 0"}, ) - + # Filter options based on search text filtered_options = [ - opt for opt in tag_options - if search_text.lower() in opt['label'].lower() + opt + for opt in tag_options + if search_text.lower() in opt["label"].lower() ] - + return dcc.RadioItems( - id='tag-selector', + id="tag-selector", options=filtered_options, - value=current_value if any(opt['value'] == current_value for opt in filtered_options) else None, - labelStyle={'display': 'block', 'margin': '5px 0'} + value=current_value + if any(opt["value"] == current_value for opt in filtered_options) + else None, + labelStyle={"display": "block", "margin": "5px 0"}, ) # Example of how to use these components in the PlotlyDashboardGenerator class def create_sidebar_with_tag_tree(tag_options, default_value): """Create a sidebar with a hierarchical tag tree for larger applications""" - # Organize tags into a tree structure tag_tree = {} for opt in tag_options: - parts = opt['label'].split('/') + parts = opt["label"].split("/") if len(parts) == 1: - category = 'General' + category = "General" tag = parts[0] else: category = parts[0] - tag = '/'.join(parts[1:]) # Join remaining parts as the tag - + tag = "/".join(parts[1:]) # Join remaining parts as the tag + if category not in tag_tree: tag_tree[category] = [] - - tag_tree[category].append({ - 'label': tag, - 'value': opt['value'] - }) - + + tag_tree[category].append({"label": tag, "value": opt["value"]}) + # Sort categories and tags categories = sorted(tag_tree.keys()) - + # Create the sidebar - return html.Div([ - html.H3("Tags"), - html.Hr(), - html.Div([ - html.Div([ - html.Details([ - html.Summary(category), - html.Div([ - html.Div([ - dcc.RadioItems( - id={'type': 'tag-radio', 'category': category}, - options=tag_tree[category], - value=None, - labelStyle={'display': 'block', 'margin': '5px 0', 'paddingLeft': '20px'} + return html.Div( + [ + html.H3("Tags"), + html.Hr(), + html.Div( + [ + html.Div( + [ + html.Details( + [ + html.Summary(category), + html.Div( + [ + html.Div( + [ + dcc.RadioItems( + id={ + "type": "tag-radio", + "category": category, + }, + options=tag_tree[category], + value=None, + labelStyle={ + "display": "block", + "margin": "5px 0", + "paddingLeft": "20px", + }, + ) + ] + ) + ] + ), + ], + open=category + == next( + ( + parts[0] + for opt in tag_options + if opt["value"] == default_value + and len(parts := opt["label"].split("/")) > 1 + ), + "General", + ), ) - ]) - ]) - ], open=category == next((parts[0] for opt in tag_options - if opt['value'] == default_value - and len(parts := opt['label'].split('/')) > 1), - 'General')) - ]) for category in categories - ]), - # Hidden input to store the selected tag - dcc.Input(id='tag-selector', value=default_value, type='hidden') - ], style={'width': '250px', 'backgroundColor': '#f8f9fa', 'padding': '15px', 'borderRight': '1px solid #ddd', - 'height': '100vh', 'position': 'fixed', 'overflowY': 'auto'}) + ] + ) + for category in categories + ] + ), + # Hidden input to store the selected tag + dcc.Input(id="tag-selector", value=default_value, type="hidden"), + ], + style={ + "width": "250px", + "backgroundColor": "#f8f9fa", + "padding": "15px", + "borderRight": "1px solid #ddd", + "height": "100vh", + "position": "fixed", + "overflowY": "auto", + }, + ) + def register_tag_tree_callbacks(app, tag_options): """Register callbacks for the sidebar tag tree""" - # Create a list of Output objects, one for each category categories = set() for opt in tag_options: - parts = opt['label'].split('/') + parts = opt["label"].split("/") if len(parts) > 1: categories.add(parts[0]) else: - categories.add('General') - + categories.add("General") + categories = sorted(list(categories)) - + @app.callback( - Output('tag-selector', 'value'), - [Input({'type': 'tag-radio', 'category': cat}, 'value') for cat in categories] + Output("tag-selector", "value"), + [Input({"type": "tag-radio", "category": cat}, "value") for cat in categories], ) def update_selected_tag(*values): # Find the first non-None value (the selected tag) for val in values: if val is not None: return val - + # Default to the first tag if nothing is selected - return tag_options[0]['value'] if tag_options else None \ No newline at end of file + return tag_options[0]["value"] if tag_options else None diff --git a/custom_handler.py b/custom_handler.py index e057bfd..8d21fa2 100644 --- a/custom_handler.py +++ b/custom_handler.py @@ -1,6 +1,7 @@ import requests from mkdocstrings.handlers.base import BaseHandler + class CustomPythonHandler(BaseHandler): def get_source(self): # Override the method to disable SSL verification diff --git a/gallery/gallery.py b/gallery/gallery.py index 6b3886c..90a824f 100644 --- a/gallery/gallery.py +++ b/gallery/gallery.py @@ -10,83 +10,86 @@ import numpy as np from pathlib import Path from trendify.api.api import ( - Trace2D, Point2D, TableEntry, HistogramEntry, AxLine, - Pen, Marker, Format2D, LineOrientation, HistogramStyle, - DataProductCollection + Trace2D, + Point2D, + TableEntry, + HistogramEntry, + AxLine, + Pen, + Marker, + Format2D, + LineOrientation, + HistogramStyle, + DataProductCollection, ) + def generate_sine_wave_example(): # Create sine wave with noise - x = np.linspace(0, 4*np.pi, 100) + x = np.linspace(0, 4 * np.pi, 100) y = np.sin(x) + np.random.normal(0, 0.1, len(x)) - + # Create main trace trace = Trace2D.from_xy( - tags=('waves', 'sine'), + tags=("waves", "sine"), x=x, y=y, - pen=Pen( - color='blue', - label='Noisy Sine', - size=2 - ), + pen=Pen(color="blue", label="Noisy Sine", size=2), format2d=Format2D( title_fig="Sine Wave with Reference Lines", title_ax="Sine Function with Noise", label_x="X (radians)", label_y="Amplitude", lim_x_min=0, - lim_x_max=4*np.pi, + lim_x_max=4 * np.pi, lim_y_min=-1.5, - lim_y_max=1.5 - ) + lim_y_max=1.5, + ), ) - + # Add reference lines hline_upper = AxLine( - tags=('waves', 'sine'), + tags=("waves", "sine"), value=1.0, orientation=LineOrientation.HORIZONTAL, - pen=Pen(color='red', label='Upper Bound', size=1.5) + pen=Pen(color="red", label="Upper Bound", size=1.5), ) - + hline_lower = AxLine( - tags=('waves', 'sine'), + tags=("waves", "sine"), value=-1.0, orientation=LineOrientation.HORIZONTAL, - pen=Pen(color='red', label='Lower Bound', size=1.5) + pen=Pen(color="red", label="Lower Bound", size=1.5), ) - + # Add some peak points peaks = [] for i, (x_val, y_val) in enumerate(zip(x[1:-1], y[1:-1])): - if y_val > y[i] and y_val > y[i+2]: # Simple peak detection - peaks.append(Point2D( - tags=('waves', 'sine'), - x=x_val, - y=y_val, - marker=Marker( - color='green', - symbol='o', - size=100, - label='Peak' + if y_val > y[i] and y_val > y[i + 2]: # Simple peak detection + peaks.append( + Point2D( + tags=("waves", "sine"), + x=x_val, + y=y_val, + marker=Marker(color="green", symbol="o", size=100, label="Peak"), ) - )) - + ) + return [trace, hline_upper, hline_lower] + peaks + def generate_scatter_plot_example(): # Create two clusters of points n_points = 50 - + # Cluster 1 x1 = np.random.normal(2, 0.5, n_points) y1 = np.random.normal(2, 0.5, n_points) - + # Cluster 2 x2 = np.random.normal(4, 0.5, n_points) y2 = np.random.normal(4, 0.5, n_points) - + format2d = Format2D( title_fig="Cluster Analysis", title_ax="Two Clusters with Centroids", @@ -95,179 +98,159 @@ def generate_scatter_plot_example(): lim_x_min=0, lim_x_max=6, lim_y_min=0, - lim_y_max=6 + lim_y_max=6, ) - + # Create points for each cluster cluster1 = [ Point2D( - tags=('clusters', 'analysis'), + tags=("clusters", "analysis"), x=x, y=y, - marker=Marker( - color='blue', - symbol='o', - size=50, - label='Cluster 1' - ), - format2d=format2d + marker=Marker(color="blue", symbol="o", size=50, label="Cluster 1"), + format2d=format2d, ) for x, y in zip(x1, y1) ] - + cluster2 = [ Point2D( - tags=('clusters', 'analysis'), + tags=("clusters", "analysis"), x=x, y=y, - marker=Marker( - color='red', - symbol='o', - size=50, - label='Cluster 2' - ), - format2d=format2d + marker=Marker(color="red", symbol="o", size=50, label="Cluster 2"), + format2d=format2d, ) for x, y in zip(x2, y2) ] - + # Add centroids centroids = [ Point2D( - tags=('clusters', 'analysis'), + tags=("clusters", "analysis"), x=np.mean(x1), y=np.mean(y1), - marker=Marker( - color='darkblue', - symbol='*', - size=200, - label='Centroid 1' - ), - format2d=format2d + marker=Marker(color="darkblue", symbol="*", size=200, label="Centroid 1"), + format2d=format2d, ), Point2D( - tags=('clusters', 'analysis'), + tags=("clusters", "analysis"), x=np.mean(x2), y=np.mean(y2), - marker=Marker( - color='darkred', - symbol='*', - size=200, - label='Centroid 2' - ), - format2d=format2d - ) + marker=Marker(color="darkred", symbol="*", size=200, label="Centroid 2"), + format2d=format2d, + ), ] - + return cluster1 + cluster2 + centroids + def generate_histogram_example(): # Generate three different distributions n_samples = 1000 - + # Normal distribution normal_data = np.random.normal(0, 1, n_samples) normal_entries = [ HistogramEntry( - tags=('distributions', 'comparison'), + tags=("distributions", "comparison"), value=v, - style=HistogramStyle( - color='blue', - label='Normal', - alpha_face=0.3, - bins=30 - ), + style=HistogramStyle(color="blue", label="Normal", alpha_face=0.3, bins=30), format2d=Format2D( title_fig="Distribution Comparison", title_ax="Multiple Distributions", label_x="Value", - label_y="Count" - ) + label_y="Count", + ), ) for v in normal_data ] - + # Uniform distribution uniform_data = np.random.uniform(-2, 2, n_samples) uniform_entries = [ HistogramEntry( - tags=('distributions', 'comparison'), + tags=("distributions", "comparison"), value=v, - style=HistogramStyle( - color='red', - label='Uniform', - alpha_face=0.3, - bins=30 - ) + style=HistogramStyle(color="red", label="Uniform", alpha_face=0.3, bins=30), ) for v in uniform_data ] - + # Exponential distribution exp_data = np.random.exponential(1, n_samples) exp_entries = [ HistogramEntry( - tags=('distributions', 'comparison'), + tags=("distributions", "comparison"), value=v, style=HistogramStyle( - color='green', - label='Exponential', - alpha_face=0.3, - bins=30 - ) + color="green", label="Exponential", alpha_face=0.3, bins=30 + ), ) for v in exp_data ] - + return normal_entries + uniform_entries + exp_entries + def generate_table_example(): # Create a comparison table of distribution statistics - distributions = ['Normal', 'Uniform', 'Exponential'] - metrics = ['Mean', 'Std Dev', 'Skewness'] - + distributions = ["Normal", "Uniform", "Exponential"] + metrics = ["Mean", "Std Dev", "Skewness"] + # Generate some example statistics stats = { - 'Normal': [0.01, 1.02, 0.05], - 'Uniform': [0.0, 1.15, 0.0], - 'Exponential': [1.0, 1.0, 2.0] + "Normal": [0.01, 1.02, 0.05], + "Uniform": [0.0, 1.15, 0.0], + "Exponential": [1.0, 1.0, 2.0], } - + table_entries = [] for dist in distributions: for i, metric in enumerate(metrics): table_entries.append( TableEntry( - tags=('distributions', 'statistics'), + tags=("distributions", "statistics"), row=dist, col=metric, value=f"{stats[dist][i]:.2f}", - unit=None + unit=None, ) ) - + return table_entries + def run_trendify_commands(input_dir: str | Path, output_dir: str | Path): """ Runs trendify commands with proper OS handling - + Args: input_dir (str | Path): Input directory containing data products output_dir (str | Path): Output directory for sorted products and assets + """ # Convert to Path objects input_dir = Path(input_dir).resolve() output_dir = Path(output_dir).resolve() - + # Determine if we need shell=True based on OS use_shell = platform.system() == "Windows" - + commands = [ - ["trendify", "products-sort", "-i", str(input_dir), "-o", str(output_dir), "-n", "1"], - ["trendify", "assets-make-static", str(output_dir), "-n", "1"] + [ + "trendify", + "products-sort", + "-i", + str(input_dir), + "-o", + str(output_dir), + "-n", + "1", + ], + ["trendify", "assets-make-static", str(output_dir), "-n", "1"], ] - + for cmd in commands: print(f"\nExecuting command: {' '.join(cmd)}") try: @@ -278,16 +261,16 @@ def run_trendify_commands(input_dir: str | Path, output_dir: str | Path): check=True, text=True, stdout=subprocess.PIPE, - stderr=subprocess.PIPE + stderr=subprocess.PIPE, ) print("Command output:") print(result.stdout) - + # Print any stderr output if it exists if result.stderr: print("Warnings/Errors:") print(result.stderr) - + except subprocess.CalledProcessError as e: print(f"Error executing command: {' '.join(cmd)}") print(f"Exit code: {e.returncode}") @@ -296,47 +279,49 @@ def run_trendify_commands(input_dir: str | Path, output_dir: str | Path): sys.exit(1) except Exception as e: print(f"Unexpected error executing command: {' '.join(cmd)}") - print(f"Error: {str(e)}") + print(f"Error: {e!s}") sys.exit(1) + def make_gallery(output_dir: Path): """ Generates the gallery """ - # Create output directory output_dir.mkdir(exist_ok=True, parents=True) - data_dir = output_dir.joinpath('data') + data_dir = output_dir.joinpath("data") data_dir.mkdir(exist_ok=True, parents=True) - trendify_dir=output_dir.joinpath('trendify') - + trendify_dir = output_dir.joinpath("trendify") + try: from importlib.resources import files from shutil import copy + copy( - str(files('trendify').joinpath('gallery.py').resolve()), - str(output_dir.joinpath('gallery.py')), + str(files("trendify").joinpath("gallery.py").resolve()), + str(output_dir.joinpath("gallery.py")), ) except Exception as e: print(e) # Generate all examples products = ( - generate_sine_wave_example() + - generate_scatter_plot_example() + - generate_histogram_example() + - generate_table_example() + generate_sine_wave_example() + + generate_scatter_plot_example() + + generate_histogram_example() + + generate_table_example() ) - + # Save to JSON file collection = DataProductCollection(elements=products) - data_dir.joinpath('data_products.json').write_text(collection.model_dump_json()) - + data_dir.joinpath("data_products.json").write_text(collection.model_dump_json()) + # Run trendify commands run_trendify_commands( input_dir=data_dir, output_dir=trendify_dir, ) + if __name__ == "__main__": - make_gallery(Path('gallery')) \ No newline at end of file + make_gallery(Path("gallery")) diff --git a/implementation_recommendation.py b/implementation_recommendation.py index 769f28e..9e1566d 100644 --- a/implementation_recommendation.py +++ b/implementation_recommendation.py @@ -5,70 +5,73 @@ # Here's how you could modify your PlotlyDashboardGenerator class # to implement a dropdown-based selection with search capabilities + def _create_app_layout(self, title: str, tab_contents: List[Dict]) -> html.Div: """Create the main application layout with improved tag selection.""" if not tab_contents: - return html.Div([ - html.H1(title), - html.Div("No data products found to visualize.") - ]) - + return html.Div( + [html.H1(title), html.Div("No data products found to visualize.")] + ) + # Create dropdown options from tab_contents dropdown_options = [ - {'label': tab['label'], 'value': tab['value']} - for tab in tab_contents + {"label": tab["label"], "value": tab["value"]} for tab in tab_contents ] - + # Create content divs for each tag content_divs = [] for tab in tab_contents: content_divs.append( html.Div( - tab['content'], - id=f"content-{tab['value']}", - style={'display': 'none'} + tab["content"], id=f"content-{tab['value']}", style={"display": "none"} ) ) - + # Only show the first one by default - content_divs[0]['style'] = {'display': 'block'} - - return html.Div([ - html.H1(title), - html.Div([ - html.Label("Select Tag:"), - dcc.Dropdown( - id='tag-selector', - options=dropdown_options, - value=tab_contents[0]['value'], - clearable=False, - searchable=True, - style={'width': '100%'} + content_divs[0]["style"] = {"display": "block"} + + return html.Div( + [ + html.H1(title), + html.Div( + [ + html.Label("Select Tag:"), + dcc.Dropdown( + id="tag-selector", + options=dropdown_options, + value=tab_contents[0]["value"], + clearable=False, + searchable=True, + style={"width": "100%"}, + ), + ], + style={"width": "30%", "margin": "10px 0"}, ), - ], style={'width': '30%', 'margin': '10px 0'}), - html.Div(id='tag-content-container', children=content_divs), - - # Stores - dcc.Store(id='active-tag', data=tab_contents[0]['value']), - dcc.Store(id='color-maps'), - ]) + html.Div(id="tag-content-container", children=content_divs), + # Stores + dcc.Store(id="active-tag", data=tab_contents[0]["value"]), + dcc.Store(id="color-maps"), + ] + ) + def _register_callbacks(self): """Register all the callbacks for the dashboard.""" + # Tag selection callback @self.app.callback( - [Output('active-tag', 'data')] + - [Output(f'content-{tag}', 'style') for tag in self.tag_data.keys()], - [Input('tag-selector', 'value')] + [Output("active-tag", "data")] + + [Output(f"content-{tag}", "style") for tag in self.tag_data.keys()], + [Input("tag-selector", "value")], ) def switch_tag(tag_value): styles = [] for tag in self.tag_data.keys(): if tag == tag_value: - styles.append({'display': 'block'}) + styles.append({"display": "block"}) else: - styles.append({'display': 'none'}) + styles.append({"display": "none"}) return [tag_value] + styles - + # Register callbacks for each tag's components as before... - # ... \ No newline at end of file + # ... diff --git a/improved_tag_selection.py b/improved_tag_selection.py index 31e6a5c..06aab5b 100644 --- a/improved_tag_selection.py +++ b/improved_tag_selection.py @@ -2,67 +2,72 @@ Code snippet showing improved tag selection for PlotlyDashboardGenerator to replace the existing tab-based navigation """ -import dash + from dash import dcc, html from dash.dependencies import Input, Output + # This would replace the _create_app_layout method in your class -def _create_app_layout_with_dropdown(self, title: str, tab_contents: List[Dict]) -> html.Div: +def _create_app_layout_with_dropdown( + self, title: str, tab_contents: List[Dict] +) -> html.Div: """Create the main application layout with dropdown selection instead of tabs.""" if not tab_contents: - return html.Div([ - html.H1(title), - html.Div("No data products found to visualize.") - ]) - + return html.Div( + [html.H1(title), html.Div("No data products found to visualize.")] + ) + # Create dropdown options from tab_contents dropdown_options = [ - {'label': tab['label'], 'value': tab['value']} - for tab in tab_contents + {"label": tab["label"], "value": tab["value"]} for tab in tab_contents ] - + # Create divs for each tag's content content_divs = [] for tab in tab_contents: content_divs.append( html.Div( - tab['content'], - id=f"content-{tab['value']}", - style={'display': 'none'} + tab["content"], id=f"content-{tab['value']}", style={"display": "none"} ) ) - - return html.Div([ - html.H1(title), - html.Div([ - html.Label("Select Tag:"), - dcc.Dropdown( - id='tag-selector', - options=dropdown_options, - value=tab_contents[0]['value'], - clearable=False, - style={'width': '100%'} + + return html.Div( + [ + html.H1(title), + html.Div( + [ + html.Label("Select Tag:"), + dcc.Dropdown( + id="tag-selector", + options=dropdown_options, + value=tab_contents[0]["value"], + clearable=False, + style={"width": "100%"}, + ), + ], + style={"width": "30%", "margin": "10px 0"}, ), - ], style={'width': '30%', 'margin': '10px 0'}), - html.Div(id='tag-content-container', children=content_divs), - - # Stores - dcc.Store(id='active-tag', data=tab_contents[0]['value']), - ]) + html.Div(id="tag-content-container", children=content_divs), + # Stores + dcc.Store(id="active-tag", data=tab_contents[0]["value"]), + ] + ) + # This would replace the callback in _register_callbacks def tag_selection_callback(self): """Register callback for tag selection dropdown.""" + @self.app.callback( - [Output('active-tag', 'data')] + - [Output(f'content-{tag}', 'style') for tag in self.tag_data.keys()], - [Input('tag-selector', 'value')] + [Output("active-tag", "data")] + + [Output(f"content-{tag}", "style") for tag in self.tag_data.keys()], + [Input("tag-selector", "value")], ) def switch_tag(tag_value): styles = [] for tag in self.tag_data.keys(): if tag == tag_value: - styles.append({'display': 'block'}) + styles.append({"display": "block"}) else: - styles.append({'display': 'none'}) - return [tag_value] + styles \ No newline at end of file + styles.append({"display": "none"}) + return [tag_value] + styles diff --git a/plotly_examples/histogram_1.py b/plotly_examples/histogram_1.py index 7c35d28..fa4d139 100644 --- a/plotly_examples/histogram_1.py +++ b/plotly_examples/histogram_1.py @@ -1,33 +1,40 @@ """Interactive histogram visualization with interactive legend features.""" + import dash from dash import dcc, html -from dash.dependencies import Input, Output, State, MATCH, ALL +from dash.dependencies import Input, Output, State, ALL import plotly.graph_objects as go import pandas as pd import numpy as np -from typing import Dict, Any +from typing import Any # Debug Configuration PYTHON_DEBUG = True MIN_BINS = 1 + def debug_log(message: str) -> None: """Python-side debug logging.""" if PYTHON_DEBUG: print(f"[PY] {message}") -def calculate_histogram_statistics(series: pd.Series, num_bins: int = 20) -> Dict[str, Any]: + +def calculate_histogram_statistics( + series: pd.Series, num_bins: int = 20 +) -> dict[str, Any]: """Calculate statistics for a histogram series.""" try: # Drop NA values clean_series = series.dropna() - + if len(clean_series) < 2: return { "samples": len(clean_series), "missing": len(series) - len(clean_series), - "mean": "N/A" if len(clean_series) == 0 else f"{clean_series.mean():.3f}", - "bins": num_bins + "mean": "N/A" + if len(clean_series) == 0 + else f"{clean_series.mean():.3f}", + "bins": num_bins, } stats_dict = { @@ -39,9 +46,11 @@ def calculate_histogram_statistics(series: pd.Series, num_bins: int = 20) -> Dic "min": f"{clean_series.min():.3f}", "max": f"{clean_series.max():.3f}", "bins": num_bins, - "bin_width": f"{(clean_series.max() - clean_series.min()) / num_bins:.3f}" if clean_series.max() != clean_series.min() else "N/A" + "bin_width": f"{(clean_series.max() - clean_series.min()) / num_bins:.3f}" + if clean_series.max() != clean_series.min() + else "N/A", } - + # Add skewness and kurtosis if enough data points if len(clean_series) > 3: try: @@ -49,174 +58,249 @@ def calculate_histogram_statistics(series: pd.Series, num_bins: int = 20) -> Dic stats_dict["kurtosis"] = f"{clean_series.kurtosis():.3f}" except: pass - + return stats_dict except Exception as e: return {"error": str(e), "bins": num_bins} + # Create sample data -df = pd.DataFrame({ - 'A': np.random.normal(0, 1, 1000), - 'B': np.random.normal(2, 1.5, 1000), - 'C': np.random.exponential(2, 1000), - 'D': np.random.gamma(2, 2, 1000), - 'Category': np.random.choice(['X', 'Y', 'Z'], 1000) -}) +df = pd.DataFrame( + { + "A": np.random.normal(0, 1, 1000), + "B": np.random.normal(2, 1.5, 1000), + "C": np.random.exponential(2, 1000), + "D": np.random.gamma(2, 2, 1000), + "Category": np.random.choice(["X", "Y", "Z"], 1000), + } +) # Initialize the Dash app app = dash.Dash(__name__, suppress_callback_exceptions=True) # Styles dropdown_style = { - 'width': '100%', - 'marginBottom': '20px', - 'zIndex': 999, + "width": "100%", + "marginBottom": "20px", + "zIndex": 999, } info_panel_style = { - 'position': 'fixed', # Fixed positioning for hover panel - 'backgroundColor': 'white', - 'padding': '15px', - 'borderRadius': '5px', - 'boxShadow': '0 0 10px rgba(0,0,0,0.3)', - 'zIndex': 1000, - 'display': 'none', - 'minWidth': '200px', - 'maxWidth': '300px', - 'fontSize': '12px' + "position": "fixed", # Fixed positioning for hover panel + "backgroundColor": "white", + "padding": "15px", + "borderRadius": "5px", + "boxShadow": "0 0 10px rgba(0,0,0,0.3)", + "zIndex": 1000, + "display": "none", + "minWidth": "200px", + "maxWidth": "300px", + "fontSize": "12px", } color_picker_style = { - 'position': 'fixed', - 'top': '50%', - 'left': '50%', - 'transform': 'translate(-50%, -50%)', - 'backgroundColor': 'white', - 'padding': '20px', - 'borderRadius': '5px', - 'boxShadow': '0 0 10px rgba(0,0,0,0.3)', - 'zIndex': 1001, - 'display': 'none' + "position": "fixed", + "top": "50%", + "left": "50%", + "transform": "translate(-50%, -50%)", + "backgroundColor": "white", + "padding": "20px", + "borderRadius": "5px", + "boxShadow": "0 0 10px rgba(0,0,0,0.3)", + "zIndex": 1001, + "display": "none", } # Available colors COLORS = [ - '#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', - '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf', - '#aec7e8', '#ffbb78', '#98df8a', '#ff9896', '#c5b0d5', - '#c49c94', '#f7b6d2', '#c7c7c7', '#dbdb8d', '#9edae5' + "#1f77b4", + "#ff7f0e", + "#2ca02c", + "#d62728", + "#9467bd", + "#8c564b", + "#e377c2", + "#7f7f7f", + "#bcbd22", + "#17becf", + "#aec7e8", + "#ffbb78", + "#98df8a", + "#ff9896", + "#c5b0d5", + "#c49c94", + "#f7b6d2", + "#c7c7c7", + "#dbdb8d", + "#9edae5", ] # Define the layout -app.layout = html.Div([ - html.H1("Interactive Histogram Visualization"), - - # Control panel for column selection - html.Div([ - html.Div([ - html.H3("Select Columns for Histograms"), - dcc.Dropdown( - id='histogram-columns', - options=[{'label': col, 'value': col} for col in df.columns if col != 'Category'], - value=[], - multi=True, - style=dropdown_style - ), - ], style={'width': '45%', 'display': 'inline-block', 'margin': '5px'}), - - ]), - - # Graph and custom legend - html.Div([ - html.Div([ - html.H3("Histogram Settings"), - dcc.Checklist( - id='histogram-settings', - options=[ - {'label': ' Show KDE (density curve)', 'value': 'kde'}, - {'label': ' Normalize histograms', 'value': 'normalize'}, - {'label': ' Show rug plot', 'value': 'rug'} - ], - value=['kde'], - style={'display': 'block', 'gap': '10px'}, - inputStyle={'marginRight': '5px'}, - labelStyle={'display': 'block', 'marginBottom': '10px'} - ), - html.Div([ - html.Label("Global Number of Bins:"), - dcc.Slider( - id='bins-slider', - min=MIN_BINS, - max=100, - step=1, - value=20, - marks={i: str(i) for i in [1, 20, 40, 60, 80, 100]}, - ) - ], style={'marginTop': '15px'}), - html.Div([ - html.Label("Lock all bins to global setting:", - style={'display': 'inline-block', 'marginRight': '10px'}), - dcc.Checklist( - id='global-bins-toggle', - options=[{'label': '', 'value': 'enabled'}], - value=['enabled'], # Enabled by default - inline=True, - inputStyle={'marginRight': '5px'} - ) - ], style={'marginTop': '15px', 'marginBottom': '5px'}), - ], style={'width': '20%', 'display': 'inline-block', 'margin': '5px'}), - # Main plot - html.Div([ - dcc.Graph( - id='histogram-plot', - style={'height': '700px'} - ), - ], style={'width': '50%', 'display': 'inline-block', 'verticalAlign': 'top'}), - - # Custom legend - html.Div([ - html.H3("Series"), - html.Div(id='custom-legend', style={ - 'overflowY': 'auto', - 'maxHeight': '600px' - }) - ], style={'width': '20%', 'display': 'inline-block', 'verticalAlign': 'top'}), - - ]), - - # Info panel (hidden by default) - html.Div(id='series-info-panel', style=info_panel_style), - - # Color picker panel (hidden by default) - html.Div([ - html.H3("Select Color"), +app.layout = html.Div( + [ + html.H1("Interactive Histogram Visualization"), + # Control panel for column selection html.Div( - [html.Button( - style={'backgroundColor': color, 'width': '30px', 'height': '30px', 'margin': '5px'}, - id={'type': 'color-option', 'index': i} - ) for i, color in enumerate(COLORS)], - style={'display': 'grid', 'gridTemplateColumns': 'repeat(5, 1fr)', 'gap': '5px'} + [ + html.Div( + [ + html.H3("Select Columns for Histograms"), + dcc.Dropdown( + id="histogram-columns", + options=[ + {"label": col, "value": col} + for col in df.columns + if col != "Category" + ], + value=[], + multi=True, + style=dropdown_style, + ), + ], + style={"width": "45%", "display": "inline-block", "margin": "5px"}, + ), + ] ), - html.Button("Close", id='close-color-picker', style={'marginTop': '10px'}) - ], id='color-picker-panel', style=color_picker_style), - - # Store components - dcc.Store(id='series-colors'), - dcc.Store(id='series-data'), - dcc.Store(id='active-series'), - dcc.Store(id='bins-settings'), - dcc.Store(id='global-bins-active', data=True), # True by default# Add this to your layout -dcc.Interval(id='interval-component', interval=60*1000, n_intervals=0) # Just a trigger -]) + # Graph and custom legend + html.Div( + [ + html.Div( + [ + html.H3("Histogram Settings"), + dcc.Checklist( + id="histogram-settings", + options=[ + {"label": " Show KDE (density curve)", "value": "kde"}, + { + "label": " Normalize histograms", + "value": "normalize", + }, + {"label": " Show rug plot", "value": "rug"}, + ], + value=["kde"], + style={"display": "block", "gap": "10px"}, + inputStyle={"marginRight": "5px"}, + labelStyle={"display": "block", "marginBottom": "10px"}, + ), + html.Div( + [ + html.Label("Global Number of Bins:"), + dcc.Slider( + id="bins-slider", + min=MIN_BINS, + max=100, + step=1, + value=20, + marks={i: str(i) for i in [1, 20, 40, 60, 80, 100]}, + ), + ], + style={"marginTop": "15px"}, + ), + html.Div( + [ + html.Label( + "Lock all bins to global setting:", + style={ + "display": "inline-block", + "marginRight": "10px", + }, + ), + dcc.Checklist( + id="global-bins-toggle", + options=[{"label": "", "value": "enabled"}], + value=["enabled"], # Enabled by default + inline=True, + inputStyle={"marginRight": "5px"}, + ), + ], + style={"marginTop": "15px", "marginBottom": "5px"}, + ), + ], + style={"width": "20%", "display": "inline-block", "margin": "5px"}, + ), + # Main plot + html.Div( + [ + dcc.Graph(id="histogram-plot", style={"height": "700px"}), + ], + style={ + "width": "50%", + "display": "inline-block", + "verticalAlign": "top", + }, + ), + # Custom legend + html.Div( + [ + html.H3("Series"), + html.Div( + id="custom-legend", + style={"overflowY": "auto", "maxHeight": "600px"}, + ), + ], + style={ + "width": "20%", + "display": "inline-block", + "verticalAlign": "top", + }, + ), + ] + ), + # Info panel (hidden by default) + html.Div(id="series-info-panel", style=info_panel_style), + # Color picker panel (hidden by default) + html.Div( + [ + html.H3("Select Color"), + html.Div( + [ + html.Button( + style={ + "backgroundColor": color, + "width": "30px", + "height": "30px", + "margin": "5px", + }, + id={"type": "color-option", "index": i}, + ) + for i, color in enumerate(COLORS) + ], + style={ + "display": "grid", + "gridTemplateColumns": "repeat(5, 1fr)", + "gap": "5px", + }, + ), + html.Button( + "Close", id="close-color-picker", style={"marginTop": "10px"} + ), + ], + id="color-picker-panel", + style=color_picker_style, + ), + # Store components + dcc.Store(id="series-colors"), + dcc.Store(id="series-data"), + dcc.Store(id="active-series"), + dcc.Store(id="bins-settings"), + dcc.Store( + id="global-bins-active", data=True + ), # True by default# Add this to your layout + dcc.Interval( + id="interval-component", interval=60 * 1000, n_intervals=0 + ), # Just a trigger + ] +) + # Update whether global bins control is active based on toggle @app.callback( - Output('global-bins-active', 'data'), - [Input('global-bins-toggle', 'value')] + Output("global-bins-active", "data"), [Input("global-bins-toggle", "value")] ) def update_global_bins_state(toggle_value): """Update whether global bins control is active based on toggle.""" - return 'enabled' in toggle_value if toggle_value else False + return "enabled" in toggle_value if toggle_value else False + # # Initialize series-colors and statistics when columns are selected # @app.callback( @@ -232,161 +316,184 @@ def update_global_bins_state(toggle_value): # color_data = existing_colors or {} # series_stats = {} # bins_settings = bins_settings or {} - + # if not selected_cols: # return color_data, series_stats - + # for col in selected_cols: # series_name = f'Histogram of {col}' - + # # Assign a color if not already assigned # if series_name not in color_data: # color_data[series_name] = COLORS[len(color_data) % len(COLORS)] - + # # Get bin count (default 20) # num_bins = bins_settings.get(series_name, 20) - + # # Calculate statistics with the current bin count # stats = calculate_histogram_statistics(df[col], num_bins) # series_stats[series_name] = stats - + # # Keep only the colors for selected columns -# color_data = {k: v for k, v in color_data.items() +# color_data = {k: v for k, v in color_data.items() # if any(f'Histogram of {x}' == k for x in selected_cols)} - + # return color_data, series_stats # Update custom legend with bin controls @app.callback( - Output('custom-legend', 'children'), - [Input('histogram-columns', 'value'), - Input('series-colors', 'data'), - Input('bins-settings', 'data')] + Output("custom-legend", "children"), + [ + Input("histogram-columns", "value"), + Input("series-colors", "data"), + Input("bins-settings", "data"), + ], ) def update_legend_with_bin_controls(selected_cols, color_data, bins_settings): """Update custom legend with interactive elements including bin controls.""" legend_items = [] bins_settings = bins_settings or {} - + if not selected_cols or not color_data: return legend_items - + for col in selected_cols: - series_name = f'Histogram of {col}' + series_name = f"Histogram of {col}" color = color_data.get(series_name, COLORS[0]) - + # Get bin count for this series (default: 20 bins) bin_count = bins_settings.get(series_name, 20) - - legend_items.append(html.Div([ - # Color button - html.Button( - style={ - 'backgroundColor': color, - 'width': '20px', - 'height': '20px', - 'marginRight': '10px', - 'verticalAlign': 'middle', - 'cursor': 'pointer', - 'border': '1px solid #ddd' + + legend_items.append( + html.Div( + [ + # Color button + html.Button( + style={ + "backgroundColor": color, + "width": "20px", + "height": "20px", + "marginRight": "10px", + "verticalAlign": "middle", + "cursor": "pointer", + "border": "1px solid #ddd", + }, + id={"type": "color-button", "index": series_name}, + ), + # Series name + html.Span( + series_name, + style={ + "cursor": "default", + "userSelect": "none", + "display": "inline-block", + "marginRight": "10px", + }, + ), + # Bin control + html.Div( + [ + html.Label( + "Bins:", + style={"fontSize": "11px", "marginRight": "5px"}, + ), + dcc.Input( + id={"type": "bin-input", "index": series_name}, + type="number", + min=5, + max=100, + step=1, + value=bin_count, + style={"width": "50px", "fontSize": "11px"}, + debounce=False, # Process changes immediately + persistence=True, # Remember values + ), + ], + style={"display": "inline-block"}, + ), + ], + className="legend-item", + id={"type": "legend-item", "index": series_name}, + **{ + "data-series": series_name, + "style": { + "margin": "10px", + "padding": "5px", + "borderRadius": "3px", + "display": "flex", + "alignItems": "center", + }, }, - id={'type': 'color-button', 'index': series_name} - ), - # Series name - html.Span( - series_name, - style={ - 'cursor': 'default', - 'userSelect': 'none', - 'display': 'inline-block', - 'marginRight': '10px' - } - ), - # Bin control - html.Div([ - html.Label("Bins:", style={'fontSize': '11px', 'marginRight': '5px'}), - dcc.Input( - id={'type': 'bin-input', 'index': series_name}, - type="number", - min=5, - max=100, - step=1, - value=bin_count, - style={'width': '50px', 'fontSize': '11px'}, - debounce=False, # Process changes immediately - persistence=True # Remember values - ) - ], style={'display': 'inline-block'}) - ], - className='legend-item', - id={'type': 'legend-item', 'index': series_name}, - **{ - 'data-series': series_name, - 'style': { - 'margin': '10px', - 'padding': '5px', - 'borderRadius': '3px', - 'display': 'flex', - 'alignItems': 'center' - } - })) - + ) + ) + return legend_items + @app.callback( - [Output('color-picker-panel', 'style'), - Output('active-series', 'data')], - [Input({'type': 'color-button', 'index': ALL}, 'n_clicks'), - Input('close-color-picker', 'n_clicks')], - [State({'type': 'color-button', 'index': ALL}, 'id'), - State('color-picker-panel', 'style')] + [Output("color-picker-panel", "style"), Output("active-series", "data")], + [ + Input({"type": "color-button", "index": ALL}, "n_clicks"), + Input("close-color-picker", "n_clicks"), + ], + [ + State({"type": "color-button", "index": ALL}, "id"), + State("color-picker-panel", "style"), + ], ) def toggle_color_picker(button_clicks, close_clicks, button_ids, current_style): """Show/hide color picker when clicking color buttons.""" ctx = dash.callback_context if not ctx.triggered: - return dict(current_style, display='none'), None - - triggered_id = ctx.triggered[0]['prop_id'] - + return dict(current_style, display="none"), None + + triggered_id = ctx.triggered[0]["prop_id"] + # Close on close button click - if 'close-color-picker' in triggered_id: + if "close-color-picker" in triggered_id: debug_log("Closing color picker") - return dict(current_style, display='none'), None - + return dict(current_style, display="none"), None + # Open on color button click - if 'color-button' in triggered_id and button_clicks and any(button_clicks): + if "color-button" in triggered_id and button_clicks and any(button_clicks): try: # Find which button was clicked - button_idx = next((i for i, clicks in enumerate(button_clicks) if clicks), None) + button_idx = next( + (i for i, clicks in enumerate(button_clicks) if clicks), None + ) if button_idx is not None: - series_name = button_ids[button_idx]['index'] + series_name = button_ids[button_idx]["index"] debug_log(f"Opening color picker for {series_name}") - return dict(current_style, display='block'), series_name + return dict(current_style, display="block"), series_name except Exception as e: debug_log(f"Error in toggle_color_picker: {e}") - - return dict(current_style, display='none'), None + + return dict(current_style, display="none"), None + @app.callback( - Output('series-colors', 'data', allow_duplicate=True), - [Input({'type': 'color-option', 'index': ALL}, 'n_clicks')], - [State({'type': 'color-option', 'index': ALL}, 'id'), - State('active-series', 'data'), - State('series-colors', 'data')], - prevent_initial_call=True + Output("series-colors", "data", allow_duplicate=True), + [Input({"type": "color-option", "index": ALL}, "n_clicks")], + [ + State({"type": "color-option", "index": ALL}, "id"), + State("active-series", "data"), + State("series-colors", "data"), + ], + prevent_initial_call=True, ) def update_series_color(color_clicks, color_ids, active_series, current_colors): """Update color when selecting from color picker.""" ctx = dash.callback_context if not ctx.triggered or not active_series or not current_colors: return dash.no_update - - triggered_id = ctx.triggered[0]['prop_id'] - if 'color-option' not in triggered_id or not any(click for click in color_clicks if click): + + triggered_id = ctx.triggered[0]["prop_id"] + if "color-option" not in triggered_id or not any( + click for click in color_clicks if click + ): return dash.no_update - + try: # Find which color was clicked color_idx = next((i for i, clicks in enumerate(color_clicks) if clicks), None) @@ -396,117 +503,130 @@ def update_series_color(color_clicks, color_ids, active_series, current_colors): return current_colors except Exception as e: debug_log(f"Error in update_series_color: {e}") - + return dash.no_update + @app.callback( - [Output('color-picker-panel', 'style', allow_duplicate=True), - Output('active-series', 'data', allow_duplicate=True)], - [Input({'type': 'color-option', 'index': ALL}, 'n_clicks')], - [State('color-picker-panel', 'style')], - prevent_initial_call=True + [ + Output("color-picker-panel", "style", allow_duplicate=True), + Output("active-series", "data", allow_duplicate=True), + ], + [Input({"type": "color-option", "index": ALL}, "n_clicks")], + [State("color-picker-panel", "style")], + prevent_initial_call=True, ) def close_color_picker_after_selection(color_clicks, current_style): """Close the color picker after a color is selected.""" if any(click for click in color_clicks if click): - return dict(current_style, display='none'), None + return dict(current_style, display="none"), None return dash.no_update, dash.no_update + @app.callback( - Output('histogram-plot', 'figure'), - [Input('histogram-columns', 'value'), - Input('histogram-settings', 'value'), - Input('series-colors', 'data'), - Input('bins-settings', 'data')] + Output("histogram-plot", "figure"), + [ + Input("histogram-columns", "value"), + Input("histogram-settings", "value"), + Input("series-colors", "data"), + Input("bins-settings", "data"), + ], ) def update_histogram(selected_cols, settings, color_data, bins_settings): """Update the histogram based on selected columns and settings.""" debug_log("Updating histogram plot") fig = go.Figure() - + if not selected_cols or not color_data: fig.add_annotation( text="Please select columns for the histogram", - xref="paper", yref="paper", - x=0.5, y=0.5, showarrow=False + xref="paper", + yref="paper", + x=0.5, + y=0.5, + showarrow=False, ) return fig - - normalize = 'normalize' in settings - show_kde = 'kde' in settings - show_rug = 'rug' in settings + + normalize = "normalize" in settings + show_kde = "kde" in settings + show_rug = "rug" in settings bins_settings = bins_settings or {} - + for col in selected_cols: - series_name = f'Histogram of {col}' + series_name = f"Histogram of {col}" color = color_data.get(series_name) if not color: # Skip if no color assigned continue - + # Get bins setting for this series (default: 20) num_bins = bins_settings.get(series_name, 20) - + # Clean the data data = df[col].dropna() - + # Add histogram with precisely num_bins bins - histnorm = 'probability' if normalize else None - + histnorm = "probability" if normalize else None + # Calculate bin range directly min_val, max_val = min(data), max(data) bin_width = (max_val - min_val) / num_bins if max_val > min_val else 1 - + # Use xbins to control exactly how many bins are displayed - fig.add_trace(go.Histogram( - x=data, - name=series_name, - histnorm=histnorm, - marker_color=color, - opacity=0.7, - showlegend=False, - xbins=dict( - start=min_val, - end=max_val, - size=bin_width - ), - autobinx=False # Disable autobinning - )) - + fig.add_trace( + go.Histogram( + x=data, + name=series_name, + histnorm=histnorm, + marker_color=color, + opacity=0.7, + showlegend=False, + xbins=dict(start=min_val, end=max_val, size=bin_width), + autobinx=False, # Disable autobinning + ) + ) + # Add KDE if requested if show_kde and len(data) > 1: try: # Calculate KDE values from scipy import stats as scipy_stats - + # Make sure we have enough unique values for KDE if len(np.unique(data)) > 5: kde = scipy_stats.gaussian_kde(data) x_range = np.linspace(min(data), max(data), 200) y_range = kde(x_range) - + # Scale KDE to match histogram height if normalize: # Scale for normalized histogram - y_range = y_range / np.trapz(y_range, x_range) + y_range = y_range / np.trapz(y_range, x_range) else: # Scale for count histogram hist, edges = np.histogram(data, bins=num_bins) max_hist_height = np.max(hist) max_kde_height = np.max(y_range) - scale_factor = max_hist_height / max_kde_height if max_kde_height > 0 else 1 + scale_factor = ( + max_hist_height / max_kde_height + if max_kde_height > 0 + else 1 + ) y_range = y_range * scale_factor - - fig.add_trace(go.Scatter( - x=x_range, - y=y_range, - mode='lines', - name=f'KDE of {col}', - line=dict(color=color, width=2), - showlegend=False - )) + + fig.add_trace( + go.Scatter( + x=x_range, + y=y_range, + mode="lines", + name=f"KDE of {col}", + line=dict(color=color, width=2), + showlegend=False, + ) + ) except Exception as e: debug_log(f"Error computing KDE: {e}") - + # Add rug plot if requested if show_rug: try: @@ -515,55 +635,55 @@ def update_histogram(selected_cols, settings, color_data, bins_settings): if normalize: # Position below x-axis for normalized histogram min_y = -0.02 - - fig.add_trace(go.Scatter( - x=data, - y=[min_y] * len(data), - mode='markers', - marker=dict( - symbol='line-ns', - size=10, - color=color, - opacity=0.5 - ), - showlegend=False, - hoverinfo='x' - )) + + fig.add_trace( + go.Scatter( + x=data, + y=[min_y] * len(data), + mode="markers", + marker=dict( + symbol="line-ns", size=10, color=color, opacity=0.5 + ), + showlegend=False, + hoverinfo="x", + ) + ) except Exception as e: debug_log(f"Error adding rug plot: {e}") - + # Display histograms using overlay mode fig.update_layout( - barmode='overlay', - title='Interactive Histogram Visualization', - plot_bgcolor='white', - paper_bgcolor='white', + barmode="overlay", + title="Interactive Histogram Visualization", + plot_bgcolor="white", + paper_bgcolor="white", showlegend=False, # Using custom legend xaxis=dict( - title='Value', + title="Value", showgrid=True, gridwidth=1, - gridcolor='LightGray', + gridcolor="LightGray", zeroline=True, zerolinewidth=2, - zerolinecolor='LightGray' + zerolinecolor="LightGray", ), yaxis=dict( - title='Frequency' if not normalize else 'Probability', + title="Frequency" if not normalize else "Probability", showgrid=True, gridwidth=1, - gridcolor='LightGray', + gridcolor="LightGray", zeroline=True, zerolinewidth=2, - zerolinecolor='LightGray' + zerolinecolor="LightGray", ), height=700, - margin=dict(t=50, b=50, l=50, r=50) + margin=dict(t=50, b=50, l=50, r=50), ) - + debug_log("Histogram plot updated") return fig + app.clientside_callback( """ function(children, series_data) { @@ -673,9 +793,8 @@ def update_histogram(selected_cols, settings, color_data, bins_settings): return window.dash_clientside.no_update; } """, - Output('custom-legend', 'data-update'), - [Input('custom-legend', 'children'), - Input('series-data', 'data')] + Output("custom-legend", "data-update"), + [Input("custom-legend", "children"), Input("series-data", "data")], ) # # Remove the existing update_bin_settings callback and replace with this: # @app.callback( @@ -689,114 +808,126 @@ def update_histogram(selected_cols, settings, color_data, bins_settings): # ctx = dash.callback_context # if not ctx.triggered: # return current_stats or {} - + # if not selected_cols: # return current_stats or {} - + # bins_settings = bins_settings or {} # stats = current_stats or {} - + # # Update statistics for all selected columns with current bin settings # for col in selected_cols: # series_name = f'Histogram of {col}' # num_bins = bins_settings.get(series_name, 20) # Default to 20 bins # updated_stats = calculate_histogram_statistics(df[col], num_bins) # stats[series_name] = updated_stats - + # # Remove stats for unselected columns # for series_name in list(stats.keys()): # if not any(f'Histogram of {col}' == series_name for col in selected_cols): # del stats[series_name] - + # return stats # Replace the existing initialize_series_data callback with this one: + @app.callback( - [Output('series-colors', 'data'), - Output('series-data', 'data')], - [Input('histogram-columns', 'value'), - Input('bins-settings', 'data')], - [State('series-colors', 'data')] + [Output("series-colors", "data"), Output("series-data", "data")], + [Input("histogram-columns", "value"), Input("bins-settings", "data")], + [State("series-colors", "data")], ) def initialize_and_update_series_data(selected_cols, bins_settings, existing_colors): """Initialize color data and update statistics for selected columns.""" ctx = dash.callback_context - debug_log(f"Series data update triggered by: {ctx.triggered[0]['prop_id'] if ctx.triggered else 'none'}") - + debug_log( + f"Series data update triggered by: {ctx.triggered[0]['prop_id'] if ctx.triggered else 'none'}" + ) + color_data = existing_colors or {} series_stats = {} bins_settings = bins_settings or {} - + if not selected_cols: return color_data, series_stats - + for col in selected_cols: - series_name = f'Histogram of {col}' - + series_name = f"Histogram of {col}" + # Assign a color if not already assigned if series_name not in color_data: color_data[series_name] = COLORS[len(color_data) % len(COLORS)] - + # Get bin count (default 20) num_bins = bins_settings.get(series_name, 20) - + # Calculate statistics with the current bin count stats = calculate_histogram_statistics(df[col], num_bins) series_stats[series_name] = stats - + # Keep only the colors for selected columns - color_data = {k: v for k, v in color_data.items() - if any(f'Histogram of {x}' == k for x in selected_cols)} - + color_data = { + k: v + for k, v in color_data.items() + if any(f"Histogram of {x}" == k for x in selected_cols) + } + return color_data, series_stats + + @app.callback( - Output('bins-settings', 'data'), - [Input({'type': 'bin-input', 'index': ALL}, 'value'), - Input('bins-slider', 'value'), - Input('histogram-columns', 'value')], - [State({'type': 'bin-input', 'index': ALL}, 'id'), - State('bins-settings', 'data'), - State('global-bins-active', 'data')] + Output("bins-settings", "data"), + [ + Input({"type": "bin-input", "index": ALL}, "value"), + Input("bins-slider", "value"), + Input("histogram-columns", "value"), + ], + [ + State({"type": "bin-input", "index": ALL}, "id"), + State("bins-settings", "data"), + State("global-bins-active", "data"), + ], ) -def update_all_bin_settings(bin_values, slider_value, selected_cols, bin_ids, current_settings, global_active): +def update_all_bin_settings( + bin_values, slider_value, selected_cols, bin_ids, current_settings, global_active +): """Master callback that handles ALL bin settings updates.""" ctx = dash.callback_context if not ctx.triggered: return current_settings or {} - + settings = current_settings or {} - triggered_id = ctx.triggered[0]['prop_id'] - + triggered_id = ctx.triggered[0]["prop_id"] + debug_log(f"Bin settings update triggered by: {triggered_id}") - + # When columns change, initialize bin settings for new columns - if 'histogram-columns.value' in triggered_id: + if "histogram-columns.value" in triggered_id: if selected_cols: for col in selected_cols: - series_name = f'Histogram of {col}' + series_name = f"Histogram of {col}" # Only set if not already set if series_name not in settings: settings[series_name] = slider_value - + # Clean up any series that are no longer selected to_remove = [] for series_name in settings: - if not any(f'Histogram of {col}' == series_name for col in selected_cols): + if not any(f"Histogram of {col}" == series_name for col in selected_cols): to_remove.append(series_name) - + for series_name in to_remove: del settings[series_name] - + # Handle global slider update - elif 'bins-slider.value' in triggered_id and global_active: + elif "bins-slider.value" in triggered_id and global_active: if selected_cols: for col in selected_cols: - series_name = f'Histogram of {col}' + series_name = f"Histogram of {col}" settings[series_name] = slider_value debug_log(f"Updated all bins to {slider_value} from global slider") - + # Handle individual bin input updates - elif 'bin-input' in triggered_id: + elif "bin-input" in triggered_id: for i, value in enumerate(bin_values): if value is not None: try: @@ -808,17 +939,19 @@ def update_all_bin_settings(bin_values, slider_value, selected_cols, bin_ids, cu continue else: num_value = int(value) - + if num_value >= MIN_BINS: - series_name = bin_ids[i]['index'] + series_name = bin_ids[i]["index"] settings[series_name] = num_value debug_log(f"Updated bin for {series_name} to {num_value}") - + except (ValueError, TypeError) as e: debug_log(f"Error converting bin value: {e}") pass - + return settings + + # Add this clientside callback to handle both typing and arrow clicks app.clientside_callback( """ @@ -867,9 +1000,9 @@ def update_all_bin_settings(bin_values, slider_value, selected_cols, bin_ids, cu return window.dash_clientside.no_update; } """, - Output('custom-legend', 'data-update', allow_duplicate=True), - [Input('bins-settings', 'data')], - prevent_initial_call=True + Output("custom-legend", "data-update", allow_duplicate=True), + [Input("bins-settings", "data")], + prevent_initial_call=True, ) # Add this to monitor and update from the clientside app.clientside_callback( @@ -896,10 +1029,10 @@ def update_all_bin_settings(bin_values, slider_value, selected_cols, bin_ids, cu return window.dash_clientside.no_update; } """, - Output('custom-legend', 'data-update', allow_duplicate=True), - [Input('interval-component', 'n_intervals')], - prevent_initial_call=True + Output("custom-legend", "data-update", allow_duplicate=True), + [Input("interval-component", "n_intervals")], + prevent_initial_call=True, ) # Run the app -if __name__ == '__main__': - app.run_server(debug=True, port=8051) \ No newline at end of file +if __name__ == "__main__": + app.run_server(debug=True, port=8051) diff --git a/plotly_examples/sensitivity_1.py b/plotly_examples/sensitivity_1.py index a4a509a..63e6815 100644 --- a/plotly_examples/sensitivity_1.py +++ b/plotly_examples/sensitivity_1.py @@ -1,13 +1,12 @@ """Interactive multi-column scatter plot interface with statistics.""" + import dash from dash import dcc, html -from dash.dependencies import Input, Output, State, MATCH, ALL +from dash.dependencies import Input, Output, State, ALL import plotly.graph_objects as go -import plotly.express as px import pandas as pd import numpy as np -from scipy import stats -from typing import Dict, Any +from typing import Any # # Debug Configuration # PYTHON_DEBUG = True # Controls Python-side logging @@ -20,26 +19,28 @@ # Debug Configuration PYTHON_DEBUG = True + def debug_log(message: str) -> None: """Python-side debug logging.""" if PYTHON_DEBUG: print(f"[PY] {message}") -def calculate_statistics(series1: pd.Series, series2: pd.Series) -> Dict[str, Any]: + +def calculate_statistics(series1: pd.Series, series2: pd.Series) -> dict[str, Any]: """Calculate statistics for a pair of series.""" try: # Drop NA values that exist in either series mask = ~(series1.isna() | series2.isna()) clean_s1 = series1[mask] clean_s2 = series2[mask] - + if len(clean_s1) < 2: # Need at least 2 points for most statistics return { "correlation": "N/A", "samples": len(clean_s1), "missing": len(series1) - len(clean_s1), "x_mean": f"{clean_s1.mean():.3f}" if len(clean_s1) > 0 else "N/A", - "y_mean": f"{clean_s2.mean():.3f}" if len(clean_s2) > 0 else "N/A" + "y_mean": f"{clean_s2.mean():.3f}" if len(clean_s2) > 0 else "N/A", } stats_dict = { @@ -49,35 +50,40 @@ def calculate_statistics(series1: pd.Series, series2: pd.Series) -> Dict[str, An "x_mean": f"{clean_s1.mean():.3f}", "y_mean": f"{clean_s2.mean():.3f}", "x_std": f"{clean_s1.std():.3f}", - "y_std": f"{clean_s2.std():.3f}" + "y_std": f"{clean_s2.std():.3f}", } - + # Calculate linear regression slope, intercept = np.polyfit(clean_s1, clean_s2, 1) stats_dict["regression"] = f"y = {slope:.3f}x + {intercept:.3f}" - + return stats_dict except Exception as e: if PYTHON_DEBUG: print(f"[PY] Error calculating statistics: {e}") return {"error": str(e)} + # Create sample data -df1 = pd.DataFrame({ - 'A': np.random.randn(100), - 'B': np.random.randn(100), - 'C': np.random.randn(100), - 'D': np.random.randn(100), - 'Category': np.random.choice(['X', 'Y', 'Z'], 100) -}) - -df2 = pd.DataFrame({ - 'W': np.random.randn(100), - 'X': np.random.randn(100), - 'Y': np.random.randn(100), - 'Z': np.random.randn(100), - 'Group': np.random.choice(['P', 'Q', 'R'], 100) -}) +df1 = pd.DataFrame( + { + "A": np.random.randn(100), + "B": np.random.randn(100), + "C": np.random.randn(100), + "D": np.random.randn(100), + "Category": np.random.choice(["X", "Y", "Z"], 100), + } +) + +df2 = pd.DataFrame( + { + "W": np.random.randn(100), + "X": np.random.randn(100), + "Y": np.random.randn(100), + "Z": np.random.randn(100), + "Group": np.random.choice(["P", "Q", "R"], 100), + } +) # # Initialize the Dash app # app = dash.Dash(__name__) @@ -86,43 +92,59 @@ def calculate_statistics(series1: pd.Series, series2: pd.Series) -> Dict[str, An # Styles with debug-friendly positioning dropdown_style = { - 'width': '100%', - 'marginBottom': '20px', - 'zIndex': 999, + "width": "100%", + "marginBottom": "20px", + "zIndex": 999, } info_panel_style = { - 'position': 'fixed', # Fixed positioning for hover panel - 'backgroundColor': 'white', - 'padding': '15px', - 'borderRadius': '5px', - 'boxShadow': '0 0 10px rgba(0,0,0,0.3)', - 'zIndex': 1000, - 'display': 'none', - 'minWidth': '200px', - 'maxWidth': '300px', - 'fontSize': '12px' + "position": "fixed", # Fixed positioning for hover panel + "backgroundColor": "white", + "padding": "15px", + "borderRadius": "5px", + "boxShadow": "0 0 10px rgba(0,0,0,0.3)", + "zIndex": 1000, + "display": "none", + "minWidth": "200px", + "maxWidth": "300px", + "fontSize": "12px", } color_picker_style = { - 'position': 'fixed', - 'top': '50%', - 'left': '50%', - 'transform': 'translate(-50%, -50%)', - 'backgroundColor': 'white', - 'padding': '20px', - 'borderRadius': '5px', - 'boxShadow': '0 0 10px rgba(0,0,0,0.3)', - 'zIndex': 1001, - 'display': 'none' + "position": "fixed", + "top": "50%", + "left": "50%", + "transform": "translate(-50%, -50%)", + "backgroundColor": "white", + "padding": "20px", + "borderRadius": "5px", + "boxShadow": "0 0 10px rgba(0,0,0,0.3)", + "zIndex": 1001, + "display": "none", } # Available colors COLORS = [ - '#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', - '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf', - '#aec7e8', '#ffbb78', '#98df8a', '#ff9896', '#c5b0d5', - '#c49c94', '#f7b6d2', '#c7c7c7', '#dbdb8d', '#9edae5' + "#1f77b4", + "#ff7f0e", + "#2ca02c", + "#d62728", + "#9467bd", + "#8c564b", + "#e377c2", + "#7f7f7f", + "#bcbd22", + "#17becf", + "#aec7e8", + "#ffbb78", + "#98df8a", + "#ff9896", + "#c5b0d5", + "#c49c94", + "#f7b6d2", + "#c7c7c7", + "#dbdb8d", + "#9edae5", ] # JavaScript code for hover handling @@ -204,111 +226,158 @@ def calculate_statistics(series1: pd.Series, series2: pd.Series) -> Dict[str, An """ # Define the layout -app.layout = html.Div([ - html.H1("DataFrame Column Comparison"), - - # Add the hover handling script - html.Script(hover_script), - - # Control panel for column selection - html.Div([ - html.Div([ - html.H3("Select X-axis Columns (DataFrame 1)"), - dcc.Dropdown( - id='df1-columns', - options=[{'label': col, 'value': col} for col in df1.columns if col != 'Category'], - value=[], - multi=True, - style=dropdown_style - ), - ], style={'width': '45%', 'display': 'inline-block', 'margin': '5px'}), - - html.Div([ - html.H3("Select Y-axis Columns (DataFrame 2)"), - dcc.Dropdown( - id='df2-columns', - options=[{'label': col, 'value': col} for col in df2.columns if col != 'Group'], - value=[], - multi=True, - style=dropdown_style - ), - ], style={'width': '45%', 'display': 'inline-block', 'margin': '5px'}), - ]), - - # Plot settings - # html.Div([ - # html.H3("Plot Settings", style={'marginBottom': '5px'}), - # dcc.Checklist( - # id='plot-settings', - # options=[ - # {'label': ' Show trend lines', 'value': 'trend'}, - # {'label': ' Show point labels', 'value': 'labels'} - # ], - # value=['trend'], - # style={'display': 'flex', 'gap': '20px'}, - # inputStyle={'marginRight': '5px'}, - # labelStyle={'display': 'flex', 'alignItems': 'center'} - # ) - # ], style={'margin': '10px'}), - - # Graph and custom legend - html.Div([ +app.layout = html.Div( + [ + html.H1("DataFrame Column Comparison"), + # Add the hover handling script + html.Script(hover_script), + # Control panel for column selection + html.Div( + [ + html.Div( + [ + html.H3("Select X-axis Columns (DataFrame 1)"), + dcc.Dropdown( + id="df1-columns", + options=[ + {"label": col, "value": col} + for col in df1.columns + if col != "Category" + ], + value=[], + multi=True, + style=dropdown_style, + ), + ], + style={"width": "45%", "display": "inline-block", "margin": "5px"}, + ), + html.Div( + [ + html.H3("Select Y-axis Columns (DataFrame 2)"), + dcc.Dropdown( + id="df2-columns", + options=[ + {"label": col, "value": col} + for col in df2.columns + if col != "Group" + ], + value=[], + multi=True, + style=dropdown_style, + ), + ], + style={"width": "45%", "display": "inline-block", "margin": "5px"}, + ), + ] + ), # Plot settings - html.Div([ - html.H3("Plot Settings", style={'marginBottom': '5px'}), - dcc.Checklist( - id='plot-settings', - options=[ - {'label': ' Show trend lines', 'value': 'trend'}, - {'label': ' Show point labels', 'value': 'labels'} - ], - value=['trend'], - style={'display': 'block', 'gap': '10px'}, # Changed from 'flex' to 'block' - inputStyle={'marginRight': '5px'}, - labelStyle={'display': 'block', 'marginBottom': '10px'} # Added marginBottom and changed to block - ) - ], style={'width': '10%', 'display': 'inline-block', 'verticalAlign': 'top'}), - - # Main plot - html.Div([ - dcc.Graph( - id='scatter-plot', - style={'height': '700px'} - ), - ], style={'width': '70%', 'display': 'inline-block'}), - - # Custom legend - html.Div([ - html.H3("Series"), - html.Div(id='custom-legend', style={ - 'overflowY': 'auto', - 'maxHeight': '600px' - }) - ], style={'width': '20%', 'display': 'inline-block', 'verticalAlign': 'top'}), - - ]), - - # Info panel (hidden by default) - html.Div(id='series-info-panel', style=info_panel_style), - - # Color picker panel (hidden by default) - html.Div([ - html.H3("Select Color"), + # html.Div([ + # html.H3("Plot Settings", style={'marginBottom': '5px'}), + # dcc.Checklist( + # id='plot-settings', + # options=[ + # {'label': ' Show trend lines', 'value': 'trend'}, + # {'label': ' Show point labels', 'value': 'labels'} + # ], + # value=['trend'], + # style={'display': 'flex', 'gap': '20px'}, + # inputStyle={'marginRight': '5px'}, + # labelStyle={'display': 'flex', 'alignItems': 'center'} + # ) + # ], style={'margin': '10px'}), + # Graph and custom legend html.Div( - [html.Button( - style={'backgroundColor': color, 'width': '30px', 'height': '30px', 'margin': '5px'}, - id={'type': 'color-option', 'index': i} - ) for i, color in enumerate(COLORS)], - style={'display': 'grid', 'gridTemplateColumns': 'repeat(5, 1fr)', 'gap': '5px'} + [ + # Plot settings + html.Div( + [ + html.H3("Plot Settings", style={"marginBottom": "5px"}), + dcc.Checklist( + id="plot-settings", + options=[ + {"label": " Show trend lines", "value": "trend"}, + {"label": " Show point labels", "value": "labels"}, + ], + value=["trend"], + style={ + "display": "block", + "gap": "10px", + }, # Changed from 'flex' to 'block' + inputStyle={"marginRight": "5px"}, + labelStyle={ + "display": "block", + "marginBottom": "10px", + }, # Added marginBottom and changed to block + ), + ], + style={ + "width": "10%", + "display": "inline-block", + "verticalAlign": "top", + }, + ), + # Main plot + html.Div( + [ + dcc.Graph(id="scatter-plot", style={"height": "700px"}), + ], + style={"width": "70%", "display": "inline-block"}, + ), + # Custom legend + html.Div( + [ + html.H3("Series"), + html.Div( + id="custom-legend", + style={"overflowY": "auto", "maxHeight": "600px"}, + ), + ], + style={ + "width": "20%", + "display": "inline-block", + "verticalAlign": "top", + }, + ), + ] ), - html.Button("Close", id='close-color-picker', style={'marginTop': '10px'}) - ], id='color-picker-panel', style=color_picker_style), - - # Store components - dcc.Store(id='series-colors'), - dcc.Store(id='series-data'), - dcc.Store(id='active-series') -]) + # Info panel (hidden by default) + html.Div(id="series-info-panel", style=info_panel_style), + # Color picker panel (hidden by default) + html.Div( + [ + html.H3("Select Color"), + html.Div( + [ + html.Button( + style={ + "backgroundColor": color, + "width": "30px", + "height": "30px", + "margin": "5px", + }, + id={"type": "color-option", "index": i}, + ) + for i, color in enumerate(COLORS) + ], + style={ + "display": "grid", + "gridTemplateColumns": "repeat(5, 1fr)", + "gap": "5px", + }, + ), + html.Button( + "Close", id="close-color-picker", style={"marginTop": "10px"} + ), + ], + id="color-picker-panel", + style=color_picker_style, + ), + # Store components + dcc.Store(id="series-colors"), + dcc.Store(id="series-data"), + dcc.Store(id="active-series"), + ] +) app.clientside_callback( """ @@ -357,17 +426,19 @@ def calculate_statistics(series1: pd.Series, series2: pd.Series) -> Dict[str, An return window.dash_clientside.no_update; } """, - Output('custom-legend', 'data-update'), - Input('custom-legend', 'children') + Output("custom-legend", "data-update"), + Input("custom-legend", "children"), ) + @app.callback( - [Output('custom-legend', 'children'), - Output('series-colors', 'data'), - Output('series-data', 'data')], - [Input('df1-columns', 'value'), - Input('df2-columns', 'value')], - [State('series-colors', 'data')] + [ + Output("custom-legend", "children"), + Output("series-colors", "data"), + Output("series-data", "data"), + ], + [Input("df1-columns", "value"), Input("df2-columns", "value")], + [State("series-colors", "data")], ) def update_legend(df1_cols, df2_cols, existing_colors): """Update custom legend with interactive elements.""" @@ -375,106 +446,128 @@ def update_legend(df1_cols, df2_cols, existing_colors): legend_items = [] color_data = existing_colors or {} series_stats = {} - + if not df1_cols or not df2_cols: debug_log("No columns selected") return [], color_data, {} - + for x_col in df1_cols: for y_col in df2_cols: - series_name = f'{x_col} vs {y_col}' + series_name = f"{x_col} vs {y_col}" debug_log(f"Processing series: {series_name}") - + if series_name not in color_data: color_data[series_name] = COLORS[len(color_data) % len(COLORS)] - + color = color_data[series_name] stats = calculate_statistics(df1[x_col], df2[y_col]) series_stats[series_name] = stats - - legend_items.append(html.Div([ - html.Button( - style={ - 'backgroundColor': color, - 'width': '20px', - 'height': '20px', - 'marginRight': '10px', - 'verticalAlign': 'middle', - 'cursor': 'pointer', - 'border': '1px solid #ddd' + + legend_items.append( + html.Div( + [ + html.Button( + style={ + "backgroundColor": color, + "width": "20px", + "height": "20px", + "marginRight": "10px", + "verticalAlign": "middle", + "cursor": "pointer", + "border": "1px solid #ddd", + }, + id={"type": "color-button", "index": series_name}, + ), + html.Span( + series_name, + style={ + "cursor": "default", + "userSelect": "none", + "display": "inline-block", + }, + ), + ], + className="legend-item", + id={"type": "legend-item", "index": series_name}, + **{ + "data-series": series_name, + "style": { + "margin": "10px", + "padding": "5px", + "borderRadius": "3px", + }, }, - id={'type': 'color-button', 'index': series_name} - ), - html.Span( - series_name, - style={ - 'cursor': 'default', - 'userSelect': 'none', - 'display': 'inline-block' - } ) - ], - className='legend-item', - id={'type': 'legend-item', 'index': series_name}, - **{ - 'data-series': series_name, - 'style': { - 'margin': '10px', - 'padding': '5px', - 'borderRadius': '3px', - } - })) - - color_data = {k: v for k, v in color_data.items() - if any(x in k for x in df1_cols) and any(y in k for y in df2_cols)} - + ) + + color_data = { + k: v + for k, v in color_data.items() + if any(x in k for x in df1_cols) and any(y in k for y in df2_cols) + } + debug_log(f"Created {len(legend_items)} legend items") return legend_items, color_data, series_stats + @app.callback( - [Output('series-info-panel', 'children'), - Output('series-info-panel', 'style')], - [Input({'type': 'legend-item', 'index': ALL}, 'mouseenter'), - Input({'type': 'legend-item', 'index': ALL}, 'mouseleave')], - [State('series-data', 'data'), - State('series-info-panel', 'style')] + [Output("series-info-panel", "children"), Output("series-info-panel", "style")], + [ + Input({"type": "legend-item", "index": ALL}, "mouseenter"), + Input({"type": "legend-item", "index": ALL}, "mouseleave"), + ], + [State("series-data", "data"), State("series-info-panel", "style")], ) def update_info_panel(mouseenter, mouseleave, series_data, current_style): """Update the info panel content and visibility.""" if not dash.callback_context.triggered: return dash.no_update, dash.no_update - + trigger = dash.callback_context.triggered[0] - + # Handle mouseleave - if '.mouseleave' in trigger['prop_id']: - return dash.no_update, dict(current_style, display='none') - + if ".mouseleave" in trigger["prop_id"]: + return dash.no_update, dict(current_style, display="none") + # Handle mouseenter - if '.mouseenter' in trigger['prop_id']: + if ".mouseenter" in trigger["prop_id"]: try: - series_name = eval(trigger['prop_id'].split('.')[0])['index'] + series_name = eval(trigger["prop_id"].split(".")[0])["index"] stats = series_data.get(series_name, {}) - - content = html.Div([ - html.H4(f"Statistics: {series_name}", - style={'marginTop': 0, 'marginBottom': '10px'}), - html.Table([ - html.Tr([ - html.Td(k, style={'padding': '3px', 'fontWeight': 'bold'}), - html.Td(str(v), style={'padding': '3px'}) - ]) for k, v in stats.items() - ], style={'borderCollapse': 'collapse'}) - ]) - - return content, dict(current_style, display='block') - + + content = html.Div( + [ + html.H4( + f"Statistics: {series_name}", + style={"marginTop": 0, "marginBottom": "10px"}, + ), + html.Table( + [ + html.Tr( + [ + html.Td( + k, + style={"padding": "3px", "fontWeight": "bold"}, + ), + html.Td(str(v), style={"padding": "3px"}), + ] + ) + for k, v in stats.items() + ], + style={"borderCollapse": "collapse"}, + ), + ] + ) + + return content, dict(current_style, display="block") + except Exception as e: debug_log(f"Error updating info panel: {e}") return dash.no_update, dash.no_update - + return dash.no_update, dash.no_update + # @app.callback( # [Output('color-picker-panel', 'style'), # Output('active-series', 'data')], @@ -487,19 +580,19 @@ def update_info_panel(mouseenter, mouseleave, series_data, current_style): # ctx = dash.callback_context # if not ctx.triggered: # return dict(color_picker_style, display='none'), None - + # triggered_id = ctx.triggered[0]['prop_id'] - + # if 'close-color-picker' in triggered_id or not any(button_clicks): # debug_log("Closing color picker") # return dict(color_picker_style, display='none'), None - + # if 'color-button' in triggered_id: # button_idx = next(i for i, c in enumerate(button_clicks) if c) # series_name = button_ids[button_idx]['index'] # debug_log(f"Opening color picker for {series_name}") # return dict(color_picker_style, display='block'), series_name - + # return dict(color_picker_style, display='none'), None # @app.callback( @@ -515,219 +608,243 @@ def update_info_panel(mouseenter, mouseleave, series_data, current_style): # ctx = dash.callback_context # if not ctx.triggered or not active_series or not current_colors: # return dash.no_update - + # triggered_id = ctx.triggered[0]['prop_id'] # if 'color-option' not in triggered_id: # return dash.no_update - + # color_idx = eval(triggered_id.split('.')[0])['index'] # current_colors[active_series] = COLORS[color_idx] # debug_log(f"Updated color for {active_series}") # return current_colors + @app.callback( - Output('scatter-plot', 'figure'), - [Input('df1-columns', 'value'), - Input('df2-columns', 'value'), - Input('plot-settings', 'value'), - Input('series-colors', 'data')] + Output("scatter-plot", "figure"), + [ + Input("df1-columns", "value"), + Input("df2-columns", "value"), + Input("plot-settings", "value"), + Input("series-colors", "data"), + ], ) def update_graph(df1_cols, df2_cols, settings, color_data): """Update the scatter plot based on selected columns.""" debug_log("Updating scatter plot") fig = go.Figure() - + if not df1_cols or not df2_cols or not color_data: fig.add_annotation( text="Please select columns from both dataframes", - xref="paper", yref="paper", - x=0.5, y=0.5, showarrow=False + xref="paper", + yref="paper", + x=0.5, + y=0.5, + showarrow=False, ) return fig - + for x_col in df1_cols: for y_col in df2_cols: - series_name = f'{x_col} vs {y_col}' + series_name = f"{x_col} vs {y_col}" color = color_data.get(series_name) if not color: # Skip if no color assigned continue - + # Add scatter plot - fig.add_trace(go.Scatter( - x=df1[x_col], - y=df2[y_col], - mode='markers', - name=series_name, - marker=dict( - color=color, - size=8, - opacity=0.6 - ), - showlegend=False # Using custom legend instead - )) - + fig.add_trace( + go.Scatter( + x=df1[x_col], + y=df2[y_col], + mode="markers", + name=series_name, + marker=dict(color=color, size=8, opacity=0.6), + showlegend=False, # Using custom legend instead + ) + ) + # Add trend line if requested - if 'trend' in settings: + if "trend" in settings: mask = ~(df1[x_col].isna() | df2[y_col].isna()) x = df1[x_col][mask] y = df2[y_col][mask] if len(x) > 1: z = np.polyfit(x, y, 1) p = np.poly1d(z) - - fig.add_trace(go.Scatter( - x=[x.min(), x.max()], - y=[p(x.min()), p(x.max())], - mode='lines', - name=f'Trend: {series_name}', - line=dict(color=color, dash='dash'), - showlegend=False - )) - + + fig.add_trace( + go.Scatter( + x=[x.min(), x.max()], + y=[p(x.min()), p(x.max())], + mode="lines", + name=f"Trend: {series_name}", + line=dict(color=color, dash="dash"), + showlegend=False, + ) + ) + fig.update_layout( - title='Column Comparisons', - plot_bgcolor='white', - paper_bgcolor='white', + title="Column Comparisons", + plot_bgcolor="white", + paper_bgcolor="white", showlegend=False, # Using custom legend xaxis=dict( - title='DataFrame 1 Columns', + title="DataFrame 1 Columns", showgrid=True, gridwidth=1, - gridcolor='LightGray', + gridcolor="LightGray", zeroline=True, zerolinewidth=2, - zerolinecolor='LightGray' + zerolinecolor="LightGray", ), yaxis=dict( - title='DataFrame 2 Columns', + title="DataFrame 2 Columns", showgrid=True, gridwidth=1, - gridcolor='LightGray', + gridcolor="LightGray", zeroline=True, zerolinewidth=2, - zerolinecolor='LightGray' + zerolinecolor="LightGray", ), height=700, - margin=dict(t=50, b=50, l=50, r=50) + margin=dict(t=50, b=50, l=50, r=50), ) - + debug_log("Scatter plot updated") return fig + """""" + @app.callback( - Output('series-colors', 'data', allow_duplicate=True), - [Input({'type': 'color-option', 'index': ALL}, 'n_clicks')], - [State({'type': 'color-option', 'index': ALL}, 'id'), - State('active-series', 'data'), - State('series-colors', 'data')], - prevent_initial_call=True + Output("series-colors", "data", allow_duplicate=True), + [Input({"type": "color-option", "index": ALL}, "n_clicks")], + [ + State({"type": "color-option", "index": ALL}, "id"), + State("active-series", "data"), + State("series-colors", "data"), + ], + prevent_initial_call=True, ) def update_series_color(color_clicks, color_ids, active_series, current_colors): """Update color when selecting from color picker.""" ctx = dash.callback_context if not ctx.triggered or not active_series or not current_colors: return dash.no_update - - triggered_id = ctx.triggered[0]['prop_id'] - if 'color-option' not in triggered_id: + + triggered_id = ctx.triggered[0]["prop_id"] + if "color-option" not in triggered_id: return dash.no_update - - color_idx = eval(triggered_id.split('.')[0])['index'] + + color_idx = eval(triggered_id.split(".")[0])["index"] current_colors[active_series] = COLORS[color_idx] debug_log(f"Updated color for {active_series} to {COLORS[color_idx]}") return current_colors + @app.callback( - [Output('color-picker-panel', 'style'), - Output('active-series', 'data')], - [Input({'type': 'color-button', 'index': ALL}, 'n_clicks'), - Input('close-color-picker', 'n_clicks'), - Input({'type': 'color-option', 'index': ALL}, 'n_clicks')], # Add this input - [State({'type': 'color-button', 'index': ALL}, 'id'), - State('color-picker-panel', 'style')] + [Output("color-picker-panel", "style"), Output("active-series", "data")], + [ + Input({"type": "color-button", "index": ALL}, "n_clicks"), + Input("close-color-picker", "n_clicks"), + Input({"type": "color-option", "index": ALL}, "n_clicks"), + ], # Add this input + [ + State({"type": "color-button", "index": ALL}, "id"), + State("color-picker-panel", "style"), + ], ) -def toggle_color_picker(button_clicks, close_clicks, color_option_clicks, button_ids, current_style): +def toggle_color_picker( + button_clicks, close_clicks, color_option_clicks, button_ids, current_style +): """Show/hide color picker when clicking color buttons.""" ctx = dash.callback_context if not ctx.triggered: - return dict(current_style, display='none'), None - - triggered_id = ctx.triggered[0]['prop_id'] - + return dict(current_style, display="none"), None + + triggered_id = ctx.triggered[0]["prop_id"] + # Close on color selection or close button click - if 'color-option' in triggered_id or 'close-color-picker' in triggered_id: + if "color-option" in triggered_id or "close-color-picker" in triggered_id: debug_log("Closing color picker after selection") - return dict(current_style, display='none'), None - + return dict(current_style, display="none"), None + # Open on color button click - if 'color-button' in triggered_id and any(button_clicks): + if "color-button" in triggered_id and any(button_clicks): button_idx = next(i for i, c in enumerate(button_clicks) if c) - series_name = button_ids[button_idx]['index'] + series_name = button_ids[button_idx]["index"] debug_log(f"Opening color picker for {series_name}") - return dict(current_style, display='block'), series_name - - return dict(current_style, display='none'), None + return dict(current_style, display="block"), series_name + + return dict(current_style, display="none"), None + # Update the legend immediately when colors change @app.callback( - Output('custom-legend', 'children', allow_duplicate=True), - [Input('series-colors', 'data')], - [State('df1-columns', 'value'), - State('df2-columns', 'value'), - State('series-data', 'data')], - prevent_initial_call=True + Output("custom-legend", "children", allow_duplicate=True), + [Input("series-colors", "data")], + [ + State("df1-columns", "value"), + State("df2-columns", "value"), + State("series-data", "data"), + ], + prevent_initial_call=True, ) def update_legend_colors(color_data, df1_cols, df2_cols, series_data): """Update legend when colors change.""" if not color_data or not df1_cols or not df2_cols: return dash.no_update - + debug_log("Updating legend colors") legend_items = [] - + for x_col in df1_cols: for y_col in df2_cols: - series_name = f'{x_col} vs {y_col}' + series_name = f"{x_col} vs {y_col}" color = color_data.get(series_name) if not color: continue - - legend_items.append(html.Div([ - html.Button( - style={ - 'backgroundColor': color, - 'width': '20px', - 'height': '20px', - 'marginRight': '10px', - 'verticalAlign': 'middle', - 'cursor': 'pointer', - 'border': '1px solid #ddd' + + legend_items.append( + html.Div( + [ + html.Button( + style={ + "backgroundColor": color, + "width": "20px", + "height": "20px", + "marginRight": "10px", + "verticalAlign": "middle", + "cursor": "pointer", + "border": "1px solid #ddd", + }, + id={"type": "color-button", "index": series_name}, + ), + html.Span( + series_name, + style={ + "cursor": "default", + "userSelect": "none", + "display": "inline-block", + }, + ), + ], + className="legend-item", + id={"type": "legend-item", "index": series_name}, + **{ + "data-series": series_name, + "style": { + "margin": "10px", + "padding": "5px", + "borderRadius": "3px", + }, }, - id={'type': 'color-button', 'index': series_name} - ), - html.Span( - series_name, - style={ - 'cursor': 'default', - 'userSelect': 'none', - 'display': 'inline-block' - } ) - ], - className='legend-item', - id={'type': 'legend-item', 'index': series_name}, - **{ - 'data-series': series_name, - 'style': { - 'margin': '10px', - 'padding': '5px', - 'borderRadius': '3px', - } - })) - + ) + return legend_items -if __name__ == '__main__': - app.run_server(debug=True, port=8050) \ No newline at end of file + +if __name__ == "__main__": + app.run_server(debug=True, port=8050) diff --git a/pyproject.toml b/pyproject.toml index ce0a3a6..012b0d1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,105 +1,152 @@ [build-system] -requires = ["hatchling"] +requires = ["hatchling", "hatch-vcs"] build-backend = "hatchling.build" [project] -license-files = ["LICENSE"] +name = "trendify" +dynamic = ["version"] +description = "Tools for generating data products, storing, and interacting with them" +readme = "README.md" +requires-python = ">=3.11" dependencies = [ - "dash", - "filelock", - "flask", - "jinja2", - "pandas", - "plotly", - "pydantic", - "matplotlib", - "numpy", - "numpydantic", - "scipy", - "strenum", - "supervisor", - "waitress", - "grafana_api", - "streamlit", + "dash>=4.3.0", + "filelock>=3.29.4", + "flask>=3.1.3", + "grafana-api>=1.0.3", + "jinja2>=3.1.6", + "matplotlib>=3.11.0", + "numpy>=2.4.6", + "numpydantic>=1.10.0", + "pandas>=3.0.3", + "plotly>=6.8.0", + "pydantic>=2.13.4", + "scipy>=1.17.1", + "streamlit>=1.58.0", + "strenum>=0.4.15", + "supervisor>=4.3.0", + "waitress>=3.0.2", +] + +keywords = ["Data", "Products", "Process", "Post", "Database"] + +classifiers = [ + "Development Status :: 3 - Alpha", + # "Intended Audience :: Science/Research", + # "Topic :: Scientific/Engineering :: Mathematics", + "Typing :: Typed", + "Operating System :: OS Independent", + "Natural Language :: English", + "License :: OSI Approved :: MIT License", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Programming Language :: Python :: 3.14", ] -name = "trendify" -version = "1.2.11" -requires-python = ">= 3.11" authors = [ - { name = "Talbot Knighton", email = "talbotknighton@gmail.com" }, - { name = "David Gable", email = "david@david-gable.com" }, + { name = "Talbot Knighton", email = "talbotknighton@gmail.com" }, + { name = "David Gable", email = "david@david-gable.com" }, ] maintainers = [ - { name = "Talbot Knighton", email = "talbotknighton@gmail.com" }, - { name = "David Gable", email = "david@david-gable.com" }, + { name = "Talbot Knighton", email = "talbotknighton@gmail.com" }, + { name = "David Gable", email = "david@david-gable.com" }, ] -description = "Tools for generating data products, storing, and interacting with them" -readme = "README.md" -keywords = ["Data", "Products", "Process", "Post", "Database"] -classifiers = [ - # How mature is this project? Common values are - # 3 - Alpha - # 4 - Beta - # 5 - Production/Stable - "Development Status :: 3 - Alpha", +license = { text = "MIT" } + +[tool.hatch.version] +source = "vcs" +fallback-version = "0.0.0" + +[tool.hatch.build.targets.wheel] +packages = ["src/trendify"] + +[tool.ruff] +exclude = ["typings"] + + +[tool.ruff.lint] +select = ["D", "RUF", "E", "F", "W", "UP"] # some suggest using "ALL" +ignore = [ + # modules + "ANN", # flake8-annotations + "COM", # flake8-commas + "C90", # mccabe complexity + "DJ", # django + "EXE", # flake8-executable + "T10", # debugger + "TID", # flake8-tidy-imports - # Specify the Python versions you support here. - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.11", + # specific rules + # "D100", # ignore missing docs + "D101", + "D102", + "D103", + "D104", + "D105", + "D106", + "D107", + "D200", + "D203", + "D205", + "D212", + "D400", + "D401", + "D404", + "D415", + "E402", # false positives for local imports + "E501", # line too long + "TRY003", # external messages in exceptions are too verbose + "TD002", + "TD003", + "FIX002", # too verbose descriptions of todos ] -[project.optional-dependencies] +[tool.pyright] +stubPath = "typings" +venvPath = ".." +venv = ".venv" +ignore = ["typings"] + +[tool.coverage.report] +exclude_also = ["if __name__ == .__main__.:", "@(abc\\.)?abstractmethod"] + +[dependency-groups] dev = [ - "mkdocs", - "black", - "pymdown-extensions", - "mkdocs-material", - "markdown-callouts", - "mdx-gh-links", - "mkdocs-click", - "markdown_include", - "mkdocs-glightbox", - "mkdocs-gen-files", - "mkdocs-section-index", - "mkdocs-autorefs", - "mkdocs-literate-nav", - "mkdocstrings", - "mkdocs-exclude", - "mkdocs-snippets", - "mkdocstrings-python", - "griffe-pydantic", - "pytest", - "pytest-asyncio", - "pytest-cov", - "mike", - "isort>=5.12.0", - "flake8>=6.0.0", - "mkdocs-material>=9.2.0", - "mkdocs-redirects>=1.0.0", - "hatchling>=1.11.0", + "black>=26.5.1", + "flake8>=7.3.0", + "griffe-pydantic>=1.3.1", + "hatchling>=1.30.1", + "isort>=8.0.1", + "markdown-callouts>=0.4.0", + "markdown-include>=0.8.1", + "mdx-gh-links>=0.4", + "mike>=2.2.0", + "mkdocs>=1.6.1", + "mkdocs-autorefs>=1.4.4", + "mkdocs-click>=0.9.0", + "mkdocs-exclude>=1.0.2", + "mkdocs-gen-files>=0.6.1", + "mkdocs-glightbox>=0.5.2", + "mkdocs-literate-nav>=0.6.3", + "mkdocs-material>=9.7.6", + "mkdocs-redirects>=1.2.3", + "mkdocs-section-index>=0.3.12", + "mkdocs-snippets>=1.3.2", + "mkdocstrings>=1.0.4", + "mkdocstrings-python>=2.0.5", + "pymdown-extensions>=11.0", + "pytest>=9.1.1", + "pytest-asyncio>=1.4.0", + "pytest-cov>=7.1.0", ] [project.urls] "Homepage" = "https://github.com/talbotknighton/trendify" -"Bug Tracker" = "https://github.com/talbotknighton/trendify/issues" "Documentation" = "https://talbotknighton.github.io/trendify/" -"Source Code" = "https://github.com/talbotknighton/trendify" +"Repository" = "https://github.com/talbotknighton/trendify" +"Issues" = "https://github.com/talbotknighton/trendify/issues" "PyPI" = "https://pypi.org/project/trendify/" [project.scripts] trendify_make_sample_data = "trendify.examples:make_sample_data" trendify = "trendify.CLI:trendify" -# trendify_server = "trendify.CLI:serve" - -[tool.setuptools] -include-package-data = true - -[tool.setuptools.package-data] -"trendify.assets" = ["*.svg"] - -[tool.pytest.ini_options] -testpaths = ["tests"] -python_files = "*.py" -asyncio_mode = "strict" -asyncio_default_fixture_loop_scope = "function" diff --git a/sample_hierarchical_data.py b/sample_hierarchical_data.py index d310e57..6ae0c08 100644 --- a/sample_hierarchical_data.py +++ b/sample_hierarchical_data.py @@ -4,8 +4,6 @@ """ import numpy as np -import pandas as pd -import os import sys from pathlib import Path @@ -16,7 +14,6 @@ from trendify.api.api import ( DataProductCollection, - Tags, Point2D, Trace2D, TableEntry, @@ -25,82 +22,86 @@ LineOrientation, Format2D, Pen, - Marker ) + def generate_sample_data(): """Generate a collection of data products with hierarchical tags.""" # First, create a list of data products data_products = [] - + # Define hierarchical tag categories categories = { "Performance": ["CPU", "Memory", "Disk", "Network"], "Analytics": ["Users", "Sessions", "Conversions", "Revenue"], "Infrastructure": ["Servers", "Databases", "Load Balancers", "Cache"], "Monitoring": ["Errors", "Latency", "Uptime", "Throughput"], - "Finance": ["Revenue", "Costs", "ROI", "Growth"] + "Finance": ["Revenue", "Costs", "ROI", "Growth"], } - + # Create Format2D objects for different plot types time_series_format = Format2D( title_ax="Time Series Data", label_x="Time (s)", label_y="Value", - title_legend="Metrics" + title_legend="Metrics", ) - + scatter_format = Format2D( title_ax="Correlation Analysis", label_x="X Value", label_y="Y Value", - title_legend="Data Points" + title_legend="Data Points", ) - + histogram_format = Format2D( title_ax="Distribution Analysis", label_x="Value", label_y="Frequency", - title_legend="Distributions" + title_legend="Distributions", ) - + # Generate data for each category for category, subcategories in categories.items(): for subcategory in subcategories: # Create a hierarchical tag tag_str = f"{category}/{subcategory}" - + # Add time series traces (1-3 per subcategory) for i in range(1, np.random.randint(2, 5)): x = np.linspace(0, 10, 100) noise = np.random.normal(0, 0.5, 100) amplitude = np.random.uniform(1, 5) frequency = np.random.uniform(0.5, 2) - + y = amplitude * np.sin(frequency * x) + noise - - data_products.append(Trace2D.from_xy( - x=x, - y=y, - pen=Pen(label=f"{subcategory} Metric {i}"), - format2d=time_series_format, - tags=[tag_str] - )) - + + data_products.append( + Trace2D.from_xy( + x=x, + y=y, + pen=Pen(label=f"{subcategory} Metric {i}"), + format2d=time_series_format, + tags=[tag_str], + ) + ) + # Add scatter points (for some subcategories) if np.random.random() > 0.3: # 70% chance of adding points num_points = np.random.randint(20, 100) x = np.random.normal(5, 2, num_points) y = 2 * x + np.random.normal(0, 3, num_points) - - data_products.append(Trace2D.from_xy( - x=x, - y=y, - pen=Pen(label=f"{subcategory} Data Points"), - format2d=scatter_format, - tags=[tag_str], - )) - + + data_products.append( + Trace2D.from_xy( + x=x, + y=y, + pen=Pen(label=f"{subcategory} Data Points"), + format2d=scatter_format, + tags=[tag_str], + ) + ) + # Add histogram data (for some subcategories) if np.random.random() > 0.5: # 50% chance of adding histogram # Generate some sample distribution data @@ -110,120 +111,129 @@ def generate_sample_data(): dist_type = "Normal" else: # Bimodal distribution - values = np.concatenate([ - np.random.normal(30, 10, 500), - np.random.normal(70, 10, 500) - ]) + values = np.concatenate( + [np.random.normal(30, 10, 500), np.random.normal(70, 10, 500)] + ) dist_type = "Bimodal" from trendify import HistogramStyle + for v in values: - data_products.append(HistogramEntry( - value=v, - style=HistogramStyle(label=f"{subcategory} {dist_type}"), - format2d=histogram_format, - tags=[tag_str], - )) - + data_products.append( + HistogramEntry( + value=v, + style=HistogramStyle(label=f"{subcategory} {dist_type}"), + format2d=histogram_format, + tags=[tag_str], + ) + ) + # Add table data (for some subcategories) if np.random.random() > 0.6: # 40% chance of adding table # Create a sample table with metrics metrics = ["Count", "Avg", "Min", "Max", "Std Dev"] features = [f"Feature {i}" for i in range(1, 4)] - + for metric in metrics: for feature in features: value = np.random.uniform(10, 100) - data_products.append(TableEntry( - row=metric, - col=feature, - value=value, - tags=[tag_str], - unit=None, - )) - + data_products.append( + TableEntry( + row=metric, + col=feature, + value=value, + tags=[tag_str], + unit=None, + ) + ) + # Add reference lines (for some subcategories) if np.random.random() > 0.7: # 30% chance of adding reference lines # Add horizontal threshold line threshold = np.random.uniform(1, 4) - data_products.append(AxLine( - value=threshold, - orientation=LineOrientation.HORIZONTAL, - pen=Pen(label=f"{subcategory} Threshold"), - tags=[tag_str] - )) - + data_products.append( + AxLine( + value=threshold, + orientation=LineOrientation.HORIZONTAL, + pen=Pen(label=f"{subcategory} Threshold"), + tags=[tag_str], + ) + ) + # Maybe add vertical reference if np.random.random() > 0.5: ref_x = np.random.uniform(3, 7) - data_products.append(AxLine( - value=ref_x, - orientation=LineOrientation.VERTICAL, - pen=Pen(label=f"{subcategory} Reference"), - tags=[tag_str] - )) - + data_products.append( + AxLine( + value=ref_x, + orientation=LineOrientation.VERTICAL, + pen=Pen(label=f"{subcategory} Reference"), + tags=[tag_str], + ) + ) + # Add some flat (non-hierarchical) tags as well for comparison flat_tags = ["Summary", "Overview", "Highlights", "Alerts"] - + for tag_name in flat_tags: # Add a couple of traces x = np.linspace(0, 10, 100) for i in range(2): y = np.random.normal(5, 2, 100) + i * 2 - data_products.append(Trace2D.from_xy( - x=x, - y=y, - pen=Pen(label=f"Metric {i+1}"), - format2d=time_series_format, - tags=[tag_name] - )) - + data_products.append( + Trace2D.from_xy( + x=x, + y=y, + pen=Pen(label=f"Metric {i + 1}"), + format2d=time_series_format, + tags=[tag_name], + ) + ) + # Add a table for row in ["Total", "Average", "Change"]: for col in ["Yesterday", "Today", "Weekly"]: value = np.random.uniform(100, 1000) - data_products.append(TableEntry( - row=row, - col=col, - value=value, - tags=[tag_name] - )) - + data_products.append( + TableEntry(row=row, col=col, value=value, tags=[tag_name]) + ) + # Create collection from data products collection = DataProductCollection(elements=data_products) - + return collection + def main(): """Main function to generate and display sample data.""" print("Generating sample hierarchical data for Trendify Plotly dashboard...") collection = generate_sample_data() - + # Print summary of what was generated tags = collection.get_tags() print(f"Generated {len(collection.elements)} data products across {len(tags)} tags") - + print("\nTag hierarchy:") for tag in sorted([str(t) for t in tags]): print(f" - {tag}") - + print("\nData product counts by type:") trace_count = len(collection.get_products(object_type=Trace2D).elements or []) point_count = len(collection.get_products(object_type=Point2D).elements or []) table_count = len(collection.get_products(object_type=TableEntry).elements or []) hist_count = len(collection.get_products(object_type=HistogramEntry).elements or []) axline_count = len(collection.get_products(object_type=AxLine).elements or []) - + print(f" - Traces: {trace_count}") print(f" - Points: {point_count}") print(f" - Tables: {table_count}") print(f" - Histograms: {hist_count}") print(f" - AxLines: {axline_count}") - + # Launch the Plotly dashboard print("\nLaunching Plotly dashboard...") app = collection.generate_plotly_dashboard(title="Hierarchical Tag Demo") app.run_server(debug=True) + if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/scripts/gen_ref_pages.py b/scripts/gen_ref_pages.py index 20302a2..4e356a8 100644 --- a/scripts/gen_ref_pages.py +++ b/scripts/gen_ref_pages.py @@ -32,4 +32,4 @@ mkdocs_gen_files.set_edit_path(full_doc_path, path.relative_to(root)) with mkdocs_gen_files.open("reference/SUMMARY.md", "w") as nav_file: - nav_file.writelines(nav.build_literate_nav()) \ No newline at end of file + nav_file.writelines(nav.build_literate_nav()) diff --git a/src/.DS_Store b/src/.DS_Store deleted file mode 100644 index e5417baf4acd0652ecaf34a63e7d9493b72e8c85..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeHK!Ab)`3{BP^D!ue5c$tGgQ1AoBvYxyM{QzyPwuP=+L_u%!bsqc#zs8qIs9m-z zcoCHZGH*7M%w*rdW?S|jT zfZW}H7W9BSyMun--)g-WO>XMSZu0cK+q0_6a<;B#@R0qUFP5)oUwO~J;k{YzW)ZP1 z&Ui$=x5#x)D`frB$h0^<+2_;DF}+!it-Y#xH@j}XMqb6GTckfw z^wAk`2AqK{1M+`r1jA^U6tMzf4Fzf_TZ_RO4tp@a zXqXf=oY 1: with ProcessPoolExecutor( @@ -179,7 +179,7 @@ def map_callable( return result -def get_sorted_dirs(dirs: List[Path]): +def get_sorted_dirs(dirs: list[Path]): """ Sorts dirs numerically if possible, else alphabetically @@ -188,6 +188,7 @@ def get_sorted_dirs(dirs: List[Path]): Returns: (List[Path]): Sorted list of directories + """ dirs = list(dirs) try: @@ -199,7 +200,7 @@ def get_sorted_dirs(dirs: List[Path]): def make_products( product_generator: Callable[[Path], DataProductCollection] | None, - data_dirs: List[Path], + data_dirs: list[Path], n_procs: int = 1, data_products_fname: str = DATA_PRODUCTS_FNAME_DEFAULT, ): @@ -218,6 +219,7 @@ def make_products( If `n_procs > 1`, a [ProcessPoolExecutor][concurrent.futures.ProcessPoolExecutor] will be used to load and process directories and/or tags in parallel. data_products_fname (str): File name to be used for storing generated data products + """ sorted_dirs = get_sorted_dirs(dirs=data_dirs) @@ -235,7 +237,7 @@ def make_products( def sort_products( - data_dirs: List[Path], + data_dirs: list[Path], output_dir: Path, n_procs: int = 1, data_products_fname: str = DATA_PRODUCTS_FNAME_DEFAULT, @@ -247,10 +249,11 @@ def sort_products( data_dirs (List[Path]): Directories containing JSON data product files output_dir (Path): Directory to which sorted products will be written data_products_fname (str): File name in which the data products to be sorted are stored + """ sorted_data_dirs = get_sorted_dirs(dirs=data_dirs) - logger.info(f"Sorting data by tags") + logger.info("Sorting data by tags") output_dir.mkdir(parents=True, exist_ok=True) map_callable( @@ -261,7 +264,7 @@ def sort_products( n_procs=n_procs, ) - logger.info(f"Finished sorting by tags") + logger.info("Finished sorting by tags") # def make_grafana_dashboard( @@ -339,8 +342,8 @@ def make_tables_and_figures( no_histograms (bool): Whether or not to generate histograms of the [`HistogramEntry`][trendify.api.HistogramEntry] products using matplotlib. - """ + """ if not (no_tables and no_xy_plots and no_histograms): product_dirs = list(products_dir.glob("**/*/")) map_callable( @@ -362,7 +365,7 @@ def _mkdir(p: Path): def make_it_trendy( data_product_generator: ProductGenerator | None, - input_dirs: List[Path], + input_dirs: list[Path], output_dir: Path, n_procs: int = 1, dpi_static_plots: int = 500, @@ -401,6 +404,7 @@ def make_it_trendy( no_grafana_dashboard (bool): Suppresses generation of Grafana dashboard JSON definition file no_include_files (bool): Suppresses generation of include files for importing static assets to markdown or LaTeX reports data_products_fname (str): File name to be used for storing generated data products + """ input_dirs = [ Path(p).parent if Path(p).is_file() else Path(p) for p in list(input_dirs) diff --git a/src/trendify/api/base/data_product.py b/src/trendify/api/base/data_product.py index ce56666..080aa8b 100644 --- a/src/trendify/api/base/data_product.py +++ b/src/trendify/api/base/data_product.py @@ -1,7 +1,8 @@ from __future__ import annotations from pathlib import Path -from typing import Callable, List, Any +from typing import Any +from collections.abc import Callable import logging from pydantic import ( @@ -18,7 +19,7 @@ logger = logging.getLogger(__name__) -__all__ = ["DataProduct", "ProductList", "ProductGenerator"] +__all__ = ["DataProduct", "ProductGenerator", "ProductList"] _data_product_subclass_registry: dict[str, DataProduct] = {} @@ -32,6 +33,7 @@ class DataProduct(BaseModel): The product type is used to search for products from a [DataProductCollection][trendify.api.DataProductCollection]. tags (Tags): Tags to be used for sorting data. metadata (dict[str, str]): A dictionary of metadata to be used as a tool tip for mousover in grafana + """ tags: Tags @@ -48,6 +50,7 @@ def _remove_computed_fields(cls, data: dict[str, Any]) -> dict[str, Any]: Returns: (dict[str, Any]): Sanitized data to be passed to class constructor. + """ for f in cls.model_computed_fields: data.pop(f, None) @@ -61,6 +64,7 @@ def product_type(self) -> str: (str): Product type should be the same as the class name. The product type is used to search for products from a [DataProductCollection][trendify.api.DataProductCollection]. + """ return type(self).__name__ @@ -74,7 +78,7 @@ def __init_subclass__(cls, **kwargs: Any) -> None: model_config = ConfigDict(extra="allow") - def append_to_list(self, l: List): + def append_to_list(self, l: list): """ Appends self to list. @@ -83,6 +87,7 @@ def append_to_list(self, l: List): Returns: (Self): returns instance of `self` + """ l.append(self) return self @@ -95,6 +100,7 @@ def deserialize_child_classes(cls, key: str, **kwargs): Args: key (str): json key kwargs (dict): json entries stored under given key + """ type_key = "product_type" elements = kwargs.get(key, None) @@ -111,7 +117,7 @@ def set_metadata(self, new: dict[str, str]): return self -ProductList = List[SerializeAsAny[InstanceOf[DataProduct]]] +ProductList = list[SerializeAsAny[InstanceOf[DataProduct]]] """List of serializable [DataProduct][trendify.api.DataProduct] or child classes thereof""" ProductGenerator = Callable[[Path], ProductList] diff --git a/src/trendify/api/base/helpers.py b/src/trendify/api/base/helpers.py index 3fe8005..c167742 100644 --- a/src/trendify/api/base/helpers.py +++ b/src/trendify/api/base/helpers.py @@ -2,7 +2,8 @@ from enum import auto from strenum import StrEnum -from typing import Union, List, Tuple, TypeVar, Hashable +from typing import Union, TypeVar +from collections.abc import Hashable import logging from pydantic import BaseModel @@ -11,11 +12,11 @@ __all__ = [ "DATA_PRODUCTS_FNAME_DEFAULT", + "HashableBase", + "ProductType", "R", "Tag", "Tags", - "HashableBase", - "ProductType", ] DATA_PRODUCTS_FNAME_DEFAULT = "data_products.json" @@ -25,12 +26,12 @@ R = TypeVar("R") -Tag = Union[Tuple[Hashable, ...], Hashable] +Tag = Union[tuple[Hashable, ...], Hashable] """ Determines what types can be used to define a tag """ -Tags = List[Tag] +Tags = list[Tag] """ List of tags """ @@ -59,6 +60,7 @@ class ProductType(StrEnum): Point2D (str): class name TableEntry (str): class name HistogramEntry (str): class name + """ DataProduct = auto() diff --git a/src/trendify/api/base/pen.py b/src/trendify/api/base/pen.py index dc44fef..132c6e5 100644 --- a/src/trendify/api/base/pen.py +++ b/src/trendify/api/base/pen.py @@ -1,10 +1,8 @@ from __future__ import annotations -from typing import Optional, Tuple, Union from pydantic import ConfigDict -from matplotlib.colors import to_rgba, to_rgb -import numpy as np +from matplotlib.colors import to_rgba from trendify.api.base.helpers import HashableBase @@ -22,14 +20,15 @@ class Pen(HashableBase): linestyle (Union[str, Tuple[int, Tuple[int, ...]]]): Linestyle to plot. Supports `str` or `tuple` definition ([matplotlib documentation](https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html)). zorder (float): Prioritization label (Union[str, None]): Legend label + """ - color: Tuple[float, float, float] | Tuple[float, float, float, float] | str = "k" + color: tuple[float, float, float] | tuple[float, float, float, float] | str = "k" size: float = 1 alpha: float = 1 zorder: float = 0 - linestyle: Union[str, Tuple[int, Tuple[int, ...]]] = "-" - label: Union[str, None] = None + linestyle: str | tuple[int, tuple[int, ...]] = "-" + label: str | None = None model_config = ConfigDict(extra="forbid") @@ -68,6 +67,7 @@ def rgba(self) -> str: Returns: str: Color in 'rgba(r,g,b,a)' format where r,g,b are 0-255 and a is 0-1 + """ # Handle different color input formats if isinstance(self.color, tuple): @@ -101,6 +101,7 @@ def get_contrast_color(self, background_luminance: float = 1.0) -> str: Returns: str: 'white' or 'black' + """ # Convert the pen's color to RGB (0-255 range) and get alpha if isinstance(self.color, tuple): diff --git a/src/trendify/api/formats/format2d.py b/src/trendify/api/formats/format2d.py index 81ecc1b..28bbde8 100644 --- a/src/trendify/api/formats/format2d.py +++ b/src/trendify/api/formats/format2d.py @@ -1,7 +1,8 @@ from __future__ import annotations from enum import StrEnum -from typing import TYPE_CHECKING, Iterable, Optional +from typing import TYPE_CHECKING +from collections.abc import Iterable import logging import numpy as np @@ -16,7 +17,7 @@ from trendify.api.plotting.plotting import PlotlyFigure logger = logging.getLogger(__name__) -__all__ = ["Format2D", "PlottableData2D", "XYData", "AxisScale"] +__all__ = ["AxisScale", "Format2D", "PlottableData2D", "XYData"] class AxisScale(StrEnum): @@ -45,13 +46,14 @@ class Format2D(HashableBase): scale_y (AxisScale, optional): Sets the y axis scale to an option from [AxisScale][trendify.api.formats.format2d.AxisScale]. Defaults to AxisScale.LINEAR figure_width (float, optional): Sets the of the width of rendered figure in inches. Defaults to 6.4. figure_height (float, optional): Sets the of the height of rendered figure in inches. Defaults to 4.8. + """ - title_fig: Optional[str] | None = None - legend: Optional[Legend] = Legend() - title_ax: Optional[str] | None = None - label_x: Optional[str] | None = None - label_y: Optional[str] | None = None + title_fig: str | None | None = None + legend: Legend | None = Legend() + title_ax: str | None | None = None + label_x: str | None | None = None + label_y: str | None | None = None lim_x_min: float | None = None lim_x_max: float | None = None lim_y_min: float | None = None @@ -75,6 +77,7 @@ def union_from_iterable(cls, format2ds: Iterable[Format2D]): Returns: (Format2D): Single format object from list of objects. + """ formats = list(set(format2ds) - {None}) @@ -127,15 +130,18 @@ class PlottableData2D(DataProduct): format2d (Format2D|None): Format to apply to plot tags (Tags): Tags to be used for sorting data. metadata (dict[str, str]): A dictionary of metadata to be used as a tool tip for mousover in grafana + """ format2d: Format2D | None = None def add_to_plotly(self, plotly_figure: PlotlyFigure): - """Add this data product to a plotly figure + """ + Add this data product to a plotly figure Args: plotly_figure (PlotlyFigure): Plotly figure to add data to + """ raise NotImplementedError("Subclasses must implement add_to_plotly") diff --git a/src/trendify/api/formats/table.py b/src/trendify/api/formats/table.py index db3ae51..877b296 100644 --- a/src/trendify/api/formats/table.py +++ b/src/trendify/api/formats/table.py @@ -27,6 +27,7 @@ class TableEntry(DataProduct): value (float | str): Value unit (str | None): Units for value metadata (dict[str, str]): A dictionary of metadata to be used as a tool tip for mousover in grafana + """ row: float | str @@ -42,6 +43,7 @@ def get_entry_dict(self): Returns: (dict[str, str | float]): Dictionary of entries to be used in creating a melted [DataFrame][pandas.DataFrame] + """ return { "row": self.row, @@ -61,10 +63,11 @@ def pivot_table(cls, melted: pd.DataFrame): Returns: (pd.DataFrame | None): pivoted DataFrame if pivot works else `None`. Pivot operation fails if row or column index pairs are repeated. + """ try: result = melted.pivot(index="row", columns="col", values="value") - except ValueError as e: + except ValueError: logger.debug(traceback.format_exc()) result = None return result @@ -80,5 +83,6 @@ def load_and_pivot(cls, path: Path): Returns: (pd.DataFrame | None): Pivoted data frame or elese `None` if pivot operation fails. + """ return cls.pivot_table(melted=pd.read_csv(path)) diff --git a/src/trendify/api/generator/data_product_collection.py b/src/trendify/api/generator/data_product_collection.py index bdf7131..dce647f 100644 --- a/src/trendify/api/generator/data_product_collection.py +++ b/src/trendify/api/generator/data_product_collection.py @@ -3,15 +3,15 @@ import os from itertools import chain from pathlib import Path -from typing import TYPE_CHECKING, Union, List, Iterable, Any, Callable, Tuple, Type +from typing import TYPE_CHECKING, Any +from collections.abc import Iterable import logging -import pandas as pd try: from typing import Self except: - from typing_extensions import Self + from typing import Self import dash from filelock import FileLock @@ -35,9 +35,9 @@ __all__ = [ "DataProductCollection", - "flatten", "ProductEntryMetadata", "ProductIndexMap", + "flatten", ] @@ -115,6 +115,7 @@ def _should_be_flattened(obj: Any): Returns: (bool): Whether or not to flatten object + """ return isinstance(obj, Iterable) and not isinstance(obj, (str, bytes, DataProduct)) @@ -128,6 +129,7 @@ def flatten(obj: Iterable): Returns: (Iterable): Flattned iterable + """ if not _should_be_flattened(obj): yield obj @@ -145,6 +147,7 @@ def atleast_1d(obj: Any) -> Iterable: Returns: (Iterable): Returns an iterable + """ if not _should_be_flattened(obj): return [obj] @@ -152,7 +155,7 @@ def atleast_1d(obj: Any) -> Iterable: return obj -def _squeeze(obj: Union[Iterable, Any]): +def _squeeze(obj: Iterable | Any): """ Returns a scalar if object is iterable of length 1 else returns object. @@ -161,6 +164,7 @@ def _squeeze(obj: Union[Iterable, Any]): Returns: (Any): Either iterable or scalar if possible + """ if _should_be_flattened(obj) and len(obj) == 1: return obj[0] @@ -176,6 +180,7 @@ class DataProductCollection(BaseModel): Attributes: elements (ProductList): A list of data products. + """ derived_from: Path | None = None @@ -186,7 +191,7 @@ def __init__(self, **kwargs: Any): super().__init__(**kwargs) @classmethod - def from_iterable(cls, *products: Tuple[ProductList, ...]): + def from_iterable(cls, *products: tuple[ProductList, ...]): """ Returns a new instance containing all of the products provided in the `*products` argument. @@ -195,10 +200,11 @@ def from_iterable(cls, *products: Tuple[ProductList, ...]): Returns: (cls): A data product collection containing all of the provided products in the `*products` argument. + """ return cls(elements=list(flatten(products))) - def get_tags(self, data_product_type: Type[DataProduct] | None = None) -> set: + def get_tags(self, data_product_type: type[DataProduct] | None = None) -> set: """ Gets the tags related to a given type of `DataProduct`. Parent classes will match all child class types. @@ -207,6 +213,7 @@ def get_tags(self, data_product_type: Type[DataProduct] | None = None) -> set: Returns: (set): set of tags applying to the given `data_product_type`. + """ tags = [] for e in flatten(self.elements): @@ -220,6 +227,7 @@ def add_products(self, *products: DataProduct): Args: products (Tuple[DataProduct|ProductList, ...]): Products or lists of products to be appended to collection elements. + """ self.elements.extend(flatten(products)) @@ -293,7 +301,7 @@ def collect_and_serve_plotly_dashboard( def drop_products( self, tag: Tag | None = None, - object_type: Type[R] | None = None, + object_type: type[R] | None = None, ) -> Self[R]: """ Removes products matching `tag` and/or `object_type` from collection elements. @@ -304,6 +312,7 @@ def drop_products( Returns: (DataProductCollection): A new collection from which matching elements have been dropped. + """ match_key = tag is None, object_type is None match match_key: @@ -319,7 +328,7 @@ def drop_products( case (False, True): # assert self.elements is not None return type(self)( - elements=[e for e in self.elements if not tag in e.tags] + elements=[e for e in self.elements if tag not in e.tags] ) case (False, False): # assert self.elements is not None @@ -334,7 +343,7 @@ def drop_products( raise ValueError("Something is wrong with match statement") def get_products( - self, tag: Tag | None = None, object_type: Type[R] | None = None + self, tag: Tag | None = None, object_type: type[R] | None = None ) -> Self[R]: """ Returns a new collection containing products matching `tag` and/or `object_type`. @@ -346,6 +355,7 @@ def get_products( Returns: (DataProductCollection): A new collection containing matching elements. + """ match_key = tag is None, object_type is None match match_key: @@ -383,6 +393,7 @@ def union(cls, *collections: DataProductCollection): Returns: (Type[Self]): A new data product collection containing all products from the provided `*collections`. + """ return cls(elements=list(flatten(chain(c.elements for c in collections)))) @@ -406,13 +417,14 @@ def collect_from_all_jsons( Returns: (Type[Self] | None): Data product collection if JSON files are found. Otherwise, returns None if no product JSON files were found. + """ if not recursive: - jsons: List[Path] = list( + jsons: list[Path] = list( flatten(chain(list(d.glob(data_products_filename)) for d in dirs)) ) else: - jsons: List[Path] = list( + jsons: list[Path] = list( flatten( chain(list(d.glob(f"**/{data_products_filename}")) for d in dirs) ) @@ -427,7 +439,7 @@ def collect_from_all_jsons( @classmethod def sort_by_tags( cls, - dirs_in: List[Path], + dirs_in: list[Path], dir_out: Path, data_products_fname: str = DATA_PRODUCTS_FNAME_DEFAULT, ): @@ -442,6 +454,7 @@ def sort_by_tags( dir_out (Path): Directory to which the sorted data products will be written into a nested folder structure generated according to the data tags. data_products_fname (str): Name of data products file + """ dirs_in = list(dirs_in) dirs_in.sort() @@ -470,6 +483,7 @@ def sort_by_tags_single_directory( dir_out (Path): Directory to which the sorted data products will be written into a nested folder structure generated according to the data tags. data_products_fname (str): Name of data products file + """ products_file = dir_in.joinpath(data_products_fname) if products_file.exists(): @@ -517,12 +531,11 @@ def process_collection( no_xy_plots (bool): Suppresses xy plot asset creation no_histograms (bool): Suppresses histogram asset creation dpi (int): Sets resolution of asset output - """ + """ collection = cls.collect_from_all_jsons(dir_in) if collection is not None: - for tag in collection.get_tags(): # tags = collection.get_tags() # try: @@ -533,7 +546,7 @@ def process_collection( format_2ds: list[Format2D] = [] if not no_tables: - table_entries: List[TableEntry] = collection.get_products( + table_entries: list[TableEntry] = collection.get_products( tag=tag, object_type=TableEntry, ).elements @@ -550,15 +563,15 @@ def process_collection( logger.info(f"Finished tables for {tag = }") if not no_xy_plots: - traces: List[Trace2D] = collection.get_products( + traces: list[Trace2D] = collection.get_products( tag=tag, object_type=Trace2D, ).elements - points: List[Point2D] = collection.get_products( + points: list[Point2D] = collection.get_products( tag=tag, object_type=Point2D, ).elements - axlines: List[AxLine] = collection.get_products( + axlines: list[AxLine] = collection.get_products( tag=tag, object_type=AxLine, ).elements @@ -595,7 +608,7 @@ def process_collection( logger.info(f"Finished xy plot for {tag = }") if not no_histograms: - histogram_entries: List[HistogramEntry] = collection.get_products( + histogram_entries: list[HistogramEntry] = collection.get_products( tag=tag, object_type=HistogramEntry, ).elements @@ -633,7 +646,7 @@ def process_collection( def process_tables_for_tag( self, tag: Tag, - in_dirs: List[Path], + in_dirs: list[Path], out_dir: Path, data_products_fname: str = DATA_PRODUCTS_FNAME_DEFAULT, ) -> TableBuilder: @@ -648,6 +661,7 @@ def process_tables_for_tag( Returns: TableBuilder: The TableBuilder instance with generated tables. + """ from trendify.api.generator.table_builder import TableBuilder @@ -658,14 +672,13 @@ def process_tables_for_tag( @classmethod def process_tag_for_streamlit( - cls, jsons: List[Path], tag: Tag + cls, jsons: list[Path], tag: Tag ) -> PlotlyFigure | None: collection = cls.union( *tuple([cls.model_validate_json(p.read_text()) for p in jsons]) ) if collection is not None: - plotly_figure: PlotlyFigure | None = None format_2ds: list[Format2D] = [] @@ -676,10 +689,10 @@ def process_tag_for_streamlit( tag=tag, object_type=HistogramEntry ).elements - traces: List[Trace2D] - points: List[Point2D] - axlines: List[AxLine] - hist_entries: List[HistogramEntry] + traces: list[Trace2D] + points: list[Point2D] + axlines: list[AxLine] + hist_entries: list[HistogramEntry] traces = [e for e in collection.elements if isinstance(e, Trace2D)] points = [e for e in collection.elements if isinstance(e, Point2D)] diff --git a/src/trendify/api/generator/data_product_generator.py b/src/trendify/api/generator/data_product_generator.py index 66595a2..507fb71 100644 --- a/src/trendify/api/generator/data_product_generator.py +++ b/src/trendify/api/generator/data_product_generator.py @@ -19,6 +19,7 @@ class DataProductGenerator: Args: processor (ProductGenerator): A callable that receives a working directory and returns a list of data products. + """ def __init__(self, processor: ProductGenerator): @@ -35,8 +36,8 @@ def process_and_save( Args: workdir (Path): working directory on which to run the processor method. data_products_fname (str): Name of data products file - """ + """ logger.info(f"Processing {workdir = } with {self._processor = }") collection = DataProductCollection.from_iterable(self._processor(workdir)) if collection.elements: diff --git a/src/trendify/api/generator/histogrammer.py b/src/trendify/api/generator/histogrammer.py index 4846aa6..aaea04f 100644 --- a/src/trendify/api/generator/histogrammer.py +++ b/src/trendify/api/generator/histogrammer.py @@ -1,11 +1,10 @@ from __future__ import annotations from pathlib import Path -from typing import List import logging # from trendify.api.data_product_collection import DataProductCollection -from trendify.api.base.helpers import Tag, DATA_PRODUCTS_FNAME_DEFAULT +from trendify.api.base.helpers import Tag from trendify.api.plotting.histogram import HistogramEntry from trendify.api.plotting.plotting import SingleAxisFigure, PlotlyFigure @@ -22,11 +21,12 @@ class Histogrammer: in_dirs (List[Path]): Directories from which the data products are to be loaded. out_dir (Path): Directory to which the generated histogram will be stored dpi (int): resolution of plot + """ def __init__( self, - in_dirs: List[Path], + in_dirs: list[Path], out_dir: Path, dpi: int, ): @@ -69,7 +69,7 @@ def __init__( def handle_histogram_entries( cls, tag: Tag, - histogram_entries: List[HistogramEntry], + histogram_entries: list[HistogramEntry], dir_out: Path, dpi: int, saf: SingleAxisFigure | None = None, @@ -82,6 +82,7 @@ def handle_histogram_entries( histogram_entries (List[HistogramEntry]): A list of [`HistogramEntry`][trendify.api.HistogramEntry]s dir_out (Path): Directory to which the generated histogram will be stored dpi (int): resolution of plot + """ if saf is None: saf = SingleAxisFigure.new(tag=tag) @@ -116,7 +117,7 @@ def handle_histogram_entries( def plotly_histogram( cls, tag: Tag, - histogram_entries: List[HistogramEntry], + histogram_entries: list[HistogramEntry], plotly_figure: PlotlyFigure | None = None, ) -> PlotlyFigure: if plotly_figure is None: diff --git a/src/trendify/api/generator/table_builder.py b/src/trendify/api/generator/table_builder.py index 6dc5db0..8dcd79d 100644 --- a/src/trendify/api/generator/table_builder.py +++ b/src/trendify/api/generator/table_builder.py @@ -1,7 +1,6 @@ from __future__ import annotations from pathlib import Path -from typing import List import logging import pandas as pd @@ -25,11 +24,12 @@ class TableBuilder: Args: in_dirs (List[Path]): directories from which to load data products out_dir (Path): directory in which tables should be saved + """ def __init__( self, - in_dirs: List[Path], + in_dirs: list[Path], out_dir: Path, ): self.in_dirs = in_dirs @@ -50,10 +50,11 @@ def load_table( Args: tag (Tag): product tag for which to collect and process. + """ logger.info(f"Making table for {tag = }") - table_entries: List[TableEntry] = [] + table_entries: list[TableEntry] = [] for subdir in self.in_dirs: collection = DataProductCollection.model_validate_json( subdir.joinpath(data_products_fname).read_text() @@ -70,7 +71,7 @@ def load_table( def process_table_entries( cls, tag: Tag, - table_entries: List[TableEntry], + table_entries: list[TableEntry], out_dir: Path, ): """ @@ -84,13 +85,14 @@ def process_table_entries( tag (Tag): product tag for which to collect and process. table_entries (List[TableEntry]): List of table entries out_dir (Path): Directory to which table CSV files should be saved + """ melted = pd.DataFrame([t.get_entry_dict() for t in table_entries]) pivot = TableEntry.pivot_table(melted=melted) save_path_partial = out_dir.joinpath(*tuple(atleast_1d(tag))) save_path_partial.parent.mkdir(exist_ok=True, parents=True) - logger.info(f"Saving to {str(save_path_partial)}_*.csv") + logger.info(f"Saving to {save_path_partial!s}_*.csv") melted.to_csv( save_path_partial.with_stem(save_path_partial.stem + "_melted").with_suffix( @@ -118,7 +120,7 @@ def process_table_entries( ) except Exception as e: logger.error( - f"Could not generate pivot table for {tag = }. Error: {str(e)}" + f"Could not generate pivot table for {tag = }. Error: {e!s}" ) @classmethod @@ -135,6 +137,7 @@ def get_stats_table( Returns: (pd.DataFrame): Dataframe having statistics (column headers) for each of the columns of the input `df`. The columns of `df` will be the row indices of the stats table. + """ # Try to convert to numeric, coerce errors to NaN numeric_df = df.apply(pd.to_numeric, errors="coerce") diff --git a/src/trendify/api/generator/xy_data_plotter.py b/src/trendify/api/generator/xy_data_plotter.py index bb47a16..78a993f 100644 --- a/src/trendify/api/generator/xy_data_plotter.py +++ b/src/trendify/api/generator/xy_data_plotter.py @@ -1,7 +1,6 @@ from __future__ import annotations from pathlib import Path -from typing import List import logging from trendify.api.generator.data_product_collection import ( @@ -29,11 +28,12 @@ class XYDataPlotter: in_dirs (List[Path]): Directories in which to search for data products from JSON files out_dir (Path): directory to which figure will be output dpi (int): Saved image resolution + """ def __init__( self, - in_dirs: List[Path], + in_dirs: list[Path], out_dir: Path, dpi: int = 500, ): @@ -56,6 +56,7 @@ def plot( Args: tag (Tag): data tag for which products are to be collected and plotted. data_products_fname (str): Data products file name + """ logger.info(f"Making xy plot for {tag = }") saf = SingleAxisFigure.new(tag=tag) @@ -64,10 +65,10 @@ def plot( collection = DataProductCollection.model_validate_json( subdir.joinpath(data_products_fname).read_text() ) - traces: List[Trace2D] = collection.get_products( + traces: list[Trace2D] = collection.get_products( tag=tag, object_type=Trace2D ).elements - points: List[Point2D] = collection.get_products( + points: list[Point2D] = collection.get_products( tag=tag, object_type=Point2D ).elements @@ -108,9 +109,9 @@ def plot( def handle_points_and_traces( cls, tag: Tag, - points: List[Point2D], - traces: List[Trace2D], - axlines: List[AxLine], # Add this parameter + points: list[Point2D], + traces: list[Trace2D], + axlines: list[AxLine], # Add this parameter dir_out: Path, dpi: int, saf: SingleAxisFigure | None = None, @@ -125,8 +126,8 @@ def handle_points_and_traces( axlines (List[AxLine]): List of axis lines to be plotted dir_out (Path): directory to output the plot dpi (int): resolution of plot - """ + """ if saf is None: saf = SingleAxisFigure.new(tag=tag) @@ -170,10 +171,10 @@ def handle_points_and_traces( def plotly_handle_points_and_traces( cls, tag: Tag, - points: List[Point2D], - traces: List[Trace2D], - axlines: List[AxLine], - hist_entries: List[HistogramEntry], + points: list[Point2D], + traces: list[Trace2D], + axlines: list[AxLine], + hist_entries: list[HistogramEntry], plotly_figure: PlotlyFigure | None = None, ) -> PlotlyFigure: if plotly_figure is None: diff --git a/src/trendify/api/plotting/axline.py b/src/trendify/api/plotting/axline.py index e2a7429..6bac333 100644 --- a/src/trendify/api/plotting/axline.py +++ b/src/trendify/api/plotting/axline.py @@ -10,17 +10,19 @@ from trendify.api.base.pen import Pen from trendify.api.plotting.plotting import PlotlyFigure -__all__ = ["LineOrientation", "AxLine"] +__all__ = ["AxLine", "LineOrientation"] logger = logging.getLogger(__name__) class LineOrientation(Enum): - """Defines orientation for axis lines + """ + Defines orientation for axis lines Attributes: HORIZONTAL (LineOrientation): Horizontal line VERTICAL (LineOrientation): Vertical line + """ HORIZONTAL = "horizontal" @@ -37,6 +39,7 @@ class AxLine(PlottableData2D): pen (Pen): Style and label information for drawing to matplotlib axes tags (Tags): Tags to be used for sorting data metadata (dict[str, str]): A dictionary of metadata + """ value: float @@ -51,6 +54,7 @@ def plot_to_ax(self, ax: Axes): Args: ax (Axes): axes to which line should be plotted + """ match self.orientation: case LineOrientation.HORIZONTAL: diff --git a/src/trendify/api/plotting/histogram.py b/src/trendify/api/plotting/histogram.py index f4e0dbe..45ecd3f 100644 --- a/src/trendify/api/plotting/histogram.py +++ b/src/trendify/api/plotting/histogram.py @@ -1,6 +1,5 @@ from __future__ import annotations -from typing import Tuple import logging from matplotlib.colors import to_rgba import plotly.graph_objects as go @@ -12,7 +11,7 @@ from trendify.api.base.helpers import HashableBase, Tags from trendify.api.plotting.plotting import PlotlyFigure -__all__ = ["HistogramStyle", "HistogramEntry"] +__all__ = ["HistogramEntry", "HistogramStyle"] logger = logging.getLogger(__name__) @@ -29,6 +28,7 @@ class HistogramStyle(HashableBase): alpha_face (float): Opacity of bar face linewidth (float): Line width of bar outline bins (int | list[int] | Tuple[int] | NDArray[Shape["*"], int] | None): Number of bins (see [matplotlib docs][matplotlib.pyplot.hist]) + """ color: str = "k" @@ -38,12 +38,13 @@ class HistogramStyle(HashableBase): alpha_face: float = 0.3 linewidth: float = 2 zorder: int = 1 - bins: int | list[int] | Tuple[int] | NDArray[Shape["*"], int] | None = None + bins: int | list[int] | tuple[int] | NDArray[Shape["*"], int] | None = None def as_plot_kwargs(self): """ Returns: (dict): kwargs for matplotlib `hist` method + """ return { "facecolor": (self.color, self.alpha_face), @@ -62,6 +63,7 @@ def rgba_face(self) -> str: Returns: str: Color in 'rgba(r,g,b,a)' format where r,g,b are 0-255 and a is 0-1 + """ # Handle different color input formats if isinstance(self.color, tuple): @@ -88,6 +90,7 @@ def rgba_edge(self) -> str: Returns: str: Color in 'rgba(r,g,b,a)' format where r,g,b are 0-255 and a is 0-1 + """ # Handle different color input formats if isinstance(self.color, tuple): @@ -125,6 +128,7 @@ def get_face_contrast_color(self, background_luminance: float = 1.0) -> str: Returns: str: 'white' or 'black' + """ # Convert the pen's color to RGB (0-255 range) and get alpha if isinstance(self.color, tuple): @@ -167,6 +171,7 @@ class HistogramEntry(PlottableData2D): tags (Tags): Tags used to sort data products value (float | str): Value to be binned style (HistogramStyle): Style of histogram display + """ value: float | str diff --git a/src/trendify/api/plotting/plotting.py b/src/trendify/api/plotting/plotting.py index 8818a64..3955f60 100644 --- a/src/trendify/api/plotting/plotting.py +++ b/src/trendify/api/plotting/plotting.py @@ -8,19 +8,17 @@ import numpy as np import plotly.graph_objects as go -from plotly.subplots import make_subplots from trendify.api.formats.format2d import AxisScale, PlottableData2D try: from typing import Self, TYPE_CHECKING except: - from typing_extensions import Self, TYPE_CHECKING + from typing import Self, TYPE_CHECKING from pydantic import ConfigDict -from trendify.api.base.data_product import DataProduct -from trendify.api.base.helpers import HashableBase, Tag +from trendify.api.base.helpers import Tag if TYPE_CHECKING: from trendify.api.formats.format2d import Format2D, Grid @@ -28,7 +26,7 @@ from matplotlib.figure import Figure -__all__ = ["SingleAxisFigure", "PlotlyFigure"] +__all__ = ["PlotlyFigure", "SingleAxisFigure"] logger = logging.getLogger(__name__) @@ -42,6 +40,7 @@ class SingleAxisFigure: ax (Axes): Matplotlib axis to which data will be plotted fig (Figure): Matplotlib figure. tag (Tag): Figure tag. Not yet used. + """ model_config = ConfigDict(arbitrary_types_allowed=True) @@ -59,6 +58,7 @@ def new(cls, tag: Tag): Returns: (Type[Self]): New single axis figure + """ fig = plt.figure() ax = fig.add_subplot(1, 1, 1) @@ -74,6 +74,7 @@ def apply_format(self, format2d: Format2D): Args: format2d (Format2D): format information to apply to the single axis figure + """ if format2d.title_ax is not None: self.ax.set_title(format2d.title_ax) @@ -162,6 +163,7 @@ def savefig(self, path: Path, dpi: int = 500): Returns: (Self): Returns self + """ self.fig.savefig(path, dpi=dpi) return self @@ -190,6 +192,7 @@ def new(cls, tag: Tag): Returns: (Type[Self]): New single axis figure + """ fig = go.Figure() return cls(tag=tag, fig=fig) @@ -200,6 +203,7 @@ def apply_format(self, format2d: Format2D): Args: format2d (Format2D): format information to apply to the figure + """ layout_updates = {} @@ -263,7 +267,6 @@ def _log10(v: float | None): # Set legend if format2d.legend is not None: - layout_updates["showlegend"] = format2d.legend.visible layout_updates["legend"] = dict( title=format2d.legend.title, @@ -285,6 +288,7 @@ def apply_grid(self, grid: Grid): Args: grid (Grid): Grid configuration to apply + """ # Major grid @@ -323,13 +327,15 @@ def apply_grid(self, grid: Grid): self.fig.update_yaxes(**yaxis_updates) def add_data_product(self, product: PlottableData2D) -> Self: - """Add a data product to the figure + """ + Add a data product to the figure Args: product (PlottableData2D): Data product to add to figure Returns: Self: Returns self for method chaining + """ raise NotImplementedError() diff --git a/src/trendify/api/plotting/point.py b/src/trendify/api/plotting/point.py index 1c50486..8979722 100644 --- a/src/trendify/api/plotting/point.py +++ b/src/trendify/api/plotting/point.py @@ -28,6 +28,7 @@ class Point2D(XYData): Only the label information is used in Grafana. Eventually style information will be used in grafana. metadata (dict[str, str]): A dictionary of metadata to be used as a tool tip for mousover in grafana + """ x: float | str diff --git a/src/trendify/api/plotting/trace.py b/src/trendify/api/plotting/trace.py index 5b90af3..51f86a5 100644 --- a/src/trendify/api/plotting/trace.py +++ b/src/trendify/api/plotting/trace.py @@ -1,15 +1,14 @@ from __future__ import annotations -from typing import List import logging import plotly.graph_objects as go from trendify.api.plotting.plotting import PlotlyFigure try: - from typing import Self, TYPE_CHECKING + from typing import TYPE_CHECKING except: - from typing_extensions import Self, TYPE_CHECKING + from typing import TYPE_CHECKING import numpy as np from numpydantic import NDArray, Shape @@ -44,11 +43,12 @@ class Trace2D(XYData): Eventually style information will be used in grafana. tags (Tags): Tags to be used for sorting data. metadata (dict[str, str]): A dictionary of metadata to be used as a tool tip for mousover in grafana + """ model_config = ConfigDict(extra="forbid") - points: List[Point2D] + points: list[Point2D] pen: Pen = Pen() @property @@ -58,7 +58,9 @@ def x(self) -> NDArray[Shape["*"], float]: Returns: (NDArray[Shape["*"], float]): array of x values from `self.points` - '""" + ' + + """ return np.array([p.x for p in self.points]) @property @@ -68,6 +70,7 @@ def y(self) -> NDArray[Shape["*"], float]: Returns: (NDArray[Shape["*"], float]): array of y values from `self.points` + """ return np.array([p.y for p in self.points]) @@ -79,6 +82,7 @@ def propagate_format2d_and_pen(self, marker_symbol: str = ".") -> None: Args: marker_symbol (str): Valid matplotlib marker symbol + """ self.points = [ p.model_copy( @@ -109,8 +113,8 @@ def from_xy( y (NDArray[Shape["*"], float]): y values pen (Pen): Style and label for trace format2d (Format2D | None): Format to apply to plot - """ + """ return cls( tags=tags, points=[ @@ -133,6 +137,7 @@ def plot_to_ax(self, ax: Axes): Args: ax (Axes): axes to which xy data should be plotted + """ ax.plot(self.x, self.y, **self.pen.as_scatter_plot_kwargs()) diff --git a/src/trendify/api/styling/grid.py b/src/trendify/api/styling/grid.py index 7386569..593f092 100644 --- a/src/trendify/api/styling/grid.py +++ b/src/trendify/api/styling/grid.py @@ -1,10 +1,9 @@ from __future__ import annotations from enum import Enum, auto -from typing import Iterable, Optional, Tuple, Union +from collections.abc import Iterable import logging -import numpy as np from pydantic import ConfigDict from trendify.api.base.helpers import HashableBase @@ -29,6 +28,7 @@ class GridAxis(HashableBase): Attributes: show (bool): Whether to display this grid axis. pen (Pen): Style and label information for drawing to matplotlib axes. + """ show: bool = False @@ -51,6 +51,7 @@ class Grid(HashableBase): major (GridAxis): Configuration for major grid lines. minor (GridAxis): Configuration for minor grid lines. enable_minor_ticks (bool): Whether to enable minor ticks on the axes. + """ major: GridAxis = GridAxis(show=False) diff --git a/src/trendify/api/styling/legend.py b/src/trendify/api/styling/legend.py index f1b60e4..f51a5bb 100644 --- a/src/trendify/api/styling/legend.py +++ b/src/trendify/api/styling/legend.py @@ -4,7 +4,7 @@ from trendify.api.base.helpers import HashableBase -__all__ = ["LegendLocation", "Legend"] +__all__ = ["Legend", "LegendLocation"] class LegendLocation(StrEnum): @@ -65,6 +65,7 @@ class Legend(HashableBase): loc=LegendLocation.UPPER_CENTER bbox_to_anchor=(0.5, -0.02) ``` + """ visible: bool = True @@ -93,6 +94,7 @@ def plotly_location(self) -> dict: Returns: dict: Dictionary containing Plotly legend position parameters (x, y, xanchor, yanchor) + """ # Default position mappings for standard locations location_map = { diff --git a/src/trendify/api/styling/marker.py b/src/trendify/api/styling/marker.py index 9bdf763..f0c7b76 100644 --- a/src/trendify/api/styling/marker.py +++ b/src/trendify/api/styling/marker.py @@ -24,6 +24,7 @@ class Marker(HashableBase): zorder (float): Prioritization label (Union[str, None]): Legend label symbol (str): Matplotlib symbol string + """ color: str = "k" @@ -50,6 +51,7 @@ def as_scatter_plot_kwargs(self): """ Returns: (dict): dictionary of `kwargs` for [matplotlib scatter][matplotlib.axes.Axes.scatter] + """ return { "marker": self.symbol, @@ -88,6 +90,7 @@ def rgba(self) -> str: Returns: str: Color in 'rgba(r,g,b,a)' format where r,g,b are 0-255 and a is 0-1 + """ # Handle different color input formats if isinstance(self.color, tuple): @@ -117,6 +120,7 @@ def get_contrast_color(self, background_luminance: float = 1.0) -> str: Returns: str: 'white' or 'black' + """ # Convert the pen's color to RGB (0-255 range) and get alpha if isinstance(self.color, tuple): diff --git a/src/trendify/CLI.py b/src/trendify/cli.py similarity index 100% rename from src/trendify/CLI.py rename to src/trendify/cli.py diff --git a/src/trendify/examples.py b/src/trendify/examples.py index b84eef1..14eb9e3 100644 --- a/src/trendify/examples.py +++ b/src/trendify/examples.py @@ -12,12 +12,11 @@ # Common imports import numpy as np import pandas as pd -import matplotlib.pyplot as plt # Local imports import trendify -__all__ = ["make_example_data", "example_data_product_generator"] +__all__ = ["example_data_product_generator", "make_example_data"] class Channels(StrEnum): @@ -27,6 +26,7 @@ class Channels(StrEnum): wave1 (str): `'wave1'` wave2 (str): `'wave2'` wave3 (str): `'wave3'` + """ time = auto() @@ -42,6 +42,7 @@ def make_example_data(workdir: Path, n_folders: int = 10): Args: workdir (Path): Directory in which the sample data is to be generated n_folders (int): Number of sample data files to generate (in separate subfolders). + """ models_dir = workdir.joinpath("models") models_dir.mkdir(parents=True, exist_ok=True) @@ -97,6 +98,7 @@ def example_data_product_generator(workdir: Path) -> trendify.ProductList: Args: workdir (Path): Directory containing sample data. + """ products = [] diff --git a/src/trendify/gallery.py b/src/trendify/gallery.py index e7f496f..e66b6f7 100644 --- a/src/trendify/gallery.py +++ b/src/trendify/gallery.py @@ -228,6 +228,7 @@ def run_trendify_commands(input_dir: str | Path, output_dir: str | Path): Args: input_dir (str | Path): Input directory containing data products output_dir (str | Path): Output directory for sorted products and assets + """ # Convert to Path objects input_dir = Path(input_dir).resolve() @@ -278,7 +279,7 @@ def run_trendify_commands(input_dir: str | Path, output_dir: str | Path): sys.exit(1) except Exception as e: print(f"Unexpected error executing command: {' '.join(cmd)}") - print(f"Error: {str(e)}") + print(f"Error: {e!s}") sys.exit(1) @@ -286,7 +287,6 @@ def make_gallery(output_dir: Path): """ Generates the gallery """ - # Create output directory output_dir.mkdir(exist_ok=True, parents=True) data_dir = output_dir.joinpath("data") diff --git a/src/trendify/local_server.py b/src/trendify/local_server.py index c83fca6..133d85d 100644 --- a/src/trendify/local_server.py +++ b/src/trendify/local_server.py @@ -5,8 +5,6 @@ # Standard impots from dataclasses import dataclass from pathlib import Path -import pandas as pd -from typing import Type # External imports from flask import Flask @@ -15,12 +13,11 @@ # Local imports from trendify.api import api -from flask import Flask __all__ = ["TrendifyProductServerLocal"] -def _to_product_type(type_name: str) -> Type: +def _to_product_type(type_name: str) -> type: """ Converts type name str to trendify type for searching data collection @@ -29,6 +26,7 @@ def _to_product_type(type_name: str) -> Type: Returns: (Type): Class type corresponding to given `type_name` + """ # breakpoint() product_type = api.ProductType[type_name] @@ -44,6 +42,7 @@ class ProductGetter: """ Attributes: trendy_dir (Path): Path for trendify + """ trendy_dir: Path @@ -76,8 +75,8 @@ def _get_data_products( | project "label", "x", "y" | pivot sum("y"), "x", "label" | project "label", "x", "y" - """ + """ # Interpret Product Type product_type = _to_product_type(type_name=str(product_type_name)) if product_type is None: diff --git a/src/trendify/plotly_dashboard.py b/src/trendify/plotly_dashboard.py index 63bb4f4..9908eff 100644 --- a/src/trendify/plotly_dashboard.py +++ b/src/trendify/plotly_dashboard.py @@ -4,16 +4,11 @@ import dash from dash import dcc, html, dash_table -from dash.dependencies import Input, Output, State, ALL +from dash.dependencies import Input, Output, State import plotly.graph_objects as go -from plotly.subplots import make_subplots import pandas as pd import numpy as np -from pathlib import Path -from typing import Dict, List, Any, Union, Optional, Tuple, Set -import warnings from collections import defaultdict -import os # Import from trendify # from trendify.api.API import ( @@ -70,6 +65,7 @@ def __init__(self, debug=False): Args: debug (bool): Whether to enable debug logging + """ self.debug = debug self.app = dash.Dash(__name__, suppress_callback_exceptions=True) @@ -93,6 +89,7 @@ def process_collection( Returns: dash.Dash: The configured Dash application + """ self.debug_log( f"Processing collection with {len(collection.elements or [])} elements" @@ -140,7 +137,7 @@ def process_collection( return self.app - def _create_tab_content(self, tag_str: str) -> List[html.Div]: + def _create_tab_content(self, tag_str: str) -> list[html.Div]: """Create content for a single tab.""" data = self.tag_data[tag_str] content = [] @@ -414,7 +411,7 @@ def _create_histogram_section(self, tag_str: str) -> html.Div: ) def _create_xy_figure( - self, tag_str: str, format2d: Optional[Format2D] = None + self, tag_str: str, format2d: Format2D | None = None ) -> go.Figure: """Create the XY plot figure.""" data = self.tag_data[tag_str] @@ -612,9 +609,9 @@ def _create_xy_figure( def _create_histogram_figure( self, tag_str: str, - histogram_groups: Dict[str, List[HistogramEntry]], + histogram_groups: dict[str, list[HistogramEntry]], num_bins: int, - options: List[str], + options: list[str], ) -> go.Figure: """Create the histogram figure.""" fig = go.Figure() @@ -757,7 +754,7 @@ def _create_histogram_figure( fig.update_layout(**layout_args) return fig - def _create_app_layout(self, title: str, tab_contents: List[Dict]) -> html.Div: + def _create_app_layout(self, title: str, tab_contents: list[dict]) -> html.Div: """Create the main application layout with category-based tag selection.""" if not tab_contents: return html.Div( @@ -993,6 +990,7 @@ def generate_plotly_dashboard( Returns: dash.Dash: The configured Dash application ready to run + """ generator = PlotlyDashboardGenerator(debug=debug) return generator.process_collection(collection, title) diff --git a/src/trendify/production_server.py b/src/trendify/production_server.py index a467bd9..0f9ca12 100644 --- a/src/trendify/production_server.py +++ b/src/trendify/production_server.py @@ -5,10 +5,9 @@ using the Waitress WSGI server. """ -import os import sys from pathlib import Path -from typing import Optional, Union +from typing import Union from trendify.api.api import DataProductCollection @@ -32,6 +31,7 @@ def run_production_server( Raises: ImportError: If Waitress is not installed + """ try: import waitress @@ -63,11 +63,11 @@ def run_production_server( def serve_from_data_dir( - data_dir: Union[str, Path], + data_dir: str | Path, host: str = "0.0.0.0", port: int = 8000, - title: Optional[str] = None, - tag: Optional[str] = None, + title: str | None = None, + tag: str | None = None, ) -> None: """ Serve a dashboard from a directory containing data products. @@ -78,6 +78,7 @@ def serve_from_data_dir( port: Port to listen on title: Optional title for the dashboard. If None, will use the directory name tag: Optional tag to filter data products by + """ from trendify.api.api import DataProductCollection diff --git a/src/trendify/streamlit.py b/src/trendify/streamlit.py index 30d7f34..a560a2f 100644 --- a/src/trendify/streamlit.py +++ b/src/trendify/streamlit.py @@ -2,9 +2,8 @@ from importlib.metadata import version from pathlib import Path import time -from typing import List, Sequence, Tuple +from collections.abc import Sequence -import pandas as pd import streamlit as st from trendify.api.generator.data_product_collection import DataProductCollection @@ -57,12 +56,12 @@ def make_streamlit(trendify_dir: Path): ) -def get_index_map_path(trendify_dir: Path, tag: Tuple[str, ...]) -> Path: +def get_index_map_path(trendify_dir: Path, tag: tuple[str, ...]) -> Path: products_dir = trendify_dir.joinpath("products") return products_dir.joinpath(*tag, "index_map") -def get_tags(trendify_dir: Path) -> Sequence[Tuple[str, ...]]: +def get_tags(trendify_dir: Path) -> Sequence[tuple[str, ...]]: products_dir = trendify_dir.joinpath("products") tags = [ p.parent.relative_to(products_dir).parts @@ -73,7 +72,7 @@ def get_tags(trendify_dir: Path) -> Sequence[Tuple[str, ...]]: def create_nested_expanders( - tags: Sequence[Tuple[str, ...]], current_level: int = 0 + tags: Sequence[tuple[str, ...]], current_level: int = 0 ) -> dict: # Group tags by their current level level_groups = {} @@ -93,9 +92,9 @@ def create_nested_expanders( def render_nested_expanders( - tags: Sequence[Tuple[str, ...]], + tags: Sequence[tuple[str, ...]], current_level: int = 0, - selected_tags: Tuple[str, ...] | None = None, + selected_tags: tuple[str, ...] | None = None, ): if selected_tags is None: selected_tags = None @@ -127,7 +126,7 @@ def render_nested_expanders( if group_info["complete"]: if st.button( button_text, - key=f"btn_{str('_').join(current_tag)}", # Use the full tag tuple as the key + key=f"btn_{'_'.join(current_tag)}", # Use the full tag tuple as the key type=button_type, ): st.session_state.selected_tags = current_tag @@ -141,7 +140,7 @@ def render_nested_expanders( if group_info["complete"]: if st.button( button_text, - key=f"btn_{str('_').join(current_tag)}", # Use the full tag tuple as the key + key=f"btn_{'_'.join(current_tag)}", # Use the full tag tuple as the key type=button_type, use_container_width=True, ): @@ -151,8 +150,8 @@ def render_nested_expanders( return st.session_state.get("selected_tags", None) -def make_sidebar(trendify_dir: Path) -> Tuple[str, ...] | None: - st.title(f"Trendify (v{version("trendify")})") +def make_sidebar(trendify_dir: Path) -> tuple[str, ...] | None: + st.title(f"Trendify (v{version('trendify')})") tags = get_tags(trendify_dir=trendify_dir) st.caption(f"Viewing {len(tags)} assets for {trendify_dir}") @@ -167,7 +166,7 @@ def make_sidebar(trendify_dir: Path) -> Tuple[str, ...] | None: @st.cache_data(show_time=True) -def process_tag(tag: Tuple[str, ...], trendify_dir: Path): +def process_tag(tag: tuple[str, ...], trendify_dir: Path): products_paths = list( trendify_dir.joinpath("products").joinpath(*tag).glob("*.json") ) @@ -175,10 +174,9 @@ def process_tag(tag: Tuple[str, ...], trendify_dir: Path): return DataProductCollection.process_tag_for_streamlit(products_paths, tag=tag) -def make_main_page(tag: Tuple[str, ...], trendify_dir: Path): +def make_main_page(tag: tuple[str, ...], trendify_dir: Path): """Display the main page content for the selected tag""" - - st.title(f"{" | ".join(tag)}") + st.title(f"{' | '.join(tag)}") # Process the tag for tables and plots proccessed_tag = process_tag(tag=tag, trendify_dir=trendify_dir) @@ -287,11 +285,12 @@ def make_dashboard(trendify_dir: str | Path): make_main_page(tag=selected_tag, trendify_dir=trendify_dir) with st.sidebar: - st.caption(f"Site built in {time.perf_counter()-start:.2f} seconds") + st.caption(f"Site built in {time.perf_counter() - start:.2f} seconds") def main(): - """To run use + """ + To run use streamlit run src/trendify/streamlit.py """ diff --git a/test.py b/test.py new file mode 100644 index 0000000..7af5c38 --- /dev/null +++ b/test.py @@ -0,0 +1,39 @@ +import matplotlib.pyplot as plt + +from dataclasses import dataclass + + +@dataclass +class Coordinate: + xyz = tuple[float, float, float] + + +def main(): + coords = [ + [(-1681, 52, -1274), (-2377, 62, -967), (2996, 67, 2797), (-238, 69, -211)], + [(2295, 73, -514), (1490, 70, -1091), (265, 60, -836), (-1061, 63, 217)], + [(-70, 63, -926), (2107, 60, 2934), (1223, 61, 1574), (-954, 74, 579)], + [(-2196, 58, -1390), (917, 77, -907), (1167, 70, -2741), (49, 51, 758)], + [(251, 54, -1118), (1858, 75, -2807), (-128, 52, 1605), (-2144, 60, 971)], + ] + flattened_list = [] + for sublist in coords: + for item in sublist: + flattened_list.append(item) + coords = flattened_list + + fig, ax = plt.subplots() + + for coord in coords: + ax.scatter(x=coord[0], y=coord[2]) + + min_x = min([x[0] for x in coords]) + max_x = max([x[0] for x in coords]) + min_z = min([x[2] for x in coords]) + max_z = max([x[2] for x in coords]) + fig.show() + breakpoint() + + +if __name__ == "__main__": + main() diff --git a/tests/unittests.py b/tests/unittests.py index 383ce36..d17f2e1 100644 --- a/tests/unittests.py +++ b/tests/unittests.py @@ -2,7 +2,6 @@ import numpy as np from pathlib import Path import tempfile -import json import matplotlib.pyplot as plt from trendify import ( Trace2D, diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..f97bd75 --- /dev/null +++ b/uv.lock @@ -0,0 +1,3211 @@ +version = 1 +revision = 3 +requires-python = ">=3.11" +resolution-markers = [ + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.12' and 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command: +# uv export --frozen --output-file=requirements.txt +-e . +altair==6.2.2 \ + --hash=sha256:94014f8ad8617c3cb163d1137359cd6db5ba134b9b46d93cfd8b609fd245a583 \ + --hash=sha256:a1ff9d9cfe81c75414641826312b9471780e19d39293ba0b012933f6b6cba0fe + # via streamlit +annotated-types==0.7.0 \ + --hash=sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53 \ + --hash=sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89 + # via pydantic +anyio==4.14.1 \ + --hash=sha256:4e5533c5b8ff0a24f5d7a176cbe6877129cd183893f66b537f8f227d10527d72 \ + --hash=sha256:8d648a3544c1a700e3ff78615cd679e4c5c3f149904287e73687b2596963629e + # via + # httpx + # mcp + # sse-starlette + # starlette + # streamlit +attrs==26.1.0 \ + --hash=sha256:c647aa4a12dfbad9333ca4e71fe62ddc36f4e63b2d260a37a8b83d2f043ac309 \ + --hash=sha256:d03ceb89cb322a8fd706d4fb91940737b6642aa36998fe130a9bc96c985eff32 + # via + # jsonschema + # referencing +babel==2.18.0 \ + --hash=sha256:b80b99a14bd085fcacfa15c9165f651fbb3406e66cc603abf11c5750937c992d \ + --hash=sha256:e2b422b277c2b9a9630c1d7903c2a00d0830c409c59ac8cae9081c92f1aeba35 + # via mkdocs-material +backrefs==7.0 \ + --hash=sha256:4989bb9e1e99eb23647c7160ed51fb21d0b41b5d200f2d3017da41e023097e82 \ + --hash=sha256:a0fa7360c63509e9e077e174ef4e6d3c21c8db94189b9d957289ae6d794b9475 \ + --hash=sha256:a6448b28180e3ca01134c9cf09dcebafad8531072e09903c5451748a05f24bc9 \ + --hash=sha256:b57cd227ea556b0aed3dc9b8da4628db4eabc0402c6d7fcfc69283a93955f7e9 \ + --hash=sha256:ca42ce6a49ace3d75684dfa9937f3373902a63284ecb385ce36d15e5dcb41c12 \ + --hash=sha256:f2c52955d631b9e1ac4cd56209f0a3a946d592b98e7790e77699339ae01c102a + # via mkdocs-material +black==26.5.1 \ + --hash=sha256:0e48b87e03bf109288e55cfceadcfa15ff5470aca2851a851950ed2926f450d7 \ + --hash=sha256:1037d5ac7b7b310b2632ad867ec8d0e4c4819dcdb0b820f63135da746a24e418 \ + --hash=sha256:1ef92b76f7733f282fd096ea406200b5a286c42947412b0eaff3a74e3616cefe \ + --hash=sha256:1f7ea64ebfa01b50f693508fc39f875e264446d3b097088f84f203b9d09618a0 \ + --hash=sha256:2b36cf2ddf5566e205f6535f782a62194a184d33e175b64ae8c40b1737522be3 \ + --hash=sha256:30d3c14661f2792e9142cce3eeeb1cbc175b3eb5f733be0c8eeb99651e52b0c3 \ + --hash=sha256:32d5ea7f6c8bdfa6e648326ebca1f02b0764e2a029edc6f8dce2627e19d468c3 \ + --hash=sha256:3915f256e75a2d7cf88d8953d37f780455dc586cc72dee059c528fe77f581217 \ + --hash=sha256:4ad6fa01f941920f54f2bbb35f3df7673428a0ef98a0b0840c2eaef3b110efa8 \ + --hash=sha256:4ed7f7da04046d2e488437170797d3b4a4ad83906683bcb7dfc68b673bbce5e2 \ + --hash=sha256:5119fa92ae61f786e8c3662fd60aece1d0a2dd5cca5d0c79417a95e7a4272a59 \ + --hash=sha256:577f21094ea469ef92ec1adaf2c9441a226d2144d01a5be2fa823cecf6543e50 \ + --hash=sha256:5c34b25da232ead53a6f335b76dbea124f4d152ad568b9080d6f944bc2b34b52 \ + --hash=sha256:96ae2c733b2aabdd9986e2c5df628ff3473676cd1c5faded1ff496cf6d74083c \ + --hash=sha256:9d98d4137277c75dfb898ec8d846c4fd68ba1e9cf77f95e2865c203dc18f4c3d \ + --hash=sha256:a1dca32d9f1784af512a13410ec204c6f7f0aa9797a111c42e1c03449821c264 \ + --hash=sha256:dd321f668053961824bcc1be1cc1df748b2d7e4fa28086b08331e577b0100a73 \ + --hash=sha256:e1a26503279b6b310669fb0b219c39e4820b77e8189fe80f522bb511f247db0a \ + --hash=sha256:e88976690a64b0af98312ca958415849cb42423423c5f2ee74af4b49a97a2168 \ + --hash=sha256:ea8d16dc41655aa113cd64665e7219446cd7e4ff2248d7178eaa905190c86b18 \ + --hash=sha256:ecb3e624844c798144e9bd986954e0adc81d8911a1f30f375e1252fe26e8c294 \ + --hash=sha256:ed1a20af114c301a0269bf01163d51dbef72737fd65f850001e7cbe7f3c7abae +blinker==1.9.0 \ + --hash=sha256:b4ce2265a7abece45e7cc896e98dbebe6cead56bcf805a3d23136d145f5445bf \ + --hash=sha256:ba0efaa9080b619ff2f3459d1d500c57bddea4a6b424b60a91141db6fd2f08bc + # via + # flask + # streamlit +cachetools==7.1.4 \ + --hash=sha256:323dc4127934744db5b54eb4924482d7edafbf9554e820d1531c2e08c0e4ef54 \ + --hash=sha256:437f55a4e0c1b01a4f3077cc470e6991d47430970e36fbcb77e2be0df4fc1cd6 + # via streamlit +certifi==2026.6.17 \ + --hash=sha256:024c88eeec92ca068db80f02b8b07c9cef7b9fe261d1d535abfd5abd6f6af432 \ + --hash=sha256:2227dcbaafe0d2f59279d1762ddddc37783ed4354594f194ffc31d20f41fc3db + # via + # httpcore + # httpx + # requests +cffi==2.0.0 ; platform_python_implementation != 'PyPy' \ + --hash=sha256:00bdf7acc5f795150faa6957054fbbca2439db2f775ce831222b66f192f03beb \ + --hash=sha256:07b271772c100085dd28b74fa0cd81c8fb1a3ba18b21e03d7c27f3436a10606b \ + --hash=sha256:087067fa8953339c723661eda6b54bc98c5625757ea62e95eb4898ad5e776e9f \ + --hash=sha256:0a1527a803f0a659de1af2e1fd700213caba79377e27e4693648c2923da066f9 \ + --hash=sha256:12873ca6cb9b0f0d3a0da705d6086fe911591737a59f28b7936bdfed27c0d47c \ + --hash=sha256:19f705ada2530c1167abacb171925dd886168931e0a7b78f5bffcae5c6b5be75 \ + --hash=sha256:1e3a615586f05fc4065a8b22b8152f0c1b00cdbc60596d187c2a74f9e3036e4e \ + --hash=sha256:1fc9ea04857caf665289b7a75923f2c6ed559b8298a1b8c49e59f7dd95c8481e \ + --hash=sha256:203a48d1fb583fc7d78a4c6655692963b860a417c0528492a6bc21f1aaefab25 \ + --hash=sha256:21d1152871b019407d8ac3985f6775c079416c282e431a4da6afe7aefd2bccbe \ + --hash=sha256:24b6f81f1983e6df8db3adc38562c83f7d4a0c36162885ec7f7b77c7dcbec97b \ + --hash=sha256:256f80b80ca3853f90c21b23ee78cd008713787b1b1e93eae9f3d6a7134abd91 \ + --hash=sha256:28a3a209b96630bca57cce802da70c266eb08c6e97e5afd61a75611ee6c64592 \ + --hash=sha256:2c8f814d84194c9ea681642fd164267891702542f028a15fc97d4674b6206187 \ + --hash=sha256:2de9a304e27f7596cd03d16f1b7c72219bd944e99cc52b84d0145aefb07cbd3c \ + --hash=sha256:38100abb9d1b1435bc4cc340bb4489635dc2f0da7456590877030c9b3d40b0c1 \ + --hash=sha256:3925dd22fa2b7699ed2617149842d2e6adde22b262fcbfada50e3d195e4b3a94 \ + --hash=sha256:3e17ed538242334bf70832644a32a7aae3d83b57567f9fd60a26257e992b79ba \ + --hash=sha256:44d1b5909021139fe36001ae048dbdde8214afa20200eda0f64c068cac5d5529 \ + --hash=sha256:45d5e886156860dc35862657e1494b9bae8dfa63bf56796f2fb56e1679fc0bca \ + --hash=sha256:4671d9dd5ec934cb9a73e7ee9676f9362aba54f7f34910956b84d727b0d73fb6 \ + --hash=sha256:5fed36fccc0612a53f1d4d9a816b50a36702c28a2aa880cb8a122b3466638743 \ + --hash=sha256:66f011380d0e49ed280c789fbd08ff0d40968ee7b665575489afa95c98196ab5 \ + --hash=sha256:6824f87845e3396029f3820c206e459ccc91760e8fa24422f8b0c3d1731cbec5 \ + --hash=sha256:6c6c373cfc5c83a975506110d17457138c8c63016b563cc9ed6e056a82f13ce4 \ + --hash=sha256:6d02d6655b0e54f54c4ef0b94eb6be0607b70853c45ce98bd278dc7de718be5d \ + --hash=sha256:6d50360be4546678fc1b79ffe7a66265e28667840010348dd69a314145807a1b \ + --hash=sha256:730cacb21e1bdff3ce90babf007d0a0917cc3e6492f336c2f0134101e0944f93 \ + --hash=sha256:737fe7d37e1a1bffe70bd5754ea763a62a066dc5913ca57e957824b72a85e205 \ + --hash=sha256:74a03b9698e198d47562765773b4a8309919089150a0bb17d829ad7b44b60d27 \ + --hash=sha256:7553fb2090d71822f02c629afe6042c299edf91ba1bf94951165613553984512 \ + --hash=sha256:7a66c7204d8869299919db4d5069a82f1561581af12b11b3c9f48c584eb8743d \ + --hash=sha256:7cc09976e8b56f8cebd752f7113ad07752461f48a58cbba644139015ac24954c \ + --hash=sha256:81afed14892743bbe14dacb9e36d9e0e504cd204e0b165062c488942b9718037 \ + --hash=sha256:8941aaadaf67246224cee8c3803777eed332a19d909b47e29c9842ef1e79ac26 \ + --hash=sha256:8eca2a813c1cb7ad4fb74d368c2ffbbb4789d377ee5bb8df98373c2cc0dee76c \ + --hash=sha256:92b68146a71df78564e4ef48af17551a5ddd142e5190cdf2c5624d0c3ff5b2e8 \ + --hash=sha256:94698a9c5f91f9d138526b48fe26a199609544591f859c870d477351dc7b2414 \ + --hash=sha256:9a67fc9e8eb39039280526379fb3a70023d77caec1852002b4da7e8b270c4dd9 \ + --hash=sha256:9de40a7b0323d889cf8d23d1ef214f565ab154443c42737dfe52ff82cf857664 \ + --hash=sha256:a05d0c237b3349096d3981b727493e22147f934b20f6f125a3eba8f994bec4a9 \ + --hash=sha256:afb8db5439b81cf9c9d0c80404b60c3cc9c3add93e114dcae767f1477cb53775 \ + --hash=sha256:b1e74d11748e7e98e2f426ab176d4ed720a64412b6a15054378afdb71e0f37dc \ + --hash=sha256:b21e08af67b8a103c71a250401c78d5e0893beff75e28c53c98f4de42f774062 \ + --hash=sha256:b4c854ef3adc177950a8dfc81a86f5115d2abd545751a304c5bcf2c2c7283cfe \ + --hash=sha256:baf5215e0ab74c16e2dd324e8ec067ef59e41125d3eade2b863d294fd5035c92 \ + --hash=sha256:c649e3a33450ec82378822b3dad03cc228b8f5963c0c12fc3b1e0ab940f768a5 \ + --hash=sha256:c654de545946e0db659b3400168c9ad31b5d29593291482c43e3564effbcee13 \ + --hash=sha256:c6638687455baf640e37344fe26d37c404db8b80d037c3d29f58fe8d1c3b194d \ + --hash=sha256:c8d3b5532fc71b7a77c09192b4a5a200ea992702734a2e9279a37f2478236f26 \ + --hash=sha256:d48a880098c96020b02d5a1f7d9251308510ce8858940e6fa99ece33f610838b \ + --hash=sha256:d68b6cef7827e8641e8ef16f4494edda8b36104d79773a334beaa1e3521430f6 \ + --hash=sha256:d9b29c1f0ae438d5ee9acb31cadee00a58c46cc9c0b2f9038c6b0b3470877a8c \ + --hash=sha256:d9b97165e8aed9272a6bb17c01e3cc5871a594a446ebedc996e2397a1c1ea8ef \ + --hash=sha256:da68248800ad6320861f129cd9c1bf96ca849a2771a59e0344e88681905916f5 \ + --hash=sha256:da902562c3e9c550df360bfa53c035b2f241fed6d9aef119048073680ace4a18 \ + --hash=sha256:dbd5c7a25a7cb98f5ca55d258b103a2054f859a46ae11aaf23134f9cc0d356ad \ + --hash=sha256:dd4f05f54a52fb558f1ba9f528228066954fee3ebe629fc1660d874d040ae5a3 \ + --hash=sha256:f93fd8e5c8c0a4aa1f424d6173f14a892044054871c771f8566e4008eaa359d2 \ + --hash=sha256:fc33c5141b55ed366cfaad382df24fe7dcbc686de5be719b207bb248e3053dc5 + # via cryptography +charset-normalizer==3.4.7 \ + --hash=sha256:03853ed82eeebbce3c2abfdbc98c96dc205f32a79627688ac9a27370ea61a49c \ + --hash=sha256:08e721811161356f97b4059a9ba7bafb23ea5ee2255402c42881c214e173c6b4 \ + --hash=sha256:0c96c3b819b5c3e9e165495db84d41914d6894d55181d2d108cc1a69bfc9cce0 \ + --hash=sha256:0ea948db76d31190bf08bd371623927ee1339d5f2a0b4b1b4a4439a65298703c \ + --hash=sha256:0f7eb884681e3938906ed0434f20c63046eacd0111c4ba96f27b76084cd679f5 \ + --hash=sha256:1a87ca9d5df6fe460483d9a5bbf2b18f620cbed41b432e2bddb686228282d10b \ + --hash=sha256:1c2a768fdd44ee4a9339a9b0b130049139b8ce3c01d2ce09f67f5a68048d477c \ + --hash=sha256:1c2aed2e5e41f24ea8ef1590b8e848a79b56f3a5564a65ceec43c9d692dc7d8a \ + --hash=sha256:202389074300232baeb53ae2569a60901f7efadd4245cf3a3bf0617d60b439d7 \ + --hash=sha256:203104ed3e428044fd943bc4bf45fa73c0730391f9621e37fe39ecf477b128cb \ + --hash=sha256:2257141f39fe65a3fdf38aeccae4b953e5f3b3324f4ff0daf9f15b8518666a2c \ + --hash=sha256:298930cec56029e05497a76988377cbd7457ba864beeea92ad7e844fe74cd1f1 \ + --hash=sha256:2d6eb928e13016cea4f1f21d1e10c1cebd5a421bc57ddf5b1142ae3f86824fab \ + --hash=sha256:2fe249cb4651fd12605b7288b24751d8bfd46d35f12a20b1ba33dea122e690df \ + --hash=sha256:30b8d1d8c52a48c2c5690e152c169b673487a2a58de1ec7393196753063fcd5e \ + --hash=sha256:3534e7dcbdcf757da6b85a0bbf5b6868786d5982dd959b065e65481644817a18 \ + --hash=sha256:38c0109396c4cfc574d502df99742a45c72c08eff0a36158b6f04000043dbf38 \ + --hash=sha256:3946fa46a0cf3e4c8cb1cc52f56bb536310d34f25f01ca9b6c16afa767dab110 \ + --hash=sha256:3bec022aec2c514d9cf199522a802bd007cd588ab17ab2525f20f9c34d067c18 \ + --hash=sha256:3c9a494bc5ec77d43cea229c4f6db1e4d8fe7e1bbffa8b6f0f0032430ff8ab44 \ + --hash=sha256:3dce51d0f5e7951f8bb4900c257dad282f49190fdbebecd4ba99bcc41fef404d \ + --hash=sha256:3dedcc22d73ec993f42055eff4fcfed9318d1eeb9a6606c55892a26964964e48 \ + --hash=sha256:4042d5c8f957e15221d423ba781e85d553722fc4113f523f2feb7b188cc34c5e \ + --hash=sha256:481551899c856c704d58119b5025793fa6730adda3571971af568f66d2424bb5 \ + --hash=sha256:4dc1e73c36828f982bfe79fadf5919923f8a6f4df2860804db9a98c48824ce8d \ + --hash=sha256:532bc9bf33a68613fd7d65e4b1c71a6a38d7d42604ecf239c77392e9b4e8998c \ + --hash=sha256:54523e136b8948060c0fa0bc7b1b50c32c186f2fceee897a495406bb6e311d2b \ + --hash=sha256:5649fd1c7bade02f320a462fdefd0b4bd3ce036065836d4f42e0de958038e116 \ + --hash=sha256:56be790f86bfb2c98fb742ce566dfb4816e5a83384616ab59c49e0604d49c51d \ + --hash=sha256:5b77459df20e08151cd6f8b9ef8ef1f961ef73d85c21a555c7eed5b79410ec10 \ + --hash=sha256:5ed6ab538499c8644b8a3e18debabcd7ce684f3fa91cf867521a7a0279cab2d6 \ + --hash=sha256:6178f72c5508bfc5fd446a5905e698c6212932f25bcdd4b47a757a50605a90e2 \ + --hash=sha256:64f02c6841d7d83f832cd97ccf8eb8a906d06eb95d5276069175c696b024b60a \ + --hash=sha256:65bcd23054beab4d166035cabbc868a09c1a49d1efe458fe8e4361215df40265 \ + --hash=sha256:67f6279d125ca0046a7fd386d01b311c6363844deac3e5b069b514ba3e63c246 \ + --hash=sha256:6c114670c45346afedc0d947faf3c7f701051d2518b943679c8ff88befe14f8e \ + --hash=sha256:708838739abf24b2ceb208d0e22403dd018faeef86ddac04319a62ae884c4f15 \ + --hash=sha256:715479b9a2802ecac752a3b0efa2b0b60285cf962ee38414211abdfccc233b41 \ + --hash=sha256:733784b6d6def852c814bce5f318d25da2ee65dd4839a0718641c696e09a2960 \ + --hash=sha256:752a45dc4a6934060b3b0dab47e04edc3326575f82be64bc4fc293914566503e \ + --hash=sha256:7579e913a5339fb8fa133f6bbcfd8e6749696206cf05acdbdca71a1b436d8e72 \ + --hash=sha256:7641bb8895e77f921102f72833904dcd9901df5d6d72a2ab8f31d04b7e51e4e7 \ + --hash=sha256:7804338df6fcc08105c7745f1502ba68d900f45fd770d5bdd5288ddccb8a42d8 \ + --hash=sha256:80d04837f55fc81da168b98de4f4b797ef007fc8a79ab71c6ec9bc4dd662b15b \ + --hash=sha256:8778f0c7a52e56f75d12dae53ae320fae900a8b9b4164b981b9c5ce059cd1fcb \ + --hash=sha256:8d828b6667a32a728a1ad1d93957cdf37489c57b97ae6c4de2860fa749b8fc1e \ + --hash=sha256:8e385e4267ab76874ae30db04c627faaaf0b509e1ccc11a95b3fc3e83f855c00 \ + --hash=sha256:92a0a01ead5e668468e952e4238cccd7c537364eb7d851ab144ab6627dbbe12f \ + --hash=sha256:a180c5e59792af262bf263b21a3c49353f25945d8d9f70628e73de370d55e1e1 \ + --hash=sha256:a277ab8928b9f299723bc1a2dabb1265911b1a76341f90a510368ca44ad9ab66 \ + --hash=sha256:a5fe03b42827c13cdccd08e6c0247b6a6d4b5e3cdc53fd1749f5896adcdc2356 \ + --hash=sha256:a89c23ef8d2c6b27fd200a42aa4ac72786e7c60d40efdc76e6011260b6e949c4 \ + --hash=sha256:adb2597b428735679446b46c8badf467b4ca5f5056aae4d51a19f9570301b1ad \ + --hash=sha256:ae196f021b5e7c78e918242d217db021ed2a6ace2bc6ae94c0fc596221c7f58d \ + --hash=sha256:ae89db9e5f98a11a4bf50407d4363e7b09b31e55bc117b4f7d80aab97ba009e5 \ + --hash=sha256:aed52fea0513bac0ccde438c188c8a471c4e0f457c2dd20cdbf6ea7a450046c7 \ + --hash=sha256:bb6d88045545b26da47aa879dd4a89a71d1dce0f0e549b1abcb31dfe4a8eac49 \ + --hash=sha256:bd6c2a1c7573c64738d716488d2cdd3c00e340e4835707d8fdb8dc1a66ef164e \ + --hash=sha256:c03a41a8784091e67a39648f70c5f97b5b6a37f216896d44d2cdcb82615339a0 \ + --hash=sha256:c35abb8bfff0185efac5878da64c45dafd2b37fb0383add1be155a763c1f083d \ + --hash=sha256:c36c333c39be2dbca264d7803333c896ab8fa7d4d6f0ab7edb7dfd7aea6e98c0 \ + --hash=sha256:c45e9440fb78f8ddabcf714b68f936737a121355bf59f3907f4e17721b9d1aae \ + --hash=sha256:ce3412fbe1e31eb81ea42f4169ed94861c56e643189e1e75f0041f3fe7020abe \ + --hash=sha256:cf1493cd8607bec4d8a7b9b004e699fcf8f9103a9284cc94962cb73d20f9d4a3 \ + --hash=sha256:d4a48e5b3c2a489fae013b7589308a40146ee081f6f509e047e0e096084ceca1 \ + --hash=sha256:d6038d37043bced98a66e68d3aa2b6a35505dc01328cd65217cefe82f25def44 \ + --hash=sha256:d635aab80466bc95771bb78d5370e74d36d1fe31467b6b29b8b57b2a3cd7d22c \ + --hash=sha256:e044c39e41b92c845bc815e5ae4230804e8e7bc29e399b0437d64222d92809dd \ + --hash=sha256:e060d01aec0a910bdccb8be71faf34e7799ce36950f8294c8bf612cba65a2c9e \ + --hash=sha256:e1421b502d83040e6d7fb2fb18dff63957f720da3d77b2fbd3187ceb63755d7b \ + --hash=sha256:e712b419df8ba5e42b226c510472b37bd57b38e897d3eca5e8cfd410a29fa859 \ + --hash=sha256:e74327fb75de8986940def6e8dee4f127cc9752bee7355bb323cc5b2659b6d46 \ + --hash=sha256:e8ac484bf18ce6975760921bb6148041faa8fef0547200386ea0b52b5d27bf7b \ + --hash=sha256:eca9705049ad3c7345d574e3510665cb2cf844c2f2dcfe675332677f081cbd46 \ + --hash=sha256:edac0f1ab77644605be2cbba52e6b7f630731fc42b34cb0f634be1a6eface56a \ + --hash=sha256:effc3f449787117233702311a1b7d8f59cba9ced946ba727bdc329ec69028e24 \ + --hash=sha256:f495a1652cf3fbab2eb0639776dad966c2fb874d79d87ca07f9d5f059b8bd215 \ + --hash=sha256:f496c9c3cc02230093d8330875c4c3cdfc3b73612a5fd921c65d39cbcef08063 \ + --hash=sha256:f59099f9b66f0d7145115e6f80dd8b1d847176df89b234a5a6b3f00437aa0832 \ + --hash=sha256:f59ad4c0e8f6bba240a9bb85504faa1ab438237199d4cce5f622761507b8f6a6 \ + --hash=sha256:fbccdc05410c9ee21bbf16a35f4c1d16123dcdeb8a1d38f33654fa21d0234f79 \ + --hash=sha256:fea24543955a6a729c45a73fe90e08c743f0b3334bbf3201e6c4bc1b0c7fa464 + # via requests +click==8.4.2 \ + --hash=sha256:9a6cea6e60b17ebe0a44c5cc636d94f09bd66142c1cd7d8b4cd731c4917a15f6 \ + --hash=sha256:e6f9f66136c816745b9d65817da91d61d957fb16e02e4dcd0552553c5a197b76 + # via + # black + # flask + # mkdocs + # mkdocs-click + # properdocs + # streamlit + # uvicorn +colorama==0.4.6 \ + --hash=sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44 \ + --hash=sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6 + # via + # click + # mkdocs + # mkdocs-material + # properdocs + # pytest +contourpy==1.3.3 \ + --hash=sha256:023b44101dfe49d7d53932be418477dba359649246075c996866106da069af69 \ + --hash=sha256:07ce5ed73ecdc4a03ffe3e1b3e3c1166db35ae7584be76f65dbbe28a7791b0cc \ + --hash=sha256:083e12155b210502d0bca491432bb04d56dc3432f95a979b429f2848c3dbe880 \ + --hash=sha256:0bf67e0e3f482cb69779dd3061b534eb35ac9b17f163d851e2a547d56dba0a3a \ + --hash=sha256:0c1fc238306b35f246d61a1d416a627348b5cf0648648a031e14bb8705fcdfe8 \ + --hash=sha256:13b68d6a62db8eafaebb8039218921399baf6e47bf85006fd8529f2a08ef33fc \ + --hash=sha256:15ff10bfada4bf92ec8b31c62bf7c1834c244019b4a33095a68000d7075df470 \ + --hash=sha256:177fb367556747a686509d6fef71d221a4b198a3905fe824430e5ea0fda54eb5 \ + --hash=sha256:1cadd8b8969f060ba45ed7c1b714fe69185812ab43bd6b86a9123fe8f99c3263 \ + --hash=sha256:1fd43c3be4c8e5fd6e4f2baeae35ae18176cf2e5cced681cca908addf1cdd53b \ + --hash=sha256:22e9b1bd7a9b1d652cd77388465dc358dafcd2e217d35552424aa4f996f524f5 \ + --hash=sha256:23416f38bfd74d5d28ab8429cc4d63fa67d5068bd711a85edb1c3fb0c3e2f381 \ + --hash=sha256:283edd842a01e3dcd435b1c5116798d661378d83d36d337b8dde1d16a5fc9ba3 \ + --hash=sha256:2a2a8b627d5cc6b7c41a4beff6c5ad5eb848c88255fda4a8745f7e901b32d8e4 \ + --hash=sha256:2b7e9480ffe2b0cd2e787e4df64270e3a0440d9db8dc823312e2c940c167df7e \ + --hash=sha256:322ab1c99b008dad206d406bb61d014cf0174df491ae9d9d0fac6a6fda4f977f \ + --hash=sha256:33c82d0138c0a062380332c861387650c82e4cf1747aaa6938b9b6516762e772 \ + --hash=sha256:348ac1f5d4f1d66d3322420f01d42e43122f43616e0f194fc1c9f5d830c5b286 \ + --hash=sha256:3519428f6be58431c56581f1694ba8e50626f2dd550af225f82fb5f5814d2a42 \ + --hash=sha256:3c30273eb2a55024ff31ba7d052dde990d7d8e5450f4bbb6e913558b3d6c2301 \ + --hash=sha256:3d1a3799d62d45c18bafd41c5fa05120b96a28079f2393af559b843d1a966a77 \ + --hash=sha256:451e71b5a7d597379ef572de31eeb909a87246974d960049a9848c3bc6c41bf7 \ + --hash=sha256:459c1f020cd59fcfe6650180678a9993932d80d44ccde1fa1868977438f0b411 \ + --hash=sha256:4d00e655fcef08aba35ec9610536bfe90267d7ab5ba944f7032549c55a146da1 \ + --hash=sha256:4debd64f124ca62069f313a9cb86656ff087786016d76927ae2cf37846b006c9 \ + --hash=sha256:4feffb6537d64b84877da813a5c30f1422ea5739566abf0bd18065ac040e120a \ + --hash=sha256:50ed930df7289ff2a8d7afeb9603f8289e5704755c7e5c3bbd929c90c817164b \ + --hash=sha256:51e79c1f7470158e838808d4a996fa9bac72c498e93d8ebe5119bc1e6becb0db \ + --hash=sha256:556dba8fb6f5d8742f2923fe9457dbdd51e1049c4a43fd3986a0b14a1d815fc6 \ + --hash=sha256:598c3aaece21c503615fd59c92a3598b428b2f01bfb4b8ca9c4edeecc2438620 \ + --hash=sha256:5ed3657edf08512fc3fe81b510e35c2012fbd3081d2e26160f27ca28affec989 \ + --hash=sha256:626d60935cf668e70a5ce6ff184fd713e9683fb458898e4249b63be9e28286ea \ + --hash=sha256:644a6853d15b2512d67881586bd03f462c7ab755db95f16f14d7e238f2852c67 \ + --hash=sha256:655456777ff65c2c548b7c454af9c6f33f16c8884f11083244b5819cc214f1b5 \ + --hash=sha256:66c8a43a4f7b8df8b71ee1840e4211a3c8d93b214b213f590e18a1beca458f7d \ + --hash=sha256:6afc576f7b33cf00996e5c1102dc2a8f7cc89e39c0b55df93a0b78c1bd992b36 \ + --hash=sha256:6c3d53c796f8647d6deb1abe867daeb66dcc8a97e8455efa729516b997b8ed99 \ + --hash=sha256:709a48ef9a690e1343202916450bc48b9e51c049b089c7f79a267b46cffcdaa1 \ + --hash=sha256:70f9aad7de812d6541d29d2bbf8feb22ff7e1c299523db288004e3157ff4674e \ + --hash=sha256:8153b8bfc11e1e4d75bcb0bff1db232f9e10b274e0929de9d608027e0d34ff8b \ + --hash=sha256:87acf5963fc2b34825e5b6b048f40e3635dd547f590b04d2ab317c2619ef7ae8 \ + --hash=sha256:88df9880d507169449d434c293467418b9f6cbe82edd19284aa0409e7fdb933d \ + --hash=sha256:929ddf8c4c7f348e4c0a5a3a714b5c8542ffaa8c22954862a46ca1813b667ee7 \ + --hash=sha256:92d9abc807cf7d0e047b95ca5d957cf4792fcd04e920ca70d48add15c1a90ea7 \ + --hash=sha256:95b181891b4c71de4bb404c6621e7e2390745f887f2a026b2d99e92c17892339 \ + --hash=sha256:9e999574eddae35f1312c2b4b717b7885d4edd6cb46700e04f7f02db454e67c1 \ + --hash=sha256:a15459b0f4615b00bbd1e91f1b9e19b7e63aea7483d03d804186f278c0af2659 \ + --hash=sha256:a22738912262aa3e254e4f3cb079a95a67132fc5a063890e224393596902f5a4 \ + --hash=sha256:ab2fd90904c503739a75b7c8c5c01160130ba67944a7b77bbf36ef8054576e7f \ + --hash=sha256:ab3074b48c4e2cf1a960e6bbeb7f04566bf36b1861d5c9d4d8ac04b82e38ba20 \ + --hash=sha256:afe5a512f31ee6bd7d0dda52ec9864c984ca3d66664444f2d72e0dc4eb832e36 \ + --hash=sha256:b08a32ea2f8e42cf1d4be3169a98dd4be32bafe4f22b6c4cb4ba810fa9e5d2cb \ + --hash=sha256:b20c7c9a3bf701366556e1b1984ed2d0cedf999903c51311417cf5f591d8c78d \ + --hash=sha256:b2e8faa0ed68cb29af51edd8e24798bb661eac3bd9f65420c1887b6ca89987c8 \ + --hash=sha256:b7301b89040075c30e5768810bc96a8e8d78085b47d8be6e4c3f5a0b4ed478a0 \ + --hash=sha256:b7448cb5a725bb1e35ce88771b86fba35ef418952474492cf7c764059933ff8b \ + --hash=sha256:ca0fdcd73925568ca027e0b17ab07aad764be4706d0a925b89227e447d9737b7 \ + --hash=sha256:ca658cd1a680a5c9ea96dc61cdbae1e85c8f25849843aa799dfd3cb370ad4fbe \ + --hash=sha256:cbedb772ed74ff5be440fa8eee9bd49f64f6e3fc09436d9c7d8f1c287b121d77 \ + --hash=sha256:cd5dfcaeb10f7b7f9dc8941717c6c2ade08f587be2226222c12b25f0483ed497 \ + --hash=sha256:cf9022ef053f2694e31d630feaacb21ea24224be1c3ad0520b13d844274614fd \ + --hash=sha256:d002b6f00d73d69333dac9d0b8d5e84d9724ff9ef044fd63c5986e62b7c9e1b1 \ + --hash=sha256:d06bb1f751ba5d417047db62bca3c8fde202b8c11fb50742ab3ab962c81e8216 \ + --hash=sha256:d304906ecc71672e9c89e87c4675dc5c2645e1f4269a5063b99b0bb29f232d13 \ + --hash=sha256:e4e6b05a45525357e382909a4c1600444e2a45b4795163d3b22669285591c1ae \ + --hash=sha256:e74a9a0f5e3fff48fb5a7f2fd2b9b70a3fe014a67522f79b7cca4c0c7e43c9ae \ + --hash=sha256:ea37e7b45949df430fe649e5de8351c423430046a2af20b1c1961cae3afcda77 \ + --hash=sha256:f64836de09927cba6f79dcd00fdd7d5329f3fccc633468507079c829ca4db4e3 \ + --hash=sha256:fd6ec6be509c787f1caf6b247f0b1ca598bef13f4ddeaa126b7658215529ba0f \ + --hash=sha256:fd907ae12cd483cd83e414b12941c632a969171bf90fc937d0c9f268a31cafff \ + --hash=sha256:fd914713266421b7536de2bfa8181aa8c699432b6763a0ea64195ebe28bff6a9 \ + --hash=sha256:fde6c716d51c04b1c25d0b90364d0be954624a0ee9d60e23e850e8d48353d07a + # via matplotlib +coverage==7.14.3 \ + --hash=sha256:02c41de2a88011b893050fc9830267d927a50a215f7ad5ec17349db7090ccf26 \ + --hash=sha256:0423d64c013057a06e70f070f073cec4b0cbc7d2b27f3c7007292f2ff1d52965 \ + --hash=sha256:0ee68f5c34812780f3a7063382c0a9fcbb99985b7ddcdcaa626e4f3fb2e0783a \ + --hash=sha256:11a7ec9f97ab950f4c5af62229befc7faf208fdbc0116d3902d7e306cf2c5abd \ + --hash=sha256:16b206e521feb8b7133a45754643dead0538489cf8b783b90cf5f4e3299625fd \ + --hash=sha256:1a7563a443f3d53fdeb040ec8c9f7466aed7ca3dc5891aa09d3ca3625fa4387f \ + --hash=sha256:1bb93c2aa61d2a5b38f1526546d95cf4132cb681e541a337bf8dfd092be816e5 \ + --hash=sha256:1e3b91f9c4740aeb571ecf82e5e8d8e4ab62d34fcb5a5d4e5baa38c6f7d2857c \ + --hash=sha256:2415902f385a23dcc4ccd26e0ba803249a169af6a930c003a4c715eeb9a5444e \ + --hash=sha256:27d07a46500ba23515b838dbcf52512026af04090755cf6cc64166d88c9b9a1a \ + --hash=sha256:2bfc4dd0a912329eccc7484a7d0b2a38032b38c40663b1e1ac595f10c457954b \ + --hash=sha256:2e41fd3aab806770008279a93879b0924b16247e09ab537c043d08bbca53b4ab \ + --hash=sha256:338b19131ab1a6b767b462bfcbaa692e7ae22f24463e39d49b02a83410ff6b37 \ + --hash=sha256:39e1dbbb6ff2c338e0196a482558a792a1de3aa64261196f5cdb3da016ad9cda \ + --hash=sha256:3c68df8e61f1e09633fefc7538297145623957a048534368c9d212782aa5e845 \ + --hash=sha256:3d74ff26299c4879ce3a4d826f9d3d4d556fd285fde7bbce3c0ef5a8ab1cec24 \ + --hash=sha256:3e5b550a128419373c2f6cec28a244207013ef15f5cbcff6a5ca09d1dfaaf027 \ + --hash=sha256:41de778bd41780586e2b04912079c73089ab5d839624e28db3bdb26de638da92 \ + --hash=sha256:47968988b367990ae4ab17523790c38cd125e02c6bfd379b6022be2d40bdc38c \ + --hash=sha256:4b60ca6d8af70473491a15a343cbabab2e8f9ea66a4376e81c7aa24876a6f977 \ + --hash=sha256:4d310baf69a4fbe8a098ce727e4808a34866ac718a6f759ae659cbd3221358bc \ + --hash=sha256:526ce9721116af23b1065089f0b75046fe521e7772ab94b641cd66b7a0421889 \ + --hash=sha256:5952f8c1bda2a5347154450379316e6dfa4d934d62ca35f6784451e6f55074fb \ + --hash=sha256:5d788e5fd55347eef06ca0732c77d04a264de67e8ff24631270cdff3767a60cf \ + --hash=sha256:605ab2b566a22bd94834529d66d295c364aba84afd3e5498285c7a524017b1fc \ + --hash=sha256:611e62cb9386096d81b63e0a05330750268617231e7bd598e1fe77482a2c58a5 \ + --hash=sha256:621e13c6108234d7960aaf5762ab5c3c00f33c30c15af06dcbff0c73bf112727 \ + --hash=sha256:62c7f79db2851c95ef020e5d28b97afde3daf9f7febcd35b53e05638f729063f \ + --hash=sha256:68520c90babfa2d560eca6d497921ed3a4f469623bd709733124491b2aa8ef3f \ + --hash=sha256:69918344541ed9c8368566c2adc03c0e33d4550d7faa87d1b35e49b6a3286ea9 \ + --hash=sha256:6a3693b4153394d265f44fb855fdc80e72403024d4d6f91c4871b334d028e4e0 \ + --hash=sha256:74fdd718d88fe144f4579b8747873a07ec3f04cb837d5faec5a25d9e22fa31a8 \ + --hash=sha256:7b27c822a8161afbe48e99f1adfb098d270ae7e0f7d7b0555ce110529bdb69cc \ + --hash=sha256:7ea52fc08f007bcc494d4bb3df3851e95843d881860ba38fe2c64dc100db5e7d \ + --hash=sha256:8427f370ca67db4c975d2a26acfc0e5783ca0b52444dbc50278ace0f35445949 \ + --hash=sha256:878832eaac515b62decfa76965aed558775f86bf1fc8cca76993c0c84ae31aed \ + --hash=sha256:8ac012839ff7e396030f1e94e10553a431d14e4de2ab65cb3acb72bbd5628ca2 \ + --hash=sha256:8cec0ad652ec57790970d817490105bd917d783c2f7b38d6b58a0ca312e1a336 \ + --hash=sha256:8cf0f2509acb4619e2471a1951089054dd58ebea7a912066d2ea56dd4c24ca4a \ + --hash=sha256:90f7608aeb5d9b60b523b9fb2a4ee1973867cc4865a3f26fe6c7577073b70205 \ + --hash=sha256:92c22e19ce64ca3f2ad751f16f14df1468b4c231bd6af97185063a9c292a0cb3 \ + --hash=sha256:96150a9cf3468ea20f0bc5d0e21b3df8972c31480ef90fa7614b773cc6429665 \ + --hash=sha256:98a0859b0e98e43e1178a9402e19c8127766b14f7109a374d976e5a62c0e5c73 \ + --hash=sha256:9973ef2463f8e6cfb61a6324126bb3e17d67a85f22f58d856e583ea2e3ca6501 \ + --hash=sha256:9be4e7d4c5ca0427889f8f9d614bd630c2be741b1de7699bca3b2b6c0e41003e \ + --hash=sha256:a090cbf9521e78ffdb2fcf448b72902afe9f5923ff6a12d5c0d0120200348af9 \ + --hash=sha256:a2335ea5fed26af2e831094964fa3f8fae60b45f7e37fcc2d3b615b2add3ad87 \ + --hash=sha256:a3c2134809e80fac091bfed18a6991b5a5eb5df5ae32b17ac4f4f99864b73dd7 \ + --hash=sha256:a571bd889cd36c5922ce8e42e059f9d37d02301531d11374afa4c87a578625d5 \ + --hash=sha256:a574912f3bde4b0619f6e97d01aa590b70998859244793769eb3a6df78ee56d3 \ + --hash=sha256:ac082660de8f429ba0ea363595abb838998570b9a7546777c60f413ab902bbde \ + --hash=sha256:b3d77f7f196abdef7e01415de1bce09f216189e83e58159cfeef2b92d0464994 \ + --hash=sha256:b3ff255799f5a1676c71c1c32ec01fd043aa09d57b3d95764b24992757184784 \ + --hash=sha256:b488bd4b23397db62e7a9459129d01ff06a846582a732efd24834b24a6ada498 \ + --hash=sha256:b75ee850fc2d7c831e883220c445b035f2224de2ba6103f1e56dbd237ab913f7 \ + --hash=sha256:b7f300ac92cd4b570724c8ffbbd0c130fee298d2447f41d5a3abf58976fae1de \ + --hash=sha256:beaab199b9e5ceaf5a225e16a9d4df136f2a1eae0a5c20de1e277c8a5225f388 \ + --hash=sha256:c02efd507227bde9969cab0db8f48890eb3b5dcad6afac57a4792df4133543ce \ + --hash=sha256:c66f9f9d4f1e9712eb9b1de5310f881d4e2188cfcba5065e1a8490f38687f2c4 \ + --hash=sha256:c90a7cdd5e380e1ce02f19792e2ac2fbfbf177e35a27e69fd3e873b30d895c0c \ + --hash=sha256:c946099774a7699de03cbd0ff0a64e21aed4525eed9d959adde4afe6d15758ef \ + --hash=sha256:cc96aa922e21d4bc5d5ed3c915cef27dfcbc13686f47d5e378d647fbfba655a2 \ + --hash=sha256:d20a15c622194234161535459affa8f7905830391c9ccfa060d495dbfe3a1c7f \ + --hash=sha256:d8e88f335544a47e22ae2e45b344772925ec65166555c958720d5ed971880891 \ + --hash=sha256:dc9b4e35e7c3920e925ba7f14886fd5fbe481232754624e832ddba66c7535635 \ + --hash=sha256:de76caefc8deabb0dd1678b6a980be97d14c8d87e213ac194dbf8b09e96d63fb \ + --hash=sha256:e343fb086c9cd780b38622fea7c369acd64c1a0724312149b5d769c387a2b1f5 \ + --hash=sha256:e4ed44705ca4bead6fc977a8b741f2145608289b33c8a9b42a95d0f15aedbf4d \ + --hash=sha256:e574801e1d643561594aa021206c46d80b257e9853087090ba97bed8b0a509d3 \ + --hash=sha256:e6230e688c7c3e65cedd41a774eb4ec221adc6bfee13768231015b702d5e4150 \ + --hash=sha256:ea3169c7116eb6cdf7608c6c7da9ecfcb3da40688e3a510fac2d1d2bafd6dc35 \ + --hash=sha256:f0a47095963cfe054e0df178daca95aec21e680d6076da807c3add28dfe920f7 \ + --hash=sha256:f502e948e03e866538048bba081c075caaa62e5bda6ea5b7432e45f587eb462a \ + --hash=sha256:f82b6bb7d75a2613e85d07cefa3a8c973d0544a8993337f6e2728e4a1e94c305 \ + --hash=sha256:fa9e5c6857a7e80fa22ace5cf3550ae392bbfc322f1d8dd2d2d5a8be38cec027 \ + --hash=sha256:fb7e18afb6e903c1a92401a2f0501ac277dca527bb9ca6fe1f691a8a0026a0e8 \ + --hash=sha256:fbb8c3a98e779013786ae01d229662aeacbc77100efbd3f2f245219ace5af700 + # via pytest-cov +cryptography==49.0.0 \ + --hash=sha256:026ac7423e6fa66872d3bf889be5974507da3944f866f704fa200eadacd00001 \ + --hash=sha256:07cab27cc7b7e0fd28e5e26bb9eeedde5c135c868b46de4a27845abe94af6122 \ + --hash=sha256:084ef1af862eb07ec46d25f68689f2102a9fc0e05ce7b80f14f5fe51e4eef0f6 \ + --hash=sha256:0b82e28ee398a386f0807bba7884d30f25218855690f45115831bcce5d90822c \ + --hash=sha256:0e959b578856a3924bc0cbb710fc12c387b9412a951389f3ca61704a9e25f325 \ + --hash=sha256:0f21641cf4b30fca7aee061ced0ec7ad7b073518088b7c9969a297c0ae796c69 \ + --hash=sha256:196ecd6a36e4e9aa10270393bb98d8df88fccee0bf1e5128b91ae4eb4375896d \ + --hash=sha256:2400ef9c9e2299a25614eb1dea3db54a69b1349efd043bfac9c67630d136df36 \ + --hash=sha256:28d8b15e6275f12c8a207dc309dfa957903c927d08d0cc937ee3f63f200693cc \ + --hash=sha256:2afe9051da7ae7bd5905da5a949280c7d2bb75682e188f650a9d0f2756b834c6 \ + --hash=sha256:2eda353d8a27bcbcaa4cbed18994a74ab4d19a2ca897db188ea269ab9b71419b \ + --hash=sha256:32703d93296f5c1f4b53349ad3a250c2cae0fdecd3a3dd5d47e616d8d616af27 \ + --hash=sha256:33cd0565932807baddb67b96dbee92f2c374b5c89dee09fd74079aeb8c8dba61 \ + --hash=sha256:35b151772baff2c74cba7fa290ceaff4c3b11c0c881eb93eb5dbc05a7cfbba18 \ + --hash=sha256:36d1709f992593689b45bda411498d62c6e365f2ca00b84657d4dadd24de16db \ + --hash=sha256:42b0684e0e40cf26122427802486f6d93aea593612603a94fbf260c7eb1e9c1b \ + --hash=sha256:4ae387c9cb68ea569ca17e490d66d8142b81c3cc814bf179974b7d146e490bbb \ + --hash=sha256:53ecee2e23f7169b6117e99fc8a944e5e50f79e69758a83b52a00cb98ab2b2d2 \ + --hash=sha256:66ec79c3904820572d7e987abdf304281f141d37ad9a489b8e97066e7b9b6459 \ + --hash=sha256:67e1d20ad9ef3a563c59ef22e7a8a0b8210bd26604369ea4a30a7c66aefe504e \ + --hash=sha256:6f2debedf9ca60cf1d5bd466475638af5130f89965605cd818484d19987d3a21 \ + --hash=sha256:6fc361c34fb6aac015ce19435876635e5c6d21db31998b0920f675f131e043b8 \ + --hash=sha256:73a205dce83953d131a4aa1e0fd917a2fd1c5b1eef251e9d7152efefcbf5caf7 \ + --hash=sha256:7abcee80084cda3f7691f3eb1ce480d8df49cec637b429aa35986c1de71738aa \ + --hash=sha256:8c25ceb16df5b9435f3f6a9829204985b0e0cbee3b48aacd432c7d2c850b44d9 \ + --hash=sha256:966fe0e9c67490071f14c0d2b1cb2dfb3023c5ce39457343931415f08382f2db \ + --hash=sha256:9e82dcc8e56052715fb18b2429e3bca4823b1629136a2084fc45a9a5cecb9b64 \ + --hash=sha256:b20133d204d2bb56ba047642199603876c872026ca53e79c35b83772ab2cc505 \ + --hash=sha256:b39efa323140595abd3ecca8529d321ae50f55f3aa3ba9cc81ea56a6011953d5 \ + --hash=sha256:b47db11c2c3525083296069b98ac5221907455e989ae0c2e3008bde851921615 \ + --hash=sha256:b87e65d263b3e5d3bb92a57e2a6638e2f31110fa7aa890c7b2dbba42248d0a3f \ + --hash=sha256:b970c6da94d5bb18629db453d14f2a1300f6bf59b61e9b82377931ef95504866 \ + --hash=sha256:be9fcb48a55f023493482827d4f459bd263cc20efde64f204b97c123201850c6 \ + --hash=sha256:c2bc30226390d60ea19d9f82b19db005fe0452154a23c1c410c12ea801e43561 \ + --hash=sha256:c83782480a4a9da4d0feb51950131ba32e12e70813848b3343f6e18c28a66838 \ + --hash=sha256:cbc77da8c523d5abd028635ba850a6966fcee2c82e2bf65a41d1d8afe0f98be9 \ + --hash=sha256:ccac2bfebc306b862133e3bb71f3f6ee8bb525240089b2d952e4144b3a6d5da7 \ + --hash=sha256:d0527ce944105f257f605a827d6ebead966c752038b6e8656abb9c5edee6fc68 \ + --hash=sha256:d8ecde755e2e91bf773fc94e8c9d730cd7f2007004cb492263a794ec3899a1c8 \ + --hash=sha256:e3fb64c420688e5319ae25113a354015abbd8dffbfbc41781a1ea66fc7622ac3 \ + --hash=sha256:e5dfc1e64de5677cec922ffa8da89c546d0415bf6efdf081842e5d44c84e1f0e \ + --hash=sha256:ec5e529fb80935c94fe7b729f9972b50e351a0e6b50aa294fd5cabb109fcc29a \ + --hash=sha256:f37d847238971164fdbc68ade6f6574aecc9c0af714190e2083429ff68f4ce9d \ + --hash=sha256:f78ff2c9ed8dc2d036b0f4d640e22522213d047c1b14e61205a7e55c80a494d4 \ + --hash=sha256:f89660a348f4f78a92366240a61404e337586ef7f5909a2fef59ca88ef505493 \ + --hash=sha256:fc1e275c2f1d97b1a6450b8b0ea3ebfa6e087a611c2b26cb2404d48588abab7b + # via pyjwt +cycler==0.12.1 \ + --hash=sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30 \ + --hash=sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c + # via matplotlib +dash==4.3.0 \ + --hash=sha256:1ec96641d5e3c06b7dfffad8241e345533d70327d96f722a8e13b827c287341b \ + --hash=sha256:cbbe79a2bb1a5e46fbbbddc33f9f558419442c2d55cd12526078f489b5801cc0 + # via trendify +filelock==3.29.4 \ + --hash=sha256:10cdb3656fc44541cdf30652a93fb10ec6b05325620eb316bd26893e4201538a \ + --hash=sha256:dac1648087d5115554850d113e7dd8c83ab2d38e3435dde2d4f163847e57b767 + # via trendify +flake8==7.3.0 \ + --hash=sha256:b9696257b9ce8beb888cdbe31cf885c90d31928fe202be0889a7cdafad32f01e \ + --hash=sha256:fe044858146b9fc69b551a4b490d69cf960fcb78ad1edcb84e7fbb1b4a8e3872 +flask==3.1.3 \ + --hash=sha256:0ef0e52b8a9cd932855379197dd8f94047b359ca0a78695144304cb45f87c9eb \ + --hash=sha256:f4bcbefc124291925f1a26446da31a5178f9483862233b23c0c96a20701f670c + # via + # dash + # trendify +fonttools==4.63.0 \ + --hash=sha256:063e08bd17bd5a90127a14123de0d6a952dbc847695fd98b63c043d58057f90c \ + --hash=sha256:0eac00b9118c3c2f87d272e45341871c5b3066baa3c86897fa634a7c3fb59096 \ + --hash=sha256:1e874792a8212b44583ea02189d9e693906b2f78b261f372f95d6c563210ac1d \ + --hash=sha256:22135da48a348785c5e2d5d2d9d6bec5ed44adacbaeb9db12d9493bf6c6bfa68 \ + --hash=sha256:22693918177bd9ceabec4736d338045f357769416fc6b0b2508eefef75b08616 \ + --hash=sha256:27fdc65af8da6f88b9c6121c47a464cbe359fcfff7ff6fc2d37a1f395d755b78 \ + --hash=sha256:2b8ae05d9eacf6081414d759c0a352769ac28ce31280d6bb8e77b03f9e3c449f \ + --hash=sha256:2c14b4fd138c4bafcca294765c547914e1aa431ae1ca94ab99d8db08c958bd3b \ + --hash=sha256:308f957cdeaf8abe4e5f2f124902ef405448af92c90f80e302a3b771c2e6116b \ + --hash=sha256:37dd23e621e3b0aef1baa70a303b80aaf38449632cfc8fd2a55fb285bbccfc02 \ + --hash=sha256:445af2eab030a16b9171ea8bdda7ebf7d96bda2df88ee182a464252f6e05e20d \ + --hash=sha256:51394295f1a51de8b5f30bdb1e1b9a4231536c7064ef5c6e211eec19fa36036f \ + --hash=sha256:58dc6bb86a78d782f00f9190ca02c119cf5bbe2807536e361e18d42019f877d8 \ + --hash=sha256:59ac449f8cca9b4ffa08d2e7bbadad87ce710d69d1eda5c3c1ce579baa987272 \ + --hash=sha256:6b2248c5decb223562f7902ff6325077a073f608ee8e33e88ad88db734eb9f49 \ + --hash=sha256:6d4741eb179121cab9eea4cb2393d24492373a260d7945006358c08cfbf45419 \ + --hash=sha256:6db5140a60a5d731d21ec076745b40a310607731b0a565b50776393188649001 \ + --hash=sha256:6e528da43bc3791085f8cb6141b1d13e459226790240340fcbb4625649238b03 \ + --hash=sha256:796f27556dbe094c4824f75ca85267e4df776c79036c8441469a4df37038c196 \ + --hash=sha256:79cdc9f567aec74a72918fd060283911406750cbc9fd28c1316023deb6ce31a9 \ + --hash=sha256:7d76edbff9014094dbf03bd2d074709dfa6ec7aba13d838c937a2b33d2d6a86e \ + --hash=sha256:7d782fac32985914c351556f68ac0855391572bcd87de50e05970d3cd4c96fc5 \ + --hash=sha256:7dd683fef0663e9f0f45cf541d788d24caa3ec9db50796b588e1757d8b3bc007 \ + --hash=sha256:85be818f5506e8a7753153def2c9550178f0ecae6a47b5e0e8dbb23f7cc90380 \ + --hash=sha256:948428a275741f0b64b113c955425a953314f4b9ab9997f73a72c83e68e569c8 \ + --hash=sha256:9ced0bd02ac751dd6319b0da88aaef24414e3b0dbc32bb4f24944821a3741a27 \ + --hash=sha256:9e12f105d2b6342c559c298afb674006bb2893afc7102dcf8a1b55b0486b4e40 \ + --hash=sha256:a9faff9e0c1f76f9fd55899d2ce785832efebab37eb8ae13995853aef178bef0 \ + --hash=sha256:af2fd1664d00a397d75f806985ddb36282091c2131a73a6485c23b4a34722263 \ + --hash=sha256:afefc1ed0a59785a7fb06ea7e1678e849c193e1e387db783579bc7b3056fcfcb \ + --hash=sha256:ba04cb5891d4c0c21b6da95eda8d7b090021508a294fff33464fc7d241e0856b \ + --hash=sha256:bf00f21eb5fb721dbaf73d1e9da6d02a1af7768f2ebcf9798be98beab8ba90f6 \ + --hash=sha256:c1aaa4b9c75798400ac043ce04d74e7830376c85095a5a6ed7cba2f17a266bf4 \ + --hash=sha256:c2a2a42198b696a6f48fad91709afb55176e66a5e566131219dba372fb7f8c59 \ + --hash=sha256:caeb583deeb5168e694b65cda8b4ee62abedfa66cf88488734466f2366b9c4e0 \ + --hash=sha256:ccf41f2efdf56994d22d73bef4ced1052161958169428d06ba9724ea9e9a64be \ + --hash=sha256:cd7e9857e5e63738b9d9fd707bc1f59c8b09e5177726d23664db393c59bb08bd \ + --hash=sha256:d76ac49f929aecaf82d83250b8347e099d7aecba0f4726c1d9b6df3b8bb5fe18 \ + --hash=sha256:dcf076a4474fe0d7367e5bbf5b052c7284fa1feca729c04176ce513521afd8a0 \ + --hash=sha256:ee08ebfa58f6e1aeff5697ab9582105bb620008c1caafb681e4c557e7483027b \ + --hash=sha256:ef3048ef05dbb552b89817713d9cac912e00d0fde4a3105c00d29e52e10c89af \ + --hash=sha256:fd1e3094f42d806d3d7c79162fc59e5910fcbe3a7360c385b8da969bc4493745 + # via matplotlib +ghp-import==2.1.0 \ + --hash=sha256:8337dd7b50877f163d4c0289bc1f1c7f127550241988d568c1db512c4324a619 \ + --hash=sha256:9c535c4c61193c2df8871222567d7fd7e5014d835f97dc7b7439069e2413d343 + # via + # mkdocs + # properdocs +gitdb==4.0.12 \ + --hash=sha256:5ef71f855d191a3326fcfbc0d5da835f26b13fbcba60c32c21091c349ffdb571 \ + --hash=sha256:67073e15955400952c6565cc3e707c554a4eea2e428946f7a4c162fab9bd9bcf + # via gitpython +gitpython==3.1.50 \ + --hash=sha256:80da2d12504d52e1f998772dc5baf6e553f8d2fcfe1fcc226c9d9a2ee3372dcc \ + --hash=sha256:d352abe2908d07355014abdd21ddf798c2a961469239afec4962e9da884858f9 + # via streamlit +grafana-api==1.0.3 \ + --hash=sha256:30caef227025ecdb5662b242aaae921b89b58959566d83a5948523c8138e98ce \ + --hash=sha256:d541ea1a5b7efafa56c5f04053ed42b2d972a6aa313bc4881dd192aeb0706e2e + # via trendify +griffe-pydantic==1.3.1 \ + --hash=sha256:475103794a1d5da3933d3968e82a2bd9d9a1fa507c06d5ee1a39ee1fadcca628 \ + --hash=sha256:f7caedfa0effedb22893bf01cc411fd567614f7b4de7ce0c1f4293eb7acb5c44 +griffelib==2.1.0 \ + --hash=sha256:762a186d2c6fd6794d4ea20d428d597ffb857cb56b66421651cbba15bdd5e813 \ + --hash=sha256:cc7b3d2d2865ad0b909fcc38086e3f554b5ea7acbaa7bbb7ecaa3f5dfb7d9f00 + # via + # griffe-pydantic + # mkdocstrings-python +h11==0.16.0 \ + --hash=sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1 \ + --hash=sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86 + # via + # httpcore + # uvicorn +hatchling==1.30.1 \ + --hash=sha256:161eacafb3c6f91526e92116d21426369f2c36e98c36a864f11a96345ad4ee31 \ + --hash=sha256:eee4fd45357f72ebb3d7a42e5d72cfb5e29ed426d79e8836288926c4258d5f2e +httpcore==1.0.9 \ + --hash=sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55 \ + --hash=sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8 + # via httpx +httptools==0.8.0 \ + --hash=sha256:0770728beb05094c809b98e814edff5fef69d26ad7d21185f2f6d5884a0ba683 \ + --hash=sha256:159e9ab5f701ccd42e555a12f1ad8ff69702910fc1c996cf2bb66e5fcb7a231b \ + --hash=sha256:19d1ee275bb59ba2643ba9a3a1e51cc0c788caf2b8df506368e03f56fdd08527 \ + --hash=sha256:20b4aac66ff65f7db06a375808b78f42a94970aa22e826b3cb2b43eb09174124 \ + --hash=sha256:2a021c3a8e65cc125390d72f59b968afca3bdcaff25bd67965e0a055a14946ca \ + --hash=sha256:2c032fa028f46871ec7e1fc59fc15e8023eab3e6bbe6ece786a1611719a5d081 \ + --hash=sha256:2d689918c15a013c65ef52d9fd495d766893ab831a2c8d89f2ac5940a5df847c \ + --hash=sha256:384c17174464c8e873398b7af24f0b1f44d992c820328413951a625323155d77 \ + --hash=sha256:425f83884fd6343828d8c565f046cb72b6d19063f6924093e11bcd8e1548cd09 \ + --hash=sha256:48774d39cbb70e2b1f71f88852a3087ae1d3a1eb80482bb48c13067ab080c14f \ + --hash=sha256:52dd695b865fe96d9d2b16b64a895f3f57bf3cb064e8383cd3b5713a069e8085 \ + --hash=sha256:57278e6fa0424c42a8a3e454828ab4f0aff27b40cddf9679579b98c6dce6a376 \ + --hash=sha256:5931891fb7b441b8a3853cf1b85c82c903defce084dd5f6771ca46e31bf862c5 \ + --hash=sha256:614ceea8ea606848bece2338ac03b3ce5324bcb4be8dc7d377ed708012fa4db8 \ + --hash=sha256:6a43c9dd399758ccc0531acb0a3c4a6c299ee893ee9400e9c893b7bdcfae0681 \ + --hash=sha256:6b2a32f18d97e16e90827d7a819ffa8dbd8cc245fc4e1fa9d1095b54ef4bd999 \ + --hash=sha256:7685df791fad561384bfb139e77fde27a1ffd93134e016f95a0db424ffbf77b1 \ + --hash=sha256:880490234c10f70a9830743097e8958d6e4b9f5a0ffc24515023afeef984054d \ + --hash=sha256:88bdd940f2b5d487b4d032c6afa5489a7dc4694410d43de3c38c4fb3af0dc45d \ + --hash=sha256:88eead8ec8680a9f146c655bc88445a325bd7921cfd8194c7337e9467282427d \ + --hash=sha256:9518c406d7b310f05adb1a37f80acabac40504a575d7c0da6d3e365c695ac20d \ + --hash=sha256:a1afd7c9fbff0d9f5d489c4ce2768bd09c84a46ddefc7161e6aa82ae35c85745 \ + --hash=sha256:a1b4c8e7a489a0d750d91894e9a8cdc295838f1924c0ca903ae993456fddec07 \ + --hash=sha256:a3b7387147361c3fd47a0bde763c5c91b5b4cd4dc9989b8ece84ff436c99843b \ + --hash=sha256:b15fc622b0f869d19207c4089a501d9bcc63ca5e071ffdd2f03f922df882dcb2 \ + --hash=sha256:bbb8caadb2b742d293169d2b458b5c001ef70e3158704aa3d3ef9597624c5d1d \ + --hash=sha256:c4a9f1707e4823d54dfec6c33fa3697d302aed536ed352a7ebb5a061ddb869d0 \ + --hash=sha256:cd96f29b4bab1d42fa6e3d008711c75e0f79e94e06827330160e3a304227f150 \ + --hash=sha256:d76ad7b951387e3632c8716a9bb03ac5b45c5f16119aa409db0459520887944e \ + --hash=sha256:de1ed58a974e75d56560acc7e7fed01a454994429456f65209789992e41f2568 \ + --hash=sha256:de242a49b5d18e0a8776e654e9f6bf6d89f3875a5c35b425a0e7ce940feb3fd6 \ + --hash=sha256:e93c227b595c6926c1acee96891dd9da4be338cfbe82e5cd3bb9d8dd7dc4ac0b \ + --hash=sha256:eb3028cca2fc0a6d720e52ef61d8ebb62fcbfeb1de56874546d858d3f25a26b7 \ + --hash=sha256:ed377e64805bdba4943c82717333f8f8603a13b09aff9cead2717c6c817fb168 \ + --hash=sha256:ef7c3c97f4311c7be57e2986629df89d49cb434dbff78eafcd48c2bff986b15a \ + --hash=sha256:f256d6ce930c52ca1cb2a960b7da03548c454e7d28b06059ad41bfe789036ce0 + # via streamlit +httpx==0.28.1 \ + --hash=sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc \ + --hash=sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad + # via mcp +httpx-sse==0.4.3 \ + --hash=sha256:0ac1c9fe3c0afad2e0ebb25a934a59f4c7823b60792691f779fad2c5568830fc \ + --hash=sha256:9b1ed0127459a66014aec3c56bebd93da3c1bc8bb6618c8082039a44889a755d + # via mcp +idna==3.18 \ + --hash=sha256:7f952cbe720b688055e3f87de14f5c3e5fdaa8bc3928985c4077ca689de849a2 \ + --hash=sha256:ffb385a7e039654cef1ab9ef32c6fafe283c0c0467bba1d9029738ce4a14a848 + # via + # anyio + # httpx + # requests +importlib-metadata==9.0.0 \ + --hash=sha256:2d21d1cc5a017bd0559e36150c21c830ab1dc304dedd1b7ea85d20f45ef3edd7 \ + --hash=sha256:a4f57ab599e6a2e3016d7595cfd72eb4661a5106e787a95bcc90c7105b831efc + # via dash +iniconfig==2.3.0 \ + --hash=sha256:c76315c77db068650d49c5b56314774a7804df16fee4402c1f19d6d15d8c4730 \ + --hash=sha256:f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12 + # via pytest +isort==8.0.1 \ + --hash=sha256:171ac4ff559cdc060bcfff550bc8404a486fee0caab245679c2abe7cb253c78d \ + --hash=sha256:28b89bc70f751b559aeca209e6120393d43fbe2490de0559662be7a9787e3d75 +itsdangerous==2.2.0 \ + --hash=sha256:c6242fc49e35958c8b15141343aa660db5fc54d4f13a1db01a3f5891b98700ef \ + --hash=sha256:e0050c0b7da1eea53ffaf149c0cfbb5c6e2e2b69c4bef22c81fa6eb73e5f6173 + # via + # flask + # streamlit +janus==2.0.0 \ + --hash=sha256:0970f38e0e725400496c834a368a67ee551dc3b5ad0a257e132f5b46f2e77770 \ + --hash=sha256:7e6449d34eab04cd016befbd7d8c0d8acaaaab67cb59e076a69149f9031745f9 + # via dash +jinja2==3.1.6 \ + --hash=sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d \ + --hash=sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67 + # via + # altair + # flask + # mike + # mkdocs + # mkdocs-material + # mkdocstrings + # properdocs + # pydeck + # trendify +jsonschema==4.26.0 \ + --hash=sha256:0c26707e2efad8aa1bfc5b7ce170f3fccc2e4918ff85989ba9ffa9facb2be326 \ + --hash=sha256:d489f15263b8d200f8387e64b4c3a75f06629559fb73deb8fdfb525f2dab50ce + # via + # altair + # mcp +jsonschema-specifications==2025.9.1 \ + --hash=sha256:98802fee3a11ee76ecaca44429fda8a41bff98b00a0f2838151b113f210cc6fe \ + --hash=sha256:b540987f239e745613c7a9176f3edb72b832a4ac465cf02712288397832b5e8d + # via jsonschema +kiwisolver==1.5.0 \ + --hash=sha256:012b1eb16e28718fa782b5e61dc6f2da1f0792ca73bd05d54de6cb9561665fc9 \ + --hash=sha256:01808c6d15f4c3e8559595d6d1fe6411c68e4a3822b4b9972b44473b24f4e679 \ + --hash=sha256:0255a027391d52944eae1dbb5d4cc5903f57092f3674e8e544cdd2622826b3f0 \ + --hash=sha256:0b85aad90cea8ac6797a53b5d5f2e967334fa4d1149f031c4537569972596cb8 \ + --hash=sha256:0bf3acf1419fa93064a4c2189ac0b58e3be7872bf6ee6177b0d4c63dc4cea276 \ + --hash=sha256:0c50b89ffd3e1a911c69a1dd3de7173c0cd10b130f56222e57898683841e4f96 \ + --hash=sha256:0cbe94b69b819209a62cb27bdfa5dc2a8977d8de2f89dfd97ba4f53ed3af754e \ + --hash=sha256:0df54df7e686afa55e6f21fb86195224a6d9beb71d637e8d7920c95cf0f89aac \ + --hash=sha256:0e3aafb33aed7479377e5e9a82e9d4bf87063741fc99fc7ae48b0f16e32bdd6f \ + --hash=sha256:12e91c215a96e39f57989c8912ae761286ac5a9584d04030ceb3368a357f017a \ + --hash=sha256:1465387ac63576c3e125e5337a6892b9e99e0627d52317f3ca79e6930d889d15 \ + --hash=sha256:16b85d37c2cbb3253226d26e64663f755d88a03439a9c47df6246b35defbdfb7 \ + --hash=sha256:1b0feb50971481a2cc44d94e88bdb02cdd497618252ae226b8eb1201b957e368 \ + --hash=sha256:1d49a49ac4cbfb7c1375301cd1ec90169dfeae55ff84710d782260ce77a75a02 \ + --hash=sha256:1d9daea4ea6b9be74fe2f01f7fbade8d6ffab263e781274cffca0dba9be9eec9 \ + --hash=sha256:1dd9b0b119a350976a6d781e7278ec7aca0b201e1a9e2d23d9804afecb6ca681 \ + --hash=sha256:1f1489f769582498610e015a8ef2d36f28f505ab3096d0e16b4858a9ec214f57 \ + --hash=sha256:2517e24d7315eb51c10664cdb865195df38ab74456c677df67bb47f12d088a27 \ + --hash=sha256:2a075bd7bd19c70cf67c8badfa36cf7c5d8de3c9ddb8420c51e10d9c50e94920 \ + --hash=sha256:332b4f0145c30b5f5ad9374881133e5aa64320428a57c2c2b61e9d891a51c2f3 \ + --hash=sha256:377815a8616074cabbf3f53354e1d040c35815a134e01d7614b7692e4bf8acfa \ + --hash=sha256:38f4a703656f493b0ad185211ccfca7f0386120f022066b018eb5296d8613e23 \ + --hash=sha256:3ac2360e93cb41be81121755c6462cff3beaa9967188c866e5fce5cf13170859 \ + --hash=sha256:3c4923e404d6bcd91b6779c009542e5647fef32e4a5d75e115e3bbac6f2335eb \ + --hash=sha256:3cdcb35dc9d807259c981a85531048ede628eabcffb3239adf3d17463518992d \ + --hash=sha256:413b820229730d358efd838ecbab79902fe97094565fdc80ddb6b0a18c18a581 \ + --hash=sha256:4432b835675f0ea7414aab3d37d119f7226d24869b7a829caeab49ebda407b0c \ + --hash=sha256:4db576bb8c3ef9365f8b40fe0f671644de6736ae2c27a2c62d7d8a1b4329f099 \ + --hash=sha256:4e7f886f47ab881692f278ae901039a234e4025a68e6dfab514263a0b1c4ae05 \ + --hash=sha256:4e9750bc21b886308024f8a54ccb9a2cc38ac9fa813bf4348434e3d54f337ff9 \ + --hash=sha256:5060731cc3ed12ca3a8b57acd4aeca5bbc2f49216dd0bec1650a1acd89486bcd \ + --hash=sha256:50847dca5d197fcbd389c805aa1a1cf32f25d2e7273dc47ab181a517666b68cc \ + --hash=sha256:5092eb5b1172947f57d6ea7d89b2f29650414e4293c47707eb499ec07a0ac796 \ + --hash=sha256:5124d1ea754509b09e53738ec185584cc609aae4a3b510aaf4ed6aa047ef9303 \ + --hash=sha256:530a3fd64c87cffa844d4b6b9768774763d9caa299e9b75d8eca6a4423b31314 \ + --hash=sha256:56fa888f10d0f367155e76ce849fa1166fc9730d13bd2d65a2aa13b6f5424489 \ + --hash=sha256:58f812017cd2985c21fbffb4864d59174d4903dd66fa23815e74bbc7a0e2dd57 \ + --hash=sha256:59cd8683f575d96df5bb48f6add94afc055012c29e28124fcae2b63661b9efb1 \ + --hash=sha256:5ae8e62c147495b01a0f4765c878e9bfdf843412446a247e28df59936e99e797 \ + --hash=sha256:5b233ea3e165e43e35dba1d2b8ecc21cf070b45b65ae17dd2747d2713d942021 \ + --hash=sha256:6176c1811d9d5a04fa391c490cc44f451e240697a16977f11c6f722efb9041db \ + --hash=sha256:6783e069732715ad0c3ce96dbf21dbc2235ab0593f2baf6338101f70371f4028 \ + --hash=sha256:6ab8ba9152203feec73758dad83af9a0bbe05001eb4639e547207c40cfb52083 \ + --hash=sha256:70d593af6a6ca332d1df73d519fddb5148edb15cd90d5f0155e3746a6d4fcc65 \ + --hash=sha256:72ec46b7eba5b395e0a7b63025490d3214c11013f4aacb4f5e8d6c3041829588 \ + --hash=sha256:7a32f72973f0f950c1920475d5c5ea3d971b81b6f0ec53b8d0a956cc965f22e0 \ + --hash=sha256:7a4aa69609f40fce3cbc3f87b2061f042eee32f94b8f11db707b66a26461591a \ + --hash=sha256:7c60d3c9b06fb23bd9c6139281ccbdc384297579ae037f08ae90c69f6845c0b1 \ + --hash=sha256:800ee55980c18545af444d93fdd60c56b580db5cc54867d8cbf8a1dc0829938c \ + --hash=sha256:80aa065ffd378ff784822a6d7c3212f2d5f5e9c3589614b5c228b311fd3063ac \ + --hash=sha256:893ff3a711d1b515ba9da14ee090519bad4610ed1962fbe298a434e8c5f8db53 \ + --hash=sha256:89fc958c702ee9a745e4700378f5d23fddbc46ff89e8fdbf5395c24d5c1452a3 \ + --hash=sha256:8c63c91f95173f9c2a67c7c526b2cea976828a0e7fced9cdcead2802dc10f8a4 \ + --hash=sha256:8df31fe574b8b3993cc61764f40941111b25c2d9fea13d3ce24a49907cd2d615 \ + --hash=sha256:9027d773c4ff81487181a925945743413f6069634d0b122d0b37684ccf4f1e18 \ + --hash=sha256:940dda65d5e764406b9fb92761cbf462e4e63f712ab60ed98f70552e496f3bf1 \ + --hash=sha256:9eed0f7edbb274413b6ee781cca50541c8c0facd3d6fd289779e494340a2b85c \ + --hash=sha256:ad4ae4ffd1ee9cd11357b4c66b612da9888f4f4daf2f36995eda64bd45370cac \ + --hash=sha256:b0f172dc8ffaccb8522d7c5d899de00133f2f1ca7b0a49b7da98e901de87bf2d \ + --hash=sha256:b2af221f268f5af85e776a73d62b0845fc8baf8ef0abfae79d29c77d0e776aaf \ + --hash=sha256:b7d335370ae48a780c6e6a6bbfa97342f563744c39c35562f3f367665f5c1de2 \ + --hash=sha256:bb5136fb5352d3f422df33f0c879a1b0c204004324150cc3b5e3c4f310c9049f \ + --hash=sha256:bc4d8e252f532ab46a1de9349e2d27b91fce46736a9eedaa37beaca66f574ed4 \ + --hash=sha256:bdd3e53429ff02aa319ba59dfe4ceeec345bf46cf180ec2cf6fd5b942e7975e9 \ + --hash=sha256:be12f931839a3bdfe28b584db0e640a65a8bcbc24560ae3fdb025a449b3d754e \ + --hash=sha256:be4a51a55833dc29ab5d7503e7bcb3b3af3402d266018137127450005cdfe737 \ + --hash=sha256:beb7f344487cdcb9e1efe4b7a29681b74d34c08f0043a327a74da852a6749e7b \ + --hash=sha256:c0e1403fd7c26d77c1f03e096dc58a5c726503fa0db0456678b8668f76f521e3 \ + --hash=sha256:c31c13da98624f957b0fb1b5bae5383b2333c2c3f6793d9825dd5ce79b525cb7 \ + --hash=sha256:c438f6ca858697c9ab67eb28246c92508af972e114cac34e57a6d4ba17a3ac08 \ + --hash=sha256:c95cab08d1965db3d84a121f1c7ce7479bdd4072c9b3dafd8fecce48a2e6b902 \ + --hash=sha256:cdee07c4d7f6d72008d3f73b9bf027f4e11550224c7c50d8df1ae4a37c1402a6 \ + --hash=sha256:ce9bf03dad3b46408c08649c6fbd6ca28a9fce0eb32fdfffa6775a13103b5310 \ + --hash=sha256:d168fda2dbff7b9b5f38e693182d792a938c31db4dac3a80a4888de603c99554 \ + --hash=sha256:d1ffeb80b5676463d7a7d56acbe8e37a20ce725570e09549fe738e02ca6b7e1e \ + --hash=sha256:d36ca54cb4c6c4686f7cbb7b817f66f5911c12ddb519450bbe86707155028f87 \ + --hash=sha256:d4193f3d9dc3f6f79aaed0e5637f45d98850ebf01f7ca20e69457f3e8946b66a \ + --hash=sha256:d618fd27420381a4f6044faa71f46d8bfd911bd077c555f7138ed88729bfbe79 \ + --hash=sha256:d76e2d8c75051d58177e762164d2e9ab92886534e3a12e795f103524f221dd8e \ + --hash=sha256:daae526907e262de627d8f70058a0f64acc9e2641c164c99c8f594b34a799a16 \ + --hash=sha256:db485b3847d182b908b483b2ed133c66d88d49cacf98fd278fadafe11b4478d1 \ + --hash=sha256:dda366d548e89a90d88a86c692377d18d8bd64b39c1fb2b92cb31370e2896bbd \ + --hash=sha256:e315e5ec90d88e140f57696ff85b484ff68bb311e36f2c414aa4286293e6dee0 \ + --hash=sha256:e4415a8db000bf49a6dd1c478bf70062eaacff0f462b92b0ba68791a905861f9 \ + --hash=sha256:e7a116ae737f0000343218c4edf5bd45893bfeaff0993c0b215d7124c9f77646 \ + --hash=sha256:e7c4c09a490dc4d4a7f8cbee56c606a320f9dc28cf92a7157a39d1ce7676a657 \ + --hash=sha256:ec4c85dc4b687c7f7f15f553ff26a98bfe8c58f5f7f0ac8905f0ba4c7be60232 \ + --hash=sha256:ed3a984b31da7481b103f68776f7128a89ef26ed40f4dc41a2223cda7fb24819 \ + --hash=sha256:f18c2d9782259a6dc132fdc7a63c168cbc74b35284b6d75c673958982a378384 \ + --hash=sha256:f1f9f4121ec58628c96baa3de1a55a4e3a333c5102c8e94b64e23bf7b2083309 \ + --hash=sha256:f443b4825c50a51ee68585522ab4a1d1257fac65896f282b4c6763337ac9f5d2 \ + --hash=sha256:f6764a4ccab3078db14a632420930f6186058750df066b8ea2a7106df91d3203 \ + --hash=sha256:f7c7553b13f69c1b29a5bde08ddc6d9d0c8bfb84f9ed01c30db25944aeb852a7 \ + --hash=sha256:fa6248cd194edff41d7ea9425ced8ca3a6f838bfb295f6f1d6e6bb694a8518df \ + --hash=sha256:fa8eb9ecdb7efb0b226acec134e0d709e87a909fa4971a54c0c4f6e88635484c \ + --hash=sha256:fc20894c3d21194d8041a28b65622d5b86db786da6e3cfe73f0c762951a61167 \ + --hash=sha256:fc4d3f1fb9ca0ae9f97b095963bc6326f1dbfd3779d6679a1e016b9baaa153d3 \ + --hash=sha256:fd40bb9cd0891c4c3cb1ddf83f8bbfa15731a248fdc8162669405451e2724b09 \ + --hash=sha256:ff710414307fefa903e0d9bdf300972f892c23477829f49504e59834f4195398 + # via matplotlib +markdown==3.10.2 \ + --hash=sha256:994d51325d25ad8aa7ce4ebaec003febcce822c3f8c911e3b17c52f7f589f950 \ + --hash=sha256:e91464b71ae3ee7afd3017d9f358ef0baf158fd9a298db92f1d4761133824c36 + # via + # markdown-callouts + # markdown-include + # mdx-gh-links + # mkdocs + # mkdocs-autorefs + # mkdocs-click + # mkdocs-material + # mkdocstrings + # properdocs + # pymdown-extensions +markdown-callouts==0.4.0 \ + --hash=sha256:7ed2c90486967058a73a547781121983839522d67041ae52c4979616f1b2b746 \ + --hash=sha256:ed0da38f29158d93116a0d0c6ecaf9df90b37e0d989b5337d678ee6e6d6550b7 +markdown-include==0.8.1 \ + --hash=sha256:1d0623e0fc2757c38d35df53752768356162284259d259c486b4ab6285cdbbe3 \ + --hash=sha256:32f0635b9cfef46997b307e2430022852529f7a5b87c0075c504283e7cc7db53 +markupsafe==3.0.3 \ + --hash=sha256:068f375c472b3e7acbe2d5318dea141359e6900156b5b2ba06a30b169086b91a \ + --hash=sha256:0bf2a864d67e76e5c9a34dc26ec616a66b9888e25e7b9460e1c76d3293bd9dbf \ + --hash=sha256:0db14f5dafddbb6d9208827849fad01f1a2609380add406671a26386cdf15a19 \ + --hash=sha256:0eb9ff8191e8498cca014656ae6b8d61f39da5f95b488805da4bb029cccbfbaf \ + --hash=sha256:1085e7fbddd3be5f89cc898938f42c0b3c711fdcb37d75221de2666af647c175 \ + --hash=sha256:116bb52f642a37c115f517494ea5feb03889e04df47eeff5b130b1808ce7c219 \ + --hash=sha256:12c63dfb4a98206f045aa9563db46507995f7ef6d83b2f68eda65c307c6829eb \ + --hash=sha256:133a43e73a802c5562be9bbcd03d090aa5a1fe899db609c29e8c8d815c5f6de6 \ + --hash=sha256:1353ef0c1b138e1907ae78e2f6c63ff67501122006b0f9abad68fda5f4ffc6ab \ + --hash=sha256:1872df69a4de6aead3491198eaf13810b565bdbeec3ae2dc8780f14458ec73ce \ + --hash=sha256:1b4b79e8ebf6b55351f0d91fe80f893b4743f104bff22e90697db1590e47a218 \ + --hash=sha256:1b52b4fb9df4eb9ae465f8d0c228a00624de2334f216f178a995ccdcf82c4634 \ + --hash=sha256:1cc7ea17a6824959616c525620e387f6dd30fec8cb44f649e31712db02123dad \ + --hash=sha256:218551f6df4868a8d527e3062d0fb968682fe92054e89978594c28e642c43a73 \ + --hash=sha256:26a5784ded40c9e318cfc2bdb30fe164bdb8665ded9cd64d500a34fb42067b1c \ + --hash=sha256:2713baf880df847f2bece4230d4d094280f4e67b1e813eec43b4c0e144a34ffe \ + --hash=sha256:32001d6a8fc98c8cb5c947787c5d08b0a50663d139f1305bac5885d98d9b40fa \ + --hash=sha256:3524b778fe5cfb3452a09d31e7b5adefeea8c5be1d43c4f810ba09f2ceb29d37 \ + --hash=sha256:35add3b638a5d900e807944a078b51922212fb3dedb01633a8defc4b01a3c85f \ + --hash=sha256:3a7e8ae81ae39e62a41ec302f972ba6ae23a5c5396c8e60113e9066ef893da0d \ + --hash=sha256:3b562dd9e9ea93f13d53989d23a7e775fdfd1066c33494ff43f5418bc8c58a5c \ + --hash=sha256:457a69a9577064c05a97c41f4e65148652db078a3a509039e64d3467b9e7ef97 \ + --hash=sha256:4bd4cd07944443f5a265608cc6aab442e4f74dff8088b0dfc8238647b8f6ae9a \ + --hash=sha256:4e885a3d1efa2eadc93c894a21770e4bc67899e3543680313b09f139e149ab19 \ + --hash=sha256:4faffd047e07c38848ce017e8725090413cd80cbc23d86e55c587bf979e579c9 \ + --hash=sha256:509fa21c6deb7a7a273d629cf5ec029bc209d1a51178615ddf718f5918992ab9 \ + --hash=sha256:5678211cb9333a6468fb8d8be0305520aa073f50d17f089b5b4b477ea6e67fdc \ + --hash=sha256:5a7d5dc5140555cf21a6fefbdbf8723f06fcd2f63ef108f2854de715e4422cb4 \ + --hash=sha256:69c0b73548bc525c8cb9a251cddf1931d1db4d2258e9599c28c07ef3580ef354 \ + --hash=sha256:6b5420a1d9450023228968e7e6a9ce57f65d148ab56d2313fcd589eee96a7a50 \ + --hash=sha256:722695808f4b6457b320fdc131280796bdceb04ab50fe1795cd540799ebe1698 \ + --hash=sha256:729586769a26dbceff69f7a7dbbf59ab6572b99d94576a5592625d5b411576b9 \ + --hash=sha256:77f0643abe7495da77fb436f50f8dab76dbc6e5fd25d39589a0f1fe6548bfa2b \ + --hash=sha256:795e7751525cae078558e679d646ae45574b47ed6e7771863fcc079a6171a0fc \ + --hash=sha256:7be7b61bb172e1ed687f1754f8e7484f1c8019780f6f6b0786e76bb01c2ae115 \ + --hash=sha256:7e68f88e5b8799aa49c85cd116c932a1ac15caaa3f5db09087854d218359e485 \ + --hash=sha256:83891d0e9fb81a825d9a6d61e3f07550ca70a076484292a70fde82c4b807286f \ + --hash=sha256:8485f406a96febb5140bfeca44a73e3ce5116b2501ac54fe953e488fb1d03b12 \ + --hash=sha256:8709b08f4a89aa7586de0aadc8da56180242ee0ada3999749b183aa23df95025 \ + --hash=sha256:8f71bc33915be5186016f675cd83a1e08523649b0e33efdb898db577ef5bb009 \ + --hash=sha256:915c04ba3851909ce68ccc2b8e2cd691618c4dc4c4232fb7982bca3f41fd8c3d \ + --hash=sha256:94c6f0bb423f739146aec64595853541634bde58b2135f27f61c1ffd1cd4d16a \ + --hash=sha256:9a1abfdc021a164803f4d485104931fb8f8c1efd55bc6b748d2f5774e78b62c5 \ + --hash=sha256:9b79b7a16f7fedff2495d684f2b59b0457c3b493778c9eed31111be64d58279f \ + --hash=sha256:a4afe79fb3de0b7097d81da19090f4df4f8d3a2b3adaa8764138aac2e44f3af1 \ + --hash=sha256:ad2cf8aa28b8c020ab2fc8287b0f823d0a7d8630784c31e9ee5edea20f406287 \ + --hash=sha256:b8512a91625c9b3da6f127803b166b629725e68af71f8184ae7e7d54686a56d6 \ + --hash=sha256:bc51efed119bc9cfdf792cdeaa4d67e8f6fcccab66ed4bfdd6bde3e59bfcbb2f \ + --hash=sha256:bdc919ead48f234740ad807933cdf545180bfbe9342c2bb451556db2ed958581 \ + --hash=sha256:bdd37121970bfd8be76c5fb069c7751683bdf373db1ed6c010162b2a130248ed \ + --hash=sha256:be8813b57049a7dc738189df53d69395eba14fb99345e0a5994914a3864c8a4b \ + --hash=sha256:c47a551199eb8eb2121d4f0f15ae0f923d31350ab9280078d1e5f12b249e0026 \ + --hash=sha256:ccfcd093f13f0f0b7fdd0f198b90053bf7b2f02a3927a30e63f3ccc9df56b676 \ + --hash=sha256:d53197da72cc091b024dd97249dfc7794d6a56530370992a5e1a08983ad9230e \ + --hash=sha256:d6dd0be5b5b189d31db7cda48b91d7e0a9795f31430b7f271219ab30f1d3ac9d \ + --hash=sha256:d88b440e37a16e651bda4c7c2b930eb586fd15ca7406cb39e211fcff3bf3017d \ + --hash=sha256:de8a88e63464af587c950061a5e6a67d3632e36df62b986892331d4620a35c01 \ + --hash=sha256:e1cf1972137e83c5d4c136c43ced9ac51d0e124706ee1c8aa8532c1287fa8795 \ + --hash=sha256:e56b7d45a839a697b5eb268c82a71bd8c7f6c94d6fd50c3d577fa39a9f1409f5 \ + --hash=sha256:e8afc3f2ccfa24215f8cb28dcf43f0113ac3c37c2f0f0806d8c70e4228c5cf4d \ + --hash=sha256:eaa9599de571d72e2daf60164784109f19978b327a3910d3e9de8c97b5b70cfe \ + --hash=sha256:ec15a59cf5af7be74194f7ab02d0f59a62bdcf1a537677ce67a2537c9b87fcda \ + --hash=sha256:f190daf01f13c72eac4efd5c430a8de82489d9cff23c364c3ea822545032993e \ + --hash=sha256:f34c41761022dd093b4b6896d4810782ffbabe30f2d443ff5f083e0cbbb8c737 \ + --hash=sha256:f3e98bb3798ead92273dc0e5fd0f31ade220f59a266ffd8a4f6065e0a3ce0523 \ + --hash=sha256:f9e130248f4462aaa8e2552d547f36ddadbeaa573879158d721bbd33dfe4743a \ + --hash=sha256:fed51ac40f757d41b7c48425901843666a6677e3e8eb0abcff09e4ba6e664f50 + # via + # flask + # jinja2 + # mkdocs + # mkdocs-autorefs + # mkdocstrings + # properdocs + # werkzeug +matplotlib==3.11.0 \ + --hash=sha256:03acfeddf87b0dddb11b081ef7740ad445a3ca8bcb6b8e3011b08f2cf802b75c \ + --hash=sha256:0515d495124be3124340e59f164d901ed4484e2246a5b74cfa483cac3b80bd97 \ + --hash=sha256:06b5872e9cf11adc8f589ded3ce11bc3e1061ad498259664fabc1f6615beb918 \ + --hash=sha256:126f256df600652d7e4b394cf3164ff75210a00038f287c95a012a6f58d0e83f \ + --hash=sha256:15b0d160079cb10699a0e98b5989c70677b2df7cacdc62af67c30f2facec46d9 \ + --hash=sha256:1644db30e759199443493ac5e5caec24fdb775a8f6123021f85ba47c4133c3cb \ + --hash=sha256:19c16c61dea63b3582918503e6b294193961261d9daa806d4ae2151f1ad05430 \ + --hash=sha256:1c02da0a629dfa9debf52725ea06866b74c1fb70a895bae05e4493d34074f9f2 \ + --hash=sha256:25c2e5455efd8d99f41fb79871a31feb7d301569642e332ec58d72cfe9282bc3 \ + --hash=sha256:2746cd2c113742ff6ce37a864c5ac5fd7aa644568f445e66166e457ac78e40e0 \ + --hash=sha256:2d72ea8b7924f3cb955e61518d21e43b3df1e6c8a793b480a0c1214f185d30ba \ + --hash=sha256:3338e3e3de128cf50d0d2fb92a122815daf9c755bd882a474343c05f8fd7ec79 \ + --hash=sha256:3489c3dc487669b4a980bc3068f87856de7a1564248d3f6c629efb2a58b03f24 \ + --hash=sha256:373db8f91214e8ccaf35ac833cc1dd59dd961e148bbd55dd027141591dde1313 \ + --hash=sha256:3d68266213e73823ac3be90615bab0cf31f88851e114cdb1dd25dacf3b01e1a7 \ + --hash=sha256:41635d7909d19e52e924a521dde6d8f670b0f53ab1d0e8c331fa831554f681d1 \ + --hash=sha256:446307e6b04b57b1f1239e228a1ec2af0d589a1008cebc3dfa3f5441d095cfb6 \ + --hash=sha256:4d7aea652b58e686444079be3376ef546bffa1eee9b9bb9c472b9fcf6cf410d3 \ + --hash=sha256:5106c444d0bf966eee2853548c03772af4ab7199118e086c62fbac8ccb07c055 \ + --hash=sha256:565af866fd63e4bd3f987d580afe27c44c2552a3b3305f4ecbb85133601ea6f3 \ + --hash=sha256:57baa92fdc82948ed716eae6d2579d4d6f40965cd8d2f416755b4a72580a3233 \ + --hash=sha256:630eee0e67d35cce2019a0e670719f4816e3b86aff0fa72729f6c69786fceb45 \ + --hash=sha256:652fb5696271d4c50f196d22a5ff4f8e4444c74f847423570d7dc0aa2bbd0159 \ + --hash=sha256:68c0c7be01b30dcca3638934f7f591df73401235cbdbf0d1ab1c71e7db7f8b57 \ + --hash=sha256:6a98f5476ce784a50ce09998f4ae1e6a9f25043cef8a480c98949902eda74620 \ + --hash=sha256:6ce3b839b34ae1f430b4616893a2945a2999debaa7e94e7e29a2a8bbf286f7b5 \ + --hash=sha256:70a5b3e9a5dab708c0f039709ae7c68d5b4d254e291ef76492cdba230c8bb5e4 \ + --hash=sha256:81ae77077a1e16d37a5b61096ccb07c8d90a99b518fa8256b8f21578932f2f62 \ + --hash=sha256:942b37c5db1899610bd1543ce8e13e4ecff9a4633e7f63bb6aa9205d2644ebd1 \ + --hash=sha256:94f5000f67ca9faa300863ea17f8bce9175cb67b88bec4bc7780502d53dd7c9e \ + --hash=sha256:9dd11fb612ce7bc60b1de5b4fc87ff959d22317b5de42aabf392f66f97af22eb \ + --hash=sha256:a9d8c6e7cd2f0ddf11d8d92e520dd1d9d2abb0cf6ac8831e338666c81e905847 \ + --hash=sha256:aa55d73b3117d4b07f959cd9eb6f69b375d8df3414139c479388e551aa5d999d \ + --hash=sha256:ab3722f04f3ff34c23b5012c5873d2894174e06c3822fcdac3610965a5ac7d06 \ + --hash=sha256:ac6f1ef39f3d0f9e2463303013094992cdbe0f85f43bc54155bc472b2042768e \ + --hash=sha256:be050fcf32f729eda99f7f75a80bf67612ce16ab9ac1c23a387dcaede95cb70e \ + --hash=sha256:be152b7570324dc8d01574cc9474dd2d803237acf528bcbb5b211fa347461a09 \ + --hash=sha256:be5f93a1d21981bfb802ded0d77a0caa92d4342a47d45754fac77e314a506344 \ + --hash=sha256:c08e649a6313e1291e713623b97a38e5bb4aa580b2a100a94a3309bc6b9c8eb3 \ + --hash=sha256:c945824670fb8915b4ac879e5e61f3c58e0913022f70a0de4c082b17372f8771 \ + --hash=sha256:cf662e5ac5707658cb931e19972c4bd99f7b4f8b7bf79d3c821d239fa6b71e64 \ + --hash=sha256:d9695457a467ff86d23f35037a43deb6f1134dd6d3e2ac8ce1e2087cff09ffb9 \ + --hash=sha256:ddef37840695f5eef65f9f070fe2d2f510f584c2156203f9f622a5b0584efffd \ + --hash=sha256:dfabef0230d0697aa0d717385194dd41162e00207a68bf4abf94c2bf4c27dca0 \ + --hash=sha256:e6b3e64dea5062c570f04358e2711859f3531b459f29516274fbad889079e4f3 \ + --hash=sha256:f857524b442f0f36e641868ce2171aafa88cb0bc0644f4e1d8a5df9b32649fef + # via trendify +mccabe==0.7.0 \ + --hash=sha256:348e0240c33b60bbdf4e523192ef919f28cb2c3d7d5c7794f74009290f236325 \ + --hash=sha256:6c2d30ab6be0e4a46919781807b4f0d834ebdd6c6e3dca0bda5a15f863427b6e + # via flake8 +mcp==1.28.1 \ + --hash=sha256:2726bca5e7193f61c5dde8b12500a6de2d9acf6d1a1c0be9e8c2e706437991df \ + --hash=sha256:d51e36a5f5644faea4f85ea649bfffa6bc6c26770d42798ad6a3de3d2ba69683 + # via dash +mdx-gh-links==0.4 \ + --hash=sha256:41d5aac2ab201425aa0a19373c4095b79e5e015fdacfe83c398199fe55ca3686 \ + --hash=sha256:9057bca1fa5280bf1fcbf354381e46c9261cc32c2d5c0407801f8a910be5f099 +mergedeep==1.3.4 \ + --hash=sha256:0096d52e9dad9939c3d975a774666af186eda617e6ca84df4c94dec30004f2a8 \ + --hash=sha256:70775750742b25c0d8f36c55aed03d24c3384d17c951b3175d898bd778ef0307 + # via + # mkdocs + # mkdocs-get-deps +mike==2.2.0 \ + --hash=sha256:1e3858e32c0f125aac14432fc7848434358f9ae0962c5c5cde387ad47f6ad25e \ + --hash=sha256:e1f4981c1152eec7c2490a3401142292cc47d686194188416db2648fdfe1d040 +mkdocs==1.6.1 \ + --hash=sha256:7b432f01d928c084353ab39c57282f29f92136665bdd6abf7c1ec8d822ef86f2 \ + --hash=sha256:db91759624d1647f3f34aa0c3f327dd2601beae39a366d6e064c03468d35c20e + # via + # mike + # mkdocs-autorefs + # mkdocs-exclude + # mkdocs-gen-files + # mkdocs-literate-nav + # mkdocs-material + # mkdocs-redirects + # mkdocs-section-index + # mkdocstrings +mkdocs-autorefs==1.4.4 \ + --hash=sha256:834ef5408d827071ad1bc69e0f39704fa34c7fc05bc8e1c72b227dfdc5c76089 \ + --hash=sha256:d54a284f27a7346b9c38f1f852177940c222da508e66edc816a0fa55fc6da197 + # via + # mkdocstrings + # mkdocstrings-python +mkdocs-click==0.9.0 \ + --hash=sha256:5208e828f4f68f63c847c1ef7be48edee9964090390afc8f5b3d4cbe5ea9bbed \ + --hash=sha256:6050917628d4740517541422b607404d044117bc31b770c4f9e9e1939a50c908 +mkdocs-exclude==1.0.2 \ + --hash=sha256:ba6fab3c80ddbe3fd31d3e579861fd3124513708271180a5f81846da8c7e2a51 +mkdocs-gen-files==0.6.1 \ + --hash=sha256:57d7ff2229e23d077e46d14a33db6d37c8823f6ce1a503c874c1764a71679763 \ + --hash=sha256:b3182bfc6219e35b8d26658cb988368659d5d023aac30c2a819247558fc12189 +mkdocs-get-deps==0.2.2 \ + --hash=sha256:8ee8d5f316cdbbb2834bc1df6e69c08fe769a83e040060de26d3c19fad3599a1 \ + --hash=sha256:e7878cbeac04860b8b5e0ca31d3abad3df9411a75a32cde82f8e44b6c16ff650 + # via mkdocs +mkdocs-glightbox==0.5.2 \ + --hash=sha256:23a431ea802b60b1030c73323db2eed6ba859df1a0822ce575afa43e0ea3f47e \ + --hash=sha256:c7622799347c32310878e01ccf14f70648445561010911c80590cec0353370ac +mkdocs-literate-nav==0.6.3 \ + --hash=sha256:2c421561280fa9184f88cbf399bebbd4cc17ee507e978a31ce11fd6f3aabf233 \ + --hash=sha256:edbaca22343f861fe4e34aac47d55a0c9955c640dbf02eea99fe631e914cf9ee +mkdocs-material==9.7.6 \ + --hash=sha256:00bdde50574f776d328b1862fe65daeaf581ec309bd150f7bff345a098c64a69 \ + --hash=sha256:71b84353921b8ea1ba84fe11c50912cc512da8fe0881038fcc9a0761c0e635ba +mkdocs-material-extensions==1.3.1 \ + --hash=sha256:10c9511cea88f568257f960358a467d12b970e1f7b2c0e5fb2bb48cab1928443 \ + --hash=sha256:adff8b62700b25cb77b53358dad940f3ef973dd6db797907c49e3c2ef3ab4e31 + # via mkdocs-material +mkdocs-redirects==1.2.3 \ + --hash=sha256:5e980330999299729a2d6a125347d1af78023d68a23681a4de3053ce7dfe2e51 \ + --hash=sha256:ec7312fff462d03ec16395d0c001006a418f8d0c21cdf2b47ff11cf839dc3ce0 +mkdocs-section-index==0.3.12 \ + --hash=sha256:285635bf86c643b0fc7a343053d7a818049817bff4408f52b80c4367bd5e7268 \ + --hash=sha256:a1100039546beb4ebef63ce6fc91f3195fb9c0c3763105d4d3d7cd31e0a046eb +mkdocs-snippets==1.3.2 \ + --hash=sha256:0a2be9ced4169acb3449d89a2d882f7e0e516a8c9cf3ffb8b9c6565af83a9a61 \ + --hash=sha256:d46d2700dec2dd8dab1d2977a656bed11ad89fdfd4dab30f2008c99d42086c53 +mkdocstrings==1.0.4 \ + --hash=sha256:3969a6515b77db65fd097b53c1b7aa4ae840bd71a2ee62a6a3e89503446d7172 \ + --hash=sha256:63464b4b29053514f32a1dbbf604e52876d5e638111b0c295ab7ed3cac73ca9b + # via mkdocstrings-python +mkdocstrings-python==2.0.5 \ + --hash=sha256:30c837bbff016549f659fcba6539ac351303f0fd7e713c89a040611072236e9d \ + --hash=sha256:3a4d92556ad39637e88af94a5374213af9a8e3040c3824ceaed04b486c017594 +mypy-extensions==1.1.0 \ + --hash=sha256:1be4cccdb0f2482337c4743e60421de3a356cd97508abadd57d47403e94f5505 \ + --hash=sha256:52e68efc3284861e772bbcd66823fde5ae21fd2fdb51c62a211403730b916558 + # via black +narwhals==2.23.0 \ + --hash=sha256:13e7ff5b4bb4a2f77b907c2e4d8a76e273dfc1323a3c997440a2f9fd26aed408 \ + --hash=sha256:769e7b9ab102c93d8fa019f6b4cd1a657909b04a20bf6210e5a35aae06814ae9 + # via + # altair + # plotly +nest-asyncio==1.6.0 \ + --hash=sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe \ + --hash=sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c + # via dash +numpy==2.4.6 ; python_full_version < '3.12' \ + --hash=sha256:001fbb8e08d942dd57599e781f2472269ee7f2755fae407b4f67b2f0b17da3f1 \ + --hash=sha256:0280e0356c0829a18d9de1cb7eee50ec22ca639878d7240307ca0943d73cd2c4 \ + --hash=sha256:043191bfa8eab18c776647b62723ac9dddece59743b13f49b2016094129c2b3f \ + --hash=sha256:06ca2f61ec4385a07a6977c55ba998a4466c123642b4a32694d3128fce18c079 \ + --hash=sha256:0a041d3d761dc3c35cc56ce0351506a02bcbc25f7b169f652435141a17db9096 \ + --hash=sha256:0ab0a9c4ffb1a6d95ef519fe4247dba8eb6b18ad93999f76b7f657039acabd47 \ + --hash=sha256:0c9136e14ed34a9e343a31c533d78a9813a69a3148332bce5e9821cb2f996e66 \ + --hash=sha256:110f8b71aacb688ec69062bb7f6938a0f8acb01b7c1c4beb453c65b6d234584d \ + --hash=sha256:112b06a867b235ef466ed3508ddf0238050df9c727cafb5301ac385b899189a1 \ + --hash=sha256:17f9ade344e7d9b464a084d69bcf18fc691cb1db67c62ed80820bf4926d78f0e \ + --hash=sha256:1e254a00cdf42b1e4d5b3d68d33af63268d41340d8885df2ab6470f2e1500147 \ + --hash=sha256:1e978ec1e8bd0e0e4de6bb75de9d30cbb74db6b6a2bb727618613703ca0167dd \ + --hash=sha256:25c692919ac5a01f170a3bfcd62d745b24fd095c353d50812637d6fcab442e75 \ + --hash=sha256:260a5d70215b61ab4fadf5c7baacd64821842975eea312125ed3c39a6391b063 \ + --hash=sha256:2803abfebfc990042cd494d8ce2d5f82e9d847af6d35ec486923aa19dbad5e73 \ + --hash=sha256:29a287e0cf63ff528da061de6b9f64a4618da591ca1046aafc54062e40ca7eab \ + --hash=sha256:29cb7f67d10b479ff07c17d33e39f78c07f71c40ef30d63c153d340e96cd3fb4 \ + --hash=sha256:3213d622a0283a39a93d188f3cf72b26862df52fbb4ca3697f51705016523d41 \ + --hash=sha256:33111801a01c12a8a1e3721f0a9232f8cfc8ae2c6b7098167e6f623c6073f402 \ + --hash=sha256:357cc07a6d7b0b182ff02249616a03742827ebb1277546b5c7cd7f7620a45698 \ + --hash=sha256:38efbc8de75c7a0fc1ac190162d892787f3f47b57cc291231aafee36b80982b7 \ + --hash=sha256:4081eb135ac24158bd51cdfbef16f1c64df7063b1143f24731387137c092bec8 \ + --hash=sha256:40fdc1ae7125e518ea98e53e69a4ebc27e1fd50510c47b7ea130cf21e5e1d42b \ + --hash=sha256:4cfe66903cc32a9921a6733d96b19bb6abf310397581bbad89c228f5abaf0ee8 \ + --hash=sha256:511dbaf848decaaaf4b4ca48032619fb3138710c4bf7da7617765edad1ef96b0 \ + --hash=sha256:55cced7c52e981362f708ad635198e97a752dfba412cc03c23bbf3bd8d5cd662 \ + --hash=sha256:56b39e5e0622a09a25bf5baf62f4bcf0cb8a41ae6e2819cf49bbc5a74c083f91 \ + --hash=sha256:5dbbdb29840ca3d91ee0fece42fc29278886d908280bfec0a5846c6f901a3eb0 \ + --hash=sha256:5f9fb9157b4ce2971008323afe46053787b526ef624fea915b261468a8421a0f \ + --hash=sha256:6180d8b35af935aed8ece3a85e0a43f87393ae0ac87c8d2c8bd2c993f7270ef3 \ + --hash=sha256:68a5124b13fa6cc2086764a20005d30bc0548146f7f5322f02fce212ca14317f \ + --hash=sha256:68bb27509ac1b9a3443094260f6326150663b06abe40b73a2f81160623da5b67 \ + --hash=sha256:6f41ae150c4e32db4f3310cdaf64b1593a03dbabe29eec77fc9b50fe64061df6 \ + --hash=sha256:7265a2f3d436e54ef9f2b52b5c937e6be778781bd97a590319d7348f1c1ca997 \ + --hash=sha256:72fbe16c6fac95aedf5937fa873445cec2110be35d8a4e9433d7501fd98dae6b \ + --hash=sha256:7d92c3819208a60205a12a245c91ad70cb0a85336659b19b834205573ac8456e \ + --hash=sha256:8155154c7c691289fe18f510b5d4657c68c67989f293f0535a91360392ff6538 \ + --hash=sha256:81a1cca95ed5bb92aa8b10dd2cdc9a0d3853a50fad926c28b5d7e8ea54389627 \ + --hash=sha256:89cd468399cfd2504718f0ba50e410dca55a170b61a02ad92bb18c8a65186e93 \ + --hash=sha256:8ad03c0965fb3c692200e74d458ca28c1dbb4ce96f9a479a8aa041ad5fabca02 \ + --hash=sha256:90f9849678c75fe7afa2d348ac842c168b0a4d3d61919687216dfc547976d853 \ + --hash=sha256:948424b06129ce883307e8cff868c31396d8dc7630a59c61d70d98dbe70f222c \ + --hash=sha256:9cd5ffd25db4e7ba6a375693b3fc0fc1791ec636c17db3720da19bde7180ec43 \ + --hash=sha256:a0df0043bdb289bde1f62da130d20df23d58b45429f752bc7a8fc5325a225ecd \ + --hash=sha256:a2c306dea656c12c68f51f4cea133cbe78ca7435eb28c735eac1d3ebe73be6e8 \ + --hash=sha256:a7830bab239b79cda9c08c2da014761cafb48da6150e1da17ac06283f43b6089 \ + --hash=sha256:a7c711e21628b52034bb5ab8d1bce291f752fcc5e92accc615778acee1ff4778 \ + --hash=sha256:aaf159caa35993cb1f56fb9b8e4610d35758e7ca005412eb1daa856a78c9c4b1 \ + --hash=sha256:ae506e6902902557576a26ff33eda8695e7ecb3cb36c3b573a0765dee114ebdb \ + --hash=sha256:b507f5c4c1d508876d1819b6bf9a49d365b96320b5d4993426b33a23ca4b8261 \ + --hash=sha256:bf162abab1c1a736333192707cef898e735a5ca00f38f27eeedf44b39d9e85eb \ + --hash=sha256:c1a2af6c6ef86344a6b0db6b97834208bf598db514f2b155042439b62605601a \ + --hash=sha256:c2d37ab77531417474168eb79d6d80b14f821a966818505d03013d0833edb7a8 \ + --hash=sha256:c4fc99836233ea196540b17ab0983aff60ed07941751930f5f4d05bc3b3b7359 \ + --hash=sha256:d581b735e177fdcdce6fed8e7e8880a3fb6ee4e3653a3ac6af01c6f4c03effc5 \ + --hash=sha256:d6da64deb6b8ed903e7560180a92f2d804ee1ba5eeb849ac2748b8c1aba1f6d7 \ + --hash=sha256:d8e8286dd7cea7895157318d1b91cdacac64c479f3cbc8dce548331728484751 \ + --hash=sha256:ddea102b48f9e339f3948bf22040944184627a30fdf7f858667673b9c5f033c8 \ + --hash=sha256:dfa20cc6ca228e6b155b11da03825975ce66aea520985dbbddf0f2a5a495c605 \ + --hash=sha256:e3e5193ef5a3dc73bceee50f7fdc2c90dbb76c42df8d8fae3d1067a583df579e \ + --hash=sha256:e3eeb0aabd6bd5ce64faae67e9935203a6991b4bc2a485a767fbafb2c5125f45 \ + --hash=sha256:e5805d5a22fd19c8ccff10a9561f9df94436b0545619ea579db2d3c35294bce2 \ + --hash=sha256:e85b752a1e912b70eaad4fafbd4d1238007ab221de2009b9a2f5ae7461239895 \ + --hash=sha256:eaf7fa2de5c0be8ae6ff8e9bea2ccd725e980541244521d8d4b5f3354a27babe \ + --hash=sha256:ebfb099f8dcf083deef3ac1ca4c1503f387cf76296fcb3816b66f5ecb5f54fdb \ + --hash=sha256:ece3d2cfe132e7d51f44a832b303895e6f2d499c5e74dfbdb06ee246147a304a \ + --hash=sha256:ed9749eef4cbd126da3dc1d6bcb3a57f5eb7ac6a6484146bdbf743f552dfc577 \ + --hash=sha256:ede83e07a75dd06bc501566c1eca2afc0d61677c1472ac9ad93fdee6e638a48d \ + --hash=sha256:ef4aea96ce4d3b074422cb4f2f64e216bf9e213004bb58ecfdf50ea02ea8eb9a \ + --hash=sha256:f3a3570c4a2a16746ac2c31a7c7c7b0c186b95ce902e33db6f28094ed7387dda \ + --hash=sha256:f407cb6b8e9d6d8c626bc73c945db1706035af8fd632295547bf1c9e46d092d6 \ + --hash=sha256:f74a575920ab21fe304421a3fc28793d82e299cae9eccb37084e9fc7f3617c20 + # via + # contourpy + # matplotlib + # numpydantic + # pandas + # pydeck + # scipy + # streamlit + # trendify +numpy==2.5.0 ; python_full_version >= '3.12' \ + --hash=sha256:016623417bb330d719d579daf2d6b9a01ddc52e41a9ed61a47f39fde46dcd865 \ + --hash=sha256:0b525be4744b60bb0557ac872d53ef07d085b5f39622bc579c98d3809d05b988 \ + --hash=sha256:126b88d95e8ff9b00c9e717aa540469f21d6180162f84c0caec51b16215d49cd \ + --hash=sha256:146b81cdd3967fdb6beca8ba25f00c58741d8f3cbd797f55af0fbe0bfec3469c \ + --hash=sha256:1a7569a7b53c77716f036bb28cb1c91f166a26ec7d9502cd1e4bdfe502fdec22 \ + --hash=sha256:1c0121101093d2bd74981b10f8837d78e794a8ff57834eb27179f49e1ba11ac6 \ + --hash=sha256:22f3d43e362d650bc39db1f17851302874a148ca95ba6981c1dfb5fa6862f35b \ + --hash=sha256:243563efb4cd7528a264567e9fd206c87826457322521d06206a00bfa316c927 \ + --hash=sha256:28e7137057d551e4a83c4ae414e3451f50568409db7569aacc7f9811ee06a446 \ + --hash=sha256:3893adc2dc7c0412ba76777db55a049215d99c9aa3113003be8f49f4f1290ab9 \ + --hash=sha256:39a0433bd4086ebd462960cf375e19195bb07b53dc1d87dd5fcf47ad78576f03 \ + --hash=sha256:3b94d0d0deceebfad3e67ae5c0e5eb87371e8f7a0581cd04a779928c2450cf1e \ + --hash=sha256:44353e2878930039db472b99dc353d749826e4010bd4d2a7f835e94a97a5c748 \ + --hash=sha256:489780423903667933b4ed6197b6ec3b75ea5dd17d1d8f0f38d798feb6921561 \ + --hash=sha256:48f54b00711f83a5f796b70c518e8c2b3c5848dda03a54911f23eb68519b9b60 \ + --hash=sha256:520e6b8be0a4b65840ac8090d4f51cef4bed66e2b0894d5a520f099adc24a9b2 \ + --hash=sha256:5a129578019311b6e56bdd714250f19b518f7dceeeb8d1af5490f4942d3f891c \ + --hash=sha256:5dc71423499fab3f46f7a7201155ade1669ea101f2f429d332df9e72f8161731 \ + --hash=sha256:6206db0af545d73d068add6d992279145f158428d1da6cc49adc4b630c5d6ee5 \ + --hash=sha256:694d8f74e156f7fd01179f1aa8faa2f648ab6ae0f70b6c3fe57a03249aea2303 \ + --hash=sha256:6f2d6873e2940c860a309d21e25b1e69af6aaffdd80aa056b04c16380db1c4f2 \ + --hash=sha256:6f9836778081a0a3c02a6a21493f3e9f5b311f8d2541934f31f05583dc999ea4 \ + --hash=sha256:7174ce8265fc7f7417d171c9ea8fe905220748893ea67a2a7abe726ec331c4b0 \ + --hash=sha256:750fb097caf26fa878746d9d119f6f9da12dedcbff1eea966c3e3447647c4a9e \ + --hash=sha256:835e454dd99b238cdc5a3f63bce2371296f5ebc53ca1e0f8e6ddbb6d92a29aab \ + --hash=sha256:84881d825ca75249b189bbee875fcfe3238aa5c479e6100893cda566e8e86826 \ + --hash=sha256:929f0c79ac38bcbd7154fe631dc907abfeddbcc5027a896bd1f7767323271e7a \ + --hash=sha256:9990713e9c38154c6861e7547f1e3fc7a87e75ff09bab24ef1cc81d81c2835e9 \ + --hash=sha256:a1a4874217b36d5ac8fc876f52e39df56f8182c88463e9e2dceabf7ca8b7efb8 \ + --hash=sha256:a55e1eb2bca2cfd17a16b213c99dfc8502d47b0d494224d2122277d0400935ca \ + --hash=sha256:aaa760137137e8d3c920d27927748215b56014f92667dc9b6c27dfc61249255a \ + --hash=sha256:b8c3daaf99de52415d20b42f8e8155c78642cb04207d02f9d317a0dcf1b3fb54 \ + --hash=sha256:bf80333980bf37f523341ddd72c783f39d6829ec7736b9eb99086388a2d52cc2 \ + --hash=sha256:c83b664b0e6eee9594fa920cf0639d8af796606d3fad6cc70180c87e4b97c7be \ + --hash=sha256:cc4f247a47bbf070bfd70be53ccdcf47b800af563535e7bbe172322197c30e21 \ + --hash=sha256:cda12aa4779d42b8771180aba759c96f527d43446d8f380ab59e2b35e8489efd \ + --hash=sha256:d371c92cfa09da00022f501ab67fafaea813d752eb30ac44336d45b1e5b0268a \ + --hash=sha256:d4313cef1594c5ce46c31b6e54e918338f63f16ee9322304e8c9114d6d81c8bd \ + --hash=sha256:e1da54b53e75cd9fcfc23efcc7edab2c6aecf97b6037566d8a0fe804af8ec57c \ + --hash=sha256:ebb81d9d5443e0309d6c54894c3fbed74ad7da0714352a67b6d773cd189eae73 \ + --hash=sha256:ece55976ced6bca95a03ae2839e2e5ccffe8eb6a3e7022415645eb154a81e4e6 \ + --hash=sha256:edadfbd4794b1086c0d822f81863e8a68fc129d132fd0bb9e31e955d7fbbbdb7 \ + --hash=sha256:f27582c55ba4c750b7c58c8faf021d2cd9324a662b466229db8a417b41368af9 \ + --hash=sha256:f7e5fa4382967ae6548bd2f174219afb908e294b0d5f625af01166edd5f7d9aa + # via + # contourpy + # matplotlib + # numpydantic + # pandas + # pydeck + # scipy + # streamlit + # trendify +numpydantic==1.10.0 \ + --hash=sha256:1c62a445d305da69a5fa4510f7e7e1c25218f58fe462e5d59548042f1f769e66 \ + --hash=sha256:a17d5ccc3b893c4a2e539c81c2f18960d70884ad7fcaa09c9eb56a95e8daf4a3 + # via trendify +packaging==26.2 \ + --hash=sha256:5fc45236b9446107ff2415ce77c807cee2862cb6fac22b8a73826d0693b0980e \ + --hash=sha256:ff452ff5a3e828ce110190feff1178bb1f2ea2281fa2075aadb987c2fb221661 + # via + # altair + # black + # hatchling + # matplotlib + # mkdocs + # plotly + # properdocs + # pytest + # streamlit +paginate==0.5.7 \ + --hash=sha256:22bd083ab41e1a8b4f3690544afb2c60c25e5c9a63a30fa2f483f6c60c8e5945 \ + --hash=sha256:b885e2af73abcf01d9559fd5216b57ef722f8c42affbb63942377668e35c7591 + # via mkdocs-material +pandas==3.0.3 \ + --hash=sha256:0383c72c75cdcca61a9e116e611143902dbfd08bff356829c2f6d1cf40a9ca8c \ + --hash=sha256:05f1f1752b8533ea03f7f39a9c15b1a058d067bb48f4748948e7a8691e0510f2 \ + --hash=sha256:08d789b41f87e0905880e293cedf6197ce71fe67cc081358b1e148a491b9bd13 \ + --hash=sha256:0d589105b3c14645af1738ff279b2995102d8f7a03b0a66dc8d95550eb513e04 \ + --hash=sha256:13fc1e853d9e04743d11ba75a985ccbc2a317fe07d8af61e445a6fd24dacd6a6 \ + --hash=sha256:14da8316da4d0c5a77618425996bfb1248ca87fc2c1486e6fde4652bd18b5824 \ + --hash=sha256:1928e07221f82db493cd4af1e23c1bfca524a19a4699887975bff68f49a72bfb \ + --hash=sha256:261e308dfb22448384b7580cf719d2f998fe2966c92893c3e77d14008af1f066 \ + --hash=sha256:275c14e0fce14a2ec20eee474aecd305478ea3c1e6f6a9d8fe219a165542717e \ + --hash=sha256:335f62418ed562cfc3c49e9e196375c28b729dcef8543abf4f9438e381bf3c76 \ + --hash=sha256:3650109c0f22879df8bd6179ab9ee3d7f1d1d4e7e0094a3f0032d9f51e2e64ac \ + --hash=sha256:39436b377d56d2a2e52d0395bdbee171f01068e99af5250509aceeb929f765c7 \ + --hash=sha256:3c20a521bbb85902f79f7270c80a59e1b5452d96d170c034f207181870f97ac5 \ + --hash=sha256:3e91cec1879ada0624fc3dc9953c5cbd60208e59c0db28f540c5d6d47502422f \ + --hash=sha256:455f6f8139d4282188f526868dbc3c828470e88a3d9d59a891bd46a455f21b98 \ + --hash=sha256:46997386d528eb40376ecd6b033cf4a8a1e5282580f68f43de875b78cba2199d \ + --hash=sha256:4db8c527972a821cf5286b40ccc57642a39bc62e62022b42f99f8a67fca8c3a1 \ + --hash=sha256:4e15135e2ee5df1063313e2425ceef8ac0f4ae775893815b0923651b806a5639 \ + --hash=sha256:51b1fe551acb77dac643c6fda86084d8d446c10fe64b06a9cc29c4cc8540e7f2 \ + --hash=sha256:557409bc4178e70ee8d9ddb494798e51ebf6ea59330f6be22c51bab2a7db6c49 \ + --hash=sha256:5cc09a68b3120e0f54870dede8287a7bb1fa463907e4fcec1ea77cab6179bf7a \ + --hash=sha256:60ae316d3fd75d1858d450d0db0103ea2be3e7d4a95ec2f064f7e2ae63f7b028 \ + --hash=sha256:6674ab18ad8c57802867264b00e15e7bb904700cdd9046e3b2fa1fce237439ea \ + --hash=sha256:67b3b64c11910cfa29f4e94a14d3bff9ee693b6fc76055e7cad549cee0aec5fa \ + --hash=sha256:696a4a00a2a2a35d4e5deb3fc946641b96c944f02230e4f76137fe35d806c4fc \ + --hash=sha256:6dc0b3fd2169c9157deed50b4d519553a3655c8c6a96027136d654592be973a9 \ + --hash=sha256:7e65d5407dc0b394f509699650e4a2ec01c0514f21850f453fa60f3be79a5dbf \ + --hash=sha256:819959dab7bbd0049c15623fbac4e29a191b9528160a61fb1032242d8ced2d9c \ + --hash=sha256:8a1e45c80cceb3b4a21bc5939d52e8cbd8d9b7305309219d59e9754d9ce09e27 \ + --hash=sha256:9c39be2d709d01fa972a0cabc522389fceca4f3969332ba25a7d6c5802cf976a \ + --hash=sha256:9d71c63ae4ebdbf70209742096f1fc46a83a0613c99d4b23766cced9ff8cd62a \ + --hash=sha256:a2d2dff8a04f3917b55ab3910c32990f8ddf7eceba114947838cefa976a68977 \ + --hash=sha256:a4eeb6830daf35a71cc09649bd823e2b542dac246cdee9614c6e4bd65028cd6a \ + --hash=sha256:a55066a0505dae0ba2b50a46637db34b46f9094c65c5d4800794ef6335010938 \ + --hash=sha256:a82d532a3351d435432cd913edbccaf8b8e01d4dd0e5ced5a8d2e8ecd94c7e44 \ + --hash=sha256:b168fc218fd80a6cbdbdbc1a97ddc7889ed057d7eb45f50d866ceab5f39904c4 \ + --hash=sha256:b2c95f8bfc1ee412bf482605d7bfd30c12d1d26bd59fdd91efeef1d4718decb1 \ + --hash=sha256:ba7e08b9ac1d54569cd1e256e3668975ed624d6826f7b68df0342b012007bddb \ + --hash=sha256:bab900348131a7db1f69a7309ef141fd5680f1487094193bcbbb61791573bf8f \ + --hash=sha256:bd3a518890b400d32f9023722dc9a9a5c969f00b415419a3c06c043f09bb5d7d \ + --hash=sha256:c7be265b62cef88e253a941e4698604973736dcfe242fdb5198f0f7bc473cdcc \ + --hash=sha256:d26cbe1fcfc12e8fd900e2454163e466b2d3af84f7c75481df7683ffc073d870 \ + --hash=sha256:d4be06d68f9ddcfc645b87534911da79a8fbffc7573c80e0edcf42a5020624d8 \ + --hash=sha256:d72828c20c6d6e83e1e22a6a3b47b326b71664112fa9705dcbccfd7a39b62085 \ + --hash=sha256:dd1a5d1def6a46002e964510bdc67c368aa0951df5d1d9f8365336f5a1f490cd \ + --hash=sha256:e3a2ec42c98ffa2565a67e08e218d06d72576d758d90facb7c00805194d8f360 \ + --hash=sha256:f8894dc474d648fe7b6ff0ca9b0bd73950d19952bc1a6534540762c5d79d305c \ + --hash=sha256:fed2ff7fd9779120e388e285fc029bd5cf9490cdd2e4166a9ee22c0e49a9ab09 + # via + # streamlit + # trendify +pathspec==1.1.1 \ + --hash=sha256:17db5ecd524104a120e173814c90367a96a98d07c45b2e10c2f3919fff91bf5a \ + --hash=sha256:a00ce642f577bf7f473932318056212bc4f8bfdf53128c78bbd5af0b9b20b189 + # via + # black + # hatchling + # mkdocs + # properdocs +pillow==12.3.0 \ + --hash=sha256:00808c5e14ef63ac5161091d242999076604ff74b883423a11e5d7bbb38bf756 \ + --hash=sha256:04f01d28a6aaff387bf842a13be313df23ba0597a44f1a976c9feb3c6ff4711a \ + --hash=sha256:06ff022112bc9cbf83b60f8e028d94ad87b60621706487e65f673de61610ab59 \ + --hash=sha256:0740a512dc522224c77d9aa5a8d70d8b7d73fb91f2c21125d8d025d3b8990e45 \ + --hash=sha256:0847a763afefb695bc912d7c131e7e0632d4edc1d8698f58ddabec8e46b8b6d3 \ + --hash=sha256:0dd2064cbc55aaec028ef5fbb60fa47bb6c3e7918e07ff17935284b227a9d2df \ + --hash=sha256:0feb2e9d6ad6c9e3c06effe9d00f3f1e618a6643273576b016f591e9315a7139 \ + --hash=sha256:10e41f0fbf1eec8cfd234b8fe17a4caac7c9d0db4c204d3c173a8f9f6ef3232b \ + --hash=sha256:1182d52bc2d5e5d7d0949503aa7e36d12f42205dc287e4883f407b1988820d39 \ + --hash=sha256:164b31cd1a0490ab6efae01aa5df49da7061be0af1b30e035b6e9a1bfe34ee6e \ + --hash=sha256:1657923d2d45afb66526e5b933e5b3052e6bdea196c90d3abb2424e18c77dae8 \ + --hash=sha256:186941b6aef820ad110fb01fb06eb925374dc3a21b17e37ec9a53b250c6fe2d1 \ + --hash=sha256:1cca606cd25738df4ed873d5ad46bbdb3d83b5cbca291f6b4ff13a4df6b0bbe8 \ + --hash=sha256:21900ce7ba264168cd50defae43cd75d25c833ad4ad6e73ffc5596d12e25ac89 \ + --hash=sha256:236ff70b9312fb68943c703aa842ca6a758abfa45ac187a5e7c1452e96ef72b5 \ + --hash=sha256:23aceaa007d6172b02c277f0cd359c79492bbb14f7072b4ede9fbcaf20648130 \ + --hash=sha256:23d27a3e0307ec2244cc51e7287b919aa68d097504ebe19df4e76a98a3eea5bd \ + --hash=sha256:24870b09b224f7ae3c39ed07d10e819d06f8720bc551847b1d623832b5b0e28d \ + --hash=sha256:251bf95b67017e27b13d82f5b326234ca62d70f9cf4c2b9032de2358a3b12c7b \ + --hash=sha256:28ce87c5ab450a9dd970b52e5aca5fe63ed432d18a2eaddd1979a00a1ba24ace \ + --hash=sha256:30f2aa603c41533cc25c05acd0da21636e84a315768feb631c937177db558931 \ + --hash=sha256:331b624368d4f1d069149002f25f44bc61c8919ce8ddb3c45bdad8f6e2d89510 \ + --hash=sha256:37d6d0a00072fd2948eb22bce7e1475f34569d90c87c59f7a2ec59541b77f7a6 \ + --hash=sha256:3b8182a766685eaa002637e28b4ec8d6b18819a0c71f579bf0dbaa5830297cce \ + --hash=sha256:3edce1d53195db527e0191f84b71d02022de0540bf43a16ed734ed7537b07385 \ + --hash=sha256:446c34dcc4324b084a53b705127dc15717b22c5e140ae0a3c38349d4efec071e \ + --hash=sha256:4998562bf62a445225f22e07c896bb04b35b1b1f2eb6d760584c9c51d7a5f78c \ + --hash=sha256:4b0a7fe987b14c31ebda6083f74f22b561fd3739bc0ac51e019622e3d72668c7 \ + --hash=sha256:4e8c2a84d977f50b9daed6eeaf3baef67d00d5d74d932288f02cb94518ee3ace \ + --hash=sha256:4f883547d4b7f0495ebe7056b0cc2aea76094e7a4abc8e933540f3271df27d9c \ + --hash=sha256:53aa02d20d10c3d814d536aa4e5ac9b84ca0ff5a88377963b085ad6822f93e64 \ + --hash=sha256:571b9fcb07b97ef3a492028fb3d2dc0993ca23a06138b0315286566d29ef718a \ + --hash=sha256:57b3d78c95ba9059768b10e28b813002261d3f3dfc55cc48b0c988f625175827 \ + --hash=sha256:5afb51d599ea772b8365ae807ae557f18bccfe46ab261fd1c2a9ed700fc6eb17 \ + --hash=sha256:6b02afb9b97f65fbca5f31db6a2a3ba21aa93030225f150fa3f249717e938fb4 \ + --hash=sha256:71d6097b330eea8fd15097780c8e89cb1a8ce7838669f48c5bacd6f663dd4701 \ + --hash=sha256:756c768d0c9c2955feb7a56c37ea24aea2e369f8d36a88da270b6a9f19e62b5e \ + --hash=sha256:78cb2c6865a35ab8ff8b75fd122f6033b92a62c82801110e48ddd6c936a45d91 \ + --hash=sha256:7a743ff716f746fc19a9557f60dab1600d4613255f8a7aeb3cdde4db7eb15a66 \ + --hash=sha256:8728f216dcdb6e6d555cf971cb34076139ad74b31fc2c14da4fafc741c5f6217 \ + --hash=sha256:877c3f311ff35410f690861c4409e7ccbf0cd2f878e50628a28e5a0bb689e658 \ + --hash=sha256:8cd2f7bdda092d99c9fc2fb7391354f306d01443d22785d0cbfafa2e2c8bb418 \ + --hash=sha256:8e95e1385e4998ae9694eeaa4730ba5457ff61185b3a55e2e7bea0880aef452a \ + --hash=sha256:962864dc93511324d51ddbb5b9f8731bf71675b93ca612a07441896f4688fb8c \ + --hash=sha256:9cf95fe4d0f84c82d282745d9bb08ad9f926efa00be4697e767b814ce40d4330 \ + --hash=sha256:9e881fca225083806662a5c43d627d215f258ff43c890f831966c7d7ba9c7402 \ + --hash=sha256:a2b55dd6b2a4c4b7d87ffa56bdb33fdc5fdb9a462173861a7bc097f17d91cb09 \ + --hash=sha256:a45650e8ce7fafffd731db8550230db6b0d306d181a90b67d3e6bca2f1990930 \ + --hash=sha256:a876864214e136f0eb367788dbd7df045f4806801518e2cfe9e13229cfe06d8f \ + --hash=sha256:ae26d61dfa7a47befdc7572b521024e8745f3d809bd95ca9505a7bba9ef849ec \ + --hash=sha256:af8d94b0db561cf68b88a267c5c44b49e134f525d0dc2cb7ed413a66bc23559a \ + --hash=sha256:b343699e8308bdc51978310e1c959c584e7869cc8c40780058c87da7781a1e94 \ + --hash=sha256:b3c777e849237620b022f7f297dd67705f9f5cf1685f09f02e46f93e92725468 \ + --hash=sha256:b629de27fda84b42cde7edef0d85f13b958b47f6e9bbcbba9b673c562a89bd8b \ + --hash=sha256:ba09209fbe443b4acccebe845d8a138b89a8f4fbaeedd44953490b5315d5e965 \ + --hash=sha256:ba54cfebe86920a559a7c4d6b9050791c20513650a1952ebe3368c7dc70306f8 \ + --hash=sha256:bcb46e2f9feff8d06323983bd83ed00c201fdcab3d74973e7072a889b3979fcd \ + --hash=sha256:bf16ba1b4d0b6b7c8e534936632270cf70eb00dbe09005bc345b2677b726855c \ + --hash=sha256:cf1845d02ad822a369a49f2bb9345b1614744267682e7a03527dc3bf6eea1777 \ + --hash=sha256:d69141514cc30b774ceea5e3ed3a6635c8d8a96edf664689b890f4089111fb35 \ + --hash=sha256:d9c7f76c0673154f044e9d78c8655fb4213f6ca31a836df48b40fe5d187717b9 \ + --hash=sha256:dbce0b29841537a2fa4a214c2bbf14de3587c9680caa9b4e217568472490b28f \ + --hash=sha256:dc624f6bc473dacdf7ef7eb8678d0d08edf15cd94fad6ae5c7d6cc67a4e4902f \ + --hash=sha256:e158cb00350dc278f3b91551101aa7d12415a66ebf2c91d8d5ac14e56ddd3ad0 \ + --hash=sha256:e491916b378fba47242221bb9ead245211b70d504f495d105d17b14a24b4907c \ + --hash=sha256:e795b7eb908249c4e43c7c99fac7c2c75dab0c43566e37db472a355f63693d71 \ + --hash=sha256:e7e480451b9fa137494bccd3a7d69adbe8ac65a87d97be61e11f1b1050a5bac3 \ + --hash=sha256:e91206ee562682b51b98ef4b26a6ef48fd84e15fd4c4bc5ec768eb641d206838 \ + --hash=sha256:e9871b1ffbfa9656b60aeee92ed5136a5742696006fa322b29ea3d8da0ecc9cf \ + --hash=sha256:e9aeb04d6aef139de265b29683e119b638208f88cf73cdd1658aa07221165321 \ + --hash=sha256:ebaea975e03d3141d9d3a507df75c9b3ec90fa9d2ffd07567b3a978d9d790b26 \ + --hash=sha256:f13c32a3abd6079a66d9526e18dad9b6d280384d49d7c54040cd57b6424041d9 \ + --hash=sha256:f7401aebd7f581d7f83a439d87d474999317ee099218e5ad25d125290990ba65 \ + --hash=sha256:fa4ecea169a355be7a3ade2c783e2ed12f0e40d2c5621cda8b3297faf7fbb9f5 \ + --hash=sha256:fbd139c8447d25dd750ab79ee274cc5e1fe80fc56340ab10b18a195e1b6eca3e \ + --hash=sha256:fdafc9cce40277e0f7a0feabce0ee50dd2fa1800f3b38015e51296b5e814048d \ + --hash=sha256:fe3cca2e4e8a592be0f269a1ca4835c25199d9f3ce815c8491048f785b0a0198 \ + --hash=sha256:ffd0c5368496f41b0944be820fcb7a838aa6e623d250b01acf2643939c3f99d7 + # via + # matplotlib + # streamlit +platformdirs==4.10.0 \ + --hash=sha256:31e761a6a0ca04faf7353ea759bdba55652be214725111e5aac52dfa29d4bef7 \ + --hash=sha256:fb516cdb12eb0d857d0cd85a7c57cea4d060bee4578d6cf5a14dfdf8cbf8784a + # via + # black + # mkdocs-get-deps + # properdocs +plotly==6.8.0 \ + --hash=sha256:13c5c4a0f70b74cab1913eda0de49b826df5931708eb6f9c3010040614700ec8 \ + --hash=sha256:e088e7ddc68d4f70e3d66659224727a45296d71d2b8284181862d3d8f1f0d88f + # via + # dash + # trendify +pluggy==1.6.0 \ + --hash=sha256:7dcc130b76258d33b90f61b658791dede3486c3e6bfb003ee5c9bfb396dd22f3 \ + --hash=sha256:e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746 + # via + # hatchling + # pytest + # pytest-cov +properdocs==1.6.7 \ + --hash=sha256:6fa0cfa2e01bf338f684892c8a506cf70ea88ae7f3479c933b6fa20168101cbd \ + --hash=sha256:adc7b16e562890af0e098a7e5b02e3a81c20894a87d6a28d345c9300de73c26e + # via + # mkdocs-gen-files + # mkdocs-literate-nav + # mkdocs-redirects + # mkdocs-section-index +protobuf==7.35.1 \ + --hash=sha256:11d6b0ec246892d85215b0a13ca6e0233cf5284b68f0ac02646427f4ff88a799 \ + --hash=sha256:230a75ddfc2de4806e56696ce9640c1cdfdb6543b7cfce98d42a4c0a0e7bdb87 \ + --hash=sha256:24f857477359a85c0c235261b8ba905fd51b2562f4a64ca1df5473f29850cbf6 \ + --hash=sha256:353652e4efd0bca5b5fc2656abf8307ef351f0cf938c9eba09f0e09c20a25c30 \ + --hash=sha256:4bc97768d8fe4ad6743c8a19403e314511ed9f6d13205b687e52421c023ac1b9 \ + --hash=sha256:74758715c53d7158fb76caf4f0cfdacc5329a4b1bb994f865d6cf302d413a1c4 \ + --hash=sha256:b73f9489a4b8b1c9cb1f8ed951c736392592edb24b9d6819f36d2e10b171d5b4 \ + --hash=sha256:ce115a26fe0c39a2c29973d914d327e516a6455464489fe3cd1e51a1b354f81a + # via streamlit +pyarrow==24.0.0 \ + --hash=sha256:02b001b3ed4723caa44f6cd1af2d5c86aa2cf9971dacc2ffa55b21237713dfba \ + --hash=sha256:04920d6a71aabd08a0417709efce97d45ea8e6fb733d9ca9ecffb13c67839f68 \ + --hash=sha256:0b3537c00fb8d384f15ac1e79b6eb6db04a16514c8c1d22e59a9b95c8ba42868 \ + --hash=sha256:1183baeb14c5f587b1ec52831e665718ce632caab84b7cd6b85fd44f96114495 \ + --hash=sha256:14e31a3c9e35f1ab6356c6378f6f72830e6d2d5f1791df3774a7b097d18a6a1e \ + --hash=sha256:1617043b99bd33e5318ae18eb2919af09c71322ef1ca46566cdafc6e6712fb66 \ + --hash=sha256:1a4e45017efbf115032e4475ee876d525e0e36c742214fbe405332480ecd6275 \ + --hash=sha256:1b2fe7f9a5566401a0ef2571f197eb92358925c1f0c8dba305d6e43ea0871bb3 \ + --hash=sha256:1cc9057f0319e26333b357e17f3c2c022f1a83739b48a88b25bfd5fa2dc18838 \ + --hash=sha256:25ea65d868eb04015cd18e6df2fbe98f07e5bda2abefabcb88fce39a947716f6 \ + --hash=sha256:295f0a7f2e242dabd513737cf076007dc5b2d59237e3eca37b05c0c6446f3826 \ + --hash=sha256:2f16197705a230a78270cdd4ea8a1d57e86b2fdcbc34a1f6aebc72e65c986f9a \ + --hash=sha256:35405aecb474e683fb36af650618fd5340ee5471fc65a21b36076a18bbc6c981 \ + --hash=sha256:38be1808cdd068605b787e6ca9119b27eb275a0234e50212c3492331680c3b1e \ + --hash=sha256:3b13dedfe76a0ad2d1d859b0811b53827a4e9d93a0bcb05cf59333ab4980cc7e \ + --hash=sha256:418e48ce50a45a6a6c73c454677203a9c75c966cb1e92ca3370959185f197a05 \ + --hash=sha256:55a3bc1e3df3b5567b7d27ef551b2283f0c68a5e86f1cd56abc569da4f31335b \ + --hash=sha256:6165461f55ef6314f026de6638d661188e3455d3ec49834556a0ebbdbace18bb \ + --hash=sha256:61a3d7eaa97a14768b542f3d284dc6400dd2470d9f080708b13cd46b6ae18136 \ + --hash=sha256:6233c9ed9ab9d1db47de57d9753256d9dcffbf42db341576099f0fd9f6bf4810 \ + --hash=sha256:641f795b361874ac9da5294f8f443dfdbee355cf2bd9e3b8d97aaac2306b9b37 \ + --hash=sha256:6f066b179d68c413374294bc1735f68475457c933258df594443bb9d88ddc2a0 \ + --hash=sha256:7986f1fa71cee060ad00758bcc79d3a93bab8559bf978fab9e53472a2e25a17b \ + --hash=sha256:806f24b4085453c197a5078218d1ee08783ebbba271badd153d1ae22a3ee804f \ + --hash=sha256:85fe721a14dd823aca09127acbb06c3ca723efbd436c004f16bca601b04dcc83 \ + --hash=sha256:8adc8e6ce5fccf5dc707046ae4914fd537def529709cc0d285d37a7f9cd442ca \ + --hash=sha256:9700ebd9a51f5895ce75ff4ac4b3c47a7d4b42bc618be8e713e5d56bacf5f931 \ + --hash=sha256:9b18371ad2f44044b81a8d23bc2d8a9b6a6226dca775e8e16cfee640473d6c5d \ + --hash=sha256:a964266397740257f16f7bb2e4f08a0c81454004beab8ff59dd531b73610e9f2 \ + --hash=sha256:adbbedc55506cbdabb830890444fb856bfb0060c46c6f8026c6c2f2cf86ae795 \ + --hash=sha256:ae8a1145af31d903fa9bb166824d7abe9b4681a000b0159c9fb99c11bc11ad26 \ + --hash=sha256:b0e131f880cda8d04e076cee175a46fc0e8bc8b65c99c6c09dff6669335fde74 \ + --hash=sha256:b196eb3f931862af3fa84c2a253514d859c08e0d8fe020e07be12e75a5a9780c \ + --hash=sha256:b7d9a514e73bc42711e6a35aaccf3587c520024fe0a25d830a1a8a27c15f4f57 \ + --hash=sha256:c91d00057f23b8d353039520dc3a6c09d8608164c692e9f59a175a42b2ae0c19 \ + --hash=sha256:d3e0b61e8efb24ed38898e5cdc5fffa9124be480008d401a1f8071500494ae42 \ + --hash=sha256:d7027eba1df3b2069e2e8d80f644fa0918b68c46432af3d088ddd390d063ecde \ + --hash=sha256:d8ddd2768da81d3ee08cfea9b597f4abb4e8e1dc8ae7e204b608d23a0d3ab699 \ + --hash=sha256:e4505fc6583f7b05ab854934896bcac8253b04ac1171a77dfb73efef92076d91 \ + --hash=sha256:e56a1ffe9bf7b727432b89104cc0849c21582949dd7bdcb34f17b2001a351a76 \ + --hash=sha256:e6f1278ee4785b6db21229374a1c9e54ec7c549de5d1efc9630b6207de7e170b \ + --hash=sha256:f7616236ec1bc2b15bfdec22a71ab38851c86f8f05ff64f379e1278cf20c634a \ + --hash=sha256:fb24ac194bfc5e86839d7dcd52092ee31e5fe6733fe11f5e3b06ef0812b20072 + # via streamlit +pycodestyle==2.14.0 \ + --hash=sha256:c4b5b517d278089ff9d0abdec919cd97262a3367449ea1c8b49b91529167b783 \ + --hash=sha256:dd6bf7cb4ee77f8e016f9c8e74a35ddd9f67e1d5fd4184d86c3b98e07099f42d + # via flake8 +pycparser==3.0 ; implementation_name != 'PyPy' and platform_python_implementation != 'PyPy' \ + --hash=sha256:600f49d217304a5902ac3c37e1281c9fe94e4d0489de643a9504c5cdfdfc6b29 \ + --hash=sha256:b727414169a36b7d524c1c3e31839a521725078d7b2ff038656844266160a992 + # via cffi +pydantic==2.13.4 \ + --hash=sha256:45a282cde31d808236fd7ea9d919b128653c8b38b393d1c4ab335c62924d9aba \ + --hash=sha256:c40756b57adaa8b1efeeced5c196f3f3b7c435f90e84ea7f443901bec8099ef6 + # via + # dash + # mcp + # numpydantic + # pydantic-settings + # trendify +pydantic-core==2.46.4 \ + --hash=sha256:00c603d540afdd6b80eb39f078f33ebd46211f02f33e34a32d9f053bba711de0 \ + --hash=sha256:0186750b482eefa11d7f435892b09c5c606193ef3375bcf94aa00ae6bfb66262 \ + --hash=sha256:041bde0a48fd37cf71cab1c9d56d3e8625a3793fef1f7dd232b3ff37e978ecda \ + --hash=sha256:0c563b08bca408dc7f65f700633d8442fffb2421fc47b8101377e9fd65051ff0 \ + --hash=sha256:0cbe8b01f948de4286c74cdd6c667aceb38f5c1e26f0693b3983d9d74887c65e \ + --hash=sha256:0ce40cd7b21210e99342afafbd4d0f76d784eb5b1d60f3bdc566be4983c6c73b \ + --hash=sha256:0e96592440881c74a213e5ad528e2b24d3d4f940de2766bed9010ab1d9e51594 \ + --hash=sha256:133878133d271ade3d41d1bfb2a45ec38dbdbda40bc065921c6b04e4630127e2 \ + --hash=sha256:14d4edf427bdcf950a8a02d7cb44a08614388dd6e1bdcbf4f67504fa7887da9c \ + --hash=sha256:14f4c5d6db102bd796a627bbb3a17b4cf4574b9ae861d8b7c9a9661c6dd3362d \ + --hash=sha256:17299feefe090f2caa5b8e37222bb5f663e4935a8bfa6931d4102e5df1a9f398 \ + --hash=sha256:184c081504d17f1c1066e430e117142b2c77d9448a97f7b65c6ac9fd9aee238d \ + --hash=sha256:18e5ceec2ab67e6d5f1a9085e5a24c9c4e2ac4545730bfe668680bca05e555f3 \ + --hash=sha256:1a7dd0b3ee80d90150e3495a3a13ac34dbcbfd4f012996a6a1d8900e91b5c0fb \ + --hash=sha256:1d8ba486450b14f3b1d63bc521d410ec7565e52f887b9fb671791886436a42f7 \ + --hash=sha256:2108ba5c1c1eca18030634489dc544844144ee36357f2f9f780b93e7ddbb44b5 \ + --hash=sha256:23ace664830ee0bfe014a0c7bc248b1f7f25ed7ad103852c317624a1083af462 \ + --hash=sha256:2412e734dcb48da14d4e4006b82b46b74f2518b8a26ee7e58c6844a6cd6d03c4 \ + --hash=sha256:29c61fc04a3d840155ff08e475a04809278972fe6aef51e2720554e96367e34b \ + --hash=sha256:3009f12e4e90b7f88b4f9adb1b0c4a3d58fe7820f3238c190047209d148026df \ + --hash=sha256:3245406455a5d98187ec35530fd772b1d799b26667980872c8d4614991e2c4a2 \ + --hash=sha256:372429a130e469c9cd698925ce5fc50940b7a1336b0d82038e63d5bbc4edc519 \ + --hash=sha256:3a233125ac121aa3ffba9a2b59edfc4a985a76092dc8279586ab4b71390875e7 \ + --hash=sha256:3bf92c5d0e00fefaab325a4d27828fe6b6e2a21848686b5b60d2d9eeb09d76c6 \ + --hash=sha256:3ecbc122d18468d06ca279dc26a8c2e2d5acb10943bb35e36ae92096dc3b5565 \ + --hash=sha256:3fb702cd90b0446a3a1c5e470bfa0dd23c0233b676a9099ddcc964fa6ca13898 \ + --hash=sha256:428e04521a40150c85216fc8b85e8d39fece235a9cf5e383761238c7fa9b96fb \ + --hash=sha256:4c63ebc82684aa89d9a3bcbd13d515b3be44250dc68dd3bd81526c1cb31286c3 \ + --hash=sha256:4fc73cb559bdb54b1134a706a2802a4cddd27a0633f5abb7e53056268751ac6a \ + --hash=sha256:4fcbe087dbc2068af7eda3aa87634eba216dbda64d1ae73c8684b621d33f6596 \ + --hash=sha256:56cb4851bcaf3d117eddcef4fe66afd750a50274b0da8e22be256d10e5611987 \ + --hash=sha256:5855698a4856556d86e8e6cd8434bc3ac0314ee8e12089ae0e143f64c6256e4e \ + --hash=sha256:5a4330cdbc57162e4b3aa303f588ba752257694c9c9be3e7ebb11b4aca659b5d \ + --hash=sha256:5b712b53160b79a5850310b912a5ef8e57e56947c8ad690c227f5c9d7e561712 \ + --hash=sha256:5d5902252db0d3cedf8d4a1bc68f70eeb430f7e4c7104c8c476753519b423008 \ + --hash=sha256:617d7e2ca7dcb8c5cf6bcb8c59b8832c94b36196bbf1cbd1bfb56ed341905edd \ + --hash=sha256:62f875393d7f270851f20523dd2e29f082bcc82292d66db2b64ea71f64b6e1c1 \ + --hash=sha256:633147d34cf4550417f12e2b1a0383973bdf5cdfde212cb09e9a581cf10820be \ + --hash=sha256:66ce7632c22d837c95301830e111ad0128a32b8207533b60896a96c4915192ea \ + --hash=sha256:6b3ace8194b0e5204818c92802dcdca7fc6d88aabbb799d7c795540d9cd6d292 \ + --hash=sha256:6f2eeda33a839975441c86a4119e1383c50b47faf0cbb5176985565c6bb02c33 \ + --hash=sha256:7027560ee92211647d0d34e3f7cd6f50da56399d26a9c8ad0da286d3869a53f3 \ + --hash=sha256:7283d57845ecf5a163403eb0702dfc220cc4fbdd18919cb5ccea4f95ee1cdab4 \ + --hash=sha256:7a5f930472650a82629163023e630d160863fce524c616f4e5186e5de9d9a49b \ + --hash=sha256:7bfb192b3f4b9e8a89b6277b6ce787564f62cfd272055f6e685726b111dc7826 \ + --hash=sha256:811ff8e9c313ab425368bcbb36e5c4ebd7108c2bbf4e4089cfbb0b01eff63fac \ + --hash=sha256:8233f2947cf85404441fd7e0085f53b10c93e0ee78611099b5c7237e36aacbf7 \ + --hash=sha256:82cf5301172168103724d49a1444d3378cb20cdee30b116a1bd6031236298a5d \ + --hash=sha256:85bb3611ff1802f3ee7fdd7dbff26b56f343fb432d57a4728fdd49b6ef35e2f4 \ + --hash=sha256:86e1a4418c6cd97d60c95c71164158eaf7324fae7b0923264016baa993eba6fc \ + --hash=sha256:8c5dac79fa1614d1e06ca695109c6105923bd9c7d1d6c918d4e637b7e6b32fd3 \ + --hash=sha256:8d0820e8192167f80d88d64038e609c31452eeca865b4e1d9950a27a4609b00b \ + --hash=sha256:8daafc69c93ee8a0204506a3b6b30f586ef54028f52aeeeb5c4cfc5184fd5914 \ + --hash=sha256:9037063db01f09b09e237c282b6792bd4da634b5402c4e7f0c61effed7701a04 \ + --hash=sha256:90884113d8b48f760e9587002789ddd741e76ab9f89518cd1e43b1f1a52ec44b \ + --hash=sha256:926c9541b14b12b1681dca8a0b75feb510b06c6341b70a8e500c2fdcff837cce \ + --hash=sha256:9401557acd873c3a7f3eb9383edef8ac4968f9510e340f4808d427e75667e7b4 \ + --hash=sha256:9551187363ffc0de2a00b2e47c25aeaeb1020b69b668762966df15fc5659dd5a \ + --hash=sha256:962ccbab7b642487b1d8b7df90ef677e03134cf1fd8880bf698649b22a69371f \ + --hash=sha256:97e7cf2be5c77b7d1a9713a05605d49460d02c6078d38d8bef3cbe323c548424 \ + --hash=sha256:9aa768456404a8bf48a4406685ac2bec8e72b62c69313734fa3b73cf33b3a894 \ + --hash=sha256:9bc519fbf2b7578398853d815009ae5e4d4603d12f4e3f91da8c06852d3da3e9 \ + --hash=sha256:9d56801be94b86a9da183e5f3766e6310752b99ff647e38b09a9500d88e46e76 \ + --hash=sha256:9fa8ae11da9e2b3126c6426f147e0fba88d96d65921799bb30c6abd1cb2c97fb \ + --hash=sha256:a0f62d0a58f4e7da165457e995725421e0064f2255d8eccebc49f41bbc23b109 \ + --hash=sha256:aaa2a54443eff1950ba5ddc6b6ccda0d9c84a364276a62f969bdf2a390650848 \ + --hash=sha256:ad785e92e6dc634c21555edc8bd6b64957ab844541bcb96a1366c202951ae526 \ + --hash=sha256:af8244b2bef6aaad6d92cda81372de7f8c8d36c9f0c3ea36e827c60e7d9467a0 \ + --hash=sha256:b2f69dec1725e79a012d920df1707de5caf7ed5e08f3be4435e25803efc47458 \ + --hash=sha256:b8458003118a712e66286df6a707db01c52c0f52f7db8e4a38f0da1d3b94fc4e \ + --hash=sha256:bfec22eab3c8cc2ceec0248aec886624116dc079afa027ecc8ad4a7e62010f8a \ + --hash=sha256:c1b3f518abeca3aa13c712fd202306e145abf59a18b094a6bafb2d2bbf59192c \ + --hash=sha256:c50f2528cf200c5eed56faf3f4e22fcd5f38c157a8b78576e6ba3168ec35f000 \ + --hash=sha256:c68fcd102d71ea85c5b2dfac3f4f8476eff42a9e078fd5faefff6d145063536b \ + --hash=sha256:c7a7bd4e39e8e4c12c39cd480356842b6a8a06e41b23a55a5e3e191718838ddf \ + --hash=sha256:c94f0688e7b8d0a67abf40e57a7eaaecd17cc9586706a31b76c031f63df052b4 \ + --hash=sha256:cbaf13819775b7f769bf4a1f066cb6df7a28d4480081a589828ef190226881cd \ + --hash=sha256:cd2213145bcc2ba85884d0ac63d222fece9209678f77b9b4d76f054c561adb28 \ + --hash=sha256:ce5c1d2a8b27468f433ca974829c44060b8097eedc39933e3c206a90ee49c4a9 \ + --hash=sha256:d396ec2b979760aaf3218e76c24e65bd0aca24983298653b3a9d7a45f9e47b30 \ + --hash=sha256:d51026d73fcfd93610abc7b27789c26b313920fcfb20e27462d74a7f8b06e983 \ + --hash=sha256:db06ffe51636ffe9ca531fe9023dd64bdd794be8754cb5df57c5498ae5b518a7 \ + --hash=sha256:e0d65b8c354be7fb5f720c3caa8bc940bc2d20ce749c8e06135f07f8ed95dd7c \ + --hash=sha256:e739fee756ba1010f8bcccb534252e85a35fe45ae92c295a06059ce58b74ccd3 \ + --hash=sha256:e846ae7835bf0703ae43f534ab79a867146dadd59dc9ca5c8b53d5c8f7c9ef02 \ + --hash=sha256:e9c26f834c65f5752f3f06cb08cb86a913ceb7274d0db6e267808a708b46bc89 \ + --hash=sha256:f027324c56cd5406ca49c124b0db10e56c69064fec039acc571c29020cc87c76 \ + --hash=sha256:f99626688942fb746e545232e7726926f3be91b5975f8b55327665fafda991c7 \ + --hash=sha256:f9fa868638bf362d3d138ea55829cefb3d5f4b0d7f142234382a15e2485dbec4 \ + --hash=sha256:fbdb89b3e1c94a30cc5edfce477c6e6a5dc4d8f84665b455c27582f211a1c72c \ + --hash=sha256:fc010ab034c8c7452522748bf937df58020d256ccae0874463d1f4d01758af8e \ + --hash=sha256:fc3e9034a63de20e15e8ade85358bc6efc614008cab72898b4b4952bea0509ff + # via pydantic +pydantic-settings==2.14.2 \ + --hash=sha256:a20c97b37910b6550d5ea50fbcc2d4187defe58cd57070b73863d069419c9440 \ + --hash=sha256:c19dd64b19097f1de80184f0cc7b0272a13ae6e170cbf240a3e27e381ed14a5f + # via mcp +pydeck==0.9.2 \ + --hash=sha256:8213dfeacc5f6bfe6825f61c8ee34e3850e8a31fc43924379ec98edb34a75b25 \ + --hash=sha256:c10d9035e81ead6385264cac8d19402471f6866a15ca1f7df1400f52142bcf87 + # via streamlit +pyflakes==3.4.0 \ + --hash=sha256:b24f96fafb7d2ab0ec5075b7350b3d2d2218eab42003821c06344973d3ea2f58 \ + --hash=sha256:f742a7dbd0d9cb9ea41e9a24a918996e8170c799fa528688d40dd582c8265f4f + # via flake8 +pygments==2.20.0 \ + --hash=sha256:6757cd03768053ff99f3039c1a36d6c0aa0b263438fcab17520b30a303a82b5f \ + --hash=sha256:81a9e26dd42fd28a23a2d169d86d7ac03b46e2f8b59ed4698fb4785f946d0176 + # via + # mkdocs-material + # pytest +pyjwt==2.13.0 \ + --hash=sha256:41571c89ca91598c79e8ef18a2d07367d4810fbbd6f637794879baf1b7703423 \ + --hash=sha256:66adcc2aff09b3f1bbd95fc1e1577df8ac8723c978552fd43304c8a290ac5728 + # via mcp +pymdown-extensions==11.0 \ + --hash=sha256:8269cef0247f9e2d0a62fcea10860aba05c1cbab5470fd4b63230b96434dc589 \ + --hash=sha256:fbc4acb641814fa9d17521bbd21a5240ef739a662f11c06330c4b78c93e954d6 + # via + # mkdocs-material + # mkdocstrings +pyparsing==3.3.2 \ + --hash=sha256:850ba148bd908d7e2411587e247a1e4f0327839c40e2e5e6d05a007ecc69911d \ + --hash=sha256:c777f4d763f140633dcb6d8a3eda953bf7a214dc4eff598413c070bcdc117cbc + # via + # matplotlib + # mike +pytest==9.1.1 \ + --hash=sha256:1088fbde8f2b49d95a549a195707afa7a76a3ce9bcadc26b6d71f0ffda5fe313 \ + --hash=sha256:37a86b45efb9a47a61a36449063e8e18d0cab3161329fc099eb21783169c4f0c + # via + # pytest-asyncio + # pytest-cov +pytest-asyncio==1.4.0 \ + --hash=sha256:933ca923a23075a87fb7070c0ec272a6848489824d887c85c812670932835aa1 \ + --hash=sha256:c6c0d2259945122819f171a32ecea2c349ead889ee28176caaf492143424be42 +pytest-cov==7.1.0 \ + --hash=sha256:30674f2b5f6351aa09702a9c8c364f6a01c27aae0c1366ae8016160d1efc56b2 \ + --hash=sha256:a0461110b7865f9a271aa1b51e516c9a95de9d696734a2f71e3e78f46e1d4678 +python-dateutil==2.9.0.post0 \ + --hash=sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3 \ + --hash=sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427 + # via + # ghp-import + # matplotlib + # pandas +python-dotenv==1.2.2 \ + --hash=sha256:1d8214789a24de455a8b8bd8ae6fe3c6b69a5e3d64aa8a8e5d68e694bbcb285a \ + --hash=sha256:2c371a91fbd7ba082c2c1dc1f8bf89ca22564a087c2c287cd9b662adde799cf3 + # via pydantic-settings +python-multipart==0.0.32 \ + --hash=sha256:be54b7f3fa167bb83e4fcd936b887b708f4e57fe75911c02aebf53efaf8d938e \ + --hash=sha256:ff6d3f776f16878c894e52e107296ffc890e913c611b1a4ec6c44e2821fe2e23 + # via + # mcp + # streamlit +pytokens==0.4.1 \ + --hash=sha256:140709331e846b728475786df8aeb27d24f48cbcf7bcd449f8de75cae7a45083 \ + --hash=sha256:24afde1f53d95348b5a0eb19488661147285ca4dd7ed752bbc3e1c6242a304d1 \ + --hash=sha256:26cef14744a8385f35d0e095dc8b3a7583f6c953c2e3d269c7f82484bf5ad2de \ + --hash=sha256:292052fe80923aae2260c073f822ceba21f3872ced9a68bb7953b348e561179a \ + --hash=sha256:29d1d8fb1030af4d231789959f21821ab6325e463f0503a61d204343c9b355d1 \ + --hash=sha256:3f901fe783e06e48e8cbdc82d631fca8f118333798193e026a50ce1b3757ea68 \ + --hash=sha256:4a14d5f5fc78ce85e426aa159489e2d5961acf0e47575e08f35584009178e321 \ + --hash=sha256:4a58d057208cb9075c144950d789511220b07636dd2e4708d5645d24de666bdc \ + --hash=sha256:5502408cab1cb18e128570f8d598981c68a50d0cbd7c61312a90507cd3a1276f \ + --hash=sha256:584c80c24b078eec1e227079d56dc22ff755e0ba8654d8383b2c549107528918 \ + --hash=sha256:5ad948d085ed6c16413eb5fec6b3e02fa00dc29a2534f088d3302c47eb59adf9 \ + --hash=sha256:6d6c4268598f762bc8e91f5dbf2ab2f61f7b95bdc07953b602db879b3c8c18e1 \ + --hash=sha256:79fc6b8699564e1f9b521582c35435f1bd32dd06822322ec44afdeba666d8cb3 \ + --hash=sha256:8bdb9d0ce90cbf99c525e75a2fa415144fd570a1ba987380190e8b786bc6ef9b \ + --hash=sha256:8fcb9ba3709ff77e77f1c7022ff11d13553f3c30299a9fe246a166903e9091eb \ + --hash=sha256:967cf6e3fd4adf7de8fc73cd3043754ae79c36475c1c11d514fc72cf5490094a \ + --hash=sha256:970b08dd6b86058b6dc07efe9e98414f5102974716232d10f32ff39701e841c4 \ + --hash=sha256:97f50fd18543be72da51dd505e2ed20d2228c74e0464e4262e4899797803d7fa \ + --hash=sha256:9bd7d7f544d362576be74f9d5901a22f317efc20046efe2034dced238cbbfe78 \ + --hash=sha256:b35d7e5ad269804f6697727702da3c517bb8a5228afa450ab0fa787732055fc9 \ + --hash=sha256:b49750419d300e2b5a3813cf229d4e5a4c728dae470bcc89867a9ad6f25a722d \ + --hash=sha256:d31b97b3de0f61571a124a00ffe9a81fb9939146c122c11060725bd5aea79975 \ + --hash=sha256:d70e77c55ae8380c91c0c18dea05951482e263982911fc7410b1ffd1dadd3440 \ + --hash=sha256:d9907d61f15bf7261d7e775bd5d7ee4d2930e04424bab1972591918497623a16 \ + --hash=sha256:dc74c035f9bfca0255c1af77ddd2d6ae8419012805453e4b0e7513e17904545d \ + --hash=sha256:ee44d0f85b803321710f9239f335aafe16553b39106384cef8e6de40cb4ef2f6 \ + --hash=sha256:f66a6bbe741bd431f6d741e617e0f39ec7257ca1f89089593479347cc4d13324 + # via black +pywin32==312 ; sys_platform == 'win32' \ + --hash=sha256:17948aeadbdb091f0ced6ef0841620794e68327b94ee415571c1203594b7215c \ + --hash=sha256:59aba5d5940842075343a5ddc6b11f1cdf0d1567fe745290359dfbcc7c2eb831 \ + --hash=sha256:5c1fbe4a937a73ae9297384a3da38518cbc694c68ad8a809b2e19acd350f03ed \ + --hash=sha256:6017c58e12f6809fbb0555b75df144c2922a9ffd18e4b9b5afa863b6c1a9d950 \ + --hash=sha256:7a27df850933d16a8eabfbaeb73d52b273e2da667f80d70b01a89d1f6828d02c \ + --hash=sha256:9fce94568364e0155e6dfb781ac5d95903be8baf28670632beab1b523f300daa \ + --hash=sha256:a4dd3a848290ef724347b19f301045831d8e802fa4464f491b98b1e0a081432e \ + --hash=sha256:a77a90fbb6881238d2ca9c6fd797b25817f3768fe78d214a90137ff055a75f5b \ + --hash=sha256:a8597d28f267b39074aef51fa593530082b39cbe5a074226096857b1fed2dfb9 \ + --hash=sha256:b2200a054ca6d6625c4842fc56a4976a4b47f96b73dbe5538c3f813a80359f47 \ + --hash=sha256:b457f6d628a47e8a7346ce22acb7e1a46a4a78b52e1d17e1af56871bd19a93bc \ + --hash=sha256:c2f03a0f73f804a13c2735b99392b0cd426bb4f2c4d0178e5ac966a0f21618d5 \ + --hash=sha256:c53e878d15a1c44788082bfe712a905433473aa38f86375b7cf8b45e3acbaaf9 \ + --hash=sha256:d11417d84412f859b722fad0841b3614459ed0047f7542d8362e77884f6b6e8a \ + --hash=sha256:dab4f65ac9c4e48400a2a0530c46c3c579cd5905ecd11b80692373915269208b + # via mcp +pyyaml==6.0.3 \ + --hash=sha256:00c4bdeba853cc34e7dd471f16b4114f4162dc03e6b7afcc2128711f0eca823c \ + --hash=sha256:02893d100e99e03eda1c8fd5c441d8c60103fd175728e23e431db1b589cf5ab3 \ + --hash=sha256:0f29edc409a6392443abf94b9cf89ce99889a1dd5376d94316ae5145dfedd5d6 \ + --hash=sha256:10892704fc220243f5305762e276552a0395f7beb4dbf9b14ec8fd43b57f126c \ + --hash=sha256:16249ee61e95f858e83976573de0f5b2893b3677ba71c9dd36b9cf8be9ac6d65 \ + --hash=sha256:1d37d57ad971609cf3c53ba6a7e365e40660e3be0e5175fa9f2365a379d6095a \ + --hash=sha256:2283a07e2c21a2aa78d9c4442724ec1eb15f5e42a723b99cb3d822d48f5f7ad1 \ + --hash=sha256:34d5fcd24b8445fadc33f9cf348c1047101756fd760b4dacb5c3e99755703310 \ + --hash=sha256:37503bfbfc9d2c40b344d06b2199cf0e96e97957ab1c1b546fd4f87e53e5d3e4 \ + --hash=sha256:41715c910c881bc081f1e8872880d3c650acf13dfa8214bad49ed4cede7c34ea \ + --hash=sha256:44edc647873928551a01e7a563d7452ccdebee747728c1080d881d68af7b997e \ + --hash=sha256:4a2e8cebe2ff6ab7d1050ecd59c25d4c8bd7e6f400f5f82b96557ac0abafd0ac \ + --hash=sha256:4ad1906908f2f5ae4e5a8ddfce73c320c2a1429ec52eafd27138b7f1cbe341c9 \ + --hash=sha256:501a031947e3a9025ed4405a168e6ef5ae3126c59f90ce0cd6f2bfc477be31b7 \ + --hash=sha256:5190d403f121660ce8d1d2c1bb2ef1bd05b5f68533fc5c2ea899bd15f4399b35 \ + --hash=sha256:5498cd1645aa724a7c71c8f378eb29ebe23da2fc0d7a08071d89469bf1d2defb \ + --hash=sha256:5fcd34e47f6e0b794d17de1b4ff496c00986e1c83f7ab2fb8fcfe9616ff7477b \ + --hash=sha256:5fdec68f91a0c6739b380c83b951e2c72ac0197ace422360e6d5a959d8d97b2c \ + --hash=sha256:64386e5e707d03a7e172c0701abfb7e10f0fb753ee1d773128192742712a98fd \ + --hash=sha256:652cb6edd41e718550aad172851962662ff2681490a8a711af6a4d288dd96824 \ + --hash=sha256:66e1674c3ef6f541c35191caae2d429b967b99e02040f5ba928632d9a7f0f065 \ + --hash=sha256:6adc77889b628398debc7b65c073bcb99c4a0237b248cacaf3fe8a557563ef6c \ + --hash=sha256:79005a0d97d5ddabfeeea4cf676af11e647e41d81c9a7722a193022accdb6b7c \ + --hash=sha256:7c6610def4f163542a622a73fb39f534f8c101d690126992300bf3207eab9764 \ + --hash=sha256:7f047e29dcae44602496db43be01ad42fc6f1cc0d8cd6c83d342306c32270196 \ + --hash=sha256:8098f252adfa6c80ab48096053f512f2321f0b998f98150cea9bd23d83e1467b \ + --hash=sha256:850774a7879607d3a6f50d36d04f00ee69e7fc816450e5f7e58d7f17f1ae5c00 \ + --hash=sha256:8d1fab6bb153a416f9aeb4b8763bc0f22a5586065f86f7664fc23339fc1c1fac \ + --hash=sha256:8da9669d359f02c0b91ccc01cac4a67f16afec0dac22c2ad09f46bee0697eba8 \ + --hash=sha256:8dc52c23056b9ddd46818a57b78404882310fb473d63f17b07d5c40421e47f8e \ + --hash=sha256:9149cad251584d5fb4981be1ecde53a1ca46c891a79788c0df828d2f166bda28 \ + --hash=sha256:93dda82c9c22deb0a405ea4dc5f2d0cda384168e466364dec6255b293923b2f3 \ + --hash=sha256:96b533f0e99f6579b3d4d4995707cf36df9100d67e0c8303a0c55b27b5f99bc5 \ + --hash=sha256:9f3bfb4965eb874431221a3ff3fdcddc7e74e3b07799e0e84ca4a0f867d449bf \ + --hash=sha256:a33284e20b78bd4a18c8c2282d549d10bc8408a2a7ff57653c0cf0b9be0afce5 \ + --hash=sha256:a80cb027f6b349846a3bf6d73b5e95e782175e52f22108cfa17876aaeff93702 \ + --hash=sha256:b3bc83488de33889877a0f2543ade9f70c67d66d9ebb4ac959502e12de895788 \ + --hash=sha256:b8bb0864c5a28024fac8a632c443c87c5aa6f215c0b126c449ae1a150412f31d \ + --hash=sha256:ba1cc08a7ccde2d2ec775841541641e4548226580ab850948cbfda66a1befcdc \ + --hash=sha256:c1ff362665ae507275af2853520967820d9124984e0f7466736aea23d8611fba \ + --hash=sha256:c458b6d084f9b935061bc36216e8a69a7e293a2f1e68bf956dcd9e6cbcd143f5 \ + --hash=sha256:d0eae10f8159e8fdad514efdc92d74fd8d682c933a6dd088030f3834bc8e6b26 \ + --hash=sha256:d76623373421df22fb4cf8817020cbb7ef15c725b9d5e45f17e189bfc384190f \ + --hash=sha256:ebc55a14a21cb14062aa4162f906cd962b28e2e9ea38f9b4391244cd8de4ae0b \ + --hash=sha256:eda16858a3cab07b80edaf74336ece1f986ba330fdb8ee0d6c0d68fe82bc96be \ + --hash=sha256:ee2922902c45ae8ccada2c5b501ab86c36525b883eff4255313a253a3160861c \ + --hash=sha256:f7057c9a337546edc7973c0d3ba84ddcdf0daa14533c2065749c9075001090e6 \ + --hash=sha256:fc09d0aa354569bc501d4e787133afc08552722d3ab34836a80547331bb5d4a0 + # via + # mike + # mkdocs + # mkdocs-get-deps + # properdocs + # pymdown-extensions + # pyyaml-env-tag +pyyaml-env-tag==1.1 \ + --hash=sha256:17109e1a528561e32f026364712fee1264bc2ea6715120891174ed1b980d2e04 \ + --hash=sha256:2eb38b75a2d21ee0475d6d97ec19c63287a7e140231e4214969d0eac923cd7ff + # via + # mike + # mkdocs + # properdocs +referencing==0.37.0 \ + --hash=sha256:381329a9f99628c9069361716891d34ad94af76e461dcb0335825aecc7692231 \ + --hash=sha256:44aefc3142c5b842538163acb373e24cce6632bd54bdb01b21ad5863489f50d8 + # via + # jsonschema + # jsonschema-specifications +requests==2.34.2 \ + --hash=sha256:2a0d60c172f83ac6ab31e4554906c0f3b3588d37b5cb939b1c061f4907e278e0 \ + --hash=sha256:f288924cae4e29463698d6d60bc6a4da69c89185ad1e0bcc4104f584e960b9ed + # via + # dash + # grafana-api + # mkdocs-material + # streamlit +retrying==1.4.2 \ + --hash=sha256:bbc004aeb542a74f3569aeddf42a2516efefcdaff90df0eb38fbfbf19f179f59 \ + --hash=sha256:d102e75d53d8d30b88562d45361d6c6c934da06fab31bd81c0420acb97a8ba39 + # via dash +rpds-py==2026.6.3 \ + --hash=sha256:0be972be84cfcaf46c8c6edf690ca0f154ac17babf1f6a955a51579b34ad2dc5 \ + --hash=sha256:127565fead0a10943b282957bd5447804ff3160ad79f2ad2635e6d249e380680 \ + --hash=sha256:127e08c0642d880cf32ca47ec2a4a77b901f7e2dd1ad9762adb13955d72ffcc9 \ + --hash=sha256:166cf54d9f44fc6ceb53c7860258dde44a81406646de79f8ed3234fca3b6e538 \ + --hash=sha256:168c733a7112e071bb7a66460e667edfcff06c017a3c523f7a8a8e08d0140804 \ + --hash=sha256:1967debc37f64f2c4dc90a7f563aec558b471966e12adcac4e1c4240496b6ebf \ + --hash=sha256:1cebd1337c242e4ec2293e541f712b2da849b29f48f0c293684b71c0632625d4 \ + --hash=sha256:1cf01971c4f2c5553b772a542e4aaf191789cd331bc2cd4ff0e6e65ba49e1e97 \ + --hash=sha256:1e5822dfc2f0d4ab7e745eaa6d85945069329beeccef965af3f3bb26058fcab6 \ + --hash=sha256:22bffe6042b9bcb0822bcd1955ec00e245daf17b4344e4ed8e9551b976b63e96 \ + --hash=sha256:23a439f31ccbeff1574e24889128821d1f7917470e830cf6544dced1c662262a \ + --hash=sha256:24e9c5386e16669b674a69c156c8eeefcb578f3b3397b713b08e6d60f3c7b187 \ + --hash=sha256:270b293dae9058fc9fcedab50f13cebf46fb8ed1d1d54e0521a9da5d6b211975 \ + --hash=sha256:29dfa0533a5d4c94d4dfa1b694fcb56c9c63aad8330ffdd816fd225d0a7a162f \ + --hash=sha256:2a9c6f195058cb45335e8cc3802745c603d716eb96bc9625950c1aac71c0c703 \ + --hash=sha256:2bfd04c19ddbd6640de0b51894d764bd2758854d5b75bd102d2ef10cb9c293a9 \ + --hash=sha256:2c54a076ca4d370980ab57bc0e31df57bbe8d41340436a90ef8b1219a3cbb127 \ + --hash=sha256:2c958bf94822e9290a40aaf2a822d4bc5c88099093e3948ad6c571eca9272e5f \ + --hash=sha256:2c99f7e8ccb3dd6e3e4bfeac657a7b208c9bac8075f4b078c02d7404c34107fa \ + --hash=sha256:2f7c26fbc5acd2522b95d4177fe4710ffd8e9b20529e703ffbf8db4d93903f05 \ + --hash=sha256:30c6dc199b24a5e3e81d50da0f00858c5bbdb2617a750395687f4339c5818171 \ + --hash=sha256:38a2fea2787428f811719ceb9114cb78964a3138838320c29ac39526c79c16ba \ + --hash=sha256:3a83ae6c67b7676b9878378547ca8e93ed77a580037bcbcd1d32f739e1e6089c \ + --hash=sha256:3cfe765c1da0072636ca06628261e0ea05688e160d5c8a03e0217c3854037223 \ + --hash=sha256:421aba32367055614287a4292b6a17f1939c9452299f7a0209c117e990b646d4 \ + --hash=sha256:425560c6fa0415f27261727bb20bd097568485e5eb0c121f1949417d1c516885 \ + --hash=sha256:4470ce197d4090875cf6affbf1f853338387428df97c4fb7b7106317b8214698 \ + --hash=sha256:4cf2d36a2357e4d07bb5a4f98801265327b48256867816cfd2ceb001e9754a8f \ + --hash=sha256:4f4bca01b63096f606e095734dd56e74e175f94cfbf24ff3d63281cec61f7bb7 \ + --hash=sha256:501f9f04a588d6a09179368c57071301445191767c64e4b52a6aa9871f1ef5ed \ + --hash=sha256:536bceea4fa4acf7e1c61da2b5786304367c816c8895be71b8f537c480b0ea1f \ + --hash=sha256:538949e262e46caa31ac01bdb3c1e8f642622922cacbabbae6a8445d9dc33eaf \ + --hash=sha256:539d75de9e0d536c84ff18dfeb805398e58227001ce09231a26a08b9aed1ee0e \ + --hash=sha256:54f45a148e28767bf343d33a684693c70e451c6f4c0e9904709a723fafbdfc1f \ + --hash=sha256:55927d532399c2c646100ff7feb48eaa940ad70f42cd68e1328f3ded9f81ca24 \ + --hash=sha256:58eadac9cd119677b60e1cf8ac4052f35949d71b8a9e5556efccbe82533cf22a \ + --hash=sha256:5e8d07bddee435a2ff6f1920e18feff28d0bc4533e42f4bf6927fbd073312c41 \ + --hash=sha256:62698275682bf121181861295c9181e789030a2d516071f5b8f3c23c170cd0fc \ + --hash=sha256:639c8929aa0afe81be836b04de888460d6bed38b9c54cfc18da8f6bfabf5af5d \ + --hash=sha256:67e3a721ffc5d8d2210d3671872298c4a84e4b8035cfe42ffd7cde35d772b146 \ + --hash=sha256:6de4744d05bd1aa1be4ed7ea1189e3979196808008113bbbf899a460966b925e \ + --hash=sha256:6e84adbcf4bf841aed8116a8264b9f50b4cb3e7bd89b516122e616ac56ca269e \ + --hash=sha256:7491ee23305ac3eb59e492b6945881f5cd77a6f731061a3f25b77fd40f9e99a4 \ + --hash=sha256:79486287de1730dbaff3dbd124d0ca4d2ef7f9d29bf2544f1f93c09b5bcbbd12 \ + --hash=sha256:7b689145a1485c335569bd056464f3243a29af7ed3871c7be31ad624ba239bc7 \ + --hash=sha256:7f88d653e7b3b779d71ae7454e20dcc9b6bae903f33c269db9f2be41bda3f261 \ + --hash=sha256:8020133a74bd81b4572dd8e4be028a6b1ebcd70e6726edc3918008c08bee6ee6 \ + --hash=sha256:808345f53cb952433ca2816f1604ff3515608a81784954f38d4452acfe8e61d5 \ + --hash=sha256:83e35b57523816c8613fd0776b40cd8bb9f596b37ddd2692eb4a6bb5ab2f8c93 \ + --hash=sha256:842e7b070435622248c7a2c44ae53fa1440e073cc3023bc919fed570884097a7 \ + --hash=sha256:847927daf4cffbd4e90e42bc890069897101edd015f956cb8721b3473372edda \ + --hash=sha256:882076c00c0a608b131187055ddc5ae29f2e7eaf870d6168980420d58528a5c8 \ + --hash=sha256:8b95977e7211527ab0ba576e286d023389fbeeb32a6b7b771665d333c60e5342 \ + --hash=sha256:8bb68f03f395eb793220b45c097bd4d8c32944393da0fad8b999efac0868fc8c \ + --hash=sha256:8c2642a7603ec0b16ed77da4555db3b4b472341904873788327c0b0d7b95f1bb \ + --hash=sha256:8c3d1e9c15b9d51ca0391e13da1a25a0a4df3c58a37c9dc368e0736cf7f69df0 \ + --hash=sha256:8c6e5a2f750cc71c3e3b11d71661f21d6f9bc6cebc6564b1466417a1ec03ec77 \ + --hash=sha256:8d2294a31386bfa251d8c8a39472beee17db67d4f1a6eabea665d35c9a4461c3 \ + --hash=sha256:8e4320744c1ffdd95a603def63344bfab2d33edeab301c5007e7de9f9f5b3885 \ + --hash=sha256:8e65860d238379ed982fd9ba690579b5e95af2f4840f99c772816dbe573cb826 \ + --hash=sha256:8f2e5c5ee828d42cb11760761c0af6507927bec42d0ad5458f97c9203b054617 \ + --hash=sha256:900a67df3fd1660b035a4761c4ce73c382ea6b35f90f9863c36c6fd8bf8b09bb \ + --hash=sha256:913ca42ccad3f8cc6e292b587ae8ae49c8c823e5dce51a736252fc7c7cdfa577 \ + --hash=sha256:9250a9a0a6fd4648b3f868da8d91a4c52b5811a62df58e753d50ae4454a36f80 \ + --hash=sha256:931908d9fc855d8f74783377822be318edb6dcb19e47169dc038f9a1bf60b06e \ + --hash=sha256:9826217f048f620d9a712672818bf231442c1b35d96b227a07eabd11b4bb6945 \ + --hash=sha256:9891e594296ab9dada6551c8e7b387b2721f27a67eecd528412e8906247a7b90 \ + --hash=sha256:9c1255b302953c86a486b81d330d5ee1d5bd937691ce271b6be0ef0e299eaab7 \ + --hash=sha256:a0811d33247c3d6128a3001d763f2aa056bb3425204335400ac54f89eec3a0d0 \ + --hash=sha256:a136d453475ac0fcbda502ef1e6504bd28d6d904700915d278deeab0d00fe140 \ + --hash=sha256:a214c993455f99a89aaeadc9b21241900037adc9d97203e374d75513c5911822 \ + --hash=sha256:a3086b538543802f84c843911242db20447de00d8752dd0efc936dbcf02218ba \ + --hash=sha256:a3450b693fde92133e9f51060568a4c31fcca76d5e53bbd611e689ca446517e9 \ + --hash=sha256:a550fb4950a06dde3beb4721f5ad4b25bf4513784665b0a8522c792e2bd822a4 \ + --hash=sha256:a9f4645593036b81bbdb36b9c8e0ea0d1c3fee968c4d59db0344c14087ef143a \ + --hash=sha256:aca6c1ef08a82bfe327cc156da694660f599923e2e6665b6d81c9c2d0ac9ffc8 \ + --hash=sha256:acac386b453c2516111b50985d60ce46e7fadb5ea71ae7b25f4c946935bf27cf \ + --hash=sha256:acc992ab27b15f852c76755eb2ab7dce86585ddadba6fa5946e58556088845b4 \ + --hash=sha256:ae3d4fe8c0b9213624fdce7279d70e3b148b682ca20719ebd193a23ebfa47324 \ + --hash=sha256:ae50181a047c871561212bb97f7932a2d45fb53e947bd9b57ebad85b529cbc53 \ + --hash=sha256:ae6dd8f10bd17aad820876d24caec9efdafd80a318d16c0a48edb5e136902c6b \ + --hash=sha256:af05d726809bff6b141be124d4c7ce998f9c9c7f30edb1f46c07aa103d540b41 \ + --hash=sha256:afd70d95892096cdb26f15a00c45907b17817577aa8d1c76b2dcc2788391f9e9 \ + --hash=sha256:b5c2dc92304aa48a4a60443b548bb12f12e119d4b72f314015e67b9e1be97fca \ + --hash=sha256:bc0011654b91cc4fb2ae701bec0a0ba1e552c0714247fa7af6c59e0ccfa3a4e1 \ + --hash=sha256:bcfbcf66006befb9fd2aeaa9e01feaf881b4dc330a02ba07d2322b1c11be7b5d \ + --hash=sha256:bdbd97738551fca3917c1bd7188bec1920bb520104f28e7e1007f9ceb17b7690 \ + --hash=sha256:c60924535c75f1566b6eb75b5c31a48a43fef04fa2d0d201acbad8a9969c6107 \ + --hash=sha256:c7b9a2f8f4d8e90af72571d3d495deebdd7e3c75451f5b41719aee166e940fc2 \ + --hash=sha256:ca6546b66be9dc4738b1b043d5ebd5488c66c578c5ff0fd0e8065313fe3afb76 \ + --hash=sha256:ccffae9a092a00deb7efd545fe5e2c33c33b88e7c054337e9a74c179347d0b7d \ + --hash=sha256:cdc7e35386f3847df728fbcb5e887e2d79c19e2fa1eba9e51b6621d23e3243af \ + --hash=sha256:d15fde0e6fb0d88a60d221204873743e5d9f0b7d29165e62cd86d0413ad74ba6 \ + --hash=sha256:d34c20167764fbcf927194d532dd7e0c56772f0a5f943fa5ef9e9afbba8fb9db \ + --hash=sha256:d483fe17f01ad64b7bf7cc38fcefff1ca9fb83f8c2b2542b68f97ffe0611b369 \ + --hash=sha256:d7469697dce35be237db177d42e2a2ee26e6dcc5fc052078a6fefabd288c6edd \ + --hash=sha256:db08f45aecde626498fb3df07bcf6d2ec040af42e859a4f5040d79c200342911 \ + --hash=sha256:dc319e5a1de4b6913aac94bf6a2f9e847371e0a140a43dd4991db1a09bc2d504 \ + --hash=sha256:de3eceba0b683bcbb1ab93da016d0270df1f9ae7be716b40214c5dafac6ea45a \ + --hash=sha256:dfcc8b909769d19db55c7cc9541eb64b9b774b1057ffffb4f1048070475bb9f9 \ + --hash=sha256:e059c5dde6452b44424bd1834557556c226b57781dee1227af23518459722b13 \ + --hash=sha256:e4316bf32babbed84e691e352faf967ce2f0f024174a8643c37c94a1080374fc \ + --hash=sha256:e52655eaf81e32593abedaa4bfe33170c8cfedf3365ed9be6e11e07f148f0278 \ + --hash=sha256:e55d236be29255554da47abe5c577637db7c24a02b8b46f0ca9524c855801868 \ + --hash=sha256:ea7bb13b7c9a29791f87a0387ba7d3ad3a6d783d827e4d3f27b40a0ff44495e2 \ + --hash=sha256:ea964164cc9afa72d4d9b23cc28dafae93693c0a53e0b42acbff15b22c3f9ddd \ + --hash=sha256:ec829541c45bca16e61c7ae50c20501f213605beb75d1aba91a6ee37fbbb56a4 \ + --hash=sha256:ecabd69db66de867690f9797f2f8fa27ba501bbc24540cbdbdc649cd15888ba6 \ + --hash=sha256:ed0c1e5d10cdc7135537988c74a0188da68e2f3c30813ba3744ab1e42e0480f9 \ + --hash=sha256:f0840b5b17057f7fd918b76183a4b5a0635f43e14eb2ce60dce1d4ee4707ea00 \ + --hash=sha256:f4d78253f6996be4901669ad25319f842f740eccf4d58e3c7f3dd39e6dde1d8f \ + --hash=sha256:f56f1695bc5c0871cbc33dc0130fcf503aab0c57dcc5a6700a4f49eba4f2652e \ + --hash=sha256:f826877d462181e5eb1c26a0026b8d0cab05d99844ecb6d8bf3627a2ca0c0442 \ + --hash=sha256:f8f23ead891a3b762f35ab3b04623da7056545b48aa60d59957e6789914545da \ + --hash=sha256:f90938e92afda60266da758ee7d363447f7f0138c9559f9e1811629580582d90 \ + --hash=sha256:faa679d19a6696fd54259ad321251ad77a13e70e03dd834daa762a44fb6196ef + # via + # jsonschema + # referencing +scipy==1.17.1 ; python_full_version < '3.12' \ + --hash=sha256:010f4333c96c9bb1a4516269e33cb5917b08ef2166d5556ca2fd9f082a9e6ea0 \ + --hash=sha256:02ae3b274fde71c5e92ac4d54bc06c42d80e399fec704383dcd99b301df37458 \ + --hash=sha256:08b900519463543aa604a06bec02461558a6e1cef8fdbb8098f77a48a83c8118 \ + --hash=sha256:131f5aaea57602008f9822e2115029b55d4b5f7c070287699fe45c661d051e39 \ + --hash=sha256:158dd96d2207e21c966063e1635b1063cd7787b627b6f07305315dd73d9c679e \ + --hash=sha256:1cc682cea2ae55524432f3cdff9e9a3be743d52a7443d0cba9017c23c87ae2f6 \ + --hash=sha256:1f95b894f13729334fb990162e911c9e5dc1ab390c58aa6cbecb389c5b5e28ec \ + --hash=sha256:200e1050faffacc162be6a486a984a0497866ec54149a01270adc8a59b7c7d21 \ + --hash=sha256:2040ad4d1795a0ae89bfc7e8429677f365d45aa9fd5e4587cf1ea737f927b4a1 \ + --hash=sha256:2b64ca7d4aee0102a97f3ba22124052b4bd2152522355073580bf4845e2550b6 \ + --hash=sha256:2ceb2d3e01c5f1d83c4189737a42d9cb2fc38a6eeed225e7515eef71ad301dce \ + --hash=sha256:35c3a56d2ef83efc372eaec584314bd0ef2e2f0d2adb21c55e6ad5b344c0dcb8 \ + --hash=sha256:37425bc9175607b0268f493d79a292c39f9d001a357bebb6b88fdfaff13f6448 \ + --hash=sha256:3877ac408e14da24a6196de0ddcace62092bfc12a83823e92e49e40747e52c19 \ + --hash=sha256:3fd1fcdab3ea951b610dc4cef356d416d5802991e7e32b5254828d342f7b7e0b \ + --hash=sha256:41b71f4a3a4cab9d366cd9065b288efc4d4f3c0b37a91a8e0947fb5bd7f31d87 \ + --hash=sha256:43af8d1f3bea642559019edfe64e9b11192a8978efbd1539d7bc2aaa23d92de4 \ + --hash=sha256:45abad819184f07240d8a696117a7aacd39787af9e0b719d00285549ed19a1e9 \ + --hash=sha256:4b400bdc6f79fa02a4d86640310dde87a21fba0c979efff5248908c6f15fad1b \ + --hash=sha256:4eb6c25dd62ee8d5edf68a8e1c171dd71c292fdae95d8aeb3dd7d7de4c364082 \ + --hash=sha256:581b2264fc0aa555f3f435a5944da7504ea3a065d7029ad60e7c3d1ae09c5464 \ + --hash=sha256:5cf36e801231b6a2059bf354720274b7558746f3b1a4efb43fcf557ccd484a87 \ + --hash=sha256:5e3c5c011904115f88a39308379c17f91546f77c1667cea98739fe0fccea804c \ + --hash=sha256:6609bc224e9568f65064cfa72edc0f24ee6655b47575954ec6339534b2798369 \ + --hash=sha256:6e3dcd57ab780c741fde8dc68619de988b966db759a3c3152e8e9142c26295ad \ + --hash=sha256:6fac755ca3d2c3edcb22f479fceaa241704111414831ddd3bc6056e18516892f \ + --hash=sha256:744b2bf3640d907b79f3fd7874efe432d1cf171ee721243e350f55234b4cec4c \ + --hash=sha256:74cbb80d93260fe2ffa334efa24cb8f2f0f622a9b9febf8b483c0b865bfb3475 \ + --hash=sha256:766e0dc5a616d026a3a1cffa379af959671729083882f50307e18175797b3dfd \ + --hash=sha256:7bdf2da170b67fdf10bca777614b1c7d96ae3ca5794fd9587dce41eb2966e866 \ + --hash=sha256:7ff200bf9d24f2e4d5dc6ee8c3ac64d739d3a89e2326ba68aaf6c4a2b838fd7d \ + --hash=sha256:844e165636711ef41f80b4103ed234181646b98a53c8f05da12ca5ca289134f6 \ + --hash=sha256:8a604bae87c6195d8b1045eddece0514d041604b14f2727bbc2b3020172045eb \ + --hash=sha256:94055a11dfebe37c656e70317e1996dc197e1a15bbcc351bcdd4610e128fe1ca \ + --hash=sha256:95d8e012d8cb8816c226aef832200b1d45109ed4464303e997c5b13122b297c0 \ + --hash=sha256:9cdc1a2fcfd5c52cfb3045feb399f7b3ce822abdde3a193a6b9a60b3cb5854ca \ + --hash=sha256:9ecb4efb1cd6e8c4afea0daa91a87fbddbce1b99d2895d151596716c0b2e859d \ + --hash=sha256:a3472cfbca0a54177d0faa68f697d8ba4c80bbdc19908c3465556d9f7efce9ee \ + --hash=sha256:a4328d245944d09fd639771de275701ccadf5f781ba0ff092ad141e017eccda4 \ + --hash=sha256:a48a72c77a310327f6a3a920092fa2b8fd03d7deaa60f093038f22d98e096717 \ + --hash=sha256:a720477885a9d2411f94a93d16f9d89bad0f28ca23c3f8daa521e2dcc3f44d49 \ + --hash=sha256:a77cbd07b940d326d39a1d1b37817e2ee4d79cb30e7338f3d0cddffae70fcaa2 \ + --hash=sha256:a9956e4d4f4a301ebf6cde39850333a6b6110799d470dbbb1e25326ac447f52a \ + --hash=sha256:adb2642e060a6549c343603a3851ba76ef0b74cc8c079a9a58121c7ec9fe2350 \ + --hash=sha256:beeda3d4ae615106d7094f7e7cef6218392e4465cc95d25f900bebabfded0950 \ + --hash=sha256:c80be5ede8f3f8eded4eff73cc99a25c388ce98e555b17d31da05287015ffa5b \ + --hash=sha256:cc90d2e9c7e5c7f1a482c9875007c095c3194b1cfedca3c2f3291cdc2bc7c086 \ + --hash=sha256:cd96a1898c0a47be4520327e01f874acfd61fb48a9420f8aa9f6483412ffa444 \ + --hash=sha256:d2650c1fb97e184d12d8ba010493ee7b322864f7d3d00d3f9bb97d9c21de4068 \ + --hash=sha256:d30e57c72013c2a4fe441c2fcb8e77b14e152ad48b5464858e07e2ad9fbfceff \ + --hash=sha256:d59c30000a16d8edc7e64152e30220bfbd724c9bbb08368c054e24c651314f0a \ + --hash=sha256:dbc12c9f3d185f5c737d801da555fb74b3dcfa1a50b66a1a93e09190f41fab50 \ + --hash=sha256:e18f12c6b0bc5a592ed23d3f7b891f68fd7f8241d69b7883769eb5d5dfb52696 \ + --hash=sha256:e19ebea31758fac5893a2ac360fedd00116cbb7628e650842a6691ba7ca28a21 \ + --hash=sha256:e30bdeaa5deed6bc27b4cc490823cd0347d7dae09119b8803ae576ea0ce52e4c \ + --hash=sha256:eb092099205ef62cd1782b006658db09e2fed75bffcae7cc0d44052d8aa0f484 \ + --hash=sha256:eee2cfda04c00a857206a4330f0c5e3e56535494e30ca445eb19ec624ae75118 \ + --hash=sha256:f4115102802df98b2b0db3cce5cb9b92572633a1197c77b7553e5203f284a5b3 \ + --hash=sha256:f590cd684941912d10becc07325a3eeb77886fe981415660d9265c4c418d0bea \ + --hash=sha256:f8885db0bc2bffa59d5c1b72fad7a6a92d3e80e7257f967dd81abb553a90d293 \ + --hash=sha256:fcb310ddb270a06114bb64bbe53c94926b943f5b7f0842194d585c65eb4edd76 + # via trendify +scipy==1.18.0 ; python_full_version >= '3.12' \ + --hash=sha256:09143f676d157d9f546d663504ef9c1becb819824f1afc018814176411942446 \ + --hash=sha256:0d13bca67c096d89fb95ced0d8921807300fce0275643aef9533cc63a0773468 \ + --hash=sha256:18e9575f1569b2c54174e6159d32942e03731177f63dce7975f0a0c88d102f5b \ + --hash=sha256:1a4441f15d620578772a49e5ab48c0ee1f7a0220e387110283062729136b2553 \ + --hash=sha256:1ad44305cfa24b1ba5803cbbebf033590ccbac1aa5d612d727b785325ab408b0 \ + --hash=sha256:1afac4a847207c7ff8efd321734a50b06d0280b3b2a2c0fc2f413101747ad7c7 \ + --hash=sha256:1f55797419e16e7f30cf88ffb3113ce0467f00cfe3f70d5c281730b21769bfc2 \ + --hash=sha256:265915e79107de9f946b855e50d7470d5893ec3f54b342e1aa6201cbdcd8bb6b \ + --hash=sha256:2d8bbdc6c817f5b4006a54d799d4f5bab6f910193cbb9a1ff310833d4d270f61 \ + --hash=sha256:2ef3abc54a4ffc53765374b0d5728532dfdd2585ed23f6b11c206a1f0b1b9af8 \ + --hash=sha256:368e0a705903c466aa5f08eefb39e6b1b6b2d659e7352a31fd9e2438365be0f8 \ + --hash=sha256:3f1ac564d3bf6c03d861d2cd87a1bea0da2887136f7fb1bf519c05a8971452d6 \ + --hash=sha256:40395a5fcd1abee49a5c7aaa98c29db393eedc835138560a588c47ec16156690 \ + --hash=sha256:4a55985d54c769c872e64b7f4c8a81cc30ef700cc04296abbbf3705439c126de \ + --hash=sha256:4c256ee70c0d1a8a2ace807e199ccd4e3f57037433842abb3fb36bc17eaa9578 \ + --hash=sha256:52a96e21517c7292375c0e27dd796a811f03fcea5fd4d108fdfea8145dcf17ab \ + --hash=sha256:56abf29a7c067dde59be8b9a22d606a4ea1b2f2a4b756d9d903c62818f5dacce \ + --hash=sha256:5aba46108853ddfc77906b6557aac839d2b52e900c1d72a1180adaaab58d265f \ + --hash=sha256:5efe260f69417b97ddae455bfb5a95e8359f7f66ad7fa9522a60feb66f169520 \ + --hash=sha256:67b2ad2ad54c72ca6d04975a9b2df8c3638c34ddd5b28738e94fc2b57929d378 \ + --hash=sha256:68363b7eaacd8b5dd426df56d782cc156468ac79a127a1b87ca597d6e2e82197 \ + --hash=sha256:6aa94e78ec192a30063a5e72e561c28af769dc311190b24fe91774eff1969709 \ + --hash=sha256:71ccc8faa2dd16ac310233203474a8b5cb67f10dedd54a3116d34943f4b19132 \ + --hash=sha256:7a7f3b01647384dbc3a711e8c6778e0aabbe93959249fef5c7393396bcac0867 \ + --hash=sha256:7bd21faaf5a1a3b2eff922d02db5f191b99a6518db9078a8fb23169f6d22259a \ + --hash=sha256:7c7a51b33ce387193c97f228320cf8e87361daa1bba750638677729598b3e677 \ + --hash=sha256:84031d7b052a54fae2f8632e0ec802073d385476eb9a63079bce6e23ef9283d4 \ + --hash=sha256:8ca01e8ae69f1b18e9a58d91afead31be3cef0dd905a10249dac559ee15460a0 \ + --hash=sha256:945c1761b93f38d7f99ae81ae80c63e621471608c7eeead563f6df025585cd58 \ + --hash=sha256:97b6cddaaee0a779ef6b5ca83c9604b27cc16b2b8fc22c142652df8793319fb8 \ + --hash=sha256:9aac6192fac56bf2ca534389d24623f07b39ff83317d58287285e7fbd622ff76 \ + --hash=sha256:9ab7b758be6940954a713ee466e2043e9f6e2ed965c1fce5c91039f4be3d90a9 \ + --hash=sha256:a46f9273dbd0eb1cefba61c9b8648b4dfe3cbc14a080176f9a73e44b8336dc7f \ + --hash=sha256:ad033410e2e0672ffdc1042110cef20e1c46f8fd0616cee1d44d8d58fad8fc11 \ + --hash=sha256:b6f758e35f12757b5d95c00bc6de2438e229c2664b7a92e96f205959d9f2dfa4 \ + --hash=sha256:c5557d8be5da8e41353fcd4d21491fdbab83b062fc579e94dc09a7c8ab4f669b \ + --hash=sha256:c5dbddf60e58c2312316d097271a8e73d40eaf2eabfa4d95ed7d3695bbf2ce7b \ + --hash=sha256:d88363fd9d8fbd3511bd273f1a49efb2a540773ddf92a91d57498ce7dd7f3e76 \ + --hash=sha256:e40baea28ae7f5475c779741e2d90b1247c78531207b49c7030e698ff81cee3f \ + --hash=sha256:f2a6af57bd9e4a75d70e4117e78a1bbee84f79ae3fbb6d0111005d6ebcc4cb8d \ + --hash=sha256:f351e0dd702687d12a402b867a1b4146a256923e1c38317cbc472f6372b94707 + # via trendify +selectolax==0.4.10 \ + --hash=sha256:01c1354b158f8c87b72ab50a12b4b6d7b276150ded39210d1078d65d1e24ae0d \ + --hash=sha256:16055f712bd93507ce61ecac156bb7acf96b2e46c4d4d30c616e810f74f4da6e \ + --hash=sha256:1bb589f6ed0f1ec28784c2cd29de111a5b6f8129c3ecf0e71b9665e588667b97 \ + --hash=sha256:1ff2585eaf13ddc5c6614b10a7e47679b0c18a78821495e0c448364f8871592e \ + --hash=sha256:2428f04d2a48ba5f4c182f0d7234a7e38ac799b3358b3063f0ae41f754ee1c4a \ + --hash=sha256:3236fc0fbea9e237ee963274ebae700e68b9d6784bf91e0b3693eda63b393fa2 \ + --hash=sha256:373298d5c2e22a73740ba8f60a5813fd6bdb8fa2c0fd170841341ad0119bdf6b \ + --hash=sha256:3c21749e3d252419581160f3a3e43c1ad95dd0f83a13fe0d8c8fe6256bf7bbe6 \ + --hash=sha256:40babac4aea579edfb32b74acdacdb4c38bacb1ee3d1c4189f9665c52c67586f \ + --hash=sha256:40ecd0ff315dffa4a55840107367b0a54f2cd35be66e700c619364e0cd087ace \ + --hash=sha256:425288d4f5b18cfb0049fd1d6f3bddfba319657b9547b0e2bd24103b4b69435e \ + --hash=sha256:48c238f4f2b4ebd3d94ec260363d2bbab7df2825b592a1f9b12d59a9a9e9ee9a \ + --hash=sha256:4b0e1ad8b3d3d11bc173d33bb0fcf9d3ef8c667f1f3debd31ac8e9e3880ee174 \ + --hash=sha256:4d41b7e95ab0e025053efcba71267af12a6c12bc624e3bdf5dd83c6534f3d696 \ + --hash=sha256:547de570413e22ae5cbc8432d6224dd5a2d5bc66d1a50e34b705e5940d035caa \ + --hash=sha256:5d5b5437ce7548e7bc0b7712114de07dd0e4f94a30b06c968a6344148621df50 \ + --hash=sha256:6517b40e41ae5cc7756f92d88c59f178eb4a3683c7ce39c66bd5617587f628e5 \ + --hash=sha256:67f826152635521e1751665e315f3be82d027fe79d6446bf8434e0f7069e55db \ + --hash=sha256:68e1ef717b47f5cdcd1b151b2176d7184c38cb3772f8964509979840575203ef \ + --hash=sha256:746838a34df35ceff5f6154991b09aa6c27f044dc1fe9a4137f8c57e3e9f1203 \ + --hash=sha256:794a10f0c2cb9662ad85c5a970a72ca072057d68f7f0b3cf7b3230e5f2a8f221 \ + --hash=sha256:7c596b424c55ae87003140f55e6aa6f88e060b645781fa69e947ef60691b2bde \ + --hash=sha256:88475ef523fe5426d113e8319eeb741806a51cc025840f337661734c65cf1aa4 \ + --hash=sha256:89764b4d1e32d38e635dfb270a639fc707af4315b863fd161357a517321e5046 \ + --hash=sha256:8b57c64690e3c86b5d07386e2e598f4d3b4990144b1d5717db7038d0b675a97e \ + --hash=sha256:8c74168deeb116a1271e74e7a157e329930b7f54e328b323ce00bf7089f39dc9 \ + --hash=sha256:975bf1199c80965307168a05c4d88312a62b8151ac27216ed1248fbd175695bb \ + --hash=sha256:98e41ac4a761c5d4589ce9c40866a80fa2c5d413ac6b3af9546b43a2b3a8da18 \ + --hash=sha256:98f93b92d23a8feb88efc7c8e692221456bae30dc551d86d376931491152b909 \ + --hash=sha256:9c4c9afbd28b81892806e9699ecd323656e1eff7318c1245e80cd6bb78566a99 \ + --hash=sha256:a5c80477a3a93f0ee350d832c6fc764cd2df1299914a816bcd5ed4f0c9701b9b \ + --hash=sha256:a95f67ed9d5947562e9268332cc7165660c7db0cd3faea959e13b6901b4d323f \ + --hash=sha256:a96d59ba2a8ba01e4f732913816f5684d30d0646b7cd0fa17377fc9d1032cc0d \ + --hash=sha256:ab07fc342cf477c0320d22fac52917b824871caf5ab177a0fd94377a901ab657 \ + --hash=sha256:ab8f95b196b2dfb2be3ea7274673d45bb251ec2e16a0e7a3c1fc21c1c20d0722 \ + --hash=sha256:b3d199a73894368b83e5d744f64bddf80c22a37c160253a8e81c1a6000be607b \ + --hash=sha256:b43da45acece07f94e4b0d555e073f1a91314c98cb86d10860a1b291bc498976 \ + --hash=sha256:b65ce508ed7f3951a2f36f807494c253d0ebe99d2b18ede149f9a97b99be7d7a \ + --hash=sha256:c1933f53fe7410777b3373b9900010ae8d392a6d245f019d8f011d12f700e389 \ + --hash=sha256:c1bb382bd7009676716814ee81327015ab3d472aff0148fccbd20322d37af83d \ + --hash=sha256:c4b4b7c5d09a20539d369891332a107a869ee170453254048fbc18f893deb4f9 \ + --hash=sha256:d42566a7e649d4e5461e763a241f46542df2613876422e0530bc59999063f36d \ + --hash=sha256:d60cabbf1899916a6389fa36f2908b1a76e00dd044f710ae3dd2d0b14919dfc7 \ + --hash=sha256:db236a92e49b27369a98b9e8ac6c97e1534368d83281d097f3e50c2ba597f112 \ + --hash=sha256:e053064c5e00788a6bcc5427a753e41c058f29e4285bf84a3e663989b7218cdb \ + --hash=sha256:ec333fe02c4b7d8a03c0aa58c7c2265edd3312b1ef309f03efd020f595f6dae1 + # via mkdocs-glightbox +setuptools==82.0.1 \ + --hash=sha256:7d872682c5d01cfde07da7bccc7b65469d3dca203318515ada1de5eda35efbf9 \ + --hash=sha256:a59e362652f08dcd477c78bb6e7bd9d80a7995bc73ce773050228a348ce2e5bb + # via dash +six==1.17.0 \ + --hash=sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274 \ + --hash=sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81 + # via python-dateutil +smmap==5.0.3 \ + --hash=sha256:4d9debb8b99007ae47165abc08670bd74cb74b5227dda7f643eccc4e9eb5642c \ + --hash=sha256:c106e05d5a61449cf6ba9a1e650227ecfb141590d2a98412103ff35d89fc7b2f + # via gitdb +sse-starlette==3.4.5 \ + --hash=sha256:83072538bc211a2f68b7b0422226c4af3e9b62e106e07034664b832ca019842a \ + --hash=sha256:e71bad53323f65573c3864a6c3bd0c1eb6e5f092b2e48082b0c35927d19ca296 + # via mcp +starlette==1.3.1 \ + --hash=sha256:05d0213193f2fbaae60e2ecb593b4add4262ad4e46536b54abe36f11a71724e0 \ + --hash=sha256:c7372aae11c3c3f26a42df7bd626cec2f47d03483d261d369516a615a53714c6 + # via + # mcp + # sse-starlette + # streamlit +streamlit==1.58.0 \ + --hash=sha256:4ca8a7afc5bd16a5f176ccf4be1e34e8121cad0240becd127fb58a103ea3178d \ + --hash=sha256:78a22e7085b053af7ce544442bf4b670771e68c509ba1bdaa056ba0708f49c3d + # via trendify +strenum==0.4.15 \ + --hash=sha256:878fb5ab705442070e4dd1929bb5e2249511c0bcf2b0eeacf3bcd80875c82eff \ + --hash=sha256:a30cda4af7cc6b5bf52c8055bc4bf4b2b6b14a93b574626da33df53cf7740659 + # via trendify +supervisor==4.3.0 \ + --hash=sha256:0bcb763fddafba410f35cbde226aa7f8514b9fb82eb05a0c85f6588d1c13f8db \ + --hash=sha256:4a2bf149adf42997e1bb44b70c43b613275ec9852c3edacca86a9166b27e945e + # via trendify +tenacity==9.1.4 \ + --hash=sha256:6095a360c919085f28c6527de529e76a06ad89b23659fa881ae0649b867a9d55 \ + --hash=sha256:adb31d4c263f2bd041081ab33b498309a57c77f9acf2db65aadf0898179cf93a + # via streamlit +toml==0.10.2 \ + --hash=sha256:806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b \ + --hash=sha256:b3bda1d108d5dd99f4a20d24d9c348e91c4db7ab1b749200bded2f839ccbe68f + # via streamlit +tomli==2.4.1 ; python_full_version <= '3.11' \ + --hash=sha256:01f520d4f53ef97964a240a035ec2a869fe1a37dde002b57ebc4417a27ccd853 \ + --hash=sha256:0d85819802132122da43cb86656f8d1f8c6587d54ae7dcaf30e90533028b49fe \ + --hash=sha256:136443dbd7e1dee43c68ac2694fde36b2849865fa258d39bf822c10e8068eac5 \ + --hash=sha256:1d8591993e228b0c930c4bb0db464bdad97b3289fb981255d6c9a41aedc84b2d \ + --hash=sha256:2190f2e9dd7508d2a90ded5ed369255980a1bcdd58e52f7fe24b8162bf9fedbd \ + --hash=sha256:2c1c351919aca02858f740c6d33adea0c5deea37f9ecca1cc1ef9e884a619d26 \ + --hash=sha256:36d2bd2ad5fb9eaddba5226aa02c8ec3fa4f192631e347b3ed28186d43be6b54 \ + --hash=sha256:3d48a93ee1c9b79c04bb38772ee1b64dcf18ff43085896ea460ca8dec96f35f6 \ + --hash=sha256:47149d5bd38761ac8be13a84864bf0b7b70bc051806bc3669ab1cbc56216b23c \ + --hash=sha256:4ab97e64ccda8756376892c53a72bd1f964e519c77236368527f758fbc36a53a \ + --hash=sha256:4b605484e43cdc43f0954ddae319fb75f04cc10dd80d830540060ee7cd0243cd \ + --hash=sha256:504aa796fe0569bb43171066009ead363de03675276d2d121ac1a4572397870f \ + --hash=sha256:51529d40e3ca50046d7606fa99ce3956a617f9b36380da3b7f0dd3dd28e68cb5 \ + --hash=sha256:52c8ef851d9a240f11a88c003eacb03c31fc1c9c4ec64a99a0f922b93874fda9 \ + --hash=sha256:559db847dc486944896521f68d8190be1c9e719fced785720d2216fe7022b662 \ + --hash=sha256:5a881ab208c0baf688221f8cecc5401bd291d67e38a1ac884d6736cbcd8247e9 \ + --hash=sha256:5cb41aa38891e073ee49d55fbc7839cfdb2bc0e600add13874d048c94aadddd1 \ + --hash=sha256:5e262d41726bc187e69af7825504c933b6794dc3fbd5945e41a79bb14c31f585 \ + --hash=sha256:5ee18d9ebdb417e384b58fe414e8d6af9f4e7a0ae761519fb50f721de398dd4e \ + --hash=sha256:7008df2e7655c495dd12d2a4ad038ff878d4ca4b81fccaf82b714e07eae4402c \ + --hash=sha256:734e20b57ba95624ecf1841e72b53f6e186355e216e5412de414e3c51e5e3c41 \ + --hash=sha256:7c7e1a961a0b2f2472c1ac5b69affa0ae1132c39adcb67aba98568702b9cc23f \ + --hash=sha256:7f86fd587c4ed9dd76f318225e7d9b29cfc5a9d43de44e5754db8d1128487085 \ + --hash=sha256:7f94b27a62cfad8496c8d2513e1a222dd446f095fca8987fceef261225538a15 \ + --hash=sha256:88dceee75c2c63af144e456745e10101eb67361050196b0b6af5d717254dddf7 \ + --hash=sha256:8a650c2dbafa08d42e51ba0b62740dae4ecb9338eefa093aa5c78ceb546fcd5c \ + --hash=sha256:8d65a2fbf9d2f8352685bc1364177ee3923d6baf5e7f43ea4959d7d8bc326a36 \ + --hash=sha256:96481a5786729fd470164b47cdb3e0e58062a496f455ee41b4403be77cb5a076 \ + --hash=sha256:a120733b01c45e9a0c34aeef92bf0cf1d56cfe81ed9d47d562f9ed591a9828ac \ + --hash=sha256:b1d22e6e9387bf4739fbe23bfa80e93f6b0373a7f1b96c6227c32bef95a4d7a8 \ + --hash=sha256:b8c198f8c1805dc42708689ed6864951fd2494f924149d3e4bce7710f8eb5232 \ + --hash=sha256:c2541745709bad0264b7d4705ad453b76ccd191e64aa6f0fc66b69a293a45ece \ + --hash=sha256:c742f741d58a28940ce01d58f0ab2ea3ced8b12402f162f4d534dfe18ba1cd6a \ + --hash=sha256:c7f2c7f2b9ca6bdeef8f0fa897f8e05085923eb091721675170254cbc5b02897 \ + --hash=sha256:d312ef37c91508b0ab2cee7da26ec0b3ed2f03ce12bd87a588d771ae15dcf82d \ + --hash=sha256:d4d8fe59808a54658fcc0160ecfb1b30f9089906c50b23bcb4c69eddc19ec2b4 \ + --hash=sha256:da25dc3563bff5965356133435b757a795a17b17d01dbc0f42fb32447ddfd917 \ + --hash=sha256:eab21f45c7f66c13f2a9e0e1535309cee140182a9cdae1e041d02e47291e8396 \ + --hash=sha256:eb0dc4e38e6a1fd579e5d50369aa2e10acfc9cace504579b2faabb478e76941a \ + --hash=sha256:ec9bfaf3ad2df51ace80688143a6a4ebc09a248f6ff781a9945e51937008fcbc \ + --hash=sha256:ede3e6487c5ef5d28634ba3f31f989030ad6af71edfb0055cbbd14189ff240ba \ + --hash=sha256:f3c6818a1a86dd6dca7ddcaaf76947d5ba31aecc28cb1b67009a5877c9a64f3f \ + --hash=sha256:f758f1b9299d059cc3f6546ae2af89670cb1c4d48ea29c3cacc4fe7de3058257 \ + --hash=sha256:f8f0fc26ec2cc2b965b7a3b87cd19c5c6b8c5e5f436b984e85f486d652285c30 \ + --hash=sha256:fd0409a3653af6c147209d267a0e4243f0ae46b011aa978b1080359fddc9b6cf \ + --hash=sha256:ff18e6a727ee0ab0388507b89d1bc6a22b138d1e2fa56d1ad494586d61d2eae9 \ + --hash=sha256:ff2983983d34813c1aeb0fa89091e76c3a22889ee83ab27c5eeb45100560c049 + # via coverage +trove-classifiers==2026.6.1.19 \ + --hash=sha256:ab4c4ec93cc4a4e7815fa759906e05e6bb3f2fbd92ea0f897288c6a43efd15b3 \ + --hash=sha256:c5132b4b61a829d11cfbd2d72e97f20a45ed6edb95e45c5efdeb5e00836b2745 + # via hatchling +typing-extensions==4.15.0 \ + --hash=sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466 \ + --hash=sha256:f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548 + # via + # altair + # anyio + # dash + # mcp + # pydantic + # pydantic-core + # pytest-asyncio + # referencing + # starlette + # streamlit + # typing-inspection +typing-inspection==0.4.2 \ + --hash=sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7 \ + --hash=sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464 + # via + # mcp + # pydantic + # pydantic-settings +tzdata==2026.2 ; sys_platform == 'emscripten' or sys_platform == 'win32' \ + --hash=sha256:9173fde7d80d9018e02a662e168e5a2d04f87c41ea174b139fbef642eda62d10 \ + --hash=sha256:bbe9af844f658da81a5f95019480da3a89415801f6cc966806612cc7169bffe7 + # via pandas +urllib3==2.7.0 \ + --hash=sha256:231e0ec3b63ceb14667c67be60f2f2c40a518cb38b03af60abc813da26505f4c \ + --hash=sha256:9fb4c81ebbb1ce9531cce37674bbc6f1360472bc18ca9a553ede278ef7276897 + # via requests +uvicorn==0.49.0 \ + --hash=sha256:ba3d14c3ee7e41c6c654c46c9eb489d33213cdd30aa1696eab1374337c13f68f \ + --hash=sha256:ebf4271aa580d9de97f93192d4595176df6e91f9aae919ca73e4fc07df1e66a3 + # via + # mcp + # streamlit +verspec==0.1.0 \ + --hash=sha256:741877d5633cc9464c45a469ae2a31e801e6dbbaa85b9675d481cda100f11c31 \ + --hash=sha256:c4504ca697b2056cdb4bfa7121461f5a0e81809255b41c03dda4ba823637c01e + # via mike +waitress==3.0.2 \ + --hash=sha256:682aaaf2af0c44ada4abfb70ded36393f0e307f4ab9456a215ce0020baefc31f \ + --hash=sha256:c56d67fd6e87c2ee598b76abdd4e96cfad1f24cacdea5078d382b1f9d7b5ed2e + # via trendify +watchdog==6.0.0 \ + --hash=sha256:07df1fdd701c5d4c8e55ef6cf55b8f0120fe1aef7ef39a1c6fc6bc2e606d517a \ + --hash=sha256:20ffe5b202af80ab4266dcd3e91aae72bf2da48c0d33bdb15c66658e685e94e2 \ + --hash=sha256:212ac9b8bf1161dc91bd09c048048a95ca3a4c4f5e5d4a7d1b1a7d5752a7f96f \ + --hash=sha256:2cce7cfc2008eb51feb6aab51251fd79b85d9894e98ba847408f662b3395ca3c \ + --hash=sha256:490ab2ef84f11129844c23fb14ecf30ef3d8a6abafd3754a6f75ca1e6654136c \ + --hash=sha256:6eb11feb5a0d452ee41f824e271ca311a09e250441c262ca2fd7ebcf2461a06c \ + --hash=sha256:6f10cb2d5902447c7d0da897e2c6768bca89174d0c6e1e30abec5421af97a5b0 \ + --hash=sha256:7607498efa04a3542ae3e05e64da8202e58159aa1fa4acddf7678d34a35d4f13 \ + --hash=sha256:76aae96b00ae814b181bb25b1b98076d5fc84e8a53cd8885a318b42b6d3a5134 \ + --hash=sha256:82dc3e3143c7e38ec49d61af98d6558288c415eac98486a5c581726e0737c00e \ + --hash=sha256:9041567ee8953024c83343288ccc458fd0a2d811d6a0fd68c4c22609e3490379 \ + --hash=sha256:9ddf7c82fda3ae8e24decda1338ede66e1c99883db93711d8fb941eaa2d8c282 \ + --hash=sha256:a175f755fc2279e0b7312c0035d52e27211a5bc39719dd529625b1930917345b \ + --hash=sha256:a1914259fa9e1454315171103c6a30961236f508b9b623eae470268bbcc6a22f \ + --hash=sha256:afd0fe1b2270917c5e23c2a65ce50c2a4abb63daafb0d419fde368e272a76b7c \ + --hash=sha256:bdd4e6f14b8b18c334febb9c4425a878a2ac20efd1e0b231978e7b150f92a948 \ + --hash=sha256:c7c15dda13c4eb00d6fb6fc508b3c0ed88b9d5d374056b239c4ad1611125c860 \ + --hash=sha256:cbafb470cf848d93b5d013e2ecb245d4aa1c8fd0504e863ccefa32445359d680 \ + --hash=sha256:e3df4cbb9a450c6d49318f6d14f4bbc80d763fa587ba46ec86f99f9e6876bb26 \ + --hash=sha256:ef810fbf7b781a5a593894e4f439773830bdecb885e6880d957d5b9382a960d2 + # via + # mkdocs + # properdocs + # streamlit +websockets==16.0 \ + --hash=sha256:0298d07ee155e2e9fda5be8a9042200dd2e3bb0b8a38482156576f863a9d457c \ + --hash=sha256:08d7af67b64d29823fed316505a89b86705f2b7981c07848fb5e3ea3020c1abe \ + --hash=sha256:152284a83a00c59b759697b7f9e9cddf4e3c7861dd0d964b472b70f78f89e80e \ + --hash=sha256:1637db62fad1dc833276dded54215f2c7fa46912301a24bd94d45d46a011ceec \ + --hash=sha256:19c4dc84098e523fd63711e563077d39e90ec6702aff4b5d9e344a60cb3c0cb1 \ + --hash=sha256:1c1b30e4f497b0b354057f3467f56244c603a79c0d1dafce1d16c283c25f6e64 \ + --hash=sha256:31a52addea25187bde0797a97d6fc3d2f92b6f72a9370792d65a6e84615ac8a8 \ + --hash=sha256:32da954ffa2814258030e5a57bc73a3635463238e797c7375dc8091327434206 \ + --hash=sha256:3425ac5cf448801335d6fdc7ae1eb22072055417a96cc6b31b3861f455fbc156 \ + --hash=sha256:349f83cd6c9a415428ee1005cadb5c2c56f4389bc06a9af16103c3bc3dcc8b7d \ + --hash=sha256:417b28978cdccab24f46400586d128366313e8a96312e4b9362a4af504f3bbad \ + --hash=sha256:485c49116d0af10ac698623c513c1cc01c9446c058a4e61e3bf6c19dff7335a2 \ + --hash=sha256:4a1aba3340a8dca8db6eb5a7986157f52eb9e436b74813764241981ca4888f03 \ + --hash=sha256:50f23cdd8343b984957e4077839841146f67a3d31ab0d00e6b824e74c5b2f6e8 \ + --hash=sha256:52a0fec0e6c8d9a784c2c78276a48a2bdf099e4ccc2a4cad53b27718dbfd0230 \ + --hash=sha256:52ac480f44d32970d66763115edea932f1c5b1312de36df06d6b219f6741eed8 \ + --hash=sha256:5569417dc80977fc8c2d43a86f78e0a5a22fee17565d78621b6bb264a115d4ea \ + --hash=sha256:569d01a4e7fba956c5ae4fc988f0d4e187900f5497ce46339c996dbf24f17641 \ + --hash=sha256:5a4b4cc550cb665dd8a47f868c8d04c8230f857363ad3c9caf7a0c3bf8c61ca6 \ + --hash=sha256:5f451484aeb5cafee1ccf789b1b66f535409d038c56966d6101740c1614b86c6 \ + --hash=sha256:5f6261a5e56e8d5c42a4497b364ea24d94d9563e8fbd44e78ac40879c60179b5 \ + --hash=sha256:6e5a82b677f8f6f59e8dfc34ec06ca6b5b48bc4fcda346acd093694cc2c24d8f \ + --hash=sha256:71c989cbf3254fbd5e84d3bff31e4da39c43f884e64f2551d14bb3c186230f00 \ + --hash=sha256:781caf5e8eee67f663126490c2f96f40906594cb86b408a703630f95550a8c3e \ + --hash=sha256:7be95cfb0a4dae143eaed2bcba8ac23f4892d8971311f1b06f3c6b78952ee70b \ + --hash=sha256:86890e837d61574c92a97496d590968b23c2ef0aeb8a9bc9421d174cd378ae39 \ + --hash=sha256:878b336ac47938b474c8f982ac2f7266a540adc3fa4ad74ae96fea9823a02cc9 \ + --hash=sha256:8b6e209ffee39ff1b6d0fa7bfef6de950c60dfb91b8fcead17da4ee539121a79 \ + --hash=sha256:8cc451a50f2aee53042ac52d2d053d08bf89bcb31ae799cb4487587661c038a0 \ + --hash=sha256:8d7f0659570eefb578dacde98e24fb60af35350193e4f56e11190787bee77dac \ + --hash=sha256:95724e638f0f9c350bb1c2b0a7ad0e83d9cc0c9259f3ea94e40d7b02a2179ae5 \ + --hash=sha256:9b5aca38b67492ef518a8ab76851862488a478602229112c4b0d58d63a7a4d5c \ + --hash=sha256:a069d734c4a043182729edd3e9f247c3b2a4035415a9172fd0f1b71658a320a8 \ + --hash=sha256:a0b31e0b424cc6b5a04b8838bbaec1688834b2383256688cf47eb97412531da1 \ + --hash=sha256:a35539cacc3febb22b8f4d4a99cc79b104226a756aa7400adc722e83b0d03244 \ + --hash=sha256:a5e18a238a2b2249c9a9235466b90e96ae4795672598a58772dd806edc7ac6d3 \ + --hash=sha256:a653aea902e0324b52f1613332ddf50b00c06fdaf7e92624fbf8c77c78fa5767 \ + --hash=sha256:abf050a199613f64c886ea10f38b47770a65154dc37181bfaff70c160f45315a \ + --hash=sha256:af80d74d4edfa3cb9ed973a0a5ba2b2a549371f8a741e0800cb07becdd20f23d \ + --hash=sha256:b14dc141ed6d2dde437cddb216004bcac6a1df0935d79656387bd41632ba0bbd \ + --hash=sha256:b784ca5de850f4ce93ec85d3269d24d4c82f22b7212023c974c401d4980ebc5e \ + --hash=sha256:bc59589ab64b0022385f429b94697348a6a234e8ce22544e3681b2e9331b5944 \ + --hash=sha256:c0204dc62a89dc9d50d682412c10b3542d748260d743500a85c13cd1ee4bde82 \ + --hash=sha256:c0ee0e63f23914732c6d7e0cce24915c48f3f1512ec1d079ed01fc629dab269d \ + --hash=sha256:caab51a72c51973ca21fa8a18bd8165e1a0183f1ac7066a182ff27107b71e1a4 \ + --hash=sha256:d6297ce39ce5c2e6feb13c1a996a2ded3b6832155fcfc920265c76f24c7cceb5 \ + --hash=sha256:daa3b6ff70a9241cf6c7fc9e949d41232d9d7d26fd3522b1ad2b4d62487e9904 \ + --hash=sha256:e0334872c0a37b606418ac52f6ab9cfd17317ac26365f7f65e203e2d0d0d359f \ + --hash=sha256:e6578ed5b6981005df1860a56e3617f14a6c307e6a71b4fff8c48fdc50f3ed2c \ + --hash=sha256:eaded469f5e5b7294e2bdca0ab06becb6756ea86894a47806456089298813c89 \ + --hash=sha256:f4a32d1bd841d4bcbffdcb3d2ce50c09c3909fbead375ab28d0181af89fd04da \ + --hash=sha256:fd3cb4adb94a2a6e2b7c0d8d05cb94e6f1c81a0cf9dc2694fb65c7e8d94c42e4 + # via streamlit +werkzeug==3.1.8 \ + --hash=sha256:63a77fb8892bf28ebc3178683445222aa500e48ebad5ec77b0ad80f8726b1f50 \ + --hash=sha256:9bad61a4268dac112f1c5cd4630a56ede601b6ed420300677a869083d70a4c44 + # via + # dash + # flask +zipp==4.1.0 \ + --hash=sha256:25ad4e16390cd314347dd8f1de67a2ac538ae658ed4ab9db16029c07c188e97f \ + --hash=sha256:4cb57381f544315db7688e976e922a2b18cdb513d21cc194eb42232ba2a3e602 + # via importlib-metadata diff --git a/src/trendify/cli.py b/src/trendify/cli.py index 5163fdd..b325ebf 100644 --- a/src/trendify/cli.py +++ b/src/trendify/cli.py @@ -14,7 +14,7 @@ import os from pathlib import Path import sys -from typing import List, Iterable +from collections.abc import Iterable import logging import matplotlib.pyplot as plt @@ -25,7 +25,6 @@ from trendify.api import api from trendify.api.base.helpers import DATA_PRODUCTS_FNAME_DEFAULT -from trendify.local_server import TrendifyProductServerLocal from trendify.streamlit import make_streamlit __all__ = [] @@ -120,6 +119,7 @@ def process_argument(arg: str): Returns; (int): Number of processes capped to `5*os.cpu_count()` + """ n_proc = int(arg) @@ -184,6 +184,7 @@ def process_argument(arg: str) -> api.ProductGenerator: Returns: (Callable): User-specified method to be mapped over raw data directories. + """ msplit = arg.split(":") assert 1 <= len(msplit) <= 2 @@ -243,6 +244,7 @@ def process_argument(arg: str) -> str: Returns: (str): String (file name to be used for generated data products) + """ return str(arg) @@ -255,7 +257,7 @@ class InputDirectories: _NAME = "input-directories" @classmethod - def get_from_namespace(cls, namespace: argparse.Namespace) -> List[Path]: + def get_from_namespace(cls, namespace: argparse.Namespace) -> list[Path]: return cls.process_argument(getattr(namespace, cls._NAME.replace("-", "_"))) @classmethod @@ -272,7 +274,7 @@ def add_argument(cls, parser: argparse.ArgumentParser): ) @staticmethod - def process_argument(arg: str) -> List[Path]: + def process_argument(arg: str) -> list[Path]: """ Converts CLI input to list of directories over which user-specified data product generator method will be mapped. @@ -281,6 +283,7 @@ def process_argument(arg: str) -> List[Path]: Returns: (List[Path]): List of directories over which to map the user-specified product generator + """ if isinstance(arg, str): return [ @@ -334,6 +337,7 @@ def process_argument(self, arg: str) -> FileManager: Returns: (FileManager): List of directories over which to map the user-specified product generator + """ return FileManager(output_dir=Path(arg).resolve()) @@ -347,8 +351,8 @@ def trendify(*pargs): Args: *pargs (list[Any]): List of flags and arguments to pass to commandline. Simulates running from commandline in pythong script. - """ + """ # Main parser parser = argparse.ArgumentParser( prog="trendify", From 5c5a3b5e8e45cad2d57db9a04194b9908da3ff4b Mon Sep 17 00:00:00 2001 From: David Gable Date: Fri, 3 Jul 2026 16:21:01 -0700 Subject: [PATCH 03/19] Beginning refactor --- .pre-commit-config.yaml | 14 +- benchmarks/stress_test.py | 139 + pyproject.toml | 42 +- requirements.txt | 1591 +----- rewrite_reference/OVERVIEW.md | 390 ++ rewrite_reference/dump_schemas.py | 48 + rewrite_reference/pydantic_schemas.json | 4479 +++++++++++++++++ src/trendify/__init__.py | 124 +- src/trendify/base/__init__.py | 34 + src/trendify/base/data_product.py | 138 + src/trendify/base/helpers.py | 68 + src/trendify/base/pen.py | 136 + src/trendify/formats/__init__.py | 22 + src/trendify/formats/format2d.py | 152 + src/trendify/formats/table.py | 50 + src/trendify/plotting/__init__.py | 40 + src/trendify/plotting/axline.py | 123 + src/trendify/plotting/figure.py | 339 ++ src/trendify/plotting/histogram.py | 249 + src/trendify/plotting/point.py | 89 + src/trendify/plotting/trace.py | 186 + src/trendify/store/__init__.py | 26 + src/trendify/store/db.py | 109 + src/trendify/store/product_store.py | 300 ++ src/trendify/store/tags.py | 51 + src/trendify/styling/__init__.py | 28 + src/trendify/styling/grid.py | 149 + src/trendify/styling/legend.py | 179 + src/trendify/styling/marker.py | 163 + src/trendify_old/__init__.py | 6 + .../api/__init__.py | 0 src/{trendify => trendify_old}/api/api.py | 0 .../api/base/__init__.py | 0 .../api/base/data_product.py | 2 +- .../api/base/helpers.py | 0 .../api/base/pen.py | 0 .../api/formats/__init__.py | 0 .../api/formats/format2d.py | 0 .../api/formats/table.py | 0 .../api/generator/__init__.py | 0 .../api/generator/data_product_collection.py | 94 +- .../api/generator/data_product_generator.py | 0 .../api/generator/histogrammer.py | 0 .../api/generator/table_builder.py | 2 +- .../api/generator/xy_data_plotter.py | 16 +- .../api/plotting/__init__.py | 0 .../api/plotting/axline.py | 0 .../api/plotting/histogram.py | 4 +- .../api/plotting/plotting.py | 0 .../api/plotting/point.py | 0 .../api/plotting/trace.py | 0 .../api/styling/__init__.py | 0 .../api/styling/grid.py | 0 .../api/styling/legend.py | 0 .../api/styling/marker.py | 0 .../assets/logo.svg | 0 .../assets/logo_white_bg.svg | 0 src/{trendify => trendify_old}/cli.py | 0 src/{trendify => trendify_old}/examples.py | 0 src/{trendify => trendify_old}/gallery.py | 0 .../local_server.py | 0 .../plotly_dashboard.py | 54 +- .../production_server.py | 0 src/{trendify => trendify_old}/server.py | 0 src/{trendify => trendify_old}/streamlit.py | 0 tests/test.sh | 25 - tests/test2.sh | 16 - tests/test_basic.py | 8 - tests/test_product_store.py | 171 + tests/unittests.py | 137 - uv.lock | 2366 +-------- 71 files changed, 8219 insertions(+), 4140 deletions(-) create mode 100644 benchmarks/stress_test.py create mode 100644 rewrite_reference/OVERVIEW.md create mode 100644 rewrite_reference/dump_schemas.py create mode 100644 rewrite_reference/pydantic_schemas.json create mode 100644 src/trendify/base/__init__.py create mode 100644 src/trendify/base/data_product.py create mode 100644 src/trendify/base/helpers.py create mode 100644 src/trendify/base/pen.py create mode 100644 src/trendify/formats/__init__.py create mode 100644 src/trendify/formats/format2d.py create mode 100644 src/trendify/formats/table.py create mode 100644 src/trendify/plotting/__init__.py create mode 100644 src/trendify/plotting/axline.py create mode 100644 src/trendify/plotting/figure.py create mode 100644 src/trendify/plotting/histogram.py create mode 100644 src/trendify/plotting/point.py create mode 100644 src/trendify/plotting/trace.py create mode 100644 src/trendify/store/__init__.py create mode 100644 src/trendify/store/db.py create mode 100644 src/trendify/store/product_store.py create mode 100644 src/trendify/store/tags.py create mode 100644 src/trendify/styling/__init__.py create mode 100644 src/trendify/styling/grid.py create mode 100644 src/trendify/styling/legend.py create mode 100644 src/trendify/styling/marker.py create mode 100644 src/trendify_old/__init__.py rename src/{trendify => trendify_old}/api/__init__.py (100%) rename src/{trendify => trendify_old}/api/api.py (100%) rename src/{trendify => trendify_old}/api/base/__init__.py (100%) rename src/{trendify => trendify_old}/api/base/data_product.py (98%) rename src/{trendify => trendify_old}/api/base/helpers.py (100%) rename src/{trendify => trendify_old}/api/base/pen.py (100%) rename src/{trendify => trendify_old}/api/formats/__init__.py (100%) rename src/{trendify => trendify_old}/api/formats/format2d.py (100%) rename src/{trendify => trendify_old}/api/formats/table.py (100%) rename src/{trendify => trendify_old}/api/generator/__init__.py (100%) rename src/{trendify => trendify_old}/api/generator/data_product_collection.py (93%) rename src/{trendify => trendify_old}/api/generator/data_product_generator.py (100%) rename src/{trendify => trendify_old}/api/generator/histogrammer.py (100%) rename src/{trendify => trendify_old}/api/generator/table_builder.py (98%) rename src/{trendify => trendify_old}/api/generator/xy_data_plotter.py (93%) rename src/{trendify => trendify_old}/api/plotting/__init__.py (100%) rename src/{trendify => trendify_old}/api/plotting/axline.py (100%) rename src/{trendify => trendify_old}/api/plotting/histogram.py (98%) rename src/{trendify => trendify_old}/api/plotting/plotting.py (100%) rename src/{trendify => trendify_old}/api/plotting/point.py (100%) rename src/{trendify => trendify_old}/api/plotting/trace.py (100%) rename src/{trendify => trendify_old}/api/styling/__init__.py (100%) rename src/{trendify => trendify_old}/api/styling/grid.py (100%) rename src/{trendify => trendify_old}/api/styling/legend.py (100%) rename src/{trendify => trendify_old}/api/styling/marker.py (100%) rename src/{trendify => trendify_old}/assets/logo.svg (100%) rename src/{trendify => trendify_old}/assets/logo_white_bg.svg (100%) rename src/{trendify => trendify_old}/cli.py (100%) rename src/{trendify => trendify_old}/examples.py (100%) rename src/{trendify => trendify_old}/gallery.py (100%) rename src/{trendify => trendify_old}/local_server.py (100%) rename src/{trendify => trendify_old}/plotly_dashboard.py (96%) rename src/{trendify => trendify_old}/production_server.py (100%) rename src/{trendify => trendify_old}/server.py (100%) rename src/{trendify => trendify_old}/streamlit.py (100%) delete mode 100644 tests/test.sh delete mode 100755 tests/test2.sh delete mode 100644 tests/test_basic.py create mode 100644 tests/test_product_store.py delete mode 100644 tests/unittests.py diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index afbe37a..560e980 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -4,22 +4,26 @@ repos: hooks: - id: check-added-large-files args: ["--maxkb=500"] + - repo: local + hooks: + - id: mkinit + name: auto-generate __init__.py files + entry: uv run mkinit + args: ["src/trendify", "-w", "--recursive"] + language: system + files: ^(src/trendify.*\.py)$ + pass_filenames: false - repo: https://github.com/astral-sh/ruff-pre-commit - # Ruff version. rev: v0.15.17 hooks: - # Run the linter. - id: ruff-check args: [--fix] - # Run the formatter. - id: ruff-format - repo: https://github.com/astral-sh/uv-pre-commit - # uv version. rev: 0.11.21 hooks: - id: uv-lock - repo: https://github.com/astral-sh/uv-pre-commit - # uv version. rev: 0.11.21 hooks: - id: uv-export diff --git a/benchmarks/stress_test.py b/benchmarks/stress_test.py new file mode 100644 index 0000000..88a6fb5 --- /dev/null +++ b/benchmarks/stress_test.py @@ -0,0 +1,139 @@ +""" +M1 concurrency stress test (the go/no-go gate for skipping the two-phase per-run-file -> merge +design, per the v2 architecture plan). Simulates N worker processes, each writing many runs' +worth of products directly to one shared SQLite `.db` file via `ProductStore.write_run`, and +measures wall-clock throughput plus checks for `SQLITE_BUSY` failures under `busy_timeout`. + +Uses the same synthetic workload shape as `bench_v1.py` (5 tags, ~42 products/run: 40 Point2D + +5 TableEntry + 5 HistogramEntry + 1 multi-tag Trace2D + 1 AxLine) for an apples-to-apples +comparison against v1's generate+sort pipeline. + +Run with the v2 venv: + + PYTHONPATH=src/v2 .venv-v2/bin/python src/v2/benchmarks/stress_test.py \ + --n-runs 10000 --n-procs 8 +""" + +from __future__ import annotations + +import argparse +import json +import time +from concurrent.futures import ProcessPoolExecutor +from pathlib import Path + +from trendify.base.pen import Pen +from trendify.plotting.axline import AxLine, LineOrientation +from trendify.plotting.histogram import HistogramEntry +from trendify.plotting.point import Point2D +from trendify.plotting.trace import Trace2D +from trendify.formats.table import TableEntry +from trendify.store.product_store import ProductStore + +TAGS = ["tag0", "tag1", "tag2", "tag3", "tag4"] + +_store: ProductStore | None = None + + +def _init_worker(db_path: str) -> None: + global _store + _store = ProductStore.open(Path(db_path)) + + +def _build_products(run_id: int): + products = [] + for i, tag in enumerate(TAGS): + for j in range(8): + products.append( + Point2D(tags=[tag], x=float(run_id + i + j), y=float(i * j)) + ) + products.append( + TableEntry(tags=[tag], row=f"r{run_id}", col=f"c{j}", value=float(run_id)) + ) + products.append(HistogramEntry(tags=[tag], value=float(run_id % 17))) + products.append( + Trace2D.from_xy( + tags=[TAGS[0], TAGS[1]], + x=list(range(20)), + y=[float(v * run_id) for v in range(20)], + pen=Pen(label="trace"), + ) + ) + products.append( + AxLine( + tags=[TAGS[2]], + value=1.0, + orientation=LineOrientation.HORIZONTAL, + pen=Pen(), + ) + ) + return products + + +def _write_one_run(run_id: int) -> tuple[int, str | None]: + assert _store is not None + try: + n = _store.write_run(Path(f"/synthetic/run/{run_id}"), _build_products(run_id)) + return n, None + except Exception as e: + return 0, f"{type(e).__name__}: {e}" + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--n-runs", type=int, default=10000) + parser.add_argument("--n-procs", type=int, default=8) + parser.add_argument( + "--db-path", type=str, default="/tmp/trendify_bench_v2/trendify.db" + ) + args = parser.parse_args() + + db_path = Path(args.db_path) + if db_path.parent.exists(): + for f in db_path.parent.glob("trendify.db*"): + f.unlink() + db_path.parent.mkdir(parents=True, exist_ok=True) + + # Bootstrap schema once, up front, from the main process, before workers race to open it. + ProductStore.open(db_path).close() + + t0 = time.perf_counter() + errors = [] + total_written = 0 + with ProcessPoolExecutor( + max_workers=args.n_procs, + initializer=_init_worker, + initargs=(str(db_path),), + ) as executor: + for n, err in executor.map(_write_one_run, range(args.n_runs), chunksize=32): + total_written += n + if err: + errors.append(err) + t1 = time.perf_counter() + + with ProductStore.open(db_path, readonly=True) as store: + (run_count,) = store._conn.execute("SELECT COUNT(*) FROM runs").fetchone() + (product_count,) = store._conn.execute( + "SELECT COUNT(*) FROM products" + ).fetchone() + (tag_row_count,) = store._conn.execute( + "SELECT COUNT(*) FROM product_tags" + ).fetchone() + + result = { + "n_runs": args.n_runs, + "n_procs": args.n_procs, + "write_seconds": t1 - t0, + "products_written_reported": total_written, + "runs_in_db": run_count, + "products_in_db": product_count, + "product_tag_rows_in_db": tag_row_count, + "db_file_size_bytes": db_path.stat().st_size, + "n_errors": len(errors), + "sample_errors": errors[:5], + } + print(json.dumps(result, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/pyproject.toml b/pyproject.toml index 012b0d1..64f50b8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -9,22 +9,14 @@ description = "Tools for generating data products, storing, and interacting with readme = "README.md" requires-python = ">=3.11" dependencies = [ - "dash>=4.3.0", - "filelock>=3.29.4", - "flask>=3.1.3", - "grafana-api>=1.0.3", - "jinja2>=3.1.6", "matplotlib>=3.11.0", "numpy>=2.4.6", "numpydantic>=1.10.0", - "pandas>=3.0.3", "plotly>=6.8.0", + "polars>=1.42.1", "pydantic>=2.13.4", - "scipy>=1.17.1", - "streamlit>=1.58.0", - "strenum>=0.4.15", - "supervisor>=4.3.0", - "waitress>=3.0.2", + "rich>=15.0.0", + "typer>=0.26.8", ] keywords = ["Data", "Products", "Process", "Post", "Database"] @@ -103,7 +95,7 @@ ignore = [ [tool.pyright] stubPath = "typings" -venvPath = ".." +venvPath = "." venv = ".venv" ignore = ["typings"] @@ -112,32 +104,8 @@ exclude_also = ["if __name__ == .__main__.:", "@(abc\\.)?abstractmethod"] [dependency-groups] dev = [ - "black>=26.5.1", - "flake8>=7.3.0", - "griffe-pydantic>=1.3.1", - "hatchling>=1.30.1", - "isort>=8.0.1", - "markdown-callouts>=0.4.0", - "markdown-include>=0.8.1", - "mdx-gh-links>=0.4", - "mike>=2.2.0", - "mkdocs>=1.6.1", - "mkdocs-autorefs>=1.4.4", - "mkdocs-click>=0.9.0", - "mkdocs-exclude>=1.0.2", - "mkdocs-gen-files>=0.6.1", - "mkdocs-glightbox>=0.5.2", - "mkdocs-literate-nav>=0.6.3", - "mkdocs-material>=9.7.6", - "mkdocs-redirects>=1.2.3", - "mkdocs-section-index>=0.3.12", - "mkdocs-snippets>=1.3.2", - "mkdocstrings>=1.0.4", - "mkdocstrings-python>=2.0.5", - "pymdown-extensions>=11.0", + "mkinit>=1.2.0", "pytest>=9.1.1", - "pytest-asyncio>=1.4.0", - "pytest-cov>=7.1.0", ] [project.urls] diff --git a/requirements.txt b/requirements.txt index 9b77c05..0d99763 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,247 +1,20 @@ # This file was autogenerated by uv via the following command: # uv export --frozen --output-file=requirements.txt -e . -altair==6.2.2 \ - --hash=sha256:94014f8ad8617c3cb163d1137359cd6db5ba134b9b46d93cfd8b609fd245a583 \ - --hash=sha256:a1ff9d9cfe81c75414641826312b9471780e19d39293ba0b012933f6b6cba0fe - # via streamlit +annotated-doc==0.0.4 \ + --hash=sha256:571ac1dc6991c450b25a9c2d84a3705e2ae7a53467b5d111c24fa8baabbed320 \ + --hash=sha256:fbcda96e87e9c92ad167c2e53839e57503ecfda18804ea28102353485033faa4 + # via typer annotated-types==0.7.0 \ --hash=sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53 \ --hash=sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89 # via pydantic -anyio==4.14.1 \ - --hash=sha256:4e5533c5b8ff0a24f5d7a176cbe6877129cd183893f66b537f8f227d10527d72 \ - --hash=sha256:8d648a3544c1a700e3ff78615cd679e4c5c3f149904287e73687b2596963629e - # via - # httpx - # mcp - # sse-starlette - # starlette - # streamlit -attrs==26.1.0 \ - --hash=sha256:c647aa4a12dfbad9333ca4e71fe62ddc36f4e63b2d260a37a8b83d2f043ac309 \ - --hash=sha256:d03ceb89cb322a8fd706d4fb91940737b6642aa36998fe130a9bc96c985eff32 - # via - # jsonschema - # referencing -babel==2.18.0 \ - --hash=sha256:b80b99a14bd085fcacfa15c9165f651fbb3406e66cc603abf11c5750937c992d \ - --hash=sha256:e2b422b277c2b9a9630c1d7903c2a00d0830c409c59ac8cae9081c92f1aeba35 - # via mkdocs-material -backrefs==7.0 \ - --hash=sha256:4989bb9e1e99eb23647c7160ed51fb21d0b41b5d200f2d3017da41e023097e82 \ - --hash=sha256:a0fa7360c63509e9e077e174ef4e6d3c21c8db94189b9d957289ae6d794b9475 \ - --hash=sha256:a6448b28180e3ca01134c9cf09dcebafad8531072e09903c5451748a05f24bc9 \ - --hash=sha256:b57cd227ea556b0aed3dc9b8da4628db4eabc0402c6d7fcfc69283a93955f7e9 \ - --hash=sha256:ca42ce6a49ace3d75684dfa9937f3373902a63284ecb385ce36d15e5dcb41c12 \ - --hash=sha256:f2c52955d631b9e1ac4cd56209f0a3a946d592b98e7790e77699339ae01c102a - # via mkdocs-material -black==26.5.1 \ - --hash=sha256:0e48b87e03bf109288e55cfceadcfa15ff5470aca2851a851950ed2926f450d7 \ - --hash=sha256:1037d5ac7b7b310b2632ad867ec8d0e4c4819dcdb0b820f63135da746a24e418 \ - --hash=sha256:1ef92b76f7733f282fd096ea406200b5a286c42947412b0eaff3a74e3616cefe \ - --hash=sha256:1f7ea64ebfa01b50f693508fc39f875e264446d3b097088f84f203b9d09618a0 \ - --hash=sha256:2b36cf2ddf5566e205f6535f782a62194a184d33e175b64ae8c40b1737522be3 \ - --hash=sha256:30d3c14661f2792e9142cce3eeeb1cbc175b3eb5f733be0c8eeb99651e52b0c3 \ - --hash=sha256:32d5ea7f6c8bdfa6e648326ebca1f02b0764e2a029edc6f8dce2627e19d468c3 \ - --hash=sha256:3915f256e75a2d7cf88d8953d37f780455dc586cc72dee059c528fe77f581217 \ - --hash=sha256:4ad6fa01f941920f54f2bbb35f3df7673428a0ef98a0b0840c2eaef3b110efa8 \ - --hash=sha256:4ed7f7da04046d2e488437170797d3b4a4ad83906683bcb7dfc68b673bbce5e2 \ - --hash=sha256:5119fa92ae61f786e8c3662fd60aece1d0a2dd5cca5d0c79417a95e7a4272a59 \ - --hash=sha256:577f21094ea469ef92ec1adaf2c9441a226d2144d01a5be2fa823cecf6543e50 \ - --hash=sha256:5c34b25da232ead53a6f335b76dbea124f4d152ad568b9080d6f944bc2b34b52 \ - --hash=sha256:96ae2c733b2aabdd9986e2c5df628ff3473676cd1c5faded1ff496cf6d74083c \ - --hash=sha256:9d98d4137277c75dfb898ec8d846c4fd68ba1e9cf77f95e2865c203dc18f4c3d \ - --hash=sha256:a1dca32d9f1784af512a13410ec204c6f7f0aa9797a111c42e1c03449821c264 \ - --hash=sha256:dd321f668053961824bcc1be1cc1df748b2d7e4fa28086b08331e577b0100a73 \ - --hash=sha256:e1a26503279b6b310669fb0b219c39e4820b77e8189fe80f522bb511f247db0a \ - --hash=sha256:e88976690a64b0af98312ca958415849cb42423423c5f2ee74af4b49a97a2168 \ - --hash=sha256:ea8d16dc41655aa113cd64665e7219446cd7e4ff2248d7178eaa905190c86b18 \ - --hash=sha256:ecb3e624844c798144e9bd986954e0adc81d8911a1f30f375e1252fe26e8c294 \ - --hash=sha256:ed1a20af114c301a0269bf01163d51dbef72737fd65f850001e7cbe7f3c7abae -blinker==1.9.0 \ - --hash=sha256:b4ce2265a7abece45e7cc896e98dbebe6cead56bcf805a3d23136d145f5445bf \ - --hash=sha256:ba0efaa9080b619ff2f3459d1d500c57bddea4a6b424b60a91141db6fd2f08bc - # via - # flask - # streamlit -cachetools==7.1.4 \ - --hash=sha256:323dc4127934744db5b54eb4924482d7edafbf9554e820d1531c2e08c0e4ef54 \ - --hash=sha256:437f55a4e0c1b01a4f3077cc470e6991d47430970e36fbcb77e2be0df4fc1cd6 - # via streamlit -certifi==2026.6.17 \ - --hash=sha256:024c88eeec92ca068db80f02b8b07c9cef7b9fe261d1d535abfd5abd6f6af432 \ - --hash=sha256:2227dcbaafe0d2f59279d1762ddddc37783ed4354594f194ffc31d20f41fc3db - # via - # httpcore - # httpx - # requests -cffi==2.0.0 ; platform_python_implementation != 'PyPy' \ - --hash=sha256:00bdf7acc5f795150faa6957054fbbca2439db2f775ce831222b66f192f03beb \ - --hash=sha256:07b271772c100085dd28b74fa0cd81c8fb1a3ba18b21e03d7c27f3436a10606b \ - --hash=sha256:087067fa8953339c723661eda6b54bc98c5625757ea62e95eb4898ad5e776e9f \ - --hash=sha256:0a1527a803f0a659de1af2e1fd700213caba79377e27e4693648c2923da066f9 \ - --hash=sha256:12873ca6cb9b0f0d3a0da705d6086fe911591737a59f28b7936bdfed27c0d47c \ - --hash=sha256:19f705ada2530c1167abacb171925dd886168931e0a7b78f5bffcae5c6b5be75 \ - --hash=sha256:1e3a615586f05fc4065a8b22b8152f0c1b00cdbc60596d187c2a74f9e3036e4e \ - --hash=sha256:1fc9ea04857caf665289b7a75923f2c6ed559b8298a1b8c49e59f7dd95c8481e \ - --hash=sha256:203a48d1fb583fc7d78a4c6655692963b860a417c0528492a6bc21f1aaefab25 \ - --hash=sha256:21d1152871b019407d8ac3985f6775c079416c282e431a4da6afe7aefd2bccbe \ - --hash=sha256:24b6f81f1983e6df8db3adc38562c83f7d4a0c36162885ec7f7b77c7dcbec97b \ - --hash=sha256:256f80b80ca3853f90c21b23ee78cd008713787b1b1e93eae9f3d6a7134abd91 \ - --hash=sha256:28a3a209b96630bca57cce802da70c266eb08c6e97e5afd61a75611ee6c64592 \ - 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--hash=sha256:d9b29c1f0ae438d5ee9acb31cadee00a58c46cc9c0b2f9038c6b0b3470877a8c \ - --hash=sha256:d9b97165e8aed9272a6bb17c01e3cc5871a594a446ebedc996e2397a1c1ea8ef \ - --hash=sha256:da68248800ad6320861f129cd9c1bf96ca849a2771a59e0344e88681905916f5 \ - --hash=sha256:da902562c3e9c550df360bfa53c035b2f241fed6d9aef119048073680ace4a18 \ - --hash=sha256:dbd5c7a25a7cb98f5ca55d258b103a2054f859a46ae11aaf23134f9cc0d356ad \ - --hash=sha256:dd4f05f54a52fb558f1ba9f528228066954fee3ebe629fc1660d874d040ae5a3 \ - --hash=sha256:f93fd8e5c8c0a4aa1f424d6173f14a892044054871c771f8566e4008eaa359d2 \ - --hash=sha256:fc33c5141b55ed366cfaad382df24fe7dcbc686de5be719b207bb248e3053dc5 - # via cryptography -charset-normalizer==3.4.7 \ - --hash=sha256:03853ed82eeebbce3c2abfdbc98c96dc205f32a79627688ac9a27370ea61a49c \ - --hash=sha256:08e721811161356f97b4059a9ba7bafb23ea5ee2255402c42881c214e173c6b4 \ - --hash=sha256:0c96c3b819b5c3e9e165495db84d41914d6894d55181d2d108cc1a69bfc9cce0 \ - --hash=sha256:0ea948db76d31190bf08bd371623927ee1339d5f2a0b4b1b4a4439a65298703c \ - 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--hash=sha256:bb6d88045545b26da47aa879dd4a89a71d1dce0f0e549b1abcb31dfe4a8eac49 \ - --hash=sha256:bd6c2a1c7573c64738d716488d2cdd3c00e340e4835707d8fdb8dc1a66ef164e \ - --hash=sha256:c03a41a8784091e67a39648f70c5f97b5b6a37f216896d44d2cdcb82615339a0 \ - --hash=sha256:c35abb8bfff0185efac5878da64c45dafd2b37fb0383add1be155a763c1f083d \ - --hash=sha256:c36c333c39be2dbca264d7803333c896ab8fa7d4d6f0ab7edb7dfd7aea6e98c0 \ - --hash=sha256:c45e9440fb78f8ddabcf714b68f936737a121355bf59f3907f4e17721b9d1aae \ - --hash=sha256:ce3412fbe1e31eb81ea42f4169ed94861c56e643189e1e75f0041f3fe7020abe \ - --hash=sha256:cf1493cd8607bec4d8a7b9b004e699fcf8f9103a9284cc94962cb73d20f9d4a3 \ - --hash=sha256:d4a48e5b3c2a489fae013b7589308a40146ee081f6f509e047e0e096084ceca1 \ - --hash=sha256:d6038d37043bced98a66e68d3aa2b6a35505dc01328cd65217cefe82f25def44 \ - --hash=sha256:d635aab80466bc95771bb78d5370e74d36d1fe31467b6b29b8b57b2a3cd7d22c \ - --hash=sha256:e044c39e41b92c845bc815e5ae4230804e8e7bc29e399b0437d64222d92809dd \ - --hash=sha256:e060d01aec0a910bdccb8be71faf34e7799ce36950f8294c8bf612cba65a2c9e \ - --hash=sha256:e1421b502d83040e6d7fb2fb18dff63957f720da3d77b2fbd3187ceb63755d7b \ - --hash=sha256:e712b419df8ba5e42b226c510472b37bd57b38e897d3eca5e8cfd410a29fa859 \ - --hash=sha256:e74327fb75de8986940def6e8dee4f127cc9752bee7355bb323cc5b2659b6d46 \ - --hash=sha256:e8ac484bf18ce6975760921bb6148041faa8fef0547200386ea0b52b5d27bf7b \ - --hash=sha256:eca9705049ad3c7345d574e3510665cb2cf844c2f2dcfe675332677f081cbd46 \ - --hash=sha256:edac0f1ab77644605be2cbba52e6b7f630731fc42b34cb0f634be1a6eface56a \ - --hash=sha256:effc3f449787117233702311a1b7d8f59cba9ced946ba727bdc329ec69028e24 \ - --hash=sha256:f495a1652cf3fbab2eb0639776dad966c2fb874d79d87ca07f9d5f059b8bd215 \ - --hash=sha256:f496c9c3cc02230093d8330875c4c3cdfc3b73612a5fd921c65d39cbcef08063 \ - --hash=sha256:f59099f9b66f0d7145115e6f80dd8b1d847176df89b234a5a6b3f00437aa0832 \ - --hash=sha256:f59ad4c0e8f6bba240a9bb85504faa1ab438237199d4cce5f622761507b8f6a6 \ - --hash=sha256:fbccdc05410c9ee21bbf16a35f4c1d16123dcdeb8a1d38f33654fa21d0234f79 \ - --hash=sha256:fea24543955a6a729c45a73fe90e08c743f0b3334bbf3201e6c4bc1b0c7fa464 - # via requests -click==8.4.2 \ - --hash=sha256:9a6cea6e60b17ebe0a44c5cc636d94f09bd66142c1cd7d8b4cd731c4917a15f6 \ - --hash=sha256:e6f9f66136c816745b9d65817da91d61d957fb16e02e4dcd0552553c5a197b76 - # via - # black - # flask - # mkdocs - # mkdocs-click - # properdocs - # streamlit - # uvicorn -colorama==0.4.6 \ +colorama==0.4.6 ; sys_platform == 'win32' \ --hash=sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44 \ --hash=sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6 # via - # click - # mkdocs - # mkdocs-material - # properdocs # pytest + # typer contourpy==1.3.3 \ --hash=sha256:023b44101dfe49d7d53932be418477dba359649246075c996866106da069af69 \ --hash=sha256:07ce5ed73ecdc4a03ffe3e1b3e3c1166db35ae7584be76f65dbbe28a7791b0cc \ @@ -316,154 +89,10 @@ contourpy==1.3.3 \ --hash=sha256:fd914713266421b7536de2bfa8181aa8c699432b6763a0ea64195ebe28bff6a9 \ --hash=sha256:fde6c716d51c04b1c25d0b90364d0be954624a0ee9d60e23e850e8d48353d07a # via matplotlib -coverage==7.14.3 \ - --hash=sha256:02c41de2a88011b893050fc9830267d927a50a215f7ad5ec17349db7090ccf26 \ - --hash=sha256:0423d64c013057a06e70f070f073cec4b0cbc7d2b27f3c7007292f2ff1d52965 \ - --hash=sha256:0ee68f5c34812780f3a7063382c0a9fcbb99985b7ddcdcaa626e4f3fb2e0783a \ - --hash=sha256:11a7ec9f97ab950f4c5af62229befc7faf208fdbc0116d3902d7e306cf2c5abd \ - --hash=sha256:16b206e521feb8b7133a45754643dead0538489cf8b783b90cf5f4e3299625fd \ - --hash=sha256:1a7563a443f3d53fdeb040ec8c9f7466aed7ca3dc5891aa09d3ca3625fa4387f \ - --hash=sha256:1bb93c2aa61d2a5b38f1526546d95cf4132cb681e541a337bf8dfd092be816e5 \ - --hash=sha256:1e3b91f9c4740aeb571ecf82e5e8d8e4ab62d34fcb5a5d4e5baa38c6f7d2857c \ - --hash=sha256:2415902f385a23dcc4ccd26e0ba803249a169af6a930c003a4c715eeb9a5444e \ - --hash=sha256:27d07a46500ba23515b838dbcf52512026af04090755cf6cc64166d88c9b9a1a \ - --hash=sha256:2bfc4dd0a912329eccc7484a7d0b2a38032b38c40663b1e1ac595f10c457954b \ - --hash=sha256:2e41fd3aab806770008279a93879b0924b16247e09ab537c043d08bbca53b4ab \ - --hash=sha256:338b19131ab1a6b767b462bfcbaa692e7ae22f24463e39d49b02a83410ff6b37 \ - --hash=sha256:39e1dbbb6ff2c338e0196a482558a792a1de3aa64261196f5cdb3da016ad9cda \ - --hash=sha256:3c68df8e61f1e09633fefc7538297145623957a048534368c9d212782aa5e845 \ - --hash=sha256:3d74ff26299c4879ce3a4d826f9d3d4d556fd285fde7bbce3c0ef5a8ab1cec24 \ - --hash=sha256:3e5b550a128419373c2f6cec28a244207013ef15f5cbcff6a5ca09d1dfaaf027 \ - --hash=sha256:41de778bd41780586e2b04912079c73089ab5d839624e28db3bdb26de638da92 \ - --hash=sha256:47968988b367990ae4ab17523790c38cd125e02c6bfd379b6022be2d40bdc38c \ - --hash=sha256:4b60ca6d8af70473491a15a343cbabab2e8f9ea66a4376e81c7aa24876a6f977 \ - --hash=sha256:4d310baf69a4fbe8a098ce727e4808a34866ac718a6f759ae659cbd3221358bc \ - --hash=sha256:526ce9721116af23b1065089f0b75046fe521e7772ab94b641cd66b7a0421889 \ - --hash=sha256:5952f8c1bda2a5347154450379316e6dfa4d934d62ca35f6784451e6f55074fb \ - --hash=sha256:5d788e5fd55347eef06ca0732c77d04a264de67e8ff24631270cdff3767a60cf \ - --hash=sha256:605ab2b566a22bd94834529d66d295c364aba84afd3e5498285c7a524017b1fc \ - --hash=sha256:611e62cb9386096d81b63e0a05330750268617231e7bd598e1fe77482a2c58a5 \ - --hash=sha256:621e13c6108234d7960aaf5762ab5c3c00f33c30c15af06dcbff0c73bf112727 \ - --hash=sha256:62c7f79db2851c95ef020e5d28b97afde3daf9f7febcd35b53e05638f729063f \ - --hash=sha256:68520c90babfa2d560eca6d497921ed3a4f469623bd709733124491b2aa8ef3f \ - --hash=sha256:69918344541ed9c8368566c2adc03c0e33d4550d7faa87d1b35e49b6a3286ea9 \ - --hash=sha256:6a3693b4153394d265f44fb855fdc80e72403024d4d6f91c4871b334d028e4e0 \ - --hash=sha256:74fdd718d88fe144f4579b8747873a07ec3f04cb837d5faec5a25d9e22fa31a8 \ - --hash=sha256:7b27c822a8161afbe48e99f1adfb098d270ae7e0f7d7b0555ce110529bdb69cc \ - --hash=sha256:7ea52fc08f007bcc494d4bb3df3851e95843d881860ba38fe2c64dc100db5e7d \ - --hash=sha256:8427f370ca67db4c975d2a26acfc0e5783ca0b52444dbc50278ace0f35445949 \ - --hash=sha256:878832eaac515b62decfa76965aed558775f86bf1fc8cca76993c0c84ae31aed \ - --hash=sha256:8ac012839ff7e396030f1e94e10553a431d14e4de2ab65cb3acb72bbd5628ca2 \ - --hash=sha256:8cec0ad652ec57790970d817490105bd917d783c2f7b38d6b58a0ca312e1a336 \ - --hash=sha256:8cf0f2509acb4619e2471a1951089054dd58ebea7a912066d2ea56dd4c24ca4a \ - --hash=sha256:90f7608aeb5d9b60b523b9fb2a4ee1973867cc4865a3f26fe6c7577073b70205 \ - --hash=sha256:92c22e19ce64ca3f2ad751f16f14df1468b4c231bd6af97185063a9c292a0cb3 \ - --hash=sha256:96150a9cf3468ea20f0bc5d0e21b3df8972c31480ef90fa7614b773cc6429665 \ - --hash=sha256:98a0859b0e98e43e1178a9402e19c8127766b14f7109a374d976e5a62c0e5c73 \ - --hash=sha256:9973ef2463f8e6cfb61a6324126bb3e17d67a85f22f58d856e583ea2e3ca6501 \ - --hash=sha256:9be4e7d4c5ca0427889f8f9d614bd630c2be741b1de7699bca3b2b6c0e41003e \ - --hash=sha256:a090cbf9521e78ffdb2fcf448b72902afe9f5923ff6a12d5c0d0120200348af9 \ - --hash=sha256:a2335ea5fed26af2e831094964fa3f8fae60b45f7e37fcc2d3b615b2add3ad87 \ - --hash=sha256:a3c2134809e80fac091bfed18a6991b5a5eb5df5ae32b17ac4f4f99864b73dd7 \ - --hash=sha256:a571bd889cd36c5922ce8e42e059f9d37d02301531d11374afa4c87a578625d5 \ - --hash=sha256:a574912f3bde4b0619f6e97d01aa590b70998859244793769eb3a6df78ee56d3 \ - --hash=sha256:ac082660de8f429ba0ea363595abb838998570b9a7546777c60f413ab902bbde \ - --hash=sha256:b3d77f7f196abdef7e01415de1bce09f216189e83e58159cfeef2b92d0464994 \ - --hash=sha256:b3ff255799f5a1676c71c1c32ec01fd043aa09d57b3d95764b24992757184784 \ - --hash=sha256:b488bd4b23397db62e7a9459129d01ff06a846582a732efd24834b24a6ada498 \ - --hash=sha256:b75ee850fc2d7c831e883220c445b035f2224de2ba6103f1e56dbd237ab913f7 \ - --hash=sha256:b7f300ac92cd4b570724c8ffbbd0c130fee298d2447f41d5a3abf58976fae1de \ - --hash=sha256:beaab199b9e5ceaf5a225e16a9d4df136f2a1eae0a5c20de1e277c8a5225f388 \ - --hash=sha256:c02efd507227bde9969cab0db8f48890eb3b5dcad6afac57a4792df4133543ce \ - --hash=sha256:c66f9f9d4f1e9712eb9b1de5310f881d4e2188cfcba5065e1a8490f38687f2c4 \ - --hash=sha256:c90a7cdd5e380e1ce02f19792e2ac2fbfbf177e35a27e69fd3e873b30d895c0c \ - --hash=sha256:c946099774a7699de03cbd0ff0a64e21aed4525eed9d959adde4afe6d15758ef \ - --hash=sha256:cc96aa922e21d4bc5d5ed3c915cef27dfcbc13686f47d5e378d647fbfba655a2 \ - --hash=sha256:d20a15c622194234161535459affa8f7905830391c9ccfa060d495dbfe3a1c7f \ - --hash=sha256:d8e88f335544a47e22ae2e45b344772925ec65166555c958720d5ed971880891 \ - --hash=sha256:dc9b4e35e7c3920e925ba7f14886fd5fbe481232754624e832ddba66c7535635 \ - --hash=sha256:de76caefc8deabb0dd1678b6a980be97d14c8d87e213ac194dbf8b09e96d63fb \ - --hash=sha256:e343fb086c9cd780b38622fea7c369acd64c1a0724312149b5d769c387a2b1f5 \ - --hash=sha256:e4ed44705ca4bead6fc977a8b741f2145608289b33c8a9b42a95d0f15aedbf4d \ - --hash=sha256:e574801e1d643561594aa021206c46d80b257e9853087090ba97bed8b0a509d3 \ - --hash=sha256:e6230e688c7c3e65cedd41a774eb4ec221adc6bfee13768231015b702d5e4150 \ - --hash=sha256:ea3169c7116eb6cdf7608c6c7da9ecfcb3da40688e3a510fac2d1d2bafd6dc35 \ - --hash=sha256:f0a47095963cfe054e0df178daca95aec21e680d6076da807c3add28dfe920f7 \ - --hash=sha256:f502e948e03e866538048bba081c075caaa62e5bda6ea5b7432e45f587eb462a \ - --hash=sha256:f82b6bb7d75a2613e85d07cefa3a8c973d0544a8993337f6e2728e4a1e94c305 \ - --hash=sha256:fa9e5c6857a7e80fa22ace5cf3550ae392bbfc322f1d8dd2d2d5a8be38cec027 \ - --hash=sha256:fb7e18afb6e903c1a92401a2f0501ac277dca527bb9ca6fe1f691a8a0026a0e8 \ - --hash=sha256:fbb8c3a98e779013786ae01d229662aeacbc77100efbd3f2f245219ace5af700 - # via pytest-cov -cryptography==49.0.0 \ - --hash=sha256:026ac7423e6fa66872d3bf889be5974507da3944f866f704fa200eadacd00001 \ - --hash=sha256:07cab27cc7b7e0fd28e5e26bb9eeedde5c135c868b46de4a27845abe94af6122 \ - --hash=sha256:084ef1af862eb07ec46d25f68689f2102a9fc0e05ce7b80f14f5fe51e4eef0f6 \ - --hash=sha256:0b82e28ee398a386f0807bba7884d30f25218855690f45115831bcce5d90822c \ - --hash=sha256:0e959b578856a3924bc0cbb710fc12c387b9412a951389f3ca61704a9e25f325 \ - --hash=sha256:0f21641cf4b30fca7aee061ced0ec7ad7b073518088b7c9969a297c0ae796c69 \ - --hash=sha256:196ecd6a36e4e9aa10270393bb98d8df88fccee0bf1e5128b91ae4eb4375896d \ - --hash=sha256:2400ef9c9e2299a25614eb1dea3db54a69b1349efd043bfac9c67630d136df36 \ - --hash=sha256:28d8b15e6275f12c8a207dc309dfa957903c927d08d0cc937ee3f63f200693cc \ - --hash=sha256:2afe9051da7ae7bd5905da5a949280c7d2bb75682e188f650a9d0f2756b834c6 \ - --hash=sha256:2eda353d8a27bcbcaa4cbed18994a74ab4d19a2ca897db188ea269ab9b71419b \ - --hash=sha256:32703d93296f5c1f4b53349ad3a250c2cae0fdecd3a3dd5d47e616d8d616af27 \ - --hash=sha256:33cd0565932807baddb67b96dbee92f2c374b5c89dee09fd74079aeb8c8dba61 \ - --hash=sha256:35b151772baff2c74cba7fa290ceaff4c3b11c0c881eb93eb5dbc05a7cfbba18 \ - --hash=sha256:36d1709f992593689b45bda411498d62c6e365f2ca00b84657d4dadd24de16db \ - --hash=sha256:42b0684e0e40cf26122427802486f6d93aea593612603a94fbf260c7eb1e9c1b \ - --hash=sha256:4ae387c9cb68ea569ca17e490d66d8142b81c3cc814bf179974b7d146e490bbb \ - --hash=sha256:53ecee2e23f7169b6117e99fc8a944e5e50f79e69758a83b52a00cb98ab2b2d2 \ - --hash=sha256:66ec79c3904820572d7e987abdf304281f141d37ad9a489b8e97066e7b9b6459 \ - --hash=sha256:67e1d20ad9ef3a563c59ef22e7a8a0b8210bd26604369ea4a30a7c66aefe504e \ - --hash=sha256:6f2debedf9ca60cf1d5bd466475638af5130f89965605cd818484d19987d3a21 \ - --hash=sha256:6fc361c34fb6aac015ce19435876635e5c6d21db31998b0920f675f131e043b8 \ - --hash=sha256:73a205dce83953d131a4aa1e0fd917a2fd1c5b1eef251e9d7152efefcbf5caf7 \ - --hash=sha256:7abcee80084cda3f7691f3eb1ce480d8df49cec637b429aa35986c1de71738aa \ - --hash=sha256:8c25ceb16df5b9435f3f6a9829204985b0e0cbee3b48aacd432c7d2c850b44d9 \ - --hash=sha256:966fe0e9c67490071f14c0d2b1cb2dfb3023c5ce39457343931415f08382f2db \ - --hash=sha256:9e82dcc8e56052715fb18b2429e3bca4823b1629136a2084fc45a9a5cecb9b64 \ - --hash=sha256:b20133d204d2bb56ba047642199603876c872026ca53e79c35b83772ab2cc505 \ - --hash=sha256:b39efa323140595abd3ecca8529d321ae50f55f3aa3ba9cc81ea56a6011953d5 \ - --hash=sha256:b47db11c2c3525083296069b98ac5221907455e989ae0c2e3008bde851921615 \ - --hash=sha256:b87e65d263b3e5d3bb92a57e2a6638e2f31110fa7aa890c7b2dbba42248d0a3f \ - --hash=sha256:b970c6da94d5bb18629db453d14f2a1300f6bf59b61e9b82377931ef95504866 \ - --hash=sha256:be9fcb48a55f023493482827d4f459bd263cc20efde64f204b97c123201850c6 \ - --hash=sha256:c2bc30226390d60ea19d9f82b19db005fe0452154a23c1c410c12ea801e43561 \ - --hash=sha256:c83782480a4a9da4d0feb51950131ba32e12e70813848b3343f6e18c28a66838 \ - --hash=sha256:cbc77da8c523d5abd028635ba850a6966fcee2c82e2bf65a41d1d8afe0f98be9 \ - --hash=sha256:ccac2bfebc306b862133e3bb71f3f6ee8bb525240089b2d952e4144b3a6d5da7 \ - --hash=sha256:d0527ce944105f257f605a827d6ebead966c752038b6e8656abb9c5edee6fc68 \ - --hash=sha256:d8ecde755e2e91bf773fc94e8c9d730cd7f2007004cb492263a794ec3899a1c8 \ - --hash=sha256:e3fb64c420688e5319ae25113a354015abbd8dffbfbc41781a1ea66fc7622ac3 \ - --hash=sha256:e5dfc1e64de5677cec922ffa8da89c546d0415bf6efdf081842e5d44c84e1f0e \ - --hash=sha256:ec5e529fb80935c94fe7b729f9972b50e351a0e6b50aa294fd5cabb109fcc29a \ - --hash=sha256:f37d847238971164fdbc68ade6f6574aecc9c0af714190e2083429ff68f4ce9d \ - --hash=sha256:f78ff2c9ed8dc2d036b0f4d640e22522213d047c1b14e61205a7e55c80a494d4 \ - --hash=sha256:f89660a348f4f78a92366240a61404e337586ef7f5909a2fef59ca88ef505493 \ - --hash=sha256:fc1e275c2f1d97b1a6450b8b0ea3ebfa6e087a611c2b26cb2404d48588abab7b - # via pyjwt cycler==0.12.1 \ --hash=sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30 \ --hash=sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c # via matplotlib -dash==4.3.0 \ - --hash=sha256:1ec96641d5e3c06b7dfffad8241e345533d70327d96f722a8e13b827c287341b \ - --hash=sha256:cbbe79a2bb1a5e46fbbbddc33f9f558419442c2d55cd12526078f489b5801cc0 - # via trendify -filelock==3.29.4 \ - --hash=sha256:10cdb3656fc44541cdf30652a93fb10ec6b05325620eb316bd26893e4201538a \ - --hash=sha256:dac1648087d5115554850d113e7dd8c83ab2d38e3435dde2d4f163847e57b767 - # via trendify -flake8==7.3.0 \ - --hash=sha256:b9696257b9ce8beb888cdbe31cf885c90d31928fe202be0889a7cdafad32f01e \ - --hash=sha256:fe044858146b9fc69b551a4b490d69cf960fcb78ad1edcb84e7fbb1b4a8e3872 -flask==3.1.3 \ - --hash=sha256:0ef0e52b8a9cd932855379197dd8f94047b359ca0a78695144304cb45f87c9eb \ - --hash=sha256:f4bcbefc124291925f1a26446da31a5178f9483862233b23c0c96a20701f670c - # via - # dash - # trendify fonttools==4.63.0 \ --hash=sha256:063e08bd17bd5a90127a14123de0d6a952dbc847695fd98b63c043d58057f90c \ --hash=sha256:0eac00b9118c3c2f87d272e45341871c5b3066baa3c86897fa634a7c3fb59096 \ @@ -508,143 +137,10 @@ fonttools==4.63.0 \ --hash=sha256:ef3048ef05dbb552b89817713d9cac912e00d0fde4a3105c00d29e52e10c89af \ --hash=sha256:fd1e3094f42d806d3d7c79162fc59e5910fcbe3a7360c385b8da969bc4493745 # via matplotlib -ghp-import==2.1.0 \ - --hash=sha256:8337dd7b50877f163d4c0289bc1f1c7f127550241988d568c1db512c4324a619 \ - --hash=sha256:9c535c4c61193c2df8871222567d7fd7e5014d835f97dc7b7439069e2413d343 - # via - # mkdocs - # properdocs -gitdb==4.0.12 \ - --hash=sha256:5ef71f855d191a3326fcfbc0d5da835f26b13fbcba60c32c21091c349ffdb571 \ - --hash=sha256:67073e15955400952c6565cc3e707c554a4eea2e428946f7a4c162fab9bd9bcf - # via gitpython -gitpython==3.1.50 \ - --hash=sha256:80da2d12504d52e1f998772dc5baf6e553f8d2fcfe1fcc226c9d9a2ee3372dcc \ - --hash=sha256:d352abe2908d07355014abdd21ddf798c2a961469239afec4962e9da884858f9 - # via streamlit -grafana-api==1.0.3 \ - --hash=sha256:30caef227025ecdb5662b242aaae921b89b58959566d83a5948523c8138e98ce \ - --hash=sha256:d541ea1a5b7efafa56c5f04053ed42b2d972a6aa313bc4881dd192aeb0706e2e - # via trendify -griffe-pydantic==1.3.1 \ - --hash=sha256:475103794a1d5da3933d3968e82a2bd9d9a1fa507c06d5ee1a39ee1fadcca628 \ - --hash=sha256:f7caedfa0effedb22893bf01cc411fd567614f7b4de7ce0c1f4293eb7acb5c44 -griffelib==2.1.0 \ - --hash=sha256:762a186d2c6fd6794d4ea20d428d597ffb857cb56b66421651cbba15bdd5e813 \ - --hash=sha256:cc7b3d2d2865ad0b909fcc38086e3f554b5ea7acbaa7bbb7ecaa3f5dfb7d9f00 - # via - # griffe-pydantic - # mkdocstrings-python -h11==0.16.0 \ - --hash=sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1 \ - --hash=sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86 - # via - # httpcore - # uvicorn -hatchling==1.30.1 \ - --hash=sha256:161eacafb3c6f91526e92116d21426369f2c36e98c36a864f11a96345ad4ee31 \ - --hash=sha256:eee4fd45357f72ebb3d7a42e5d72cfb5e29ed426d79e8836288926c4258d5f2e -httpcore==1.0.9 \ - --hash=sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55 \ - --hash=sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8 - # via httpx -httptools==0.8.0 \ - --hash=sha256:0770728beb05094c809b98e814edff5fef69d26ad7d21185f2f6d5884a0ba683 \ - --hash=sha256:159e9ab5f701ccd42e555a12f1ad8ff69702910fc1c996cf2bb66e5fcb7a231b \ - --hash=sha256:19d1ee275bb59ba2643ba9a3a1e51cc0c788caf2b8df506368e03f56fdd08527 \ - --hash=sha256:20b4aac66ff65f7db06a375808b78f42a94970aa22e826b3cb2b43eb09174124 \ - --hash=sha256:2a021c3a8e65cc125390d72f59b968afca3bdcaff25bd67965e0a055a14946ca \ - --hash=sha256:2c032fa028f46871ec7e1fc59fc15e8023eab3e6bbe6ece786a1611719a5d081 \ - --hash=sha256:2d689918c15a013c65ef52d9fd495d766893ab831a2c8d89f2ac5940a5df847c \ - --hash=sha256:384c17174464c8e873398b7af24f0b1f44d992c820328413951a625323155d77 \ - --hash=sha256:425f83884fd6343828d8c565f046cb72b6d19063f6924093e11bcd8e1548cd09 \ - --hash=sha256:48774d39cbb70e2b1f71f88852a3087ae1d3a1eb80482bb48c13067ab080c14f \ - --hash=sha256:52dd695b865fe96d9d2b16b64a895f3f57bf3cb064e8383cd3b5713a069e8085 \ - --hash=sha256:57278e6fa0424c42a8a3e454828ab4f0aff27b40cddf9679579b98c6dce6a376 \ - --hash=sha256:5931891fb7b441b8a3853cf1b85c82c903defce084dd5f6771ca46e31bf862c5 \ - --hash=sha256:614ceea8ea606848bece2338ac03b3ce5324bcb4be8dc7d377ed708012fa4db8 \ - --hash=sha256:6a43c9dd399758ccc0531acb0a3c4a6c299ee893ee9400e9c893b7bdcfae0681 \ - --hash=sha256:6b2a32f18d97e16e90827d7a819ffa8dbd8cc245fc4e1fa9d1095b54ef4bd999 \ - --hash=sha256:7685df791fad561384bfb139e77fde27a1ffd93134e016f95a0db424ffbf77b1 \ - --hash=sha256:880490234c10f70a9830743097e8958d6e4b9f5a0ffc24515023afeef984054d \ - --hash=sha256:88bdd940f2b5d487b4d032c6afa5489a7dc4694410d43de3c38c4fb3af0dc45d \ - --hash=sha256:88eead8ec8680a9f146c655bc88445a325bd7921cfd8194c7337e9467282427d \ - --hash=sha256:9518c406d7b310f05adb1a37f80acabac40504a575d7c0da6d3e365c695ac20d \ - --hash=sha256:a1afd7c9fbff0d9f5d489c4ce2768bd09c84a46ddefc7161e6aa82ae35c85745 \ - --hash=sha256:a1b4c8e7a489a0d750d91894e9a8cdc295838f1924c0ca903ae993456fddec07 \ - --hash=sha256:a3b7387147361c3fd47a0bde763c5c91b5b4cd4dc9989b8ece84ff436c99843b \ - --hash=sha256:b15fc622b0f869d19207c4089a501d9bcc63ca5e071ffdd2f03f922df882dcb2 \ - --hash=sha256:bbb8caadb2b742d293169d2b458b5c001ef70e3158704aa3d3ef9597624c5d1d \ - --hash=sha256:c4a9f1707e4823d54dfec6c33fa3697d302aed536ed352a7ebb5a061ddb869d0 \ - --hash=sha256:cd96f29b4bab1d42fa6e3d008711c75e0f79e94e06827330160e3a304227f150 \ - --hash=sha256:d76ad7b951387e3632c8716a9bb03ac5b45c5f16119aa409db0459520887944e \ - --hash=sha256:de1ed58a974e75d56560acc7e7fed01a454994429456f65209789992e41f2568 \ - --hash=sha256:de242a49b5d18e0a8776e654e9f6bf6d89f3875a5c35b425a0e7ce940feb3fd6 \ - --hash=sha256:e93c227b595c6926c1acee96891dd9da4be338cfbe82e5cd3bb9d8dd7dc4ac0b \ - --hash=sha256:eb3028cca2fc0a6d720e52ef61d8ebb62fcbfeb1de56874546d858d3f25a26b7 \ - --hash=sha256:ed377e64805bdba4943c82717333f8f8603a13b09aff9cead2717c6c817fb168 \ - --hash=sha256:ef7c3c97f4311c7be57e2986629df89d49cb434dbff78eafcd48c2bff986b15a \ - --hash=sha256:f256d6ce930c52ca1cb2a960b7da03548c454e7d28b06059ad41bfe789036ce0 - # via streamlit -httpx==0.28.1 \ - --hash=sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc \ - --hash=sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad - # via mcp -httpx-sse==0.4.3 \ - --hash=sha256:0ac1c9fe3c0afad2e0ebb25a934a59f4c7823b60792691f779fad2c5568830fc \ - --hash=sha256:9b1ed0127459a66014aec3c56bebd93da3c1bc8bb6618c8082039a44889a755d - # via mcp -idna==3.18 \ - --hash=sha256:7f952cbe720b688055e3f87de14f5c3e5fdaa8bc3928985c4077ca689de849a2 \ - --hash=sha256:ffb385a7e039654cef1ab9ef32c6fafe283c0c0467bba1d9029738ce4a14a848 - # via - # anyio - # httpx - # requests -importlib-metadata==9.0.0 \ - --hash=sha256:2d21d1cc5a017bd0559e36150c21c830ab1dc304dedd1b7ea85d20f45ef3edd7 \ - --hash=sha256:a4f57ab599e6a2e3016d7595cfd72eb4661a5106e787a95bcc90c7105b831efc - # via dash iniconfig==2.3.0 \ --hash=sha256:c76315c77db068650d49c5b56314774a7804df16fee4402c1f19d6d15d8c4730 \ --hash=sha256:f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12 # via pytest -isort==8.0.1 \ - --hash=sha256:171ac4ff559cdc060bcfff550bc8404a486fee0caab245679c2abe7cb253c78d \ - --hash=sha256:28b89bc70f751b559aeca209e6120393d43fbe2490de0559662be7a9787e3d75 -itsdangerous==2.2.0 \ - --hash=sha256:c6242fc49e35958c8b15141343aa660db5fc54d4f13a1db01a3f5891b98700ef \ - --hash=sha256:e0050c0b7da1eea53ffaf149c0cfbb5c6e2e2b69c4bef22c81fa6eb73e5f6173 - # via - # flask - # streamlit -janus==2.0.0 \ - --hash=sha256:0970f38e0e725400496c834a368a67ee551dc3b5ad0a257e132f5b46f2e77770 \ - --hash=sha256:7e6449d34eab04cd016befbd7d8c0d8acaaaab67cb59e076a69149f9031745f9 - # via dash -jinja2==3.1.6 \ - --hash=sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d \ - --hash=sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67 - # via - # altair - # flask - # mike - # mkdocs - # mkdocs-material - # mkdocstrings - # properdocs - # pydeck - # trendify -jsonschema==4.26.0 \ - --hash=sha256:0c26707e2efad8aa1bfc5b7ce170f3fccc2e4918ff85989ba9ffa9facb2be326 \ - --hash=sha256:d489f15263b8d200f8387e64b4c3a75f06629559fb73deb8fdfb525f2dab50ce - # via - # altair - # mcp -jsonschema-specifications==2025.9.1 \ - --hash=sha256:98802fee3a11ee76ecaca44429fda8a41bff98b00a0f2838151b113f210cc6fe \ - --hash=sha256:b540987f239e745613c7a9176f3edb72b832a4ac465cf02712288397832b5e8d - # via jsonschema kiwisolver==1.5.0 \ --hash=sha256:012b1eb16e28718fa782b5e61dc6f2da1f0792ca73bd05d54de6cb9561665fc9 \ --hash=sha256:01808c6d15f4c3e8559595d6d1fe6411c68e4a3822b4b9972b44473b24f4e679 \ @@ -746,102 +242,10 @@ kiwisolver==1.5.0 \ --hash=sha256:fd40bb9cd0891c4c3cb1ddf83f8bbfa15731a248fdc8162669405451e2724b09 \ --hash=sha256:ff710414307fefa903e0d9bdf300972f892c23477829f49504e59834f4195398 # via matplotlib -markdown==3.10.2 \ - --hash=sha256:994d51325d25ad8aa7ce4ebaec003febcce822c3f8c911e3b17c52f7f589f950 \ - --hash=sha256:e91464b71ae3ee7afd3017d9f358ef0baf158fd9a298db92f1d4761133824c36 - # via - # markdown-callouts - # markdown-include - # mdx-gh-links - # mkdocs - # mkdocs-autorefs - # mkdocs-click - # mkdocs-material - # mkdocstrings - # properdocs - # pymdown-extensions -markdown-callouts==0.4.0 \ - --hash=sha256:7ed2c90486967058a73a547781121983839522d67041ae52c4979616f1b2b746 \ - --hash=sha256:ed0da38f29158d93116a0d0c6ecaf9df90b37e0d989b5337d678ee6e6d6550b7 -markdown-include==0.8.1 \ - --hash=sha256:1d0623e0fc2757c38d35df53752768356162284259d259c486b4ab6285cdbbe3 \ - --hash=sha256:32f0635b9cfef46997b307e2430022852529f7a5b87c0075c504283e7cc7db53 -markupsafe==3.0.3 \ - --hash=sha256:068f375c472b3e7acbe2d5318dea141359e6900156b5b2ba06a30b169086b91a \ - --hash=sha256:0bf2a864d67e76e5c9a34dc26ec616a66b9888e25e7b9460e1c76d3293bd9dbf \ - --hash=sha256:0db14f5dafddbb6d9208827849fad01f1a2609380add406671a26386cdf15a19 \ - --hash=sha256:0eb9ff8191e8498cca014656ae6b8d61f39da5f95b488805da4bb029cccbfbaf \ - --hash=sha256:1085e7fbddd3be5f89cc898938f42c0b3c711fdcb37d75221de2666af647c175 \ - --hash=sha256:116bb52f642a37c115f517494ea5feb03889e04df47eeff5b130b1808ce7c219 \ - --hash=sha256:12c63dfb4a98206f045aa9563db46507995f7ef6d83b2f68eda65c307c6829eb \ - --hash=sha256:133a43e73a802c5562be9bbcd03d090aa5a1fe899db609c29e8c8d815c5f6de6 \ - --hash=sha256:1353ef0c1b138e1907ae78e2f6c63ff67501122006b0f9abad68fda5f4ffc6ab \ - --hash=sha256:1872df69a4de6aead3491198eaf13810b565bdbeec3ae2dc8780f14458ec73ce \ - --hash=sha256:1b4b79e8ebf6b55351f0d91fe80f893b4743f104bff22e90697db1590e47a218 \ - --hash=sha256:1b52b4fb9df4eb9ae465f8d0c228a00624de2334f216f178a995ccdcf82c4634 \ - --hash=sha256:1cc7ea17a6824959616c525620e387f6dd30fec8cb44f649e31712db02123dad \ - --hash=sha256:218551f6df4868a8d527e3062d0fb968682fe92054e89978594c28e642c43a73 \ - --hash=sha256:26a5784ded40c9e318cfc2bdb30fe164bdb8665ded9cd64d500a34fb42067b1c \ - --hash=sha256:2713baf880df847f2bece4230d4d094280f4e67b1e813eec43b4c0e144a34ffe \ - --hash=sha256:32001d6a8fc98c8cb5c947787c5d08b0a50663d139f1305bac5885d98d9b40fa \ - --hash=sha256:3524b778fe5cfb3452a09d31e7b5adefeea8c5be1d43c4f810ba09f2ceb29d37 \ - --hash=sha256:35add3b638a5d900e807944a078b51922212fb3dedb01633a8defc4b01a3c85f \ - --hash=sha256:3a7e8ae81ae39e62a41ec302f972ba6ae23a5c5396c8e60113e9066ef893da0d \ - --hash=sha256:3b562dd9e9ea93f13d53989d23a7e775fdfd1066c33494ff43f5418bc8c58a5c \ - --hash=sha256:457a69a9577064c05a97c41f4e65148652db078a3a509039e64d3467b9e7ef97 \ - --hash=sha256:4bd4cd07944443f5a265608cc6aab442e4f74dff8088b0dfc8238647b8f6ae9a \ - --hash=sha256:4e885a3d1efa2eadc93c894a21770e4bc67899e3543680313b09f139e149ab19 \ - --hash=sha256:4faffd047e07c38848ce017e8725090413cd80cbc23d86e55c587bf979e579c9 \ - --hash=sha256:509fa21c6deb7a7a273d629cf5ec029bc209d1a51178615ddf718f5918992ab9 \ - --hash=sha256:5678211cb9333a6468fb8d8be0305520aa073f50d17f089b5b4b477ea6e67fdc \ - --hash=sha256:5a7d5dc5140555cf21a6fefbdbf8723f06fcd2f63ef108f2854de715e4422cb4 \ - --hash=sha256:69c0b73548bc525c8cb9a251cddf1931d1db4d2258e9599c28c07ef3580ef354 \ - --hash=sha256:6b5420a1d9450023228968e7e6a9ce57f65d148ab56d2313fcd589eee96a7a50 \ - --hash=sha256:722695808f4b6457b320fdc131280796bdceb04ab50fe1795cd540799ebe1698 \ - --hash=sha256:729586769a26dbceff69f7a7dbbf59ab6572b99d94576a5592625d5b411576b9 \ - --hash=sha256:77f0643abe7495da77fb436f50f8dab76dbc6e5fd25d39589a0f1fe6548bfa2b \ - --hash=sha256:795e7751525cae078558e679d646ae45574b47ed6e7771863fcc079a6171a0fc \ - --hash=sha256:7be7b61bb172e1ed687f1754f8e7484f1c8019780f6f6b0786e76bb01c2ae115 \ - --hash=sha256:7e68f88e5b8799aa49c85cd116c932a1ac15caaa3f5db09087854d218359e485 \ - --hash=sha256:83891d0e9fb81a825d9a6d61e3f07550ca70a076484292a70fde82c4b807286f \ - --hash=sha256:8485f406a96febb5140bfeca44a73e3ce5116b2501ac54fe953e488fb1d03b12 \ - --hash=sha256:8709b08f4a89aa7586de0aadc8da56180242ee0ada3999749b183aa23df95025 \ - --hash=sha256:8f71bc33915be5186016f675cd83a1e08523649b0e33efdb898db577ef5bb009 \ - --hash=sha256:915c04ba3851909ce68ccc2b8e2cd691618c4dc4c4232fb7982bca3f41fd8c3d \ - --hash=sha256:94c6f0bb423f739146aec64595853541634bde58b2135f27f61c1ffd1cd4d16a \ - --hash=sha256:9a1abfdc021a164803f4d485104931fb8f8c1efd55bc6b748d2f5774e78b62c5 \ - --hash=sha256:9b79b7a16f7fedff2495d684f2b59b0457c3b493778c9eed31111be64d58279f \ - --hash=sha256:a4afe79fb3de0b7097d81da19090f4df4f8d3a2b3adaa8764138aac2e44f3af1 \ - --hash=sha256:ad2cf8aa28b8c020ab2fc8287b0f823d0a7d8630784c31e9ee5edea20f406287 \ - --hash=sha256:b8512a91625c9b3da6f127803b166b629725e68af71f8184ae7e7d54686a56d6 \ - --hash=sha256:bc51efed119bc9cfdf792cdeaa4d67e8f6fcccab66ed4bfdd6bde3e59bfcbb2f \ - --hash=sha256:bdc919ead48f234740ad807933cdf545180bfbe9342c2bb451556db2ed958581 \ - --hash=sha256:bdd37121970bfd8be76c5fb069c7751683bdf373db1ed6c010162b2a130248ed \ - --hash=sha256:be8813b57049a7dc738189df53d69395eba14fb99345e0a5994914a3864c8a4b \ - --hash=sha256:c47a551199eb8eb2121d4f0f15ae0f923d31350ab9280078d1e5f12b249e0026 \ - --hash=sha256:ccfcd093f13f0f0b7fdd0f198b90053bf7b2f02a3927a30e63f3ccc9df56b676 \ - --hash=sha256:d53197da72cc091b024dd97249dfc7794d6a56530370992a5e1a08983ad9230e \ - --hash=sha256:d6dd0be5b5b189d31db7cda48b91d7e0a9795f31430b7f271219ab30f1d3ac9d \ - --hash=sha256:d88b440e37a16e651bda4c7c2b930eb586fd15ca7406cb39e211fcff3bf3017d \ - --hash=sha256:de8a88e63464af587c950061a5e6a67d3632e36df62b986892331d4620a35c01 \ - --hash=sha256:e1cf1972137e83c5d4c136c43ced9ac51d0e124706ee1c8aa8532c1287fa8795 \ - --hash=sha256:e56b7d45a839a697b5eb268c82a71bd8c7f6c94d6fd50c3d577fa39a9f1409f5 \ - --hash=sha256:e8afc3f2ccfa24215f8cb28dcf43f0113ac3c37c2f0f0806d8c70e4228c5cf4d \ - --hash=sha256:eaa9599de571d72e2daf60164784109f19978b327a3910d3e9de8c97b5b70cfe \ - --hash=sha256:ec15a59cf5af7be74194f7ab02d0f59a62bdcf1a537677ce67a2537c9b87fcda \ - --hash=sha256:f190daf01f13c72eac4efd5c430a8de82489d9cff23c364c3ea822545032993e \ - --hash=sha256:f34c41761022dd093b4b6896d4810782ffbabe30f2d443ff5f083e0cbbb8c737 \ - --hash=sha256:f3e98bb3798ead92273dc0e5fd0f31ade220f59a266ffd8a4f6065e0a3ce0523 \ - --hash=sha256:f9e130248f4462aaa8e2552d547f36ddadbeaa573879158d721bbd33dfe4743a \ - --hash=sha256:fed51ac40f757d41b7c48425901843666a6677e3e8eb0abcff09e4ba6e664f50 - # via - # flask - # jinja2 - # mkdocs - # mkdocs-autorefs - # mkdocstrings - # properdocs - # werkzeug +markdown-it-py==4.2.0 \ + --hash=sha256:04a21681d6fbb623de53f6f364d352309d4094dd4194040a10fd51833e418d49 \ + --hash=sha256:9f7ebbcd14fe59494226453aed97c1070d83f8d24b6fc3a3bcf9a38092641c4a + # via rich matplotlib==3.11.0 \ --hash=sha256:03acfeddf87b0dddb11b081ef7740ad445a3ca8bcb6b8e3011b08f2cf802b75c \ --hash=sha256:0515d495124be3124340e59f164d901ed4484e2246a5b74cfa483cac3b80bd97 \ @@ -890,100 +294,17 @@ matplotlib==3.11.0 \ --hash=sha256:e6b3e64dea5062c570f04358e2711859f3531b459f29516274fbad889079e4f3 \ --hash=sha256:f857524b442f0f36e641868ce2171aafa88cb0bc0644f4e1d8a5df9b32649fef # via trendify -mccabe==0.7.0 \ - --hash=sha256:348e0240c33b60bbdf4e523192ef919f28cb2c3d7d5c7794f74009290f236325 \ - --hash=sha256:6c2d30ab6be0e4a46919781807b4f0d834ebdd6c6e3dca0bda5a15f863427b6e - # via flake8 -mcp==1.28.1 \ - --hash=sha256:2726bca5e7193f61c5dde8b12500a6de2d9acf6d1a1c0be9e8c2e706437991df \ - --hash=sha256:d51e36a5f5644faea4f85ea649bfffa6bc6c26770d42798ad6a3de3d2ba69683 - # via dash -mdx-gh-links==0.4 \ - --hash=sha256:41d5aac2ab201425aa0a19373c4095b79e5e015fdacfe83c398199fe55ca3686 \ - --hash=sha256:9057bca1fa5280bf1fcbf354381e46c9261cc32c2d5c0407801f8a910be5f099 -mergedeep==1.3.4 \ - --hash=sha256:0096d52e9dad9939c3d975a774666af186eda617e6ca84df4c94dec30004f2a8 \ - --hash=sha256:70775750742b25c0d8f36c55aed03d24c3384d17c951b3175d898bd778ef0307 - # via - # mkdocs - # mkdocs-get-deps -mike==2.2.0 \ - --hash=sha256:1e3858e32c0f125aac14432fc7848434358f9ae0962c5c5cde387ad47f6ad25e \ - --hash=sha256:e1f4981c1152eec7c2490a3401142292cc47d686194188416db2648fdfe1d040 -mkdocs==1.6.1 \ - --hash=sha256:7b432f01d928c084353ab39c57282f29f92136665bdd6abf7c1ec8d822ef86f2 \ - --hash=sha256:db91759624d1647f3f34aa0c3f327dd2601beae39a366d6e064c03468d35c20e - # via - # mike - # mkdocs-autorefs - # mkdocs-exclude - # mkdocs-gen-files - # mkdocs-literate-nav - # mkdocs-material - # mkdocs-redirects - # mkdocs-section-index - # mkdocstrings -mkdocs-autorefs==1.4.4 \ - --hash=sha256:834ef5408d827071ad1bc69e0f39704fa34c7fc05bc8e1c72b227dfdc5c76089 \ - --hash=sha256:d54a284f27a7346b9c38f1f852177940c222da508e66edc816a0fa55fc6da197 - # via - # mkdocstrings - # mkdocstrings-python -mkdocs-click==0.9.0 \ - --hash=sha256:5208e828f4f68f63c847c1ef7be48edee9964090390afc8f5b3d4cbe5ea9bbed \ - --hash=sha256:6050917628d4740517541422b607404d044117bc31b770c4f9e9e1939a50c908 -mkdocs-exclude==1.0.2 \ - --hash=sha256:ba6fab3c80ddbe3fd31d3e579861fd3124513708271180a5f81846da8c7e2a51 -mkdocs-gen-files==0.6.1 \ - --hash=sha256:57d7ff2229e23d077e46d14a33db6d37c8823f6ce1a503c874c1764a71679763 \ - --hash=sha256:b3182bfc6219e35b8d26658cb988368659d5d023aac30c2a819247558fc12189 -mkdocs-get-deps==0.2.2 \ - --hash=sha256:8ee8d5f316cdbbb2834bc1df6e69c08fe769a83e040060de26d3c19fad3599a1 \ - --hash=sha256:e7878cbeac04860b8b5e0ca31d3abad3df9411a75a32cde82f8e44b6c16ff650 - # via mkdocs -mkdocs-glightbox==0.5.2 \ - --hash=sha256:23a431ea802b60b1030c73323db2eed6ba859df1a0822ce575afa43e0ea3f47e \ - --hash=sha256:c7622799347c32310878e01ccf14f70648445561010911c80590cec0353370ac -mkdocs-literate-nav==0.6.3 \ - --hash=sha256:2c421561280fa9184f88cbf399bebbd4cc17ee507e978a31ce11fd6f3aabf233 \ - --hash=sha256:edbaca22343f861fe4e34aac47d55a0c9955c640dbf02eea99fe631e914cf9ee -mkdocs-material==9.7.6 \ - --hash=sha256:00bdde50574f776d328b1862fe65daeaf581ec309bd150f7bff345a098c64a69 \ - --hash=sha256:71b84353921b8ea1ba84fe11c50912cc512da8fe0881038fcc9a0761c0e635ba -mkdocs-material-extensions==1.3.1 \ - --hash=sha256:10c9511cea88f568257f960358a467d12b970e1f7b2c0e5fb2bb48cab1928443 \ - --hash=sha256:adff8b62700b25cb77b53358dad940f3ef973dd6db797907c49e3c2ef3ab4e31 - # via mkdocs-material -mkdocs-redirects==1.2.3 \ - --hash=sha256:5e980330999299729a2d6a125347d1af78023d68a23681a4de3053ce7dfe2e51 \ - --hash=sha256:ec7312fff462d03ec16395d0c001006a418f8d0c21cdf2b47ff11cf839dc3ce0 -mkdocs-section-index==0.3.12 \ - --hash=sha256:285635bf86c643b0fc7a343053d7a818049817bff4408f52b80c4367bd5e7268 \ - --hash=sha256:a1100039546beb4ebef63ce6fc91f3195fb9c0c3763105d4d3d7cd31e0a046eb -mkdocs-snippets==1.3.2 \ - --hash=sha256:0a2be9ced4169acb3449d89a2d882f7e0e516a8c9cf3ffb8b9c6565af83a9a61 \ - --hash=sha256:d46d2700dec2dd8dab1d2977a656bed11ad89fdfd4dab30f2008c99d42086c53 -mkdocstrings==1.0.4 \ - --hash=sha256:3969a6515b77db65fd097b53c1b7aa4ae840bd71a2ee62a6a3e89503446d7172 \ - --hash=sha256:63464b4b29053514f32a1dbbf604e52876d5e638111b0c295ab7ed3cac73ca9b - # via mkdocstrings-python -mkdocstrings-python==2.0.5 \ - --hash=sha256:30c837bbff016549f659fcba6539ac351303f0fd7e713c89a040611072236e9d \ - --hash=sha256:3a4d92556ad39637e88af94a5374213af9a8e3040c3824ceaed04b486c017594 -mypy-extensions==1.1.0 \ - --hash=sha256:1be4cccdb0f2482337c4743e60421de3a356cd97508abadd57d47403e94f5505 \ - --hash=sha256:52e68efc3284861e772bbcd66823fde5ae21fd2fdb51c62a211403730b916558 - # via black +mdurl==0.1.2 \ + --hash=sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8 \ + --hash=sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba + # via markdown-it-py +mkinit==1.2.0 \ + --hash=sha256:172755988cdcdc5212d0b76b586ec4765c2bff9ae41266a93f5f4b962c3d2def \ + --hash=sha256:4b776ca991ccedbb202b079bd3e573d753121baa2a4f330324247325c9a1d346 narwhals==2.23.0 \ --hash=sha256:13e7ff5b4bb4a2f77b907c2e4d8a76e273dfc1323a3c997440a2f9fd26aed408 \ --hash=sha256:769e7b9ab102c93d8fa019f6b4cd1a657909b04a20bf6210e5a35aae06814ae9 - # via - # altair - # plotly -nest-asyncio==1.6.0 \ - --hash=sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe \ - --hash=sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c - # via dash + # via plotly numpy==2.4.6 ; python_full_version < '3.12' \ --hash=sha256:001fbb8e08d942dd57599e781f2472269ee7f2755fae407b4f67b2f0b17da3f1 \ --hash=sha256:0280e0356c0829a18d9de1cb7eee50ec22ca639878d7240307ca0943d73cd2c4 \ @@ -1061,10 +382,6 @@ numpy==2.4.6 ; python_full_version < '3.12' \ # contourpy # matplotlib # numpydantic - # pandas - # pydeck - # scipy - # streamlit # trendify numpy==2.5.0 ; python_full_version >= '3.12' \ --hash=sha256:016623417bb330d719d579daf2d6b9a01ddc52e41a9ed61a47f39fde46dcd865 \ @@ -1115,10 +432,6 @@ numpy==2.5.0 ; python_full_version >= '3.12' \ # contourpy # matplotlib # numpydantic - # pandas - # pydeck - # scipy - # streamlit # trendify numpydantic==1.10.0 \ --hash=sha256:1c62a445d305da69a5fa4510f7e7e1c25218f58fe462e5d59548042f1f769e66 \ @@ -1128,79 +441,9 @@ packaging==26.2 \ --hash=sha256:5fc45236b9446107ff2415ce77c807cee2862cb6fac22b8a73826d0693b0980e \ --hash=sha256:ff452ff5a3e828ce110190feff1178bb1f2ea2281fa2075aadb987c2fb221661 # via - # altair - # black - # hatchling # matplotlib - # mkdocs # plotly - # properdocs # pytest - # streamlit -paginate==0.5.7 \ - --hash=sha256:22bd083ab41e1a8b4f3690544afb2c60c25e5c9a63a30fa2f483f6c60c8e5945 \ - --hash=sha256:b885e2af73abcf01d9559fd5216b57ef722f8c42affbb63942377668e35c7591 - # via mkdocs-material -pandas==3.0.3 \ - --hash=sha256:0383c72c75cdcca61a9e116e611143902dbfd08bff356829c2f6d1cf40a9ca8c \ - --hash=sha256:05f1f1752b8533ea03f7f39a9c15b1a058d067bb48f4748948e7a8691e0510f2 \ - --hash=sha256:08d789b41f87e0905880e293cedf6197ce71fe67cc081358b1e148a491b9bd13 \ - --hash=sha256:0d589105b3c14645af1738ff279b2995102d8f7a03b0a66dc8d95550eb513e04 \ - --hash=sha256:13fc1e853d9e04743d11ba75a985ccbc2a317fe07d8af61e445a6fd24dacd6a6 \ - --hash=sha256:14da8316da4d0c5a77618425996bfb1248ca87fc2c1486e6fde4652bd18b5824 \ - --hash=sha256:1928e07221f82db493cd4af1e23c1bfca524a19a4699887975bff68f49a72bfb \ - --hash=sha256:261e308dfb22448384b7580cf719d2f998fe2966c92893c3e77d14008af1f066 \ - --hash=sha256:275c14e0fce14a2ec20eee474aecd305478ea3c1e6f6a9d8fe219a165542717e \ - --hash=sha256:335f62418ed562cfc3c49e9e196375c28b729dcef8543abf4f9438e381bf3c76 \ - --hash=sha256:3650109c0f22879df8bd6179ab9ee3d7f1d1d4e7e0094a3f0032d9f51e2e64ac \ - --hash=sha256:39436b377d56d2a2e52d0395bdbee171f01068e99af5250509aceeb929f765c7 \ - --hash=sha256:3c20a521bbb85902f79f7270c80a59e1b5452d96d170c034f207181870f97ac5 \ - --hash=sha256:3e91cec1879ada0624fc3dc9953c5cbd60208e59c0db28f540c5d6d47502422f \ - --hash=sha256:455f6f8139d4282188f526868dbc3c828470e88a3d9d59a891bd46a455f21b98 \ - --hash=sha256:46997386d528eb40376ecd6b033cf4a8a1e5282580f68f43de875b78cba2199d \ - --hash=sha256:4db8c527972a821cf5286b40ccc57642a39bc62e62022b42f99f8a67fca8c3a1 \ - --hash=sha256:4e15135e2ee5df1063313e2425ceef8ac0f4ae775893815b0923651b806a5639 \ - --hash=sha256:51b1fe551acb77dac643c6fda86084d8d446c10fe64b06a9cc29c4cc8540e7f2 \ - --hash=sha256:557409bc4178e70ee8d9ddb494798e51ebf6ea59330f6be22c51bab2a7db6c49 \ - --hash=sha256:5cc09a68b3120e0f54870dede8287a7bb1fa463907e4fcec1ea77cab6179bf7a \ - --hash=sha256:60ae316d3fd75d1858d450d0db0103ea2be3e7d4a95ec2f064f7e2ae63f7b028 \ - --hash=sha256:6674ab18ad8c57802867264b00e15e7bb904700cdd9046e3b2fa1fce237439ea \ - --hash=sha256:67b3b64c11910cfa29f4e94a14d3bff9ee693b6fc76055e7cad549cee0aec5fa \ - --hash=sha256:696a4a00a2a2a35d4e5deb3fc946641b96c944f02230e4f76137fe35d806c4fc \ - --hash=sha256:6dc0b3fd2169c9157deed50b4d519553a3655c8c6a96027136d654592be973a9 \ - --hash=sha256:7e65d5407dc0b394f509699650e4a2ec01c0514f21850f453fa60f3be79a5dbf \ - --hash=sha256:819959dab7bbd0049c15623fbac4e29a191b9528160a61fb1032242d8ced2d9c \ - --hash=sha256:8a1e45c80cceb3b4a21bc5939d52e8cbd8d9b7305309219d59e9754d9ce09e27 \ - --hash=sha256:9c39be2d709d01fa972a0cabc522389fceca4f3969332ba25a7d6c5802cf976a \ - --hash=sha256:9d71c63ae4ebdbf70209742096f1fc46a83a0613c99d4b23766cced9ff8cd62a \ - --hash=sha256:a2d2dff8a04f3917b55ab3910c32990f8ddf7eceba114947838cefa976a68977 \ - --hash=sha256:a4eeb6830daf35a71cc09649bd823e2b542dac246cdee9614c6e4bd65028cd6a \ - --hash=sha256:a55066a0505dae0ba2b50a46637db34b46f9094c65c5d4800794ef6335010938 \ - --hash=sha256:a82d532a3351d435432cd913edbccaf8b8e01d4dd0e5ced5a8d2e8ecd94c7e44 \ - --hash=sha256:b168fc218fd80a6cbdbdbc1a97ddc7889ed057d7eb45f50d866ceab5f39904c4 \ - --hash=sha256:b2c95f8bfc1ee412bf482605d7bfd30c12d1d26bd59fdd91efeef1d4718decb1 \ - --hash=sha256:ba7e08b9ac1d54569cd1e256e3668975ed624d6826f7b68df0342b012007bddb \ - --hash=sha256:bab900348131a7db1f69a7309ef141fd5680f1487094193bcbbb61791573bf8f \ - --hash=sha256:bd3a518890b400d32f9023722dc9a9a5c969f00b415419a3c06c043f09bb5d7d \ - --hash=sha256:c7be265b62cef88e253a941e4698604973736dcfe242fdb5198f0f7bc473cdcc \ - --hash=sha256:d26cbe1fcfc12e8fd900e2454163e466b2d3af84f7c75481df7683ffc073d870 \ - --hash=sha256:d4be06d68f9ddcfc645b87534911da79a8fbffc7573c80e0edcf42a5020624d8 \ - --hash=sha256:d72828c20c6d6e83e1e22a6a3b47b326b71664112fa9705dcbccfd7a39b62085 \ - --hash=sha256:dd1a5d1def6a46002e964510bdc67c368aa0951df5d1d9f8365336f5a1f490cd \ - --hash=sha256:e3a2ec42c98ffa2565a67e08e218d06d72576d758d90facb7c00805194d8f360 \ - --hash=sha256:f8894dc474d648fe7b6ff0ca9b0bd73950d19952bc1a6534540762c5d79d305c \ - --hash=sha256:fed2ff7fd9779120e388e285fc029bd5cf9490cdd2e4166a9ee22c0e49a9ab09 - # via - # streamlit - # trendify -pathspec==1.1.1 \ - --hash=sha256:17db5ecd524104a120e173814c90367a96a98d07c45b2e10c2f3919fff91bf5a \ - --hash=sha256:a00ce642f577bf7f473932318056212bc4f8bfdf53128c78bbd5af0b9b20b189 - # via - # black - # hatchling - # mkdocs - # properdocs pillow==12.3.0 \ --hash=sha256:00808c5e14ef63ac5161091d242999076604ff74b883423a11e5d7bbb38bf756 \ --hash=sha256:04f01d28a6aaff387bf842a13be313df23ba0597a44f1a976c9feb3c6ff4711a \ @@ -1280,108 +523,35 @@ pillow==12.3.0 \ --hash=sha256:fdafc9cce40277e0f7a0feabce0ee50dd2fa1800f3b38015e51296b5e814048d \ --hash=sha256:fe3cca2e4e8a592be0f269a1ca4835c25199d9f3ce815c8491048f785b0a0198 \ --hash=sha256:ffd0c5368496f41b0944be820fcb7a838aa6e623d250b01acf2643939c3f99d7 - # via - # matplotlib - # streamlit -platformdirs==4.10.0 \ - --hash=sha256:31e761a6a0ca04faf7353ea759bdba55652be214725111e5aac52dfa29d4bef7 \ - --hash=sha256:fb516cdb12eb0d857d0cd85a7c57cea4d060bee4578d6cf5a14dfdf8cbf8784a - # via - # black - # mkdocs-get-deps - # properdocs + # via matplotlib plotly==6.8.0 \ --hash=sha256:13c5c4a0f70b74cab1913eda0de49b826df5931708eb6f9c3010040614700ec8 \ --hash=sha256:e088e7ddc68d4f70e3d66659224727a45296d71d2b8284181862d3d8f1f0d88f - # via - # dash - # trendify + # via trendify pluggy==1.6.0 \ --hash=sha256:7dcc130b76258d33b90f61b658791dede3486c3e6bfb003ee5c9bfb396dd22f3 \ --hash=sha256:e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746 - # via - # hatchling - # pytest - # pytest-cov -properdocs==1.6.7 \ - --hash=sha256:6fa0cfa2e01bf338f684892c8a506cf70ea88ae7f3479c933b6fa20168101cbd \ - --hash=sha256:adc7b16e562890af0e098a7e5b02e3a81c20894a87d6a28d345c9300de73c26e - # via - # mkdocs-gen-files - # mkdocs-literate-nav - # mkdocs-redirects - # mkdocs-section-index -protobuf==7.35.1 \ - --hash=sha256:11d6b0ec246892d85215b0a13ca6e0233cf5284b68f0ac02646427f4ff88a799 \ - --hash=sha256:230a75ddfc2de4806e56696ce9640c1cdfdb6543b7cfce98d42a4c0a0e7bdb87 \ - --hash=sha256:24f857477359a85c0c235261b8ba905fd51b2562f4a64ca1df5473f29850cbf6 \ - --hash=sha256:353652e4efd0bca5b5fc2656abf8307ef351f0cf938c9eba09f0e09c20a25c30 \ - --hash=sha256:4bc97768d8fe4ad6743c8a19403e314511ed9f6d13205b687e52421c023ac1b9 \ - --hash=sha256:74758715c53d7158fb76caf4f0cfdacc5329a4b1bb994f865d6cf302d413a1c4 \ - --hash=sha256:b73f9489a4b8b1c9cb1f8ed951c736392592edb24b9d6819f36d2e10b171d5b4 \ - --hash=sha256:ce115a26fe0c39a2c29973d914d327e516a6455464489fe3cd1e51a1b354f81a - # via streamlit -pyarrow==24.0.0 \ - --hash=sha256:02b001b3ed4723caa44f6cd1af2d5c86aa2cf9971dacc2ffa55b21237713dfba \ - --hash=sha256:04920d6a71aabd08a0417709efce97d45ea8e6fb733d9ca9ecffb13c67839f68 \ - --hash=sha256:0b3537c00fb8d384f15ac1e79b6eb6db04a16514c8c1d22e59a9b95c8ba42868 \ - --hash=sha256:1183baeb14c5f587b1ec52831e665718ce632caab84b7cd6b85fd44f96114495 \ - --hash=sha256:14e31a3c9e35f1ab6356c6378f6f72830e6d2d5f1791df3774a7b097d18a6a1e \ - --hash=sha256:1617043b99bd33e5318ae18eb2919af09c71322ef1ca46566cdafc6e6712fb66 \ - --hash=sha256:1a4e45017efbf115032e4475ee876d525e0e36c742214fbe405332480ecd6275 \ - --hash=sha256:1b2fe7f9a5566401a0ef2571f197eb92358925c1f0c8dba305d6e43ea0871bb3 \ - --hash=sha256:1cc9057f0319e26333b357e17f3c2c022f1a83739b48a88b25bfd5fa2dc18838 \ - --hash=sha256:25ea65d868eb04015cd18e6df2fbe98f07e5bda2abefabcb88fce39a947716f6 \ - --hash=sha256:295f0a7f2e242dabd513737cf076007dc5b2d59237e3eca37b05c0c6446f3826 \ - --hash=sha256:2f16197705a230a78270cdd4ea8a1d57e86b2fdcbc34a1f6aebc72e65c986f9a \ - --hash=sha256:35405aecb474e683fb36af650618fd5340ee5471fc65a21b36076a18bbc6c981 \ - --hash=sha256:38be1808cdd068605b787e6ca9119b27eb275a0234e50212c3492331680c3b1e \ - --hash=sha256:3b13dedfe76a0ad2d1d859b0811b53827a4e9d93a0bcb05cf59333ab4980cc7e \ - --hash=sha256:418e48ce50a45a6a6c73c454677203a9c75c966cb1e92ca3370959185f197a05 \ - --hash=sha256:55a3bc1e3df3b5567b7d27ef551b2283f0c68a5e86f1cd56abc569da4f31335b \ - --hash=sha256:6165461f55ef6314f026de6638d661188e3455d3ec49834556a0ebbdbace18bb \ - --hash=sha256:61a3d7eaa97a14768b542f3d284dc6400dd2470d9f080708b13cd46b6ae18136 \ - --hash=sha256:6233c9ed9ab9d1db47de57d9753256d9dcffbf42db341576099f0fd9f6bf4810 \ - --hash=sha256:641f795b361874ac9da5294f8f443dfdbee355cf2bd9e3b8d97aaac2306b9b37 \ - --hash=sha256:6f066b179d68c413374294bc1735f68475457c933258df594443bb9d88ddc2a0 \ - --hash=sha256:7986f1fa71cee060ad00758bcc79d3a93bab8559bf978fab9e53472a2e25a17b \ - --hash=sha256:806f24b4085453c197a5078218d1ee08783ebbba271badd153d1ae22a3ee804f \ - --hash=sha256:85fe721a14dd823aca09127acbb06c3ca723efbd436c004f16bca601b04dcc83 \ - --hash=sha256:8adc8e6ce5fccf5dc707046ae4914fd537def529709cc0d285d37a7f9cd442ca \ - --hash=sha256:9700ebd9a51f5895ce75ff4ac4b3c47a7d4b42bc618be8e713e5d56bacf5f931 \ - --hash=sha256:9b18371ad2f44044b81a8d23bc2d8a9b6a6226dca775e8e16cfee640473d6c5d \ - --hash=sha256:a964266397740257f16f7bb2e4f08a0c81454004beab8ff59dd531b73610e9f2 \ - --hash=sha256:adbbedc55506cbdabb830890444fb856bfb0060c46c6f8026c6c2f2cf86ae795 \ - --hash=sha256:ae8a1145af31d903fa9bb166824d7abe9b4681a000b0159c9fb99c11bc11ad26 \ - --hash=sha256:b0e131f880cda8d04e076cee175a46fc0e8bc8b65c99c6c09dff6669335fde74 \ - --hash=sha256:b196eb3f931862af3fa84c2a253514d859c08e0d8fe020e07be12e75a5a9780c \ - --hash=sha256:b7d9a514e73bc42711e6a35aaccf3587c520024fe0a25d830a1a8a27c15f4f57 \ - --hash=sha256:c91d00057f23b8d353039520dc3a6c09d8608164c692e9f59a175a42b2ae0c19 \ - --hash=sha256:d3e0b61e8efb24ed38898e5cdc5fffa9124be480008d401a1f8071500494ae42 \ - --hash=sha256:d7027eba1df3b2069e2e8d80f644fa0918b68c46432af3d088ddd390d063ecde \ - --hash=sha256:d8ddd2768da81d3ee08cfea9b597f4abb4e8e1dc8ae7e204b608d23a0d3ab699 \ - --hash=sha256:e4505fc6583f7b05ab854934896bcac8253b04ac1171a77dfb73efef92076d91 \ - --hash=sha256:e56a1ffe9bf7b727432b89104cc0849c21582949dd7bdcb34f17b2001a351a76 \ - --hash=sha256:e6f1278ee4785b6db21229374a1c9e54ec7c549de5d1efc9630b6207de7e170b \ - --hash=sha256:f7616236ec1bc2b15bfdec22a71ab38851c86f8f05ff64f379e1278cf20c634a \ - --hash=sha256:fb24ac194bfc5e86839d7dcd52092ee31e5fe6733fe11f5e3b06ef0812b20072 - # via streamlit -pycodestyle==2.14.0 \ - --hash=sha256:c4b5b517d278089ff9d0abdec919cd97262a3367449ea1c8b49b91529167b783 \ - --hash=sha256:dd6bf7cb4ee77f8e016f9c8e74a35ddd9f67e1d5fd4184d86c3b98e07099f42d - # via flake8 -pycparser==3.0 ; implementation_name != 'PyPy' and platform_python_implementation != 'PyPy' \ - --hash=sha256:600f49d217304a5902ac3c37e1281c9fe94e4d0489de643a9504c5cdfdfc6b29 \ - --hash=sha256:b727414169a36b7d524c1c3e31839a521725078d7b2ff038656844266160a992 - # via cffi + # via pytest +polars==1.42.1 \ + --hash=sha256:2fe94f3059334650bd850ae19a9c165dcd5d9cb12cd95ea04de2201662e70e8a \ + --hash=sha256:3c0c65cdfa21a621650c4bdcbbccf93964d052fd766c3e70e84a55d961c259fd + # via trendify +polars-runtime-32==1.42.1 \ + --hash=sha256:4d4809e1c1b9a6611f6944f27b24abea902b5159e6b6fa262fd716e947af5afd \ + --hash=sha256:635d9dbcae2302ae223afb395d5cd220bffa61a53d0ab6871d17c8bc830101cf \ + --hash=sha256:7051226e6b42ffc395a7a9190377cd28649fbfb991b8f85c6271f4e1cfb736fb \ + --hash=sha256:8b8bf972d99d48aaaa2582e2bce966a6f43bc815bd8725d15f5cab9e2fb15d17 \ + --hash=sha256:bbdc26d68ee5b23b0ce227fa0599220aa35b77c826b6b0a6b2d8e7f6c1c36974 \ + --hash=sha256:d059e8e53cc114ff82f9bd791fd341dc53534a2c745e6f6aa37594c3a93f01fe \ + --hash=sha256:e9364c26da389a8b7339e4d29e20a3d12af730247e6ed3b7804bddce2477f428 \ + --hash=sha256:f6c0288be940b607dc4a7476c01e67fb6bbee93f5f1dd42c64970274c71008ba \ + --hash=sha256:f91f0b13588324905682809d270e1de5f1990c908721c8527657d77a044c9919 + # via polars pydantic==2.13.4 \ --hash=sha256:45a282cde31d808236fd7ea9d919b128653c8b38b393d1c4ab335c62924d9aba \ --hash=sha256:c40756b57adaa8b1efeeced5c196f3f3b7c435f90e84ea7f443901bec8099ef6 # via - # dash - # mcp # numpydantic - # pydantic-settings # trendify pydantic-core==2.46.4 \ --hash=sha256:00c603d540afdd6b80eb39f078f33ebd46211f02f33e34a32d9f053bba711de0 \ @@ -1477,696 +647,53 @@ pydantic-core==2.46.4 \ --hash=sha256:fc010ab034c8c7452522748bf937df58020d256ccae0874463d1f4d01758af8e \ --hash=sha256:fc3e9034a63de20e15e8ade85358bc6efc614008cab72898b4b4952bea0509ff # via pydantic -pydantic-settings==2.14.2 \ - --hash=sha256:a20c97b37910b6550d5ea50fbcc2d4187defe58cd57070b73863d069419c9440 \ - --hash=sha256:c19dd64b19097f1de80184f0cc7b0272a13ae6e170cbf240a3e27e381ed14a5f - # via mcp -pydeck==0.9.2 \ - --hash=sha256:8213dfeacc5f6bfe6825f61c8ee34e3850e8a31fc43924379ec98edb34a75b25 \ - --hash=sha256:c10d9035e81ead6385264cac8d19402471f6866a15ca1f7df1400f52142bcf87 - # via streamlit -pyflakes==3.4.0 \ - --hash=sha256:b24f96fafb7d2ab0ec5075b7350b3d2d2218eab42003821c06344973d3ea2f58 \ - --hash=sha256:f742a7dbd0d9cb9ea41e9a24a918996e8170c799fa528688d40dd582c8265f4f - # via flake8 pygments==2.20.0 \ --hash=sha256:6757cd03768053ff99f3039c1a36d6c0aa0b263438fcab17520b30a303a82b5f \ --hash=sha256:81a9e26dd42fd28a23a2d169d86d7ac03b46e2f8b59ed4698fb4785f946d0176 # via - # mkdocs-material # pytest -pyjwt==2.13.0 \ - --hash=sha256:41571c89ca91598c79e8ef18a2d07367d4810fbbd6f637794879baf1b7703423 \ - --hash=sha256:66adcc2aff09b3f1bbd95fc1e1577df8ac8723c978552fd43304c8a290ac5728 - # via mcp -pymdown-extensions==11.0 \ - --hash=sha256:8269cef0247f9e2d0a62fcea10860aba05c1cbab5470fd4b63230b96434dc589 \ - --hash=sha256:fbc4acb641814fa9d17521bbd21a5240ef739a662f11c06330c4b78c93e954d6 - # via - # mkdocs-material - # mkdocstrings + # rich pyparsing==3.3.2 \ --hash=sha256:850ba148bd908d7e2411587e247a1e4f0327839c40e2e5e6d05a007ecc69911d \ --hash=sha256:c777f4d763f140633dcb6d8a3eda953bf7a214dc4eff598413c070bcdc117cbc - # via - # matplotlib - # mike + # via matplotlib pytest==9.1.1 \ --hash=sha256:1088fbde8f2b49d95a549a195707afa7a76a3ce9bcadc26b6d71f0ffda5fe313 \ --hash=sha256:37a86b45efb9a47a61a36449063e8e18d0cab3161329fc099eb21783169c4f0c - # via - # pytest-asyncio - # pytest-cov -pytest-asyncio==1.4.0 \ - --hash=sha256:933ca923a23075a87fb7070c0ec272a6848489824d887c85c812670932835aa1 \ - --hash=sha256:c6c0d2259945122819f171a32ecea2c349ead889ee28176caaf492143424be42 -pytest-cov==7.1.0 \ - --hash=sha256:30674f2b5f6351aa09702a9c8c364f6a01c27aae0c1366ae8016160d1efc56b2 \ - --hash=sha256:a0461110b7865f9a271aa1b51e516c9a95de9d696734a2f71e3e78f46e1d4678 python-dateutil==2.9.0.post0 \ --hash=sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3 \ --hash=sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427 + # via matplotlib +rich==15.0.0 \ + --hash=sha256:33bd4ef74232fb73fe9279a257718407f169c09b78a87ad3d296f548e27de0bb \ + --hash=sha256:edd07a4824c6b40189fb7ac9bc4c52536e9780fbbfbddf6f1e2502c31b068c36 # via - # ghp-import - # matplotlib - # pandas -python-dotenv==1.2.2 \ - --hash=sha256:1d8214789a24de455a8b8bd8ae6fe3c6b69a5e3d64aa8a8e5d68e694bbcb285a \ - --hash=sha256:2c371a91fbd7ba082c2c1dc1f8bf89ca22564a087c2c287cd9b662adde799cf3 - # via pydantic-settings -python-multipart==0.0.32 \ - --hash=sha256:be54b7f3fa167bb83e4fcd936b887b708f4e57fe75911c02aebf53efaf8d938e \ - --hash=sha256:ff6d3f776f16878c894e52e107296ffc890e913c611b1a4ec6c44e2821fe2e23 - # via - # mcp - # streamlit -pytokens==0.4.1 \ - --hash=sha256:140709331e846b728475786df8aeb27d24f48cbcf7bcd449f8de75cae7a45083 \ - --hash=sha256:24afde1f53d95348b5a0eb19488661147285ca4dd7ed752bbc3e1c6242a304d1 \ - --hash=sha256:26cef14744a8385f35d0e095dc8b3a7583f6c953c2e3d269c7f82484bf5ad2de \ - --hash=sha256:292052fe80923aae2260c073f822ceba21f3872ced9a68bb7953b348e561179a \ - --hash=sha256:29d1d8fb1030af4d231789959f21821ab6325e463f0503a61d204343c9b355d1 \ - --hash=sha256:3f901fe783e06e48e8cbdc82d631fca8f118333798193e026a50ce1b3757ea68 \ - --hash=sha256:4a14d5f5fc78ce85e426aa159489e2d5961acf0e47575e08f35584009178e321 \ - --hash=sha256:4a58d057208cb9075c144950d789511220b07636dd2e4708d5645d24de666bdc \ - --hash=sha256:5502408cab1cb18e128570f8d598981c68a50d0cbd7c61312a90507cd3a1276f \ - --hash=sha256:584c80c24b078eec1e227079d56dc22ff755e0ba8654d8383b2c549107528918 \ - --hash=sha256:5ad948d085ed6c16413eb5fec6b3e02fa00dc29a2534f088d3302c47eb59adf9 \ - --hash=sha256:6d6c4268598f762bc8e91f5dbf2ab2f61f7b95bdc07953b602db879b3c8c18e1 \ - --hash=sha256:79fc6b8699564e1f9b521582c35435f1bd32dd06822322ec44afdeba666d8cb3 \ - --hash=sha256:8bdb9d0ce90cbf99c525e75a2fa415144fd570a1ba987380190e8b786bc6ef9b \ - --hash=sha256:8fcb9ba3709ff77e77f1c7022ff11d13553f3c30299a9fe246a166903e9091eb \ - --hash=sha256:967cf6e3fd4adf7de8fc73cd3043754ae79c36475c1c11d514fc72cf5490094a \ - --hash=sha256:970b08dd6b86058b6dc07efe9e98414f5102974716232d10f32ff39701e841c4 \ - --hash=sha256:97f50fd18543be72da51dd505e2ed20d2228c74e0464e4262e4899797803d7fa \ - --hash=sha256:9bd7d7f544d362576be74f9d5901a22f317efc20046efe2034dced238cbbfe78 \ - --hash=sha256:b35d7e5ad269804f6697727702da3c517bb8a5228afa450ab0fa787732055fc9 \ - --hash=sha256:b49750419d300e2b5a3813cf229d4e5a4c728dae470bcc89867a9ad6f25a722d \ - --hash=sha256:d31b97b3de0f61571a124a00ffe9a81fb9939146c122c11060725bd5aea79975 \ - --hash=sha256:d70e77c55ae8380c91c0c18dea05951482e263982911fc7410b1ffd1dadd3440 \ - --hash=sha256:d9907d61f15bf7261d7e775bd5d7ee4d2930e04424bab1972591918497623a16 \ - --hash=sha256:dc74c035f9bfca0255c1af77ddd2d6ae8419012805453e4b0e7513e17904545d \ - --hash=sha256:ee44d0f85b803321710f9239f335aafe16553b39106384cef8e6de40cb4ef2f6 \ - --hash=sha256:f66a6bbe741bd431f6d741e617e0f39ec7257ca1f89089593479347cc4d13324 - # via black -pywin32==312 ; sys_platform == 'win32' \ - --hash=sha256:17948aeadbdb091f0ced6ef0841620794e68327b94ee415571c1203594b7215c \ - --hash=sha256:59aba5d5940842075343a5ddc6b11f1cdf0d1567fe745290359dfbcc7c2eb831 \ - --hash=sha256:5c1fbe4a937a73ae9297384a3da38518cbc694c68ad8a809b2e19acd350f03ed \ - --hash=sha256:6017c58e12f6809fbb0555b75df144c2922a9ffd18e4b9b5afa863b6c1a9d950 \ - --hash=sha256:7a27df850933d16a8eabfbaeb73d52b273e2da667f80d70b01a89d1f6828d02c \ - --hash=sha256:9fce94568364e0155e6dfb781ac5d95903be8baf28670632beab1b523f300daa \ - --hash=sha256:a4dd3a848290ef724347b19f301045831d8e802fa4464f491b98b1e0a081432e \ - --hash=sha256:a77a90fbb6881238d2ca9c6fd797b25817f3768fe78d214a90137ff055a75f5b \ - --hash=sha256:a8597d28f267b39074aef51fa593530082b39cbe5a074226096857b1fed2dfb9 \ - --hash=sha256:b2200a054ca6d6625c4842fc56a4976a4b47f96b73dbe5538c3f813a80359f47 \ - --hash=sha256:b457f6d628a47e8a7346ce22acb7e1a46a4a78b52e1d17e1af56871bd19a93bc \ - --hash=sha256:c2f03a0f73f804a13c2735b99392b0cd426bb4f2c4d0178e5ac966a0f21618d5 \ - --hash=sha256:c53e878d15a1c44788082bfe712a905433473aa38f86375b7cf8b45e3acbaaf9 \ - --hash=sha256:d11417d84412f859b722fad0841b3614459ed0047f7542d8362e77884f6b6e8a \ - --hash=sha256:dab4f65ac9c4e48400a2a0530c46c3c579cd5905ecd11b80692373915269208b - # via mcp -pyyaml==6.0.3 \ - --hash=sha256:00c4bdeba853cc34e7dd471f16b4114f4162dc03e6b7afcc2128711f0eca823c \ - --hash=sha256:02893d100e99e03eda1c8fd5c441d8c60103fd175728e23e431db1b589cf5ab3 \ - --hash=sha256:0f29edc409a6392443abf94b9cf89ce99889a1dd5376d94316ae5145dfedd5d6 \ - --hash=sha256:10892704fc220243f5305762e276552a0395f7beb4dbf9b14ec8fd43b57f126c \ - --hash=sha256:16249ee61e95f858e83976573de0f5b2893b3677ba71c9dd36b9cf8be9ac6d65 \ - --hash=sha256:1d37d57ad971609cf3c53ba6a7e365e40660e3be0e5175fa9f2365a379d6095a \ - --hash=sha256:2283a07e2c21a2aa78d9c4442724ec1eb15f5e42a723b99cb3d822d48f5f7ad1 \ - --hash=sha256:34d5fcd24b8445fadc33f9cf348c1047101756fd760b4dacb5c3e99755703310 \ - --hash=sha256:37503bfbfc9d2c40b344d06b2199cf0e96e97957ab1c1b546fd4f87e53e5d3e4 \ - --hash=sha256:41715c910c881bc081f1e8872880d3c650acf13dfa8214bad49ed4cede7c34ea \ - --hash=sha256:44edc647873928551a01e7a563d7452ccdebee747728c1080d881d68af7b997e \ - --hash=sha256:4a2e8cebe2ff6ab7d1050ecd59c25d4c8bd7e6f400f5f82b96557ac0abafd0ac \ - --hash=sha256:4ad1906908f2f5ae4e5a8ddfce73c320c2a1429ec52eafd27138b7f1cbe341c9 \ - --hash=sha256:501a031947e3a9025ed4405a168e6ef5ae3126c59f90ce0cd6f2bfc477be31b7 \ - --hash=sha256:5190d403f121660ce8d1d2c1bb2ef1bd05b5f68533fc5c2ea899bd15f4399b35 \ - --hash=sha256:5498cd1645aa724a7c71c8f378eb29ebe23da2fc0d7a08071d89469bf1d2defb \ - --hash=sha256:5fcd34e47f6e0b794d17de1b4ff496c00986e1c83f7ab2fb8fcfe9616ff7477b \ - --hash=sha256:5fdec68f91a0c6739b380c83b951e2c72ac0197ace422360e6d5a959d8d97b2c \ - --hash=sha256:64386e5e707d03a7e172c0701abfb7e10f0fb753ee1d773128192742712a98fd \ - --hash=sha256:652cb6edd41e718550aad172851962662ff2681490a8a711af6a4d288dd96824 \ - --hash=sha256:66e1674c3ef6f541c35191caae2d429b967b99e02040f5ba928632d9a7f0f065 \ - --hash=sha256:6adc77889b628398debc7b65c073bcb99c4a0237b248cacaf3fe8a557563ef6c \ - --hash=sha256:79005a0d97d5ddabfeeea4cf676af11e647e41d81c9a7722a193022accdb6b7c \ - --hash=sha256:7c6610def4f163542a622a73fb39f534f8c101d690126992300bf3207eab9764 \ - --hash=sha256:7f047e29dcae44602496db43be01ad42fc6f1cc0d8cd6c83d342306c32270196 \ - --hash=sha256:8098f252adfa6c80ab48096053f512f2321f0b998f98150cea9bd23d83e1467b \ - --hash=sha256:850774a7879607d3a6f50d36d04f00ee69e7fc816450e5f7e58d7f17f1ae5c00 \ - --hash=sha256:8d1fab6bb153a416f9aeb4b8763bc0f22a5586065f86f7664fc23339fc1c1fac \ - --hash=sha256:8da9669d359f02c0b91ccc01cac4a67f16afec0dac22c2ad09f46bee0697eba8 \ - --hash=sha256:8dc52c23056b9ddd46818a57b78404882310fb473d63f17b07d5c40421e47f8e \ - --hash=sha256:9149cad251584d5fb4981be1ecde53a1ca46c891a79788c0df828d2f166bda28 \ - --hash=sha256:93dda82c9c22deb0a405ea4dc5f2d0cda384168e466364dec6255b293923b2f3 \ - --hash=sha256:96b533f0e99f6579b3d4d4995707cf36df9100d67e0c8303a0c55b27b5f99bc5 \ - --hash=sha256:9f3bfb4965eb874431221a3ff3fdcddc7e74e3b07799e0e84ca4a0f867d449bf \ - --hash=sha256:a33284e20b78bd4a18c8c2282d549d10bc8408a2a7ff57653c0cf0b9be0afce5 \ - --hash=sha256:a80cb027f6b349846a3bf6d73b5e95e782175e52f22108cfa17876aaeff93702 \ - --hash=sha256:b3bc83488de33889877a0f2543ade9f70c67d66d9ebb4ac959502e12de895788 \ - --hash=sha256:b8bb0864c5a28024fac8a632c443c87c5aa6f215c0b126c449ae1a150412f31d \ - --hash=sha256:ba1cc08a7ccde2d2ec775841541641e4548226580ab850948cbfda66a1befcdc \ - --hash=sha256:c1ff362665ae507275af2853520967820d9124984e0f7466736aea23d8611fba \ - --hash=sha256:c458b6d084f9b935061bc36216e8a69a7e293a2f1e68bf956dcd9e6cbcd143f5 \ - --hash=sha256:d0eae10f8159e8fdad514efdc92d74fd8d682c933a6dd088030f3834bc8e6b26 \ - --hash=sha256:d76623373421df22fb4cf8817020cbb7ef15c725b9d5e45f17e189bfc384190f \ - --hash=sha256:ebc55a14a21cb14062aa4162f906cd962b28e2e9ea38f9b4391244cd8de4ae0b \ - --hash=sha256:eda16858a3cab07b80edaf74336ece1f986ba330fdb8ee0d6c0d68fe82bc96be \ - --hash=sha256:ee2922902c45ae8ccada2c5b501ab86c36525b883eff4255313a253a3160861c \ - --hash=sha256:f7057c9a337546edc7973c0d3ba84ddcdf0daa14533c2065749c9075001090e6 \ - --hash=sha256:fc09d0aa354569bc501d4e787133afc08552722d3ab34836a80547331bb5d4a0 - # via - # mike - # mkdocs - # mkdocs-get-deps - # properdocs - # pymdown-extensions - # pyyaml-env-tag -pyyaml-env-tag==1.1 \ - --hash=sha256:17109e1a528561e32f026364712fee1264bc2ea6715120891174ed1b980d2e04 \ - --hash=sha256:2eb38b75a2d21ee0475d6d97ec19c63287a7e140231e4214969d0eac923cd7ff - # via - # mike - # mkdocs - # properdocs -referencing==0.37.0 \ - --hash=sha256:381329a9f99628c9069361716891d34ad94af76e461dcb0335825aecc7692231 \ - --hash=sha256:44aefc3142c5b842538163acb373e24cce6632bd54bdb01b21ad5863489f50d8 - # via - # jsonschema - # jsonschema-specifications -requests==2.34.2 \ - --hash=sha256:2a0d60c172f83ac6ab31e4554906c0f3b3588d37b5cb939b1c061f4907e278e0 \ - --hash=sha256:f288924cae4e29463698d6d60bc6a4da69c89185ad1e0bcc4104f584e960b9ed - # via - # dash - # grafana-api - # mkdocs-material - # streamlit -retrying==1.4.2 \ - --hash=sha256:bbc004aeb542a74f3569aeddf42a2516efefcdaff90df0eb38fbfbf19f179f59 \ - --hash=sha256:d102e75d53d8d30b88562d45361d6c6c934da06fab31bd81c0420acb97a8ba39 - # via dash -rpds-py==2026.6.3 \ - --hash=sha256:0be972be84cfcaf46c8c6edf690ca0f154ac17babf1f6a955a51579b34ad2dc5 \ - --hash=sha256:127565fead0a10943b282957bd5447804ff3160ad79f2ad2635e6d249e380680 \ - --hash=sha256:127e08c0642d880cf32ca47ec2a4a77b901f7e2dd1ad9762adb13955d72ffcc9 \ - --hash=sha256:166cf54d9f44fc6ceb53c7860258dde44a81406646de79f8ed3234fca3b6e538 \ - --hash=sha256:168c733a7112e071bb7a66460e667edfcff06c017a3c523f7a8a8e08d0140804 \ - --hash=sha256:1967debc37f64f2c4dc90a7f563aec558b471966e12adcac4e1c4240496b6ebf \ - --hash=sha256:1cebd1337c242e4ec2293e541f712b2da849b29f48f0c293684b71c0632625d4 \ - --hash=sha256:1cf01971c4f2c5553b772a542e4aaf191789cd331bc2cd4ff0e6e65ba49e1e97 \ - --hash=sha256:1e5822dfc2f0d4ab7e745eaa6d85945069329beeccef965af3f3bb26058fcab6 \ - --hash=sha256:22bffe6042b9bcb0822bcd1955ec00e245daf17b4344e4ed8e9551b976b63e96 \ - --hash=sha256:23a439f31ccbeff1574e24889128821d1f7917470e830cf6544dced1c662262a \ - --hash=sha256:24e9c5386e16669b674a69c156c8eeefcb578f3b3397b713b08e6d60f3c7b187 \ - --hash=sha256:270b293dae9058fc9fcedab50f13cebf46fb8ed1d1d54e0521a9da5d6b211975 \ - --hash=sha256:29dfa0533a5d4c94d4dfa1b694fcb56c9c63aad8330ffdd816fd225d0a7a162f \ - --hash=sha256:2a9c6f195058cb45335e8cc3802745c603d716eb96bc9625950c1aac71c0c703 \ - --hash=sha256:2bfd04c19ddbd6640de0b51894d764bd2758854d5b75bd102d2ef10cb9c293a9 \ - --hash=sha256:2c54a076ca4d370980ab57bc0e31df57bbe8d41340436a90ef8b1219a3cbb127 \ - --hash=sha256:2c958bf94822e9290a40aaf2a822d4bc5c88099093e3948ad6c571eca9272e5f \ - --hash=sha256:2c99f7e8ccb3dd6e3e4bfeac657a7b208c9bac8075f4b078c02d7404c34107fa \ - --hash=sha256:2f7c26fbc5acd2522b95d4177fe4710ffd8e9b20529e703ffbf8db4d93903f05 \ - --hash=sha256:30c6dc199b24a5e3e81d50da0f00858c5bbdb2617a750395687f4339c5818171 \ - --hash=sha256:38a2fea2787428f811719ceb9114cb78964a3138838320c29ac39526c79c16ba \ - 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--hash=sha256:a9f4645593036b81bbdb36b9c8e0ea0d1c3fee968c4d59db0344c14087ef143a \ - --hash=sha256:aca6c1ef08a82bfe327cc156da694660f599923e2e6665b6d81c9c2d0ac9ffc8 \ - --hash=sha256:acac386b453c2516111b50985d60ce46e7fadb5ea71ae7b25f4c946935bf27cf \ - --hash=sha256:acc992ab27b15f852c76755eb2ab7dce86585ddadba6fa5946e58556088845b4 \ - --hash=sha256:ae3d4fe8c0b9213624fdce7279d70e3b148b682ca20719ebd193a23ebfa47324 \ - --hash=sha256:ae50181a047c871561212bb97f7932a2d45fb53e947bd9b57ebad85b529cbc53 \ - --hash=sha256:ae6dd8f10bd17aad820876d24caec9efdafd80a318d16c0a48edb5e136902c6b \ - --hash=sha256:af05d726809bff6b141be124d4c7ce998f9c9c7f30edb1f46c07aa103d540b41 \ - --hash=sha256:afd70d95892096cdb26f15a00c45907b17817577aa8d1c76b2dcc2788391f9e9 \ - --hash=sha256:b5c2dc92304aa48a4a60443b548bb12f12e119d4b72f314015e67b9e1be97fca \ - --hash=sha256:bc0011654b91cc4fb2ae701bec0a0ba1e552c0714247fa7af6c59e0ccfa3a4e1 \ - --hash=sha256:bcfbcf66006befb9fd2aeaa9e01feaf881b4dc330a02ba07d2322b1c11be7b5d \ - --hash=sha256:bdbd97738551fca3917c1bd7188bec1920bb520104f28e7e1007f9ceb17b7690 \ - --hash=sha256:c60924535c75f1566b6eb75b5c31a48a43fef04fa2d0d201acbad8a9969c6107 \ - --hash=sha256:c7b9a2f8f4d8e90af72571d3d495deebdd7e3c75451f5b41719aee166e940fc2 \ - --hash=sha256:ca6546b66be9dc4738b1b043d5ebd5488c66c578c5ff0fd0e8065313fe3afb76 \ - --hash=sha256:ccffae9a092a00deb7efd545fe5e2c33c33b88e7c054337e9a74c179347d0b7d \ - --hash=sha256:cdc7e35386f3847df728fbcb5e887e2d79c19e2fa1eba9e51b6621d23e3243af \ - --hash=sha256:d15fde0e6fb0d88a60d221204873743e5d9f0b7d29165e62cd86d0413ad74ba6 \ - --hash=sha256:d34c20167764fbcf927194d532dd7e0c56772f0a5f943fa5ef9e9afbba8fb9db \ - --hash=sha256:d483fe17f01ad64b7bf7cc38fcefff1ca9fb83f8c2b2542b68f97ffe0611b369 \ - --hash=sha256:d7469697dce35be237db177d42e2a2ee26e6dcc5fc052078a6fefabd288c6edd \ - --hash=sha256:db08f45aecde626498fb3df07bcf6d2ec040af42e859a4f5040d79c200342911 \ - --hash=sha256:dc319e5a1de4b6913aac94bf6a2f9e847371e0a140a43dd4991db1a09bc2d504 \ - --hash=sha256:de3eceba0b683bcbb1ab93da016d0270df1f9ae7be716b40214c5dafac6ea45a \ - --hash=sha256:dfcc8b909769d19db55c7cc9541eb64b9b774b1057ffffb4f1048070475bb9f9 \ - --hash=sha256:e059c5dde6452b44424bd1834557556c226b57781dee1227af23518459722b13 \ - --hash=sha256:e4316bf32babbed84e691e352faf967ce2f0f024174a8643c37c94a1080374fc \ - --hash=sha256:e52655eaf81e32593abedaa4bfe33170c8cfedf3365ed9be6e11e07f148f0278 \ - --hash=sha256:e55d236be29255554da47abe5c577637db7c24a02b8b46f0ca9524c855801868 \ - --hash=sha256:ea7bb13b7c9a29791f87a0387ba7d3ad3a6d783d827e4d3f27b40a0ff44495e2 \ - --hash=sha256:ea964164cc9afa72d4d9b23cc28dafae93693c0a53e0b42acbff15b22c3f9ddd \ - --hash=sha256:ec829541c45bca16e61c7ae50c20501f213605beb75d1aba91a6ee37fbbb56a4 \ - --hash=sha256:ecabd69db66de867690f9797f2f8fa27ba501bbc24540cbdbdc649cd15888ba6 \ - --hash=sha256:ed0c1e5d10cdc7135537988c74a0188da68e2f3c30813ba3744ab1e42e0480f9 \ - --hash=sha256:f0840b5b17057f7fd918b76183a4b5a0635f43e14eb2ce60dce1d4ee4707ea00 \ - --hash=sha256:f4d78253f6996be4901669ad25319f842f740eccf4d58e3c7f3dd39e6dde1d8f \ - --hash=sha256:f56f1695bc5c0871cbc33dc0130fcf503aab0c57dcc5a6700a4f49eba4f2652e \ - --hash=sha256:f826877d462181e5eb1c26a0026b8d0cab05d99844ecb6d8bf3627a2ca0c0442 \ - --hash=sha256:f8f23ead891a3b762f35ab3b04623da7056545b48aa60d59957e6789914545da \ - --hash=sha256:f90938e92afda60266da758ee7d363447f7f0138c9559f9e1811629580582d90 \ - --hash=sha256:faa679d19a6696fd54259ad321251ad77a13e70e03dd834daa762a44fb6196ef - # via - # jsonschema - # referencing -scipy==1.17.1 ; python_full_version < '3.12' \ - --hash=sha256:010f4333c96c9bb1a4516269e33cb5917b08ef2166d5556ca2fd9f082a9e6ea0 \ - --hash=sha256:02ae3b274fde71c5e92ac4d54bc06c42d80e399fec704383dcd99b301df37458 \ - --hash=sha256:08b900519463543aa604a06bec02461558a6e1cef8fdbb8098f77a48a83c8118 \ - --hash=sha256:131f5aaea57602008f9822e2115029b55d4b5f7c070287699fe45c661d051e39 \ - --hash=sha256:158dd96d2207e21c966063e1635b1063cd7787b627b6f07305315dd73d9c679e \ - --hash=sha256:1cc682cea2ae55524432f3cdff9e9a3be743d52a7443d0cba9017c23c87ae2f6 \ - --hash=sha256:1f95b894f13729334fb990162e911c9e5dc1ab390c58aa6cbecb389c5b5e28ec \ - --hash=sha256:200e1050faffacc162be6a486a984a0497866ec54149a01270adc8a59b7c7d21 \ - --hash=sha256:2040ad4d1795a0ae89bfc7e8429677f365d45aa9fd5e4587cf1ea737f927b4a1 \ - --hash=sha256:2b64ca7d4aee0102a97f3ba22124052b4bd2152522355073580bf4845e2550b6 \ - --hash=sha256:2ceb2d3e01c5f1d83c4189737a42d9cb2fc38a6eeed225e7515eef71ad301dce \ - --hash=sha256:35c3a56d2ef83efc372eaec584314bd0ef2e2f0d2adb21c55e6ad5b344c0dcb8 \ - --hash=sha256:37425bc9175607b0268f493d79a292c39f9d001a357bebb6b88fdfaff13f6448 \ - --hash=sha256:3877ac408e14da24a6196de0ddcace62092bfc12a83823e92e49e40747e52c19 \ - --hash=sha256:3fd1fcdab3ea951b610dc4cef356d416d5802991e7e32b5254828d342f7b7e0b \ - --hash=sha256:41b71f4a3a4cab9d366cd9065b288efc4d4f3c0b37a91a8e0947fb5bd7f31d87 \ - --hash=sha256:43af8d1f3bea642559019edfe64e9b11192a8978efbd1539d7bc2aaa23d92de4 \ - --hash=sha256:45abad819184f07240d8a696117a7aacd39787af9e0b719d00285549ed19a1e9 \ - --hash=sha256:4b400bdc6f79fa02a4d86640310dde87a21fba0c979efff5248908c6f15fad1b \ - --hash=sha256:4eb6c25dd62ee8d5edf68a8e1c171dd71c292fdae95d8aeb3dd7d7de4c364082 \ - --hash=sha256:581b2264fc0aa555f3f435a5944da7504ea3a065d7029ad60e7c3d1ae09c5464 \ - --hash=sha256:5cf36e801231b6a2059bf354720274b7558746f3b1a4efb43fcf557ccd484a87 \ - --hash=sha256:5e3c5c011904115f88a39308379c17f91546f77c1667cea98739fe0fccea804c \ - --hash=sha256:6609bc224e9568f65064cfa72edc0f24ee6655b47575954ec6339534b2798369 \ - --hash=sha256:6e3dcd57ab780c741fde8dc68619de988b966db759a3c3152e8e9142c26295ad \ - --hash=sha256:6fac755ca3d2c3edcb22f479fceaa241704111414831ddd3bc6056e18516892f \ - --hash=sha256:744b2bf3640d907b79f3fd7874efe432d1cf171ee721243e350f55234b4cec4c \ - --hash=sha256:74cbb80d93260fe2ffa334efa24cb8f2f0f622a9b9febf8b483c0b865bfb3475 \ - --hash=sha256:766e0dc5a616d026a3a1cffa379af959671729083882f50307e18175797b3dfd \ - --hash=sha256:7bdf2da170b67fdf10bca777614b1c7d96ae3ca5794fd9587dce41eb2966e866 \ - --hash=sha256:7ff200bf9d24f2e4d5dc6ee8c3ac64d739d3a89e2326ba68aaf6c4a2b838fd7d \ - --hash=sha256:844e165636711ef41f80b4103ed234181646b98a53c8f05da12ca5ca289134f6 \ - --hash=sha256:8a604bae87c6195d8b1045eddece0514d041604b14f2727bbc2b3020172045eb \ - --hash=sha256:94055a11dfebe37c656e70317e1996dc197e1a15bbcc351bcdd4610e128fe1ca \ - --hash=sha256:95d8e012d8cb8816c226aef832200b1d45109ed4464303e997c5b13122b297c0 \ - --hash=sha256:9cdc1a2fcfd5c52cfb3045feb399f7b3ce822abdde3a193a6b9a60b3cb5854ca \ - --hash=sha256:9ecb4efb1cd6e8c4afea0daa91a87fbddbce1b99d2895d151596716c0b2e859d \ - --hash=sha256:a3472cfbca0a54177d0faa68f697d8ba4c80bbdc19908c3465556d9f7efce9ee \ - --hash=sha256:a4328d245944d09fd639771de275701ccadf5f781ba0ff092ad141e017eccda4 \ - --hash=sha256:a48a72c77a310327f6a3a920092fa2b8fd03d7deaa60f093038f22d98e096717 \ - --hash=sha256:a720477885a9d2411f94a93d16f9d89bad0f28ca23c3f8daa521e2dcc3f44d49 \ - --hash=sha256:a77cbd07b940d326d39a1d1b37817e2ee4d79cb30e7338f3d0cddffae70fcaa2 \ - --hash=sha256:a9956e4d4f4a301ebf6cde39850333a6b6110799d470dbbb1e25326ac447f52a \ - --hash=sha256:adb2642e060a6549c343603a3851ba76ef0b74cc8c079a9a58121c7ec9fe2350 \ - --hash=sha256:beeda3d4ae615106d7094f7e7cef6218392e4465cc95d25f900bebabfded0950 \ - --hash=sha256:c80be5ede8f3f8eded4eff73cc99a25c388ce98e555b17d31da05287015ffa5b \ - --hash=sha256:cc90d2e9c7e5c7f1a482c9875007c095c3194b1cfedca3c2f3291cdc2bc7c086 \ - --hash=sha256:cd96a1898c0a47be4520327e01f874acfd61fb48a9420f8aa9f6483412ffa444 \ - --hash=sha256:d2650c1fb97e184d12d8ba010493ee7b322864f7d3d00d3f9bb97d9c21de4068 \ - --hash=sha256:d30e57c72013c2a4fe441c2fcb8e77b14e152ad48b5464858e07e2ad9fbfceff \ - --hash=sha256:d59c30000a16d8edc7e64152e30220bfbd724c9bbb08368c054e24c651314f0a \ - --hash=sha256:dbc12c9f3d185f5c737d801da555fb74b3dcfa1a50b66a1a93e09190f41fab50 \ - --hash=sha256:e18f12c6b0bc5a592ed23d3f7b891f68fd7f8241d69b7883769eb5d5dfb52696 \ - --hash=sha256:e19ebea31758fac5893a2ac360fedd00116cbb7628e650842a6691ba7ca28a21 \ - --hash=sha256:e30bdeaa5deed6bc27b4cc490823cd0347d7dae09119b8803ae576ea0ce52e4c \ - --hash=sha256:eb092099205ef62cd1782b006658db09e2fed75bffcae7cc0d44052d8aa0f484 \ - --hash=sha256:eee2cfda04c00a857206a4330f0c5e3e56535494e30ca445eb19ec624ae75118 \ - --hash=sha256:f4115102802df98b2b0db3cce5cb9b92572633a1197c77b7553e5203f284a5b3 \ - --hash=sha256:f590cd684941912d10becc07325a3eeb77886fe981415660d9265c4c418d0bea \ - --hash=sha256:f8885db0bc2bffa59d5c1b72fad7a6a92d3e80e7257f967dd81abb553a90d293 \ - --hash=sha256:fcb310ddb270a06114bb64bbe53c94926b943f5b7f0842194d585c65eb4edd76 - # via trendify -scipy==1.18.0 ; python_full_version >= '3.12' \ - --hash=sha256:09143f676d157d9f546d663504ef9c1becb819824f1afc018814176411942446 \ - --hash=sha256:0d13bca67c096d89fb95ced0d8921807300fce0275643aef9533cc63a0773468 \ - --hash=sha256:18e9575f1569b2c54174e6159d32942e03731177f63dce7975f0a0c88d102f5b \ - --hash=sha256:1a4441f15d620578772a49e5ab48c0ee1f7a0220e387110283062729136b2553 \ - --hash=sha256:1ad44305cfa24b1ba5803cbbebf033590ccbac1aa5d612d727b785325ab408b0 \ - --hash=sha256:1afac4a847207c7ff8efd321734a50b06d0280b3b2a2c0fc2f413101747ad7c7 \ - --hash=sha256:1f55797419e16e7f30cf88ffb3113ce0467f00cfe3f70d5c281730b21769bfc2 \ - --hash=sha256:265915e79107de9f946b855e50d7470d5893ec3f54b342e1aa6201cbdcd8bb6b \ - --hash=sha256:2d8bbdc6c817f5b4006a54d799d4f5bab6f910193cbb9a1ff310833d4d270f61 \ - --hash=sha256:2ef3abc54a4ffc53765374b0d5728532dfdd2585ed23f6b11c206a1f0b1b9af8 \ - --hash=sha256:368e0a705903c466aa5f08eefb39e6b1b6b2d659e7352a31fd9e2438365be0f8 \ - --hash=sha256:3f1ac564d3bf6c03d861d2cd87a1bea0da2887136f7fb1bf519c05a8971452d6 \ - --hash=sha256:40395a5fcd1abee49a5c7aaa98c29db393eedc835138560a588c47ec16156690 \ - --hash=sha256:4a55985d54c769c872e64b7f4c8a81cc30ef700cc04296abbbf3705439c126de \ - --hash=sha256:4c256ee70c0d1a8a2ace807e199ccd4e3f57037433842abb3fb36bc17eaa9578 \ - --hash=sha256:52a96e21517c7292375c0e27dd796a811f03fcea5fd4d108fdfea8145dcf17ab \ - --hash=sha256:56abf29a7c067dde59be8b9a22d606a4ea1b2f2a4b756d9d903c62818f5dacce \ - --hash=sha256:5aba46108853ddfc77906b6557aac839d2b52e900c1d72a1180adaaab58d265f \ - --hash=sha256:5efe260f69417b97ddae455bfb5a95e8359f7f66ad7fa9522a60feb66f169520 \ - --hash=sha256:67b2ad2ad54c72ca6d04975a9b2df8c3638c34ddd5b28738e94fc2b57929d378 \ - --hash=sha256:68363b7eaacd8b5dd426df56d782cc156468ac79a127a1b87ca597d6e2e82197 \ - --hash=sha256:6aa94e78ec192a30063a5e72e561c28af769dc311190b24fe91774eff1969709 \ - --hash=sha256:71ccc8faa2dd16ac310233203474a8b5cb67f10dedd54a3116d34943f4b19132 \ - --hash=sha256:7a7f3b01647384dbc3a711e8c6778e0aabbe93959249fef5c7393396bcac0867 \ - --hash=sha256:7bd21faaf5a1a3b2eff922d02db5f191b99a6518db9078a8fb23169f6d22259a \ - --hash=sha256:7c7a51b33ce387193c97f228320cf8e87361daa1bba750638677729598b3e677 \ - --hash=sha256:84031d7b052a54fae2f8632e0ec802073d385476eb9a63079bce6e23ef9283d4 \ - --hash=sha256:8ca01e8ae69f1b18e9a58d91afead31be3cef0dd905a10249dac559ee15460a0 \ - --hash=sha256:945c1761b93f38d7f99ae81ae80c63e621471608c7eeead563f6df025585cd58 \ - --hash=sha256:97b6cddaaee0a779ef6b5ca83c9604b27cc16b2b8fc22c142652df8793319fb8 \ - --hash=sha256:9aac6192fac56bf2ca534389d24623f07b39ff83317d58287285e7fbd622ff76 \ - --hash=sha256:9ab7b758be6940954a713ee466e2043e9f6e2ed965c1fce5c91039f4be3d90a9 \ - --hash=sha256:a46f9273dbd0eb1cefba61c9b8648b4dfe3cbc14a080176f9a73e44b8336dc7f \ - --hash=sha256:ad033410e2e0672ffdc1042110cef20e1c46f8fd0616cee1d44d8d58fad8fc11 \ - --hash=sha256:b6f758e35f12757b5d95c00bc6de2438e229c2664b7a92e96f205959d9f2dfa4 \ - --hash=sha256:c5557d8be5da8e41353fcd4d21491fdbab83b062fc579e94dc09a7c8ab4f669b \ - --hash=sha256:c5dbddf60e58c2312316d097271a8e73d40eaf2eabfa4d95ed7d3695bbf2ce7b \ - --hash=sha256:d88363fd9d8fbd3511bd273f1a49efb2a540773ddf92a91d57498ce7dd7f3e76 \ - --hash=sha256:e40baea28ae7f5475c779741e2d90b1247c78531207b49c7030e698ff81cee3f \ - --hash=sha256:f2a6af57bd9e4a75d70e4117e78a1bbee84f79ae3fbb6d0111005d6ebcc4cb8d \ - --hash=sha256:f351e0dd702687d12a402b867a1b4146a256923e1c38317cbc472f6372b94707 - # via trendify -selectolax==0.4.10 \ - --hash=sha256:01c1354b158f8c87b72ab50a12b4b6d7b276150ded39210d1078d65d1e24ae0d \ - --hash=sha256:16055f712bd93507ce61ecac156bb7acf96b2e46c4d4d30c616e810f74f4da6e \ - --hash=sha256:1bb589f6ed0f1ec28784c2cd29de111a5b6f8129c3ecf0e71b9665e588667b97 \ - --hash=sha256:1ff2585eaf13ddc5c6614b10a7e47679b0c18a78821495e0c448364f8871592e \ - --hash=sha256:2428f04d2a48ba5f4c182f0d7234a7e38ac799b3358b3063f0ae41f754ee1c4a \ - --hash=sha256:3236fc0fbea9e237ee963274ebae700e68b9d6784bf91e0b3693eda63b393fa2 \ - --hash=sha256:373298d5c2e22a73740ba8f60a5813fd6bdb8fa2c0fd170841341ad0119bdf6b \ - --hash=sha256:3c21749e3d252419581160f3a3e43c1ad95dd0f83a13fe0d8c8fe6256bf7bbe6 \ - --hash=sha256:40babac4aea579edfb32b74acdacdb4c38bacb1ee3d1c4189f9665c52c67586f \ - --hash=sha256:40ecd0ff315dffa4a55840107367b0a54f2cd35be66e700c619364e0cd087ace \ - --hash=sha256:425288d4f5b18cfb0049fd1d6f3bddfba319657b9547b0e2bd24103b4b69435e \ - --hash=sha256:48c238f4f2b4ebd3d94ec260363d2bbab7df2825b592a1f9b12d59a9a9e9ee9a \ - --hash=sha256:4b0e1ad8b3d3d11bc173d33bb0fcf9d3ef8c667f1f3debd31ac8e9e3880ee174 \ - --hash=sha256:4d41b7e95ab0e025053efcba71267af12a6c12bc624e3bdf5dd83c6534f3d696 \ - --hash=sha256:547de570413e22ae5cbc8432d6224dd5a2d5bc66d1a50e34b705e5940d035caa \ - --hash=sha256:5d5b5437ce7548e7bc0b7712114de07dd0e4f94a30b06c968a6344148621df50 \ - --hash=sha256:6517b40e41ae5cc7756f92d88c59f178eb4a3683c7ce39c66bd5617587f628e5 \ - --hash=sha256:67f826152635521e1751665e315f3be82d027fe79d6446bf8434e0f7069e55db \ - --hash=sha256:68e1ef717b47f5cdcd1b151b2176d7184c38cb3772f8964509979840575203ef \ - --hash=sha256:746838a34df35ceff5f6154991b09aa6c27f044dc1fe9a4137f8c57e3e9f1203 \ - --hash=sha256:794a10f0c2cb9662ad85c5a970a72ca072057d68f7f0b3cf7b3230e5f2a8f221 \ - --hash=sha256:7c596b424c55ae87003140f55e6aa6f88e060b645781fa69e947ef60691b2bde \ - --hash=sha256:88475ef523fe5426d113e8319eeb741806a51cc025840f337661734c65cf1aa4 \ - --hash=sha256:89764b4d1e32d38e635dfb270a639fc707af4315b863fd161357a517321e5046 \ - --hash=sha256:8b57c64690e3c86b5d07386e2e598f4d3b4990144b1d5717db7038d0b675a97e \ - --hash=sha256:8c74168deeb116a1271e74e7a157e329930b7f54e328b323ce00bf7089f39dc9 \ - --hash=sha256:975bf1199c80965307168a05c4d88312a62b8151ac27216ed1248fbd175695bb \ - --hash=sha256:98e41ac4a761c5d4589ce9c40866a80fa2c5d413ac6b3af9546b43a2b3a8da18 \ - --hash=sha256:98f93b92d23a8feb88efc7c8e692221456bae30dc551d86d376931491152b909 \ - --hash=sha256:9c4c9afbd28b81892806e9699ecd323656e1eff7318c1245e80cd6bb78566a99 \ - --hash=sha256:a5c80477a3a93f0ee350d832c6fc764cd2df1299914a816bcd5ed4f0c9701b9b \ - --hash=sha256:a95f67ed9d5947562e9268332cc7165660c7db0cd3faea959e13b6901b4d323f \ - --hash=sha256:a96d59ba2a8ba01e4f732913816f5684d30d0646b7cd0fa17377fc9d1032cc0d \ - --hash=sha256:ab07fc342cf477c0320d22fac52917b824871caf5ab177a0fd94377a901ab657 \ - --hash=sha256:ab8f95b196b2dfb2be3ea7274673d45bb251ec2e16a0e7a3c1fc21c1c20d0722 \ - --hash=sha256:b3d199a73894368b83e5d744f64bddf80c22a37c160253a8e81c1a6000be607b \ - --hash=sha256:b43da45acece07f94e4b0d555e073f1a91314c98cb86d10860a1b291bc498976 \ - --hash=sha256:b65ce508ed7f3951a2f36f807494c253d0ebe99d2b18ede149f9a97b99be7d7a \ - --hash=sha256:c1933f53fe7410777b3373b9900010ae8d392a6d245f019d8f011d12f700e389 \ - --hash=sha256:c1bb382bd7009676716814ee81327015ab3d472aff0148fccbd20322d37af83d \ - --hash=sha256:c4b4b7c5d09a20539d369891332a107a869ee170453254048fbc18f893deb4f9 \ - --hash=sha256:d42566a7e649d4e5461e763a241f46542df2613876422e0530bc59999063f36d \ - --hash=sha256:d60cabbf1899916a6389fa36f2908b1a76e00dd044f710ae3dd2d0b14919dfc7 \ - --hash=sha256:db236a92e49b27369a98b9e8ac6c97e1534368d83281d097f3e50c2ba597f112 \ - --hash=sha256:e053064c5e00788a6bcc5427a753e41c058f29e4285bf84a3e663989b7218cdb \ - --hash=sha256:ec333fe02c4b7d8a03c0aa58c7c2265edd3312b1ef309f03efd020f595f6dae1 - # via mkdocs-glightbox -setuptools==82.0.1 \ - --hash=sha256:7d872682c5d01cfde07da7bccc7b65469d3dca203318515ada1de5eda35efbf9 \ - --hash=sha256:a59e362652f08dcd477c78bb6e7bd9d80a7995bc73ce773050228a348ce2e5bb - # via dash + # trendify + # typer +shellingham==1.5.4 \ + --hash=sha256:7ecfff8f2fd72616f7481040475a65b2bf8af90a56c89140852d1120324e8686 \ + --hash=sha256:8dbca0739d487e5bd35ab3ca4b36e11c4078f3a234bfce294b0a0291363404de + # via typer six==1.17.0 \ --hash=sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274 \ --hash=sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81 # via python-dateutil -smmap==5.0.3 \ - --hash=sha256:4d9debb8b99007ae47165abc08670bd74cb74b5227dda7f643eccc4e9eb5642c \ - --hash=sha256:c106e05d5a61449cf6ba9a1e650227ecfb141590d2a98412103ff35d89fc7b2f - # via gitdb -sse-starlette==3.4.5 \ - --hash=sha256:83072538bc211a2f68b7b0422226c4af3e9b62e106e07034664b832ca019842a \ - --hash=sha256:e71bad53323f65573c3864a6c3bd0c1eb6e5f092b2e48082b0c35927d19ca296 - # via mcp -starlette==1.3.1 \ - --hash=sha256:05d0213193f2fbaae60e2ecb593b4add4262ad4e46536b54abe36f11a71724e0 \ - --hash=sha256:c7372aae11c3c3f26a42df7bd626cec2f47d03483d261d369516a615a53714c6 - # via - # mcp - # sse-starlette - # streamlit -streamlit==1.58.0 \ - --hash=sha256:4ca8a7afc5bd16a5f176ccf4be1e34e8121cad0240becd127fb58a103ea3178d \ - --hash=sha256:78a22e7085b053af7ce544442bf4b670771e68c509ba1bdaa056ba0708f49c3d - # via trendify -strenum==0.4.15 \ - --hash=sha256:878fb5ab705442070e4dd1929bb5e2249511c0bcf2b0eeacf3bcd80875c82eff \ - --hash=sha256:a30cda4af7cc6b5bf52c8055bc4bf4b2b6b14a93b574626da33df53cf7740659 +typer==0.26.8 \ + --hash=sha256:3512ca79ac5c11113414b36e80281b872884477722440691c89d1112e321a49c \ + --hash=sha256:c244a6bd558886fe3f8780efb6bdd28bb9aff005a94eedebaa5cb32926fe2f7e # via trendify -supervisor==4.3.0 \ - --hash=sha256:0bcb763fddafba410f35cbde226aa7f8514b9fb82eb05a0c85f6588d1c13f8db \ - --hash=sha256:4a2bf149adf42997e1bb44b70c43b613275ec9852c3edacca86a9166b27e945e - # via trendify -tenacity==9.1.4 \ - --hash=sha256:6095a360c919085f28c6527de529e76a06ad89b23659fa881ae0649b867a9d55 \ - --hash=sha256:adb31d4c263f2bd041081ab33b498309a57c77f9acf2db65aadf0898179cf93a - # via streamlit -toml==0.10.2 \ - --hash=sha256:806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b \ - --hash=sha256:b3bda1d108d5dd99f4a20d24d9c348e91c4db7ab1b749200bded2f839ccbe68f - # via streamlit -tomli==2.4.1 ; python_full_version <= '3.11' \ - --hash=sha256:01f520d4f53ef97964a240a035ec2a869fe1a37dde002b57ebc4417a27ccd853 \ - --hash=sha256:0d85819802132122da43cb86656f8d1f8c6587d54ae7dcaf30e90533028b49fe \ - --hash=sha256:136443dbd7e1dee43c68ac2694fde36b2849865fa258d39bf822c10e8068eac5 \ - --hash=sha256:1d8591993e228b0c930c4bb0db464bdad97b3289fb981255d6c9a41aedc84b2d \ - --hash=sha256:2190f2e9dd7508d2a90ded5ed369255980a1bcdd58e52f7fe24b8162bf9fedbd \ - --hash=sha256:2c1c351919aca02858f740c6d33adea0c5deea37f9ecca1cc1ef9e884a619d26 \ - --hash=sha256:36d2bd2ad5fb9eaddba5226aa02c8ec3fa4f192631e347b3ed28186d43be6b54 \ - --hash=sha256:3d48a93ee1c9b79c04bb38772ee1b64dcf18ff43085896ea460ca8dec96f35f6 \ - --hash=sha256:47149d5bd38761ac8be13a84864bf0b7b70bc051806bc3669ab1cbc56216b23c \ - --hash=sha256:4ab97e64ccda8756376892c53a72bd1f964e519c77236368527f758fbc36a53a \ - --hash=sha256:4b605484e43cdc43f0954ddae319fb75f04cc10dd80d830540060ee7cd0243cd \ - --hash=sha256:504aa796fe0569bb43171066009ead363de03675276d2d121ac1a4572397870f \ - --hash=sha256:51529d40e3ca50046d7606fa99ce3956a617f9b36380da3b7f0dd3dd28e68cb5 \ - --hash=sha256:52c8ef851d9a240f11a88c003eacb03c31fc1c9c4ec64a99a0f922b93874fda9 \ - --hash=sha256:559db847dc486944896521f68d8190be1c9e719fced785720d2216fe7022b662 \ - --hash=sha256:5a881ab208c0baf688221f8cecc5401bd291d67e38a1ac884d6736cbcd8247e9 \ - --hash=sha256:5cb41aa38891e073ee49d55fbc7839cfdb2bc0e600add13874d048c94aadddd1 \ - --hash=sha256:5e262d41726bc187e69af7825504c933b6794dc3fbd5945e41a79bb14c31f585 \ - --hash=sha256:5ee18d9ebdb417e384b58fe414e8d6af9f4e7a0ae761519fb50f721de398dd4e \ - --hash=sha256:7008df2e7655c495dd12d2a4ad038ff878d4ca4b81fccaf82b714e07eae4402c \ - --hash=sha256:734e20b57ba95624ecf1841e72b53f6e186355e216e5412de414e3c51e5e3c41 \ - --hash=sha256:7c7e1a961a0b2f2472c1ac5b69affa0ae1132c39adcb67aba98568702b9cc23f \ - --hash=sha256:7f86fd587c4ed9dd76f318225e7d9b29cfc5a9d43de44e5754db8d1128487085 \ - --hash=sha256:7f94b27a62cfad8496c8d2513e1a222dd446f095fca8987fceef261225538a15 \ - --hash=sha256:88dceee75c2c63af144e456745e10101eb67361050196b0b6af5d717254dddf7 \ - --hash=sha256:8a650c2dbafa08d42e51ba0b62740dae4ecb9338eefa093aa5c78ceb546fcd5c \ - --hash=sha256:8d65a2fbf9d2f8352685bc1364177ee3923d6baf5e7f43ea4959d7d8bc326a36 \ - --hash=sha256:96481a5786729fd470164b47cdb3e0e58062a496f455ee41b4403be77cb5a076 \ - --hash=sha256:a120733b01c45e9a0c34aeef92bf0cf1d56cfe81ed9d47d562f9ed591a9828ac \ - --hash=sha256:b1d22e6e9387bf4739fbe23bfa80e93f6b0373a7f1b96c6227c32bef95a4d7a8 \ - --hash=sha256:b8c198f8c1805dc42708689ed6864951fd2494f924149d3e4bce7710f8eb5232 \ - --hash=sha256:c2541745709bad0264b7d4705ad453b76ccd191e64aa6f0fc66b69a293a45ece \ - --hash=sha256:c742f741d58a28940ce01d58f0ab2ea3ced8b12402f162f4d534dfe18ba1cd6a \ - --hash=sha256:c7f2c7f2b9ca6bdeef8f0fa897f8e05085923eb091721675170254cbc5b02897 \ - --hash=sha256:d312ef37c91508b0ab2cee7da26ec0b3ed2f03ce12bd87a588d771ae15dcf82d \ - --hash=sha256:d4d8fe59808a54658fcc0160ecfb1b30f9089906c50b23bcb4c69eddc19ec2b4 \ - --hash=sha256:da25dc3563bff5965356133435b757a795a17b17d01dbc0f42fb32447ddfd917 \ - --hash=sha256:eab21f45c7f66c13f2a9e0e1535309cee140182a9cdae1e041d02e47291e8396 \ - --hash=sha256:eb0dc4e38e6a1fd579e5d50369aa2e10acfc9cace504579b2faabb478e76941a \ - --hash=sha256:ec9bfaf3ad2df51ace80688143a6a4ebc09a248f6ff781a9945e51937008fcbc \ - --hash=sha256:ede3e6487c5ef5d28634ba3f31f989030ad6af71edfb0055cbbd14189ff240ba \ - --hash=sha256:f3c6818a1a86dd6dca7ddcaaf76947d5ba31aecc28cb1b67009a5877c9a64f3f \ - --hash=sha256:f758f1b9299d059cc3f6546ae2af89670cb1c4d48ea29c3cacc4fe7de3058257 \ - --hash=sha256:f8f0fc26ec2cc2b965b7a3b87cd19c5c6b8c5e5f436b984e85f486d652285c30 \ - --hash=sha256:fd0409a3653af6c147209d267a0e4243f0ae46b011aa978b1080359fddc9b6cf \ - --hash=sha256:ff18e6a727ee0ab0388507b89d1bc6a22b138d1e2fa56d1ad494586d61d2eae9 \ - --hash=sha256:ff2983983d34813c1aeb0fa89091e76c3a22889ee83ab27c5eeb45100560c049 - # via coverage -trove-classifiers==2026.6.1.19 \ - --hash=sha256:ab4c4ec93cc4a4e7815fa759906e05e6bb3f2fbd92ea0f897288c6a43efd15b3 \ - --hash=sha256:c5132b4b61a829d11cfbd2d72e97f20a45ed6edb95e45c5efdeb5e00836b2745 - # via hatchling -typing-extensions==4.15.0 \ - --hash=sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466 \ - --hash=sha256:f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548 +typing-extensions==4.16.0 \ + --hash=sha256:481caa481374e813c1b176ada14e97f1f67a4539ce9cfeb3f350d78d6370c2e8 \ + --hash=sha256:dc983d19a509c94dba722ee6abd33940f7c05a89e243c47e907eb4db6f1a43e5 # via - # altair - # anyio - # dash - # mcp # pydantic # pydantic-core - # pytest-asyncio - # referencing - # starlette - # streamlit # typing-inspection typing-inspection==0.4.2 \ --hash=sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7 \ --hash=sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464 - # via - # mcp - # pydantic - # pydantic-settings -tzdata==2026.2 ; sys_platform == 'emscripten' or sys_platform == 'win32' \ - --hash=sha256:9173fde7d80d9018e02a662e168e5a2d04f87c41ea174b139fbef642eda62d10 \ - --hash=sha256:bbe9af844f658da81a5f95019480da3a89415801f6cc966806612cc7169bffe7 - # via pandas -urllib3==2.7.0 \ - --hash=sha256:231e0ec3b63ceb14667c67be60f2f2c40a518cb38b03af60abc813da26505f4c \ - --hash=sha256:9fb4c81ebbb1ce9531cce37674bbc6f1360472bc18ca9a553ede278ef7276897 - # via requests -uvicorn==0.49.0 \ - --hash=sha256:ba3d14c3ee7e41c6c654c46c9eb489d33213cdd30aa1696eab1374337c13f68f \ - --hash=sha256:ebf4271aa580d9de97f93192d4595176df6e91f9aae919ca73e4fc07df1e66a3 - # via - # mcp - # streamlit -verspec==0.1.0 \ - --hash=sha256:741877d5633cc9464c45a469ae2a31e801e6dbbaa85b9675d481cda100f11c31 \ - --hash=sha256:c4504ca697b2056cdb4bfa7121461f5a0e81809255b41c03dda4ba823637c01e - # via mike -waitress==3.0.2 \ - --hash=sha256:682aaaf2af0c44ada4abfb70ded36393f0e307f4ab9456a215ce0020baefc31f \ - --hash=sha256:c56d67fd6e87c2ee598b76abdd4e96cfad1f24cacdea5078d382b1f9d7b5ed2e - # via trendify -watchdog==6.0.0 \ - --hash=sha256:07df1fdd701c5d4c8e55ef6cf55b8f0120fe1aef7ef39a1c6fc6bc2e606d517a \ - --hash=sha256:20ffe5b202af80ab4266dcd3e91aae72bf2da48c0d33bdb15c66658e685e94e2 \ - --hash=sha256:212ac9b8bf1161dc91bd09c048048a95ca3a4c4f5e5d4a7d1b1a7d5752a7f96f \ - --hash=sha256:2cce7cfc2008eb51feb6aab51251fd79b85d9894e98ba847408f662b3395ca3c \ - --hash=sha256:490ab2ef84f11129844c23fb14ecf30ef3d8a6abafd3754a6f75ca1e6654136c \ - --hash=sha256:6eb11feb5a0d452ee41f824e271ca311a09e250441c262ca2fd7ebcf2461a06c \ - --hash=sha256:6f10cb2d5902447c7d0da897e2c6768bca89174d0c6e1e30abec5421af97a5b0 \ - --hash=sha256:7607498efa04a3542ae3e05e64da8202e58159aa1fa4acddf7678d34a35d4f13 \ - --hash=sha256:76aae96b00ae814b181bb25b1b98076d5fc84e8a53cd8885a318b42b6d3a5134 \ - --hash=sha256:82dc3e3143c7e38ec49d61af98d6558288c415eac98486a5c581726e0737c00e \ - --hash=sha256:9041567ee8953024c83343288ccc458fd0a2d811d6a0fd68c4c22609e3490379 \ - --hash=sha256:9ddf7c82fda3ae8e24decda1338ede66e1c99883db93711d8fb941eaa2d8c282 \ - --hash=sha256:a175f755fc2279e0b7312c0035d52e27211a5bc39719dd529625b1930917345b \ - --hash=sha256:a1914259fa9e1454315171103c6a30961236f508b9b623eae470268bbcc6a22f \ - --hash=sha256:afd0fe1b2270917c5e23c2a65ce50c2a4abb63daafb0d419fde368e272a76b7c \ - --hash=sha256:bdd4e6f14b8b18c334febb9c4425a878a2ac20efd1e0b231978e7b150f92a948 \ - --hash=sha256:c7c15dda13c4eb00d6fb6fc508b3c0ed88b9d5d374056b239c4ad1611125c860 \ - --hash=sha256:cbafb470cf848d93b5d013e2ecb245d4aa1c8fd0504e863ccefa32445359d680 \ - --hash=sha256:e3df4cbb9a450c6d49318f6d14f4bbc80d763fa587ba46ec86f99f9e6876bb26 \ - --hash=sha256:ef810fbf7b781a5a593894e4f439773830bdecb885e6880d957d5b9382a960d2 - # via - # mkdocs - # properdocs - # streamlit -websockets==16.0 \ - --hash=sha256:0298d07ee155e2e9fda5be8a9042200dd2e3bb0b8a38482156576f863a9d457c \ - --hash=sha256:08d7af67b64d29823fed316505a89b86705f2b7981c07848fb5e3ea3020c1abe \ - --hash=sha256:152284a83a00c59b759697b7f9e9cddf4e3c7861dd0d964b472b70f78f89e80e \ - --hash=sha256:1637db62fad1dc833276dded54215f2c7fa46912301a24bd94d45d46a011ceec \ - --hash=sha256:19c4dc84098e523fd63711e563077d39e90ec6702aff4b5d9e344a60cb3c0cb1 \ - --hash=sha256:1c1b30e4f497b0b354057f3467f56244c603a79c0d1dafce1d16c283c25f6e64 \ - --hash=sha256:31a52addea25187bde0797a97d6fc3d2f92b6f72a9370792d65a6e84615ac8a8 \ - --hash=sha256:32da954ffa2814258030e5a57bc73a3635463238e797c7375dc8091327434206 \ - --hash=sha256:3425ac5cf448801335d6fdc7ae1eb22072055417a96cc6b31b3861f455fbc156 \ - --hash=sha256:349f83cd6c9a415428ee1005cadb5c2c56f4389bc06a9af16103c3bc3dcc8b7d \ - --hash=sha256:417b28978cdccab24f46400586d128366313e8a96312e4b9362a4af504f3bbad \ - --hash=sha256:485c49116d0af10ac698623c513c1cc01c9446c058a4e61e3bf6c19dff7335a2 \ - --hash=sha256:4a1aba3340a8dca8db6eb5a7986157f52eb9e436b74813764241981ca4888f03 \ - --hash=sha256:50f23cdd8343b984957e4077839841146f67a3d31ab0d00e6b824e74c5b2f6e8 \ - --hash=sha256:52a0fec0e6c8d9a784c2c78276a48a2bdf099e4ccc2a4cad53b27718dbfd0230 \ - --hash=sha256:52ac480f44d32970d66763115edea932f1c5b1312de36df06d6b219f6741eed8 \ - --hash=sha256:5569417dc80977fc8c2d43a86f78e0a5a22fee17565d78621b6bb264a115d4ea \ - --hash=sha256:569d01a4e7fba956c5ae4fc988f0d4e187900f5497ce46339c996dbf24f17641 \ - --hash=sha256:5a4b4cc550cb665dd8a47f868c8d04c8230f857363ad3c9caf7a0c3bf8c61ca6 \ - --hash=sha256:5f451484aeb5cafee1ccf789b1b66f535409d038c56966d6101740c1614b86c6 \ - --hash=sha256:5f6261a5e56e8d5c42a4497b364ea24d94d9563e8fbd44e78ac40879c60179b5 \ - --hash=sha256:6e5a82b677f8f6f59e8dfc34ec06ca6b5b48bc4fcda346acd093694cc2c24d8f \ - --hash=sha256:71c989cbf3254fbd5e84d3bff31e4da39c43f884e64f2551d14bb3c186230f00 \ - --hash=sha256:781caf5e8eee67f663126490c2f96f40906594cb86b408a703630f95550a8c3e \ - --hash=sha256:7be95cfb0a4dae143eaed2bcba8ac23f4892d8971311f1b06f3c6b78952ee70b \ - --hash=sha256:86890e837d61574c92a97496d590968b23c2ef0aeb8a9bc9421d174cd378ae39 \ - --hash=sha256:878b336ac47938b474c8f982ac2f7266a540adc3fa4ad74ae96fea9823a02cc9 \ - --hash=sha256:8b6e209ffee39ff1b6d0fa7bfef6de950c60dfb91b8fcead17da4ee539121a79 \ - --hash=sha256:8cc451a50f2aee53042ac52d2d053d08bf89bcb31ae799cb4487587661c038a0 \ - --hash=sha256:8d7f0659570eefb578dacde98e24fb60af35350193e4f56e11190787bee77dac \ - --hash=sha256:95724e638f0f9c350bb1c2b0a7ad0e83d9cc0c9259f3ea94e40d7b02a2179ae5 \ - --hash=sha256:9b5aca38b67492ef518a8ab76851862488a478602229112c4b0d58d63a7a4d5c \ - --hash=sha256:a069d734c4a043182729edd3e9f247c3b2a4035415a9172fd0f1b71658a320a8 \ - --hash=sha256:a0b31e0b424cc6b5a04b8838bbaec1688834b2383256688cf47eb97412531da1 \ - --hash=sha256:a35539cacc3febb22b8f4d4a99cc79b104226a756aa7400adc722e83b0d03244 \ - --hash=sha256:a5e18a238a2b2249c9a9235466b90e96ae4795672598a58772dd806edc7ac6d3 \ - --hash=sha256:a653aea902e0324b52f1613332ddf50b00c06fdaf7e92624fbf8c77c78fa5767 \ - --hash=sha256:abf050a199613f64c886ea10f38b47770a65154dc37181bfaff70c160f45315a \ - --hash=sha256:af80d74d4edfa3cb9ed973a0a5ba2b2a549371f8a741e0800cb07becdd20f23d \ - --hash=sha256:b14dc141ed6d2dde437cddb216004bcac6a1df0935d79656387bd41632ba0bbd \ - --hash=sha256:b784ca5de850f4ce93ec85d3269d24d4c82f22b7212023c974c401d4980ebc5e \ - --hash=sha256:bc59589ab64b0022385f429b94697348a6a234e8ce22544e3681b2e9331b5944 \ - --hash=sha256:c0204dc62a89dc9d50d682412c10b3542d748260d743500a85c13cd1ee4bde82 \ - --hash=sha256:c0ee0e63f23914732c6d7e0cce24915c48f3f1512ec1d079ed01fc629dab269d \ - --hash=sha256:caab51a72c51973ca21fa8a18bd8165e1a0183f1ac7066a182ff27107b71e1a4 \ - --hash=sha256:d6297ce39ce5c2e6feb13c1a996a2ded3b6832155fcfc920265c76f24c7cceb5 \ - --hash=sha256:daa3b6ff70a9241cf6c7fc9e949d41232d9d7d26fd3522b1ad2b4d62487e9904 \ - --hash=sha256:e0334872c0a37b606418ac52f6ab9cfd17317ac26365f7f65e203e2d0d0d359f \ - --hash=sha256:e6578ed5b6981005df1860a56e3617f14a6c307e6a71b4fff8c48fdc50f3ed2c \ - --hash=sha256:eaded469f5e5b7294e2bdca0ab06becb6756ea86894a47806456089298813c89 \ - --hash=sha256:f4a32d1bd841d4bcbffdcb3d2ce50c09c3909fbead375ab28d0181af89fd04da \ - --hash=sha256:fd3cb4adb94a2a6e2b7c0d8d05cb94e6f1c81a0cf9dc2694fb65c7e8d94c42e4 - # via streamlit -werkzeug==3.1.8 \ - --hash=sha256:63a77fb8892bf28ebc3178683445222aa500e48ebad5ec77b0ad80f8726b1f50 \ - --hash=sha256:9bad61a4268dac112f1c5cd4630a56ede601b6ed420300677a869083d70a4c44 - # via - # dash - # flask -zipp==4.1.0 \ - --hash=sha256:25ad4e16390cd314347dd8f1de67a2ac538ae658ed4ab9db16029c07c188e97f \ - --hash=sha256:4cb57381f544315db7688e976e922a2b18cdb513d21cc194eb42232ba2a3e602 - # via importlib-metadata + # via pydantic +ubelt==1.4.2 \ + --hash=sha256:40322cf3bb59c05b7567222b10bd0d553c6abc463cd480c23ea3dbcf2ef332fb \ + --hash=sha256:6cdbcbba75cd7c3e1ef9556a13b7c0e29a55f67cb454f022a981e42808f2b06e + # via mkinit diff --git a/rewrite_reference/OVERVIEW.md b/rewrite_reference/OVERVIEW.md new file mode 100644 index 0000000..75deb3b --- /dev/null +++ b/rewrite_reference/OVERVIEW.md @@ -0,0 +1,390 @@ +# Trendify: Current Behavior Reference (pre-rewrite) + +This document is a functional inventory of `trendify` as it exists today, written to support a +clean rewrite. Goal: nothing in this list should silently disappear during the rewrite, and the +on-disk/JSON schema for `DataProduct` objects should stay compatible (or the break should be a +deliberate, documented decision) so that old `data_products.json` files stay loadable. + +The full pydantic JSON Schema for every model is exported alongside this file in +[`pydantic_schemas.json`](pydantic_schemas.json) (generated by `dump_schemas.py`, re-runnable any +time with `.venv/bin/python rewrite_reference/dump_schemas.py`). + +## 1. What Trendify does, end to end + +Trendify batch-processes many "run" directories of raw data (e.g. simulation outputs) into a +common set of typed **data products** (points, traces, table entries, histogram entries, axis +lines), sorts those products by user-defined **tags** into a nested directory tree, and then +renders each tag's products into assets: matplotlib static images, CSV tables, or an interactive +Plotly/Dash/Streamlit dashboard. + +Pipeline (`trendify.api.make_it_trendy`, also reachable piecewise via CLI): + +1. **Generate** (`make_products` → `DataProductGenerator.process_and_save`) + - User supplies a `ProductGenerator`: `Callable[[Path], list[DataProduct]]`. + - Mapped over each input directory (optionally in parallel via `ProcessPoolExecutor`). + - Each directory's returned products are wrapped in a `DataProductCollection` and serialized to + `data_products.json` (name configurable) in that same directory. +2. **Sort** (`sort_products` → `DataProductCollection.sort_by_tags_single_directory`) + - Reads each directory's `data_products.json`, gets the set of tags present + (`DataProductCollection.get_tags`), and for each tag writes a sub-collection into + `output_dir//.json`. + - Tuple tags become nested folders (`tags=[("a","b")]` → `output_dir/a/b/`). + - Index numbers are stable/deduped per source file via `ProductIndexMap`, a small JSON + file (`index_map`) per output folder that maps `{source_path: index}`, guarded with a + `FileLock` (`reserving_index.lock`) so parallel workers don't clash. +3. **Render static assets** (`make_tables_and_figures` → `DataProductCollection.process_collection`) + - Runs once per sorted tag-directory. + - Loads all JSONs in that directory back into a collection, then for each tag present: + - `TableEntry`s → `TableBuilder` → 3 CSVs: `_melted.csv`, `_pivot.csv` (if pivot + succeeds), `_stats.csv` (min/mean/max/3-sigma, if pivot columns are numeric). + - `Point2D`/`Trace2D`/`AxLine` → `XYDataPlotter.handle_points_and_traces` draws onto a shared + matplotlib `SingleAxisFigure`. + - `HistogramEntry` → `Histogrammer.handle_histogram_entries` draws onto the *same* shared + figure (so XY and histogram products with the same tag are overlaid on the same axes today + — this looks like it may be accidental behavior worth revisiting in the rewrite). + - All `Format2D`s seen for the tag are merged via `Format2D.union_from_iterable` (requires + titles/labels/legend to be identical across products sharing a tag, min/max of limits) and + applied once to the figure, which is then saved as `.jpg` and closed. +4. **Include files** (`make_include_files`, optional) — walks the static-assets tree and writes + `include.md` per directory with MkDocs `{% include %}` / `{{ read_csv(...) }}` snippets for + figures/tables/subdirectories, so a docs site can pull in results without manual linking. +5. **Interactive serving** — two independent paths, not really unified: + - **Plotly/Dash** (`serve_products_to_plotly_dashboard` / + `DataProductCollection.serve_plotly_dashboard` / `trendify.plotly_dashboard`): builds one big + `dash.Dash` app with a category+tag dropdown UI, one XY figure + table + histogram section per + tag, client-side callbacks for legend/grid/autorange toggles and histogram bin count. Runs via + Flask dev server or `waitress` if installed and `debug=False`. + - **Streamlit** (`trendify.streamlit`, wired up via `trendify make dashboard`): reads the + *sorted products* directory tree directly off disk (using the presence of `index_map` files to + discover tags), renders a collapsible sidebar of nested tags built from folder structure, and + on selection produces one `PlotlyFigure` via + `DataProductCollection.process_tag_for_streamlit` (note: this path calls + `add_to_plotly` per-product rather than the matplotlib-only `plot_to_ax`/`Histogrammer` route + used for static assets — so static and interactive rendering are two separate code paths that + must each be kept in sync with new product types). + - There is a **third**, older, largely dead HTTP-serving path: `trendify.server` / a + `Flask` app with hardcoded `DATABASE_ROOT` pointing at a specific developer's machine, and + `trendify.local_server` (`TrendifyProductServerLocal`), a slightly more generic Flask JSON API + over a products directory keyed by URL path segments. Both exist to feed a **Grafana** + dashboard (see `UQL_TableEntry` / `UQL_Point2D` / `UQL_Trace2D` query strings in + `data_product_collection.py`, and the fully commented-out `make_grafana_dashboard` / + `make_grafana_panels` functions that depended on a `grafana_api` package). Grafana support is + effectively disabled today (commented out) but the dependency (`grafana-api`) is still listed + in `pyproject.toml` and `ProductType` docstring/enum still lists Grafana-flavored intent. TODO.md + says "Sunset production server, grafana, etc." — treat this whole vertical (`server.py`, + `local_server.py`, `production_server.py`, Grafana comments, UQL strings) as a candidate for + deletion rather than porting, unless the user says otherwise. + +## 2. Package layout (`src/trendify/`) + +``` +trendify/ +├── __init__.py # re-exports trendify.api.* and trendify.server.* as top-level `trendify.X` +├── api/ +│ ├── __init__.py # re-exports all submodule __all__s — this is the umbrella "trendify.api" namespace +│ ├── api.py # pipeline orchestration functions (make_products, sort_products, make_tables_and_figures, make_it_trendy, serve_products_to_plotly_dashboard, make_include_files) +│ ├── base/ +│ │ ├── data_product.py # DataProduct (pydantic base class + subclass registry), ProductList, ProductGenerator +│ │ ├── helpers.py # Tag/Tags type aliases, HashableBase, ProductType enum, DATA_PRODUCTS_FNAME_DEFAULT +│ │ └── pen.py # Pen (matplotlib line/scatter style + rgba/contrast helpers) +│ ├── formats/ +│ │ ├── format2d.py # AxisScale enum, Format2D, PlottableData2D, XYData base classes +│ │ └── table.py # TableEntry +│ ├── styling/ +│ │ ├── grid.py # GridTheme enum, GridAxis, Grid (+ theme presets: MATLAB/LIGHT/DARK) +│ │ ├── legend.py # LegendLocation enum, Legend (+ plotly position mapping) +│ │ └── marker.py # Marker (matplotlib scatter style + plotly symbol mapping) +│ ├── plotting/ +│ │ ├── plotting.py # SingleAxisFigure (matplotlib fig+ax wrapper), PlotlyFigure (go.Figure wrapper) +│ │ ├── point.py # Point2D +│ │ ├── trace.py # Trace2D +│ │ ├── axline.py # LineOrientation enum, AxLine +│ │ └── histogram.py # HistogramStyle, HistogramEntry +│ └── generator/ +│ ├── data_product_collection.py # DataProductCollection, ProductEntryMetadata, ProductIndexMap, flatten/atleast_1d/_squeeze helpers +│ ├── data_product_generator.py # DataProductGenerator (runs user callable, writes JSON) +│ ├── histogrammer.py # Histogrammer (matplotlib histogram rendering) +│ ├── table_builder.py # TableBuilder (melted/pivot/stats CSV export) +│ └── xy_data_plotter.py # XYDataPlotter (matplotlib + plotly XY rendering) +├── cli.py # argparse CLI ("trendify ..."); NOTE pyproject.toml script entry point is `trendify.CLI:trendify` (wrong case) — currently broken unless case-insensitive FS +├── examples.py # sample data generator + example_data_product_generator + `_main`/`make_sample_data` CLI entry +├── gallery.py # standalone example generator (sine/scatter/histogram/table) used by `trendify make gallery` +├── plotly_dashboard.py # PlotlyDashboardGenerator + generate_plotly_dashboard (Dash app builder) +├── streamlit.py # Streamlit dashboard (tag sidebar + PlotlyFigure viewer), `make_streamlit` scaffolding writer +├── server.py # legacy Flask/Grafana server, hardcoded path, largely dead +├── local_server.py # more generic Flask JSON API over a products dir, also Grafana-oriented +└── production_server.py # waitress wrapper to run a Dash app or DataProductCollection in "production" +``` + +Other loose files at repo root (`alternative_tag_selectors.py`, `custom_handler.py`, +`implementation_recommendation.py`, `improved_tag_selection.py`, `sample_hierarchical_data.py`, +`test.py`) are **not part of the installed package** (they live outside `src/`) — they read like +scratch experiments/prototypes for streamlit tag selection UI. Worth a skim before the rewrite in +case they contain ideas already validated, but they are not part of the current shipped behavior. + +## 3. The data model + +### 3.1 `DataProduct` — the polymorphic base (`api/base/data_product.py`) + +- Pydantic `BaseModel`, `model_config = ConfigDict(extra="allow")` on the base class (subclasses + mostly override to `extra="forbid"`). +- Fields: `tags: Tags` (required), `metadata: dict[str, str] = {}`. +- `product_type: str` is a `@computed_field` property returning `type(self).__name__` — this is + what gets serialized into JSON and is the polymorphism discriminator. +- `__init_subclass__` auto-registers every subclass by class name into a module-level + `_data_product_subclass_registry: dict[str, type[DataProduct]]`. **This means product types are + looked up by bare class name, globally, at import time** — two different classes named + `Trace2D` in different modules would collide. Worth deciding deliberately in the rewrite whether + to keep name-based polymorphism or move to `pydantic`'s built-in discriminated unions / + `Annotated[Union[...], Field(discriminator=...)]`. +- `deserialize_child_classes(cls, key, **kwargs)`: manually walks `kwargs[key]` (a list of raw + dicts), pops `"product_type"` from each, looks up the class in the registry, and constructs it. + This is how `DataProductCollection.__init__` turns a raw `elements: [...]` JSON list into actual + typed objects instead of relying on pydantic's own polymorphic validation — a hand-rolled + substitute for `SerializeAsAny`/discriminated unions. +- `append_to_list(l)` / `set_metadata(new)`: fluent helpers so example code can do + `Trace2D(...).append_to_list(products).set_metadata({...})` inline in a list comprehension. +- `ProductList = list[SerializeAsAny[InstanceOf[DataProduct]]]` — the type alias used everywhere a + list of heterogeneous products is expected; `SerializeAsAny` is what makes `model_dump_json()` + include subclass-specific fields instead of truncating to the base class's fields. +- `ProductGenerator = Callable[[Path], ProductList]` — the type user code must implement. + +### 3.2 Concrete `DataProduct` subclasses (the actual schema surface) + +All in `pydantic_schemas.json`. Summarized here with every field, default, and constraint: + +| Class | Base | Fields (name: type = default) | `extra` | +|---|---|---|---| +| `TableEntry` | `DataProduct` | `row: float\|str`, `col: float\|str`, `value: float\|str\|bool`, `unit: str\|None = None` | forbid | +| `PlottableData2D` | `DataProduct` | `format2d: Format2D\|None = None` | (inherits `allow`) | +| `XYData` | `PlottableData2D` | *(no new fields — pure marker base class)* | (inherits) | +| `Point2D` | `XYData` | `x: float\|str`, `y: float\|str`, `marker: Marker\|None = Marker()` | forbid | +| `Trace2D` | `XYData` | `points: list[Point2D]`, `pen: Pen = Pen()` | forbid | +| `AxLine` | `PlottableData2D` | `value: float`, `orientation: LineOrientation`, `pen: Pen = Pen()` | forbid | +| `HistogramEntry` | `PlottableData2D` | `value: float\|str`, `tags: Tags` (redeclared), `style: HistogramStyle\|None = HistogramStyle()` | forbid | + +Every `DataProduct` (via base) also carries `tags: Tags`, `metadata: dict[str,str] = {}`, and the +computed `product_type: str`. + +`Tag = Union[tuple[Hashable, ...], Hashable]`, `Tags = list[Tag]` — tags are either scalars +(`"foo"`) or tuples (`("foo", "bar")`, which becomes nested folders `foo/bar/` when sorting). +Note: `Hashable` is extremely permissive — in practice tags are strings or tuples of strings; the +rewrite might want to narrow this type for safety. + +### 3.3 Style / format value objects (not `DataProduct`s themselves — used as fields) + +- **`Pen`** (`HashableBase`, `extra="forbid"`): `color: tuple[float,float,float]|tuple[float,float,float,float]|str = "k"`, `size: float = 1`, `alpha: float = 1`, `zorder: float = 0`, `linestyle: str|tuple[int,tuple[int,...]] = "-"`, `label: str|None = None`. Methods: `as_scatter_plot_kwargs()` (→ matplotlib `plot` kwargs), `_convert_linestyle_to_plotly()`, `rgba`/`rgb` properties, `get_contrast_color(background_luminance=1.0)` (WCAG-style relative luminance blend for legible text/hover color). +- **`Marker`** (`HashableBase`, `extra="forbid"`): `color: str = "k"`, `size: float = 5`, `alpha: float = 1`, `zorder: float = 0`, `label: str|None = None`, `symbol: str = "."`. `from_pen(pen, symbol=".")` classmethod (note: **this method is buggy** — `pen.model_dump().pop("linestyle")` pops and returns just the *value* of `linestyle`, discarding the rest of the dict, then tries to pass that single value as `**kwargs`, which will raise `TypeError` on any real invocation; grep shows it's called from `Trace2D.propagate_format2d_and_pen`, which itself appears unused elsewhere — confirm before quietly reimplementing "the same way"). `as_scatter_plot_kwargs()` (has a duplicate `"marker"` key literal — harmless dict-literal bug, last one wins). `plotly_symbol` property maps a fixed matplotlib marker-char set to plotly symbol names (unmapped symbols silently fall back to `"circle"`). `rgba`, `get_contrast_color()` mirror `Pen`'s. +- **`Legend`** (`HashableBase`): `visible: bool = True`, `title: str|None`, `framealpha: float = 1`, `loc: LegendLocation = BEST`, `ncol: int = 1`, `fancybox: bool = True`, `edgecolor: str = "black"`, `zorder: int = 10`, `bbox_to_anchor: tuple[float,float]|None = None`. `LegendLocation` StrEnum mirrors matplotlib's location strings. `to_kwargs()` → matplotlib legend kwargs subset (does *not* include `zorder`, `edgecolor`, or `bbox_to_anchor` — those are applied separately by the caller). `plotly_location` property: hand-maintained lookup table mapping each `LegendLocation` to `{x,y,xanchor,yanchor}` plus a `bbox_to_anchor`-override branch with its own anchor-inference logic. +- **`GridAxis`** (`HashableBase`, `extra="forbid"`): `show: bool = False`, `pen: Pen = Pen(color="gray", alpha=1.0, size=0.75, linestyle="-")`. +- **`Grid`** (`HashableBase`, `extra="forbid"`): `major: GridAxis = GridAxis(show=False)`, `minor: GridAxis = GridAxis(show=False)`, `enable_minor_ticks: bool = False`, `zorder: float = -1`. `GridTheme` enum (`MATLAB`, `LIGHT`, `DARK`) with `Grid.from_theme(name)` classmethod producing hand-tuned pen presets. `union_from_iterable` requires all inputs to have *identical* major/minor/enable_minor_ticks/zorder (raises via tuple-unpack of a length-checked set if they differ) — same "must all agree" merge pattern as `Format2D`. +- **`Format2D`** (`HashableBase`, `extra="forbid"`): `title_fig`, `title_ax`, `label_x`, `label_y: str|None = None`; `legend: Legend|None = Legend()`; `lim_x_min/max`, `lim_y_min/max: float|None = None`; `grid: Grid|None = None`; `scale_x`, `scale_y: AxisScale = LINEAR`; `figure_width: float = 6.4`, `figure_height: float = 4.8`. `union_from_iterable(format2ds)`: title/legend/labels must be identical across the set (single-element `set()` unpack — raises `ValueError` on mismatch); limits take `min`/`max` across the group; grid is unioned via `Grid.union_from_iterable`. This is the mechanism by which multiple products sharing a tag "agree" on one figure's formatting — it's a hard failure mode if two products on the same tag specify conflicting titles/labels, which the rewrite should probably surface as a clearer error rather than an unpacking `ValueError`. +- **`HistogramStyle`** (`HashableBase`): `color: str = "k"`, `label: str|None`, `histtype: str = "stepfilled"`, `alpha_edge: float = 0`, `alpha_face: float = 0.3`, `linewidth: float = 2`, `zorder: int = 1`, `bins: int|list[int]|tuple[int]|NDArray|None = None`. `as_plot_kwargs()` → matplotlib `hist()` kwargs. `rgba_face`/`rgba_edge`/`rgb_face`/`rgb_edge`/`get_face_contrast_color()` mirror `Pen`. + +All the `*_contrast_color` / `rgba` implementations (`Pen`, `Marker`, `HistogramStyle`) are +**copy-pasted three times** with identical logic — a natural dedup target for the rewrite (e.g. a +shared `ColorMixin` or free functions taking `color, alpha`). + +### 3.4 Collections and bookkeeping (`api/generator/data_product_collection.py`) + +- **`DataProductCollection`** (plain `BaseModel`, not `DataProduct`): `derived_from: Path|None`, + `elements: ProductList = []`. Custom `__init__` calls + `DataProduct.deserialize_child_classes(key="elements", **kwargs)` before `super().__init__` — this + is the polymorphic-load hook. Rich query API: `get_tags(type|None)`, `add_products(*products)` + (flattens nested lists/generators via `flatten()`), `drop_products(tag=, object_type=)`, + `get_products(tag=, object_type=)` (returns a new collection), `get_products_of_type(object_type, + tag=)` (returns a typed `list[R]`, more convenient than `get_products(...).elements`), `union(*collections)` + classmethod, `collect_from_all_jsons(*dirs, recursive=, data_products_filename=)` classmethod + (glob + union), `sort_by_tags` / `sort_by_tags_single_directory` (the sort step described in §1), + `process_collection` (the static-asset-rendering step described in §1), + `process_tables_for_tag`, `process_tag_for_streamlit` (the Streamlit-only rendering path), + `generate_plotly_dashboard`/`serve_plotly_dashboard`/`collect_and_serve_plotly_dashboard`. +- **`ProductEntryMetadata`**: `{source: Path}`, hashed via its own JSON dump (used as a dict key). +- **`ProductIndexMap`**: `{entries: dict[int, ProductEntryMetadata]}` persisted as a JSON file + named literally `index_map` (no extension) inside each sorted tag directory. `get_index(save_dir, + metadata)` classmethod does read-modify-write under a `FileLock` to hand out a stable integer + index per source file per tag directory — this is what keeps re-running the pipeline from + duplicating or renumbering already-sorted product files, and is also what the Streamlit UI's + `get_tags()` uses to discover which folders are "real" tags (it globs for files literally named + `index_map`). +- Free functions `flatten(obj)` (recursive flatten that stops at `str`/`bytes`/`DataProduct`), + `atleast_1d(obj)`, `_squeeze(obj)` — small utilities used throughout for accepting either a + scalar tag or tuple tag interchangeably. +- Three multi-line UQL query string constants (`UQL_TableEntry`, `UQL_Point2D`, `UQL_Trace2D`) and + a fully commented-out `make_grafana_panels` classmethod — dead Grafana integration, see §1. + +## 4. Rendering pipelines (kept deliberately separate today — a rewrite could unify them) + +| | Static (matplotlib) | Interactive (Plotly/Dash) | Interactive (Streamlit) | +|---|---|---|---| +| Entry point | `DataProductCollection.process_collection` | `plotly_dashboard.generate_plotly_dashboard` | `streamlit.make_dashboard` | +| Driven by | sorted-by-tag directory tree on disk | one `DataProductCollection` in memory | sorted-by-tag directory tree on disk (`index_map` discovery) | +| Per-tag output | one shared matplotlib `Axes` (`SingleAxisFigure`), XY + histogram overlaid, saved to `.jpg` | one Dash "tab" per tag with XY graph + table + histogram sections, dropdown-driven | one `PlotlyFigure` per tag built by `process_tag_for_streamlit`, calling each product's `add_to_plotly` | +| `add_to_plotly` used? | No (matplotlib-only `plot_to_ax`) | No (dashboard rebuilds traces from raw fields, duplicating logic in `_create_xy_figure`/`_create_histogram_figure`) | Yes — the only caller of `Point2D.add_to_plotly`, `Trace2D.add_to_plotly`, `AxLine.add_to_plotly`, `HistogramEntry.add_to_plotly` | +| Format handling | `Format2D.union_from_iterable` across all products for the tag, applied once | ad hoc: takes the *first* product's `format2d` and applies a hand-rolled subset of fields directly to Dash's `go.Figure.update_layout` | `Format2D.union_from_iterable`, applied via `PlotlyFigure.apply_format`/`apply_grid` | + +Consequence for the rewrite: **`add_to_plotly` is a real, load-bearing interface** (each +`PlottableData2D` subclass implements it) but it's currently only exercised by the Streamlit path +— the Dash dashboard (`plotly_dashboard.py`) reimplements equivalent logic from scratch with its +own color-cycling and legend-dedup bookkeeping. `PlotlyFigure.add_data_product` in `plotting.py` +is a stub that literally raises `NotImplementedError()` before an unreachable line calling +`product.add_to_plotly` — dead/aspirational code for a unification that never happened. If the +rewrite unifies these three renderers, `add_to_plotly` + `Format2D`/`Grid`/`Legend` "union and +apply" is the design to standardize on; the Dash-specific bespoke logic in `plotly_dashboard.py` +is the one to retire. + +## 5. CLI (`cli.py`) + +`trendify` (argparse, subcommands via `add_subparsers`): + +- `trendify make static -i -g -o [-n procs] [-f data-products-filename]` + — full pipeline: generate → sort → static assets. +- `trendify make dashboard -o ` — writes a `streamlit_run.py` launcher script into + `/assets/dashboard/` (does **not** itself generate/sort products — assumes that already happened). +- `trendify make gallery [-o ]` — runs the bundled example generator (`gallery.py`), then + shells out to `trendify products-sort` and `trendify assets-make-static` as subprocesses. +- `trendify serve plotly -i -g [--title] [--host] [--port]` — + generate then serve Dash dashboard directly (no sort/asset step). +- 1:1 API wrappers: `products-make`, `products-sort`, `products-serve`, `assets-make-static` — thin + argparse shims over `api.make_products` / `api.sort_products` / + `api.serve_products_to_plotly_dashboard` / `api.make_tables_and_figures`. +- Global `-v`/`-vv` verbosity flag → `logging.basicConfig` writing to both stdout and + `./trendify.log` (relative to CWD, always append mode — this is likely the "multiprocessing + logging broken on Windows" TODO item, since each subprocess calling `basicConfig` again / writing + to the same file without coordination is a known cross-platform hazard). +- `UserMethod.process_argument` resolves `-g/--product-generator` from a string like + `package.module:Class.method` or `/path/to/file.py:function`, supporting dotted attribute access + for `Class.method` — this is the mechanism for pointing the CLI at user code without a plugin + system. +- **Known bug**: `pyproject.toml`'s `[project.scripts]` maps `trendify = "trendify.CLI:trendify"` + but the module is `trendify/cli.py` (lowercase). This only works on case-insensitive filesystems + (macOS default, Windows) and would fail to import on Linux/case-sensitive installs. Worth fixing + outright rather than "preserving" in the rewrite. + +## 6. Other entry points / helper modules + +- **`examples.py`**: `make_example_data(workdir, n_folders=10)` synthesizes noisy multi-frequency + sine-wave CSVs (`results.csv` + `stdin.csv` per numbered subfolder under `workdir/models/`). + `example_data_product_generator(workdir)` is the reference implementation of a `ProductGenerator` + — demonstrates nearly every product type and format option (traces with multiple tags on the + same data, log/linear scale combinations, `AxLine`s, `Point2D` scatter of point-count vs run, + `TableEntry` + `HistogramEntry` + companion mean `AxLine`). Good source of "what must still work" + examples for rewrite acceptance tests. Also exposes a `trendify_make_sample_data` console script + (`make_sample_data`) and a `_main()` used as `python -m trendify.examples`-style smoke test + (generates 100 runs, makes products, sorts, renders tables/figures — no dashboard). +- **`gallery.py`**: independent of `examples.py`; hand-builds a small fixed set of products (sine + wave with peak detection, two Gaussian clusters + centroids, three-distribution histogram + comparison, a distributions×metrics stats table), asserts on `product.py`... actually copies + itself (`gallery.py`) into the output dir for user reference, writes `data_products.json` + directly (skipping the generate step), then **shells out** to the `trendify` console script + (`subprocess.run(["trendify", "products-sort", ...])` then `["trendify", + "assets-make-static", ...])`, with a Windows `shell=True` branch. This is the only place in the + codebase that invokes trendify as a subprocess rather than calling the Python API directly — + worth deciding whether the rewrite keeps that indirection (it does exercise the actual installed + console script, which is a plausible intentional integration-test-like design) or calls the API + functions in-process like everywhere else. +- **`streamlit.py`**: `make_streamlit(trendify_dir)` writes a small launcher script + a + `.streamlit/config.toml` theme file (hardcoded to `trendify` pink `#e92063`). `make_dashboard` + itself does the actual UI: sidebar built by recursively grouping the tag tuples found via + `get_tags` (which walks for files named `index_map`) into nested `st.expander`s with buttons, + `st.session_state.selected_tags` tracks selection, main page uses `@st.cache_data` on + `process_tag` (keyed by tag+dir) to avoid recomputation, then renders one `PlotlyFigure` with a + small form for height/tooltip-mode controls. Only handles the "collection produced a + `PlotlyFigure`" case; anything else shows `st.warning(...)`. Note the docs/issue-tracker URLs and + `st.logo` reference bundled SVG assets (`trendify.assets/logo.svg`, `logo_white_bg.svg`) that + must ship with the package (`importlib.resources`). +- **`production_server.py`**: thin `waitress.serve(app.server, ...)` wrapper; `run_production_server` + accepts either a `dash.Dash` or a `DataProductCollection` (builds the dashboard for you in the + latter case) and requires `waitress`+`dash` be importable or exits via `sys.exit(1)` with a + printed message (not a raised exception — inconsistent with the rest of the codebase's use of + `logger`). +- **`server.py`** / **`local_server.py`**: see §1, candidates for deletion. + +## 7. Cross-cutting behaviors worth preserving deliberately + +- **Parallelism model**: `map_callable` in `api.py` is the single place that decides + sequential-`for`-loop vs `ProcessPoolExecutor`, based on `n_procs`. Every stage (generate, sort, + render) goes through it, so `n_procs=1` is explicitly documented as the "get a real traceback" + debug mode. Any rewrite of the execution model should preserve an easy "run everything in-process + with full tracebacks" switch. +- **Tag-driven nested directories**: a tuple tag `("a", "b")` becomes a two-level directory, a + scalar tag `"a"` becomes one level. This convention is depended on by: the sort step (path + construction), the Streamlit tag discovery (splitting `Path.relative_to(products_dir).parts`), + and `make_include_files`. If tag typing gets stricter in the rewrite, this folder convention is + the main external contract to keep, since users' existing sorted-output directories (and anyone + scripting around them, e.g. MkDocs includes) depend on it. +- **JSON compatibility / discriminator field**: every serialized `DataProduct` JSON object has a + `product_type` string key holding the exact class name. If the rewrite wants old + `data_products.json` files (already generated and sitting on disk from prior runs) to keep + loading, either keep class names identical or write an explicit migration/alias step — don't + rename `Trace2D`/`Point2D`/etc. without a plan for existing data. + [`pydantic_schemas.json`](pydantic_schemas.json) is the field-level source of truth to diff + against for any planned schema change. + Similarly, `HashableBase.__hash__` (`hash((type(self),) + tuple(self.__dict__.values()))`) is + used to de-duplicate `Pen`/`Marker`/`Grid`/`HistogramStyle`/`Format2D` instances via `set()` in + several places (`Trace2D` grouping by pen in `plotly_dashboard`, `Histogrammer` grouping by + style, `Format2D.union_from_iterable`, `Grid.union_from_iterable`). This requires every field on + those classes to itself be hashable — e.g. `bbox_to_anchor` is a `tuple` not a `list`, + `linestyle` accepts a `tuple[int, tuple[int,...]]` not `list[int]`, precisely so instances stay + hashable. Any new field added to these classes must preserve hashability or these `set()`-based + dedup/merge operations will start raising `TypeError: unhashable type`. +- **`extra="forbid"` vs `extra="allow"`**: the `DataProduct` base class allows extra fields (so old + JSON with now-unknown extra keys still loads), but essentially every concrete subclass and every + style class overrides to `extra="forbid"` (stricter, catches typos). Worth keeping this pattern + intentionally rather than picking one globally. +- **Metadata dict** (`dict[str,str]`) on every product is surfaced as a hover tooltip in both the + Streamlit-fed Plotly figures (`add_to_plotly` implementations build an HTML `
`-joined + `hovertemplate`) and was intended for Grafana tooltips (per docstrings) — a real, used feature to + keep, not a documentation artifact. + +## 8. Known bugs / rough edges observed while reading (not exhaustive, worth triaging before porting "as-is") + +1. `Marker.from_pen` (`marker.py`) — `pen.model_dump().pop("linestyle")` discards the whole dict + and returns one value, then splats it as `**kwargs` into `Marker(...)`. Will raise at runtime if + ever actually called with a realistic `Pen`. Only caller: `Trace2D.propagate_format2d_and_pen` + (itself grep-unreferenced elsewhere in `src/`). +2. `Marker.as_scatter_plot_kwargs` has the dict key `"marker"` twice (last wins; harmless but + dead code). +3. `PlotlyFigure.apply_format`'s x/y-range-under-log-scale branches both check + `if format2d.scale_x == AxisScale.LOG` / `elif format2d.scale_x == AxisScale.LOG` (the `elif` + should almost certainly be `AxisScale.LINEAR` given the surrounding log10-transform logic) — the + non-log branch for setting a plain range is currently unreachable. +4. `PlotlyFigure.add_data_product` raises `NotImplementedError()` unconditionally with unreachable + code after it — stub method, never completed, never called (Dash path doesn't use it). +5. `Histogrammer.plotly_histogram` also raises `NotImplementedError()` — histogram Plotly rendering + actually happens via `HistogramEntry.add_to_plotly` instead; this method looks unused/orphaned. +6. `pyproject.toml` console script points at `trendify.CLI:trendify` — wrong case vs. actual + `trendify/cli.py`. +7. `server.py` has a hardcoded absolute path (`/Users/talbotknighton/...`) as `DATABASE_ROOT` — + can't work on any other machine as-is. +8. Grid/Format2D "union" merges silently assume homogeneity (single-element `set()` unpacking + raises a bare `ValueError: too many values to unpack` with no context if two products sharing a + tag disagree on title/label/grid) — a confusing failure mode for end users that a rewrite could + turn into a clear, actionable error. +9. Static asset rendering (`process_collection`) draws XY data and histogram data onto the *same* + `SingleAxisFigure`/axes for a shared tag — if a tag is used for both `Point2D`/`Trace2D` and + `HistogramEntry` simultaneously, they'll overlay on one plot, which is very likely unintended + given the interactive dashboards keep these as separate sections per tag. +10. `TableEntry.value: float | str | bool` but the class docstring only documents `float | str` — + minor doc/type drift. + +## 9. Dependencies (from `pyproject.toml`) + +Runtime: `dash`, `filelock`, `flask`, `grafana-api` (dead weight per §1/§6), `jinja2`, `matplotlib`, +`numpy`, `numpydantic`, `pandas`, `plotly`, `pydantic>=2`, `scipy` (only used, optionally, for KDE +in the Dash histogram section), `streamlit`, `strenum` (redundant with the stdlib `enum.StrEnum` +already used directly in `format2d.py`/`legend.py`/`grid.py` on Python ≥3.11 — the project already +requires `>=3.11`, so the `strenum` dependency in `helpers.py`/`examples.py` is likely vestigial), +`supervisor`, `waitress`. Python `>=3.11`. + +## 10. Suggested reading order if resuming this rewrite later + +1. `api/base/data_product.py` + `api/base/helpers.py` — the polymorphism and tagging foundation. +2. `api/generator/data_product_collection.py` — the central data structure and sort/query/process logic. +3. `pydantic_schemas.json` — exact current wire format, field by field. +4. `api/plotting/*.py` + `api/formats/format2d.py` — the concrete product types and their + `plot_to_ax`/`add_to_plotly` dual implementations. +5. `api/generator/{histogrammer,table_builder,xy_data_plotter}.py` + `api/api.py` — how products + become static assets. +6. `plotly_dashboard.py` vs `streamlit.py` — the two divergent interactive renderers (§4 table). +7. `cli.py` — how it's all wired together for end users. diff --git a/rewrite_reference/dump_schemas.py b/rewrite_reference/dump_schemas.py new file mode 100644 index 0000000..a2c2b76 --- /dev/null +++ b/rewrite_reference/dump_schemas.py @@ -0,0 +1,48 @@ +""" +Dumps JSON schemas for every pydantic model in trendify's public API surface. +Run with the project's venv: .venv/bin/python dump_schemas.py +""" + +import json +from pathlib import Path + +import trendify as t +from trendify.api.base.data_product import DataProduct, _data_product_subclass_registry +from trendify.api.generator.data_product_collection import ( + DataProductCollection, + ProductEntryMetadata, + ProductIndexMap, +) + +# All pydantic BaseModel classes that make up the public schema surface. +models = { + # base + "DataProduct": DataProduct, + # data product subclasses (auto-registered via __init_subclass__) + **_data_product_subclass_registry, + # collection / bookkeeping + "DataProductCollection": DataProductCollection, + "ProductEntryMetadata": ProductEntryMetadata, + "ProductIndexMap": ProductIndexMap, + # style / format + "Pen": t.Pen, + "Marker": t.Marker, + "Legend": t.Legend, + "Grid": t.Grid, + "GridAxis": t.GridAxis, + "Format2D": t.Format2D, + "HistogramStyle": t.HistogramStyle, +} + +out = {} +for name, cls in sorted(models.items()): + try: + out[name] = cls.model_json_schema() + except Exception as e: + out[name] = {"__error__": str(e)} + +dest = Path("/home/gable/git/trendify/rewrite_reference/pydantic_schemas.json") +dest.write_text(json.dumps(out, indent=2, default=str)) +print(f"Wrote {len(out)} schemas to {dest}") +for name in sorted(out): + print(" -", name) diff --git a/rewrite_reference/pydantic_schemas.json b/rewrite_reference/pydantic_schemas.json new file mode 100644 index 0000000..f432280 --- /dev/null +++ b/rewrite_reference/pydantic_schemas.json @@ -0,0 +1,4479 @@ +{ + "AxLine": { + "$defs": { + "AxisScale": { + "enum": [ + "linear", + "log" + ], + "title": "AxisScale", + "type": "string" + }, + "Format2D": { + "additionalProperties": false, + "description": "Formatting data for matplotlib figure and axes\n\nAttributes:\n title_fig (Optional[str], optional): Sets [figure title][matplotlib.figure.Figure.suptitle]. Defaults to None.\n legend (Optional[Legend], optional): Sets [legend style][trendify.api.styling.legend.Legend]. Defaults to Legend().\n title_ax (Optional[str], optional): Sets [axis title][matplotlib.axes.Axes.set_title]. Defaults to None.\n label_x (Optional[str], optional): Sets [x-axis label][matplotlib.axes.Axes.set_xlabel]. Defaults to None.\n label_y (Optional[str], optional): Sets [y-axis label][matplotlib.axes.Axes.set_ylabel]. Defaults to None.\n lim_x_min (float | None, optional): Sets [x-axis lower bound][matplotlib.axes.Axes.set_xlim]. Defaults to None.\n lim_x_max (float | None, optional): Sets [x-axis upper bound][matplotlib.axes.Axes.set_xlim]. Defaults to None.\n lim_y_min (float | None, optional): Sets [y-axis lower bound][matplotlib.axes.Axes.set_ylim]. Defaults to None.\n lim_y_max (float | None, optional): Sets [y-axis upper bound][matplotlib.axes.Axes.set_ylim]. Defaults to None.\n grid (Grid | None,optional): Sets the [grid][matplotlib.pyplot.grid]. Defaults to None.\n scale_x (AxisScale, optional): Sets the x axis scale to an option from [AxisScale][trendify.api.formats.format2d.AxisScale]. Defaults to AxisScale.LINEAR\n scale_y (AxisScale, optional): Sets the y axis scale to an option from [AxisScale][trendify.api.formats.format2d.AxisScale]. Defaults to AxisScale.LINEAR\n figure_width (float, optional): Sets the of the width of rendered figure in inches. Defaults to 6.4.\n figure_height (float, optional): Sets the of the height of rendered figure in inches. Defaults to 4.8.", + "properties": { + "title_fig": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Title Fig" + }, + "legend": { + "anyOf": [ + { + "$ref": "#/$defs/Legend" + }, + { + "type": "null" + } + ], + "default": { + "visible": true, + "title": null, + "framealpha": 1.0, + "loc": "best", + "ncol": 1, + "fancybox": true, + "edgecolor": "black", + "zorder": 10, + "bbox_to_anchor": null + } + }, + "title_ax": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Title Ax" + }, + "label_x": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Label X" + }, + "label_y": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Label Y" + }, + "lim_x_min": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Lim X Min" + }, + "lim_x_max": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Lim X Max" + }, + "lim_y_min": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Lim Y Min" + }, + "lim_y_max": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Lim Y Max" + }, + "grid": { + "anyOf": [ + { + "$ref": "#/$defs/Grid" + }, + { + "type": "null" + } + ], + "default": null + }, + "scale_x": { + "$ref": "#/$defs/AxisScale", + "default": "linear" + }, + "scale_y": { + "$ref": "#/$defs/AxisScale", + "default": "linear" + }, + "figure_width": { + "default": 6.4, + "title": "Figure Width", + "type": "number" + }, + "figure_height": { + "default": 4.8, + "title": "Figure Height", + "type": "number" + } + }, + "title": "Format2D", + "type": "object" + }, + "Grid": { + "additionalProperties": false, + "description": "Container for major and minor grid line configuration.\n\nAttributes:\n major (GridAxis): Configuration for major grid lines.\n minor (GridAxis): Configuration for minor grid lines.\n enable_minor_ticks (bool): Whether to enable minor ticks on the axes.", + "properties": { + "major": { + "$ref": "#/$defs/GridAxis", + "default": { + "show": false, + "pen": { + "alpha": 1.0, + "color": "gray", + "label": null, + "linestyle": "-", + "size": 0.75, + "zorder": 0.0 + } + } + }, + "minor": { + "$ref": "#/$defs/GridAxis", + "default": { + "show": false, + "pen": { + "alpha": 1.0, + "color": "gray", + "label": null, + "linestyle": "-", + "size": 0.75, + "zorder": 0.0 + } + } + }, + "enable_minor_ticks": { + "default": false, + "title": "Enable Minor Ticks", + "type": "boolean" + }, + "zorder": { + "default": -1, + "title": "Zorder", + "type": "number" + } + }, + "title": "Grid", + "type": "object" + }, + "GridAxis": { + "additionalProperties": false, + "description": "Controls styling and visibility for one type of grid (major or minor).\n\nAttributes:\n show (bool): Whether to display this grid axis.\n pen (Pen): Style and label information for drawing to matplotlib axes.", + "properties": { + "show": { + "default": false, + "title": "Show", + "type": "boolean" + }, + "pen": { + "$ref": "#/$defs/Pen", + "default": { + "color": "gray", + "size": 0.75, + "alpha": 1.0, + "zorder": 0.0, + "linestyle": "-", + "label": null + } + } + }, + "title": "GridAxis", + "type": "object" + }, + "Legend": { + "description": "Configuration container for Matplotlib legend styling and placement.\n\nThe `Legend` class controls the appearance and position of the plot legend.\nPlacement is governed by a combination of the `loc` and `bbox_to_anchor`\nparameters, mirroring Matplotlib's `Axes.legend()`.\n\nAttributes:\n visible (bool): Whether the legend should be displayed. Defaults to True.\n title (str | None): Title displayed above the legend entries.\n framealpha (float): Opacity of the legend background. 1 = fully opaque, 0 = fully transparent.\n loc (LegendLocation): Anchor point for the legend (e.g., upper right, lower left). See `LegendLocation` enum for options.\n ncol (int): Number of columns to arrange legend entries into.\n fancybox (bool): Whether to draw a rounded (True) or square (False) legend frame.\n edgecolor (str): Color of the legend frame border. Default is \"black\".\n bbox_to_anchor (tuple[float, float] | None): Offset position of the legend in figure or axes coordinates. If None, the legend is placed inside the axes using `loc`.\n\n Good starter values for common placements:\n\n - **Inside (default)**:\n ```python\n bbox_to_anchor=None\n ```\n - **Outside right**:\n ```python\n loc=LegendLocation.CENTER_LEFT\n bbox_to_anchor=(1.02, 0.5)\n ```\n - **Outside left**:\n ```python\n loc=LegendLocation.CENTER_RIGHT\n bbox_to_anchor=(-0.02, 0.5)\n ```\n - **Outside top**:\n ```python\n loc=LegendLocation.LOWER_CENTER\n bbox_to_anchor=(0.5, 1.02)\n ```\n - **Outside bottom**:\n ```python\n loc=LegendLocation.UPPER_CENTER\n bbox_to_anchor=(0.5, -0.02)\n ```", + "properties": { + "visible": { + "default": true, + "title": "Visible", + "type": "boolean" + }, + "title": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Title" + }, + "framealpha": { + "default": 1, + "title": "Framealpha", + "type": "number" + }, + "loc": { + "$ref": "#/$defs/LegendLocation", + "default": "best" + }, + "ncol": { + "default": 1, + "title": "Ncol", + "type": "integer" + }, + "fancybox": { + "default": true, + "title": "Fancybox", + "type": "boolean" + }, + "edgecolor": { + "default": "black", + "title": "Edgecolor", + "type": "string" + }, + "zorder": { + "default": 10, + "title": "Zorder", + "type": "integer" + }, + "bbox_to_anchor": { + "anyOf": [ + { + "maxItems": 2, + "minItems": 2, + "prefixItems": [ + { + "type": "number" + }, + { + "type": "number" + } + ], + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Bbox To Anchor" + } + }, + "title": "Legend", + "type": "object" + }, + "LegendLocation": { + "enum": [ + "best", + "upper right", + "upper left", + "lower left", + "lower right", + "right", + "center left", + "center right", + "lower center", + "upper center", + "center" + ], + "title": "LegendLocation", + "type": "string" + }, + "LineOrientation": { + "description": "Defines orientation for axis lines\n\nAttributes:\n HORIZONTAL (LineOrientation): Horizontal line\n VERTICAL (LineOrientation): Vertical line", + "enum": [ + "horizontal", + "vertical" + ], + "title": "LineOrientation", + "type": "string" + }, + "Pen": { + "additionalProperties": false, + "description": "Defines the pen drawing to matplotlib.\n\nAttributes:\n color (str): Color of line\n size (float): Line width\n alpha (float): Opacity from 0 to 1 (inclusive)\n linestyle (Union[str, Tuple[int, Tuple[int, ...]]]): Linestyle to plot. Supports `str` or `tuple` definition ([matplotlib documentation](https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html)).\n zorder (float): Prioritization\n label (Union[str, None]): Legend label", + "properties": { + "color": { + "anyOf": [ + { + "maxItems": 3, + "minItems": 3, + "prefixItems": [ + { + "type": "number" + }, + { + "type": "number" + }, + { + "type": "number" + } + ], + "type": "array" + }, + { + "maxItems": 4, + "minItems": 4, + "prefixItems": [ + { + "type": "number" + }, + { + "type": "number" + }, + { + "type": "number" + }, + { + "type": "number" + } + ], + "type": "array" + }, + { + "type": "string" + } + ], + "default": "k", + "title": "Color" + }, + "size": { + "default": 1, + "title": "Size", + "type": "number" + }, + "alpha": { + "default": 1, + "title": "Alpha", + "type": "number" + }, + "zorder": { + "default": 0, + "title": "Zorder", + "type": "number" + }, + "linestyle": { + "anyOf": [ + { + "type": "string" + }, + { + "maxItems": 2, + "minItems": 2, + "prefixItems": [ + { + "type": "integer" + }, + { + "items": { + "type": "integer" + }, + "type": "array" + } + ], + "type": "array" + } + ], + "default": "-", + "title": "Linestyle" + }, + "label": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Label" + } + }, + "title": "Pen", + "type": "object" + } + }, + "additionalProperties": false, + "description": "Defines a horizontal or vertical line to be drawn on a plot.\n\nAttributes:\n value (float): Value at which to draw the line (x-value for vertical, y-value for horizontal)\n orientation (LineOrientation): Whether line should be horizontal or vertical\n pen (Pen): Style and label information for drawing to matplotlib axes\n tags (Tags): Tags to be used for sorting data\n metadata (dict[str, str]): A dictionary of metadata", + "properties": { + "tags": { + "items": { + "anyOf": [ + { + "items": {}, + "type": "array" + }, + {} + ] + }, + "title": "Tags", + "type": "array" + }, + "metadata": { + "additionalProperties": { + "type": "string" + }, + "default": {}, + "title": "Metadata", + "type": "object" + }, + "format2d": { + "anyOf": [ + { + "$ref": "#/$defs/Format2D" + }, + { + "type": "null" + } + ], + "default": null + }, + "value": { + "title": "Value", + "type": "number" + }, + "orientation": { + "$ref": "#/$defs/LineOrientation" + }, + "pen": { + "$ref": "#/$defs/Pen", + "default": { + "color": "k", + "size": 1.0, + "alpha": 1.0, + "zorder": 0.0, + "linestyle": "-", + "label": null + } + } + }, + "required": [ + "tags", + "value", + "orientation" + ], + "title": "AxLine", + "type": "object" + }, + "DataProduct": { + "additionalProperties": true, + "description": "Base class for data products to be generated and handled.\n\nAttributes:\n product_type (str): Product type should be the same as the class name.\n The product type is used to search for products from a [DataProductCollection][trendify.api.DataProductCollection].\n tags (Tags): Tags to be used for sorting data.\n metadata (dict[str, str]): A dictionary of metadata to be used as a tool tip for mousover in grafana", + "properties": { + "tags": { + "items": { + "anyOf": [ + { + "items": {}, + "type": "array" + }, + {} + ] + }, + "title": "Tags", + "type": "array" + }, + "metadata": { + "additionalProperties": { + "type": "string" + }, + "default": {}, + "title": "Metadata", + "type": "object" + } + }, + "required": [ + "tags" + ], + "title": "DataProduct", + "type": "object" + }, + "DataProductCollection": { + "$defs": { + "DataProduct": { + "additionalProperties": true, + "description": "Base class for data products to be generated and handled.\n\nAttributes:\n product_type (str): Product type should be the same as the class name.\n The product type is used to search for products from a [DataProductCollection][trendify.api.DataProductCollection].\n tags (Tags): Tags to be used for sorting data.\n metadata (dict[str, str]): A dictionary of metadata to be used as a tool tip for mousover in grafana", + "properties": { + "tags": { + "items": { + "anyOf": [ + { + "items": {}, + "type": "array" + }, + {} + ] + }, + "title": "Tags", + "type": "array" + }, + "metadata": { + "additionalProperties": { + "type": "string" + }, + "default": {}, + "title": "Metadata", + "type": "object" + } + }, + "required": [ + "tags" + ], + "title": "DataProduct", + "type": "object" + } + }, + "description": "A collection of data products.\n\nUse this class to serialize data products to JSON, de-serialized them from JSON, filter the products, etc.\n\nAttributes:\n elements (ProductList): A list of data products.", + "properties": { + "derived_from": { + "anyOf": [ + { + "format": "path", + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Derived From" + }, + "elements": { + "items": { + "$ref": "#/$defs/DataProduct" + }, + "title": "Elements", + "type": "array" + } + }, + "title": "DataProductCollection", + "type": "object" + }, + "Format2D": { + "$defs": { + "AxisScale": { + "enum": [ + "linear", + "log" + ], + "title": "AxisScale", + "type": "string" + }, + "Grid": { + "additionalProperties": false, + "description": "Container for major and minor grid line configuration.\n\nAttributes:\n major (GridAxis): Configuration for major grid lines.\n minor (GridAxis): Configuration for minor grid lines.\n enable_minor_ticks (bool): Whether to enable minor ticks on the axes.", + "properties": { + "major": { + "$ref": "#/$defs/GridAxis", + "default": { + "show": false, + "pen": { + "alpha": 1.0, + "color": "gray", + "label": null, + "linestyle": "-", + "size": 0.75, + "zorder": 0.0 + } + } + }, + "minor": { + "$ref": "#/$defs/GridAxis", + "default": { + "show": false, + "pen": { + "alpha": 1.0, + "color": "gray", + "label": null, + "linestyle": "-", + "size": 0.75, + "zorder": 0.0 + } + } + }, + "enable_minor_ticks": { + "default": false, + "title": "Enable Minor Ticks", + "type": "boolean" + }, + "zorder": { + "default": -1, + "title": "Zorder", + "type": "number" + } + }, + "title": "Grid", + "type": "object" + }, + "GridAxis": { + "additionalProperties": false, + "description": "Controls styling and visibility for one type of grid (major or minor).\n\nAttributes:\n show (bool): Whether to display this grid axis.\n pen (Pen): Style and label information for drawing to matplotlib axes.", + "properties": { + "show": { + "default": false, + "title": "Show", + "type": "boolean" + }, + "pen": { + "$ref": "#/$defs/Pen", + "default": { + "color": "gray", + "size": 0.75, + "alpha": 1.0, + "zorder": 0.0, + "linestyle": "-", + "label": null + } + } + }, + "title": "GridAxis", + "type": "object" + }, + "Legend": { + "description": "Configuration container for Matplotlib legend styling and placement.\n\nThe `Legend` class controls the appearance and position of the plot legend.\nPlacement is governed by a combination of the `loc` and `bbox_to_anchor`\nparameters, mirroring Matplotlib's `Axes.legend()`.\n\nAttributes:\n visible (bool): Whether the legend should be displayed. Defaults to True.\n title (str | None): Title displayed above the legend entries.\n framealpha (float): Opacity of the legend background. 1 = fully opaque, 0 = fully transparent.\n loc (LegendLocation): Anchor point for the legend (e.g., upper right, lower left). See `LegendLocation` enum for options.\n ncol (int): Number of columns to arrange legend entries into.\n fancybox (bool): Whether to draw a rounded (True) or square (False) legend frame.\n edgecolor (str): Color of the legend frame border. Default is \"black\".\n bbox_to_anchor (tuple[float, float] | None): Offset position of the legend in figure or axes coordinates. If None, the legend is placed inside the axes using `loc`.\n\n Good starter values for common placements:\n\n - **Inside (default)**:\n ```python\n bbox_to_anchor=None\n ```\n - **Outside right**:\n ```python\n loc=LegendLocation.CENTER_LEFT\n bbox_to_anchor=(1.02, 0.5)\n ```\n - **Outside left**:\n ```python\n loc=LegendLocation.CENTER_RIGHT\n bbox_to_anchor=(-0.02, 0.5)\n ```\n - **Outside top**:\n ```python\n loc=LegendLocation.LOWER_CENTER\n bbox_to_anchor=(0.5, 1.02)\n ```\n - **Outside bottom**:\n ```python\n loc=LegendLocation.UPPER_CENTER\n bbox_to_anchor=(0.5, -0.02)\n ```", + "properties": { + "visible": { + "default": true, + "title": "Visible", + "type": "boolean" + }, + "title": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Title" + }, + "framealpha": { + "default": 1, + "title": "Framealpha", + "type": "number" + }, + "loc": { + "$ref": "#/$defs/LegendLocation", + "default": "best" + }, + "ncol": { + "default": 1, + "title": "Ncol", + "type": "integer" + }, + "fancybox": { + "default": true, + "title": "Fancybox", + "type": "boolean" + }, + "edgecolor": { + "default": "black", + "title": "Edgecolor", + "type": "string" + }, + "zorder": { + "default": 10, + "title": "Zorder", + "type": "integer" + }, + "bbox_to_anchor": { + "anyOf": [ + { + "maxItems": 2, + "minItems": 2, + "prefixItems": [ + { + "type": "number" + }, + { + "type": "number" + } + ], + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Bbox To Anchor" + } + }, + "title": "Legend", + "type": "object" + }, + "LegendLocation": { + "enum": [ + "best", + "upper right", + "upper left", + "lower left", + "lower right", + "right", + "center left", + "center right", + "lower center", + "upper center", + "center" + ], + "title": "LegendLocation", + "type": "string" + }, + "Pen": { + "additionalProperties": false, + "description": "Defines the pen drawing to matplotlib.\n\nAttributes:\n color (str): Color of line\n size (float): Line width\n alpha (float): Opacity from 0 to 1 (inclusive)\n linestyle (Union[str, Tuple[int, Tuple[int, ...]]]): Linestyle to plot. Supports `str` or `tuple` definition ([matplotlib documentation](https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html)).\n zorder (float): Prioritization\n label (Union[str, None]): Legend label", + "properties": { + "color": { + "anyOf": [ + { + "maxItems": 3, + "minItems": 3, + "prefixItems": [ + { + "type": "number" + }, + { + "type": "number" + }, + { + "type": "number" + } + ], + "type": "array" + }, + { + "maxItems": 4, + "minItems": 4, + "prefixItems": [ + { + "type": "number" + }, + { + "type": "number" + }, + { + "type": "number" + }, + { + "type": "number" + } + ], + "type": "array" + }, + { + "type": "string" + } + ], + "default": "k", + "title": "Color" + }, + "size": { + "default": 1, + "title": "Size", + "type": "number" + }, + "alpha": { + "default": 1, + "title": "Alpha", + "type": "number" + }, + "zorder": { + "default": 0, + "title": "Zorder", + "type": "number" + }, + "linestyle": { + "anyOf": [ + { + "type": "string" + }, + { + "maxItems": 2, + "minItems": 2, + "prefixItems": [ + { + "type": "integer" + }, + { + "items": { + "type": "integer" + }, + "type": "array" + } + ], + "type": "array" + } + ], + "default": "-", + "title": "Linestyle" + }, + "label": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Label" + } + }, + "title": "Pen", + "type": "object" + } + }, + "additionalProperties": false, + "description": "Formatting data for matplotlib figure and axes\n\nAttributes:\n title_fig (Optional[str], optional): Sets [figure title][matplotlib.figure.Figure.suptitle]. Defaults to None.\n legend (Optional[Legend], optional): Sets [legend style][trendify.api.styling.legend.Legend]. Defaults to Legend().\n title_ax (Optional[str], optional): Sets [axis title][matplotlib.axes.Axes.set_title]. Defaults to None.\n label_x (Optional[str], optional): Sets [x-axis label][matplotlib.axes.Axes.set_xlabel]. Defaults to None.\n label_y (Optional[str], optional): Sets [y-axis label][matplotlib.axes.Axes.set_ylabel]. Defaults to None.\n lim_x_min (float | None, optional): Sets [x-axis lower bound][matplotlib.axes.Axes.set_xlim]. Defaults to None.\n lim_x_max (float | None, optional): Sets [x-axis upper bound][matplotlib.axes.Axes.set_xlim]. Defaults to None.\n lim_y_min (float | None, optional): Sets [y-axis lower bound][matplotlib.axes.Axes.set_ylim]. Defaults to None.\n lim_y_max (float | None, optional): Sets [y-axis upper bound][matplotlib.axes.Axes.set_ylim]. Defaults to None.\n grid (Grid | None,optional): Sets the [grid][matplotlib.pyplot.grid]. Defaults to None.\n scale_x (AxisScale, optional): Sets the x axis scale to an option from [AxisScale][trendify.api.formats.format2d.AxisScale]. Defaults to AxisScale.LINEAR\n scale_y (AxisScale, optional): Sets the y axis scale to an option from [AxisScale][trendify.api.formats.format2d.AxisScale]. Defaults to AxisScale.LINEAR\n figure_width (float, optional): Sets the of the width of rendered figure in inches. Defaults to 6.4.\n figure_height (float, optional): Sets the of the height of rendered figure in inches. Defaults to 4.8.", + "properties": { + "title_fig": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Title Fig" + }, + "legend": { + "anyOf": [ + { + "$ref": "#/$defs/Legend" + }, + { + "type": "null" + } + ], + "default": { + "visible": true, + "title": null, + "framealpha": 1.0, + "loc": "best", + "ncol": 1, + "fancybox": true, + "edgecolor": "black", + "zorder": 10, + "bbox_to_anchor": null + } + }, + "title_ax": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Title Ax" + }, + "label_x": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Label X" + }, + "label_y": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Label Y" + }, + "lim_x_min": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Lim X Min" + }, + "lim_x_max": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Lim X Max" + }, + "lim_y_min": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Lim Y Min" + }, + "lim_y_max": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Lim Y Max" + }, + "grid": { + "anyOf": [ + { + "$ref": "#/$defs/Grid" + }, + { + "type": "null" + } + ], + "default": null + }, + "scale_x": { + "$ref": "#/$defs/AxisScale", + "default": "linear" + }, + "scale_y": { + "$ref": "#/$defs/AxisScale", + "default": "linear" + }, + "figure_width": { + "default": 6.4, + "title": "Figure Width", + "type": "number" + }, + "figure_height": { + "default": 4.8, + "title": "Figure Height", + "type": "number" + } + }, + "title": "Format2D", + "type": "object" + }, + "Grid": { + "$defs": { + "GridAxis": { + "additionalProperties": false, + "description": "Controls styling and visibility for one type of grid (major or minor).\n\nAttributes:\n show (bool): Whether to display this grid axis.\n pen (Pen): Style and label information for drawing to matplotlib axes.", + "properties": { + "show": { + "default": false, + "title": "Show", + "type": "boolean" + }, + "pen": { + "$ref": "#/$defs/Pen", + "default": { + "color": "gray", + "size": 0.75, + "alpha": 1.0, + "zorder": 0.0, + "linestyle": "-", + "label": null + } + } + }, + "title": "GridAxis", + "type": "object" + }, + "Pen": { + "additionalProperties": false, + "description": "Defines the pen drawing to matplotlib.\n\nAttributes:\n color (str): Color of line\n size (float): Line width\n alpha (float): Opacity from 0 to 1 (inclusive)\n linestyle (Union[str, Tuple[int, Tuple[int, ...]]]): Linestyle to plot. Supports `str` or `tuple` definition ([matplotlib documentation](https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html)).\n zorder (float): Prioritization\n label (Union[str, None]): Legend label", + "properties": { + "color": { + "anyOf": [ + { + "maxItems": 3, + "minItems": 3, + "prefixItems": [ + { + "type": "number" + }, + { + "type": "number" + }, + { + "type": "number" + } + ], + "type": "array" + }, + { + "maxItems": 4, + "minItems": 4, + "prefixItems": [ + { + "type": "number" + }, + { + "type": "number" + }, + { + "type": "number" + }, + { + "type": "number" + } + ], + "type": "array" + }, + { + "type": "string" + } + ], + "default": "k", + "title": "Color" + }, + "size": { + "default": 1, + "title": "Size", + "type": "number" + }, + "alpha": { + "default": 1, + "title": "Alpha", + "type": "number" + }, + "zorder": { + "default": 0, + "title": "Zorder", + "type": "number" + }, + "linestyle": { + "anyOf": [ + { + "type": "string" + }, + { + "maxItems": 2, + "minItems": 2, + "prefixItems": [ + { + "type": "integer" + }, + { + "items": { + "type": "integer" + }, + "type": "array" + } + ], + "type": "array" + } + ], + "default": "-", + "title": "Linestyle" + }, + "label": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Label" + } + }, + "title": "Pen", + "type": "object" + } + }, + "additionalProperties": false, + "description": "Container for major and minor grid line configuration.\n\nAttributes:\n major (GridAxis): Configuration for major grid lines.\n minor (GridAxis): Configuration for minor grid lines.\n enable_minor_ticks (bool): Whether to enable minor ticks on the axes.", + "properties": { + "major": { + "$ref": "#/$defs/GridAxis", + "default": { + "show": false, + "pen": { + "alpha": 1.0, + "color": "gray", + "label": null, + "linestyle": "-", + "size": 0.75, + "zorder": 0.0 + } + } + }, + "minor": { + "$ref": "#/$defs/GridAxis", + "default": { + "show": false, + "pen": { + "alpha": 1.0, + "color": "gray", + "label": null, + "linestyle": "-", + "size": 0.75, + "zorder": 0.0 + } + } + }, + "enable_minor_ticks": { + "default": false, + "title": "Enable Minor Ticks", + "type": "boolean" + }, + "zorder": { + "default": -1, + "title": "Zorder", + "type": "number" + } + }, + "title": "Grid", + "type": "object" + }, + "GridAxis": { + "$defs": { + "Pen": { + "additionalProperties": false, + "description": "Defines the pen drawing to matplotlib.\n\nAttributes:\n color (str): Color of line\n size (float): Line width\n alpha (float): Opacity from 0 to 1 (inclusive)\n linestyle (Union[str, Tuple[int, Tuple[int, ...]]]): Linestyle to plot. Supports `str` or `tuple` definition ([matplotlib documentation](https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html)).\n zorder (float): Prioritization\n label (Union[str, None]): Legend label", + "properties": { + "color": { + "anyOf": [ + { + "maxItems": 3, + "minItems": 3, + "prefixItems": [ + { + "type": "number" + }, + { + "type": "number" + }, + { + "type": "number" + } + ], + "type": "array" + }, + { + "maxItems": 4, + "minItems": 4, + "prefixItems": [ + { + "type": "number" + }, + { + "type": "number" + }, + { + "type": "number" + }, + { + "type": "number" + } + ], + "type": "array" + }, + { + "type": "string" + } + ], + "default": "k", + "title": "Color" + }, + "size": { + "default": 1, + "title": "Size", + "type": "number" + }, + "alpha": { + "default": 1, + "title": "Alpha", + "type": "number" + }, + "zorder": { + "default": 0, + "title": "Zorder", + "type": "number" + }, + "linestyle": { + "anyOf": [ + { + "type": "string" + }, + { + "maxItems": 2, + "minItems": 2, + "prefixItems": [ + { + "type": "integer" + }, + { + "items": { + "type": "integer" + }, + "type": "array" + } + ], + "type": "array" + } + ], + "default": "-", + "title": "Linestyle" + }, + "label": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Label" + } + }, + "title": "Pen", + "type": "object" + } + }, + "additionalProperties": false, + "description": "Controls styling and visibility for one type of grid (major or minor).\n\nAttributes:\n show (bool): Whether to display this grid axis.\n pen (Pen): Style and label information for drawing to matplotlib axes.", + "properties": { + "show": { + "default": false, + "title": "Show", + "type": "boolean" + }, + "pen": { + "$ref": "#/$defs/Pen", + "default": { + "color": "gray", + "size": 0.75, + "alpha": 1.0, + "zorder": 0.0, + "linestyle": "-", + "label": null + } + } + }, + "title": "GridAxis", + "type": "object" + }, + "HistogramEntry": { + "$defs": { + "AxisScale": { + "enum": [ + "linear", + "log" + ], + "title": "AxisScale", + "type": "string" + }, + "Format2D": { + "additionalProperties": false, + "description": "Formatting data for matplotlib figure and axes\n\nAttributes:\n title_fig (Optional[str], optional): Sets [figure title][matplotlib.figure.Figure.suptitle]. Defaults to None.\n legend (Optional[Legend], optional): Sets [legend style][trendify.api.styling.legend.Legend]. Defaults to Legend().\n title_ax (Optional[str], optional): Sets [axis title][matplotlib.axes.Axes.set_title]. Defaults to None.\n label_x (Optional[str], optional): Sets [x-axis label][matplotlib.axes.Axes.set_xlabel]. Defaults to None.\n label_y (Optional[str], optional): Sets [y-axis label][matplotlib.axes.Axes.set_ylabel]. Defaults to None.\n lim_x_min (float | None, optional): Sets [x-axis lower bound][matplotlib.axes.Axes.set_xlim]. Defaults to None.\n lim_x_max (float | None, optional): Sets [x-axis upper bound][matplotlib.axes.Axes.set_xlim]. Defaults to None.\n lim_y_min (float | None, optional): Sets [y-axis lower bound][matplotlib.axes.Axes.set_ylim]. Defaults to None.\n lim_y_max (float | None, optional): Sets [y-axis upper bound][matplotlib.axes.Axes.set_ylim]. Defaults to None.\n grid (Grid | None,optional): Sets the [grid][matplotlib.pyplot.grid]. Defaults to None.\n scale_x (AxisScale, optional): Sets the x axis scale to an option from [AxisScale][trendify.api.formats.format2d.AxisScale]. Defaults to AxisScale.LINEAR\n scale_y (AxisScale, optional): Sets the y axis scale to an option from [AxisScale][trendify.api.formats.format2d.AxisScale]. Defaults to AxisScale.LINEAR\n figure_width (float, optional): Sets the of the width of rendered figure in inches. Defaults to 6.4.\n figure_height (float, optional): Sets the of the height of rendered figure in inches. Defaults to 4.8.", + "properties": { + "title_fig": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Title Fig" + }, + "legend": { + "anyOf": [ + { + "$ref": "#/$defs/Legend" + }, + { + "type": "null" + } + ], + "default": { + "visible": true, + "title": null, + "framealpha": 1.0, + "loc": "best", + "ncol": 1, + "fancybox": true, + "edgecolor": "black", + "zorder": 10, + "bbox_to_anchor": null + } + }, + "title_ax": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Title Ax" + }, + "label_x": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Label X" + }, + "label_y": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Label Y" + }, + "lim_x_min": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Lim X Min" + }, + "lim_x_max": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Lim X Max" + }, + "lim_y_min": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Lim Y Min" + }, + "lim_y_max": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Lim Y Max" + }, + "grid": { + "anyOf": [ + { + "$ref": "#/$defs/Grid" + }, + { + "type": "null" + } + ], + "default": null + }, + "scale_x": { + "$ref": "#/$defs/AxisScale", + "default": "linear" + }, + "scale_y": { + "$ref": "#/$defs/AxisScale", + "default": "linear" + }, + "figure_width": { + "default": 6.4, + "title": "Figure Width", + "type": "number" + }, + "figure_height": { + "default": 4.8, + "title": "Figure Height", + "type": "number" + } + }, + "title": "Format2D", + "type": "object" + }, + "Grid": { + "additionalProperties": false, + "description": "Container for major and minor grid line configuration.\n\nAttributes:\n major (GridAxis): Configuration for major grid lines.\n minor (GridAxis): Configuration for minor grid lines.\n enable_minor_ticks (bool): Whether to enable minor ticks on the axes.", + "properties": { + "major": { + "$ref": "#/$defs/GridAxis", + "default": { + "show": false, + "pen": { + "alpha": 1.0, + "color": "gray", + "label": null, + "linestyle": "-", + "size": 0.75, + "zorder": 0.0 + } + } + }, + "minor": { + "$ref": "#/$defs/GridAxis", + "default": { + "show": false, + "pen": { + "alpha": 1.0, + "color": "gray", + "label": null, + "linestyle": "-", + "size": 0.75, + "zorder": 0.0 + } + } + }, + "enable_minor_ticks": { + "default": false, + "title": "Enable Minor Ticks", + "type": "boolean" + }, + "zorder": { + "default": -1, + "title": "Zorder", + "type": "number" + } + }, + "title": "Grid", + "type": "object" + }, + "GridAxis": { + "additionalProperties": false, + "description": "Controls styling and visibility for one type of grid (major or minor).\n\nAttributes:\n show (bool): Whether to display this grid axis.\n pen (Pen): Style and label information for drawing to matplotlib axes.", + "properties": { + "show": { + "default": false, + "title": "Show", + "type": "boolean" + }, + "pen": { + "$ref": "#/$defs/Pen", + "default": { + "color": "gray", + "size": 0.75, + "alpha": 1.0, + "zorder": 0.0, + "linestyle": "-", + "label": null + } + } + }, + "title": "GridAxis", + "type": "object" + }, + "HistogramStyle": { + "description": "Label and style data for generating histogram bars\n\nAttributes:\n color (str): Color of bars\n label (str|None): Legend entry\n histtype (str): Histogram type corresponding to matplotlib argument of same name\n alpha_edge (float): Opacity of bar edge\n alpha_face (float): Opacity of bar face\n linewidth (float): Line width of bar outline\n bins (int | list[int] | Tuple[int] | NDArray[Shape[\"*\"], int] | None): Number of bins (see [matplotlib docs][matplotlib.pyplot.hist])", + "properties": { + "color": { + "default": "k", + "title": "Color", + "type": "string" + }, + "label": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Label" + }, + "histtype": { + "default": "stepfilled", + "title": "Histtype", + "type": "string" + }, + "alpha_edge": { + "default": 0, + "title": "Alpha Edge", + "type": "number" + }, + "alpha_face": { + "default": 0.3, + "title": "Alpha Face", + "type": "number" + }, + "linewidth": { + "default": 2, + "title": "Linewidth", + "type": "number" + }, + "zorder": { + "default": 1, + "title": "Zorder", + "type": "integer" + }, + "bins": { + "anyOf": [ + { + "type": "integer" + }, + { + "items": { + "type": "integer" + }, + "type": "array" + }, + { + "maxItems": 1, + "minItems": 1, + "prefixItems": [ + { + "type": "integer" + } + ], + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Bins" + } + }, + "title": "HistogramStyle", + "type": "object" + }, + "Legend": { + "description": "Configuration container for Matplotlib legend styling and placement.\n\nThe `Legend` class controls the appearance and position of the plot legend.\nPlacement is governed by a combination of the `loc` and `bbox_to_anchor`\nparameters, mirroring Matplotlib's `Axes.legend()`.\n\nAttributes:\n visible (bool): Whether the legend should be displayed. Defaults to True.\n title (str | None): Title displayed above the legend entries.\n framealpha (float): Opacity of the legend background. 1 = fully opaque, 0 = fully transparent.\n loc (LegendLocation): Anchor point for the legend (e.g., upper right, lower left). See `LegendLocation` enum for options.\n ncol (int): Number of columns to arrange legend entries into.\n fancybox (bool): Whether to draw a rounded (True) or square (False) legend frame.\n edgecolor (str): Color of the legend frame border. Default is \"black\".\n bbox_to_anchor (tuple[float, float] | None): Offset position of the legend in figure or axes coordinates. If None, the legend is placed inside the axes using `loc`.\n\n Good starter values for common placements:\n\n - **Inside (default)**:\n ```python\n bbox_to_anchor=None\n ```\n - **Outside right**:\n ```python\n loc=LegendLocation.CENTER_LEFT\n bbox_to_anchor=(1.02, 0.5)\n ```\n - **Outside left**:\n ```python\n loc=LegendLocation.CENTER_RIGHT\n bbox_to_anchor=(-0.02, 0.5)\n ```\n - **Outside top**:\n ```python\n loc=LegendLocation.LOWER_CENTER\n bbox_to_anchor=(0.5, 1.02)\n ```\n - **Outside bottom**:\n ```python\n loc=LegendLocation.UPPER_CENTER\n bbox_to_anchor=(0.5, -0.02)\n ```", + "properties": { + "visible": { + "default": true, + "title": "Visible", + "type": "boolean" + }, + "title": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Title" + }, + "framealpha": { + "default": 1, + "title": "Framealpha", + "type": "number" + }, + "loc": { + "$ref": "#/$defs/LegendLocation", + "default": "best" + }, + "ncol": { + "default": 1, + "title": "Ncol", + "type": "integer" + }, + "fancybox": { + "default": true, + "title": "Fancybox", + "type": "boolean" + }, + "edgecolor": { + "default": "black", + "title": "Edgecolor", + "type": "string" + }, + "zorder": { + "default": 10, + "title": "Zorder", + "type": "integer" + }, + "bbox_to_anchor": { + "anyOf": [ + { + "maxItems": 2, + "minItems": 2, + "prefixItems": [ + { + "type": "number" + }, + { + "type": "number" + } + ], + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Bbox To Anchor" + } + }, + "title": "Legend", + "type": "object" + }, + "LegendLocation": { + "enum": [ + "best", + "upper right", + "upper left", + "lower left", + "lower right", + "right", + "center left", + "center right", + "lower center", + "upper center", + "center" + ], + "title": "LegendLocation", + "type": "string" + }, + "Pen": { + "additionalProperties": false, + "description": "Defines the pen drawing to matplotlib.\n\nAttributes:\n color (str): Color of line\n size (float): Line width\n alpha (float): Opacity from 0 to 1 (inclusive)\n linestyle (Union[str, Tuple[int, Tuple[int, ...]]]): Linestyle to plot. Supports `str` or `tuple` definition ([matplotlib documentation](https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html)).\n zorder (float): Prioritization\n label (Union[str, None]): Legend label", + "properties": { + "color": { + "anyOf": [ + { + "maxItems": 3, + "minItems": 3, + "prefixItems": [ + { + "type": "number" + }, + { + "type": "number" + }, + { + "type": "number" + } + ], + "type": "array" + }, + { + "maxItems": 4, + "minItems": 4, + "prefixItems": [ + { + "type": "number" + }, + { + "type": "number" + }, + { + "type": "number" + }, + { + "type": "number" + } + ], + "type": "array" + }, + { + "type": "string" + } + ], + "default": "k", + "title": "Color" + }, + "size": { + "default": 1, + "title": "Size", + "type": "number" + }, + "alpha": { + "default": 1, + "title": "Alpha", + "type": "number" + }, + "zorder": { + "default": 0, + "title": "Zorder", + "type": "number" + }, + "linestyle": { + "anyOf": [ + { + "type": "string" + }, + { + "maxItems": 2, + "minItems": 2, + "prefixItems": [ + { + "type": "integer" + }, + { + "items": { + "type": "integer" + }, + "type": "array" + } + ], + "type": "array" + } + ], + "default": "-", + "title": "Linestyle" + }, + "label": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Label" + } + }, + "title": "Pen", + "type": "object" + } + }, + "additionalProperties": false, + "description": "Use this class to specify a value to be collected into a matplotlib histogram.\n\nAttributes:\n tags (Tags): Tags used to sort data products\n value (float | str): Value to be binned\n style (HistogramStyle): Style of histogram display", + "properties": { + "tags": { + "items": { + "anyOf": [ + { + "items": {}, + "type": "array" + }, + {} + ] + }, + "title": "Tags", + "type": "array" + }, + "metadata": { + "additionalProperties": { + "type": "string" + }, + "default": {}, + "title": "Metadata", + "type": "object" + }, + "format2d": { + "anyOf": [ + { + "$ref": "#/$defs/Format2D" + }, + { + "type": "null" + } + ], + "default": null + }, + "value": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "string" + } + ], + "title": "Value" + }, + "style": { + "anyOf": [ + { + "$ref": "#/$defs/HistogramStyle" + }, + { + "type": "null" + } + ] + } + }, + "required": [ + "tags", + "value" + ], + "title": "HistogramEntry", + "type": "object" + }, + "HistogramStyle": { + "description": "Label and style data for generating histogram bars\n\nAttributes:\n color (str): Color of bars\n label (str|None): Legend entry\n histtype (str): Histogram type corresponding to matplotlib argument of same name\n alpha_edge (float): Opacity of bar edge\n alpha_face (float): Opacity of bar face\n linewidth (float): Line width of bar outline\n bins (int | list[int] | Tuple[int] | NDArray[Shape[\"*\"], int] | None): Number of bins (see [matplotlib docs][matplotlib.pyplot.hist])", + "properties": { + "color": { + "default": "k", + "title": "Color", + "type": "string" + }, + "label": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Label" + }, + "histtype": { + "default": "stepfilled", + "title": "Histtype", + "type": "string" + }, + "alpha_edge": { + "default": 0, + "title": "Alpha Edge", + "type": "number" + }, + "alpha_face": { + "default": 0.3, + "title": "Alpha Face", + "type": "number" + }, + "linewidth": { + "default": 2, + "title": "Linewidth", + "type": "number" + }, + "zorder": { + "default": 1, + "title": "Zorder", + "type": "integer" + }, + "bins": { + "anyOf": [ + { + "type": "integer" + }, + { + "items": { + "type": "integer" + }, + "type": "array" + }, + { + "maxItems": 1, + "minItems": 1, + "prefixItems": [ + { + "type": "integer" + } + ], + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Bins" + } + }, + "title": "HistogramStyle", + "type": "object" + }, + "Legend": { + "$defs": { + "LegendLocation": { + "enum": [ + "best", + "upper right", + "upper left", + "lower left", + "lower right", + "right", + "center left", + "center right", + "lower center", + "upper center", + "center" + ], + "title": "LegendLocation", + "type": "string" + } + }, + "description": "Configuration container for Matplotlib legend styling and placement.\n\nThe `Legend` class controls the appearance and position of the plot legend.\nPlacement is governed by a combination of the `loc` and `bbox_to_anchor`\nparameters, mirroring Matplotlib's `Axes.legend()`.\n\nAttributes:\n visible (bool): Whether the legend should be displayed. Defaults to True.\n title (str | None): Title displayed above the legend entries.\n framealpha (float): Opacity of the legend background. 1 = fully opaque, 0 = fully transparent.\n loc (LegendLocation): Anchor point for the legend (e.g., upper right, lower left). See `LegendLocation` enum for options.\n ncol (int): Number of columns to arrange legend entries into.\n fancybox (bool): Whether to draw a rounded (True) or square (False) legend frame.\n edgecolor (str): Color of the legend frame border. Default is \"black\".\n bbox_to_anchor (tuple[float, float] | None): Offset position of the legend in figure or axes coordinates. If None, the legend is placed inside the axes using `loc`.\n\n Good starter values for common placements:\n\n - **Inside (default)**:\n ```python\n bbox_to_anchor=None\n ```\n - **Outside right**:\n ```python\n loc=LegendLocation.CENTER_LEFT\n bbox_to_anchor=(1.02, 0.5)\n ```\n - **Outside left**:\n ```python\n loc=LegendLocation.CENTER_RIGHT\n bbox_to_anchor=(-0.02, 0.5)\n ```\n - **Outside top**:\n ```python\n loc=LegendLocation.LOWER_CENTER\n bbox_to_anchor=(0.5, 1.02)\n ```\n - **Outside bottom**:\n ```python\n loc=LegendLocation.UPPER_CENTER\n bbox_to_anchor=(0.5, -0.02)\n ```", + "properties": { + "visible": { + "default": true, + "title": "Visible", + "type": "boolean" + }, + "title": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Title" + }, + "framealpha": { + "default": 1, + "title": "Framealpha", + "type": "number" + }, + "loc": { + "$ref": "#/$defs/LegendLocation", + "default": "best" + }, + "ncol": { + "default": 1, + "title": "Ncol", + "type": "integer" + }, + "fancybox": { + "default": true, + "title": "Fancybox", + "type": "boolean" + }, + "edgecolor": { + "default": "black", + "title": "Edgecolor", + "type": "string" + }, + "zorder": { + "default": 10, + "title": "Zorder", + "type": "integer" + }, + "bbox_to_anchor": { + "anyOf": [ + { + "maxItems": 2, + "minItems": 2, + "prefixItems": [ + { + "type": "number" + }, + { + "type": "number" + } + ], + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Bbox To Anchor" + } + }, + "title": "Legend", + "type": "object" + }, + "Marker": { + "additionalProperties": false, + "description": "Defines marker for scattering to matplotlib\n\nAttributes:\n color (str): Color of line\n size (float): Line width\n alpha (float): Opacity from 0 to 1 (inclusive)\n zorder (float): Prioritization\n label (Union[str, None]): Legend label\n symbol (str): Matplotlib symbol string", + "properties": { + "color": { + "default": "k", + "title": "Color", + "type": "string" + }, + "size": { + "default": 5, + "title": "Size", + "type": "number" + }, + "alpha": { + "default": 1, + "title": "Alpha", + "type": "number" + }, + "zorder": { + "default": 0, + "title": "Zorder", + "type": "number" + }, + "label": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Label" + }, + "symbol": { + "default": ".", + "title": "Symbol", + "type": "string" + } + }, + "title": "Marker", + "type": "object" + }, + "Pen": { + "additionalProperties": false, + "description": "Defines the pen drawing to matplotlib.\n\nAttributes:\n color (str): Color of line\n size (float): Line width\n alpha (float): Opacity from 0 to 1 (inclusive)\n linestyle (Union[str, Tuple[int, Tuple[int, ...]]]): Linestyle to plot. Supports `str` or `tuple` definition ([matplotlib documentation](https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html)).\n zorder (float): Prioritization\n label (Union[str, None]): Legend label", + "properties": { + "color": { + "anyOf": [ + { + "maxItems": 3, + "minItems": 3, + "prefixItems": [ + { + "type": "number" + }, + { + "type": "number" + }, + { + "type": "number" + } + ], + "type": "array" + }, + { + "maxItems": 4, + "minItems": 4, + "prefixItems": [ + { + "type": "number" + }, + { + "type": "number" + }, + { + "type": "number" + }, + { + "type": "number" + } + ], + "type": "array" + }, + { + "type": "string" + } + ], + "default": "k", + "title": "Color" + }, + "size": { + "default": 1, + "title": "Size", + "type": "number" + }, + "alpha": { + "default": 1, + "title": "Alpha", + "type": "number" + }, + "zorder": { + "default": 0, + "title": "Zorder", + "type": "number" + }, + "linestyle": { + "anyOf": [ + { + "type": "string" + }, + { + "maxItems": 2, + "minItems": 2, + "prefixItems": [ + { + "type": "integer" + }, + { + "items": { + "type": "integer" + }, + "type": "array" + } + ], + "type": "array" + } + ], + "default": "-", + "title": "Linestyle" + }, + "label": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Label" + } + }, + "title": "Pen", + "type": "object" + }, + "PlottableData2D": { + "$defs": { + "AxisScale": { + "enum": [ + "linear", + "log" + ], + "title": "AxisScale", + "type": "string" + }, + "Format2D": { + "additionalProperties": false, + "description": "Formatting data for matplotlib figure and axes\n\nAttributes:\n title_fig (Optional[str], optional): Sets [figure title][matplotlib.figure.Figure.suptitle]. Defaults to None.\n legend (Optional[Legend], optional): Sets [legend style][trendify.api.styling.legend.Legend]. Defaults to Legend().\n title_ax (Optional[str], optional): Sets [axis title][matplotlib.axes.Axes.set_title]. Defaults to None.\n label_x (Optional[str], optional): Sets [x-axis label][matplotlib.axes.Axes.set_xlabel]. Defaults to None.\n label_y (Optional[str], optional): Sets [y-axis label][matplotlib.axes.Axes.set_ylabel]. Defaults to None.\n lim_x_min (float | None, optional): Sets [x-axis lower bound][matplotlib.axes.Axes.set_xlim]. Defaults to None.\n lim_x_max (float | None, optional): Sets [x-axis upper bound][matplotlib.axes.Axes.set_xlim]. Defaults to None.\n lim_y_min (float | None, optional): Sets [y-axis lower bound][matplotlib.axes.Axes.set_ylim]. Defaults to None.\n lim_y_max (float | None, optional): Sets [y-axis upper bound][matplotlib.axes.Axes.set_ylim]. Defaults to None.\n grid (Grid | None,optional): Sets the [grid][matplotlib.pyplot.grid]. Defaults to None.\n scale_x (AxisScale, optional): Sets the x axis scale to an option from [AxisScale][trendify.api.formats.format2d.AxisScale]. Defaults to AxisScale.LINEAR\n scale_y (AxisScale, optional): Sets the y axis scale to an option from [AxisScale][trendify.api.formats.format2d.AxisScale]. Defaults to AxisScale.LINEAR\n figure_width (float, optional): Sets the of the width of rendered figure in inches. Defaults to 6.4.\n figure_height (float, optional): Sets the of the height of rendered figure in inches. Defaults to 4.8.", + "properties": { + "title_fig": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Title Fig" + }, + "legend": { + "anyOf": [ + { + "$ref": "#/$defs/Legend" + }, + { + "type": "null" + } + ], + "default": { + "visible": true, + "title": null, + "framealpha": 1.0, + "loc": "best", + "ncol": 1, + "fancybox": true, + "edgecolor": "black", + "zorder": 10, + "bbox_to_anchor": null + } + }, + "title_ax": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Title Ax" + }, + "label_x": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Label X" + }, + "label_y": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Label Y" + }, + "lim_x_min": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Lim X Min" + }, + "lim_x_max": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Lim X Max" + }, + "lim_y_min": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Lim Y Min" + }, + "lim_y_max": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Lim Y Max" + }, + "grid": { + "anyOf": [ + { + "$ref": "#/$defs/Grid" + }, + { + "type": "null" + } + ], + "default": null + }, + "scale_x": { + "$ref": "#/$defs/AxisScale", + "default": "linear" + }, + "scale_y": { + "$ref": "#/$defs/AxisScale", + "default": "linear" + }, + "figure_width": { + "default": 6.4, + "title": "Figure Width", + "type": "number" + }, + "figure_height": { + "default": 4.8, + "title": "Figure Height", + "type": "number" + } + }, + "title": "Format2D", + "type": "object" + }, + "Grid": { + "additionalProperties": false, + "description": "Container for major and minor grid line configuration.\n\nAttributes:\n major (GridAxis): Configuration for major grid lines.\n minor (GridAxis): Configuration for minor grid lines.\n enable_minor_ticks (bool): Whether to enable minor ticks on the axes.", + "properties": { + "major": { + "$ref": "#/$defs/GridAxis", + "default": { + "show": false, + "pen": { + "alpha": 1.0, + "color": "gray", + "label": null, + "linestyle": "-", + "size": 0.75, + "zorder": 0.0 + } + } + }, + "minor": { + "$ref": "#/$defs/GridAxis", + "default": { + "show": false, + "pen": { + "alpha": 1.0, + "color": "gray", + "label": null, + "linestyle": "-", + "size": 0.75, + "zorder": 0.0 + } + } + }, + "enable_minor_ticks": { + "default": false, + "title": "Enable Minor Ticks", + "type": "boolean" + }, + "zorder": { + "default": -1, + "title": "Zorder", + "type": "number" + } + }, + "title": "Grid", + "type": "object" + }, + "GridAxis": { + "additionalProperties": false, + "description": "Controls styling and visibility for one type of grid (major or minor).\n\nAttributes:\n show (bool): Whether to display this grid axis.\n pen (Pen): Style and label information for drawing to matplotlib axes.", + "properties": { + "show": { + "default": false, + "title": "Show", + "type": "boolean" + }, + "pen": { + "$ref": "#/$defs/Pen", + "default": { + "color": "gray", + "size": 0.75, + "alpha": 1.0, + "zorder": 0.0, + "linestyle": "-", + "label": null + } + } + }, + "title": "GridAxis", + "type": "object" + }, + "Legend": { + "description": "Configuration container for Matplotlib legend styling and placement.\n\nThe `Legend` class controls the appearance and position of the plot legend.\nPlacement is governed by a combination of the `loc` and `bbox_to_anchor`\nparameters, mirroring Matplotlib's `Axes.legend()`.\n\nAttributes:\n visible (bool): Whether the legend should be displayed. Defaults to True.\n title (str | None): Title displayed above the legend entries.\n framealpha (float): Opacity of the legend background. 1 = fully opaque, 0 = fully transparent.\n loc (LegendLocation): Anchor point for the legend (e.g., upper right, lower left). See `LegendLocation` enum for options.\n ncol (int): Number of columns to arrange legend entries into.\n fancybox (bool): Whether to draw a rounded (True) or square (False) legend frame.\n edgecolor (str): Color of the legend frame border. Default is \"black\".\n bbox_to_anchor (tuple[float, float] | None): Offset position of the legend in figure or axes coordinates. If None, the legend is placed inside the axes using `loc`.\n\n Good starter values for common placements:\n\n - **Inside (default)**:\n ```python\n bbox_to_anchor=None\n ```\n - **Outside right**:\n ```python\n loc=LegendLocation.CENTER_LEFT\n bbox_to_anchor=(1.02, 0.5)\n ```\n - **Outside left**:\n ```python\n loc=LegendLocation.CENTER_RIGHT\n bbox_to_anchor=(-0.02, 0.5)\n ```\n - **Outside top**:\n ```python\n loc=LegendLocation.LOWER_CENTER\n bbox_to_anchor=(0.5, 1.02)\n ```\n - **Outside bottom**:\n ```python\n loc=LegendLocation.UPPER_CENTER\n bbox_to_anchor=(0.5, -0.02)\n ```", + "properties": { + "visible": { + "default": true, + "title": "Visible", + "type": "boolean" + }, + "title": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Title" + }, + "framealpha": { + "default": 1, + "title": "Framealpha", + "type": "number" + }, + "loc": { + "$ref": "#/$defs/LegendLocation", + "default": "best" + }, + "ncol": { + "default": 1, + "title": "Ncol", + "type": "integer" + }, + "fancybox": { + "default": true, + "title": "Fancybox", + "type": "boolean" + }, + "edgecolor": { + "default": "black", + "title": "Edgecolor", + "type": "string" + }, + "zorder": { + "default": 10, + "title": "Zorder", + "type": "integer" + }, + "bbox_to_anchor": { + "anyOf": [ + { + "maxItems": 2, + "minItems": 2, + "prefixItems": [ + { + "type": "number" + }, + { + "type": "number" + } + ], + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Bbox To Anchor" + } + }, + "title": "Legend", + "type": "object" + }, + "LegendLocation": { + "enum": [ + "best", + "upper right", + "upper left", + "lower left", + "lower right", + "right", + "center left", + "center right", + "lower center", + "upper center", + "center" + ], + "title": "LegendLocation", + "type": "string" + }, + "Pen": { + "additionalProperties": false, + "description": "Defines the pen drawing to matplotlib.\n\nAttributes:\n color (str): Color of line\n size (float): Line width\n alpha (float): Opacity from 0 to 1 (inclusive)\n linestyle (Union[str, Tuple[int, Tuple[int, ...]]]): Linestyle to plot. Supports `str` or `tuple` definition ([matplotlib documentation](https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html)).\n zorder (float): Prioritization\n label (Union[str, None]): Legend label", + "properties": { + "color": { + "anyOf": [ + { + "maxItems": 3, + "minItems": 3, + "prefixItems": [ + { + "type": "number" + }, + { + "type": "number" + }, + { + "type": "number" + } + ], + "type": "array" + }, + { + "maxItems": 4, + "minItems": 4, + "prefixItems": [ + { + "type": "number" + }, + { + "type": "number" + }, + { + "type": "number" + }, + { + "type": "number" + } + ], + "type": "array" + }, + { + "type": "string" + } + ], + "default": "k", + "title": "Color" + }, + "size": { + "default": 1, + "title": "Size", + "type": "number" + }, + "alpha": { + "default": 1, + "title": "Alpha", + "type": "number" + }, + "zorder": { + "default": 0, + "title": "Zorder", + "type": "number" + }, + "linestyle": { + "anyOf": [ + { + "type": "string" + }, + { + "maxItems": 2, + "minItems": 2, + "prefixItems": [ + { + "type": "integer" + }, + { + "items": { + "type": "integer" + }, + "type": "array" + } + ], + "type": "array" + } + ], + "default": "-", + "title": "Linestyle" + }, + "label": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Label" + } + }, + "title": "Pen", + "type": "object" + } + }, + "additionalProperties": true, + "description": "Base class for children of DataProduct to be plotted ax xy data on a 2D plot\n\nAttributes:\n format2d (Format2D|None): Format to apply to plot\n tags (Tags): Tags to be used for sorting data.\n metadata (dict[str, str]): A dictionary of metadata to be used as a tool tip for mousover in grafana", + "properties": { + "tags": { + "items": { + "anyOf": [ + { + "items": {}, + "type": "array" + }, + {} + ] + }, + "title": "Tags", + "type": "array" + }, + "metadata": { + "additionalProperties": { + "type": "string" + }, + "default": {}, + "title": "Metadata", + "type": "object" + }, + "format2d": { + "anyOf": [ + { + "$ref": "#/$defs/Format2D" + }, + { + "type": "null" + } + ], + "default": null + } + }, + "required": [ + "tags" + ], + "title": "PlottableData2D", + "type": "object" + }, + "Point2D": { + "$defs": { + "AxisScale": { + "enum": [ + "linear", + "log" + ], + "title": "AxisScale", + "type": "string" + }, + "Format2D": { + "additionalProperties": false, + "description": "Formatting data for matplotlib figure and axes\n\nAttributes:\n title_fig (Optional[str], optional): Sets [figure title][matplotlib.figure.Figure.suptitle]. Defaults to None.\n legend (Optional[Legend], optional): Sets [legend style][trendify.api.styling.legend.Legend]. Defaults to Legend().\n title_ax (Optional[str], optional): Sets [axis title][matplotlib.axes.Axes.set_title]. Defaults to None.\n label_x (Optional[str], optional): Sets [x-axis label][matplotlib.axes.Axes.set_xlabel]. Defaults to None.\n label_y (Optional[str], optional): Sets [y-axis label][matplotlib.axes.Axes.set_ylabel]. Defaults to None.\n lim_x_min (float | None, optional): Sets [x-axis lower bound][matplotlib.axes.Axes.set_xlim]. Defaults to None.\n lim_x_max (float | None, optional): Sets [x-axis upper bound][matplotlib.axes.Axes.set_xlim]. Defaults to None.\n lim_y_min (float | None, optional): Sets [y-axis lower bound][matplotlib.axes.Axes.set_ylim]. Defaults to None.\n lim_y_max (float | None, optional): Sets [y-axis upper bound][matplotlib.axes.Axes.set_ylim]. Defaults to None.\n grid (Grid | None,optional): Sets the [grid][matplotlib.pyplot.grid]. Defaults to None.\n scale_x (AxisScale, optional): Sets the x axis scale to an option from [AxisScale][trendify.api.formats.format2d.AxisScale]. Defaults to AxisScale.LINEAR\n scale_y (AxisScale, optional): Sets the y axis scale to an option from [AxisScale][trendify.api.formats.format2d.AxisScale]. Defaults to AxisScale.LINEAR\n figure_width (float, optional): Sets the of the width of rendered figure in inches. Defaults to 6.4.\n figure_height (float, optional): Sets the of the height of rendered figure in inches. Defaults to 4.8.", + "properties": { + "title_fig": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Title Fig" + }, + "legend": { + "anyOf": [ + { + "$ref": "#/$defs/Legend" + }, + { + "type": "null" + } + ], + "default": { + "visible": true, + "title": null, + "framealpha": 1.0, + "loc": "best", + "ncol": 1, + "fancybox": true, + "edgecolor": "black", + "zorder": 10, + "bbox_to_anchor": null + } + }, + "title_ax": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Title Ax" + }, + "label_x": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Label X" + }, + "label_y": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Label Y" + }, + "lim_x_min": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Lim X Min" + }, + "lim_x_max": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Lim X Max" + }, + "lim_y_min": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Lim Y Min" + }, + "lim_y_max": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Lim Y Max" + }, + "grid": { + "anyOf": [ + { + "$ref": "#/$defs/Grid" + }, + { + "type": "null" + } + ], + "default": null + }, + "scale_x": { + "$ref": "#/$defs/AxisScale", + "default": "linear" + }, + "scale_y": { + "$ref": "#/$defs/AxisScale", + "default": "linear" + }, + "figure_width": { + "default": 6.4, + "title": "Figure Width", + "type": "number" + }, + "figure_height": { + "default": 4.8, + "title": "Figure Height", + "type": "number" + } + }, + "title": "Format2D", + "type": "object" + }, + "Grid": { + "additionalProperties": false, + "description": "Container for major and minor grid line configuration.\n\nAttributes:\n major (GridAxis): Configuration for major grid lines.\n minor (GridAxis): Configuration for minor grid lines.\n enable_minor_ticks (bool): Whether to enable minor ticks on the axes.", + "properties": { + "major": { + "$ref": "#/$defs/GridAxis", + "default": { + "show": false, + "pen": { + "alpha": 1.0, + "color": "gray", + "label": null, + "linestyle": "-", + "size": 0.75, + "zorder": 0.0 + } + } + }, + "minor": { + "$ref": "#/$defs/GridAxis", + "default": { + "show": false, + "pen": { + "alpha": 1.0, + "color": "gray", + "label": null, + "linestyle": "-", + "size": 0.75, + "zorder": 0.0 + } + } + }, + "enable_minor_ticks": { + "default": false, + "title": "Enable Minor Ticks", + "type": "boolean" + }, + "zorder": { + "default": -1, + "title": "Zorder", + "type": "number" + } + }, + "title": "Grid", + "type": "object" + }, + "GridAxis": { + "additionalProperties": false, + "description": "Controls styling and visibility for one type of grid (major or minor).\n\nAttributes:\n show (bool): Whether to display this grid axis.\n pen (Pen): Style and label information for drawing to matplotlib axes.", + "properties": { + "show": { + "default": false, + "title": "Show", + "type": "boolean" + }, + "pen": { + "$ref": "#/$defs/Pen", + "default": { + "color": "gray", + "size": 0.75, + "alpha": 1.0, + "zorder": 0.0, + "linestyle": "-", + "label": null + } + } + }, + "title": "GridAxis", + "type": "object" + }, + "Legend": { + "description": "Configuration container for Matplotlib legend styling and placement.\n\nThe `Legend` class controls the appearance and position of the plot legend.\nPlacement is governed by a combination of the `loc` and `bbox_to_anchor`\nparameters, mirroring Matplotlib's `Axes.legend()`.\n\nAttributes:\n visible (bool): Whether the legend should be displayed. Defaults to True.\n title (str | None): Title displayed above the legend entries.\n framealpha (float): Opacity of the legend background. 1 = fully opaque, 0 = fully transparent.\n loc (LegendLocation): Anchor point for the legend (e.g., upper right, lower left). See `LegendLocation` enum for options.\n ncol (int): Number of columns to arrange legend entries into.\n fancybox (bool): Whether to draw a rounded (True) or square (False) legend frame.\n edgecolor (str): Color of the legend frame border. Default is \"black\".\n bbox_to_anchor (tuple[float, float] | None): Offset position of the legend in figure or axes coordinates. If None, the legend is placed inside the axes using `loc`.\n\n Good starter values for common placements:\n\n - **Inside (default)**:\n ```python\n bbox_to_anchor=None\n ```\n - **Outside right**:\n ```python\n loc=LegendLocation.CENTER_LEFT\n bbox_to_anchor=(1.02, 0.5)\n ```\n - **Outside left**:\n ```python\n loc=LegendLocation.CENTER_RIGHT\n bbox_to_anchor=(-0.02, 0.5)\n ```\n - **Outside top**:\n ```python\n loc=LegendLocation.LOWER_CENTER\n bbox_to_anchor=(0.5, 1.02)\n ```\n - **Outside bottom**:\n ```python\n loc=LegendLocation.UPPER_CENTER\n bbox_to_anchor=(0.5, -0.02)\n ```", + "properties": { + "visible": { + "default": true, + "title": "Visible", + "type": "boolean" + }, + "title": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Title" + }, + "framealpha": { + "default": 1, + "title": "Framealpha", + "type": "number" + }, + "loc": { + "$ref": "#/$defs/LegendLocation", + "default": "best" + }, + "ncol": { + "default": 1, + "title": "Ncol", + "type": "integer" + }, + "fancybox": { + "default": true, + "title": "Fancybox", + "type": "boolean" + }, + "edgecolor": { + "default": "black", + "title": "Edgecolor", + "type": "string" + }, + "zorder": { + "default": 10, + "title": "Zorder", + "type": "integer" + }, + "bbox_to_anchor": { + "anyOf": [ + { + "maxItems": 2, + "minItems": 2, + "prefixItems": [ + { + "type": "number" + }, + { + "type": "number" + } + ], + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Bbox To Anchor" + } + }, + "title": "Legend", + "type": "object" + }, + "LegendLocation": { + "enum": [ + "best", + "upper right", + "upper left", + "lower left", + "lower right", + "right", + "center left", + "center right", + "lower center", + "upper center", + "center" + ], + "title": "LegendLocation", + "type": "string" + }, + "Marker": { + "additionalProperties": false, + "description": "Defines marker for scattering to matplotlib\n\nAttributes:\n color (str): Color of line\n size (float): Line width\n alpha (float): Opacity from 0 to 1 (inclusive)\n zorder (float): Prioritization\n label (Union[str, None]): Legend label\n symbol (str): Matplotlib symbol string", + "properties": { + "color": { + "default": "k", + "title": "Color", + "type": "string" + }, + "size": { + "default": 5, + "title": "Size", + "type": "number" + }, + "alpha": { + "default": 1, + "title": "Alpha", + "type": "number" + }, + "zorder": { + "default": 0, + "title": "Zorder", + "type": "number" + }, + "label": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Label" + }, + "symbol": { + "default": ".", + "title": "Symbol", + "type": "string" + } + }, + "title": "Marker", + "type": "object" + }, + "Pen": { + "additionalProperties": false, + "description": "Defines the pen drawing to matplotlib.\n\nAttributes:\n color (str): Color of line\n size (float): Line width\n alpha (float): Opacity from 0 to 1 (inclusive)\n linestyle (Union[str, Tuple[int, Tuple[int, ...]]]): Linestyle to plot. Supports `str` or `tuple` definition ([matplotlib documentation](https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html)).\n zorder (float): Prioritization\n label (Union[str, None]): Legend label", + "properties": { + "color": { + "anyOf": [ + { + "maxItems": 3, + "minItems": 3, + "prefixItems": [ + { + "type": "number" + }, + { + "type": "number" + }, + { + "type": "number" + } + ], + "type": "array" + }, + { + "maxItems": 4, + "minItems": 4, + "prefixItems": [ + { + "type": "number" + }, + { + "type": "number" + }, + { + "type": "number" + }, + { + "type": "number" + } + ], + "type": "array" + }, + { + "type": "string" + } + ], + "default": "k", + "title": "Color" + }, + "size": { + "default": 1, + "title": "Size", + "type": "number" + }, + "alpha": { + "default": 1, + "title": "Alpha", + "type": "number" + }, + "zorder": { + "default": 0, + "title": "Zorder", + "type": "number" + }, + "linestyle": { + "anyOf": [ + { + "type": "string" + }, + { + "maxItems": 2, + "minItems": 2, + "prefixItems": [ + { + "type": "integer" + }, + { + "items": { + "type": "integer" + }, + "type": "array" + } + ], + "type": "array" + } + ], + "default": "-", + "title": "Linestyle" + }, + "label": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Label" + } + }, + "title": "Pen", + "type": "object" + } + }, + "additionalProperties": false, + "description": "Defines a point to be scattered onto xy plot.\n\nAttributes:\n tags (Tags): Tags to be used for sorting data.\n x (float | str): X value for the point.\n y (float | str): Y value for the point.\n marker (Marker | None): Style and label information for scattering points to matplotlib axes.\n Only the label information is used in Grafana.\n Eventually style information will be used in grafana.\n metadata (dict[str, str]): A dictionary of metadata to be used as a tool tip for mousover in grafana", + "properties": { + "tags": { + "items": { + "anyOf": [ + { + "items": {}, + "type": "array" + }, + {} + ] + }, + "title": "Tags", + "type": "array" + }, + "metadata": { + "additionalProperties": { + "type": "string" + }, + "default": {}, + "title": "Metadata", + "type": "object" + }, + "format2d": { + "anyOf": [ + { + "$ref": "#/$defs/Format2D" + }, + { + "type": "null" + } + ], + "default": null + }, + "x": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "string" + } + ], + "title": "X" + }, + "y": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "string" + } + ], + "title": "Y" + }, + "marker": { + "anyOf": [ + { + "$ref": "#/$defs/Marker" + }, + { + "type": "null" + } + ], + "default": { + "color": "k", + "size": 5.0, + "alpha": 1.0, + "zorder": 0.0, + "label": null, + "symbol": "." + } + } + }, + "required": [ + "tags", + "x", + "y" + ], + "title": "Point2D", + "type": "object" + }, + "ProductEntryMetadata": { + "properties": { + "source": { + "format": "path", + "title": "Source", + "type": "string" + } + }, + "required": [ + "source" + ], + "title": "ProductEntryMetadata", + "type": "object" + }, + "ProductIndexMap": { + "$defs": { + "ProductEntryMetadata": { + "properties": { + "source": { + "format": "path", + "title": "Source", + "type": "string" + } + }, + "required": [ + "source" + ], + "title": "ProductEntryMetadata", + "type": "object" + } + }, + "properties": { + "entries": { + "additionalProperties": { + "$ref": "#/$defs/ProductEntryMetadata" + }, + "title": "Entries", + "type": "object" + } + }, + "title": "ProductIndexMap", + "type": "object" + }, + "TableEntry": { + "additionalProperties": false, + "description": "Defines an entry to be collected into a table.\n\nCollected table entries will be printed in three forms when possible: melted, pivot (when possible), and stats (on pivot columns, when possible).\n\nAttributes:\n tags (Tags): Tags used to sort data products\n row (float | str): Row Label\n col (float | str): Column Label\n value (float | str): Value\n unit (str | None): Units for value\n metadata (dict[str, str]): A dictionary of metadata to be used as a tool tip for mousover in grafana", + "properties": { + "tags": { + "items": { + "anyOf": [ + { + "items": {}, + "type": "array" + }, + {} + ] + }, + "title": "Tags", + "type": "array" + }, + "metadata": { + "additionalProperties": { + "type": "string" + }, + "default": {}, + "title": "Metadata", + "type": "object" + }, + "row": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "string" + } + ], + "title": "Row" + }, + "col": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "string" + } + ], + "title": "Col" + }, + "value": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "string" + }, + { + "type": "boolean" + } + ], + "title": "Value" + }, + "unit": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Unit" + } + }, + "required": [ + "tags", + "row", + "col", + "value" + ], + "title": "TableEntry", + "type": "object" + }, + "Trace2D": { + "$defs": { + "AxisScale": { + "enum": [ + "linear", + "log" + ], + "title": "AxisScale", + "type": "string" + }, + "Format2D": { + "additionalProperties": false, + "description": "Formatting data for matplotlib figure and axes\n\nAttributes:\n title_fig (Optional[str], optional): Sets [figure title][matplotlib.figure.Figure.suptitle]. Defaults to None.\n legend (Optional[Legend], optional): Sets [legend style][trendify.api.styling.legend.Legend]. Defaults to Legend().\n title_ax (Optional[str], optional): Sets [axis title][matplotlib.axes.Axes.set_title]. Defaults to None.\n label_x (Optional[str], optional): Sets [x-axis label][matplotlib.axes.Axes.set_xlabel]. Defaults to None.\n label_y (Optional[str], optional): Sets [y-axis label][matplotlib.axes.Axes.set_ylabel]. Defaults to None.\n lim_x_min (float | None, optional): Sets [x-axis lower bound][matplotlib.axes.Axes.set_xlim]. Defaults to None.\n lim_x_max (float | None, optional): Sets [x-axis upper bound][matplotlib.axes.Axes.set_xlim]. Defaults to None.\n lim_y_min (float | None, optional): Sets [y-axis lower bound][matplotlib.axes.Axes.set_ylim]. Defaults to None.\n lim_y_max (float | None, optional): Sets [y-axis upper bound][matplotlib.axes.Axes.set_ylim]. Defaults to None.\n grid (Grid | None,optional): Sets the [grid][matplotlib.pyplot.grid]. Defaults to None.\n scale_x (AxisScale, optional): Sets the x axis scale to an option from [AxisScale][trendify.api.formats.format2d.AxisScale]. Defaults to AxisScale.LINEAR\n scale_y (AxisScale, optional): Sets the y axis scale to an option from [AxisScale][trendify.api.formats.format2d.AxisScale]. Defaults to AxisScale.LINEAR\n figure_width (float, optional): Sets the of the width of rendered figure in inches. Defaults to 6.4.\n figure_height (float, optional): Sets the of the height of rendered figure in inches. Defaults to 4.8.", + "properties": { + "title_fig": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Title Fig" + }, + "legend": { + "anyOf": [ + { + "$ref": "#/$defs/Legend" + }, + { + "type": "null" + } + ], + "default": { + "visible": true, + "title": null, + "framealpha": 1.0, + "loc": "best", + "ncol": 1, + "fancybox": true, + "edgecolor": "black", + "zorder": 10, + "bbox_to_anchor": null + } + }, + "title_ax": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Title Ax" + }, + "label_x": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Label X" + }, + "label_y": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Label Y" + }, + "lim_x_min": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Lim X Min" + }, + "lim_x_max": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Lim X Max" + }, + "lim_y_min": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Lim Y Min" + }, + "lim_y_max": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Lim Y Max" + }, + "grid": { + "anyOf": [ + { + "$ref": "#/$defs/Grid" + }, + { + "type": "null" + } + ], + "default": null + }, + "scale_x": { + "$ref": "#/$defs/AxisScale", + "default": "linear" + }, + "scale_y": { + "$ref": "#/$defs/AxisScale", + "default": "linear" + }, + "figure_width": { + "default": 6.4, + "title": "Figure Width", + "type": "number" + }, + "figure_height": { + "default": 4.8, + "title": "Figure Height", + "type": "number" + } + }, + "title": "Format2D", + "type": "object" + }, + "Grid": { + "additionalProperties": false, + "description": "Container for major and minor grid line configuration.\n\nAttributes:\n major (GridAxis): Configuration for major grid lines.\n minor (GridAxis): Configuration for minor grid lines.\n enable_minor_ticks (bool): Whether to enable minor ticks on the axes.", + "properties": { + "major": { + "$ref": "#/$defs/GridAxis", + "default": { + "show": false, + "pen": { + "alpha": 1.0, + "color": "gray", + "label": null, + "linestyle": "-", + "size": 0.75, + "zorder": 0.0 + } + } + }, + "minor": { + "$ref": "#/$defs/GridAxis", + "default": { + "show": false, + "pen": { + "alpha": 1.0, + "color": "gray", + "label": null, + "linestyle": "-", + "size": 0.75, + "zorder": 0.0 + } + } + }, + "enable_minor_ticks": { + "default": false, + "title": "Enable Minor Ticks", + "type": "boolean" + }, + "zorder": { + "default": -1, + "title": "Zorder", + "type": "number" + } + }, + "title": "Grid", + "type": "object" + }, + "GridAxis": { + "additionalProperties": false, + "description": "Controls styling and visibility for one type of grid (major or minor).\n\nAttributes:\n show (bool): Whether to display this grid axis.\n pen (Pen): Style and label information for drawing to matplotlib axes.", + "properties": { + "show": { + "default": false, + "title": "Show", + "type": "boolean" + }, + "pen": { + "$ref": "#/$defs/Pen", + "default": { + "color": "gray", + "size": 0.75, + "alpha": 1.0, + "zorder": 0.0, + "linestyle": "-", + "label": null + } + } + }, + "title": "GridAxis", + "type": "object" + }, + "Legend": { + "description": "Configuration container for Matplotlib legend styling and placement.\n\nThe `Legend` class controls the appearance and position of the plot legend.\nPlacement is governed by a combination of the `loc` and `bbox_to_anchor`\nparameters, mirroring Matplotlib's `Axes.legend()`.\n\nAttributes:\n visible (bool): Whether the legend should be displayed. Defaults to True.\n title (str | None): Title displayed above the legend entries.\n framealpha (float): Opacity of the legend background. 1 = fully opaque, 0 = fully transparent.\n loc (LegendLocation): Anchor point for the legend (e.g., upper right, lower left). See `LegendLocation` enum for options.\n ncol (int): Number of columns to arrange legend entries into.\n fancybox (bool): Whether to draw a rounded (True) or square (False) legend frame.\n edgecolor (str): Color of the legend frame border. Default is \"black\".\n bbox_to_anchor (tuple[float, float] | None): Offset position of the legend in figure or axes coordinates. If None, the legend is placed inside the axes using `loc`.\n\n Good starter values for common placements:\n\n - **Inside (default)**:\n ```python\n bbox_to_anchor=None\n ```\n - **Outside right**:\n ```python\n loc=LegendLocation.CENTER_LEFT\n bbox_to_anchor=(1.02, 0.5)\n ```\n - **Outside left**:\n ```python\n loc=LegendLocation.CENTER_RIGHT\n bbox_to_anchor=(-0.02, 0.5)\n ```\n - **Outside top**:\n ```python\n loc=LegendLocation.LOWER_CENTER\n bbox_to_anchor=(0.5, 1.02)\n ```\n - **Outside bottom**:\n ```python\n loc=LegendLocation.UPPER_CENTER\n bbox_to_anchor=(0.5, -0.02)\n ```", + "properties": { + "visible": { + "default": true, + "title": "Visible", + "type": "boolean" + }, + "title": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Title" + }, + "framealpha": { + "default": 1, + "title": "Framealpha", + "type": "number" + }, + "loc": { + "$ref": "#/$defs/LegendLocation", + "default": "best" + }, + "ncol": { + "default": 1, + "title": "Ncol", + "type": "integer" + }, + "fancybox": { + "default": true, + "title": "Fancybox", + "type": "boolean" + }, + "edgecolor": { + "default": "black", + "title": "Edgecolor", + "type": "string" + }, + "zorder": { + "default": 10, + "title": "Zorder", + "type": "integer" + }, + "bbox_to_anchor": { + "anyOf": [ + { + "maxItems": 2, + "minItems": 2, + "prefixItems": [ + { + "type": "number" + }, + { + "type": "number" + } + ], + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Bbox To Anchor" + } + }, + "title": "Legend", + "type": "object" + }, + "LegendLocation": { + "enum": [ + "best", + "upper right", + "upper left", + "lower left", + "lower right", + "right", + "center left", + "center right", + "lower center", + "upper center", + "center" + ], + "title": "LegendLocation", + "type": "string" + }, + "Marker": { + "additionalProperties": false, + "description": "Defines marker for scattering to matplotlib\n\nAttributes:\n color (str): Color of line\n size (float): Line width\n alpha (float): Opacity from 0 to 1 (inclusive)\n zorder (float): Prioritization\n label (Union[str, None]): Legend label\n symbol (str): Matplotlib symbol string", + "properties": { + "color": { + "default": "k", + "title": "Color", + "type": "string" + }, + "size": { + "default": 5, + "title": "Size", + "type": "number" + }, + "alpha": { + "default": 1, + "title": "Alpha", + "type": "number" + }, + "zorder": { + "default": 0, + "title": "Zorder", + "type": "number" + }, + "label": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Label" + }, + "symbol": { + "default": ".", + "title": "Symbol", + "type": "string" + } + }, + "title": "Marker", + "type": "object" + }, + "Pen": { + "additionalProperties": false, + "description": "Defines the pen drawing to matplotlib.\n\nAttributes:\n color (str): Color of line\n size (float): Line width\n alpha (float): Opacity from 0 to 1 (inclusive)\n linestyle (Union[str, Tuple[int, Tuple[int, ...]]]): Linestyle to plot. Supports `str` or `tuple` definition ([matplotlib documentation](https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html)).\n zorder (float): Prioritization\n label (Union[str, None]): Legend label", + "properties": { + "color": { + "anyOf": [ + { + "maxItems": 3, + "minItems": 3, + "prefixItems": [ + { + "type": "number" + }, + { + "type": "number" + }, + { + "type": "number" + } + ], + "type": "array" + }, + { + "maxItems": 4, + "minItems": 4, + "prefixItems": [ + { + "type": "number" + }, + { + "type": "number" + }, + { + "type": "number" + }, + { + "type": "number" + } + ], + "type": "array" + }, + { + "type": "string" + } + ], + "default": "k", + "title": "Color" + }, + "size": { + "default": 1, + "title": "Size", + "type": "number" + }, + "alpha": { + "default": 1, + "title": "Alpha", + "type": "number" + }, + "zorder": { + "default": 0, + "title": "Zorder", + "type": "number" + }, + "linestyle": { + "anyOf": [ + { + "type": "string" + }, + { + "maxItems": 2, + "minItems": 2, + "prefixItems": [ + { + "type": "integer" + }, + { + "items": { + "type": "integer" + }, + "type": "array" + } + ], + "type": "array" + } + ], + "default": "-", + "title": "Linestyle" + }, + "label": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Label" + } + }, + "title": "Pen", + "type": "object" + }, + "Point2D": { + "additionalProperties": false, + "description": "Defines a point to be scattered onto xy plot.\n\nAttributes:\n tags (Tags): Tags to be used for sorting data.\n x (float | str): X value for the point.\n y (float | str): Y value for the point.\n marker (Marker | None): Style and label information for scattering points to matplotlib axes.\n Only the label information is used in Grafana.\n Eventually style information will be used in grafana.\n metadata (dict[str, str]): A dictionary of metadata to be used as a tool tip for mousover in grafana", + "properties": { + "tags": { + "items": { + "anyOf": [ + { + "items": {}, + "type": "array" + }, + {} + ] + }, + "title": "Tags", + "type": "array" + }, + "metadata": { + "additionalProperties": { + "type": "string" + }, + "default": {}, + "title": "Metadata", + "type": "object" + }, + "format2d": { + "anyOf": [ + { + "$ref": "#/$defs/Format2D" + }, + { + "type": "null" + } + ], + "default": null + }, + "x": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "string" + } + ], + "title": "X" + }, + "y": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "string" + } + ], + "title": "Y" + }, + "marker": { + "anyOf": [ + { + "$ref": "#/$defs/Marker" + }, + { + "type": "null" + } + ], + "default": { + "color": "k", + "size": 5.0, + "alpha": 1.0, + "zorder": 0.0, + "label": null, + "symbol": "." + } + } + }, + "required": [ + "tags", + "x", + "y" + ], + "title": "Point2D", + "type": "object" + } + }, + "additionalProperties": false, + "description": "A collection of points comprising a trace.\nUse the [Trace2D.from_xy][trendify.api.Trace2D.from_xy] constructor.\n\nAttributes:\n points (List[Point2D]): List of points. Usually the points would have null values\n for `marker` and `format2d` fields to save space.\n pen (Pen): Style and label information for drawing to matplotlib axes.\n Only the label information is used in Grafana.\n Eventually style information will be used in grafana.\n tags (Tags): Tags to be used for sorting data.\n metadata (dict[str, str]): A dictionary of metadata to be used as a tool tip for mousover in grafana", + "properties": { + "tags": { + "items": { + "anyOf": [ + { + "items": {}, + "type": "array" + }, + {} + ] + }, + "title": "Tags", + "type": "array" + }, + "metadata": { + "additionalProperties": { + "type": "string" + }, + "default": {}, + "title": "Metadata", + "type": "object" + }, + "format2d": { + "anyOf": [ + { + "$ref": "#/$defs/Format2D" + }, + { + "type": "null" + } + ], + "default": null + }, + "points": { + "items": { + "$ref": "#/$defs/Point2D" + }, + "title": "Points", + "type": "array" + }, + "pen": { + "$ref": "#/$defs/Pen", + "default": { + "color": "k", + "size": 1.0, + "alpha": 1.0, + "zorder": 0.0, + "linestyle": "-", + "label": null + } + } + }, + "required": [ + "tags", + "points" + ], + "title": "Trace2D", + "type": "object" + }, + "XYData": { + "$defs": { + "AxisScale": { + "enum": [ + "linear", + "log" + ], + "title": "AxisScale", + "type": "string" + }, + "Format2D": { + "additionalProperties": false, + "description": "Formatting data for matplotlib figure and axes\n\nAttributes:\n title_fig (Optional[str], optional): Sets [figure title][matplotlib.figure.Figure.suptitle]. Defaults to None.\n legend (Optional[Legend], optional): Sets [legend style][trendify.api.styling.legend.Legend]. Defaults to Legend().\n title_ax (Optional[str], optional): Sets [axis title][matplotlib.axes.Axes.set_title]. Defaults to None.\n label_x (Optional[str], optional): Sets [x-axis label][matplotlib.axes.Axes.set_xlabel]. Defaults to None.\n label_y (Optional[str], optional): Sets [y-axis label][matplotlib.axes.Axes.set_ylabel]. Defaults to None.\n lim_x_min (float | None, optional): Sets [x-axis lower bound][matplotlib.axes.Axes.set_xlim]. Defaults to None.\n lim_x_max (float | None, optional): Sets [x-axis upper bound][matplotlib.axes.Axes.set_xlim]. Defaults to None.\n lim_y_min (float | None, optional): Sets [y-axis lower bound][matplotlib.axes.Axes.set_ylim]. Defaults to None.\n lim_y_max (float | None, optional): Sets [y-axis upper bound][matplotlib.axes.Axes.set_ylim]. Defaults to None.\n grid (Grid | None,optional): Sets the [grid][matplotlib.pyplot.grid]. Defaults to None.\n scale_x (AxisScale, optional): Sets the x axis scale to an option from [AxisScale][trendify.api.formats.format2d.AxisScale]. Defaults to AxisScale.LINEAR\n scale_y (AxisScale, optional): Sets the y axis scale to an option from [AxisScale][trendify.api.formats.format2d.AxisScale]. Defaults to AxisScale.LINEAR\n figure_width (float, optional): Sets the of the width of rendered figure in inches. Defaults to 6.4.\n figure_height (float, optional): Sets the of the height of rendered figure in inches. Defaults to 4.8.", + "properties": { + "title_fig": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Title Fig" + }, + "legend": { + "anyOf": [ + { + "$ref": "#/$defs/Legend" + }, + { + "type": "null" + } + ], + "default": { + "visible": true, + "title": null, + "framealpha": 1.0, + "loc": "best", + "ncol": 1, + "fancybox": true, + "edgecolor": "black", + "zorder": 10, + "bbox_to_anchor": null + } + }, + "title_ax": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Title Ax" + }, + "label_x": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Label X" + }, + "label_y": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Label Y" + }, + "lim_x_min": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Lim X Min" + }, + "lim_x_max": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Lim X Max" + }, + "lim_y_min": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Lim Y Min" + }, + "lim_y_max": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Lim Y Max" + }, + "grid": { + "anyOf": [ + { + "$ref": "#/$defs/Grid" + }, + { + "type": "null" + } + ], + "default": null + }, + "scale_x": { + "$ref": "#/$defs/AxisScale", + "default": "linear" + }, + "scale_y": { + "$ref": "#/$defs/AxisScale", + "default": "linear" + }, + "figure_width": { + "default": 6.4, + "title": "Figure Width", + "type": "number" + }, + "figure_height": { + "default": 4.8, + "title": "Figure Height", + "type": "number" + } + }, + "title": "Format2D", + "type": "object" + }, + "Grid": { + "additionalProperties": false, + "description": "Container for major and minor grid line configuration.\n\nAttributes:\n major (GridAxis): Configuration for major grid lines.\n minor (GridAxis): Configuration for minor grid lines.\n enable_minor_ticks (bool): Whether to enable minor ticks on the axes.", + "properties": { + "major": { + "$ref": "#/$defs/GridAxis", + "default": { + "show": false, + "pen": { + "alpha": 1.0, + "color": "gray", + "label": null, + "linestyle": "-", + "size": 0.75, + "zorder": 0.0 + } + } + }, + "minor": { + "$ref": "#/$defs/GridAxis", + "default": { + "show": false, + "pen": { + "alpha": 1.0, + "color": "gray", + "label": null, + "linestyle": "-", + "size": 0.75, + "zorder": 0.0 + } + } + }, + "enable_minor_ticks": { + "default": false, + "title": "Enable Minor Ticks", + "type": "boolean" + }, + "zorder": { + "default": -1, + "title": "Zorder", + "type": "number" + } + }, + "title": "Grid", + "type": "object" + }, + "GridAxis": { + "additionalProperties": false, + "description": "Controls styling and visibility for one type of grid (major or minor).\n\nAttributes:\n show (bool): Whether to display this grid axis.\n pen (Pen): Style and label information for drawing to matplotlib axes.", + "properties": { + "show": { + "default": false, + "title": "Show", + "type": "boolean" + }, + "pen": { + "$ref": "#/$defs/Pen", + "default": { + "color": "gray", + "size": 0.75, + "alpha": 1.0, + "zorder": 0.0, + "linestyle": "-", + "label": null + } + } + }, + "title": "GridAxis", + "type": "object" + }, + "Legend": { + "description": "Configuration container for Matplotlib legend styling and placement.\n\nThe `Legend` class controls the appearance and position of the plot legend.\nPlacement is governed by a combination of the `loc` and `bbox_to_anchor`\nparameters, mirroring Matplotlib's `Axes.legend()`.\n\nAttributes:\n visible (bool): Whether the legend should be displayed. Defaults to True.\n title (str | None): Title displayed above the legend entries.\n framealpha (float): Opacity of the legend background. 1 = fully opaque, 0 = fully transparent.\n loc (LegendLocation): Anchor point for the legend (e.g., upper right, lower left). See `LegendLocation` enum for options.\n ncol (int): Number of columns to arrange legend entries into.\n fancybox (bool): Whether to draw a rounded (True) or square (False) legend frame.\n edgecolor (str): Color of the legend frame border. Default is \"black\".\n bbox_to_anchor (tuple[float, float] | None): Offset position of the legend in figure or axes coordinates. If None, the legend is placed inside the axes using `loc`.\n\n Good starter values for common placements:\n\n - **Inside (default)**:\n ```python\n bbox_to_anchor=None\n ```\n - **Outside right**:\n ```python\n loc=LegendLocation.CENTER_LEFT\n bbox_to_anchor=(1.02, 0.5)\n ```\n - **Outside left**:\n ```python\n loc=LegendLocation.CENTER_RIGHT\n bbox_to_anchor=(-0.02, 0.5)\n ```\n - **Outside top**:\n ```python\n loc=LegendLocation.LOWER_CENTER\n bbox_to_anchor=(0.5, 1.02)\n ```\n - **Outside bottom**:\n ```python\n loc=LegendLocation.UPPER_CENTER\n bbox_to_anchor=(0.5, -0.02)\n ```", + "properties": { + "visible": { + "default": true, + "title": "Visible", + "type": "boolean" + }, + "title": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Title" + }, + "framealpha": { + "default": 1, + "title": "Framealpha", + "type": "number" + }, + "loc": { + "$ref": "#/$defs/LegendLocation", + "default": "best" + }, + "ncol": { + "default": 1, + "title": "Ncol", + "type": "integer" + }, + "fancybox": { + "default": true, + "title": "Fancybox", + "type": "boolean" + }, + "edgecolor": { + "default": "black", + "title": "Edgecolor", + "type": "string" + }, + "zorder": { + "default": 10, + "title": "Zorder", + "type": "integer" + }, + "bbox_to_anchor": { + "anyOf": [ + { + "maxItems": 2, + "minItems": 2, + "prefixItems": [ + { + "type": "number" + }, + { + "type": "number" + } + ], + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Bbox To Anchor" + } + }, + "title": "Legend", + "type": "object" + }, + "LegendLocation": { + "enum": [ + "best", + "upper right", + "upper left", + "lower left", + "lower right", + "right", + "center left", + "center right", + "lower center", + "upper center", + "center" + ], + "title": "LegendLocation", + "type": "string" + }, + "Pen": { + "additionalProperties": false, + "description": "Defines the pen drawing to matplotlib.\n\nAttributes:\n color (str): Color of line\n size (float): Line width\n alpha (float): Opacity from 0 to 1 (inclusive)\n linestyle (Union[str, Tuple[int, Tuple[int, ...]]]): Linestyle to plot. Supports `str` or `tuple` definition ([matplotlib documentation](https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html)).\n zorder (float): Prioritization\n label (Union[str, None]): Legend label", + "properties": { + "color": { + "anyOf": [ + { + "maxItems": 3, + "minItems": 3, + "prefixItems": [ + { + "type": "number" + }, + { + "type": "number" + }, + { + "type": "number" + } + ], + "type": "array" + }, + { + "maxItems": 4, + "minItems": 4, + "prefixItems": [ + { + "type": "number" + }, + { + "type": "number" + }, + { + "type": "number" + }, + { + "type": "number" + } + ], + "type": "array" + }, + { + "type": "string" + } + ], + "default": "k", + "title": "Color" + }, + "size": { + "default": 1, + "title": "Size", + "type": "number" + }, + "alpha": { + "default": 1, + "title": "Alpha", + "type": "number" + }, + "zorder": { + "default": 0, + "title": "Zorder", + "type": "number" + }, + "linestyle": { + "anyOf": [ + { + "type": "string" + }, + { + "maxItems": 2, + "minItems": 2, + "prefixItems": [ + { + "type": "integer" + }, + { + "items": { + "type": "integer" + }, + "type": "array" + } + ], + "type": "array" + } + ], + "default": "-", + "title": "Linestyle" + }, + "label": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Label" + } + }, + "title": "Pen", + "type": "object" + } + }, + "additionalProperties": true, + "description": "Base class for children of DataProduct to be plotted ax xy data on a 2D plot", + "properties": { + "tags": { + "items": { + "anyOf": [ + { + "items": {}, + "type": "array" + }, + {} + ] + }, + "title": "Tags", + "type": "array" + }, + "metadata": { + "additionalProperties": { + "type": "string" + }, + "default": {}, + "title": "Metadata", + "type": "object" + }, + "format2d": { + "anyOf": [ + { + "$ref": "#/$defs/Format2D" + }, + { + "type": "null" + } + ], + "default": null + } + }, + "required": [ + "tags" + ], + "title": "XYData", + "type": "object" + } +} \ No newline at end of file diff --git a/src/trendify/__init__.py b/src/trendify/__init__.py index 3ab8434..c7e908e 100644 --- a/src/trendify/__init__.py +++ b/src/trendify/__init__.py @@ -2,5 +2,125 @@ Provides top-level imports """ -from trendify.api import * -from trendify.server import * +from trendify import base +from trendify import formats +from trendify import plotting +from trendify import store +from trendify import styling + +from trendify.base import ( + DataProduct, + HashableBase, + Pen, + ProductGenerator, + ProductList, + ProductType, + R, + Tag, + Tags, + data_product, + helpers, + pen, +) +from trendify.formats import ( + AxisScale, + Format2D, + PlottableData2D, + TableEntry, + XYData, + format2d, + table, +) +from trendify.plotting import ( + AxLine, + HistogramEntry, + HistogramStyle, + LineOrientation, + PlotlyFigure, + Point2D, + SingleAxisFigure, + Trace2D, + axline, + figure, + histogram, + point, + trace, +) +from trendify.store import ( + ProductStore, + SCHEMA_VERSION, + connect, + db, + decode_tag, + encode_tag, + product_store, + tags, +) +from trendify.styling import ( + Grid, + GridAxis, + GridTheme, + Legend, + LegendLocation, + Marker, + grid, + legend, + marker, +) + +__all__ = [ + "SCHEMA_VERSION", + "AxLine", + "AxisScale", + "DataProduct", + "Format2D", + "Grid", + "GridAxis", + "GridTheme", + "HashableBase", + "HistogramEntry", + "HistogramStyle", + "Legend", + "LegendLocation", + "LineOrientation", + "Marker", + "Pen", + "PlotlyFigure", + "PlottableData2D", + "Point2D", + "ProductGenerator", + "ProductList", + "ProductStore", + "ProductType", + "R", + "SingleAxisFigure", + "TableEntry", + "Tag", + "Tags", + "Trace2D", + "XYData", + "axline", + "base", + "connect", + "data_product", + "db", + "decode_tag", + "encode_tag", + "figure", + "format2d", + "formats", + "grid", + "helpers", + "histogram", + "legend", + "marker", + "pen", + "plotting", + "point", + "product_store", + "store", + "styling", + "table", + "tags", + "trace", +] diff --git a/src/trendify/base/__init__.py b/src/trendify/base/__init__.py new file mode 100644 index 0000000..21bf916 --- /dev/null +++ b/src/trendify/base/__init__.py @@ -0,0 +1,34 @@ +from trendify.base import data_product +from trendify.base import helpers +from trendify.base import pen + +from trendify.base.data_product import ( + DataProduct, + ProductGenerator, + ProductList, +) +from trendify.base.helpers import ( + HashableBase, + ProductType, + R, + Tag, + Tags, +) +from trendify.base.pen import ( + Pen, +) + +__all__ = [ + "DataProduct", + "HashableBase", + "Pen", + "ProductGenerator", + "ProductList", + "ProductType", + "R", + "Tag", + "Tags", + "data_product", + "helpers", + "pen", +] diff --git a/src/trendify/base/data_product.py b/src/trendify/base/data_product.py new file mode 100644 index 0000000..a637daf --- /dev/null +++ b/src/trendify/base/data_product.py @@ -0,0 +1,138 @@ +from __future__ import annotations + +from pathlib import Path +from typing import Any +from collections.abc import Callable +import logging + +from pydantic import ( + BaseModel, + ConfigDict, + InstanceOf, + SerializeAsAny, + computed_field, + model_validator, +) + +from trendify.base.helpers import Tags + + +logger = logging.getLogger(__name__) + +__all__ = ["DataProduct", "ProductGenerator", "ProductList"] + +_data_product_subclass_registry: dict[str, type[DataProduct]] = {} + + +class DataProduct(BaseModel): + """ + Base class for data products to be generated and handled. + + Attributes: + product_type (str): Product type should be the same as the class name. + The product type is used to search for products from the store. + tags (Tags): Tags to be used for sorting data. + metadata (dict[str, str]): A dictionary of metadata to be used as a tool tip for mousover in grafana + + """ + + tags: Tags + metadata: dict[str, str] = {} + + @model_validator(mode="before") + @classmethod + def _remove_computed_fields(cls, data: dict[str, Any]) -> dict[str, Any]: + """ + Removes computed fields before passing data to constructor. + + Args: + data (dict[str, Any]): Raw data to be validated before passing to pydantic class constructor. + + Returns: + (dict[str, Any]): Sanitized data to be passed to class constructor. + + """ + for f in cls.model_computed_fields: + data.pop(f, None) + return data + + @computed_field + @property + def product_type(self) -> str: + """ + Returns: + (str): Product type should be the same as the class name. + The product type is used to search for products from the store. + + """ + return type(self).__name__ + + def __init_subclass__(cls, **kwargs: Any) -> None: + """ + Registers child subclasses to be able to parse them from JSON payloads using the + [deserialize][trendify.base.data_product.DataProduct.deserialize] method + """ + super().__init_subclass__(**kwargs) + _data_product_subclass_registry[cls.__name__] = cls + + model_config = ConfigDict(extra="allow") + + def append_to_list(self, l: list): + """ + Appends self to list. + + Args: + l (List): list to which `self` will be appended + + Returns: + (Self): returns instance of `self` + + """ + l.append(self) + return self + + @classmethod + def registry(cls) -> dict[str, type[DataProduct]]: + """ + Returns: + (dict[str, type[DataProduct]]): a copy of the product_type -> class registry, + used by the store to resolve which leaf `product_type` names correspond to a + given (possibly non-leaf) type when filtering by `object_type`. + + """ + return dict(_data_product_subclass_registry) + + @classmethod + def deserialize(cls, product_type: str, payload: str) -> DataProduct: + """ + Loads a stored JSON payload into the pydantic dataclass registered under `product_type`. + + Args: + product_type (str): the `product_type` discriminator string (same as the class name) + payload (str): the raw JSON text (e.g. from the store's `products.payload` column) + + Returns: + (DataProduct): the reconstructed, correctly-typed product instance + + """ + duck_type = _data_product_subclass_registry[product_type] + return duck_type.model_validate_json(payload) + + def set_metadata(self, new: dict[str, str]): + self.metadata = new + return self + + +ProductList = list[SerializeAsAny[InstanceOf[DataProduct]]] +"""List of serializable [DataProduct][trendify.base.data_product.DataProduct] or child classes thereof""" + +ProductGenerator = Callable[[Path], ProductList] +""" +Callable method type. Users must provide a `ProductGenerator` to map over raw data. + +Args: + path (Path): Workdir holding raw data (Should be one per run from a batch) + +Returns: + (ProductList): List of data products to be sorted and used to produce assets +""" diff --git a/src/trendify/base/helpers.py b/src/trendify/base/helpers.py new file mode 100644 index 0000000..7ed5e30 --- /dev/null +++ b/src/trendify/base/helpers.py @@ -0,0 +1,68 @@ +from __future__ import annotations + +import logging +from enum import StrEnum, auto +from typing import TypeVar + +from pydantic import BaseModel + +logger = logging.getLogger(__name__) + +__all__ = [ + "HashableBase", + "ProductType", + "R", + "Tag", + "Tags", +] + +R = TypeVar("R") + +Tag = str | int | tuple[str | int, ...] +""" +Determines what types can be used to define a tag. + +Narrower than v1's ``Union[tuple[Hashable, ...], Hashable]``: tags must be reliably +JSON-encodable (str/int, or tuples thereof) because the store canonicalizes a tag into a +`tag_key` via `json.dumps` for indexed lookup (see `trendify.store.tags`). A bare `Hashable` +can't guarantee a stable, 1:1 JSON encoding. +""" + +Tags = list[Tag] +""" +List of tags +""" + + +class HashableBase(BaseModel): + """ + Defines a base for hashable pydantic data classes so that they can be reduced to a minimal set through type-casting. + """ + + def __hash__(self): + """ + Defines hash function + """ + return hash((type(self), *tuple(self.__dict__.values()))) + + +class ProductType(StrEnum): + """ + Defines all product types. Used to type-cast URL info in server to validate. + + Attributes: + DataProduct (str): class name + XYData (str): class name + Trace2D (str): class name + Point2D (str): class name + TableEntry (str): class name + HistogramEntry (str): class name + + """ + + DataProduct = auto() + XYData = auto() + Trace2D = auto() + Point2D = auto() + TableEntry = auto() + HistogramEntry = auto() diff --git a/src/trendify/base/pen.py b/src/trendify/base/pen.py new file mode 100644 index 0000000..7d5a046 --- /dev/null +++ b/src/trendify/base/pen.py @@ -0,0 +1,136 @@ +from __future__ import annotations + + +from pydantic import ConfigDict +from matplotlib.colors import to_rgba + +from trendify.base.helpers import HashableBase + +__all__ = ["Pen"] + + +class Pen(HashableBase): + """ + Defines the pen drawing to matplotlib. + + Attributes: + color (str): Color of line + size (float): Line width + alpha (float): Opacity from 0 to 1 (inclusive) + linestyle (Union[str, Tuple[int, Tuple[int, ...]]]): Linestyle to plot. Supports `str` or `tuple` definition ([matplotlib documentation](https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html)). + zorder (float): Prioritization + label (Union[str, None]): Legend label + + """ + + color: tuple[float, float, float] | tuple[float, float, float, float] | str = "k" + size: float = 1 + alpha: float = 1 + zorder: float = 0 + linestyle: str | tuple[int, tuple[int, ...]] = "-" + label: str | None = None + + model_config = ConfigDict(extra="forbid") + + def as_scatter_plot_kwargs(self): + """ + Returns kwargs dictionary for passing to [matplotlib plot][matplotlib.axes.Axes.plot] method + """ + return { + "color": self.color, + "linewidth": self.size, + "linestyle": self.linestyle, + "alpha": self.alpha, + "zorder": self.zorder, + "label": self.label, + } + + def _convert_linestyle_to_plotly(self) -> str: + """Convert matplotlib linestyle to plotly dash style""" + # Handle string styles + style_map = { + "-": "solid", + "--": "dash", + ":": "dot", + "-.": "dashdot", + } + if isinstance(self.linestyle, str): + return style_map.get(self.linestyle, "solid") + + # Handle tuple styles - convert to 'dash' as approximation + return "dash" + + @property + def rgba(self) -> str: + """ + Convert the pen's color to rgba string format. + + Returns: + str: Color in 'rgba(r,g,b,a)' format where r,g,b are 0-255 and a is 0-1 + + """ + # Handle different color input formats + if isinstance(self.color, tuple): + if len(self.color) == 3: # RGB tuple + r, g, b = self.color + a = self.alpha + else: # RGBA tuple + r, g, b, a = self.color + # Convert 0-1 range to 0-255 for RGB + r, g, b = int(r * 255), int(g * 255), int(b * 255) + else: # String color (name or hex) + # Use matplotlib's color converter + rgba_vals = to_rgba(self.color, self.alpha) + # Convert 0-1 range to 0-255 for RGB + r, g, b = [int(x * 255) for x in rgba_vals[:3]] + a = rgba_vals[3] + + return f"rgba({r}, {g}, {b}, {a})" + + @property + def rgb(self) -> str: + return ", ".join(self.rgba.split(",")[0:-1]) + ")" + + def get_contrast_color(self, background_luminance: float = 1.0) -> str: + """ + Returns 'white' or 'black' to provide the best contrast against the pen's color, + taking into account the alpha (transparency) value of the line. + + Args: + background_luminance (float): The luminance of the background (default is 1.0 for white). + + Returns: + str: 'white' or 'black' + + """ + # Convert the pen's color to RGB (0-255 range) and get alpha + if isinstance(self.color, tuple): + if len(self.color) == 3: # RGB tuple + r, g, b = self.color + a = self.alpha + else: # RGBA tuple + r, g, b, a = self.color + r, g, b = int(r * 255), int(g * 255), int(b * 255) + else: # String color (name or hex) + rgba_vals = to_rgba(self.color, self.alpha) + r, g, b = [int(x * 255) for x in rgba_vals[:3]] + a = rgba_vals[3] + + # Calculate relative luminance of the pen's color + def luminance(channel): + channel /= 255.0 + return ( + channel / 12.92 + if channel <= 0.03928 + else ((channel + 0.055) / 1.055) ** 2.4 + ) + + color_luminance = ( + 0.2126 * luminance(r) + 0.7152 * luminance(g) + 0.0722 * luminance(b) + ) + + # Blend the color luminance with the background luminance based on alpha + blended_luminance = (1 - a) * background_luminance + a * color_luminance + + # Return white for dark blended colors, black for light blended colors + return "white" if blended_luminance < 0.5 else "black" diff --git a/src/trendify/formats/__init__.py b/src/trendify/formats/__init__.py new file mode 100644 index 0000000..5773899 --- /dev/null +++ b/src/trendify/formats/__init__.py @@ -0,0 +1,22 @@ +from trendify.formats import format2d +from trendify.formats import table + +from trendify.formats.format2d import ( + AxisScale, + Format2D, + PlottableData2D, + XYData, +) +from trendify.formats.table import ( + TableEntry, +) + +__all__ = [ + "AxisScale", + "Format2D", + "PlottableData2D", + "TableEntry", + "XYData", + "format2d", + "table", +] diff --git a/src/trendify/formats/format2d.py b/src/trendify/formats/format2d.py new file mode 100644 index 0000000..e7a6091 --- /dev/null +++ b/src/trendify/formats/format2d.py @@ -0,0 +1,152 @@ +from __future__ import annotations + +import logging +from collections.abc import Iterable +from enum import StrEnum +from typing import TYPE_CHECKING + +import numpy as np +from pydantic import ConfigDict + +from trendify.base.data_product import DataProduct +from trendify.base.helpers import HashableBase +from trendify.styling.grid import Grid +from trendify.styling.legend import Legend + +if TYPE_CHECKING: + from trendify.plotting.figure import PlotlyFigure +logger = logging.getLogger(__name__) + +__all__ = ["AxisScale", "Format2D", "PlottableData2D", "XYData"] + + +class AxisScale(StrEnum): + LINEAR = "linear" + """Format axis as linear""" + LOG = "log" + """Format axis with log base 10""" + + +class Format2D(HashableBase): + """ + Formatting data for matplotlib figure and axes + + Attributes: + title_fig (Optional[str], optional): Sets [figure title][matplotlib.figure.Figure.suptitle]. Defaults to None. + legend (Optional[Legend], optional): Sets [legend style][trendify.styling.legend.Legend]. Defaults to Legend(). + title_ax (Optional[str], optional): Sets [axis title][matplotlib.axes.Axes.set_title]. Defaults to None. + label_x (Optional[str], optional): Sets [x-axis label][matplotlib.axes.Axes.set_xlabel]. Defaults to None. + label_y (Optional[str], optional): Sets [y-axis label][matplotlib.axes.Axes.set_ylabel]. Defaults to None. + lim_x_min (float | None, optional): Sets [x-axis lower bound][matplotlib.axes.Axes.set_xlim]. Defaults to None. + lim_x_max (float | None, optional): Sets [x-axis upper bound][matplotlib.axes.Axes.set_xlim]. Defaults to None. + lim_y_min (float | None, optional): Sets [y-axis lower bound][matplotlib.axes.Axes.set_ylim]. Defaults to None. + lim_y_max (float | None, optional): Sets [y-axis upper bound][matplotlib.axes.Axes.set_ylim]. Defaults to None. + grid (Grid | None,optional): Sets the [grid][matplotlib.pyplot.grid]. Defaults to None. + scale_x (AxisScale, optional): Sets the x axis scale to an option from [AxisScale][trendify.formats.format2d.AxisScale]. Defaults to AxisScale.LINEAR + scale_y (AxisScale, optional): Sets the y axis scale to an option from [AxisScale][trendify.formats.format2d.AxisScale]. Defaults to AxisScale.LINEAR + figure_width (float, optional): Sets the of the width of rendered figure in inches. Defaults to 6.4. + figure_height (float, optional): Sets the of the height of rendered figure in inches. Defaults to 4.8. + + """ + + title_fig: str | None = None + legend: Legend | None = Legend() + title_ax: str | None = None + label_x: str | None = None + label_y: str | None = None + lim_x_min: float | None = None + lim_x_max: float | None = None + lim_y_min: float | None = None + lim_y_max: float | None = None + grid: Grid | None = None + scale_x: AxisScale = AxisScale.LINEAR + scale_y: AxisScale = AxisScale.LINEAR + figure_width: float = 6.4 + figure_height: float = 4.8 + + model_config = ConfigDict(extra="forbid") + + @classmethod + def union_from_iterable(cls, format2ds: Iterable[Format2D]): + """ + Gets the most inclusive format object (in terms of limits) from a list of `Format2D` objects. + Requires that the label and title fields are identical for all format objects in the list. + + Args: + format2ds (Iterable[Format2D]): Iterable of `Format2D` objects. + + Returns: + (Format2D): Single format object from list of objects. + + """ + formats = list(set(format2ds) - {None}) + + [title_fig] = set(i.title_fig for i in formats if i is not None) + [legend] = set(i.legend for i in formats if i is not None) + [title_ax] = set(i.title_ax for i in formats if i is not None) + [label_x] = set(i.label_x for i in formats if i is not None) + [label_y] = set(i.label_y for i in formats if i is not None) + [figure_width] = set(i.figure_width for i in formats) + [figure_height] = set(i.figure_height for i in formats) + + x_min = [i.lim_x_min for i in formats if i.lim_x_min is not None] + x_max = [i.lim_x_max for i in formats if i.lim_x_max is not None] + y_min = [i.lim_y_min for i in formats if i.lim_y_min is not None] + y_max = [i.lim_y_max for i in formats if i.lim_y_max is not None] + + lim_x_min = np.min(x_min) if len(x_min) > 0 else None + lim_x_max = np.max(x_max) if len(x_max) > 0 else None + lim_y_min = np.min(y_min) if len(y_min) > 0 else None + lim_y_max = np.max(y_max) if len(y_max) > 0 else None + + grid = Grid.union_from_iterable(f.grid for f in formats if f.grid is not None) + + [scale_x] = set(i.scale_x for i in formats) + [scale_y] = set(i.scale_y for i in formats) + + return cls( + title_fig=title_fig, + legend=legend, + title_ax=title_ax, + label_x=label_x, + label_y=label_y, + lim_x_min=lim_x_min, + lim_x_max=lim_x_max, + lim_y_min=lim_y_min, + lim_y_max=lim_y_max, + grid=grid, + scale_x=scale_x, + scale_y=scale_y, + figure_width=figure_width, + figure_height=figure_height, + ) + + +class PlottableData2D(DataProduct): + """ + Base class for children of DataProduct to be plotted ax xy data on a 2D plot + + Attributes: + format2d (Format2D|None): Format to apply to plot + tags (Tags): Tags to be used for sorting data. + metadata (dict[str, str]): A dictionary of metadata to be used as a tool tip for mousover in grafana + + """ + + format2d: Format2D | None = None + + def add_to_plotly(self, plotly_figure: PlotlyFigure): + """ + Add this data product to a plotly figure + + Args: + plotly_figure (PlotlyFigure): Plotly figure to add data to + + """ + raise NotImplementedError("Subclasses must implement add_to_plotly") + + +class XYData(PlottableData2D): + """ + Base class for children of DataProduct to be plotted ax xy data on a 2D plot + """ diff --git a/src/trendify/formats/table.py b/src/trendify/formats/table.py new file mode 100644 index 0000000..22c5c21 --- /dev/null +++ b/src/trendify/formats/table.py @@ -0,0 +1,50 @@ +from __future__ import annotations + +import logging + +from pydantic import ConfigDict + +from trendify.base.data_product import DataProduct + +logger = logging.getLogger(__name__) + +__all__ = ["TableEntry"] + + +class TableEntry(DataProduct): + """ + Defines an entry to be collected into a table. + + Collected table entries will be printed in three forms when possible: melted, pivot (when possible), and stats (on pivot columns, when possible). + + Attributes: + tags (Tags): Tags used to sort data products + row (float | str): Row Label + col (float | str): Column Label + value (float | str | bool): Value + unit (str | None): Units for value + metadata (dict[str, str]): A dictionary of metadata to be used as a tool tip for mousover in grafana + + """ + + row: float | str + col: float | str + value: float | str | bool + unit: str | None = None + + model_config = ConfigDict(extra="forbid") + + def get_entry_dict(self): + """ + Returns a dictionary of entries to be used in creating a table. + + Returns: + (dict[str, str | float]): Dictionary of entries to be used in creating a melted table + + """ + return { + "row": self.row, + "col": self.col, + "value": self.value, + "unit": self.unit, + } diff --git a/src/trendify/plotting/__init__.py b/src/trendify/plotting/__init__.py new file mode 100644 index 0000000..8da7c3b --- /dev/null +++ b/src/trendify/plotting/__init__.py @@ -0,0 +1,40 @@ +from trendify.plotting import axline +from trendify.plotting import figure +from trendify.plotting import histogram +from trendify.plotting import point +from trendify.plotting import trace + +from trendify.plotting.axline import ( + AxLine, + LineOrientation, +) +from trendify.plotting.figure import ( + PlotlyFigure, + SingleAxisFigure, +) +from trendify.plotting.histogram import ( + HistogramEntry, + HistogramStyle, +) +from trendify.plotting.point import ( + Point2D, +) +from trendify.plotting.trace import ( + Trace2D, +) + +__all__ = [ + "AxLine", + "HistogramEntry", + "HistogramStyle", + "LineOrientation", + "PlotlyFigure", + "Point2D", + "SingleAxisFigure", + "Trace2D", + "axline", + "figure", + "histogram", + "point", + "trace", +] diff --git a/src/trendify/plotting/axline.py b/src/trendify/plotting/axline.py new file mode 100644 index 0000000..b8c2ea4 --- /dev/null +++ b/src/trendify/plotting/axline.py @@ -0,0 +1,123 @@ +from __future__ import annotations + +import logging +from enum import Enum + +from matplotlib.axes import Axes +from pydantic import ConfigDict + +from trendify.base.pen import Pen +from trendify.formats.format2d import PlottableData2D +from trendify.plotting.figure import PlotlyFigure + +__all__ = ["AxLine", "LineOrientation"] + +logger = logging.getLogger(__name__) + + +class LineOrientation(Enum): + """ + Defines orientation for axis lines + + Attributes: + HORIZONTAL (LineOrientation): Horizontal line + VERTICAL (LineOrientation): Vertical line + + """ + + HORIZONTAL = "horizontal" + VERTICAL = "vertical" + + +class AxLine(PlottableData2D): + """ + Defines a horizontal or vertical line to be drawn on a plot. + + Attributes: + value (float): Value at which to draw the line (x-value for vertical, y-value for horizontal) + orientation (LineOrientation): Whether line should be horizontal or vertical + pen (Pen): Style and label information for drawing to matplotlib axes + tags (Tags): Tags to be used for sorting data + metadata (dict[str, str]): A dictionary of metadata + + """ + + value: float + orientation: LineOrientation + pen: Pen = Pen() + + model_config = ConfigDict(extra="forbid") + + def plot_to_ax(self, ax: Axes): + """ + Plots line to matplotlib axes object. + + Args: + ax (Axes): axes to which line should be plotted + + """ + match self.orientation: + case LineOrientation.HORIZONTAL: + ax.axhline(y=self.value, **self.pen.as_scatter_plot_kwargs()) + case LineOrientation.VERTICAL: + ax.axvline(x=self.value, **self.pen.as_scatter_plot_kwargs()) + case _: + logger.error(f"Unrecognized line orientation {self.orientation}") + + def add_to_plotly(self, plotly_figure: PlotlyFigure) -> PlotlyFigure: + """Add axis line to plotly figure without showing it in the legend""" + match self.orientation: + case LineOrientation.HORIZONTAL: + plotly_figure.fig.add_hline( + y=self.value, + line=dict( + color=self.pen.rgba if self.pen else None, + width=self.pen.size if self.pen else None, + dash=( + self.pen._convert_linestyle_to_plotly() + if self.pen + else None + ), + ), + showlegend=False, # Do not show in the legend + opacity=self.pen.alpha if self.pen else None, + ) + plotly_figure.fig.add_annotation( + x=1.0, + y=self.value, + xref="paper", # Reference to the entire plotting area + yref="y", + text=self.pen.label, + showarrow=False, # Hide the arrow if not needed + xanchor="left", + yanchor="middle", # Center the text horizontally with the vline + ) + case LineOrientation.VERTICAL: + plotly_figure.fig.add_vline( + x=self.value, + line=dict( + color=self.pen.rgba if self.pen else None, + width=self.pen.size if self.pen else None, + dash=( + self.pen._convert_linestyle_to_plotly() + if self.pen + else None + ), + ), + showlegend=False, # Do not show in the legend + opacity=self.pen.alpha if self.pen else None, + ) + plotly_figure.fig.add_annotation( + x=self.value, + y=1.0, # Position slightly above the plot area + yref="paper", # Reference to the entire plotting area + text=self.pen.label, + showarrow=False, # Hide the arrow if not needed + xanchor="center", # Center the text horizontally with the vline + yanchor="bottom", + ) + case _: + logger.error(f"Unrecognized line orientation {self.orientation}") + return plotly_figure + + return plotly_figure diff --git a/src/trendify/plotting/figure.py b/src/trendify/plotting/figure.py new file mode 100644 index 0000000..8bf76d4 --- /dev/null +++ b/src/trendify/plotting/figure.py @@ -0,0 +1,339 @@ +from __future__ import annotations + +from dataclasses import dataclass, field +from pathlib import Path +import matplotlib.pyplot as plt +import warnings +import logging + +import numpy as np +import plotly.graph_objects as go + +from trendify.formats.format2d import AxisScale, PlottableData2D + +from typing import Self, TYPE_CHECKING + +from pydantic import ConfigDict + +from trendify.base.helpers import Tag + +if TYPE_CHECKING: + from trendify.formats.format2d import Format2D + from trendify.styling.grid import Grid + from matplotlib.axes import Axes + from matplotlib.figure import Figure + + +__all__ = ["PlotlyFigure", "SingleAxisFigure"] + +logger = logging.getLogger(__name__) + + +@dataclass +class SingleAxisFigure: + """ + Data class storing a matlab figure and axis. The stored tag data in this class is so-far unused. + + Attributes: + ax (Axes): Matplotlib axis to which data will be plotted + fig (Figure): Matplotlib figure. + tag (Tag): Figure tag. Not yet used. + + """ + + model_config = ConfigDict(arbitrary_types_allowed=True) + tag: Tag + fig: Figure + ax: Axes + + @classmethod + def new(cls, tag: Tag): + """ + Creates new figure and axis. Returns new instance of this class. + + Args: + tag (Tag): tag (not yet used) + + Returns: + (Type[Self]): New single axis figure + + """ + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1) + return cls( + tag=tag, + fig=fig, + ax=ax, + ) + + def apply_format(self, format2d: Format2D): + """ + Applies format to figure and axes labels and limits + + Args: + format2d (Format2D): format information to apply to the single axis figure + + """ + if format2d.title_ax is not None: + self.ax.set_title(format2d.title_ax) + if format2d.title_fig is not None: + self.fig.suptitle(format2d.title_fig) + + leg = None + if format2d.legend is not None: + with warnings.catch_warnings(action="ignore", category=UserWarning): + handles, labels = self.ax.get_legend_handles_labels() + by_label = dict(zip(labels, handles)) + if by_label: + sorted_items = sorted(by_label.items(), key=lambda item: item[0]) + labels_sorted, handles_sorted = zip(*sorted_items) + + kwargs = format2d.legend.to_kwargs() + + leg = self.ax.legend( + handles=handles_sorted, + labels=labels_sorted, + bbox_to_anchor=format2d.legend.bbox_to_anchor, + **kwargs, + ) + leg.set_zorder(level=format2d.legend.zorder) + + if leg is not None and format2d.legend.edgecolor: + leg.get_frame().set_edgecolor(format2d.legend.edgecolor) + + if format2d.label_x is not None: + self.ax.set_xlabel(xlabel=format2d.label_x) + if format2d.label_y is not None: + self.ax.set_ylabel(ylabel=format2d.label_y) + + self.ax.set_xlim(left=format2d.lim_x_min, right=format2d.lim_x_max) + self.ax.set_ylim(bottom=format2d.lim_y_min, top=format2d.lim_y_max) + + self.ax.set_xscale(format2d.scale_x.value) + self.ax.set_yscale(format2d.scale_y.value) + + if format2d.grid is not None: + self.apply_grid(format2d.grid) + + self.fig.set_size_inches(format2d.figure_width, format2d.figure_height) + self.fig.tight_layout(rect=(0, 0.03, 1, 0.95)) + return self + + def apply_grid(self, grid: Grid): + self.ax.set_axisbelow(True) + + # Major grid + if grid.major.show: + self.ax.grid( + visible=True, + which="major", + color=grid.major.pen.color, + linestyle=grid.major.pen.linestyle, + linewidth=grid.major.pen.size, + alpha=grid.major.pen.alpha, + zorder=grid.zorder, + ) + else: + self.ax.grid(visible=False, which="major") + + # Minor ticks and grid + if grid.enable_minor_ticks: + self.ax.minorticks_on() + else: + self.ax.minorticks_off() + + if grid.minor.show: + self.ax.grid( + visible=True, + which="minor", + color=grid.minor.pen.color, + linestyle=grid.minor.pen.linestyle, + linewidth=grid.minor.pen.size, + alpha=grid.minor.pen.alpha, + zorder=grid.zorder, + ) + else: + self.ax.grid(visible=False, which="minor") + + def savefig(self, path: Path, dpi: int = 500): + """ + Wrapper on matplotlib savefig method. Saves figure to given path with given dpi resolution. + + Returns: + (Self): Returns self + + """ + self.fig.savefig(path, dpi=dpi) + return self + + def __del__(self): + """ + Closes stored matplotlib figure before deleting reference to object. + """ + plt.close(self.fig) + + +@dataclass +class PlotlyFigure: + model_config = ConfigDict(arbitrary_types_allowed=True) + tag: Tag + fig: go.Figure + legend_groups: set[str] = field(default_factory=set) + + @classmethod + def new(cls, tag: Tag): + """ + Creates new figure and axis. Returns new instance of this class. + + Args: + tag (Tag): tag (not yet used) + + Returns: + (Type[Self]): New single axis figure + + """ + fig = go.Figure() + return cls(tag=tag, fig=fig) + + def apply_format(self, format2d: Format2D): + """ + Applies format to Plotly figure layout including axes labels and limits + + Args: + format2d (Format2D): format information to apply to the figure + + """ + layout_updates = {} + + # Set titles + if format2d.title_fig is not None and format2d.title_ax is not None: + layout_updates["title"] = f"{format2d.title_fig} | {format2d.title_ax}" + elif format2d.title_fig is None and format2d.title_ax is not None: + layout_updates["title"] = f"{format2d.title_ax}" + elif format2d.title_fig is not None and format2d.title_ax is None: + layout_updates["title"] = f"{format2d.title_fig}" + + # Set axis labels + if format2d.label_x is not None: + layout_updates["xaxis"] = {"title": format2d.label_x} + if format2d.label_y is not None: + layout_updates["yaxis"] = {"title": format2d.label_y} + + # Set axis ranges + def _log10(v: float | None): + return None if v is None else np.log10(v) + + if format2d.lim_x_min is not None or format2d.lim_x_max is not None: + if "xaxis" not in layout_updates: + layout_updates["xaxis"] = {} + + if format2d.scale_x == AxisScale.LOG: + layout_updates["xaxis"]["range"] = [ + _log10(format2d.lim_x_min), + _log10(format2d.lim_x_max), + ] + elif format2d.scale_x == AxisScale.LINEAR: + # v1 bugfix: this branch previously re-checked `== AxisScale.LOG`, + # making the plain (non-log) range-setting path unreachable. + layout_updates["xaxis"]["range"] = [ + format2d.lim_x_min, + format2d.lim_x_max, + ] + + if format2d.lim_y_min is not None or format2d.lim_y_max is not None: + if "yaxis" not in layout_updates: + layout_updates["yaxis"] = {} + if format2d.scale_y == AxisScale.LOG: + layout_updates["yaxis"]["range"] = [ + _log10(format2d.lim_y_min), + _log10(format2d.lim_y_max), + ] + elif format2d.scale_y == AxisScale.LINEAR: + # same fix as above, for the y axis + layout_updates["yaxis"]["range"] = [ + format2d.lim_y_min, + format2d.lim_y_max, + ] + + # Set axis scales + if format2d.scale_x is not None: + if "xaxis" not in layout_updates: + layout_updates["xaxis"] = {} + layout_updates["xaxis"]["type"] = format2d.scale_x.value + + if format2d.scale_y is not None: + if "yaxis" not in layout_updates: + layout_updates["yaxis"] = {} + layout_updates["yaxis"]["type"] = format2d.scale_y.value + + # Set legend + if format2d.legend is not None: + layout_updates["showlegend"] = format2d.legend.visible + layout_updates["legend"] = dict( + title=format2d.legend.title, + bordercolor=format2d.legend.edgecolor, + **format2d.legend.plotly_location, + ) + + # Apply grid if specified + if format2d.grid is not None: + self.apply_grid(format2d.grid) + + # Update layout + self.fig.update_layout(**layout_updates) + return self + + def apply_grid(self, grid: Grid): + """ + Applies grid settings to the Plotly figure + + Args: + grid (Grid): Grid configuration to apply + + """ + # Major grid + + xaxis_updates = { + "showgrid": grid.major.show, + "gridcolor": grid.major.pen.rgba if grid.major.show else None, + "gridwidth": grid.major.pen.size if grid.major.show else None, + "griddash": "solid" if grid.major.pen.linestyle == "-" else "dash", + } + + yaxis_updates = { + "showgrid": grid.major.show, + "gridcolor": grid.major.pen.rgba if grid.major.show else None, + "gridwidth": grid.major.pen.size if grid.major.show else None, + "griddash": "solid" if grid.major.pen.linestyle == "-" else "dash", + } + + # Minor ticks and grid + if grid.enable_minor_ticks: + xaxis_updates["minor"] = { + "showgrid": grid.minor.show, + "gridcolor": grid.minor.pen.rgba if grid.minor.show else None, + "gridwidth": grid.minor.pen.size if grid.minor.show else None, + "griddash": "solid" if grid.minor.pen.linestyle == "-" else "dash", + } + yaxis_updates["minor"] = { + "showgrid": grid.minor.show, + "gridcolor": grid.minor.pen.rgba if grid.minor.show else None, + "gridwidth": grid.minor.pen.size if grid.minor.show else None, + "griddash": "solid" if grid.minor.pen.linestyle == "-" else "dash", + } + + self.fig.update_xaxes(**xaxis_updates) + self.fig.update_yaxes(**yaxis_updates) + + def add_data_product(self, product: PlottableData2D) -> Self: + """ + Add a data product to the figure + + Args: + product (PlottableData2D): Data product to add to figure + + Returns: + Self: Returns self for method chaining + + """ + return product.add_to_plotly(self) diff --git a/src/trendify/plotting/histogram.py b/src/trendify/plotting/histogram.py new file mode 100644 index 0000000..cbe8b22 --- /dev/null +++ b/src/trendify/plotting/histogram.py @@ -0,0 +1,249 @@ +from __future__ import annotations + +import logging +from typing import cast + +import plotly.graph_objects as go +from matplotlib.colors import to_rgba +from numpydantic import NDArray, Shape +from pydantic import ConfigDict, Field + +from trendify.base.helpers import HashableBase, Tags +from trendify.formats.format2d import PlottableData2D +from trendify.plotting.figure import PlotlyFigure + +__all__ = ["HistogramEntry", "HistogramStyle"] + +logger = logging.getLogger(__name__) + + +class HistogramStyle(HashableBase): + """ + Label and style data for generating histogram bars + + Attributes: + color (str): Color of bars + label (str|None): Legend entry + histtype (str): Histogram type corresponding to matplotlib argument of same name + alpha_edge (float): Opacity of bar edge + alpha_face (float): Opacity of bar face + linewidth (float): Line width of bar outline + bins (int | list[int] | Tuple[int] | NDArray[Shape["*"], int] | None): Number of bins (see [matplotlib docs][matplotlib.pyplot.hist]) + + """ + + color: str = "k" + label: str | None = None + histtype: str = "stepfilled" + alpha_edge: float = 0 + alpha_face: float = 0.3 + linewidth: float = 2 + zorder: int = 1 + bins: int | list[int] | tuple[int] | NDArray[Shape["*"], int] | None = None + + def as_plot_kwargs(self): + """ + Returns: + (dict): kwargs for matplotlib `hist` method + + """ + return { + "facecolor": (self.color, self.alpha_face), + "edgecolor": (self.color, self.alpha_edge), + "linewidth": self.linewidth, + "label": self.label, + "histtype": self.histtype, + "bins": self.bins, + "zorder": self.zorder, + } + + @property + def rgba_face(self) -> str: + """ + Convert the pen's color to rgba string format. + + Returns: + str: Color in 'rgba(r,g,b,a)' format where r,g,b are 0-255 and a is 0-1 + + """ + # Handle different color input formats + if isinstance(self.color, tuple): + if len(self.color) == 3: # RGB tuple + r, g, b = self.color + a = self.alpha_face + else: # RGBA tuple + r, g, b, a = self.color + # Convert 0-1 range to 0-255 for RGB + r, g, b = int(r * 255), int(g * 255), int(b * 255) + else: # String color (name or hex) + # Use matplotlib's color converter + rgba_vals = to_rgba(self.color, self.alpha_face) + # Convert 0-1 range to 0-255 for RGB + r, g, b = [int(x * 255) for x in rgba_vals[:3]] + a = rgba_vals[3] + + return f"rgba({r}, {g}, {b}, {a})" + + @property + def rgba_edge(self) -> str: + """ + Convert the pen's color to rgba string format. + + Returns: + str: Color in 'rgba(r,g,b,a)' format where r,g,b are 0-255 and a is 0-1 + + """ + # Handle different color input formats + if isinstance(self.color, tuple): + if len(self.color) == 3: # RGB tuple + r, g, b = self.color + a = self.alpha_edge + else: # RGBA tuple + r, g, b, a = self.color + # Convert 0-1 range to 0-255 for RGB + r, g, b = int(r * 255), int(g * 255), int(b * 255) + else: # String color (name or hex) + # Use matplotlib's color converter + rgba_vals = to_rgba(self.color, self.alpha_edge) + # Convert 0-1 range to 0-255 for RGB + r, g, b = [int(x * 255) for x in rgba_vals[:3]] + a = rgba_vals[3] + + return f"rgba({r}, {g}, {b}, {a})" + + @property + def rgb_face(self) -> str: + return ", ".join(self.rgba_face.split(",")[0:-1]) + ")" + + @property + def rgb_edge(self) -> str: + return ", ".join(self.rgba_edge.split(",")[0:-1]) + ")" + + def get_face_contrast_color(self, background_luminance: float = 1.0) -> str: + """ + Returns 'white' or 'black' to provide the best contrast against the pen's color, + taking into account the alpha (transparency) value of the line. + + Args: + background_luminance (float): The luminance of the background (default is 1.0 for white). + + Returns: + str: 'white' or 'black' + + """ + # Convert the pen's color to RGB (0-255 range) and get alpha + if isinstance(self.color, tuple): + if len(self.color) == 3: # RGB tuple + r, g, b = self.color + a = self.alpha_face + else: # RGBA tuple + r, g, b, a = self.color + r, g, b = int(r * 255), int(g * 255), int(b * 255) + else: # String color (name or hex) + rgba_vals = to_rgba(self.color, self.alpha_face) + r, g, b = [int(x * 255) for x in rgba_vals[:3]] + a = rgba_vals[3] + + # Calculate relative luminance of the pen's color + def luminance(channel): + channel /= 255.0 + return ( + channel / 12.92 + if channel <= 0.03928 + else ((channel + 0.055) / 1.055) ** 2.4 + ) + + color_luminance = ( + 0.2126 * luminance(r) + 0.7152 * luminance(g) + 0.0722 * luminance(b) + ) + + # Blend the color luminance with the background luminance based on alpha + blended_luminance = (1 - a) * background_luminance + a * color_luminance + + # Return white for dark blended colors, black for light blended colors + return "white" if blended_luminance < 0.5 else "black" + + +class HistogramEntry(PlottableData2D): + """ + Use this class to specify a value to be collected into a matplotlib histogram. + + Attributes: + tags (Tags): Tags used to sort data products + value (float | str): Value to be binned + style (HistogramStyle): Style of histogram display + + """ + + value: float | str + tags: Tags + style: HistogramStyle | None = Field(default_factory=HistogramStyle) + + model_config = ConfigDict(extra="forbid") + + def add_to_plotly(self, plotly_figure: PlotlyFigure): + """Add histogram entry to plotly figure, merging with existing traces if possible""" + if not self.style: + logger.error("HistogramEntry style is not defined.") + return plotly_figure + + # Create legend group key based on the label and color + legend_key = ( + f"{self.style.label}_{self.style.rgba_face}" if self.style.label else None + ) + + # Check if a trace with the same legend group already exists + traces = cast("tuple[go.Histogram, ...]", plotly_figure.fig.data) + for trace in traces: + if trace.legendgroup == legend_key: + # Append the value to the existing trace's x data + trace.x = list(trace.x) + [self.value] + return plotly_figure + + metadata_html = ( + "
".join([f"{key}: {value}" for key, value in self.metadata.items()]) + if self.metadata + else "" + ) + + hovertemplate = ( + f"{self.style.label if self.style.label else ''}
" + "x: %{x}
" + "y: %{y}
" + f"{metadata_html}" + ) + + # If no existing trace, add a new one + plotly_figure.fig.add_trace( + go.Histogram( + x=[self.value], + name=self.style.label, # Legend label + marker=dict( + color=self.style.rgba_face, + line=dict( + color=self.style.rgba_edge, + width=self.style.linewidth, + ), + ), + zorder=self.style.zorder, + nbinsx=self.style.bins if isinstance(self.style.bins, int) else None, + legendgroup=legend_key, # Group histograms with the same label and color + hovertemplate=hovertemplate, + hoverlabel=dict( + bgcolor=self.style.rgba_face, + bordercolor=self.style.rgba_edge, + font=dict( + color=self.style.get_face_contrast_color() # Optionally, set the font color as well + ), + ), + showlegend=( + True if self.style.label is not None else False + ), # Always show legend for the first trace + ) + ) + + # Track the legend group to avoid duplicate legend entries + if legend_key and legend_key not in plotly_figure.legend_groups: + plotly_figure.legend_groups.add(legend_key) + + return plotly_figure diff --git a/src/trendify/plotting/point.py b/src/trendify/plotting/point.py new file mode 100644 index 0000000..ee7a01f --- /dev/null +++ b/src/trendify/plotting/point.py @@ -0,0 +1,89 @@ +from __future__ import annotations + +import logging + +import plotly.graph_objects as go +from pydantic import ConfigDict + +from trendify.formats.format2d import XYData +from trendify.plotting.figure import PlotlyFigure +from trendify.styling.marker import Marker + +__all__ = ["Point2D"] + +logger = logging.getLogger(__name__) + + +class Point2D(XYData): + """ + Defines a point to be scattered onto xy plot. + + Attributes: + tags (Tags): Tags to be used for sorting data. + x (float | str): X value for the point. + y (float | str): Y value for the point. + marker (Marker | None): Style and label information for scattering points to matplotlib axes. + Only the label information is used in Grafana. + Eventually style information will be used in grafana. + metadata (dict[str, str]): A dictionary of metadata to be used as a tool tip for mousover in grafana + + """ + + x: float | str + y: float | str + marker: Marker | None = Marker() + + model_config = ConfigDict(extra="forbid") + + def add_to_plotly(self, plotly_figure: PlotlyFigure) -> PlotlyFigure: + """Add point to plotly figure with legendgroup support""" + legend_key = ( + f"{self.marker.label}_{self.marker.color}_{self.marker.symbol}" + if self.marker + else None + ) + # Prepare metadata for the tooltip + metadata_html = ( + "
".join([f"{key}: {value}" for key, value in self.metadata.items()]) + if self.metadata + else "" + ) + + # Define hovertemplate for the tooltip + hovertemplate = ( + f"{self.marker.label if self.marker else ''}
" + "x: %{x}
" + "y: %{y}
" + f"{metadata_html}" + ) + + plotly_figure.fig.add_trace( + go.Scatter( + x=[self.x], + y=[self.y], + name=self.marker.label if self.marker else None, + mode="markers", + marker=dict( + color=self.marker.rgba if self.marker else None, + size=self.marker.size if self.marker else None, + symbol=self.marker.plotly_symbol if self.marker else None, + ), + zorder=int(self.marker.zorder) if self.marker else None, + legendgroup=legend_key, + hovertemplate=hovertemplate, + hoverlabel=dict( + bgcolor=(self.marker.rgba if self.marker else None), + font=dict( + color=self.marker.get_contrast_color() if self.marker else None + ), + ), + showlegend=( + True if legend_key not in plotly_figure.legend_groups else False + ), + ) + ) + + if legend_key and legend_key not in plotly_figure.legend_groups: + plotly_figure.legend_groups.add(legend_key) + + return plotly_figure diff --git a/src/trendify/plotting/trace.py b/src/trendify/plotting/trace.py new file mode 100644 index 0000000..5970c32 --- /dev/null +++ b/src/trendify/plotting/trace.py @@ -0,0 +1,186 @@ +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING + +import numpy as np +import plotly.graph_objects as go +from numpydantic import NDArray, Shape +from pydantic import ConfigDict + +from trendify.base.helpers import Tags +from trendify.base.pen import Pen +from trendify.formats.format2d import XYData +from trendify.plotting.figure import PlotlyFigure +from trendify.plotting.point import Point2D +from trendify.styling.marker import Marker + +if TYPE_CHECKING: + from matplotlib.axes import Axes + + from trendify.formats.format2d import Format2D + +__all__ = ["Trace2D"] + +logger = logging.getLogger(__name__) + + +class Trace2D(XYData): + """ + A collection of points comprising a trace. + Use the [Trace2D.from_xy][trendify.plotting.trace.Trace2D.from_xy] constructor. + + Attributes: + points (List[Point2D]): List of points. Usually the points would have null values + for `marker` and `format2d` fields to save space. + pen (Pen): Style and label information for drawing to matplotlib axes. + Only the label information is used in Grafana. + Eventually style information will be used in grafana. + tags (Tags): Tags to be used for sorting data. + metadata (dict[str, str]): A dictionary of metadata to be used as a tool tip for mousover in grafana + + """ + + model_config = ConfigDict(extra="forbid") + + points: list[Point2D] + pen: Pen = Pen() + + @property + def x(self) -> NDArray[Shape["*"], float]: + """ + Returns an array of x values from `self.points` + + Returns: + (NDArray[Shape["*"], float]): array of x values from `self.points` + + """ + return np.array([p.x for p in self.points]) + + @property + def y(self) -> NDArray[Shape["*"], float]: + """ + Returns an array of y values from `self.points` + + Returns: + (NDArray[Shape["*"], float]): array of y values from `self.points` + + """ + return np.array([p.y for p in self.points]) + + def propagate_format2d_and_pen(self, marker_symbol: str = ".") -> None: + """ + Propagates format and style info to all `self.points` (in-place). + + Args: + marker_symbol (str): Valid matplotlib marker symbol + + """ + self.points = [ + p.model_copy( + update={ + "tags": self.tags, + "format2d": self.format2d, + "marker": Marker.from_pen(self.pen, symbol=marker_symbol), + } + ) + for p in self.points + ] + + @classmethod + def from_xy( + cls, + tags: Tags, + x: NDArray[Shape["*"], float], + y: NDArray[Shape["*"], float], + pen: Pen = Pen(), + format2d: Format2D | None = None, + ): + """ + Creates a list of [Point2D][trendify.plotting.point.Point2D]s from xy data and returns a new [Trace2D][trendify.plotting.trace.Trace2D] product. + + Args: + tags (Tags): Tags used to sort data products + x (NDArray[Shape["*"], float]): x values + y (NDArray[Shape["*"], float]): y values + pen (Pen): Style and label for trace + format2d (Format2D | None): Format to apply to plot + + """ + return cls( + tags=tags, + points=[ + Point2D( + tags=[], + x=x_, + y=y_, + marker=None, + format2d=None, + ) + for x_, y_ in zip(x, y) + ], + pen=pen, + format2d=format2d, + ) + + def plot_to_ax(self, ax: Axes): + """ + Plots xy data from trace to a matplotlib axes object. + + Args: + ax (Axes): axes to which xy data should be plotted + + """ + ax.plot(self.x, self.y, **self.pen.as_scatter_plot_kwargs()) + + def add_to_plotly(self, plotly_figure: PlotlyFigure) -> PlotlyFigure: + legend_key = ( + f"{self.pen.label}_{self.pen.color}_{self.pen._convert_linestyle_to_plotly()}" + if self.pen + else None + ) + # Prepare metadata for the tooltip + metadata_html = ( + "
".join([f"{key}: {value}" for key, value in self.metadata.items()]) + if self.metadata + else "" + ) + + # Define hovertemplate for the tooltip + hovertemplate = ( + f"{self.pen.label if self.pen else ''}
" + "x: %{x}
" + "y: %{y}
" + f"{metadata_html}" + ) + + plotly_figure.fig.add_trace( + go.Scatter( + x=[p.x for p in self.points], + y=[p.y for p in self.points], + name=self.pen.label if self.pen else None, + mode="lines", + line=dict( + color=self.pen.rgba if self.pen else None, + width=self.pen.size if self.pen else None, + dash=self.pen._convert_linestyle_to_plotly() if self.pen else None, + ), + marker=dict( + color=self.pen.rgba if self.pen else None, # Marker color + ), + zorder=int(self.pen.zorder), + hovertemplate=hovertemplate, + hoverlabel=dict( + bgcolor=(self.pen.rgba if self.pen else None), + font=dict(color=self.pen.get_contrast_color()), + ), + legendgroup=legend_key, + showlegend=( + True if legend_key not in plotly_figure.legend_groups else False + ), + ) + ) + + if legend_key and legend_key not in plotly_figure.legend_groups: + plotly_figure.legend_groups.add(legend_key) + return plotly_figure diff --git a/src/trendify/store/__init__.py b/src/trendify/store/__init__.py new file mode 100644 index 0000000..61d9609 --- /dev/null +++ b/src/trendify/store/__init__.py @@ -0,0 +1,26 @@ +from trendify.store import db +from trendify.store import product_store +from trendify.store import tags + +from trendify.store.db import ( + SCHEMA_VERSION, + connect, +) +from trendify.store.product_store import ( + ProductStore, +) +from trendify.store.tags import ( + decode_tag, + encode_tag, +) + +__all__ = [ + "SCHEMA_VERSION", + "ProductStore", + "connect", + "db", + "decode_tag", + "encode_tag", + "product_store", + "tags", +] diff --git a/src/trendify/store/db.py b/src/trendify/store/db.py new file mode 100644 index 0000000..d0ce3f0 --- /dev/null +++ b/src/trendify/store/db.py @@ -0,0 +1,109 @@ +""" +SQLite connection factory, pragmas, and schema management for the trendify v2 store. + +Design (see rewrite_reference/OVERVIEW.md and the v2 architecture plan for full context): + +- One `.db` file per trendify output directory, opened by every worker process independently. +- WAL mode gives concurrent readers + one writer without the readers blocking on the writer; + `busy_timeout` replaces v1's hand-rolled `FileLock` for the writer-vs-writer case. +- Schema versioning via `PRAGMA user_version`, checked/applied on every `connect()` call so + opening a fresh `.db` file bootstraps it and opening an existing one verifies compatibility. +""" + +from __future__ import annotations + +import sqlite3 +from pathlib import Path + +__all__ = ["SCHEMA_VERSION", "connect"] + +SCHEMA_VERSION = 1 + +_SCHEMA_DDL = """ +CREATE TABLE IF NOT EXISTS runs ( + id INTEGER PRIMARY KEY, + path TEXT NOT NULL UNIQUE, + generated_at TEXT NOT NULL +); + +CREATE TABLE IF NOT EXISTS products ( + id INTEGER PRIMARY KEY, + run_id INTEGER NOT NULL REFERENCES runs(id) ON DELETE CASCADE, + product_type TEXT NOT NULL, + payload TEXT NOT NULL, + created_at TEXT NOT NULL +); +CREATE INDEX IF NOT EXISTS idx_products_run ON products(run_id); + +CREATE TABLE IF NOT EXISTS product_tags ( + product_id INTEGER NOT NULL REFERENCES products(id) ON DELETE CASCADE, + tag_key TEXT NOT NULL, + product_type TEXT NOT NULL, + PRIMARY KEY (product_id, tag_key) +); +CREATE INDEX IF NOT EXISTS idx_product_tags_lookup ON product_tags(tag_key, product_type); + +CREATE TABLE IF NOT EXISTS table_entries ( + id INTEGER PRIMARY KEY, + product_id INTEGER NOT NULL REFERENCES products(id) ON DELETE CASCADE, + tag_key TEXT NOT NULL, + row_key TEXT NOT NULL, + col_key TEXT NOT NULL, + value_num REAL, + value_text TEXT, + value_bool INTEGER, + unit TEXT +); +CREATE INDEX IF NOT EXISTS idx_table_entries_tag ON table_entries(tag_key); +""" + + +def connect(db_path: Path, *, readonly: bool = False) -> sqlite3.Connection: + """ + Opens a WAL-mode SQLite connection to `db_path`, bootstrapping the schema on first use. + + Args: + db_path (Path): path to the trendify output directory's `.db` file + readonly (bool): open in read-only mode (`mode=ro` URI) — used by render workers that + only ever query, never write, so they can safely run concurrently with an + in-progress writer under WAL. + + Returns: + (sqlite3.Connection): a configured connection, with row access by column name. + + """ + if readonly: + uri = f"file:{db_path}?mode=ro" + conn = sqlite3.connect(uri, uri=True) + else: + db_path.parent.mkdir(parents=True, exist_ok=True) + conn = sqlite3.connect(db_path) + + conn.row_factory = sqlite3.Row + conn.execute("PRAGMA journal_mode=WAL") + conn.execute("PRAGMA synchronous=NORMAL") + conn.execute("PRAGMA busy_timeout=30000") + conn.execute("PRAGMA foreign_keys=ON") + + if not readonly: + _ensure_schema(conn) + + return conn + + +def _ensure_schema(conn: sqlite3.Connection) -> None: + """ + Bootstraps the schema on a fresh database, or verifies the on-disk schema version matches + `SCHEMA_VERSION` on an existing one. There is deliberately no migration logic yet (v2 has + only ever had one schema version) — this is the hook future migrations attach to. + """ + (version,) = conn.execute("PRAGMA user_version").fetchone() + if version == 0: + conn.executescript(_SCHEMA_DDL) + conn.execute(f"PRAGMA user_version={SCHEMA_VERSION}") + conn.commit() + elif version != SCHEMA_VERSION: + raise RuntimeError( + f"Database schema version {version} does not match expected " + f"{SCHEMA_VERSION}, and no migration path exists yet." + ) diff --git a/src/trendify/store/product_store.py b/src/trendify/store/product_store.py new file mode 100644 index 0000000..282a04a --- /dev/null +++ b/src/trendify/store/product_store.py @@ -0,0 +1,300 @@ +""" +ProductStore: the SQLite-backed replacement for v1's DataProductCollection file-glob methods. + +Pydantic objects remain what flows in and out everywhere -- only the storage/query mechanics +change. See rewrite_reference/OVERVIEW.md and the v2 architecture plan for the full design +rationale (why SQLite/WAL, why this schema shape, why no merge phase). +""" + +from __future__ import annotations + +import datetime +import sqlite3 +from collections.abc import Iterable, Iterator +from pathlib import Path + +import polars as pl + +from trendify.base.data_product import DataProduct +from trendify.base.helpers import Tag +from trendify.formats.table import TableEntry +from trendify.store import db +from trendify.store.tags import decode_tag, encode_tag + +__all__ = ["ProductStore"] + + +def _leaf_type_names(object_type: type[DataProduct]) -> list[str]: + """ + Expands a (possibly non-leaf) `DataProduct` type into the list of registered leaf + `product_type` names that are instances of it -- e.g. `PlottableData2D` expands to + `["Point2D", "Trace2D", "AxLine", "HistogramEntry"]`. This lets tag/type filtering happen + as a plain SQL `product_type IN (...)` clause instead of deserializing every tag-matched + row just to run an `isinstance` check in Python. + """ + return [ + name + for name, cls in DataProduct.registry().items() + if issubclass(cls, object_type) + ] + + +def _tag_sort_key(tag: Tag): + as_tuple = tag if isinstance(tag, tuple) else (tag,) + return (len(as_tuple), as_tuple) + + +class ProductStore: + """ + SQLite-backed store for `DataProduct`s, grouped by run and queryable by tag/type. + + Usage: + with ProductStore.open(db_path) as store: + store.write_run(run_path, products) + traces = store.get_products_of_type(Trace2D, tag="my_tag") + """ + + def __init__(self, conn: sqlite3.Connection): + self._conn = conn + + @classmethod + def open(cls, db_path: Path, *, readonly: bool = False) -> ProductStore: + return cls(db.connect(Path(db_path), readonly=readonly)) + + def __enter__(self) -> ProductStore: + return self + + def __exit__(self, *exc_info) -> None: + self.close() + + def close(self) -> None: + self._conn.close() + + def write_run(self, run_path: Path, products: Iterable[DataProduct]) -> int: + """ + Replaces all products for `run_path` in a single transaction. Idempotent: calling this + again for the same `run_path` (e.g. re-running a generator on the same input directory) + deletes and replaces that run's rows rather than accumulating duplicates -- this is what + replaces v1's `ProductIndexMap`/`FileLock` stable-indexing dance, since there's no + per-tag file to keep numbered anymore. + + Args: + run_path (Path): the raw-data run directory these products were generated from + products (Iterable[DataProduct]): products returned by the user's `ProductGenerator` + + Returns: + (int): number of products written + + """ + products = list(products) + run_path_str = str(Path(run_path).resolve()) + now = datetime.datetime.now(datetime.UTC).isoformat() + + conn = self._conn + with conn: # transaction: commits on success, rolls back on exception + row = conn.execute( + "SELECT id FROM runs WHERE path = ?", (run_path_str,) + ).fetchone() + if row is None: + run_id = conn.execute( + "INSERT INTO runs(path, generated_at) VALUES (?, ?)", + (run_path_str, now), + ).lastrowid + else: + run_id = row["id"] + conn.execute( + "UPDATE runs SET generated_at = ? WHERE id = ?", (now, run_id) + ) + + # ON DELETE CASCADE (product_tags, table_entries) requires PRAGMA foreign_keys=ON, + # set in db.connect(). + conn.execute("DELETE FROM products WHERE run_id = ?", (run_id,)) + + for product in products: + product_id = conn.execute( + "INSERT INTO products(run_id, product_type, payload, created_at) " + "VALUES (?, ?, ?, ?)", + (run_id, product.product_type, product.model_dump_json(), now), + ).lastrowid + + tag_keys = [encode_tag(t) for t in product.tags] + conn.executemany( + "INSERT INTO product_tags(product_id, tag_key, product_type) " + "VALUES (?, ?, ?)", + [(product_id, tk, product.product_type) for tk in tag_keys], + ) + + if isinstance(product, TableEntry): + value_num = value_text = value_bool = None + if isinstance(product.value, bool): + value_bool = int(product.value) + elif isinstance(product.value, (int, float)): + value_num = float(product.value) + else: + value_text = product.value + + conn.executemany( + "INSERT INTO table_entries(" + "product_id, tag_key, row_key, col_key, " + "value_num, value_text, value_bool, unit) " + "VALUES (?, ?, ?, ?, ?, ?, ?, ?)", + [ + ( + product_id, + tk, + str(product.row), + str(product.col), + value_num, + value_text, + value_bool, + product.unit, + ) + for tk in tag_keys + ], + ) + + return len(products) + + def get_tags(self, object_type: type[DataProduct] | None = None) -> set[Tag]: + """ + Args: + object_type (type[DataProduct] | None): restrict to tags used by products of this + type (or any registered subclass of it); `None` matches every product type. + + Returns: + (set[Tag]): the set of tags in use + + """ + if object_type is None: + rows = self._conn.execute("SELECT DISTINCT tag_key FROM product_tags") + else: + names = _leaf_type_names(object_type) + if not names: + return set() + placeholders = ",".join("?" * len(names)) + rows = self._conn.execute( + f"SELECT DISTINCT tag_key FROM product_tags " + f"WHERE product_type IN ({placeholders})", + names, + ) + return {decode_tag(r["tag_key"]) for r in rows} + + def tag_tree(self, object_type: type[DataProduct] | None = None) -> list[Tag]: + """ + Returns: + (list[Tag]): tags sorted the way v1's Streamlit sidebar expects (shallow tags + first, then lexicographically) -- direct replacement for v1's + `streamlit.get_tags()`, which discovered this list by globbing for files + literally named `index_map` in the sorted-products directory tree. + + """ + return sorted(self.get_tags(object_type=object_type), key=_tag_sort_key) + + def get_products( + self, + tag: Tag | None = None, + object_type: type[DataProduct] | None = None, + ) -> Iterator[DataProduct]: + """ + Streams matching products, deserialized to their concrete pydantic type. Filtering by + `tag` and/or `object_type` happens in SQL (indexed tag lookup, `product_type IN (...)` + for the type hierarchy) before anything is deserialized, so this only ever parses JSON + for rows that actually match -- unlike v1, which reparsed every JSON in a tag directory + before filtering in Python. + + Args: + tag (Tag | None): tag to filter by; `None` matches every tag + object_type (type[DataProduct] | None): type (or base type) to filter by; + `None` matches every type + + Yields: + (DataProduct): matching products, in insertion order + + """ + select = "SELECT p.product_type, p.payload FROM products p" + joins = [] + where = [] + params: list[object] = [] + + if tag is not None: + joins.append("JOIN product_tags pt ON pt.product_id = p.id") + where.append("pt.tag_key = ?") + params.append(encode_tag(tag)) + + if object_type is not None: + names = _leaf_type_names(object_type) + if not names: + return + where.append(f"p.product_type IN ({','.join('?' * len(names))})") + params.extend(names) + + query = " ".join([select, *joins]) + if where: + query += " WHERE " + " AND ".join(where) + + cursor = self._conn.execute(query, params) + for row in cursor: + yield DataProduct.deserialize(row["product_type"], row["payload"]) + + def get_products_of_type( + self, object_type: type[DataProduct], tag: Tag | None = None + ) -> list[DataProduct]: + """ + Same as `get_products`, but eager (a `list`) -- convenience for callers that don't need + streaming, mirroring v1's `DataProductCollection.get_products_of_type`. + """ + return list(self.get_products(tag=tag, object_type=object_type)) + + def get_table_entries(self, tag: Tag) -> pl.DataFrame: + """ + Fetches `TableEntry` rows for `tag` directly from the `table_entries` table -- skips + deserializing full `DataProduct` payloads entirely, since row/col/value/unit are already + real columns. This is the direct replacement for `TableBuilder.load_table`'s "reparse + every JSON in every input dir" step. + + Note: `value` is resolved to a single Python value per row (whichever of + `value_num`/`value_text`/`value_bool` is populated) before handing rows to Polars, + rather than asking Polars/SQLite to reconcile a mixed-type SQL column -- Polars expects + one dtype per column, so resolving the union type in Python first is the well-defined + choice. + + Args: + tag (Tag): tag to filter by + + Returns: + (pl.DataFrame): columns `row`, `col`, `value`, `unit` -- same shape as v1's + `TableEntry.get_entry_dict()`. + + """ + rows = self._conn.execute( + "SELECT row_key, col_key, value_num, value_text, value_bool, unit " + "FROM table_entries WHERE tag_key = ?", + (encode_tag(tag),), + ).fetchall() + + records = [] + for r in rows: + if r["value_bool"] is not None: + value = bool(r["value_bool"]) + elif r["value_num"] is not None: + value = r["value_num"] + else: + value = r["value_text"] + records.append( + { + "row": r["row_key"], + "col": r["col_key"], + "value": value, + "unit": r["unit"], + } + ) + + return pl.DataFrame( + records, + schema={ + "row": pl.Utf8, + "col": pl.Utf8, + "value": pl.Object, + "unit": pl.Utf8, + }, + ) diff --git a/src/trendify/store/tags.py b/src/trendify/store/tags.py new file mode 100644 index 0000000..911fb34 --- /dev/null +++ b/src/trendify/store/tags.py @@ -0,0 +1,51 @@ +""" +Canonical encoding between the pydantic-facing `Tag` type and the `tag_key` string +stored/indexed in the `product_tags` and `table_entries` SQL tables. + +This is the direct successor to v1's tuple-tag -> nested-folder-path convention: instead of +turning a tag into a filesystem path, we turn it into a single indexed string key. The same +`encode_tag` function must be used at write time and query time so lookups are exact-match +index seeks. +""" + +from __future__ import annotations + +import json + +from trendify.base.helpers import Tag + +__all__ = ["decode_tag", "encode_tag"] + + +def encode_tag(tag: Tag) -> str: + """ + Canonicalizes a `Tag` (`str`, `int`, or `tuple[str | int, ...]`) into the `tag_key` string + used for indexed storage/lookup. Scalars and tuples produce distinguishable encodings + (`json.dumps` renders a tuple as a JSON array), so `"foo"` and `("foo",)` never collide. + + Args: + tag (Tag): tag to encode + + Returns: + (str): canonical, indexable string key + + """ + return json.dumps(tag, separators=(",", ":")) + + +def decode_tag(tag_key: str) -> Tag: + """ + Inverse of `encode_tag`. JSON arrays decode back to `tuple`s (the `Tag` type never uses + plain `list`s) so round-tripping a tag through the store reproduces the original shape. + + Args: + tag_key (str): canonical string key, as produced by `encode_tag` + + Returns: + (Tag): decoded tag + + """ + decoded = json.loads(tag_key) + if isinstance(decoded, list): + return tuple(decoded) + return decoded diff --git a/src/trendify/styling/__init__.py b/src/trendify/styling/__init__.py new file mode 100644 index 0000000..bdf844b --- /dev/null +++ b/src/trendify/styling/__init__.py @@ -0,0 +1,28 @@ +from trendify.styling import grid +from trendify.styling import legend +from trendify.styling import marker + +from trendify.styling.grid import ( + Grid, + GridAxis, + GridTheme, +) +from trendify.styling.legend import ( + Legend, + LegendLocation, +) +from trendify.styling.marker import ( + Marker, +) + +__all__ = [ + "Grid", + "GridAxis", + "GridTheme", + "Legend", + "LegendLocation", + "Marker", + "grid", + "legend", + "marker", +] diff --git a/src/trendify/styling/grid.py b/src/trendify/styling/grid.py new file mode 100644 index 0000000..7926a54 --- /dev/null +++ b/src/trendify/styling/grid.py @@ -0,0 +1,149 @@ +from __future__ import annotations + +from enum import Enum, auto +from collections.abc import Iterable +import logging + +from pydantic import ConfigDict + +from trendify.base.helpers import HashableBase +from trendify.base.pen import Pen + + +logger = logging.getLogger(__name__) + +__all__ = ["Grid", "GridAxis", "GridTheme"] + + +class GridTheme(Enum): + MATLAB = auto() + LIGHT = auto() + DARK = auto() + + +class GridAxis(HashableBase): + """ + Controls styling and visibility for one type of grid (major or minor). + + Attributes: + show (bool): Whether to display this grid axis. + pen (Pen): Style and label information for drawing to matplotlib axes. + + """ + + show: bool = False + pen: Pen = Pen( + color="gray", + alpha=1.0, + size=0.75, + linestyle="-", + label=None, + ) + + model_config = ConfigDict(extra="forbid") + + +class Grid(HashableBase): + """ + Container for major and minor grid line configuration. + + Attributes: + major (GridAxis): Configuration for major grid lines. + minor (GridAxis): Configuration for minor grid lines. + enable_minor_ticks (bool): Whether to enable minor ticks on the axes. + + """ + + major: GridAxis = GridAxis(show=False) + minor: GridAxis = GridAxis(show=False) + enable_minor_ticks: bool = False + zorder: float = -1 + + model_config = ConfigDict(extra="forbid") + + @classmethod + def from_theme(cls, name: GridTheme) -> Grid: + """ + Predefined themes for common grid styles. + """ + themes = { + GridTheme.MATLAB: cls( + major=GridAxis( + show=True, + pen=Pen( + color="#b0b0b0", + linestyle="-", + size=0.8, + alpha=0.35, + label=None, + ), + ), + minor=GridAxis( + show=True, + pen=Pen( + color="#b0b0b0", + linestyle=(0, (3, 1, 1, 1)), + size=0.6, + alpha=0.25, + label=None, + ), + ), + enable_minor_ticks=True, + ), + GridTheme.LIGHT: cls( + major=GridAxis( + show=True, + pen=Pen( + color="#E0E0E0", + linestyle="--", + size=0.7, + alpha=0.9, + label=None, + ), + ), + minor=GridAxis(show=False), + enable_minor_ticks=False, + ), + GridTheme.DARK: cls( + major=GridAxis( + show=True, + pen=Pen( + color="#444444", + linestyle="--", + size=0.7, + alpha=0.5, + label=None, + ), + ), + minor=GridAxis(show=False), + enable_minor_ticks=False, + ), + } + + try: + return themes[name] + except KeyError: + raise ValueError(f"Unknown grid theme: {name!r}") + + @classmethod + def union_from_iterable(cls, grids: Iterable[Grid]) -> Grid: + """ + Gets the most inclusive grid format from a list of Grid objects. + Requires that all GridAxis fields (major/minor) are consistent across the objects. + """ + grids = list(set(grids) - {None}) + if not grids: + return cls() + + # Enforce consistent GridAxis settings + [major] = set(g.major for g in grids) + [minor] = set(g.minor for g in grids) + [enable_minor_ticks] = set(g.enable_minor_ticks for g in grids) + [zorder] = set(g.zorder for g in grids) + + return cls( + major=major, + minor=minor, + enable_minor_ticks=enable_minor_ticks, + zorder=zorder, + ) diff --git a/src/trendify/styling/legend.py b/src/trendify/styling/legend.py new file mode 100644 index 0000000..dfd325f --- /dev/null +++ b/src/trendify/styling/legend.py @@ -0,0 +1,179 @@ +from __future__ import annotations + +from enum import StrEnum + +from trendify.base.helpers import HashableBase + +__all__ = ["Legend", "LegendLocation"] + + +class LegendLocation(StrEnum): + BEST = "best" + UPPER_RIGHT = "upper right" + UPPER_LEFT = "upper left" + LOWER_LEFT = "lower left" + LOWER_RIGHT = "lower right" + RIGHT = "right" + CENTER_LEFT = "center left" + CENTER_RIGHT = "center right" + LOWER_CENTER = "lower center" + UPPER_CENTER = "upper center" + CENTER = "center" + + +class Legend(HashableBase): + """ + Configuration container for Matplotlib legend styling and placement. + + The `Legend` class controls the appearance and position of the plot legend. + Placement is governed by a combination of the `loc` and `bbox_to_anchor` + parameters, mirroring Matplotlib's `Axes.legend()`. + + Attributes: + visible (bool): Whether the legend should be displayed. Defaults to True. + title (str | None): Title displayed above the legend entries. + framealpha (float): Opacity of the legend background. 1 = fully opaque, 0 = fully transparent. + loc (LegendLocation): Anchor point for the legend (e.g., upper right, lower left). See `LegendLocation` enum for options. + ncol (int): Number of columns to arrange legend entries into. + fancybox (bool): Whether to draw a rounded (True) or square (False) legend frame. + edgecolor (str): Color of the legend frame border. Default is "black". + bbox_to_anchor (tuple[float, float] | None): Offset position of the legend in figure or axes coordinates. If None, the legend is placed inside the axes using `loc`. + + """ + + visible: bool = True + title: str | None = None + framealpha: float = 1 + loc: LegendLocation = LegendLocation.BEST + ncol: int = 1 + fancybox: bool = True + edgecolor: str = "black" + zorder: int = 10 + bbox_to_anchor: tuple[float, float] | None = None + + def to_kwargs(self): + return { + "title": self.title, + "framealpha": self.framealpha, + "loc": self.loc, + "ncol": self.ncol, + "fancybox": self.fancybox, + } + + @property + def plotly_location(self) -> dict: + """ + Convert matplotlib legend location to Plotly legend position parameters. + + Returns: + dict: Dictionary containing Plotly legend position parameters (x, y, xanchor, yanchor) + + """ + # Default position mappings for standard locations + location_map = { + LegendLocation.BEST: { + "x": 1.02, + "y": 1, + "xanchor": "left", + "yanchor": "top", + }, + LegendLocation.UPPER_RIGHT: { + "x": 0.98, + "y": 0.98, + "xanchor": "right", + "yanchor": "top", + }, + LegendLocation.UPPER_LEFT: { + "x": 0.02, + "y": 0.98, + "xanchor": "left", + "yanchor": "top", + }, + LegendLocation.LOWER_LEFT: { + "x": 0.02, + "y": 0.02, + "xanchor": "left", + "yanchor": "bottom", + }, + LegendLocation.LOWER_RIGHT: { + "x": 0.98, + "y": 0.02, + "xanchor": "right", + "yanchor": "bottom", + }, + LegendLocation.RIGHT: { + "x": 1.02, + "y": 0.5, + "xanchor": "left", + "yanchor": "middle", + }, + LegendLocation.CENTER_LEFT: { + "x": -0.02, + "y": 0.5, + "xanchor": "right", + "yanchor": "middle", + }, + LegendLocation.CENTER_RIGHT: { + "x": 1.02, + "y": 0.5, + "xanchor": "left", + "yanchor": "middle", + }, + LegendLocation.LOWER_CENTER: { + "x": 0.5, + "y": 0.02, + "xanchor": "center", + "yanchor": "bottom", + }, + LegendLocation.UPPER_CENTER: { + "x": 0.5, + "y": 0.98, + "xanchor": "center", + "yanchor": "top", + }, + LegendLocation.CENTER: { + "x": 0.5, + "y": 0.5, + "xanchor": "center", + "yanchor": "middle", + }, + } + + # If bbox_to_anchor is provided, use it to override the position + if self.bbox_to_anchor is not None: + x, y = self.bbox_to_anchor + # Determine anchors based on location + if self.loc in [ + LegendLocation.CENTER_LEFT, + LegendLocation.UPPER_LEFT, + LegendLocation.LOWER_LEFT, + ]: + xanchor = "right" + elif self.loc in [ + LegendLocation.CENTER_RIGHT, + LegendLocation.UPPER_RIGHT, + LegendLocation.LOWER_RIGHT, + ]: + xanchor = "left" + else: + xanchor = "center" + + if self.loc in [ + LegendLocation.UPPER_RIGHT, + LegendLocation.UPPER_LEFT, + LegendLocation.UPPER_CENTER, + ]: + yanchor = "top" + elif self.loc in [ + LegendLocation.LOWER_RIGHT, + LegendLocation.LOWER_LEFT, + LegendLocation.LOWER_CENTER, + ]: + yanchor = "bottom" + else: + yanchor = "middle" + + return {"x": x, "y": y, "xanchor": xanchor, "yanchor": yanchor} + + # Use predefined mapping if no bbox_to_anchor + return location_map[self.loc] diff --git a/src/trendify/styling/marker.py b/src/trendify/styling/marker.py new file mode 100644 index 0000000..cadbc18 --- /dev/null +++ b/src/trendify/styling/marker.py @@ -0,0 +1,163 @@ +from __future__ import annotations + +import logging + +from matplotlib.colors import to_rgba +from pydantic import ConfigDict + +from trendify.base.helpers import HashableBase +from trendify.base.pen import Pen + +__all__ = ["Marker"] + +logger = logging.getLogger(__name__) + + +class Marker(HashableBase): + """ + Defines marker for scattering to matplotlib + + Attributes: + color (str): Color of line + size (float): Line width + alpha (float): Opacity from 0 to 1 (inclusive) + zorder (float): Prioritization + label (Union[str, None]): Legend label + symbol (str): Matplotlib symbol string + + """ + + color: str = "k" + size: float = 5 + alpha: float = 1 + zorder: float = 0 + label: str | None = None + symbol: str = "." + + @classmethod + def from_pen( + cls, + pen: Pen, + symbol: str = ".", + ): + """ + Converts Pen to marker with the option to specify a symbol. + + Note (v1 bugfix): v1 did `pen.model_dump().pop("linestyle")`, which pops and + returns a single value (discarding the rest of the dict) and then tries to + splat that scalar as `**kwargs` -- guaranteed `TypeError` on any real call. + The fix is to dump the dict, drop the one field (`linestyle`) that `Pen` has + and `Marker` doesn't, and splat what's left (color/size/alpha/zorder/label, + which line up 1:1 with `Marker`'s fields). + """ + data = pen.model_dump() + data.pop("linestyle", None) + return cls(symbol=symbol, **data) + + model_config = ConfigDict(extra="forbid") + + def as_scatter_plot_kwargs(self): + """ + Returns: + (dict): dictionary of `kwargs` for [matplotlib scatter][matplotlib.axes.Axes.scatter] + + """ + return { + "marker": self.symbol, + "c": self.color, + "s": self.size, + "alpha": self.alpha, + "zorder": self.zorder, + "label": self.label, + } + + @property + def plotly_symbol(self) -> str: + """Convert matplotlib marker symbol to plotly symbol""" + symbol_map = { + ".": "circle", + "o": "circle", + "v": "triangle-down", + "^": "triangle-up", + "<": "triangle-left", + ">": "triangle-right", + "s": "square", + "p": "pentagon", + "*": "star", + "h": "hexagon", + "+": "cross", + "x": "x", + "D": "diamond", + } + return symbol_map.get(self.symbol, "circle") + + @property + def rgba(self) -> str: + """ + Convert the pen's color to rgba string format. + + Returns: + str: Color in 'rgba(r,g,b,a)' format where r,g,b are 0-255 and a is 0-1 + + """ + # Handle different color input formats + if isinstance(self.color, tuple): + if len(self.color) == 3: # RGB tuple + r, g, b = self.color + a = self.alpha + else: # RGBA tuple + r, g, b, a = self.color + # Convert 0-1 range to 0-255 for RGB + r, g, b = int(r * 255), int(g * 255), int(b * 255) + else: # String color (name or hex) + # Use matplotlib's color converter + rgba_vals = to_rgba(self.color, self.alpha) + # Convert 0-1 range to 0-255 for RGB + r, g, b = [int(x * 255) for x in rgba_vals[:3]] + a = rgba_vals[3] + + return f"rgba({r}, {g}, {b}, {a})" + + def get_contrast_color(self, background_luminance: float = 1.0) -> str: + """ + Returns 'white' or 'black' to provide the best contrast against the pen's color, + taking into account the alpha (transparency) value of the line. + + Args: + background_luminance (float): The luminance of the background (default is 1.0 for white). + + Returns: + str: 'white' or 'black' + + """ + # Convert the pen's color to RGB (0-255 range) and get alpha + if isinstance(self.color, tuple): + if len(self.color) == 3: # RGB tuple + r, g, b = self.color + a = self.alpha + else: # RGBA tuple + r, g, b, a = self.color + r, g, b = int(r * 255), int(g * 255), int(b * 255) + else: # String color (name or hex) + rgba_vals = to_rgba(self.color, self.alpha) + r, g, b = [int(x * 255) for x in rgba_vals[:3]] + a = rgba_vals[3] + + # Calculate relative luminance of the pen's color + def luminance(channel): + channel /= 255.0 + return ( + channel / 12.92 + if channel <= 0.03928 + else ((channel + 0.055) / 1.055) ** 2.4 + ) + + color_luminance = ( + 0.2126 * luminance(r) + 0.7152 * luminance(g) + 0.0722 * luminance(b) + ) + + # Blend the color luminance with the background luminance based on alpha + blended_luminance = (1 - a) * background_luminance + a * color_luminance + + # Return white for dark blended colors, black for light blended colors + return "white" if blended_luminance < 0.5 else "black" diff --git a/src/trendify_old/__init__.py b/src/trendify_old/__init__.py new file mode 100644 index 0000000..3ab8434 --- /dev/null +++ b/src/trendify_old/__init__.py @@ -0,0 +1,6 @@ +""" +Provides top-level imports +""" + +from trendify.api import * +from trendify.server import * diff --git a/src/trendify/api/__init__.py b/src/trendify_old/api/__init__.py similarity index 100% rename from src/trendify/api/__init__.py rename to src/trendify_old/api/__init__.py diff --git a/src/trendify/api/api.py b/src/trendify_old/api/api.py similarity index 100% rename from src/trendify/api/api.py rename to src/trendify_old/api/api.py diff --git a/src/trendify/api/base/__init__.py b/src/trendify_old/api/base/__init__.py similarity index 100% rename from src/trendify/api/base/__init__.py rename to src/trendify_old/api/base/__init__.py diff --git a/src/trendify/api/base/data_product.py b/src/trendify_old/api/base/data_product.py similarity index 98% rename from src/trendify/api/base/data_product.py rename to src/trendify_old/api/base/data_product.py index 080aa8b..4381082 100644 --- a/src/trendify/api/base/data_product.py +++ b/src/trendify_old/api/base/data_product.py @@ -21,7 +21,7 @@ __all__ = ["DataProduct", "ProductGenerator", "ProductList"] -_data_product_subclass_registry: dict[str, DataProduct] = {} +_data_product_subclass_registry: dict[str, type[DataProduct]] = {} class DataProduct(BaseModel): diff --git a/src/trendify/api/base/helpers.py b/src/trendify_old/api/base/helpers.py similarity index 100% rename from src/trendify/api/base/helpers.py rename to src/trendify_old/api/base/helpers.py diff --git a/src/trendify/api/base/pen.py b/src/trendify_old/api/base/pen.py similarity index 100% rename from src/trendify/api/base/pen.py rename to src/trendify_old/api/base/pen.py diff --git a/src/trendify/api/formats/__init__.py b/src/trendify_old/api/formats/__init__.py similarity index 100% rename from src/trendify/api/formats/__init__.py rename to src/trendify_old/api/formats/__init__.py diff --git a/src/trendify/api/formats/format2d.py b/src/trendify_old/api/formats/format2d.py similarity index 100% rename from src/trendify/api/formats/format2d.py rename to src/trendify_old/api/formats/format2d.py diff --git a/src/trendify/api/formats/table.py b/src/trendify_old/api/formats/table.py similarity index 100% rename from src/trendify/api/formats/table.py rename to src/trendify_old/api/formats/table.py diff --git a/src/trendify/api/generator/__init__.py b/src/trendify_old/api/generator/__init__.py similarity index 100% rename from src/trendify/api/generator/__init__.py rename to src/trendify_old/api/generator/__init__.py diff --git a/src/trendify/api/generator/data_product_collection.py b/src/trendify_old/api/generator/data_product_collection.py similarity index 93% rename from src/trendify/api/generator/data_product_collection.py rename to src/trendify_old/api/generator/data_product_collection.py index dce647f..eb2145f 100644 --- a/src/trendify/api/generator/data_product_collection.py +++ b/src/trendify_old/api/generator/data_product_collection.py @@ -4,7 +4,7 @@ from itertools import chain from pathlib import Path from typing import TYPE_CHECKING, Any -from collections.abc import Iterable +from collections.abc import Iterable, Iterator import logging @@ -74,7 +74,7 @@ class ProductIndexMap(BaseModel): entries: dict[int, ProductEntryMetadata] = Field(default_factory=dict) @property - def inverse(self) -> dict[Path, int]: + def inverse(self) -> dict[ProductEntryMetadata, int]: return {v: k for k, v in self.entries.items()} @classmethod @@ -83,7 +83,7 @@ def get_index( cls, save_dir: Path, metadata: ProductEntryMetadata, - ) -> Self: + ) -> int: if not save_dir.is_dir(): raise ValueError(f"{save_dir} is not a directory") lock_file = save_dir.joinpath("reserving_index.lock") @@ -120,7 +120,7 @@ def _should_be_flattened(obj: Any): return isinstance(obj, Iterable) and not isinstance(obj, (str, bytes, DataProduct)) -def flatten(obj: Iterable): +def flatten(obj: Iterable[Any]) -> Iterator[Any]: """ Recursively flattens iterable up to a point (leaves `str`, `bytes`, and `DataProduct` unflattened) @@ -155,7 +155,7 @@ def atleast_1d(obj: Any) -> Iterable: return obj -def _squeeze(obj: Iterable | Any): +def _squeeze(obj: Any) -> Any: """ Returns a scalar if object is iterable of length 1 else returns object. @@ -184,14 +184,14 @@ class DataProductCollection(BaseModel): """ derived_from: Path | None = None - elements: ProductList | None = None + elements: ProductList = Field(default_factory=list) def __init__(self, **kwargs: Any): DataProduct.deserialize_child_classes(key="elements", **kwargs) super().__init__(**kwargs) @classmethod - def from_iterable(cls, *products: tuple[ProductList, ...]): + def from_iterable(cls, *products: ProductList): """ Returns a new instance containing all of the products provided in the `*products` argument. @@ -302,7 +302,7 @@ def drop_products( self, tag: Tag | None = None, object_type: type[R] | None = None, - ) -> Self[R]: + ) -> Self: """ Removes products matching `tag` and/or `object_type` from collection elements. @@ -319,19 +319,18 @@ def drop_products( case (True, True): return type(self)(elements=self.elements) case (True, False): - # assert self.elements is not None + assert object_type is not None return type(self)( elements=[ e for e in self.elements if not isinstance(e, object_type) ] ) case (False, True): - # assert self.elements is not None return type(self)( elements=[e for e in self.elements if tag not in e.tags] ) case (False, False): - # assert self.elements is not None + assert object_type is not None return type(self)( elements=[ e @@ -344,7 +343,7 @@ def drop_products( def get_products( self, tag: Tag | None = None, object_type: type[R] | None = None - ) -> Self[R]: + ) -> Self: """ Returns a new collection containing products matching `tag` and/or `object_type`. Both `tag` and `object_type` default to `None` which matches all products. @@ -362,15 +361,14 @@ def get_products( case (True, True): return type(self)(elements=self.elements) case (True, False): - # assert self.elements is not None + assert object_type is not None return type(self)( elements=[e for e in self.elements if isinstance(e, object_type)] ) case (False, True): - # assert self.elements is not None return type(self)(elements=[e for e in self.elements if tag in e.tags]) case (False, False): - # assert self.elements is not None + assert object_type is not None return type(self)( elements=[ e @@ -381,6 +379,26 @@ def get_products( case _: raise ValueError("Something is wrong with match statement") + def get_products_of_type( + self, object_type: type[R], tag: Tag | None = None + ) -> list[R]: + """ + Returns the elements matching `object_type` (and `tag`, if given) as a list typed to `object_type`. + + Args: + object_type (Type[R]): Type of data product to keep. + tag (Tag | None): Tag of data products to be kept. `None` matches all products. + + Returns: + (list[R]): List of matching elements. + + """ + if tag is None: + return [e for e in self.elements if isinstance(e, object_type)] + return [ + e for e in self.elements if tag in e.tags and isinstance(e, object_type) + ] + @classmethod def union(cls, *collections: DataProductCollection): """ @@ -402,7 +420,7 @@ def collect_from_all_jsons( cls, *dirs: Path, recursive: bool = False, - data_products_filename: str | None = "*.json", + data_products_filename: str = "*.json", ): """ Loads all products from JSONs in the given list of directories. @@ -546,10 +564,10 @@ def process_collection( format_2ds: list[Format2D] = [] if not no_tables: - table_entries: list[TableEntry] = collection.get_products( + table_entries = collection.get_products_of_type( tag=tag, object_type=TableEntry, - ).elements + ) if table_entries: from trendify.api.generator.table_builder import TableBuilder @@ -563,18 +581,18 @@ def process_collection( logger.info(f"Finished tables for {tag = }") if not no_xy_plots: - traces: list[Trace2D] = collection.get_products( + traces = collection.get_products_of_type( tag=tag, object_type=Trace2D, - ).elements - points: list[Point2D] = collection.get_products( + ) + points = collection.get_products_of_type( tag=tag, object_type=Point2D, - ).elements - axlines: list[AxLine] = collection.get_products( + ) + axlines = collection.get_products_of_type( tag=tag, object_type=AxLine, - ).elements + ) if points or traces or axlines: # Update condition from trendify.api.generator.xy_data_plotter import XYDataPlotter @@ -608,10 +626,10 @@ def process_collection( logger.info(f"Finished xy plot for {tag = }") if not no_histograms: - histogram_entries: list[HistogramEntry] = collection.get_products( + histogram_entries = collection.get_products_of_type( tag=tag, object_type=HistogramEntry, - ).elements + ) if histogram_entries: logger.info(f"Making histogram for {tag = }") @@ -667,7 +685,7 @@ def process_tables_for_tag( logger.info(f"Processing tables for {tag = }") table_builder = TableBuilder(in_dirs=in_dirs, out_dir=out_dir) - table_builder.build_tables(tag=tag, data_products_fname=data_products_fname) + table_builder.load_table(tag=tag, data_products_fname=data_products_fname) return table_builder @classmethod @@ -682,24 +700,12 @@ def process_tag_for_streamlit( plotly_figure: PlotlyFigure | None = None format_2ds: list[Format2D] = [] - traces = collection.get_products(tag=tag, object_type=Trace2D).elements - points = collection.get_products(tag=tag, object_type=Point2D).elements - axlines = collection.get_products(tag=tag, object_type=AxLine).elements - hist_entries = collection.get_products( + traces = collection.get_products_of_type(tag=tag, object_type=Trace2D) + points = collection.get_products_of_type(tag=tag, object_type=Point2D) + axlines = collection.get_products_of_type(tag=tag, object_type=AxLine) + hist_entries = collection.get_products_of_type( tag=tag, object_type=HistogramEntry - ).elements - - traces: list[Trace2D] - points: list[Point2D] - axlines: list[AxLine] - hist_entries: list[HistogramEntry] - - traces = [e for e in collection.elements if isinstance(e, Trace2D)] - points = [e for e in collection.elements if isinstance(e, Point2D)] - axlines = [e for e in collection.elements if isinstance(e, AxLine)] - hist_entries = [ - e for e in collection.elements if isinstance(e, HistogramEntry) - ] + ) if points or traces or axlines or hist_entries: from trendify.api.generator.xy_data_plotter import XYDataPlotter diff --git a/src/trendify/api/generator/data_product_generator.py b/src/trendify_old/api/generator/data_product_generator.py similarity index 100% rename from src/trendify/api/generator/data_product_generator.py rename to src/trendify_old/api/generator/data_product_generator.py diff --git a/src/trendify/api/generator/histogrammer.py b/src/trendify_old/api/generator/histogrammer.py similarity index 100% rename from src/trendify/api/generator/histogrammer.py rename to src/trendify_old/api/generator/histogrammer.py diff --git a/src/trendify/api/generator/table_builder.py b/src/trendify_old/api/generator/table_builder.py similarity index 98% rename from src/trendify/api/generator/table_builder.py rename to src/trendify_old/api/generator/table_builder.py index 8dcd79d..eb78ae8 100644 --- a/src/trendify/api/generator/table_builder.py +++ b/src/trendify_old/api/generator/table_builder.py @@ -60,7 +60,7 @@ def load_table( subdir.joinpath(data_products_fname).read_text() ) table_entries.extend( - collection.get_products(tag=tag, object_type=TableEntry).elements + collection.get_products_of_type(tag=tag, object_type=TableEntry) ) self.process_table_entries( diff --git a/src/trendify/api/generator/xy_data_plotter.py b/src/trendify_old/api/generator/xy_data_plotter.py similarity index 93% rename from src/trendify/api/generator/xy_data_plotter.py rename to src/trendify_old/api/generator/xy_data_plotter.py index 78a993f..77600a9 100644 --- a/src/trendify/api/generator/xy_data_plotter.py +++ b/src/trendify_old/api/generator/xy_data_plotter.py @@ -65,12 +65,8 @@ def plot( collection = DataProductCollection.model_validate_json( subdir.joinpath(data_products_fname).read_text() ) - traces: list[Trace2D] = collection.get_products( - tag=tag, object_type=Trace2D - ).elements - points: list[Point2D] = collection.get_products( - tag=tag, object_type=Point2D - ).elements + traces = collection.get_products_of_type(tag=tag, object_type=Trace2D) + points = collection.get_products_of_type(tag=tag, object_type=Point2D) if points or traces: if points: @@ -91,9 +87,8 @@ def plot( formats = list( set( [p.format2d for p in points if p.format2d] - + [t.format2d for t in traces] + + [t.format2d for t in traces if t.format2d] ) - - {None} ) format2d = Format2D.union_from_iterable(formats) saf.apply_format(format2d) @@ -138,7 +133,10 @@ def handle_points_and_traces( x = [p.x for p in matching_points] y = [p.y for p in matching_points] if x and y: - saf.ax.scatter(x, y, **marker.as_scatter_plot_kwargs()) + if marker is not None: + saf.ax.scatter(x, y, **marker.as_scatter_plot_kwargs()) + else: + saf.ax.scatter(x, y) for trace in traces: trace.plot_to_ax(saf.ax) diff --git a/src/trendify/api/plotting/__init__.py b/src/trendify_old/api/plotting/__init__.py similarity index 100% rename from src/trendify/api/plotting/__init__.py rename to src/trendify_old/api/plotting/__init__.py diff --git a/src/trendify/api/plotting/axline.py b/src/trendify_old/api/plotting/axline.py similarity index 100% rename from src/trendify/api/plotting/axline.py rename to src/trendify_old/api/plotting/axline.py diff --git a/src/trendify/api/plotting/histogram.py b/src/trendify_old/api/plotting/histogram.py similarity index 98% rename from src/trendify/api/plotting/histogram.py rename to src/trendify_old/api/plotting/histogram.py index 45ecd3f..46a779c 100644 --- a/src/trendify/api/plotting/histogram.py +++ b/src/trendify_old/api/plotting/histogram.py @@ -1,6 +1,7 @@ from __future__ import annotations import logging +from typing import cast from matplotlib.colors import to_rgba import plotly.graph_objects as go @@ -192,7 +193,8 @@ def add_to_plotly(self, plotly_figure: PlotlyFigure): ) # Check if a trace with the same legend group already exists - for trace in plotly_figure.fig.data: + traces = cast("tuple[go.Histogram, ...]", plotly_figure.fig.data) + for trace in traces: if trace.legendgroup == legend_key: # Append the value to the existing trace's x data trace.x = list(trace.x) + [self.value] diff --git a/src/trendify/api/plotting/plotting.py b/src/trendify_old/api/plotting/plotting.py similarity index 100% rename from src/trendify/api/plotting/plotting.py rename to src/trendify_old/api/plotting/plotting.py diff --git a/src/trendify/api/plotting/point.py b/src/trendify_old/api/plotting/point.py similarity index 100% rename from src/trendify/api/plotting/point.py rename to src/trendify_old/api/plotting/point.py diff --git a/src/trendify/api/plotting/trace.py b/src/trendify_old/api/plotting/trace.py similarity index 100% rename from src/trendify/api/plotting/trace.py rename to src/trendify_old/api/plotting/trace.py diff --git a/src/trendify/api/styling/__init__.py b/src/trendify_old/api/styling/__init__.py similarity index 100% rename from src/trendify/api/styling/__init__.py rename to src/trendify_old/api/styling/__init__.py diff --git a/src/trendify/api/styling/grid.py b/src/trendify_old/api/styling/grid.py similarity index 100% rename from src/trendify/api/styling/grid.py rename to src/trendify_old/api/styling/grid.py diff --git a/src/trendify/api/styling/legend.py b/src/trendify_old/api/styling/legend.py similarity index 100% rename from src/trendify/api/styling/legend.py rename to src/trendify_old/api/styling/legend.py diff --git a/src/trendify/api/styling/marker.py b/src/trendify_old/api/styling/marker.py similarity index 100% rename from src/trendify/api/styling/marker.py rename to src/trendify_old/api/styling/marker.py diff --git a/src/trendify/assets/logo.svg b/src/trendify_old/assets/logo.svg similarity index 100% rename from src/trendify/assets/logo.svg rename to src/trendify_old/assets/logo.svg diff --git a/src/trendify/assets/logo_white_bg.svg b/src/trendify_old/assets/logo_white_bg.svg similarity index 100% rename from src/trendify/assets/logo_white_bg.svg rename to src/trendify_old/assets/logo_white_bg.svg diff --git a/src/trendify/cli.py b/src/trendify_old/cli.py similarity index 100% rename from src/trendify/cli.py rename to src/trendify_old/cli.py diff --git a/src/trendify/examples.py b/src/trendify_old/examples.py similarity index 100% rename from src/trendify/examples.py rename to src/trendify_old/examples.py diff --git a/src/trendify/gallery.py b/src/trendify_old/gallery.py similarity index 100% rename from src/trendify/gallery.py rename to src/trendify_old/gallery.py diff --git a/src/trendify/local_server.py b/src/trendify_old/local_server.py similarity index 100% rename from src/trendify/local_server.py rename to src/trendify_old/local_server.py diff --git a/src/trendify/plotly_dashboard.py b/src/trendify_old/plotly_dashboard.py similarity index 96% rename from src/trendify/plotly_dashboard.py rename to src/trendify_old/plotly_dashboard.py index 9908eff..c30f89a 100644 --- a/src/trendify/plotly_dashboard.py +++ b/src/trendify_old/plotly_dashboard.py @@ -2,6 +2,8 @@ Module for generating interactive Plotly dashboards from data products. """ +from typing import Any, cast + import dash from dash import dcc, html, dash_table from dash.dependencies import Input, Output, State @@ -11,20 +13,16 @@ from collections import defaultdict # Import from trendify -# from trendify.api.API import ( -# DataProductCollection, -# Tag, -# Tags, -# Point2D, -# Trace2D, -# TableEntry, -# HistogramEntry, -# AxLine, -# LineOrientation, -# Format2D, -# flatten, -# atleast_1d, -# ) +from trendify import ( + DataProductCollection, + Point2D, + Trace2D, + TableEntry, + HistogramEntry, + AxLine, + LineOrientation, + Format2D, +) # Constants COLORS = [ @@ -292,15 +290,21 @@ def _create_table_section(self, tag_str: str) -> html.Div: ( dash_table.DataTable( id=f"pivot-table-{tag_str}", - columns=[{"name": "row", "id": "row"}] - + [ - {"name": str(col), "id": str(col)} - for col in ( - pivot_df.columns - if pivot_df is not None - else [] - ) - ], + columns=cast( + "list[dash_table.DataTable.Columns]", + [{"name": "row", "id": "row"}] + + [ + { + "name": str(col), + "id": str(col), + } + for col in ( + pivot_df.columns + if pivot_df is not None + else [] + ) + ], + ), data=( pivot_df.reset_index().to_dict( "records" @@ -554,7 +558,7 @@ def _create_xy_figure( ) # Update layout with format information - layout_args = dict( + layout_args: dict[str, Any] = dict( title=( format2d.title_ax if format2d and format2d.title_ax @@ -700,7 +704,7 @@ def _create_histogram_figure( break # Update layout - layout_args = dict( + layout_args: dict[str, Any] = dict( title=( format2d.title_ax if format2d and format2d.title_ax diff --git a/src/trendify/production_server.py b/src/trendify_old/production_server.py similarity index 100% rename from src/trendify/production_server.py rename to src/trendify_old/production_server.py diff --git a/src/trendify/server.py b/src/trendify_old/server.py similarity index 100% rename from src/trendify/server.py rename to src/trendify_old/server.py diff --git a/src/trendify/streamlit.py b/src/trendify_old/streamlit.py similarity index 100% rename from src/trendify/streamlit.py rename to src/trendify_old/streamlit.py diff --git a/tests/test.sh b/tests/test.sh deleted file mode 100644 index 83cc62f..0000000 --- a/tests/test.sh +++ /dev/null @@ -1,25 +0,0 @@ -#! /bin/bash - -export workdir=./sample_data -export input=$workdir/models -export output=$workdir/trendify -export generator=trendify.examples:example_data_product_generator -export server_host=localhost -export server_port=8001 -export n_procs=1 - -rm -rf $output/products - -trendify_make_sample_data -wd $workdir -n 10 - -find $input -name "data_product.json" -type f -delete - -# trendify products-make -n $n_procs -g $generator -i $input -# trendify products-sort -n $n_procs -i $input -o $output -# # trendify assets-make-static $output - -# trendify assets-make-interactive grafana $output --host $server_host --port $server_port -# trendify products-serve $output --host $server_host --port $server_port - -trendify make static -i $input/**/results.csv -g $generator -n $n_procs -o $output -f data_product.json -trendify make dashboard -o $output \ No newline at end of file diff --git a/tests/test2.sh b/tests/test2.sh deleted file mode 100755 index f54a50f..0000000 --- a/tests/test2.sh +++ /dev/null @@ -1,16 +0,0 @@ -#! /bin/bash - -workdir=./testdir -inputs=$workdir/models/*/ -output=$workdir/output/ -generator=trendify.examples:example_data_product_generator -n_procs=5 -server_port=8001 -data_products_file_name='test_products.json' - -test -d $workdir && rm -r $workdir -trendify_make_sample_data -wd $workdir -n 100 - -trendify make static -g $generator -i $inputs -o $output -n $n_procs -f $data_products_file_name -# trendify make grafana -g $generator -i $inputs -o $output -n $n_procs --port $server_port -# trendify make all -g $generator -i $inputs -o $output -n $n_procs --port $server_port diff --git a/tests/test_basic.py b/tests/test_basic.py deleted file mode 100644 index 56f5096..0000000 --- a/tests/test_basic.py +++ /dev/null @@ -1,8 +0,0 @@ -"""Basic tests for trendify package.""" - - -def test_import(): - """Test that trendify can be imported.""" - import trendify - - assert trendify is not None diff --git a/tests/test_product_store.py b/tests/test_product_store.py new file mode 100644 index 0000000..0aee658 --- /dev/null +++ b/tests/test_product_store.py @@ -0,0 +1,171 @@ +from pathlib import Path + +import polars as pl +import pytest + +from trendify.base.data_product import DataProduct +from trendify.base.pen import Pen +from trendify.formats.table import TableEntry +from trendify.plotting.axline import AxLine, LineOrientation +from trendify.plotting.histogram import HistogramEntry +from trendify.plotting.point import Point2D +from trendify.plotting.trace import Trace2D +from trendify.formats.format2d import XYData +from trendify.store.product_store import ProductStore +from trendify.store.tags import decode_tag, encode_tag + + +@pytest.fixture +def store(tmp_path: Path): + with ProductStore.open(tmp_path / "trendify.db") as s: + yield s + + +def _sample_products(): + return [ + Point2D(tags=["a", ("a", "b")], x=1, y=2), + Trace2D.from_xy( + tags=[("a", "b")], x=[0, 1, 2], y=[0, 1, 4], pen=Pen(label="hi") + ), + AxLine(tags=["a"], value=0.5, orientation=LineOrientation.VERTICAL), + TableEntry(tags=["tbl"], row="r1", col="c1", value=1.5, unit="m"), + TableEntry(tags=["tbl"], row="r1", col="c2", value="hello"), + TableEntry(tags=["tbl"], row="r2", col="c1", value=True), + HistogramEntry(tags=["h"], value=3.0), + ] + + +class TestTagEncoding: + def test_scalar_round_trip(self): + assert decode_tag(encode_tag("foo")) == "foo" + assert decode_tag(encode_tag(3)) == 3 + + def test_tuple_round_trip(self): + assert decode_tag(encode_tag(("a", "b"))) == ("a", "b") + + def test_scalar_and_tuple_dont_collide(self): + assert encode_tag("a") != encode_tag(("a",)) + + +class TestWriteRun: + def test_write_run_returns_count(self, store: ProductStore, tmp_path: Path): + n = store.write_run(tmp_path / "run1", _sample_products()) + assert n == len(_sample_products()) + + def test_write_run_is_idempotent(self, store: ProductStore, tmp_path: Path): + run_dir = tmp_path / "run1" + store.write_run(run_dir, _sample_products()) + store.write_run(run_dir, _sample_products()) + + (count,) = store._conn.execute("SELECT COUNT(*) FROM products").fetchone() + assert count == len(_sample_products()) + + (run_count,) = store._conn.execute("SELECT COUNT(*) FROM runs").fetchone() + assert run_count == 1 + + def test_rerun_with_fewer_products_drops_stale_rows( + self, store: ProductStore, tmp_path: Path + ): + run_dir = tmp_path / "run1" + store.write_run(run_dir, _sample_products()) + store.write_run(run_dir, _sample_products()[:2]) + + (count,) = store._conn.execute("SELECT COUNT(*) FROM products").fetchone() + assert count == 2 + + def test_two_runs_coexist(self, store: ProductStore, tmp_path: Path): + store.write_run(tmp_path / "run1", _sample_products()) + store.write_run(tmp_path / "run2", _sample_products()) + + (count,) = store._conn.execute("SELECT COUNT(*) FROM products").fetchone() + assert count == 2 * len(_sample_products()) + + +class TestGetTags: + def test_get_tags_includes_scalar_and_tuple( + self, store: ProductStore, tmp_path: Path + ): + store.write_run(tmp_path / "run1", _sample_products()) + tags = store.get_tags() + assert tags == {"a", ("a", "b"), "tbl", "h"} + + def test_get_tags_filtered_by_type(self, store: ProductStore, tmp_path: Path): + store.write_run(tmp_path / "run1", _sample_products()) + assert store.get_tags(object_type=TableEntry) == {"tbl"} + assert store.get_tags(object_type=XYData) == {"a", ("a", "b")} + + def test_tag_tree_sorted_shallow_first(self, store: ProductStore, tmp_path: Path): + store.write_run(tmp_path / "run1", _sample_products()) + tree = store.tag_tree() + depths = [len(t) if isinstance(t, tuple) else 1 for t in tree] + assert depths == sorted(depths) + + +class TestGetProducts: + def test_filters_by_tag(self, store: ProductStore, tmp_path: Path): + store.write_run(tmp_path / "run1", _sample_products()) + products = store.get_products_of_type(DataProduct, tag="h") + assert [p.product_type for p in products] == ["HistogramEntry"] + + def test_filters_by_type_hierarchy(self, store: ProductStore, tmp_path: Path): + store.write_run(tmp_path / "run1", _sample_products()) + # tag "a" has a Point2D and an AxLine; only Point2D is XYData + xy = store.get_products_of_type(XYData, tag="a") + assert [p.product_type for p in xy] == ["Point2D"] + + def test_no_filters_returns_everything(self, store: ProductStore, tmp_path: Path): + store.write_run(tmp_path / "run1", _sample_products()) + products = store.get_products_of_type(DataProduct) + assert len(products) == len(_sample_products()) + + def test_multi_tag_product_is_not_duplicated_in_storage( + self, store: ProductStore, tmp_path: Path + ): + # Point2D above carries two tags ("a" and ("a","b")) -- this is precisely the + # v1 disk-duplication complaint: assert there's exactly one `products` row for + # it, with two matching `product_tags` index rows, not two payload copies. + store.write_run(tmp_path / "run1", _sample_products()) + (point_count,) = store._conn.execute( + "SELECT COUNT(*) FROM products WHERE product_type = 'Point2D'" + ).fetchone() + assert point_count == 1 + + (tag_row_count,) = store._conn.execute( + "SELECT COUNT(*) FROM product_tags pt " + "JOIN products p ON p.id = pt.product_id " + "WHERE p.product_type = 'Point2D'" + ).fetchone() + assert tag_row_count == 2 + + def test_round_trip_preserves_fields(self, store: ProductStore, tmp_path: Path): + store.write_run(tmp_path / "run1", _sample_products()) + [trace] = store.get_products_of_type(Trace2D, tag=("a", "b")) + assert list(trace.x) == [0, 1, 2] + assert list(trace.y) == [0, 1, 4] + assert trace.pen.label == "hi" + + +class TestGetTableEntries: + def test_returns_polars_dataframe_with_expected_shape( + self, store: ProductStore, tmp_path: Path + ): + store.write_run(tmp_path / "run1", _sample_products()) + df = store.get_table_entries("tbl") + assert isinstance(df, pl.DataFrame) + assert set(df.columns) == {"row", "col", "value", "unit"} + assert df.height == 3 + + def test_preserves_heterogeneous_value_types( + self, store: ProductStore, tmp_path: Path + ): + store.write_run(tmp_path / "run1", _sample_products()) + df = store.get_table_entries("tbl") + values = {(row["row"], row["col"]): row["value"] for row in df.to_dicts()} + assert values[("r1", "c1")] == 1.5 + assert values[("r1", "c2")] == "hello" + assert values[("r2", "c1")] is True + + def test_empty_tag_returns_empty_frame(self, store: ProductStore, tmp_path: Path): + store.write_run(tmp_path / "run1", _sample_products()) + df = store.get_table_entries("does-not-exist") + assert df.height == 0 diff --git a/tests/unittests.py b/tests/unittests.py deleted file mode 100644 index d17f2e1..0000000 --- a/tests/unittests.py +++ /dev/null @@ -1,137 +0,0 @@ -import unittest -import numpy as np -from pathlib import Path -import tempfile -import matplotlib.pyplot as plt -from trendify import ( - Trace2D, - Point2D, - TableEntry, - HistogramEntry, - AxLine, - Pen, - Marker, - Format2D, - LineOrientation, - DataProductCollection, -) - - -class TestDataProducts(unittest.TestCase): - def setUp(self): - self.temp_dir = tempfile.mkdtemp() - self.test_dir = Path(self.temp_dir) - - def test_trace2d(self): - # Test creating and manipulating a Trace2D - x = np.linspace(0, 10, 100) - y = np.sin(x) - - trace = Trace2D.from_xy( - tags=["test_trace"], - x=x, - y=y, - pen=Pen(color="blue", label="Test Sine"), - format2d=Format2D(title_fig="Test Figure", label_x="X", label_y="Y"), - ) - - # Test properties - np.testing.assert_array_equal(trace.x, x) - np.testing.assert_array_equal(trace.y, y) - self.assertEqual(trace.pen.color, "blue") - self.assertEqual(trace.pen.label, "Test Sine") - - # Test plotting - fig, ax = plt.subplots() - trace.plot_to_ax(ax) - plt.close(fig) - - def test_point2d(self): - point = Point2D( - tags=["test_point"], - x=1.0, - y=2.0, - marker=Marker(color="red", symbol="o"), - format2d=Format2D(title_fig="Test Point"), - ) - - self.assertEqual(point.x, 1.0) - self.assertEqual(point.y, 2.0) - self.assertEqual(point.marker.color, "red") - - def test_axline(self): - # Test horizontal line - hline = AxLine( - tags=["test_line"], - value=5.0, - orientation=LineOrientation.HORIZONTAL, - pen=Pen(color="red", label="Test HLine"), - ) - - self.assertEqual(hline.value, 5.0) - self.assertEqual(hline.orientation, LineOrientation.HORIZONTAL) - - # Test vertical line - vline = AxLine( - tags=["test_line"], - value=2.0, - orientation=LineOrientation.VERTICAL, - pen=Pen(color="blue", label="Test VLine"), - ) - - self.assertEqual(vline.value, 2.0) - self.assertEqual(vline.orientation, LineOrientation.VERTICAL) - - # Test plotting - fig, ax = plt.subplots() - hline.plot_to_ax(ax) - vline.plot_to_ax(ax) - plt.close(fig) - - def test_table_entry(self): - entry = TableEntry( - tags=["test_table"], - row="Test Row", - col="Test Col", - value=42.0, - unit="units", - ) - - self.assertEqual(entry.row, "Test Row") - self.assertEqual(entry.col, "Test Col") - self.assertEqual(entry.value, 42.0) - self.assertEqual(entry.unit, "units") - - def test_histogram_entry(self): - entry = HistogramEntry(tags=["test_hist"], value=3.14, style=None) - - self.assertEqual(entry.value, 3.14) - - def test_data_product_collection(self): - # Create some test products - point = Point2D(tags=["test"], x=1.0, y=2.0, marker=Marker(color="red")) - - trace = Trace2D.from_xy( - tags=["test"], - x=np.array([1, 2, 3]), - y=np.array([4, 5, 6]), - pen=Pen(color="blue"), - ) - - # Create collection - collection = DataProductCollection(elements=[point, trace]) - - # Test serialization - json_file = self.test_dir / "test.json" - json_file.write_text(collection.model_dump_json()) - - # Test deserialization - loaded = 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[PATCH 04/19] Updated to new generation logic. Removed old files. --- .pre-commit-config.yaml | 9 - alternative_tag_selectors.py | 312 -- app.log | 5 - benchmarks/stress_test.py | 139 - custom_handler.py | 9 - implementation_recommendation.py | 77 - improved_tag_selection.py | 73 - index.html | 6 - plotly_examples/histogram_1.py | 1038 ---- plotly_examples/sensitivity_1.py | 850 ---- prepare_release.sh | 212 - publish_release.sh | 68 - pyproject.toml | 10 +- requirements.txt | 179 +- rewrite_reference/OVERVIEW.md | 390 -- rewrite_reference/dump_schemas.py | 48 - rewrite_reference/pydantic_schemas.json | 4479 ----------------- sample_hierarchical_data.py | 239 - src/trendify/__init__.py | 93 +- src/trendify/base/data_product.py | 28 +- src/trendify/base/helpers.py | 37 +- src/trendify/base/pen.py | 5 + src/trendify/cli.py | 218 + src/trendify/color.py | 260 + src/{trendify_old => trendify}/examples.py | 142 +- src/trendify/formats/format2d.py | 39 +- src/trendify/formats/table.py | 6 + src/trendify/generator/__init__.py | 38 + src/trendify/generator/generate.py | 140 + src/trendify/generator/histogrammer.py | 62 + src/trendify/generator/render.py | 193 + src/trendify/generator/table_builder.py | 173 + src/trendify/generator/xy_data_plotter.py | 77 + src/trendify/log.py | 195 + src/trendify/pipeline.py | 150 + src/trendify/plotting/axline.py | 8 +- src/trendify/plotting/figure.py | 31 +- src/trendify/plotting/histogram.py | 14 +- src/trendify/plotting/point.py | 4 + src/trendify/plotting/trace.py | 23 +- src/trendify/store/__init__.py | 2 + src/trendify/store/db.py | 27 +- src/trendify/store/product_store.py | 79 +- src/trendify/store/tags.py | 24 +- src/trendify/styling/grid.py | 33 +- src/trendify/styling/legend.py | 5 + src/trendify/styling/marker.py | 18 +- src/trendify/typing.py | 26 + .../viewer/templates/static}/assets/logo.svg | 0 .../static}/assets/logo_white_bg.svg | 0 src/trendify_old/__init__.py | 6 - src/trendify_old/api/__init__.py | 7 - src/trendify_old/api/api.py | 491 -- src/trendify_old/api/base/__init__.py | 4 - src/trendify_old/api/base/data_product.py | 132 - src/trendify_old/api/base/helpers.py | 71 - src/trendify_old/api/base/pen.py | 136 - src/trendify_old/api/formats/__init__.py | 2 - src/trendify_old/api/formats/format2d.py | 152 - src/trendify_old/api/formats/table.py | 88 - src/trendify_old/api/generator/__init__.py | 5 - .../api/generator/data_product_collection.py | 872 ---- .../api/generator/data_product_generator.py | 47 - .../api/generator/histogrammer.py | 126 - .../api/generator/table_builder.py | 153 - .../api/generator/xy_data_plotter.py | 191 - src/trendify_old/api/plotting/__init__.py | 5 - src/trendify_old/api/plotting/axline.py | 123 - src/trendify_old/api/plotting/histogram.py | 249 - src/trendify_old/api/plotting/plotting.py | 343 -- src/trendify_old/api/plotting/point.py | 91 - src/trendify_old/api/plotting/trace.py | 194 - src/trendify_old/api/styling/__init__.py | 3 - src/trendify_old/api/styling/grid.py | 149 - src/trendify_old/api/styling/legend.py | 206 - src/trendify_old/api/styling/marker.py | 155 - src/trendify_old/cli.py | 597 --- src/trendify_old/gallery.py | 327 -- src/trendify_old/local_server.py | 169 - src/trendify_old/plotly_dashboard.py | 1000 ---- src/trendify_old/production_server.py | 127 - src/trendify_old/server.py | 170 - src/trendify_old/streamlit.py | 302 -- test.py | 39 - tests/test_cli.py | 152 + tests/test_generate.py | 81 + tests/test_pipeline.py | 103 + tests/test_product_store.py | 6 +- tests/test_render.py | 131 + tests/test_table_builder.py | 135 + update_docs_and_push_changes.sh | 17 - uv.lock | 212 +- 92 files changed, 2547 insertions(+), 15315 deletions(-) delete mode 100644 alternative_tag_selectors.py delete mode 100644 app.log delete mode 100644 benchmarks/stress_test.py delete mode 100644 custom_handler.py delete mode 100644 implementation_recommendation.py delete mode 100644 improved_tag_selection.py delete mode 100644 index.html delete mode 100644 plotly_examples/histogram_1.py delete mode 100644 plotly_examples/sensitivity_1.py delete mode 100755 prepare_release.sh delete mode 100755 publish_release.sh delete mode 100644 rewrite_reference/OVERVIEW.md delete mode 100644 rewrite_reference/dump_schemas.py delete mode 100644 rewrite_reference/pydantic_schemas.json delete mode 100644 sample_hierarchical_data.py create mode 100644 src/trendify/cli.py create mode 100644 src/trendify/color.py rename src/{trendify_old => trendify}/examples.py (75%) create mode 100644 src/trendify/generator/__init__.py create mode 100644 src/trendify/generator/generate.py create mode 100644 src/trendify/generator/histogrammer.py create mode 100644 src/trendify/generator/render.py create mode 100644 src/trendify/generator/table_builder.py create mode 100644 src/trendify/generator/xy_data_plotter.py create mode 100644 src/trendify/log.py create mode 100644 src/trendify/pipeline.py create mode 100644 src/trendify/typing.py rename src/{trendify_old => trendify/viewer/templates/static}/assets/logo.svg (100%) rename src/{trendify_old => trendify/viewer/templates/static}/assets/logo_white_bg.svg (100%) delete mode 100644 src/trendify_old/__init__.py delete mode 100644 src/trendify_old/api/__init__.py delete mode 100644 src/trendify_old/api/api.py delete mode 100644 src/trendify_old/api/base/__init__.py delete mode 100644 src/trendify_old/api/base/data_product.py delete mode 100644 src/trendify_old/api/base/helpers.py delete mode 100644 src/trendify_old/api/base/pen.py delete mode 100644 src/trendify_old/api/formats/__init__.py delete mode 100644 src/trendify_old/api/formats/format2d.py delete mode 100644 src/trendify_old/api/formats/table.py delete mode 100644 src/trendify_old/api/generator/__init__.py delete mode 100644 src/trendify_old/api/generator/data_product_collection.py delete mode 100644 src/trendify_old/api/generator/data_product_generator.py delete mode 100644 src/trendify_old/api/generator/histogrammer.py delete mode 100644 src/trendify_old/api/generator/table_builder.py delete mode 100644 src/trendify_old/api/generator/xy_data_plotter.py delete mode 100644 src/trendify_old/api/plotting/__init__.py delete mode 100644 src/trendify_old/api/plotting/axline.py delete mode 100644 src/trendify_old/api/plotting/histogram.py delete mode 100644 src/trendify_old/api/plotting/plotting.py delete mode 100644 src/trendify_old/api/plotting/point.py delete mode 100644 src/trendify_old/api/plotting/trace.py delete mode 100644 src/trendify_old/api/styling/__init__.py delete mode 100644 src/trendify_old/api/styling/grid.py delete mode 100644 src/trendify_old/api/styling/legend.py delete mode 100644 src/trendify_old/api/styling/marker.py delete mode 100644 src/trendify_old/cli.py delete mode 100644 src/trendify_old/gallery.py delete mode 100644 src/trendify_old/local_server.py delete mode 100644 src/trendify_old/plotly_dashboard.py delete mode 100644 src/trendify_old/production_server.py delete mode 100644 src/trendify_old/server.py delete mode 100644 src/trendify_old/streamlit.py delete mode 100644 test.py create mode 100644 tests/test_cli.py create mode 100644 tests/test_generate.py create mode 100644 tests/test_pipeline.py create mode 100644 tests/test_render.py create mode 100644 tests/test_table_builder.py delete mode 100755 update_docs_and_push_changes.sh diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 560e980..4f1b8ca 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -4,15 +4,6 @@ repos: hooks: - id: check-added-large-files args: ["--maxkb=500"] - - repo: local - hooks: - - id: mkinit - name: auto-generate __init__.py files - entry: uv run mkinit - args: ["src/trendify", "-w", "--recursive"] - language: system - files: ^(src/trendify.*\.py)$ - pass_filenames: false - repo: https://github.com/astral-sh/ruff-pre-commit rev: v0.15.17 hooks: diff --git a/alternative_tag_selectors.py b/alternative_tag_selectors.py deleted file mode 100644 index 8511def..0000000 --- a/alternative_tag_selectors.py +++ /dev/null @@ -1,312 +0,0 @@ -""" -Additional selection mechanisms for handling large numbers of tags in Trendify dashboards. -These components can be integrated into the PlotlyDashboardGenerator class. -""" - -from dash import dcc, html -from dash.dependencies import Input, Output, State - - -class TagSelectionMethods: - """Various tag selection UI components for Plotly Dash""" - - @staticmethod - def create_searchable_dropdown(tag_options, default_value): - """ - Create a searchable dropdown for tag selection - - Args: - tag_options (list): List of tag options in {'label': '...', 'value': '...'} format - default_value (str): Default tag value to select - - Returns: - html.Div: Dropdown component with search functionality - - """ - return html.Div( - [ - html.Label("Search and select tag:"), - dcc.Dropdown( - id="tag-selector", - options=tag_options, - value=default_value, - clearable=False, - searchable=True, - style={"width": "100%"}, - ), - ], - style={"width": "40%", "margin": "10px 0"}, - ) - - @staticmethod - def create_radio_with_search_filter(tag_options, default_value): - """ - Create radio buttons with a search filter above - - Args: - tag_options (list): List of tag options in {'label': '...', 'value': '...'} format - default_value (str): Default tag value to select - - Returns: - html.Div: Search filter and radio button component - - """ - return html.Div( - [ - html.Label("Filter tags:"), - dcc.Input( - id="tag-search-input", - type="text", - placeholder="Type to filter tags...", - style={"width": "100%", "marginBottom": "10px"}, - ), - html.Div( - [ - dcc.RadioItems( - id="tag-selector", - options=tag_options, - value=default_value, - labelStyle={"display": "block", "margin": "5px 0"}, - ) - ], - id="filtered-radio-container", - style={"maxHeight": "300px", "overflowY": "auto"}, - ), - ], - style={"width": "30%", "margin": "10px 0"}, - ) - - @staticmethod - def create_category_based_selection(tag_options, default_value): - """ - Create a two-level selection for tags that can be organized by category - Assumes tag names might follow a pattern like 'category/tag_name' - - Args: - tag_options (list): List of tag options in {'label': '...', 'value': '...'} format - default_value (str): Default tag value to select - - Returns: - html.Div: Two-level selection component - - """ - # Try to extract categories from tag names (e.g., 'category/tag_name') - categories = set() - for opt in tag_options: - parts = opt["label"].split("/") - if len(parts) > 1: - categories.add(parts[0]) - else: - categories.add("General") - - categories = sorted(list(categories)) - - # Default category - default_category = next( - ( - parts[0] - for opt in tag_options - if opt["value"] == default_value - and len(parts := opt["label"].split("/")) > 1 - ), - "General", - ) - - return html.Div( - [ - html.Div( - [ - html.Label("Category:"), - dcc.Dropdown( - id="category-selector", - options=[ - {"label": cat, "value": cat} for cat in categories - ], - value=default_category, - clearable=False, - style={"width": "100%"}, - ), - ], - style={"marginBottom": "10px"}, - ), - html.Div( - [ - html.Label("Tag:"), - dcc.Dropdown( - id="tag-selector", - # Options will be populated by callback - value=default_value, - clearable=False, - style={"width": "100%"}, - ), - ] - ), - ], - style={"width": "40%", "margin": "10px 0"}, - ) - - @staticmethod - def register_category_callbacks(app, tag_options): - """Register callbacks for the category-based selection""" - - @app.callback( - Output("tag-selector", "options"), [Input("category-selector", "value")] - ) - def update_tag_options(selected_category): - filtered_options = [] - for opt in tag_options: - parts = opt["label"].split("/") - if len(parts) > 1 and parts[0] == selected_category: - # Show only the tag part in the dropdown - filtered_options.append({"label": parts[1], "value": opt["value"]}) - elif len(parts) == 1 and selected_category == "General": - filtered_options.append(opt) - return filtered_options - - @staticmethod - def register_search_filter_callbacks(app, tag_options): - """Register callbacks for the search filter with radio buttons""" - - @app.callback( - Output("filtered-radio-container", "children"), - [Input("tag-search-input", "value")], - [State("tag-selector", "value")], - ) - def filter_radio_options(search_text, current_value): - if not search_text: - return dcc.RadioItems( - id="tag-selector", - options=tag_options, - value=current_value, - labelStyle={"display": "block", "margin": "5px 0"}, - ) - - # Filter options based on search text - filtered_options = [ - opt - for opt in tag_options - if search_text.lower() in opt["label"].lower() - ] - - return dcc.RadioItems( - id="tag-selector", - options=filtered_options, - value=current_value - if any(opt["value"] == current_value for opt in filtered_options) - else None, - labelStyle={"display": "block", "margin": "5px 0"}, - ) - - -# Example of how to use these components in the PlotlyDashboardGenerator class -def create_sidebar_with_tag_tree(tag_options, default_value): - """Create a sidebar with a hierarchical tag tree for larger applications""" - # Organize tags into a tree structure - tag_tree = {} - for opt in tag_options: - parts = opt["label"].split("/") - if len(parts) == 1: - category = "General" - tag = parts[0] - else: - category = parts[0] - tag = "/".join(parts[1:]) # Join remaining parts as the tag - - if category not in tag_tree: - tag_tree[category] = [] - - tag_tree[category].append({"label": tag, "value": opt["value"]}) - - # Sort categories and tags - categories = sorted(tag_tree.keys()) - - # Create the sidebar - return html.Div( - [ - html.H3("Tags"), - html.Hr(), - html.Div( - [ - html.Div( - [ - html.Details( - [ - html.Summary(category), - html.Div( - [ - html.Div( - [ - dcc.RadioItems( - id={ - "type": "tag-radio", - "category": category, - }, - options=tag_tree[category], - value=None, - labelStyle={ - "display": "block", - "margin": "5px 0", - "paddingLeft": "20px", - }, - ) - ] - ) - ] - ), - ], - open=category - == next( - ( - parts[0] - for opt in tag_options - if opt["value"] == default_value - and len(parts := opt["label"].split("/")) > 1 - ), - "General", - ), - ) - ] - ) - for category in categories - ] - ), - # Hidden input to store the selected tag - dcc.Input(id="tag-selector", value=default_value, type="hidden"), - ], - style={ - "width": "250px", - "backgroundColor": "#f8f9fa", - "padding": "15px", - "borderRight": "1px solid #ddd", - "height": "100vh", - "position": "fixed", - "overflowY": "auto", - }, - ) - - -def register_tag_tree_callbacks(app, tag_options): - """Register callbacks for the sidebar tag tree""" - # Create a list of Output objects, one for each category - categories = set() - for opt in tag_options: - parts = opt["label"].split("/") - if len(parts) > 1: - categories.add(parts[0]) - else: - categories.add("General") - - categories = sorted(list(categories)) - - @app.callback( - Output("tag-selector", "value"), - [Input({"type": "tag-radio", "category": cat}, "value") for cat in categories], - ) - def update_selected_tag(*values): - # Find the first non-None value (the selected tag) - for val in values: - if val is not None: - return val - - # Default to the first tag if nothing is selected - return tag_options[0]["value"] if tag_options else None diff --git a/app.log b/app.log deleted file mode 100644 index 766c931..0000000 --- a/app.log +++ /dev/null @@ -1,5 +0,0 @@ -2025-03-14 12:31:15,568 - dash.dash - INFO - Dash is running on http://127.0.0.1:8050/ - -2025-03-14 12:31:15,582 - werkzeug - INFO - WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead. - * Running on http://127.0.0.1:8050 -2025-03-14 12:31:15,582 - werkzeug - INFO - Press CTRL+C to quit diff --git a/benchmarks/stress_test.py b/benchmarks/stress_test.py deleted file mode 100644 index 88a6fb5..0000000 --- a/benchmarks/stress_test.py +++ /dev/null @@ -1,139 +0,0 @@ -""" -M1 concurrency stress test (the go/no-go gate for skipping the two-phase per-run-file -> merge -design, per the v2 architecture plan). Simulates N worker processes, each writing many runs' -worth of products directly to one shared SQLite `.db` file via `ProductStore.write_run`, and -measures wall-clock throughput plus checks for `SQLITE_BUSY` failures under `busy_timeout`. - -Uses the same synthetic workload shape as `bench_v1.py` (5 tags, ~42 products/run: 40 Point2D + -5 TableEntry + 5 HistogramEntry + 1 multi-tag Trace2D + 1 AxLine) for an apples-to-apples -comparison against v1's generate+sort pipeline. - -Run with the v2 venv: - - PYTHONPATH=src/v2 .venv-v2/bin/python src/v2/benchmarks/stress_test.py \ - --n-runs 10000 --n-procs 8 -""" - -from __future__ import annotations - -import argparse -import json -import time -from concurrent.futures import ProcessPoolExecutor -from pathlib import Path - -from trendify.base.pen import Pen -from trendify.plotting.axline import AxLine, LineOrientation -from trendify.plotting.histogram import HistogramEntry -from trendify.plotting.point import Point2D -from trendify.plotting.trace import Trace2D -from trendify.formats.table import TableEntry -from trendify.store.product_store import ProductStore - -TAGS = ["tag0", "tag1", "tag2", "tag3", "tag4"] - -_store: ProductStore | None = None - - -def _init_worker(db_path: str) -> None: - global _store - _store = ProductStore.open(Path(db_path)) - - -def _build_products(run_id: int): - products = [] - for i, tag in enumerate(TAGS): - for j in range(8): - products.append( - Point2D(tags=[tag], x=float(run_id + i + j), y=float(i * j)) - ) - products.append( - TableEntry(tags=[tag], row=f"r{run_id}", col=f"c{j}", value=float(run_id)) - ) - products.append(HistogramEntry(tags=[tag], value=float(run_id % 17))) - products.append( - Trace2D.from_xy( - tags=[TAGS[0], TAGS[1]], - x=list(range(20)), - y=[float(v * run_id) for v in range(20)], - pen=Pen(label="trace"), - ) - ) - products.append( - AxLine( - tags=[TAGS[2]], - value=1.0, - orientation=LineOrientation.HORIZONTAL, - pen=Pen(), - ) - ) - return products - - -def _write_one_run(run_id: int) -> tuple[int, str | None]: - assert _store is not None - try: - n = _store.write_run(Path(f"/synthetic/run/{run_id}"), _build_products(run_id)) - return n, None - except Exception as e: - return 0, f"{type(e).__name__}: {e}" - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument("--n-runs", type=int, default=10000) - parser.add_argument("--n-procs", type=int, default=8) - parser.add_argument( - "--db-path", type=str, default="/tmp/trendify_bench_v2/trendify.db" - ) - args = parser.parse_args() - - db_path = Path(args.db_path) - if db_path.parent.exists(): - for f in db_path.parent.glob("trendify.db*"): - f.unlink() - db_path.parent.mkdir(parents=True, exist_ok=True) - - # Bootstrap schema once, up front, from the main process, before workers race to open it. - ProductStore.open(db_path).close() - - t0 = time.perf_counter() - errors = [] - total_written = 0 - with ProcessPoolExecutor( - max_workers=args.n_procs, - initializer=_init_worker, - initargs=(str(db_path),), - ) as executor: - for n, err in executor.map(_write_one_run, range(args.n_runs), chunksize=32): - total_written += n - if err: - errors.append(err) - t1 = time.perf_counter() - - with ProductStore.open(db_path, readonly=True) as store: - (run_count,) = store._conn.execute("SELECT COUNT(*) FROM runs").fetchone() - (product_count,) = store._conn.execute( - "SELECT COUNT(*) FROM products" - ).fetchone() - (tag_row_count,) = store._conn.execute( - "SELECT COUNT(*) FROM product_tags" - ).fetchone() - - result = { - "n_runs": args.n_runs, - "n_procs": args.n_procs, - "write_seconds": t1 - t0, - "products_written_reported": total_written, - "runs_in_db": run_count, - "products_in_db": product_count, - "product_tag_rows_in_db": tag_row_count, - "db_file_size_bytes": db_path.stat().st_size, - "n_errors": len(errors), - "sample_errors": errors[:5], - } - print(json.dumps(result, indent=2)) - - -if __name__ == "__main__": - main() diff --git a/custom_handler.py b/custom_handler.py deleted file mode 100644 index 8d21fa2..0000000 --- a/custom_handler.py +++ /dev/null @@ -1,9 +0,0 @@ -import requests -from mkdocstrings.handlers.base import BaseHandler - - -class CustomPythonHandler(BaseHandler): - def get_source(self): - # Override the method to disable SSL verification - response = requests.get(self.get_source(), verify=False) - return response.text diff --git a/implementation_recommendation.py b/implementation_recommendation.py deleted file mode 100644 index 9e1566d..0000000 --- a/implementation_recommendation.py +++ /dev/null @@ -1,77 +0,0 @@ -""" -Implementation recommendation for integrating better tag selection into the PlotlyDashboardGenerator class -""" - -# Here's how you could modify your PlotlyDashboardGenerator class -# to implement a dropdown-based selection with search capabilities - - -def _create_app_layout(self, title: str, tab_contents: List[Dict]) -> html.Div: - """Create the main application layout with improved tag selection.""" - if not tab_contents: - return html.Div( - [html.H1(title), html.Div("No data products found to visualize.")] - ) - - # Create dropdown options from tab_contents - dropdown_options = [ - {"label": tab["label"], "value": tab["value"]} for tab in tab_contents - ] - - # Create content divs for each tag - content_divs = [] - for tab in tab_contents: - content_divs.append( - html.Div( - tab["content"], id=f"content-{tab['value']}", style={"display": "none"} - ) - ) - - # Only show the first one by default - content_divs[0]["style"] = {"display": "block"} - - return html.Div( - [ - html.H1(title), - html.Div( - [ - html.Label("Select Tag:"), - dcc.Dropdown( - id="tag-selector", - options=dropdown_options, - value=tab_contents[0]["value"], - clearable=False, - searchable=True, - style={"width": "100%"}, - ), - ], - style={"width": "30%", "margin": "10px 0"}, - ), - html.Div(id="tag-content-container", children=content_divs), - # Stores - dcc.Store(id="active-tag", data=tab_contents[0]["value"]), - dcc.Store(id="color-maps"), - ] - ) - - -def _register_callbacks(self): - """Register all the callbacks for the dashboard.""" - - # Tag selection callback - @self.app.callback( - [Output("active-tag", "data")] - + [Output(f"content-{tag}", "style") for tag in self.tag_data.keys()], - [Input("tag-selector", "value")], - ) - def switch_tag(tag_value): - styles = [] - for tag in self.tag_data.keys(): - if tag == tag_value: - styles.append({"display": "block"}) - else: - styles.append({"display": "none"}) - return [tag_value] + styles - - # Register callbacks for each tag's components as before... - # ... diff --git a/improved_tag_selection.py b/improved_tag_selection.py deleted file mode 100644 index 06aab5b..0000000 --- a/improved_tag_selection.py +++ /dev/null @@ -1,73 +0,0 @@ -""" -Code snippet showing improved tag selection for PlotlyDashboardGenerator -to replace the existing tab-based navigation -""" - -from dash import dcc, html -from dash.dependencies import Input, Output - - -# This would replace the _create_app_layout method in your class -def _create_app_layout_with_dropdown( - self, title: str, tab_contents: List[Dict] -) -> html.Div: - """Create the main application layout with dropdown selection instead of tabs.""" - if not tab_contents: - return html.Div( - [html.H1(title), html.Div("No data products found to visualize.")] - ) - - # Create dropdown options from tab_contents - dropdown_options = [ - {"label": tab["label"], "value": tab["value"]} for tab in tab_contents - ] - - # Create divs for each tag's content - content_divs = [] - for tab in tab_contents: - content_divs.append( - html.Div( - tab["content"], id=f"content-{tab['value']}", style={"display": "none"} - ) - ) - - return html.Div( - [ - html.H1(title), - html.Div( - [ - html.Label("Select Tag:"), - dcc.Dropdown( - id="tag-selector", - options=dropdown_options, - value=tab_contents[0]["value"], - clearable=False, - style={"width": "100%"}, - ), - ], - style={"width": "30%", "margin": "10px 0"}, - ), - html.Div(id="tag-content-container", children=content_divs), - # Stores - dcc.Store(id="active-tag", data=tab_contents[0]["value"]), - ] - ) - - -# This would replace the callback in _register_callbacks -def tag_selection_callback(self): - """Register callback for tag selection dropdown.""" - - @self.app.callback( - [Output("active-tag", "data")] - + [Output(f"content-{tag}", "style") for tag in self.tag_data.keys()], - [Input("tag-selector", "value")], - ) - def switch_tag(tag_value): - styles = [] - for tag in self.tag_data.keys(): - if tag == tag_value: - styles.append({"display": "block"}) - else: - styles.append({"display": "none"}) - return [tag_value] + styles diff --git a/index.html b/index.html deleted file mode 100644 index df7175d..0000000 --- a/index.html +++ /dev/null @@ -1,6 +0,0 @@ - - - - -

If you are not re-directed to docs page in 5 seconds, click here.

- \ No newline at end of file diff --git a/plotly_examples/histogram_1.py b/plotly_examples/histogram_1.py deleted file mode 100644 index fa4d139..0000000 --- a/plotly_examples/histogram_1.py +++ /dev/null @@ -1,1038 +0,0 @@ -"""Interactive histogram visualization with interactive legend features.""" - -import dash -from dash import dcc, html -from dash.dependencies import Input, Output, State, ALL -import plotly.graph_objects as go -import pandas as pd -import numpy as np -from typing import Any - -# Debug Configuration -PYTHON_DEBUG = True -MIN_BINS = 1 - - -def debug_log(message: str) -> None: - """Python-side debug logging.""" - if PYTHON_DEBUG: - print(f"[PY] {message}") - - -def calculate_histogram_statistics( - series: pd.Series, num_bins: int = 20 -) -> dict[str, Any]: - """Calculate statistics for a histogram series.""" - try: - # Drop NA values - clean_series = series.dropna() - - if len(clean_series) < 2: - return { - "samples": len(clean_series), - "missing": len(series) - len(clean_series), - "mean": "N/A" - if len(clean_series) == 0 - else f"{clean_series.mean():.3f}", - "bins": num_bins, - } - - stats_dict = { - "samples": len(clean_series), - "missing": len(series) - len(clean_series), - "mean": f"{clean_series.mean():.3f}", - "median": f"{clean_series.median():.3f}", - "std_dev": f"{clean_series.std():.3f}", - "min": f"{clean_series.min():.3f}", - "max": f"{clean_series.max():.3f}", - "bins": num_bins, - "bin_width": f"{(clean_series.max() - clean_series.min()) / num_bins:.3f}" - if clean_series.max() != clean_series.min() - else "N/A", - } - - # Add skewness and kurtosis if enough data points - if len(clean_series) > 3: - try: - stats_dict["skewness"] = f"{clean_series.skew():.3f}" - stats_dict["kurtosis"] = f"{clean_series.kurtosis():.3f}" - except: - pass - - return stats_dict - except Exception as e: - return {"error": str(e), "bins": num_bins} - - -# Create sample data -df = pd.DataFrame( - { - "A": np.random.normal(0, 1, 1000), - "B": np.random.normal(2, 1.5, 1000), - "C": np.random.exponential(2, 1000), - "D": np.random.gamma(2, 2, 1000), - "Category": np.random.choice(["X", "Y", "Z"], 1000), - } -) - -# Initialize the Dash app -app = dash.Dash(__name__, suppress_callback_exceptions=True) - -# Styles -dropdown_style = { - "width": "100%", - "marginBottom": "20px", - "zIndex": 999, -} - -info_panel_style = { - "position": "fixed", # Fixed positioning for hover panel - "backgroundColor": "white", - "padding": "15px", - "borderRadius": "5px", - "boxShadow": "0 0 10px rgba(0,0,0,0.3)", - "zIndex": 1000, - "display": "none", - "minWidth": "200px", - "maxWidth": "300px", - "fontSize": "12px", -} - -color_picker_style = { - "position": "fixed", - "top": "50%", - "left": "50%", - "transform": "translate(-50%, -50%)", - "backgroundColor": "white", - "padding": "20px", - "borderRadius": "5px", - "boxShadow": "0 0 10px rgba(0,0,0,0.3)", - "zIndex": 1001, - "display": "none", -} - -# Available colors -COLORS = [ - "#1f77b4", - "#ff7f0e", - "#2ca02c", - "#d62728", - "#9467bd", - "#8c564b", - "#e377c2", - "#7f7f7f", - "#bcbd22", - "#17becf", - "#aec7e8", - "#ffbb78", - "#98df8a", - "#ff9896", - "#c5b0d5", - "#c49c94", - "#f7b6d2", - "#c7c7c7", - "#dbdb8d", - "#9edae5", -] - -# Define the layout -app.layout = html.Div( - [ - html.H1("Interactive Histogram Visualization"), - # Control panel for column selection - html.Div( - [ - html.Div( - [ - html.H3("Select Columns for Histograms"), - dcc.Dropdown( - id="histogram-columns", - options=[ - {"label": col, "value": col} - for col in df.columns - if col != "Category" - ], - value=[], - multi=True, - style=dropdown_style, - ), - ], - style={"width": "45%", "display": "inline-block", "margin": "5px"}, - ), - ] - ), - # Graph and custom legend - html.Div( - [ - html.Div( - [ - html.H3("Histogram Settings"), - dcc.Checklist( - id="histogram-settings", - options=[ - {"label": " Show KDE (density curve)", "value": "kde"}, - { - "label": " Normalize histograms", - "value": "normalize", - }, - {"label": " Show rug plot", "value": "rug"}, - ], - value=["kde"], - style={"display": "block", "gap": "10px"}, - inputStyle={"marginRight": "5px"}, - labelStyle={"display": "block", "marginBottom": "10px"}, - ), - html.Div( - [ - html.Label("Global Number of Bins:"), - dcc.Slider( - id="bins-slider", - min=MIN_BINS, - max=100, - step=1, - value=20, - marks={i: str(i) for i in [1, 20, 40, 60, 80, 100]}, - ), - ], - style={"marginTop": "15px"}, - ), - html.Div( - [ - html.Label( - "Lock all bins to global setting:", - style={ - "display": "inline-block", - "marginRight": "10px", - }, - ), - dcc.Checklist( - id="global-bins-toggle", - options=[{"label": "", "value": "enabled"}], - value=["enabled"], # Enabled by default - inline=True, - inputStyle={"marginRight": "5px"}, - ), - ], - style={"marginTop": "15px", "marginBottom": "5px"}, - ), - ], - style={"width": "20%", "display": "inline-block", "margin": "5px"}, - ), - # Main plot - html.Div( - [ - dcc.Graph(id="histogram-plot", style={"height": "700px"}), - ], - style={ - "width": "50%", - "display": "inline-block", - "verticalAlign": "top", - }, - ), - # Custom legend - html.Div( - [ - html.H3("Series"), - html.Div( - id="custom-legend", - style={"overflowY": "auto", "maxHeight": "600px"}, - ), - ], - style={ - "width": "20%", - "display": "inline-block", - "verticalAlign": "top", - }, - ), - ] - ), - # Info panel (hidden by default) - html.Div(id="series-info-panel", style=info_panel_style), - # Color picker panel (hidden by default) - html.Div( - [ - html.H3("Select Color"), - html.Div( - [ - html.Button( - style={ - "backgroundColor": color, - "width": "30px", - "height": "30px", - "margin": "5px", - }, - id={"type": "color-option", "index": i}, - ) - for i, color in enumerate(COLORS) - ], - style={ - "display": "grid", - "gridTemplateColumns": "repeat(5, 1fr)", - "gap": "5px", - }, - ), - html.Button( - "Close", id="close-color-picker", style={"marginTop": "10px"} - ), - ], - id="color-picker-panel", - style=color_picker_style, - ), - # Store components - dcc.Store(id="series-colors"), - dcc.Store(id="series-data"), - dcc.Store(id="active-series"), - dcc.Store(id="bins-settings"), - dcc.Store( - id="global-bins-active", data=True - ), # True by default# Add this to your layout - dcc.Interval( - id="interval-component", interval=60 * 1000, n_intervals=0 - ), # Just a trigger - ] -) - - -# Update whether global bins control is active based on toggle -@app.callback( - Output("global-bins-active", "data"), [Input("global-bins-toggle", "value")] -) -def update_global_bins_state(toggle_value): - """Update whether global bins control is active based on toggle.""" - return "enabled" in toggle_value if toggle_value else False - - -# # Initialize series-colors and statistics when columns are selected -# @app.callback( -# [Output('series-colors', 'data'), -# Output('series-data', 'data')], -# [Input('histogram-columns', 'value'), -# Input('bins-settings', 'data')], -# [State('series-colors', 'data')] -# ) -# def initialize_series_data(selected_cols, bins_settings, existing_colors): -# """Initialize color data and statistics for selected columns.""" -# debug_log("Initializing series data") -# color_data = existing_colors or {} -# series_stats = {} -# bins_settings = bins_settings or {} - -# if not selected_cols: -# return color_data, series_stats - -# for col in selected_cols: -# series_name = f'Histogram of {col}' - -# # Assign a color if not already assigned -# if series_name not in color_data: -# color_data[series_name] = COLORS[len(color_data) % len(COLORS)] - -# # Get bin count (default 20) -# num_bins = bins_settings.get(series_name, 20) - -# # Calculate statistics with the current bin count -# stats = calculate_histogram_statistics(df[col], num_bins) -# series_stats[series_name] = stats - -# # Keep only the colors for selected columns -# color_data = {k: v for k, v in color_data.items() -# if any(f'Histogram of {x}' == k for x in selected_cols)} - -# return color_data, series_stats - - -# Update custom legend with bin controls -@app.callback( - Output("custom-legend", "children"), - [ - Input("histogram-columns", "value"), - Input("series-colors", "data"), - Input("bins-settings", "data"), - ], -) -def update_legend_with_bin_controls(selected_cols, color_data, bins_settings): - """Update custom legend with interactive elements including bin controls.""" - legend_items = [] - bins_settings = bins_settings or {} - - if not selected_cols or not color_data: - return legend_items - - for col in selected_cols: - series_name = f"Histogram of {col}" - color = color_data.get(series_name, COLORS[0]) - - # Get bin count for this series (default: 20 bins) - bin_count = bins_settings.get(series_name, 20) - - legend_items.append( - html.Div( - [ - # Color button - html.Button( - style={ - "backgroundColor": color, - "width": "20px", - "height": "20px", - "marginRight": "10px", - "verticalAlign": "middle", - "cursor": "pointer", - "border": "1px solid #ddd", - }, - id={"type": "color-button", "index": series_name}, - ), - # Series name - html.Span( - series_name, - style={ - "cursor": "default", - "userSelect": "none", - "display": "inline-block", - "marginRight": "10px", - }, - ), - # Bin control - html.Div( - [ - html.Label( - "Bins:", - style={"fontSize": "11px", "marginRight": "5px"}, - ), - dcc.Input( - id={"type": "bin-input", "index": series_name}, - type="number", - min=5, - max=100, - step=1, - value=bin_count, - style={"width": "50px", "fontSize": "11px"}, - debounce=False, # Process changes immediately - persistence=True, # Remember values - ), - ], - style={"display": "inline-block"}, - ), - ], - className="legend-item", - id={"type": "legend-item", "index": series_name}, - **{ - "data-series": series_name, - "style": { - "margin": "10px", - "padding": "5px", - "borderRadius": "3px", - "display": "flex", - "alignItems": "center", - }, - }, - ) - ) - - return legend_items - - -@app.callback( - [Output("color-picker-panel", "style"), Output("active-series", "data")], - [ - Input({"type": "color-button", "index": ALL}, "n_clicks"), - Input("close-color-picker", "n_clicks"), - ], - [ - State({"type": "color-button", "index": ALL}, "id"), - State("color-picker-panel", "style"), - ], -) -def toggle_color_picker(button_clicks, close_clicks, button_ids, current_style): - """Show/hide color picker when clicking color buttons.""" - ctx = dash.callback_context - if not ctx.triggered: - return dict(current_style, display="none"), None - - triggered_id = ctx.triggered[0]["prop_id"] - - # Close on close button click - if "close-color-picker" in triggered_id: - debug_log("Closing color picker") - return dict(current_style, display="none"), None - - # Open on color button click - if "color-button" in triggered_id and button_clicks and any(button_clicks): - try: - # Find which button was clicked - button_idx = next( - (i for i, clicks in enumerate(button_clicks) if clicks), None - ) - if button_idx is not None: - series_name = button_ids[button_idx]["index"] - debug_log(f"Opening color picker for {series_name}") - return dict(current_style, display="block"), series_name - except Exception as e: - debug_log(f"Error in toggle_color_picker: {e}") - - return dict(current_style, display="none"), None - - -@app.callback( - Output("series-colors", "data", allow_duplicate=True), - [Input({"type": "color-option", "index": ALL}, "n_clicks")], - [ - State({"type": "color-option", "index": ALL}, "id"), - State("active-series", "data"), - State("series-colors", "data"), - ], - prevent_initial_call=True, -) -def update_series_color(color_clicks, color_ids, active_series, current_colors): - """Update color when selecting from color picker.""" - ctx = dash.callback_context - if not ctx.triggered or not active_series or not current_colors: - return dash.no_update - - triggered_id = ctx.triggered[0]["prop_id"] - if "color-option" not in triggered_id or not any( - click for click in color_clicks if click - ): - return dash.no_update - - try: - # Find which color was clicked - color_idx = next((i for i, clicks in enumerate(color_clicks) if clicks), None) - if color_idx is not None: - current_colors[active_series] = COLORS[color_idx] - debug_log(f"Updated color for {active_series} to {COLORS[color_idx]}") - return current_colors - except Exception as e: - debug_log(f"Error in update_series_color: {e}") - - return dash.no_update - - -@app.callback( - [ - Output("color-picker-panel", "style", allow_duplicate=True), - Output("active-series", "data", allow_duplicate=True), - ], - [Input({"type": "color-option", "index": ALL}, "n_clicks")], - [State("color-picker-panel", "style")], - prevent_initial_call=True, -) -def close_color_picker_after_selection(color_clicks, current_style): - """Close the color picker after a color is selected.""" - if any(click for click in color_clicks if click): - return dict(current_style, display="none"), None - return dash.no_update, dash.no_update - - -@app.callback( - Output("histogram-plot", "figure"), - [ - Input("histogram-columns", "value"), - Input("histogram-settings", "value"), - Input("series-colors", "data"), - Input("bins-settings", "data"), - ], -) -def update_histogram(selected_cols, settings, color_data, bins_settings): - """Update the histogram based on selected columns and settings.""" - debug_log("Updating histogram plot") - fig = go.Figure() - - if not selected_cols or not color_data: - fig.add_annotation( - text="Please select columns for the histogram", - xref="paper", - yref="paper", - x=0.5, - y=0.5, - showarrow=False, - ) - return fig - - normalize = "normalize" in settings - show_kde = "kde" in settings - show_rug = "rug" in settings - bins_settings = bins_settings or {} - - for col in selected_cols: - series_name = f"Histogram of {col}" - color = color_data.get(series_name) - if not color: # Skip if no color assigned - continue - - # Get bins setting for this series (default: 20) - num_bins = bins_settings.get(series_name, 20) - - # Clean the data - data = df[col].dropna() - - # Add histogram with precisely num_bins bins - histnorm = "probability" if normalize else None - - # Calculate bin range directly - min_val, max_val = min(data), max(data) - bin_width = (max_val - min_val) / num_bins if max_val > min_val else 1 - - # Use xbins to control exactly how many bins are displayed - fig.add_trace( - go.Histogram( - x=data, - name=series_name, - histnorm=histnorm, - marker_color=color, - opacity=0.7, - showlegend=False, - xbins=dict(start=min_val, end=max_val, size=bin_width), - autobinx=False, # Disable autobinning - ) - ) - - # Add KDE if requested - if show_kde and len(data) > 1: - try: - # Calculate KDE values - from scipy import stats as scipy_stats - - # Make sure we have enough unique values for KDE - if len(np.unique(data)) > 5: - kde = scipy_stats.gaussian_kde(data) - x_range = np.linspace(min(data), max(data), 200) - y_range = kde(x_range) - - # Scale KDE to match histogram height - if normalize: - # Scale for normalized histogram - y_range = y_range / np.trapz(y_range, x_range) - else: - # Scale for count histogram - hist, edges = np.histogram(data, bins=num_bins) - max_hist_height = np.max(hist) - max_kde_height = np.max(y_range) - scale_factor = ( - max_hist_height / max_kde_height - if max_kde_height > 0 - else 1 - ) - y_range = y_range * scale_factor - - fig.add_trace( - go.Scatter( - x=x_range, - y=y_range, - mode="lines", - name=f"KDE of {col}", - line=dict(color=color, width=2), - showlegend=False, - ) - ) - except Exception as e: - debug_log(f"Error computing KDE: {e}") - - # Add rug plot if requested - if show_rug: - try: - # Create a small y value for the rug - min_y = 0 - if normalize: - # Position below x-axis for normalized histogram - min_y = -0.02 - - fig.add_trace( - go.Scatter( - x=data, - y=[min_y] * len(data), - mode="markers", - marker=dict( - symbol="line-ns", size=10, color=color, opacity=0.5 - ), - showlegend=False, - hoverinfo="x", - ) - ) - except Exception as e: - debug_log(f"Error adding rug plot: {e}") - - # Display histograms using overlay mode - fig.update_layout( - barmode="overlay", - title="Interactive Histogram Visualization", - plot_bgcolor="white", - paper_bgcolor="white", - showlegend=False, # Using custom legend - xaxis=dict( - title="Value", - showgrid=True, - gridwidth=1, - gridcolor="LightGray", - zeroline=True, - zerolinewidth=2, - zerolinecolor="LightGray", - ), - yaxis=dict( - title="Frequency" if not normalize else "Probability", - showgrid=True, - gridwidth=1, - gridcolor="LightGray", - zeroline=True, - zerolinewidth=2, - zerolinecolor="LightGray", - ), - height=700, - margin=dict(t=50, b=50, l=50, r=50), - ) - - debug_log("Histogram plot updated") - return fig - - -app.clientside_callback( - """ - function(children, series_data) { - if (!children) return window.dash_clientside.no_update; - - const JS_DEBUG = true; - - function debugLog(message) { - if (JS_DEBUG) console.log('[JS]', message); - } - - // Global reference to track which element is being hovered - window.activeHoverLegendItem = window.activeHoverLegendItem || null; - - function updateStatsPanel() { - // If no item is active, don't update - if (!window.activeHoverLegendItem) return; - - const panel = document.getElementById('series-info-panel'); - if (!panel) return; - - const series = window.activeHoverLegendItem.getAttribute('data-series'); - - // If panel is visible, update its content - if (panel.style.display === 'block' && series_data && series_data[series]) { - let statsHtml = "
"; - statsHtml += `

Statistics: ${series}

`; - statsHtml += ""; - - const stats = series_data[series]; - for (const [key, value] of Object.entries(stats)) { - statsHtml += ` - - - `; - } - - statsHtml += "
${key}${value}
"; - panel.innerHTML = statsHtml; - } - } - - function setupHoverHandlers() { - debugLog('Setting up hover handlers'); - const items = document.getElementsByClassName('legend-item'); - const panel = document.getElementById('series-info-panel'); - - Array.from(items).forEach(item => { - item.onmouseenter = (e) => { - const series = item.getAttribute('data-series'); - debugLog(`Hover enter: ${series}`); - - // Store reference to the hovered item - window.activeHoverLegendItem = item; - - // Generate HTML content for statistics - let statsHtml = "
"; - - if (series_data && series_data[series]) { - statsHtml += `

Statistics: ${series}

`; - statsHtml += ""; - - const stats = series_data[series]; - for (const [key, value] of Object.entries(stats)) { - statsHtml += ` - - - `; - } - - statsHtml += "
${key}${value}
"; - } else { - statsHtml += "

No statistics available for this series.

"; - } - - statsHtml += "
"; - panel.innerHTML = statsHtml; - - // Position the panel - const rect = item.getBoundingClientRect(); - let left = rect.right + 10; - - // Check if panel would go off-screen - if (left + 300 > window.innerWidth) { // 300px is max panel width - left = rect.left - 310; // Position to left with 10px margin - } - - panel.style.left = left + 'px'; - panel.style.top = rect.top + 'px'; - panel.style.display = 'block'; - }; - - item.onmouseleave = (e) => { - debugLog('Hover leave'); - window.activeHoverLegendItem = null; - panel.style.display = 'none'; - }; - }); - } - - // Update panel content if needed - updateStatsPanel(); - - // Allow time for DOM to update - setTimeout(setupHoverHandlers, 100); - - return window.dash_clientside.no_update; - } - """, - Output("custom-legend", "data-update"), - [Input("custom-legend", "children"), Input("series-data", "data")], -) -# # Remove the existing update_bin_settings callback and replace with this: -# @app.callback( -# Output('series-data', 'data'), -# [Input('bins-settings', 'data'), -# Input('histogram-columns', 'value')], -# [State('series-data', 'data')] -# ) -# def update_statistics_on_bin_change(bins_settings, selected_cols, current_stats): -# """Update statistics whenever bin settings change.""" -# ctx = dash.callback_context -# if not ctx.triggered: -# return current_stats or {} - -# if not selected_cols: -# return current_stats or {} - -# bins_settings = bins_settings or {} -# stats = current_stats or {} - -# # Update statistics for all selected columns with current bin settings -# for col in selected_cols: -# series_name = f'Histogram of {col}' -# num_bins = bins_settings.get(series_name, 20) # Default to 20 bins -# updated_stats = calculate_histogram_statistics(df[col], num_bins) -# stats[series_name] = updated_stats - -# # Remove stats for unselected columns -# for series_name in list(stats.keys()): -# if not any(f'Histogram of {col}' == series_name for col in selected_cols): -# del stats[series_name] - -# return stats -# Replace the existing initialize_series_data callback with this one: - - -@app.callback( - [Output("series-colors", "data"), Output("series-data", "data")], - [Input("histogram-columns", "value"), Input("bins-settings", "data")], - [State("series-colors", "data")], -) -def initialize_and_update_series_data(selected_cols, bins_settings, existing_colors): - """Initialize color data and update statistics for selected columns.""" - ctx = dash.callback_context - debug_log( - f"Series data update triggered by: {ctx.triggered[0]['prop_id'] if ctx.triggered else 'none'}" - ) - - color_data = existing_colors or {} - series_stats = {} - bins_settings = bins_settings or {} - - if not selected_cols: - return color_data, series_stats - - for col in selected_cols: - series_name = f"Histogram of {col}" - - # Assign a color if not already assigned - if series_name not in color_data: - color_data[series_name] = COLORS[len(color_data) % len(COLORS)] - - # Get bin count (default 20) - num_bins = bins_settings.get(series_name, 20) - - # Calculate statistics with the current bin count - stats = calculate_histogram_statistics(df[col], num_bins) - series_stats[series_name] = stats - - # Keep only the colors for selected columns - color_data = { - k: v - for k, v in color_data.items() - if any(f"Histogram of {x}" == k for x in selected_cols) - } - - return color_data, series_stats - - -@app.callback( - Output("bins-settings", "data"), - [ - Input({"type": "bin-input", "index": ALL}, "value"), - Input("bins-slider", "value"), - Input("histogram-columns", "value"), - ], - [ - State({"type": "bin-input", "index": ALL}, "id"), - State("bins-settings", "data"), - State("global-bins-active", "data"), - ], -) -def update_all_bin_settings( - bin_values, slider_value, selected_cols, bin_ids, current_settings, global_active -): - """Master callback that handles ALL bin settings updates.""" - ctx = dash.callback_context - if not ctx.triggered: - return current_settings or {} - - settings = current_settings or {} - triggered_id = ctx.triggered[0]["prop_id"] - - debug_log(f"Bin settings update triggered by: {triggered_id}") - - # When columns change, initialize bin settings for new columns - if "histogram-columns.value" in triggered_id: - if selected_cols: - for col in selected_cols: - series_name = f"Histogram of {col}" - # Only set if not already set - if series_name not in settings: - settings[series_name] = slider_value - - # Clean up any series that are no longer selected - to_remove = [] - for series_name in settings: - if not any(f"Histogram of {col}" == series_name for col in selected_cols): - to_remove.append(series_name) - - for series_name in to_remove: - del settings[series_name] - - # Handle global slider update - elif "bins-slider.value" in triggered_id and global_active: - if selected_cols: - for col in selected_cols: - series_name = f"Histogram of {col}" - settings[series_name] = slider_value - debug_log(f"Updated all bins to {slider_value} from global slider") - - # Handle individual bin input updates - elif "bin-input" in triggered_id: - for i, value in enumerate(bin_values): - if value is not None: - try: - # Handle both numeric and string inputs - if isinstance(value, str): - if value.strip(): # Non-empty string - num_value = int(float(value)) - else: - continue - else: - num_value = int(value) - - if num_value >= MIN_BINS: - series_name = bin_ids[i]["index"] - settings[series_name] = num_value - debug_log(f"Updated bin for {series_name} to {num_value}") - - except (ValueError, TypeError) as e: - debug_log(f"Error converting bin value: {e}") - pass - - return settings - - -# Add this clientside callback to handle both typing and arrow clicks -app.clientside_callback( - """ - function(bins_settings) { - // This function just initializes our input handlers once - if (!window.binsInputsInitialized) { - // Wait for DOM to be ready - setTimeout(() => { - // Find all bin inputs - const binInputs = document.querySelectorAll('input[id*="bin-input"]'); - - // Add event listeners to each input - binInputs.forEach(input => { - // Extract the series name from the id attribute - const idMatch = input.id.match(/"index":"([^"]+)"/); - if (!idMatch) return; - - const seriesName = idMatch[1]; - - // Handle input events (works for both typing and arrows) - input.addEventListener('input', function(e) { - const value = parseInt(this.value); - if (!isNaN(value) && value >= 5) { - // Directly update the bins-settings store - const existingSettings = JSON.parse( - document.getElementById('bins-settings').getAttribute('data-dash-store') || '{}' - ); - - existingSettings[seriesName] = value; - - // This will trigger the Dash callback - document.getElementById('bins-settings') - .setAttribute('data-dash-store', JSON.stringify(existingSettings)); - - // Create and dispatch a custom event to notify Dash - const event = new CustomEvent('bins-settings-updated'); - document.dispatchEvent(event); - } - }); - }); - - window.binsInputsInitialized = true; - }, 500); // Wait for components to render - } - - return window.dash_clientside.no_update; - } - """, - Output("custom-legend", "data-update", allow_duplicate=True), - [Input("bins-settings", "data")], - prevent_initial_call=True, -) -# Add this to monitor and update from the clientside -app.clientside_callback( - """ - function(n_intervals) { - const customEvent = new CustomEvent('_dash-update-component', { - detail: { - output: 'bins-settings.data', - changedPropIds: ['custom-event.n_events'] - } - }); - - document.addEventListener('bins-settings-updated', function() { - // Get current settings from the store - const storeEl = document.getElementById('bins-settings'); - const data = JSON.parse(storeEl.getAttribute('data-dash-store') || '{}'); - - // Trigger update in Dash - window.dash_clientside._callback.setProps({ - 'bins-settings.data': data - }); - }); - - return window.dash_clientside.no_update; - } - """, - Output("custom-legend", "data-update", allow_duplicate=True), - [Input("interval-component", "n_intervals")], - prevent_initial_call=True, -) -# Run the app -if __name__ == "__main__": - app.run_server(debug=True, port=8051) diff --git a/plotly_examples/sensitivity_1.py b/plotly_examples/sensitivity_1.py deleted file mode 100644 index 63e6815..0000000 --- a/plotly_examples/sensitivity_1.py +++ /dev/null @@ -1,850 +0,0 @@ -"""Interactive multi-column scatter plot interface with statistics.""" - -import dash -from dash import dcc, html -from dash.dependencies import Input, Output, State, ALL -import plotly.graph_objects as go -import pandas as pd -import numpy as np -from typing import Any - -# # Debug Configuration -# PYTHON_DEBUG = True # Controls Python-side logging -# JS_DEBUG_CONFIG = True # This will be passed to JavaScript - -# def debug_log(message: str) -> None: -# """Python-side debug logging.""" -# if PYTHON_DEBUG: -# print(f"[PY] {message}") -# Debug Configuration -PYTHON_DEBUG = True - - -def debug_log(message: str) -> None: - """Python-side debug logging.""" - if PYTHON_DEBUG: - print(f"[PY] {message}") - - -def calculate_statistics(series1: pd.Series, series2: pd.Series) -> dict[str, Any]: - """Calculate statistics for a pair of series.""" - try: - # Drop NA values that exist in either series - mask = ~(series1.isna() | series2.isna()) - clean_s1 = series1[mask] - clean_s2 = series2[mask] - - if len(clean_s1) < 2: # Need at least 2 points for most statistics - return { - "correlation": "N/A", - "samples": len(clean_s1), - "missing": len(series1) - len(clean_s1), - "x_mean": f"{clean_s1.mean():.3f}" if len(clean_s1) > 0 else "N/A", - "y_mean": f"{clean_s2.mean():.3f}" if len(clean_s2) > 0 else "N/A", - } - - stats_dict = { - "correlation": f"{clean_s1.corr(clean_s2):.3f}", - "samples": len(clean_s1), - "missing": len(series1) - len(clean_s1), - "x_mean": f"{clean_s1.mean():.3f}", - "y_mean": f"{clean_s2.mean():.3f}", - "x_std": f"{clean_s1.std():.3f}", - "y_std": f"{clean_s2.std():.3f}", - } - - # Calculate linear regression - slope, intercept = np.polyfit(clean_s1, clean_s2, 1) - stats_dict["regression"] = f"y = {slope:.3f}x + {intercept:.3f}" - - return stats_dict - except Exception as e: - if PYTHON_DEBUG: - print(f"[PY] Error calculating statistics: {e}") - return {"error": str(e)} - - -# Create sample data -df1 = pd.DataFrame( - { - "A": np.random.randn(100), - "B": np.random.randn(100), - "C": np.random.randn(100), - "D": np.random.randn(100), - "Category": np.random.choice(["X", "Y", "Z"], 100), - } -) - -df2 = pd.DataFrame( - { - "W": np.random.randn(100), - "X": np.random.randn(100), - "Y": np.random.randn(100), - "Z": np.random.randn(100), - "Group": np.random.choice(["P", "Q", "R"], 100), - } -) - -# # Initialize the Dash app -# app = dash.Dash(__name__) -# Initialize the Dash app -app = dash.Dash(__name__, suppress_callback_exceptions=True) - -# Styles with debug-friendly positioning -dropdown_style = { - "width": "100%", - "marginBottom": "20px", - "zIndex": 999, -} - -info_panel_style = { - "position": "fixed", # Fixed positioning for hover panel - "backgroundColor": "white", - "padding": "15px", - "borderRadius": "5px", - "boxShadow": "0 0 10px rgba(0,0,0,0.3)", - "zIndex": 1000, - "display": "none", - "minWidth": "200px", - "maxWidth": "300px", - "fontSize": "12px", -} - -color_picker_style = { - "position": "fixed", - "top": "50%", - "left": "50%", - "transform": "translate(-50%, -50%)", - "backgroundColor": "white", - "padding": "20px", - "borderRadius": "5px", - "boxShadow": "0 0 10px rgba(0,0,0,0.3)", - "zIndex": 1001, - "display": "none", -} - -# Available colors -COLORS = [ - "#1f77b4", - "#ff7f0e", - "#2ca02c", - "#d62728", - "#9467bd", - "#8c564b", - "#e377c2", - "#7f7f7f", - "#bcbd22", - "#17becf", - "#aec7e8", - "#ffbb78", - "#98df8a", - "#ff9896", - "#c5b0d5", - "#c49c94", - "#f7b6d2", - "#c7c7c7", - "#dbdb8d", - "#9edae5", -] - -# JavaScript code for hover handling -hover_script = """ -const JS_DEBUG = true; // JavaScript debug flag - -function debugLog(message) { - if (JS_DEBUG) { - console.log(`[JS] ${message}`); - } -} - -function initializeHoverHandlers() { - debugLog('Initializing hover handlers'); - const legendItems = document.getElementsByClassName('legend-item'); - const infoPanel = document.getElementById('series-info-panel'); - - function positionPanel(event, item) { - const rect = item.getBoundingClientRect(); - const panel = infoPanel.getBoundingClientRect(); - const viewport = { - width: window.innerWidth, - height: window.innerHeight - }; - - // Try positioning to the right first - let left = rect.right + 10; - if (left + panel.width > viewport.width) { - // Fall back to left side if not enough space - left = rect.left - panel.width - 10; - } - - let top = rect.top; - if (top + panel.height > viewport.height) { - top = viewport.height - panel.height - 10; - } - - debugLog(`Positioning panel at (${left}, ${top})`); - - infoPanel.style.left = `${left}px`; - infoPanel.style.top = `${top}px`; - } - - function handleMouseEnter(event) { - const item = event.currentTarget; - debugLog(`Mouse enter: ${item.getAttribute('data-series')}`); - - // Show panel and position it - infoPanel.style.display = 'block'; - positionPanel(event, item); - } - - function handleMouseLeave(event) { - debugLog('Mouse leave'); - infoPanel.style.display = 'none'; - } - - // Attach handlers to all legend items - Array.from(legendItems).forEach(item => { - item.addEventListener('mouseenter', handleMouseEnter); - item.addEventListener('mouseleave', handleMouseLeave); - }); - - debugLog(`Attached handlers to ${legendItems.length} items`); -} - -// Initialize handlers when content updates -if (window.dash_clientside) { - window.dash_clientside.clientside = { - initializeHover: function(children) { - if (!children) return window.dash_clientside.no_update; - - // Use setTimeout to ensure DOM is ready - setTimeout(initializeHoverHandlers, 100); - return window.dash_clientside.no_update; - } - }; -} -""" - -# Define the layout -app.layout = html.Div( - [ - html.H1("DataFrame Column Comparison"), - # Add the hover handling script - html.Script(hover_script), - # Control panel for column selection - html.Div( - [ - html.Div( - [ - html.H3("Select X-axis Columns (DataFrame 1)"), - dcc.Dropdown( - id="df1-columns", - options=[ - {"label": col, "value": col} - for col in df1.columns - if col != "Category" - ], - value=[], - multi=True, - style=dropdown_style, - ), - ], - style={"width": "45%", "display": "inline-block", "margin": "5px"}, - ), - html.Div( - [ - html.H3("Select Y-axis Columns (DataFrame 2)"), - dcc.Dropdown( - id="df2-columns", - options=[ - {"label": col, "value": col} - for col in df2.columns - if col != "Group" - ], - value=[], - multi=True, - style=dropdown_style, - ), - ], - style={"width": "45%", "display": "inline-block", "margin": "5px"}, - ), - ] - ), - # Plot settings - # html.Div([ - # html.H3("Plot Settings", style={'marginBottom': '5px'}), - # dcc.Checklist( - # id='plot-settings', - # options=[ - # {'label': ' Show trend lines', 'value': 'trend'}, - # {'label': ' Show point labels', 'value': 'labels'} - # ], - # value=['trend'], - # style={'display': 'flex', 'gap': '20px'}, - # inputStyle={'marginRight': '5px'}, - # labelStyle={'display': 'flex', 'alignItems': 'center'} - # ) - # ], style={'margin': '10px'}), - # Graph and custom legend - html.Div( - [ - # Plot settings - html.Div( - [ - html.H3("Plot Settings", style={"marginBottom": "5px"}), - dcc.Checklist( - id="plot-settings", - options=[ - {"label": " Show trend lines", "value": "trend"}, - {"label": " Show point labels", "value": "labels"}, - ], - value=["trend"], - style={ - "display": "block", - "gap": "10px", - }, # Changed from 'flex' to 'block' - inputStyle={"marginRight": "5px"}, - labelStyle={ - "display": "block", - "marginBottom": "10px", - }, # Added marginBottom and changed to block - ), - ], - style={ - "width": "10%", - "display": "inline-block", - "verticalAlign": "top", - }, - ), - # Main plot - html.Div( - [ - dcc.Graph(id="scatter-plot", style={"height": "700px"}), - ], - style={"width": "70%", "display": "inline-block"}, - ), - # Custom legend - html.Div( - [ - html.H3("Series"), - html.Div( - id="custom-legend", - style={"overflowY": "auto", "maxHeight": "600px"}, - ), - ], - style={ - "width": "20%", - "display": "inline-block", - "verticalAlign": "top", - }, - ), - ] - ), - # Info panel (hidden by default) - html.Div(id="series-info-panel", style=info_panel_style), - # Color picker panel (hidden by default) - html.Div( - [ - html.H3("Select Color"), - html.Div( - [ - html.Button( - style={ - "backgroundColor": color, - "width": "30px", - "height": "30px", - "margin": "5px", - }, - id={"type": "color-option", "index": i}, - ) - for i, color in enumerate(COLORS) - ], - style={ - "display": "grid", - "gridTemplateColumns": "repeat(5, 1fr)", - "gap": "5px", - }, - ), - html.Button( - "Close", id="close-color-picker", style={"marginTop": "10px"} - ), - ], - id="color-picker-panel", - style=color_picker_style, - ), - # Store components - dcc.Store(id="series-colors"), - dcc.Store(id="series-data"), - dcc.Store(id="active-series"), - ] -) - -app.clientside_callback( - """ - function(children) { - if (!children) return window.dash_clientside.no_update; - - const JS_DEBUG = true; - - function debugLog(message) { - if (JS_DEBUG) console.log('[JS]', message); - } - - function setupHoverHandlers() { - debugLog('Setting up hover handlers'); - const items = document.getElementsByClassName('legend-item'); - const panel = document.getElementById('series-info-panel'); - - Array.from(items).forEach(item => { - item.onmouseenter = (e) => { - const series = item.getAttribute('data-series'); - debugLog(`Hover enter: ${series}`); - - // Position the panel - const rect = item.getBoundingClientRect(); - let left = rect.right + 10; - - // Check if panel would go off-screen - if (left + 300 > window.innerWidth) { // 300px is max panel width - left = rect.left - 310; // Position to left with 10px margin - } - - panel.style.left = left + 'px'; - panel.style.top = rect.top + 'px'; - panel.style.display = 'block'; - }; - - item.onmouseleave = (e) => { - debugLog('Hover leave'); - panel.style.display = 'none'; - }; - }); - } - - // Allow time for DOM to update - setTimeout(setupHoverHandlers, 100); - return window.dash_clientside.no_update; - } - """, - Output("custom-legend", "data-update"), - Input("custom-legend", "children"), -) - - -@app.callback( - [ - Output("custom-legend", "children"), - Output("series-colors", "data"), - Output("series-data", "data"), - ], - [Input("df1-columns", "value"), Input("df2-columns", "value")], - [State("series-colors", "data")], -) -def update_legend(df1_cols, df2_cols, existing_colors): - """Update custom legend with interactive elements.""" - debug_log("Updating legend") - legend_items = [] - color_data = existing_colors or {} - series_stats = {} - - if not df1_cols or not df2_cols: - debug_log("No columns selected") - return [], color_data, {} - - for x_col in df1_cols: - for y_col in df2_cols: - series_name = f"{x_col} vs {y_col}" - debug_log(f"Processing series: {series_name}") - - if series_name not in color_data: - color_data[series_name] = COLORS[len(color_data) % len(COLORS)] - - color = color_data[series_name] - stats = calculate_statistics(df1[x_col], df2[y_col]) - series_stats[series_name] = stats - - legend_items.append( - html.Div( - [ - html.Button( - style={ - "backgroundColor": color, - "width": "20px", - "height": "20px", - "marginRight": "10px", - "verticalAlign": "middle", - "cursor": "pointer", - "border": "1px solid #ddd", - }, - id={"type": "color-button", "index": series_name}, - ), - html.Span( - series_name, - style={ - "cursor": "default", - "userSelect": "none", - "display": "inline-block", - }, - ), - ], - className="legend-item", - id={"type": "legend-item", "index": series_name}, - **{ - "data-series": series_name, - "style": { - "margin": "10px", - "padding": "5px", - "borderRadius": "3px", - }, - }, - ) - ) - - color_data = { - k: v - for k, v in color_data.items() - if any(x in k for x in df1_cols) and any(y in k for y in df2_cols) - } - - debug_log(f"Created {len(legend_items)} legend items") - return legend_items, color_data, series_stats - - -@app.callback( - [Output("series-info-panel", "children"), Output("series-info-panel", "style")], - [ - Input({"type": "legend-item", "index": ALL}, "mouseenter"), - Input({"type": "legend-item", "index": ALL}, "mouseleave"), - ], - [State("series-data", "data"), State("series-info-panel", "style")], -) -def update_info_panel(mouseenter, mouseleave, series_data, current_style): - """Update the info panel content and visibility.""" - if not dash.callback_context.triggered: - return dash.no_update, dash.no_update - - trigger = dash.callback_context.triggered[0] - - # Handle mouseleave - if ".mouseleave" in trigger["prop_id"]: - return dash.no_update, dict(current_style, display="none") - - # Handle mouseenter - if ".mouseenter" in trigger["prop_id"]: - try: - series_name = eval(trigger["prop_id"].split(".")[0])["index"] - stats = series_data.get(series_name, {}) - - content = html.Div( - [ - html.H4( - f"Statistics: {series_name}", - style={"marginTop": 0, "marginBottom": "10px"}, - ), - html.Table( - [ - html.Tr( - [ - html.Td( - k, - style={"padding": "3px", "fontWeight": "bold"}, - ), - html.Td(str(v), style={"padding": "3px"}), - ] - ) - for k, v in stats.items() - ], - style={"borderCollapse": "collapse"}, - ), - ] - ) - - return content, dict(current_style, display="block") - - except Exception as e: - debug_log(f"Error updating info panel: {e}") - return dash.no_update, dash.no_update - - return dash.no_update, dash.no_update - - -# @app.callback( -# [Output('color-picker-panel', 'style'), -# Output('active-series', 'data')], -# [Input({'type': 'color-button', 'index': ALL}, 'n_clicks'), -# Input('close-color-picker', 'n_clicks')], -# [State({'type': 'color-button', 'index': ALL}, 'id')] -# ) -# def toggle_color_picker(button_clicks, close_clicks, button_ids): -# """Show/hide color picker when clicking color buttons.""" -# ctx = dash.callback_context -# if not ctx.triggered: -# return dict(color_picker_style, display='none'), None - -# triggered_id = ctx.triggered[0]['prop_id'] - -# if 'close-color-picker' in triggered_id or not any(button_clicks): -# debug_log("Closing color picker") -# return dict(color_picker_style, display='none'), None - -# if 'color-button' in triggered_id: -# button_idx = next(i for i, c in enumerate(button_clicks) if c) -# series_name = button_ids[button_idx]['index'] -# debug_log(f"Opening color picker for {series_name}") -# return dict(color_picker_style, display='block'), series_name - -# return dict(color_picker_style, display='none'), None - -# @app.callback( -# Output('series-colors', 'data', allow_duplicate=True), -# [Input({'type': 'color-option', 'index': ALL}, 'n_clicks')], -# [State({'type': 'color-option', 'index': ALL}, 'id'), -# State('active-series', 'data'), -# State('series-colors', 'data')], -# prevent_initial_call=True -# ) -# def update_series_color(color_clicks, color_ids, active_series, current_colors): -# """Update color when selecting from color picker.""" -# ctx = dash.callback_context -# if not ctx.triggered or not active_series or not current_colors: -# return dash.no_update - -# triggered_id = ctx.triggered[0]['prop_id'] -# if 'color-option' not in triggered_id: -# return dash.no_update - -# color_idx = eval(triggered_id.split('.')[0])['index'] -# current_colors[active_series] = COLORS[color_idx] -# debug_log(f"Updated color for {active_series}") -# return current_colors - - -@app.callback( - Output("scatter-plot", "figure"), - [ - Input("df1-columns", "value"), - Input("df2-columns", "value"), - Input("plot-settings", "value"), - Input("series-colors", "data"), - ], -) -def update_graph(df1_cols, df2_cols, settings, color_data): - """Update the scatter plot based on selected columns.""" - debug_log("Updating scatter plot") - fig = go.Figure() - - if not df1_cols or not df2_cols or not color_data: - fig.add_annotation( - text="Please select columns from both dataframes", - xref="paper", - yref="paper", - x=0.5, - y=0.5, - showarrow=False, - ) - return fig - - for x_col in df1_cols: - for y_col in df2_cols: - series_name = f"{x_col} vs {y_col}" - color = color_data.get(series_name) - if not color: # Skip if no color assigned - continue - - # Add scatter plot - fig.add_trace( - go.Scatter( - x=df1[x_col], - y=df2[y_col], - mode="markers", - name=series_name, - marker=dict(color=color, size=8, opacity=0.6), - showlegend=False, # Using custom legend instead - ) - ) - - # Add trend line if requested - if "trend" in settings: - mask = ~(df1[x_col].isna() | df2[y_col].isna()) - x = df1[x_col][mask] - y = df2[y_col][mask] - if len(x) > 1: - z = np.polyfit(x, y, 1) - p = np.poly1d(z) - - fig.add_trace( - go.Scatter( - x=[x.min(), x.max()], - y=[p(x.min()), p(x.max())], - mode="lines", - name=f"Trend: {series_name}", - line=dict(color=color, dash="dash"), - showlegend=False, - ) - ) - - fig.update_layout( - title="Column Comparisons", - plot_bgcolor="white", - paper_bgcolor="white", - showlegend=False, # Using custom legend - xaxis=dict( - title="DataFrame 1 Columns", - showgrid=True, - gridwidth=1, - gridcolor="LightGray", - zeroline=True, - zerolinewidth=2, - zerolinecolor="LightGray", - ), - yaxis=dict( - title="DataFrame 2 Columns", - showgrid=True, - gridwidth=1, - gridcolor="LightGray", - zeroline=True, - zerolinewidth=2, - zerolinecolor="LightGray", - ), - height=700, - margin=dict(t=50, b=50, l=50, r=50), - ) - - debug_log("Scatter plot updated") - return fig - - -"""""" - - -@app.callback( - Output("series-colors", "data", allow_duplicate=True), - [Input({"type": "color-option", "index": ALL}, "n_clicks")], - [ - State({"type": "color-option", "index": ALL}, "id"), - State("active-series", "data"), - State("series-colors", "data"), - ], - prevent_initial_call=True, -) -def update_series_color(color_clicks, color_ids, active_series, current_colors): - """Update color when selecting from color picker.""" - ctx = dash.callback_context - if not ctx.triggered or not active_series or not current_colors: - return dash.no_update - - triggered_id = ctx.triggered[0]["prop_id"] - if "color-option" not in triggered_id: - return dash.no_update - - color_idx = eval(triggered_id.split(".")[0])["index"] - current_colors[active_series] = COLORS[color_idx] - debug_log(f"Updated color for {active_series} to {COLORS[color_idx]}") - return current_colors - - -@app.callback( - [Output("color-picker-panel", "style"), Output("active-series", "data")], - [ - Input({"type": "color-button", "index": ALL}, "n_clicks"), - Input("close-color-picker", "n_clicks"), - Input({"type": "color-option", "index": ALL}, "n_clicks"), - ], # Add this input - [ - State({"type": "color-button", "index": ALL}, "id"), - State("color-picker-panel", "style"), - ], -) -def toggle_color_picker( - button_clicks, close_clicks, color_option_clicks, button_ids, current_style -): - """Show/hide color picker when clicking color buttons.""" - ctx = dash.callback_context - if not ctx.triggered: - return dict(current_style, display="none"), None - - triggered_id = ctx.triggered[0]["prop_id"] - - # Close on color selection or close button click - if "color-option" in triggered_id or "close-color-picker" in triggered_id: - debug_log("Closing color picker after selection") - return dict(current_style, display="none"), None - - # Open on color button click - if "color-button" in triggered_id and any(button_clicks): - button_idx = next(i for i, c in enumerate(button_clicks) if c) - series_name = button_ids[button_idx]["index"] - debug_log(f"Opening color picker for {series_name}") - return dict(current_style, display="block"), series_name - - return dict(current_style, display="none"), None - - -# Update the legend immediately when colors change -@app.callback( - Output("custom-legend", "children", allow_duplicate=True), - [Input("series-colors", "data")], - [ - State("df1-columns", "value"), - State("df2-columns", "value"), - State("series-data", "data"), - ], - prevent_initial_call=True, -) -def update_legend_colors(color_data, df1_cols, df2_cols, series_data): - """Update legend when colors change.""" - if not color_data or not df1_cols or not df2_cols: - return dash.no_update - - debug_log("Updating legend colors") - legend_items = [] - - for x_col in df1_cols: - for y_col in df2_cols: - series_name = f"{x_col} vs {y_col}" - color = color_data.get(series_name) - if not color: - continue - - legend_items.append( - html.Div( - [ - html.Button( - style={ - "backgroundColor": color, - "width": "20px", - "height": "20px", - "marginRight": "10px", - "verticalAlign": "middle", - "cursor": "pointer", - "border": "1px solid #ddd", - }, - id={"type": "color-button", "index": series_name}, - ), - html.Span( - series_name, - style={ - "cursor": "default", - "userSelect": "none", - "display": "inline-block", - }, - ), - ], - className="legend-item", - id={"type": "legend-item", "index": series_name}, - **{ - "data-series": series_name, - "style": { - "margin": "10px", - "padding": "5px", - "borderRadius": "3px", - }, - }, - ) - ) - - return legend_items - - -if __name__ == "__main__": - app.run_server(debug=True, port=8050) diff --git a/prepare_release.sh b/prepare_release.sh deleted file mode 100755 index 4f9e4c3..0000000 --- a/prepare_release.sh +++ /dev/null @@ -1,212 +0,0 @@ -#!/bin/bash -echo "Script started" -set -e # Exit immediately if a command exits with a non-zero status -echo "Version argument: $1" - -# Check if a version argument was provided -if [ $# -ne 1 ]; then - echo "Usage: $0 " - echo "Example: $0 v0.1.1" - exit 1 -fi - -VERSION=$1 -VERSION_NO_V="${VERSION#v}" # Remove the 'v' prefix for use in pyproject.toml - -# Validate version format -if ! [[ $VERSION =~ ^v[0-9]+\.[0-9]+\.[0-9]+$ ]]; then - echo "Error: Version must be in format vX.Y.Z (e.g., v0.1.1)" - exit 1 -fi - -# Ensure we're on the dev branch -CURRENT_BRANCH=$(git rev-parse --abbrev-ref HEAD) -if [ "$CURRENT_BRANCH" != "dev" ]; then - echo "Error: You must be on the dev branch to publish a new version" - exit 1 -fi - -# Make sure test output files are in .gitignore -if ! grep -q "discriminator_tests.json" .gitignore 2>/dev/null; then - echo "Adding test output files to .gitignore..." - cat >> .gitignore << EOF -# Test output files -discriminator_tests.json -discriminator_tests.log -.coverage -coverage.xml -EOF - git add .gitignore - git commit -m "Add test output files to .gitignore" - echo "Updated .gitignore and committed changes." -fi - -# Clean up any test output files that might interfere with branch switching -echo "Cleaning up test output files..." -rm -f discriminator_tests.json discriminator_tests.log .coverage coverage.xml - -# Make sure the working directory is clean (except for ignored files) -if ! git diff-index --quiet HEAD --; then - echo "Error: Working directory has uncommitted changes" - exit 1 -fi - -# Pull the latest changes from the dev branch -echo "Pulling latest changes from dev branch..." -git pull origin dev - -# Run unit tests before proceeding -echo "Running unit tests..." -echo "Installing package with development dependencies..." -pip install -e ".[dev]" # Install the package with dev dependencies - -if ! python -m pytest; then - echo "Error: Tests failed. Please fix the failing tests before publishing." - exit 1 -fi -echo "All tests passed successfully!" - -# Clean up test output files again -echo "Cleaning up test output files..." -rm -f discriminator_tests.json discriminator_tests.log .coverage coverage.xml - -# Update version in pyproject.toml -echo "Updating version in pyproject.toml to $VERSION_NO_V..." -sed -i.bak "s/^version = \".*\"/version = \"$VERSION_NO_V\"/" pyproject.toml -rm pyproject.toml.bak # Remove backup file - -# Update version in __init__.py (accounting for src layout) -INIT_PATH="src/pydantic_discriminated/__init__.py" -if [ -f "$INIT_PATH" ]; then - if grep -q "__version__" "$INIT_PATH"; then - # Update existing version - echo "Updating version in $INIT_PATH..." - sed -i.bak "s/__version__ = \".*\"/__version__ = \"$VERSION_NO_V\"/" "$INIT_PATH" - else - # Add version if it doesn't exist - echo "Adding version to $INIT_PATH..." - echo "" >> "$INIT_PATH" # Add a newline - echo "__version__ = \"$VERSION_NO_V\"" >> "$INIT_PATH" - fi - rm -f "${INIT_PATH}.bak" # Remove backup file if it exists -else - echo "Warning: $INIT_PATH not found, skipping version update in __init__.py" -fi - -# Update documentation URL in pyproject.toml to point to the docs site -echo "Updating documentation URL in pyproject.toml..." -sed -i.bak 's|"Documentation" = ".*"|"Documentation" = "https://talbotknighton.github.io/trendify/"|' pyproject.toml -rm pyproject.toml.bak # Remove backup file - -# Check if email in pyproject.toml contains .org and fix it if needed -if grep -q "talbotknighton@gmail.org" pyproject.toml; then - echo "Fixing email address in pyproject.toml..." - sed -i.bak 's/talbotknighton@gmail.org/talbotknighton@gmail.com/g' pyproject.toml - rm pyproject.toml.bak # Remove backup file -fi - -# Update README.md if it contains badge.fury.io badges -if [ -f "README.md" ] && grep -q "badge.fury.io" README.md; then - echo "Updating badges in README.md..." - sed -i.bak 's|https://badge.fury.io/py/trendify.svg|https://img.shields.io/pypi/v/trendify.svg|g' README.md - sed -i.bak 's|https://badge.fury.io/py/trendify|https://pypi.org/project/trendify/|g' README.md - rm README.md.bak # Remove backup file -fi - -# Make sure to add README.md to the commit -git add README.md - -# Update docs/index.md badges if needed -if [ -f "docs/index.md" ]; then - echo "Checking badges in docs/index.md..." - if grep -q "badge.fury.io" docs/index.md; then - echo "Updating PyPI badge in docs/index.md..." - sed -i.bak 's|https://badge.fury.io/py/trendify.svg|https://img.shields.io/pypi/v/trendify.svg|g' docs/index.md - sed -i.bak 's|https://badge.fury.io/py/trendify|https://pypi.org/project/trendify/|g' docs/index.md - rm docs/index.md.bak # Remove backup file - fi - - # Update documentation badge to be more useful (not link to itself) - if grep -q "docs-mkdocs-blue" docs/index.md; then - echo "Updating documentation badge in docs/index.md..." - sed -i.bak 's|[![Documentation](https://img.shields.io/badge/docs-mkdocs-blue.svg)](https://talbotknighton.github.io/trendify/)|[![API Reference](https://img.shields.io/badge/api-reference-blue.svg)](https://talbotknighton.github.io/trendify/api-reference/)|g' docs/index.md - rm docs/index.md.bak # Remove backup file - fi -fi - -# Update changelog if it exists -if [ -f "CHANGELOG.md" ]; then - echo "Adding new version entry to CHANGELOG.md..." - DATE=$(date +%Y-%m-%d) - sed -i.bak "s/^# Changelog/# Changelog\n\n## $VERSION ($DATE)\n\n- TODO: Add release notes\n/" CHANGELOG.md - rm CHANGELOG.md.bak # Remove backup file - - # Open the changelog for editing - echo "Opening CHANGELOG.md for editing. Please add release notes and save..." - ${EDITOR:-vi} CHANGELOG.md -fi - -# Check for consistency in version numbers -echo "Checking for version consistency..." -PYPROJECT_VERSION=$(grep "^version = " pyproject.toml | sed 's/version = "\(.*\)"/\1/') -INIT_VERSION=$(grep "__version__" "$INIT_PATH" 2>/dev/null | sed 's/__version__ = "\(.*\)"/\1/') - -if [ "$PYPROJECT_VERSION" != "$VERSION_NO_V" ]; then - echo "Error: Version in pyproject.toml ($PYPROJECT_VERSION) doesn't match requested version ($VERSION_NO_V)" - exit 1 -fi - -if [ -n "$INIT_VERSION" ] && [ "$INIT_VERSION" != "$VERSION_NO_V" ]; then - echo "Error: Version in $INIT_PATH ($INIT_VERSION) doesn't match requested version ($VERSION_NO_V)" - exit 1 -fi - -echo "Version consistency check passed." - -# Commit the version changes -echo "Committing version changes..." -git add pyproject.toml README.md docs/index.md -if [ -f "$INIT_PATH" ]; then - git add "$INIT_PATH" -fi -if [ -f "CHANGELOG.md" ]; then - git add CHANGELOG.md -fi -git commit -m "Bump version to $VERSION" - -# Push changes to dev branch -echo "Pushing changes to dev branch..." -git push origin dev - -# Build and deploy documentation locally -echo "Building and deploying documentation version $VERSION_NO_V locally..." -mike deploy "$VERSION_NO_V" latest --update-aliases -mike set-default latest - -echo "Documentation built and deployed locally." -echo "To view the documentation locally, run: mike serve" - -# Create pull request from dev to main -echo "Creating a pull request from dev to main..." - -# Try to create PR with GitHub CLI if available -if command -v gh &> /dev/null; then - echo "GitHub CLI found. Attempting to create PR automatically..." - PR_URL=$(gh pr create --title "Release $VERSION" \ - --body "This PR prepares the release of version $VERSION. Please review the changes carefully." \ - --base main \ - --head dev) - - if [ $? -eq 0 ]; then - echo "Pull request created successfully!" - echo "PR URL: $PR_URL" - echo "After the PR is reviewed and merged, run publish_release.sh $VERSION to complete the release process." - else - echo "Failed to create PR automatically. Please create it manually:" - echo "https://github.com/TalbotKnighton/trendify/compare/main...dev" - fi -else - echo "GitHub CLI not found. Please create the PR manually:" - echo "https://github.com/TalbotKnighton/trendify/compare/main...dev" - echo "After the PR is reviewed and merged, run publish_release.sh $VERSION to complete the release process." -fi \ No newline at end of file diff --git a/publish_release.sh b/publish_release.sh deleted file mode 100755 index 8f7e375..0000000 --- a/publish_release.sh +++ /dev/null @@ -1,68 +0,0 @@ -#!/bin/bash -echo "Publish script started" -set -e # Exit immediately if a command exits with a non-zero status -echo "Version argument: $1" - -# Check if a version argument was provided -if [ $# -ne 1 ]; then - echo "Usage: $0 " - echo "Example: $0 v0.1.1" - exit 1 -fi - -VERSION=$1 -VERSION_NO_V="${VERSION#v}" # Remove the 'v' prefix for use in pyproject.toml - -# Validate version format -if ! [[ $VERSION =~ ^v[0-9]+\.[0-9]+\.[0-9]+$ ]]; then - echo "Error: Version must be in format vX.Y.Z (e.g., v0.1.1)" - exit 1 -fi - -echo "This script will complete the release process for version $VERSION" -echo "Make sure the PR has been reviewed and merged to main before proceeding." -read -p "Has the PR been merged to main? (y/n) " -n 1 -r -echo -if [[ ! $REPLY =~ ^[Yy]$ ]]; then - echo "Please complete the PR process first, then run this script again." - exit 1 -fi - -# Clean up test output files to ensure branch switching works -echo "Cleaning up test output files..." -rm -f discriminator_tests.json discriminator_tests.log .coverage coverage.xml - -# Switch to main and pull latest changes -echo "Switching to main branch and pulling latest changes..." -git checkout main -git pull origin main - -# Create and push the tag -echo "Creating and pushing tag $VERSION..." -git tag -a $VERSION -m "Release $VERSION" -git push origin $VERSION - -# Wait for the package to be published to PyPI -echo "Waiting for package to be published to PyPI..." -echo "This may take a few minutes. The GitHub Actions workflow should be building and publishing your package." -echo "You can check the progress here: https://github.com/TalbotKnighton/trendify/actions" - -read -p "Has the package been published to PyPI? (y/n) " -n 1 -r -echo -if [[ $REPLY =~ ^[Yy]$ ]]; then - # Push documentation to GitHub Pages - echo "Pushing documentation to GitHub Pages..." - mike deploy "$VERSION_NO_V" latest --update-aliases --push - mike set-default latest --push -else - echo "Skipping documentation push. You can do this manually later with:" - echo "mike deploy \"$VERSION_NO_V\" --alias latest --update-aliases --push" - echo "mike set-default latest --push" -fi - -# Switch back to dev branch -echo "Switching back to dev branch..." -git checkout dev - -echo "Release process completed successfully!" -echo "Documentation has been deployed to GitHub Pages." \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 64f50b8..c36540d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,7 +7,7 @@ name = "trendify" dynamic = ["version"] description = "Tools for generating data products, storing, and interacting with them" readme = "README.md" -requires-python = ">=3.11" +requires-python = ">=3.12" dependencies = [ "matplotlib>=3.11.0", "numpy>=2.4.6", @@ -29,7 +29,6 @@ classifiers = [ "Operating System :: OS Independent", "Natural Language :: English", "License :: OSI Approved :: MIT License", - "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", "Programming Language :: Python :: 3.13", "Programming Language :: Python :: 3.14", @@ -103,10 +102,7 @@ ignore = ["typings"] exclude_also = ["if __name__ == .__main__.:", "@(abc\\.)?abstractmethod"] [dependency-groups] -dev = [ - "mkinit>=1.2.0", - "pytest>=9.1.1", -] +dev = ["mkinit>=1.2.0", "pytest>=9.1.1"] [project.urls] "Homepage" = "https://github.com/talbotknighton/trendify" @@ -117,4 +113,4 @@ dev = [ [project.scripts] trendify_make_sample_data = "trendify.examples:make_sample_data" -trendify = "trendify.CLI:trendify" +trendify = "trendify.cli:app" diff --git a/requirements.txt b/requirements.txt index 0d99763..7dad0b6 100644 --- a/requirements.txt +++ b/requirements.txt @@ -19,49 +19,35 @@ contourpy==1.3.3 \ --hash=sha256:023b44101dfe49d7d53932be418477dba359649246075c996866106da069af69 \ --hash=sha256:07ce5ed73ecdc4a03ffe3e1b3e3c1166db35ae7584be76f65dbbe28a7791b0cc \ --hash=sha256:083e12155b210502d0bca491432bb04d56dc3432f95a979b429f2848c3dbe880 \ - --hash=sha256:0bf67e0e3f482cb69779dd3061b534eb35ac9b17f163d851e2a547d56dba0a3a \ - --hash=sha256:0c1fc238306b35f246d61a1d416a627348b5cf0648648a031e14bb8705fcdfe8 \ --hash=sha256:13b68d6a62db8eafaebb8039218921399baf6e47bf85006fd8529f2a08ef33fc \ - --hash=sha256:15ff10bfada4bf92ec8b31c62bf7c1834c244019b4a33095a68000d7075df470 \ --hash=sha256:177fb367556747a686509d6fef71d221a4b198a3905fe824430e5ea0fda54eb5 \ --hash=sha256:1cadd8b8969f060ba45ed7c1b714fe69185812ab43bd6b86a9123fe8f99c3263 \ --hash=sha256:1fd43c3be4c8e5fd6e4f2baeae35ae18176cf2e5cced681cca908addf1cdd53b \ --hash=sha256:22e9b1bd7a9b1d652cd77388465dc358dafcd2e217d35552424aa4f996f524f5 \ - --hash=sha256:23416f38bfd74d5d28ab8429cc4d63fa67d5068bd711a85edb1c3fb0c3e2f381 \ --hash=sha256:283edd842a01e3dcd435b1c5116798d661378d83d36d337b8dde1d16a5fc9ba3 \ --hash=sha256:2a2a8b627d5cc6b7c41a4beff6c5ad5eb848c88255fda4a8745f7e901b32d8e4 \ --hash=sha256:2b7e9480ffe2b0cd2e787e4df64270e3a0440d9db8dc823312e2c940c167df7e \ - --hash=sha256:322ab1c99b008dad206d406bb61d014cf0174df491ae9d9d0fac6a6fda4f977f \ --hash=sha256:33c82d0138c0a062380332c861387650c82e4cf1747aaa6938b9b6516762e772 \ --hash=sha256:348ac1f5d4f1d66d3322420f01d42e43122f43616e0f194fc1c9f5d830c5b286 \ - --hash=sha256:3519428f6be58431c56581f1694ba8e50626f2dd550af225f82fb5f5814d2a42 \ --hash=sha256:3c30273eb2a55024ff31ba7d052dde990d7d8e5450f4bbb6e913558b3d6c2301 \ - --hash=sha256:3d1a3799d62d45c18bafd41c5fa05120b96a28079f2393af559b843d1a966a77 \ --hash=sha256:451e71b5a7d597379ef572de31eeb909a87246974d960049a9848c3bc6c41bf7 \ --hash=sha256:459c1f020cd59fcfe6650180678a9993932d80d44ccde1fa1868977438f0b411 \ --hash=sha256:4d00e655fcef08aba35ec9610536bfe90267d7ab5ba944f7032549c55a146da1 \ --hash=sha256:4debd64f124ca62069f313a9cb86656ff087786016d76927ae2cf37846b006c9 \ --hash=sha256:4feffb6537d64b84877da813a5c30f1422ea5739566abf0bd18065ac040e120a \ --hash=sha256:50ed930df7289ff2a8d7afeb9603f8289e5704755c7e5c3bbd929c90c817164b \ - --hash=sha256:51e79c1f7470158e838808d4a996fa9bac72c498e93d8ebe5119bc1e6becb0db \ --hash=sha256:556dba8fb6f5d8742f2923fe9457dbdd51e1049c4a43fd3986a0b14a1d815fc6 \ - --hash=sha256:598c3aaece21c503615fd59c92a3598b428b2f01bfb4b8ca9c4edeecc2438620 \ - --hash=sha256:5ed3657edf08512fc3fe81b510e35c2012fbd3081d2e26160f27ca28affec989 \ --hash=sha256:626d60935cf668e70a5ce6ff184fd713e9683fb458898e4249b63be9e28286ea \ --hash=sha256:644a6853d15b2512d67881586bd03f462c7ab755db95f16f14d7e238f2852c67 \ --hash=sha256:655456777ff65c2c548b7c454af9c6f33f16c8884f11083244b5819cc214f1b5 \ --hash=sha256:66c8a43a4f7b8df8b71ee1840e4211a3c8d93b214b213f590e18a1beca458f7d \ --hash=sha256:6afc576f7b33cf00996e5c1102dc2a8f7cc89e39c0b55df93a0b78c1bd992b36 \ --hash=sha256:6c3d53c796f8647d6deb1abe867daeb66dcc8a97e8455efa729516b997b8ed99 \ - --hash=sha256:709a48ef9a690e1343202916450bc48b9e51c049b089c7f79a267b46cffcdaa1 \ - --hash=sha256:70f9aad7de812d6541d29d2bbf8feb22ff7e1c299523db288004e3157ff4674e \ --hash=sha256:8153b8bfc11e1e4d75bcb0bff1db232f9e10b274e0929de9d608027e0d34ff8b \ --hash=sha256:87acf5963fc2b34825e5b6b048f40e3635dd547f590b04d2ab317c2619ef7ae8 \ --hash=sha256:88df9880d507169449d434c293467418b9f6cbe82edd19284aa0409e7fdb933d \ - --hash=sha256:929ddf8c4c7f348e4c0a5a3a714b5c8542ffaa8c22954862a46ca1813b667ee7 \ --hash=sha256:92d9abc807cf7d0e047b95ca5d957cf4792fcd04e920ca70d48add15c1a90ea7 \ --hash=sha256:95b181891b4c71de4bb404c6621e7e2390745f887f2a026b2d99e92c17892339 \ - --hash=sha256:9e999574eddae35f1312c2b4b717b7885d4edd6cb46700e04f7f02db454e67c1 \ --hash=sha256:a15459b0f4615b00bbd1e91f1b9e19b7e63aea7483d03d804186f278c0af2659 \ --hash=sha256:a22738912262aa3e254e4f3cb079a95a67132fc5a063890e224393596902f5a4 \ --hash=sha256:ab2fd90904c503739a75b7c8c5c01160130ba67944a7b77bbf36ef8054576e7f \ @@ -75,7 +61,6 @@ contourpy==1.3.3 \ --hash=sha256:ca0fdcd73925568ca027e0b17ab07aad764be4706d0a925b89227e447d9737b7 \ --hash=sha256:ca658cd1a680a5c9ea96dc61cdbae1e85c8f25849843aa799dfd3cb370ad4fbe \ --hash=sha256:cbedb772ed74ff5be440fa8eee9bd49f64f6e3fc09436d9c7d8f1c287b121d77 \ - --hash=sha256:cd5dfcaeb10f7b7f9dc8941717c6c2ade08f587be2226222c12b25f0483ed497 \ --hash=sha256:cf9022ef053f2694e31d630feaacb21ea24224be1c3ad0520b13d844274614fd \ --hash=sha256:d002b6f00d73d69333dac9d0b8d5e84d9724ff9ef044fd63c5986e62b7c9e1b1 \ --hash=sha256:d06bb1f751ba5d417047db62bca3c8fde202b8c11fb50742ab3ab962c81e8216 \ @@ -85,7 +70,6 @@ contourpy==1.3.3 \ --hash=sha256:ea37e7b45949df430fe649e5de8351c423430046a2af20b1c1961cae3afcda77 \ --hash=sha256:f64836de09927cba6f79dcd00fdd7d5329f3fccc633468507079c829ca4db4e3 \ --hash=sha256:fd6ec6be509c787f1caf6b247f0b1ca598bef13f4ddeaa126b7658215529ba0f \ - --hash=sha256:fd907ae12cd483cd83e414b12941c632a969171bf90fc937d0c9f268a31cafff \ --hash=sha256:fd914713266421b7536de2bfa8181aa8c699432b6763a0ea64195ebe28bff6a9 \ --hash=sha256:fde6c716d51c04b1c25d0b90364d0be954624a0ee9d60e23e850e8d48353d07a # via matplotlib @@ -94,14 +78,11 @@ cycler==0.12.1 \ --hash=sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c # via matplotlib fonttools==4.63.0 \ - --hash=sha256:063e08bd17bd5a90127a14123de0d6a952dbc847695fd98b63c043d58057f90c \ --hash=sha256:0eac00b9118c3c2f87d272e45341871c5b3066baa3c86897fa634a7c3fb59096 \ --hash=sha256:1e874792a8212b44583ea02189d9e693906b2f78b261f372f95d6c563210ac1d \ --hash=sha256:22135da48a348785c5e2d5d2d9d6bec5ed44adacbaeb9db12d9493bf6c6bfa68 \ --hash=sha256:22693918177bd9ceabec4736d338045f357769416fc6b0b2508eefef75b08616 \ --hash=sha256:27fdc65af8da6f88b9c6121c47a464cbe359fcfff7ff6fc2d37a1f395d755b78 \ - --hash=sha256:2b8ae05d9eacf6081414d759c0a352769ac28ce31280d6bb8e77b03f9e3c449f \ - --hash=sha256:2c14b4fd138c4bafcca294765c547914e1aa431ae1ca94ab99d8db08c958bd3b \ --hash=sha256:308f957cdeaf8abe4e5f2f124902ef405448af92c90f80e302a3b771c2e6116b \ --hash=sha256:37dd23e621e3b0aef1baa70a303b80aaf38449632cfc8fd2a55fb285bbccfc02 \ --hash=sha256:445af2eab030a16b9171ea8bdda7ebf7d96bda2df88ee182a464252f6e05e20d \ @@ -113,17 +94,14 @@ fonttools==4.63.0 \ --hash=sha256:6db5140a60a5d731d21ec076745b40a310607731b0a565b50776393188649001 \ --hash=sha256:6e528da43bc3791085f8cb6141b1d13e459226790240340fcbb4625649238b03 \ --hash=sha256:796f27556dbe094c4824f75ca85267e4df776c79036c8441469a4df37038c196 \ - --hash=sha256:79cdc9f567aec74a72918fd060283911406750cbc9fd28c1316023deb6ce31a9 \ --hash=sha256:7d76edbff9014094dbf03bd2d074709dfa6ec7aba13d838c937a2b33d2d6a86e \ --hash=sha256:7d782fac32985914c351556f68ac0855391572bcd87de50e05970d3cd4c96fc5 \ - --hash=sha256:7dd683fef0663e9f0f45cf541d788d24caa3ec9db50796b588e1757d8b3bc007 \ --hash=sha256:85be818f5506e8a7753153def2c9550178f0ecae6a47b5e0e8dbb23f7cc90380 \ --hash=sha256:948428a275741f0b64b113c955425a953314f4b9ab9997f73a72c83e68e569c8 \ --hash=sha256:9ced0bd02ac751dd6319b0da88aaef24414e3b0dbc32bb4f24944821a3741a27 \ --hash=sha256:9e12f105d2b6342c559c298afb674006bb2893afc7102dcf8a1b55b0486b4e40 \ --hash=sha256:a9faff9e0c1f76f9fd55899d2ce785832efebab37eb8ae13995853aef178bef0 \ --hash=sha256:af2fd1664d00a397d75f806985ddb36282091c2131a73a6485c23b4a34722263 \ - --hash=sha256:afefc1ed0a59785a7fb06ea7e1678e849c193e1e387db783579bc7b3056fcfcb \ --hash=sha256:ba04cb5891d4c0c21b6da95eda8d7b090021508a294fff33464fc7d241e0856b \ --hash=sha256:bf00f21eb5fb721dbaf73d1e9da6d02a1af7768f2ebcf9798be98beab8ba90f6 \ --hash=sha256:c1aaa4b9c75798400ac043ce04d74e7830376c85095a5a6ed7cba2f17a266bf4 \ @@ -131,8 +109,6 @@ fonttools==4.63.0 \ --hash=sha256:caeb583deeb5168e694b65cda8b4ee62abedfa66cf88488734466f2366b9c4e0 \ --hash=sha256:ccf41f2efdf56994d22d73bef4ced1052161958169428d06ba9724ea9e9a64be \ --hash=sha256:cd7e9857e5e63738b9d9fd707bc1f59c8b09e5177726d23664db393c59bb08bd \ - --hash=sha256:d76ac49f929aecaf82d83250b8347e099d7aecba0f4726c1d9b6df3b8bb5fe18 \ - --hash=sha256:dcf076a4474fe0d7367e5bbf5b052c7284fa1feca729c04176ce513521afd8a0 \ --hash=sha256:ee08ebfa58f6e1aeff5697ab9582105bb620008c1caafb681e4c557e7483027b \ --hash=sha256:ef3048ef05dbb552b89817713d9cac912e00d0fde4a3105c00d29e52e10c89af \ --hash=sha256:fd1e3094f42d806d3d7c79162fc59e5910fcbe3a7360c385b8da969bc4493745 @@ -143,15 +119,12 @@ iniconfig==2.3.0 \ # via pytest kiwisolver==1.5.0 \ --hash=sha256:012b1eb16e28718fa782b5e61dc6f2da1f0792ca73bd05d54de6cb9561665fc9 \ - --hash=sha256:01808c6d15f4c3e8559595d6d1fe6411c68e4a3822b4b9972b44473b24f4e679 \ --hash=sha256:0255a027391d52944eae1dbb5d4cc5903f57092f3674e8e544cdd2622826b3f0 \ --hash=sha256:0b85aad90cea8ac6797a53b5d5f2e967334fa4d1149f031c4537569972596cb8 \ --hash=sha256:0bf3acf1419fa93064a4c2189ac0b58e3be7872bf6ee6177b0d4c63dc4cea276 \ --hash=sha256:0c50b89ffd3e1a911c69a1dd3de7173c0cd10b130f56222e57898683841e4f96 \ --hash=sha256:0cbe94b69b819209a62cb27bdfa5dc2a8977d8de2f89dfd97ba4f53ed3af754e \ - --hash=sha256:0df54df7e686afa55e6f21fb86195224a6d9beb71d637e8d7920c95cf0f89aac \ --hash=sha256:0e3aafb33aed7479377e5e9a82e9d4bf87063741fc99fc7ae48b0f16e32bdd6f \ - --hash=sha256:12e91c215a96e39f57989c8912ae761286ac5a9584d04030ceb3368a357f017a \ --hash=sha256:1465387ac63576c3e125e5337a6892b9e99e0627d52317f3ca79e6930d889d15 \ --hash=sha256:16b85d37c2cbb3253226d26e64663f755d88a03439a9c47df6246b35defbdfb7 \ --hash=sha256:1b0feb50971481a2cc44d94e88bdb02cdd497618252ae226b8eb1201b957e368 \ @@ -159,13 +132,11 @@ kiwisolver==1.5.0 \ --hash=sha256:1d9daea4ea6b9be74fe2f01f7fbade8d6ffab263e781274cffca0dba9be9eec9 \ --hash=sha256:1dd9b0b119a350976a6d781e7278ec7aca0b201e1a9e2d23d9804afecb6ca681 \ --hash=sha256:1f1489f769582498610e015a8ef2d36f28f505ab3096d0e16b4858a9ec214f57 \ - --hash=sha256:2517e24d7315eb51c10664cdb865195df38ab74456c677df67bb47f12d088a27 \ --hash=sha256:2a075bd7bd19c70cf67c8badfa36cf7c5d8de3c9ddb8420c51e10d9c50e94920 \ --hash=sha256:332b4f0145c30b5f5ad9374881133e5aa64320428a57c2c2b61e9d891a51c2f3 \ --hash=sha256:377815a8616074cabbf3f53354e1d040c35815a134e01d7614b7692e4bf8acfa \ --hash=sha256:38f4a703656f493b0ad185211ccfca7f0386120f022066b018eb5296d8613e23 \ --hash=sha256:3ac2360e93cb41be81121755c6462cff3beaa9967188c866e5fce5cf13170859 \ - --hash=sha256:3c4923e404d6bcd91b6779c009542e5647fef32e4a5d75e115e3bbac6f2335eb \ --hash=sha256:3cdcb35dc9d807259c981a85531048ede628eabcffb3239adf3d17463518992d \ --hash=sha256:413b820229730d358efd838ecbab79902fe97094565fdc80ddb6b0a18c18a581 \ --hash=sha256:4432b835675f0ea7414aab3d37d119f7226d24869b7a829caeab49ebda407b0c \ @@ -173,16 +144,13 @@ kiwisolver==1.5.0 \ --hash=sha256:4e7f886f47ab881692f278ae901039a234e4025a68e6dfab514263a0b1c4ae05 \ --hash=sha256:4e9750bc21b886308024f8a54ccb9a2cc38ac9fa813bf4348434e3d54f337ff9 \ --hash=sha256:5060731cc3ed12ca3a8b57acd4aeca5bbc2f49216dd0bec1650a1acd89486bcd \ - --hash=sha256:50847dca5d197fcbd389c805aa1a1cf32f25d2e7273dc47ab181a517666b68cc \ --hash=sha256:5092eb5b1172947f57d6ea7d89b2f29650414e4293c47707eb499ec07a0ac796 \ --hash=sha256:5124d1ea754509b09e53738ec185584cc609aae4a3b510aaf4ed6aa047ef9303 \ --hash=sha256:530a3fd64c87cffa844d4b6b9768774763d9caa299e9b75d8eca6a4423b31314 \ --hash=sha256:56fa888f10d0f367155e76ce849fa1166fc9730d13bd2d65a2aa13b6f5424489 \ --hash=sha256:58f812017cd2985c21fbffb4864d59174d4903dd66fa23815e74bbc7a0e2dd57 \ - --hash=sha256:59cd8683f575d96df5bb48f6add94afc055012c29e28124fcae2b63661b9efb1 \ --hash=sha256:5ae8e62c147495b01a0f4765c878e9bfdf843412446a247e28df59936e99e797 \ --hash=sha256:5b233ea3e165e43e35dba1d2b8ecc21cf070b45b65ae17dd2747d2713d942021 \ - --hash=sha256:6176c1811d9d5a04fa391c490cc44f451e240697a16977f11c6f722efb9041db \ --hash=sha256:6783e069732715ad0c3ce96dbf21dbc2235ab0593f2baf6338101f70371f4028 \ --hash=sha256:6ab8ba9152203feec73758dad83af9a0bbe05001eb4639e547207c40cfb52083 \ --hash=sha256:70d593af6a6ca332d1df73d519fddb5148edb15cd90d5f0155e3746a6d4fcc65 \ @@ -190,28 +158,20 @@ kiwisolver==1.5.0 \ --hash=sha256:7a32f72973f0f950c1920475d5c5ea3d971b81b6f0ec53b8d0a956cc965f22e0 \ --hash=sha256:7a4aa69609f40fce3cbc3f87b2061f042eee32f94b8f11db707b66a26461591a \ --hash=sha256:7c60d3c9b06fb23bd9c6139281ccbdc384297579ae037f08ae90c69f6845c0b1 \ - --hash=sha256:800ee55980c18545af444d93fdd60c56b580db5cc54867d8cbf8a1dc0829938c \ --hash=sha256:80aa065ffd378ff784822a6d7c3212f2d5f5e9c3589614b5c228b311fd3063ac \ --hash=sha256:893ff3a711d1b515ba9da14ee090519bad4610ed1962fbe298a434e8c5f8db53 \ --hash=sha256:89fc958c702ee9a745e4700378f5d23fddbc46ff89e8fdbf5395c24d5c1452a3 \ - --hash=sha256:8c63c91f95173f9c2a67c7c526b2cea976828a0e7fced9cdcead2802dc10f8a4 \ --hash=sha256:8df31fe574b8b3993cc61764f40941111b25c2d9fea13d3ce24a49907cd2d615 \ --hash=sha256:9027d773c4ff81487181a925945743413f6069634d0b122d0b37684ccf4f1e18 \ --hash=sha256:940dda65d5e764406b9fb92761cbf462e4e63f712ab60ed98f70552e496f3bf1 \ - --hash=sha256:9eed0f7edbb274413b6ee781cca50541c8c0facd3d6fd289779e494340a2b85c \ - --hash=sha256:ad4ae4ffd1ee9cd11357b4c66b612da9888f4f4daf2f36995eda64bd45370cac \ --hash=sha256:b0f172dc8ffaccb8522d7c5d899de00133f2f1ca7b0a49b7da98e901de87bf2d \ --hash=sha256:b2af221f268f5af85e776a73d62b0845fc8baf8ef0abfae79d29c77d0e776aaf \ - --hash=sha256:b7d335370ae48a780c6e6a6bbfa97342f563744c39c35562f3f367665f5c1de2 \ --hash=sha256:bb5136fb5352d3f422df33f0c879a1b0c204004324150cc3b5e3c4f310c9049f \ --hash=sha256:bc4d8e252f532ab46a1de9349e2d27b91fce46736a9eedaa37beaca66f574ed4 \ --hash=sha256:bdd3e53429ff02aa319ba59dfe4ceeec345bf46cf180ec2cf6fd5b942e7975e9 \ --hash=sha256:be12f931839a3bdfe28b584db0e640a65a8bcbc24560ae3fdb025a449b3d754e \ - --hash=sha256:be4a51a55833dc29ab5d7503e7bcb3b3af3402d266018137127450005cdfe737 \ - --hash=sha256:beb7f344487cdcb9e1efe4b7a29681b74d34c08f0043a327a74da852a6749e7b \ --hash=sha256:c0e1403fd7c26d77c1f03e096dc58a5c726503fa0db0456678b8668f76f521e3 \ --hash=sha256:c31c13da98624f957b0fb1b5bae5383b2333c2c3f6793d9825dd5ce79b525cb7 \ - --hash=sha256:c438f6ca858697c9ab67eb28246c92508af972e114cac34e57a6d4ba17a3ac08 \ --hash=sha256:c95cab08d1965db3d84a121f1c7ce7479bdd4072c9b3dafd8fecce48a2e6b902 \ --hash=sha256:cdee07c4d7f6d72008d3f73b9bf027f4e11550224c7c50d8df1ae4a37c1402a6 \ --hash=sha256:ce9bf03dad3b46408c08649c6fbd6ca28a9fce0eb32fdfffa6775a13103b5310 \ @@ -221,17 +181,14 @@ kiwisolver==1.5.0 \ --hash=sha256:d4193f3d9dc3f6f79aaed0e5637f45d98850ebf01f7ca20e69457f3e8946b66a \ --hash=sha256:d618fd27420381a4f6044faa71f46d8bfd911bd077c555f7138ed88729bfbe79 \ --hash=sha256:d76e2d8c75051d58177e762164d2e9ab92886534e3a12e795f103524f221dd8e \ - --hash=sha256:daae526907e262de627d8f70058a0f64acc9e2641c164c99c8f594b34a799a16 \ --hash=sha256:db485b3847d182b908b483b2ed133c66d88d49cacf98fd278fadafe11b4478d1 \ --hash=sha256:dda366d548e89a90d88a86c692377d18d8bd64b39c1fb2b92cb31370e2896bbd \ --hash=sha256:e315e5ec90d88e140f57696ff85b484ff68bb311e36f2c414aa4286293e6dee0 \ --hash=sha256:e4415a8db000bf49a6dd1c478bf70062eaacff0f462b92b0ba68791a905861f9 \ --hash=sha256:e7a116ae737f0000343218c4edf5bd45893bfeaff0993c0b215d7124c9f77646 \ --hash=sha256:e7c4c09a490dc4d4a7f8cbee56c606a320f9dc28cf92a7157a39d1ce7676a657 \ - --hash=sha256:ec4c85dc4b687c7f7f15f553ff26a98bfe8c58f5f7f0ac8905f0ba4c7be60232 \ --hash=sha256:ed3a984b31da7481b103f68776f7128a89ef26ed40f4dc41a2223cda7fb24819 \ --hash=sha256:f18c2d9782259a6dc132fdc7a63c168cbc74b35284b6d75c673958982a378384 \ - --hash=sha256:f1f9f4121ec58628c96baa3de1a55a4e3a333c5102c8e94b64e23bf7b2083309 \ --hash=sha256:f443b4825c50a51ee68585522ab4a1d1257fac65896f282b4c6763337ac9f5d2 \ --hash=sha256:f6764a4ccab3078db14a632420930f6186058750df066b8ea2a7106df91d3203 \ --hash=sha256:f7c7553b13f69c1b29a5bde08ddc6d9d0c8bfb84f9ed01c30db25944aeb852a7 \ @@ -239,8 +196,7 @@ kiwisolver==1.5.0 \ --hash=sha256:fa8eb9ecdb7efb0b226acec134e0d709e87a909fa4971a54c0c4f6e88635484c \ --hash=sha256:fc20894c3d21194d8041a28b65622d5b86db786da6e3cfe73f0c762951a61167 \ --hash=sha256:fc4d3f1fb9ca0ae9f97b095963bc6326f1dbfd3779d6679a1e016b9baaa153d3 \ - --hash=sha256:fd40bb9cd0891c4c3cb1ddf83f8bbfa15731a248fdc8162669405451e2724b09 \ - --hash=sha256:ff710414307fefa903e0d9bdf300972f892c23477829f49504e59834f4195398 + --hash=sha256:fd40bb9cd0891c4c3cb1ddf83f8bbfa15731a248fdc8162669405451e2724b09 # via matplotlib markdown-it-py==4.2.0 \ --hash=sha256:04a21681d6fbb623de53f6f364d352309d4094dd4194040a10fd51833e418d49 \ @@ -261,20 +217,13 @@ matplotlib==3.11.0 \ --hash=sha256:3338e3e3de128cf50d0d2fb92a122815daf9c755bd882a474343c05f8fd7ec79 \ --hash=sha256:3489c3dc487669b4a980bc3068f87856de7a1564248d3f6c629efb2a58b03f24 \ --hash=sha256:373db8f91214e8ccaf35ac833cc1dd59dd961e148bbd55dd027141591dde1313 \ - --hash=sha256:3d68266213e73823ac3be90615bab0cf31f88851e114cdb1dd25dacf3b01e1a7 \ --hash=sha256:41635d7909d19e52e924a521dde6d8f670b0f53ab1d0e8c331fa831554f681d1 \ --hash=sha256:446307e6b04b57b1f1239e228a1ec2af0d589a1008cebc3dfa3f5441d095cfb6 \ - --hash=sha256:4d7aea652b58e686444079be3376ef546bffa1eee9b9bb9c472b9fcf6cf410d3 \ - --hash=sha256:5106c444d0bf966eee2853548c03772af4ab7199118e086c62fbac8ccb07c055 \ --hash=sha256:565af866fd63e4bd3f987d580afe27c44c2552a3b3305f4ecbb85133601ea6f3 \ - --hash=sha256:57baa92fdc82948ed716eae6d2579d4d6f40965cd8d2f416755b4a72580a3233 \ - --hash=sha256:630eee0e67d35cce2019a0e670719f4816e3b86aff0fa72729f6c69786fceb45 \ --hash=sha256:652fb5696271d4c50f196d22a5ff4f8e4444c74f847423570d7dc0aa2bbd0159 \ --hash=sha256:68c0c7be01b30dcca3638934f7f591df73401235cbdbf0d1ab1c71e7db7f8b57 \ --hash=sha256:6a98f5476ce784a50ce09998f4ae1e6a9f25043cef8a480c98949902eda74620 \ --hash=sha256:6ce3b839b34ae1f430b4616893a2945a2999debaa7e94e7e29a2a8bbf286f7b5 \ - --hash=sha256:70a5b3e9a5dab708c0f039709ae7c68d5b4d254e291ef76492cdba230c8bb5e4 \ - --hash=sha256:81ae77077a1e16d37a5b61096ccb07c8d90a99b518fa8256b8f21578932f2f62 \ --hash=sha256:942b37c5db1899610bd1543ce8e13e4ecff9a4633e7f63bb6aa9205d2644ebd1 \ --hash=sha256:94f5000f67ca9faa300863ea17f8bce9175cb67b88bec4bc7780502d53dd7c9e \ --hash=sha256:9dd11fb612ce7bc60b1de5b4fc87ff959d22317b5de42aabf392f66f97af22eb \ @@ -287,12 +236,9 @@ matplotlib==3.11.0 \ --hash=sha256:be5f93a1d21981bfb802ded0d77a0caa92d4342a47d45754fac77e314a506344 \ --hash=sha256:c08e649a6313e1291e713623b97a38e5bb4aa580b2a100a94a3309bc6b9c8eb3 \ --hash=sha256:c945824670fb8915b4ac879e5e61f3c58e0913022f70a0de4c082b17372f8771 \ - --hash=sha256:cf662e5ac5707658cb931e19972c4bd99f7b4f8b7bf79d3c821d239fa6b71e64 \ --hash=sha256:d9695457a467ff86d23f35037a43deb6f1134dd6d3e2ac8ce1e2087cff09ffb9 \ - --hash=sha256:ddef37840695f5eef65f9f070fe2d2f510f584c2156203f9f622a5b0584efffd \ --hash=sha256:dfabef0230d0697aa0d717385194dd41162e00207a68bf4abf94c2bf4c27dca0 \ - --hash=sha256:e6b3e64dea5062c570f04358e2711859f3531b459f29516274fbad889079e4f3 \ - --hash=sha256:f857524b442f0f36e641868ce2171aafa88cb0bc0644f4e1d8a5df9b32649fef + --hash=sha256:e6b3e64dea5062c570f04358e2711859f3531b459f29516274fbad889079e4f3 # via trendify mdurl==0.1.2 \ --hash=sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8 \ @@ -305,85 +251,7 @@ narwhals==2.23.0 \ --hash=sha256:13e7ff5b4bb4a2f77b907c2e4d8a76e273dfc1323a3c997440a2f9fd26aed408 \ --hash=sha256:769e7b9ab102c93d8fa019f6b4cd1a657909b04a20bf6210e5a35aae06814ae9 # via plotly -numpy==2.4.6 ; python_full_version < '3.12' \ - --hash=sha256:001fbb8e08d942dd57599e781f2472269ee7f2755fae407b4f67b2f0b17da3f1 \ - --hash=sha256:0280e0356c0829a18d9de1cb7eee50ec22ca639878d7240307ca0943d73cd2c4 \ - --hash=sha256:043191bfa8eab18c776647b62723ac9dddece59743b13f49b2016094129c2b3f \ - --hash=sha256:06ca2f61ec4385a07a6977c55ba998a4466c123642b4a32694d3128fce18c079 \ - --hash=sha256:0a041d3d761dc3c35cc56ce0351506a02bcbc25f7b169f652435141a17db9096 \ - --hash=sha256:0ab0a9c4ffb1a6d95ef519fe4247dba8eb6b18ad93999f76b7f657039acabd47 \ - --hash=sha256:0c9136e14ed34a9e343a31c533d78a9813a69a3148332bce5e9821cb2f996e66 \ - --hash=sha256:110f8b71aacb688ec69062bb7f6938a0f8acb01b7c1c4beb453c65b6d234584d \ - --hash=sha256:112b06a867b235ef466ed3508ddf0238050df9c727cafb5301ac385b899189a1 \ - --hash=sha256:17f9ade344e7d9b464a084d69bcf18fc691cb1db67c62ed80820bf4926d78f0e \ - --hash=sha256:1e254a00cdf42b1e4d5b3d68d33af63268d41340d8885df2ab6470f2e1500147 \ - --hash=sha256:1e978ec1e8bd0e0e4de6bb75de9d30cbb74db6b6a2bb727618613703ca0167dd \ - --hash=sha256:25c692919ac5a01f170a3bfcd62d745b24fd095c353d50812637d6fcab442e75 \ - --hash=sha256:260a5d70215b61ab4fadf5c7baacd64821842975eea312125ed3c39a6391b063 \ - --hash=sha256:2803abfebfc990042cd494d8ce2d5f82e9d847af6d35ec486923aa19dbad5e73 \ - --hash=sha256:29a287e0cf63ff528da061de6b9f64a4618da591ca1046aafc54062e40ca7eab \ - --hash=sha256:29cb7f67d10b479ff07c17d33e39f78c07f71c40ef30d63c153d340e96cd3fb4 \ - --hash=sha256:3213d622a0283a39a93d188f3cf72b26862df52fbb4ca3697f51705016523d41 \ - --hash=sha256:33111801a01c12a8a1e3721f0a9232f8cfc8ae2c6b7098167e6f623c6073f402 \ - --hash=sha256:357cc07a6d7b0b182ff02249616a03742827ebb1277546b5c7cd7f7620a45698 \ - --hash=sha256:38efbc8de75c7a0fc1ac190162d892787f3f47b57cc291231aafee36b80982b7 \ - --hash=sha256:4081eb135ac24158bd51cdfbef16f1c64df7063b1143f24731387137c092bec8 \ - --hash=sha256:40fdc1ae7125e518ea98e53e69a4ebc27e1fd50510c47b7ea130cf21e5e1d42b \ - --hash=sha256:4cfe66903cc32a9921a6733d96b19bb6abf310397581bbad89c228f5abaf0ee8 \ - --hash=sha256:511dbaf848decaaaf4b4ca48032619fb3138710c4bf7da7617765edad1ef96b0 \ - --hash=sha256:55cced7c52e981362f708ad635198e97a752dfba412cc03c23bbf3bd8d5cd662 \ - --hash=sha256:56b39e5e0622a09a25bf5baf62f4bcf0cb8a41ae6e2819cf49bbc5a74c083f91 \ - --hash=sha256:5dbbdb29840ca3d91ee0fece42fc29278886d908280bfec0a5846c6f901a3eb0 \ - --hash=sha256:5f9fb9157b4ce2971008323afe46053787b526ef624fea915b261468a8421a0f \ - --hash=sha256:6180d8b35af935aed8ece3a85e0a43f87393ae0ac87c8d2c8bd2c993f7270ef3 \ - --hash=sha256:68a5124b13fa6cc2086764a20005d30bc0548146f7f5322f02fce212ca14317f \ - --hash=sha256:68bb27509ac1b9a3443094260f6326150663b06abe40b73a2f81160623da5b67 \ - --hash=sha256:6f41ae150c4e32db4f3310cdaf64b1593a03dbabe29eec77fc9b50fe64061df6 \ - --hash=sha256:7265a2f3d436e54ef9f2b52b5c937e6be778781bd97a590319d7348f1c1ca997 \ - --hash=sha256:72fbe16c6fac95aedf5937fa873445cec2110be35d8a4e9433d7501fd98dae6b \ - --hash=sha256:7d92c3819208a60205a12a245c91ad70cb0a85336659b19b834205573ac8456e \ - --hash=sha256:8155154c7c691289fe18f510b5d4657c68c67989f293f0535a91360392ff6538 \ - --hash=sha256:81a1cca95ed5bb92aa8b10dd2cdc9a0d3853a50fad926c28b5d7e8ea54389627 \ - --hash=sha256:89cd468399cfd2504718f0ba50e410dca55a170b61a02ad92bb18c8a65186e93 \ - --hash=sha256:8ad03c0965fb3c692200e74d458ca28c1dbb4ce96f9a479a8aa041ad5fabca02 \ - --hash=sha256:90f9849678c75fe7afa2d348ac842c168b0a4d3d61919687216dfc547976d853 \ - --hash=sha256:948424b06129ce883307e8cff868c31396d8dc7630a59c61d70d98dbe70f222c \ - --hash=sha256:9cd5ffd25db4e7ba6a375693b3fc0fc1791ec636c17db3720da19bde7180ec43 \ - --hash=sha256:a0df0043bdb289bde1f62da130d20df23d58b45429f752bc7a8fc5325a225ecd \ - --hash=sha256:a2c306dea656c12c68f51f4cea133cbe78ca7435eb28c735eac1d3ebe73be6e8 \ - --hash=sha256:a7830bab239b79cda9c08c2da014761cafb48da6150e1da17ac06283f43b6089 \ - --hash=sha256:a7c711e21628b52034bb5ab8d1bce291f752fcc5e92accc615778acee1ff4778 \ - --hash=sha256:aaf159caa35993cb1f56fb9b8e4610d35758e7ca005412eb1daa856a78c9c4b1 \ - --hash=sha256:ae506e6902902557576a26ff33eda8695e7ecb3cb36c3b573a0765dee114ebdb \ - --hash=sha256:b507f5c4c1d508876d1819b6bf9a49d365b96320b5d4993426b33a23ca4b8261 \ - --hash=sha256:bf162abab1c1a736333192707cef898e735a5ca00f38f27eeedf44b39d9e85eb \ - --hash=sha256:c1a2af6c6ef86344a6b0db6b97834208bf598db514f2b155042439b62605601a \ - --hash=sha256:c2d37ab77531417474168eb79d6d80b14f821a966818505d03013d0833edb7a8 \ - --hash=sha256:c4fc99836233ea196540b17ab0983aff60ed07941751930f5f4d05bc3b3b7359 \ - --hash=sha256:d581b735e177fdcdce6fed8e7e8880a3fb6ee4e3653a3ac6af01c6f4c03effc5 \ - --hash=sha256:d6da64deb6b8ed903e7560180a92f2d804ee1ba5eeb849ac2748b8c1aba1f6d7 \ - --hash=sha256:d8e8286dd7cea7895157318d1b91cdacac64c479f3cbc8dce548331728484751 \ - --hash=sha256:ddea102b48f9e339f3948bf22040944184627a30fdf7f858667673b9c5f033c8 \ - --hash=sha256:dfa20cc6ca228e6b155b11da03825975ce66aea520985dbbddf0f2a5a495c605 \ - --hash=sha256:e3e5193ef5a3dc73bceee50f7fdc2c90dbb76c42df8d8fae3d1067a583df579e \ - --hash=sha256:e3eeb0aabd6bd5ce64faae67e9935203a6991b4bc2a485a767fbafb2c5125f45 \ - --hash=sha256:e5805d5a22fd19c8ccff10a9561f9df94436b0545619ea579db2d3c35294bce2 \ - --hash=sha256:e85b752a1e912b70eaad4fafbd4d1238007ab221de2009b9a2f5ae7461239895 \ - --hash=sha256:eaf7fa2de5c0be8ae6ff8e9bea2ccd725e980541244521d8d4b5f3354a27babe \ - --hash=sha256:ebfb099f8dcf083deef3ac1ca4c1503f387cf76296fcb3816b66f5ecb5f54fdb \ - --hash=sha256:ece3d2cfe132e7d51f44a832b303895e6f2d499c5e74dfbdb06ee246147a304a \ - --hash=sha256:ed9749eef4cbd126da3dc1d6bcb3a57f5eb7ac6a6484146bdbf743f552dfc577 \ - --hash=sha256:ede83e07a75dd06bc501566c1eca2afc0d61677c1472ac9ad93fdee6e638a48d \ - --hash=sha256:ef4aea96ce4d3b074422cb4f2f64e216bf9e213004bb58ecfdf50ea02ea8eb9a \ - --hash=sha256:f3a3570c4a2a16746ac2c31a7c7c7b0c186b95ce902e33db6f28094ed7387dda \ - --hash=sha256:f407cb6b8e9d6d8c626bc73c945db1706035af8fd632295547bf1c9e46d092d6 \ - --hash=sha256:f74a575920ab21fe304421a3fc28793d82e299cae9eccb37084e9fc7f3617c20 - # via - # contourpy - # matplotlib - # numpydantic - # trendify -numpy==2.5.0 ; python_full_version >= '3.12' \ +numpy==2.5.0 \ --hash=sha256:016623417bb330d719d579daf2d6b9a01ddc52e41a9ed61a47f39fde46dcd865 \ --hash=sha256:0b525be4744b60bb0557ac872d53ef07d085b5f39622bc579c98d3809d05b988 \ --hash=sha256:126b88d95e8ff9b00c9e717aa540469f21d6180162f84c0caec51b16215d49cd \ @@ -445,36 +313,29 @@ packaging==26.2 \ # plotly # pytest pillow==12.3.0 \ - --hash=sha256:00808c5e14ef63ac5161091d242999076604ff74b883423a11e5d7bbb38bf756 \ - --hash=sha256:04f01d28a6aaff387bf842a13be313df23ba0597a44f1a976c9feb3c6ff4711a \ --hash=sha256:06ff022112bc9cbf83b60f8e028d94ad87b60621706487e65f673de61610ab59 \ --hash=sha256:0740a512dc522224c77d9aa5a8d70d8b7d73fb91f2c21125d8d025d3b8990e45 \ --hash=sha256:0847a763afefb695bc912d7c131e7e0632d4edc1d8698f58ddabec8e46b8b6d3 \ --hash=sha256:0dd2064cbc55aaec028ef5fbb60fa47bb6c3e7918e07ff17935284b227a9d2df \ --hash=sha256:0feb2e9d6ad6c9e3c06effe9d00f3f1e618a6643273576b016f591e9315a7139 \ - --hash=sha256:10e41f0fbf1eec8cfd234b8fe17a4caac7c9d0db4c204d3c173a8f9f6ef3232b \ --hash=sha256:1182d52bc2d5e5d7d0949503aa7e36d12f42205dc287e4883f407b1988820d39 \ --hash=sha256:164b31cd1a0490ab6efae01aa5df49da7061be0af1b30e035b6e9a1bfe34ee6e \ --hash=sha256:1657923d2d45afb66526e5b933e5b3052e6bdea196c90d3abb2424e18c77dae8 \ --hash=sha256:186941b6aef820ad110fb01fb06eb925374dc3a21b17e37ec9a53b250c6fe2d1 \ --hash=sha256:1cca606cd25738df4ed873d5ad46bbdb3d83b5cbca291f6b4ff13a4df6b0bbe8 \ --hash=sha256:21900ce7ba264168cd50defae43cd75d25c833ad4ad6e73ffc5596d12e25ac89 \ - --hash=sha256:236ff70b9312fb68943c703aa842ca6a758abfa45ac187a5e7c1452e96ef72b5 \ --hash=sha256:23aceaa007d6172b02c277f0cd359c79492bbb14f7072b4ede9fbcaf20648130 \ - --hash=sha256:23d27a3e0307ec2244cc51e7287b919aa68d097504ebe19df4e76a98a3eea5bd \ --hash=sha256:24870b09b224f7ae3c39ed07d10e819d06f8720bc551847b1d623832b5b0e28d \ --hash=sha256:251bf95b67017e27b13d82f5b326234ca62d70f9cf4c2b9032de2358a3b12c7b \ --hash=sha256:28ce87c5ab450a9dd970b52e5aca5fe63ed432d18a2eaddd1979a00a1ba24ace \ --hash=sha256:30f2aa603c41533cc25c05acd0da21636e84a315768feb631c937177db558931 \ --hash=sha256:331b624368d4f1d069149002f25f44bc61c8919ce8ddb3c45bdad8f6e2d89510 \ - --hash=sha256:37d6d0a00072fd2948eb22bce7e1475f34569d90c87c59f7a2ec59541b77f7a6 \ --hash=sha256:3b8182a766685eaa002637e28b4ec8d6b18819a0c71f579bf0dbaa5830297cce \ --hash=sha256:3edce1d53195db527e0191f84b71d02022de0540bf43a16ed734ed7537b07385 \ --hash=sha256:446c34dcc4324b084a53b705127dc15717b22c5e140ae0a3c38349d4efec071e \ --hash=sha256:4998562bf62a445225f22e07c896bb04b35b1b1f2eb6d760584c9c51d7a5f78c \ --hash=sha256:4b0a7fe987b14c31ebda6083f74f22b561fd3739bc0ac51e019622e3d72668c7 \ --hash=sha256:4e8c2a84d977f50b9daed6eeaf3baef67d00d5d74d932288f02cb94518ee3ace \ - --hash=sha256:4f883547d4b7f0495ebe7056b0cc2aea76094e7a4abc8e933540f3271df27d9c \ --hash=sha256:53aa02d20d10c3d814d536aa4e5ac9b84ca0ff5a88377963b085ad6822f93e64 \ --hash=sha256:571b9fcb07b97ef3a492028fb3d2dc0993ca23a06138b0315286566d29ef718a \ --hash=sha256:57b3d78c95ba9059768b10e28b813002261d3f3dfc55cc48b0c988f625175827 \ @@ -487,7 +348,6 @@ pillow==12.3.0 \ --hash=sha256:8728f216dcdb6e6d555cf971cb34076139ad74b31fc2c14da4fafc741c5f6217 \ --hash=sha256:877c3f311ff35410f690861c4409e7ccbf0cd2f878e50628a28e5a0bb689e658 \ --hash=sha256:8cd2f7bdda092d99c9fc2fb7391354f306d01443d22785d0cbfafa2e2c8bb418 \ - --hash=sha256:8e95e1385e4998ae9694eeaa4730ba5457ff61185b3a55e2e7bea0880aef452a \ --hash=sha256:962864dc93511324d51ddbb5b9f8731bf71675b93ca612a07441896f4688fb8c \ --hash=sha256:9cf95fe4d0f84c82d282745d9bb08ad9f926efa00be4697e767b814ce40d4330 \ --hash=sha256:9e881fca225083806662a5c43d627d215f258ff43c890f831966c7d7ba9c7402 \ @@ -496,12 +356,9 @@ pillow==12.3.0 \ --hash=sha256:a876864214e136f0eb367788dbd7df045f4806801518e2cfe9e13229cfe06d8f \ --hash=sha256:ae26d61dfa7a47befdc7572b521024e8745f3d809bd95ca9505a7bba9ef849ec \ --hash=sha256:af8d94b0db561cf68b88a267c5c44b49e134f525d0dc2cb7ed413a66bc23559a \ - --hash=sha256:b343699e8308bdc51978310e1c959c584e7869cc8c40780058c87da7781a1e94 \ - --hash=sha256:b3c777e849237620b022f7f297dd67705f9f5cf1685f09f02e46f93e92725468 \ --hash=sha256:b629de27fda84b42cde7edef0d85f13b958b47f6e9bbcbba9b673c562a89bd8b \ --hash=sha256:ba09209fbe443b4acccebe845d8a138b89a8f4fbaeedd44953490b5315d5e965 \ --hash=sha256:ba54cfebe86920a559a7c4d6b9050791c20513650a1952ebe3368c7dc70306f8 \ - --hash=sha256:bcb46e2f9feff8d06323983bd83ed00c201fdcab3d74973e7072a889b3979fcd \ --hash=sha256:bf16ba1b4d0b6b7c8e534936632270cf70eb00dbe09005bc345b2677b726855c \ --hash=sha256:cf1845d02ad822a369a49f2bb9345b1614744267682e7a03527dc3bf6eea1777 \ --hash=sha256:d69141514cc30b774ceea5e3ed3a6635c8d8a96edf664689b890f4089111fb35 \ @@ -511,15 +368,12 @@ pillow==12.3.0 \ --hash=sha256:e158cb00350dc278f3b91551101aa7d12415a66ebf2c91d8d5ac14e56ddd3ad0 \ --hash=sha256:e491916b378fba47242221bb9ead245211b70d504f495d105d17b14a24b4907c \ --hash=sha256:e795b7eb908249c4e43c7c99fac7c2c75dab0c43566e37db472a355f63693d71 \ - --hash=sha256:e7e480451b9fa137494bccd3a7d69adbe8ac65a87d97be61e11f1b1050a5bac3 \ --hash=sha256:e91206ee562682b51b98ef4b26a6ef48fd84e15fd4c4bc5ec768eb641d206838 \ --hash=sha256:e9871b1ffbfa9656b60aeee92ed5136a5742696006fa322b29ea3d8da0ecc9cf \ --hash=sha256:e9aeb04d6aef139de265b29683e119b638208f88cf73cdd1658aa07221165321 \ - --hash=sha256:ebaea975e03d3141d9d3a507df75c9b3ec90fa9d2ffd07567b3a978d9d790b26 \ --hash=sha256:f13c32a3abd6079a66d9526e18dad9b6d280384d49d7c54040cd57b6424041d9 \ --hash=sha256:f7401aebd7f581d7f83a439d87d474999317ee099218e5ad25d125290990ba65 \ --hash=sha256:fa4ecea169a355be7a3ade2c783e2ed12f0e40d2c5621cda8b3297faf7fbb9f5 \ - --hash=sha256:fbd139c8447d25dd750ab79ee274cc5e1fe80fc56340ab10b18a195e1b6eca3e \ --hash=sha256:fdafc9cce40277e0f7a0feabce0ee50dd2fa1800f3b38015e51296b5e814048d \ --hash=sha256:fe3cca2e4e8a592be0f269a1ca4835c25199d9f3ce815c8491048f785b0a0198 \ --hash=sha256:ffd0c5368496f41b0944be820fcb7a838aa6e623d250b01acf2643939c3f99d7 @@ -556,17 +410,8 @@ pydantic==2.13.4 \ pydantic-core==2.46.4 \ --hash=sha256:00c603d540afdd6b80eb39f078f33ebd46211f02f33e34a32d9f053bba711de0 \ --hash=sha256:0186750b482eefa11d7f435892b09c5c606193ef3375bcf94aa00ae6bfb66262 \ - --hash=sha256:041bde0a48fd37cf71cab1c9d56d3e8625a3793fef1f7dd232b3ff37e978ecda \ - --hash=sha256:0c563b08bca408dc7f65f700633d8442fffb2421fc47b8101377e9fd65051ff0 \ --hash=sha256:0cbe8b01f948de4286c74cdd6c667aceb38f5c1e26f0693b3983d9d74887c65e \ - --hash=sha256:0ce40cd7b21210e99342afafbd4d0f76d784eb5b1d60f3bdc566be4983c6c73b \ - --hash=sha256:0e96592440881c74a213e5ad528e2b24d3d4f940de2766bed9010ab1d9e51594 \ - --hash=sha256:133878133d271ade3d41d1bfb2a45ec38dbdbda40bc065921c6b04e4630127e2 \ - --hash=sha256:14d4edf427bdcf950a8a02d7cb44a08614388dd6e1bdcbf4f67504fa7887da9c \ - --hash=sha256:14f4c5d6db102bd796a627bbb3a17b4cf4574b9ae861d8b7c9a9661c6dd3362d \ - --hash=sha256:17299feefe090f2caa5b8e37222bb5f663e4935a8bfa6931d4102e5df1a9f398 \ --hash=sha256:184c081504d17f1c1066e430e117142b2c77d9448a97f7b65c6ac9fd9aee238d \ - --hash=sha256:18e5ceec2ab67e6d5f1a9085e5a24c9c4e2ac4545730bfe668680bca05e555f3 \ --hash=sha256:1a7dd0b3ee80d90150e3495a3a13ac34dbcbfd4f012996a6a1d8900e91b5c0fb \ --hash=sha256:1d8ba486450b14f3b1d63bc521d410ec7565e52f887b9fb671791886436a42f7 \ --hash=sha256:2108ba5c1c1eca18030634489dc544844144ee36357f2f9f780b93e7ddbb44b5 \ @@ -581,7 +426,6 @@ pydantic-core==2.46.4 \ --hash=sha256:3ecbc122d18468d06ca279dc26a8c2e2d5acb10943bb35e36ae92096dc3b5565 \ --hash=sha256:3fb702cd90b0446a3a1c5e470bfa0dd23c0233b676a9099ddcc964fa6ca13898 \ --hash=sha256:428e04521a40150c85216fc8b85e8d39fece235a9cf5e383761238c7fa9b96fb \ - --hash=sha256:4c63ebc82684aa89d9a3bcbd13d515b3be44250dc68dd3bd81526c1cb31286c3 \ --hash=sha256:4fc73cb559bdb54b1134a706a2802a4cddd27a0633f5abb7e53056268751ac6a \ --hash=sha256:4fcbe087dbc2068af7eda3aa87634eba216dbda64d1ae73c8684b621d33f6596 \ --hash=sha256:56cb4851bcaf3d117eddcef4fe66afd750a50274b0da8e22be256d10e5611987 \ @@ -592,34 +436,24 @@ pydantic-core==2.46.4 \ --hash=sha256:617d7e2ca7dcb8c5cf6bcb8c59b8832c94b36196bbf1cbd1bfb56ed341905edd \ --hash=sha256:62f875393d7f270851f20523dd2e29f082bcc82292d66db2b64ea71f64b6e1c1 \ --hash=sha256:633147d34cf4550417f12e2b1a0383973bdf5cdfde212cb09e9a581cf10820be \ - --hash=sha256:66ce7632c22d837c95301830e111ad0128a32b8207533b60896a96c4915192ea \ --hash=sha256:6b3ace8194b0e5204818c92802dcdca7fc6d88aabbb799d7c795540d9cd6d292 \ - --hash=sha256:6f2eeda33a839975441c86a4119e1383c50b47faf0cbb5176985565c6bb02c33 \ --hash=sha256:7027560ee92211647d0d34e3f7cd6f50da56399d26a9c8ad0da286d3869a53f3 \ --hash=sha256:7283d57845ecf5a163403eb0702dfc220cc4fbdd18919cb5ccea4f95ee1cdab4 \ --hash=sha256:7a5f930472650a82629163023e630d160863fce524c616f4e5186e5de9d9a49b \ - --hash=sha256:7bfb192b3f4b9e8a89b6277b6ce787564f62cfd272055f6e685726b111dc7826 \ --hash=sha256:811ff8e9c313ab425368bcbb36e5c4ebd7108c2bbf4e4089cfbb0b01eff63fac \ --hash=sha256:8233f2947cf85404441fd7e0085f53b10c93e0ee78611099b5c7237e36aacbf7 \ --hash=sha256:82cf5301172168103724d49a1444d3378cb20cdee30b116a1bd6031236298a5d \ --hash=sha256:85bb3611ff1802f3ee7fdd7dbff26b56f343fb432d57a4728fdd49b6ef35e2f4 \ - --hash=sha256:86e1a4418c6cd97d60c95c71164158eaf7324fae7b0923264016baa993eba6fc \ - --hash=sha256:8c5dac79fa1614d1e06ca695109c6105923bd9c7d1d6c918d4e637b7e6b32fd3 \ --hash=sha256:8d0820e8192167f80d88d64038e609c31452eeca865b4e1d9950a27a4609b00b \ --hash=sha256:8daafc69c93ee8a0204506a3b6b30f586ef54028f52aeeeb5c4cfc5184fd5914 \ - --hash=sha256:9037063db01f09b09e237c282b6792bd4da634b5402c4e7f0c61effed7701a04 \ - --hash=sha256:90884113d8b48f760e9587002789ddd741e76ab9f89518cd1e43b1f1a52ec44b \ --hash=sha256:926c9541b14b12b1681dca8a0b75feb510b06c6341b70a8e500c2fdcff837cce \ --hash=sha256:9401557acd873c3a7f3eb9383edef8ac4968f9510e340f4808d427e75667e7b4 \ --hash=sha256:9551187363ffc0de2a00b2e47c25aeaeb1020b69b668762966df15fc5659dd5a \ --hash=sha256:962ccbab7b642487b1d8b7df90ef677e03134cf1fd8880bf698649b22a69371f \ --hash=sha256:97e7cf2be5c77b7d1a9713a05605d49460d02c6078d38d8bef3cbe323c548424 \ --hash=sha256:9aa768456404a8bf48a4406685ac2bec8e72b62c69313734fa3b73cf33b3a894 \ - --hash=sha256:9bc519fbf2b7578398853d815009ae5e4d4603d12f4e3f91da8c06852d3da3e9 \ --hash=sha256:9d56801be94b86a9da183e5f3766e6310752b99ff647e38b09a9500d88e46e76 \ --hash=sha256:9fa8ae11da9e2b3126c6426f147e0fba88d96d65921799bb30c6abd1cb2c97fb \ - --hash=sha256:a0f62d0a58f4e7da165457e995725421e0064f2255d8eccebc49f41bbc23b109 \ - --hash=sha256:aaa2a54443eff1950ba5ddc6b6ccda0d9c84a364276a62f969bdf2a390650848 \ --hash=sha256:ad785e92e6dc634c21555edc8bd6b64957ab844541bcb96a1366c202951ae526 \ --hash=sha256:af8244b2bef6aaad6d92cda81372de7f8c8d36c9f0c3ea36e827c60e7d9467a0 \ --hash=sha256:b2f69dec1725e79a012d920df1707de5caf7ed5e08f3be4435e25803efc47458 \ @@ -628,23 +462,16 @@ pydantic-core==2.46.4 \ --hash=sha256:c1b3f518abeca3aa13c712fd202306e145abf59a18b094a6bafb2d2bbf59192c \ --hash=sha256:c50f2528cf200c5eed56faf3f4e22fcd5f38c157a8b78576e6ba3168ec35f000 \ --hash=sha256:c68fcd102d71ea85c5b2dfac3f4f8476eff42a9e078fd5faefff6d145063536b \ - --hash=sha256:c7a7bd4e39e8e4c12c39cd480356842b6a8a06e41b23a55a5e3e191718838ddf \ --hash=sha256:c94f0688e7b8d0a67abf40e57a7eaaecd17cc9586706a31b76c031f63df052b4 \ --hash=sha256:cbaf13819775b7f769bf4a1f066cb6df7a28d4480081a589828ef190226881cd \ --hash=sha256:cd2213145bcc2ba85884d0ac63d222fece9209678f77b9b4d76f054c561adb28 \ --hash=sha256:ce5c1d2a8b27468f433ca974829c44060b8097eedc39933e3c206a90ee49c4a9 \ - --hash=sha256:d396ec2b979760aaf3218e76c24e65bd0aca24983298653b3a9d7a45f9e47b30 \ - --hash=sha256:d51026d73fcfd93610abc7b27789c26b313920fcfb20e27462d74a7f8b06e983 \ - --hash=sha256:db06ffe51636ffe9ca531fe9023dd64bdd794be8754cb5df57c5498ae5b518a7 \ - --hash=sha256:e0d65b8c354be7fb5f720c3caa8bc940bc2d20ce749c8e06135f07f8ed95dd7c \ --hash=sha256:e739fee756ba1010f8bcccb534252e85a35fe45ae92c295a06059ce58b74ccd3 \ --hash=sha256:e846ae7835bf0703ae43f534ab79a867146dadd59dc9ca5c8b53d5c8f7c9ef02 \ --hash=sha256:e9c26f834c65f5752f3f06cb08cb86a913ceb7274d0db6e267808a708b46bc89 \ --hash=sha256:f027324c56cd5406ca49c124b0db10e56c69064fec039acc571c29020cc87c76 \ --hash=sha256:f99626688942fb746e545232e7726926f3be91b5975f8b55327665fafda991c7 \ - --hash=sha256:f9fa868638bf362d3d138ea55829cefb3d5f4b0d7f142234382a15e2485dbec4 \ --hash=sha256:fbdb89b3e1c94a30cc5edfce477c6e6a5dc4d8f84665b455c27582f211a1c72c \ - --hash=sha256:fc010ab034c8c7452522748bf937df58020d256ccae0874463d1f4d01758af8e \ --hash=sha256:fc3e9034a63de20e15e8ade85358bc6efc614008cab72898b4b4952bea0509ff # via pydantic pygments==2.20.0 \ diff --git a/rewrite_reference/OVERVIEW.md b/rewrite_reference/OVERVIEW.md deleted file mode 100644 index 75deb3b..0000000 --- a/rewrite_reference/OVERVIEW.md +++ /dev/null @@ -1,390 +0,0 @@ -# Trendify: Current Behavior Reference (pre-rewrite) - -This document is a functional inventory of `trendify` as it exists today, written to support a -clean rewrite. Goal: nothing in this list should silently disappear during the rewrite, and the -on-disk/JSON schema for `DataProduct` objects should stay compatible (or the break should be a -deliberate, documented decision) so that old `data_products.json` files stay loadable. - -The full pydantic JSON Schema for every model is exported alongside this file in -[`pydantic_schemas.json`](pydantic_schemas.json) (generated by `dump_schemas.py`, re-runnable any -time with `.venv/bin/python rewrite_reference/dump_schemas.py`). - -## 1. What Trendify does, end to end - -Trendify batch-processes many "run" directories of raw data (e.g. simulation outputs) into a -common set of typed **data products** (points, traces, table entries, histogram entries, axis -lines), sorts those products by user-defined **tags** into a nested directory tree, and then -renders each tag's products into assets: matplotlib static images, CSV tables, or an interactive -Plotly/Dash/Streamlit dashboard. - -Pipeline (`trendify.api.make_it_trendy`, also reachable piecewise via CLI): - -1. **Generate** (`make_products` → `DataProductGenerator.process_and_save`) - - User supplies a `ProductGenerator`: `Callable[[Path], list[DataProduct]]`. - - Mapped over each input directory (optionally in parallel via `ProcessPoolExecutor`). - - Each directory's returned products are wrapped in a `DataProductCollection` and serialized to - `data_products.json` (name configurable) in that same directory. -2. **Sort** (`sort_products` → `DataProductCollection.sort_by_tags_single_directory`) - - Reads each directory's `data_products.json`, gets the set of tags present - (`DataProductCollection.get_tags`), and for each tag writes a sub-collection into - `output_dir//.json`. - - Tuple tags become nested folders (`tags=[("a","b")]` → `output_dir/a/b/`). - - Index numbers are stable/deduped per source file via `ProductIndexMap`, a small JSON - file (`index_map`) per output folder that maps `{source_path: index}`, guarded with a - `FileLock` (`reserving_index.lock`) so parallel workers don't clash. -3. **Render static assets** (`make_tables_and_figures` → `DataProductCollection.process_collection`) - - Runs once per sorted tag-directory. - - Loads all JSONs in that directory back into a collection, then for each tag present: - - `TableEntry`s → `TableBuilder` → 3 CSVs: `_melted.csv`, `_pivot.csv` (if pivot - succeeds), `_stats.csv` (min/mean/max/3-sigma, if pivot columns are numeric). - - `Point2D`/`Trace2D`/`AxLine` → `XYDataPlotter.handle_points_and_traces` draws onto a shared - matplotlib `SingleAxisFigure`. - - `HistogramEntry` → `Histogrammer.handle_histogram_entries` draws onto the *same* shared - figure (so XY and histogram products with the same tag are overlaid on the same axes today - — this looks like it may be accidental behavior worth revisiting in the rewrite). - - All `Format2D`s seen for the tag are merged via `Format2D.union_from_iterable` (requires - titles/labels/legend to be identical across products sharing a tag, min/max of limits) and - applied once to the figure, which is then saved as `.jpg` and closed. -4. **Include files** (`make_include_files`, optional) — walks the static-assets tree and writes - `include.md` per directory with MkDocs `{% include %}` / `{{ read_csv(...) }}` snippets for - figures/tables/subdirectories, so a docs site can pull in results without manual linking. -5. **Interactive serving** — two independent paths, not really unified: - - **Plotly/Dash** (`serve_products_to_plotly_dashboard` / - `DataProductCollection.serve_plotly_dashboard` / `trendify.plotly_dashboard`): builds one big - `dash.Dash` app with a category+tag dropdown UI, one XY figure + table + histogram section per - tag, client-side callbacks for legend/grid/autorange toggles and histogram bin count. Runs via - Flask dev server or `waitress` if installed and `debug=False`. - - **Streamlit** (`trendify.streamlit`, wired up via `trendify make dashboard`): reads the - *sorted products* directory tree directly off disk (using the presence of `index_map` files to - discover tags), renders a collapsible sidebar of nested tags built from folder structure, and - on selection produces one `PlotlyFigure` via - `DataProductCollection.process_tag_for_streamlit` (note: this path calls - `add_to_plotly` per-product rather than the matplotlib-only `plot_to_ax`/`Histogrammer` route - used for static assets — so static and interactive rendering are two separate code paths that - must each be kept in sync with new product types). - - There is a **third**, older, largely dead HTTP-serving path: `trendify.server` / a - `Flask` app with hardcoded `DATABASE_ROOT` pointing at a specific developer's machine, and - `trendify.local_server` (`TrendifyProductServerLocal`), a slightly more generic Flask JSON API - over a products directory keyed by URL path segments. Both exist to feed a **Grafana** - dashboard (see `UQL_TableEntry` / `UQL_Point2D` / `UQL_Trace2D` query strings in - `data_product_collection.py`, and the fully commented-out `make_grafana_dashboard` / - `make_grafana_panels` functions that depended on a `grafana_api` package). Grafana support is - effectively disabled today (commented out) but the dependency (`grafana-api`) is still listed - in `pyproject.toml` and `ProductType` docstring/enum still lists Grafana-flavored intent. TODO.md - says "Sunset production server, grafana, etc." — treat this whole vertical (`server.py`, - `local_server.py`, `production_server.py`, Grafana comments, UQL strings) as a candidate for - deletion rather than porting, unless the user says otherwise. - -## 2. Package layout (`src/trendify/`) - -``` -trendify/ -├── __init__.py # re-exports trendify.api.* and trendify.server.* as top-level `trendify.X` -├── api/ -│ ├── __init__.py # re-exports all submodule __all__s — this is the umbrella "trendify.api" namespace -│ ├── api.py # pipeline orchestration functions (make_products, sort_products, make_tables_and_figures, make_it_trendy, serve_products_to_plotly_dashboard, make_include_files) -│ ├── base/ -│ │ ├── data_product.py # DataProduct (pydantic base class + subclass registry), ProductList, ProductGenerator -│ │ ├── helpers.py # Tag/Tags type aliases, HashableBase, ProductType enum, DATA_PRODUCTS_FNAME_DEFAULT -│ │ └── pen.py # Pen (matplotlib line/scatter style + rgba/contrast helpers) -│ ├── formats/ -│ │ ├── format2d.py # AxisScale enum, Format2D, PlottableData2D, XYData base classes -│ │ └── table.py # TableEntry -│ ├── styling/ -│ │ ├── grid.py # GridTheme enum, GridAxis, Grid (+ theme presets: MATLAB/LIGHT/DARK) -│ │ ├── legend.py # LegendLocation enum, Legend (+ plotly position mapping) -│ │ └── marker.py # Marker (matplotlib scatter style + plotly symbol mapping) -│ ├── plotting/ -│ │ ├── plotting.py # SingleAxisFigure (matplotlib fig+ax wrapper), PlotlyFigure (go.Figure wrapper) -│ │ ├── point.py # Point2D -│ │ ├── trace.py # Trace2D -│ │ ├── axline.py # LineOrientation enum, AxLine -│ │ └── histogram.py # HistogramStyle, HistogramEntry -│ └── generator/ -│ ├── data_product_collection.py # DataProductCollection, ProductEntryMetadata, ProductIndexMap, flatten/atleast_1d/_squeeze helpers -│ ├── data_product_generator.py # DataProductGenerator (runs user callable, writes JSON) -│ ├── histogrammer.py # Histogrammer (matplotlib histogram rendering) -│ ├── table_builder.py # TableBuilder (melted/pivot/stats CSV export) -│ └── xy_data_plotter.py # XYDataPlotter (matplotlib + plotly XY rendering) -├── cli.py # argparse CLI ("trendify ..."); NOTE pyproject.toml script entry point is `trendify.CLI:trendify` (wrong case) — currently broken unless case-insensitive FS -├── examples.py # sample data generator + example_data_product_generator + `_main`/`make_sample_data` CLI entry -├── gallery.py # standalone example generator (sine/scatter/histogram/table) used by `trendify make gallery` -├── plotly_dashboard.py # PlotlyDashboardGenerator + generate_plotly_dashboard (Dash app builder) -├── streamlit.py # Streamlit dashboard (tag sidebar + PlotlyFigure viewer), `make_streamlit` scaffolding writer -├── server.py # legacy Flask/Grafana server, hardcoded path, largely dead -├── local_server.py # more generic Flask JSON API over a products dir, also Grafana-oriented -└── production_server.py # waitress wrapper to run a Dash app or DataProductCollection in "production" -``` - -Other loose files at repo root (`alternative_tag_selectors.py`, `custom_handler.py`, -`implementation_recommendation.py`, `improved_tag_selection.py`, `sample_hierarchical_data.py`, -`test.py`) are **not part of the installed package** (they live outside `src/`) — they read like -scratch experiments/prototypes for streamlit tag selection UI. Worth a skim before the rewrite in -case they contain ideas already validated, but they are not part of the current shipped behavior. - -## 3. The data model - -### 3.1 `DataProduct` — the polymorphic base (`api/base/data_product.py`) - -- Pydantic `BaseModel`, `model_config = ConfigDict(extra="allow")` on the base class (subclasses - mostly override to `extra="forbid"`). -- Fields: `tags: Tags` (required), `metadata: dict[str, str] = {}`. -- `product_type: str` is a `@computed_field` property returning `type(self).__name__` — this is - what gets serialized into JSON and is the polymorphism discriminator. -- `__init_subclass__` auto-registers every subclass by class name into a module-level - `_data_product_subclass_registry: dict[str, type[DataProduct]]`. **This means product types are - looked up by bare class name, globally, at import time** — two different classes named - `Trace2D` in different modules would collide. Worth deciding deliberately in the rewrite whether - to keep name-based polymorphism or move to `pydantic`'s built-in discriminated unions / - `Annotated[Union[...], Field(discriminator=...)]`. -- `deserialize_child_classes(cls, key, **kwargs)`: manually walks `kwargs[key]` (a list of raw - dicts), pops `"product_type"` from each, looks up the class in the registry, and constructs it. - This is how `DataProductCollection.__init__` turns a raw `elements: [...]` JSON list into actual - typed objects instead of relying on pydantic's own polymorphic validation — a hand-rolled - substitute for `SerializeAsAny`/discriminated unions. -- `append_to_list(l)` / `set_metadata(new)`: fluent helpers so example code can do - `Trace2D(...).append_to_list(products).set_metadata({...})` inline in a list comprehension. -- `ProductList = list[SerializeAsAny[InstanceOf[DataProduct]]]` — the type alias used everywhere a - list of heterogeneous products is expected; `SerializeAsAny` is what makes `model_dump_json()` - include subclass-specific fields instead of truncating to the base class's fields. -- `ProductGenerator = Callable[[Path], ProductList]` — the type user code must implement. - -### 3.2 Concrete `DataProduct` subclasses (the actual schema surface) - -All in `pydantic_schemas.json`. Summarized here with every field, default, and constraint: - -| Class | Base | Fields (name: type = default) | `extra` | -|---|---|---|---| -| `TableEntry` | `DataProduct` | `row: float\|str`, `col: float\|str`, `value: float\|str\|bool`, `unit: str\|None = None` | forbid | -| `PlottableData2D` | `DataProduct` | `format2d: Format2D\|None = None` | (inherits `allow`) | -| `XYData` | `PlottableData2D` | *(no new fields — pure marker base class)* | (inherits) | -| `Point2D` | `XYData` | `x: float\|str`, `y: float\|str`, `marker: Marker\|None = Marker()` | forbid | -| `Trace2D` | `XYData` | `points: list[Point2D]`, `pen: Pen = Pen()` | forbid | -| `AxLine` | `PlottableData2D` | `value: float`, `orientation: LineOrientation`, `pen: Pen = Pen()` | forbid | -| `HistogramEntry` | `PlottableData2D` | `value: float\|str`, `tags: Tags` (redeclared), `style: HistogramStyle\|None = HistogramStyle()` | forbid | - -Every `DataProduct` (via base) also carries `tags: Tags`, `metadata: dict[str,str] = {}`, and the -computed `product_type: str`. - -`Tag = Union[tuple[Hashable, ...], Hashable]`, `Tags = list[Tag]` — tags are either scalars -(`"foo"`) or tuples (`("foo", "bar")`, which becomes nested folders `foo/bar/` when sorting). -Note: `Hashable` is extremely permissive — in practice tags are strings or tuples of strings; the -rewrite might want to narrow this type for safety. - -### 3.3 Style / format value objects (not `DataProduct`s themselves — used as fields) - -- **`Pen`** (`HashableBase`, `extra="forbid"`): `color: tuple[float,float,float]|tuple[float,float,float,float]|str = "k"`, `size: float = 1`, `alpha: float = 1`, `zorder: float = 0`, `linestyle: str|tuple[int,tuple[int,...]] = "-"`, `label: str|None = None`. Methods: `as_scatter_plot_kwargs()` (→ matplotlib `plot` kwargs), `_convert_linestyle_to_plotly()`, `rgba`/`rgb` properties, `get_contrast_color(background_luminance=1.0)` (WCAG-style relative luminance blend for legible text/hover color). -- **`Marker`** (`HashableBase`, `extra="forbid"`): `color: str = "k"`, `size: float = 5`, `alpha: float = 1`, `zorder: float = 0`, `label: str|None = None`, `symbol: str = "."`. `from_pen(pen, symbol=".")` classmethod (note: **this method is buggy** — `pen.model_dump().pop("linestyle")` pops and returns just the *value* of `linestyle`, discarding the rest of the dict, then tries to pass that single value as `**kwargs`, which will raise `TypeError` on any real invocation; grep shows it's called from `Trace2D.propagate_format2d_and_pen`, which itself appears unused elsewhere — confirm before quietly reimplementing "the same way"). `as_scatter_plot_kwargs()` (has a duplicate `"marker"` key literal — harmless dict-literal bug, last one wins). `plotly_symbol` property maps a fixed matplotlib marker-char set to plotly symbol names (unmapped symbols silently fall back to `"circle"`). `rgba`, `get_contrast_color()` mirror `Pen`'s. -- **`Legend`** (`HashableBase`): `visible: bool = True`, `title: str|None`, `framealpha: float = 1`, `loc: LegendLocation = BEST`, `ncol: int = 1`, `fancybox: bool = True`, `edgecolor: str = "black"`, `zorder: int = 10`, `bbox_to_anchor: tuple[float,float]|None = None`. `LegendLocation` StrEnum mirrors matplotlib's location strings. `to_kwargs()` → matplotlib legend kwargs subset (does *not* include `zorder`, `edgecolor`, or `bbox_to_anchor` — those are applied separately by the caller). `plotly_location` property: hand-maintained lookup table mapping each `LegendLocation` to `{x,y,xanchor,yanchor}` plus a `bbox_to_anchor`-override branch with its own anchor-inference logic. -- **`GridAxis`** (`HashableBase`, `extra="forbid"`): `show: bool = False`, `pen: Pen = Pen(color="gray", alpha=1.0, size=0.75, linestyle="-")`. -- **`Grid`** (`HashableBase`, `extra="forbid"`): `major: GridAxis = GridAxis(show=False)`, `minor: GridAxis = GridAxis(show=False)`, `enable_minor_ticks: bool = False`, `zorder: float = -1`. `GridTheme` enum (`MATLAB`, `LIGHT`, `DARK`) with `Grid.from_theme(name)` classmethod producing hand-tuned pen presets. `union_from_iterable` requires all inputs to have *identical* major/minor/enable_minor_ticks/zorder (raises via tuple-unpack of a length-checked set if they differ) — same "must all agree" merge pattern as `Format2D`. -- **`Format2D`** (`HashableBase`, `extra="forbid"`): `title_fig`, `title_ax`, `label_x`, `label_y: str|None = None`; `legend: Legend|None = Legend()`; `lim_x_min/max`, `lim_y_min/max: float|None = None`; `grid: Grid|None = None`; `scale_x`, `scale_y: AxisScale = LINEAR`; `figure_width: float = 6.4`, `figure_height: float = 4.8`. `union_from_iterable(format2ds)`: title/legend/labels must be identical across the set (single-element `set()` unpack — raises `ValueError` on mismatch); limits take `min`/`max` across the group; grid is unioned via `Grid.union_from_iterable`. This is the mechanism by which multiple products sharing a tag "agree" on one figure's formatting — it's a hard failure mode if two products on the same tag specify conflicting titles/labels, which the rewrite should probably surface as a clearer error rather than an unpacking `ValueError`. -- **`HistogramStyle`** (`HashableBase`): `color: str = "k"`, `label: str|None`, `histtype: str = "stepfilled"`, `alpha_edge: float = 0`, `alpha_face: float = 0.3`, `linewidth: float = 2`, `zorder: int = 1`, `bins: int|list[int]|tuple[int]|NDArray|None = None`. `as_plot_kwargs()` → matplotlib `hist()` kwargs. `rgba_face`/`rgba_edge`/`rgb_face`/`rgb_edge`/`get_face_contrast_color()` mirror `Pen`. - -All the `*_contrast_color` / `rgba` implementations (`Pen`, `Marker`, `HistogramStyle`) are -**copy-pasted three times** with identical logic — a natural dedup target for the rewrite (e.g. a -shared `ColorMixin` or free functions taking `color, alpha`). - -### 3.4 Collections and bookkeeping (`api/generator/data_product_collection.py`) - -- **`DataProductCollection`** (plain `BaseModel`, not `DataProduct`): `derived_from: Path|None`, - `elements: ProductList = []`. Custom `__init__` calls - `DataProduct.deserialize_child_classes(key="elements", **kwargs)` before `super().__init__` — this - is the polymorphic-load hook. Rich query API: `get_tags(type|None)`, `add_products(*products)` - (flattens nested lists/generators via `flatten()`), `drop_products(tag=, object_type=)`, - `get_products(tag=, object_type=)` (returns a new collection), `get_products_of_type(object_type, - tag=)` (returns a typed `list[R]`, more convenient than `get_products(...).elements`), `union(*collections)` - classmethod, `collect_from_all_jsons(*dirs, recursive=, data_products_filename=)` classmethod - (glob + union), `sort_by_tags` / `sort_by_tags_single_directory` (the sort step described in §1), - `process_collection` (the static-asset-rendering step described in §1), - `process_tables_for_tag`, `process_tag_for_streamlit` (the Streamlit-only rendering path), - `generate_plotly_dashboard`/`serve_plotly_dashboard`/`collect_and_serve_plotly_dashboard`. -- **`ProductEntryMetadata`**: `{source: Path}`, hashed via its own JSON dump (used as a dict key). -- **`ProductIndexMap`**: `{entries: dict[int, ProductEntryMetadata]}` persisted as a JSON file - named literally `index_map` (no extension) inside each sorted tag directory. `get_index(save_dir, - metadata)` classmethod does read-modify-write under a `FileLock` to hand out a stable integer - index per source file per tag directory — this is what keeps re-running the pipeline from - duplicating or renumbering already-sorted product files, and is also what the Streamlit UI's - `get_tags()` uses to discover which folders are "real" tags (it globs for files literally named - `index_map`). -- Free functions `flatten(obj)` (recursive flatten that stops at `str`/`bytes`/`DataProduct`), - `atleast_1d(obj)`, `_squeeze(obj)` — small utilities used throughout for accepting either a - scalar tag or tuple tag interchangeably. -- Three multi-line UQL query string constants (`UQL_TableEntry`, `UQL_Point2D`, `UQL_Trace2D`) and - a fully commented-out `make_grafana_panels` classmethod — dead Grafana integration, see §1. - -## 4. Rendering pipelines (kept deliberately separate today — a rewrite could unify them) - -| | Static (matplotlib) | Interactive (Plotly/Dash) | Interactive (Streamlit) | -|---|---|---|---| -| Entry point | `DataProductCollection.process_collection` | `plotly_dashboard.generate_plotly_dashboard` | `streamlit.make_dashboard` | -| Driven by | sorted-by-tag directory tree on disk | one `DataProductCollection` in memory | sorted-by-tag directory tree on disk (`index_map` discovery) | -| Per-tag output | one shared matplotlib `Axes` (`SingleAxisFigure`), XY + histogram overlaid, saved to `.jpg` | one Dash "tab" per tag with XY graph + table + histogram sections, dropdown-driven | one `PlotlyFigure` per tag built by `process_tag_for_streamlit`, calling each product's `add_to_plotly` | -| `add_to_plotly` used? | No (matplotlib-only `plot_to_ax`) | No (dashboard rebuilds traces from raw fields, duplicating logic in `_create_xy_figure`/`_create_histogram_figure`) | Yes — the only caller of `Point2D.add_to_plotly`, `Trace2D.add_to_plotly`, `AxLine.add_to_plotly`, `HistogramEntry.add_to_plotly` | -| Format handling | `Format2D.union_from_iterable` across all products for the tag, applied once | ad hoc: takes the *first* product's `format2d` and applies a hand-rolled subset of fields directly to Dash's `go.Figure.update_layout` | `Format2D.union_from_iterable`, applied via `PlotlyFigure.apply_format`/`apply_grid` | - -Consequence for the rewrite: **`add_to_plotly` is a real, load-bearing interface** (each -`PlottableData2D` subclass implements it) but it's currently only exercised by the Streamlit path -— the Dash dashboard (`plotly_dashboard.py`) reimplements equivalent logic from scratch with its -own color-cycling and legend-dedup bookkeeping. `PlotlyFigure.add_data_product` in `plotting.py` -is a stub that literally raises `NotImplementedError()` before an unreachable line calling -`product.add_to_plotly` — dead/aspirational code for a unification that never happened. If the -rewrite unifies these three renderers, `add_to_plotly` + `Format2D`/`Grid`/`Legend` "union and -apply" is the design to standardize on; the Dash-specific bespoke logic in `plotly_dashboard.py` -is the one to retire. - -## 5. CLI (`cli.py`) - -`trendify` (argparse, subcommands via `add_subparsers`): - -- `trendify make static -i -g -o [-n procs] [-f data-products-filename]` - — full pipeline: generate → sort → static assets. -- `trendify make dashboard -o ` — writes a `streamlit_run.py` launcher script into - `/assets/dashboard/` (does **not** itself generate/sort products — assumes that already happened). -- `trendify make gallery [-o ]` — runs the bundled example generator (`gallery.py`), then - shells out to `trendify products-sort` and `trendify assets-make-static` as subprocesses. -- `trendify serve plotly -i -g [--title] [--host] [--port]` — - generate then serve Dash dashboard directly (no sort/asset step). -- 1:1 API wrappers: `products-make`, `products-sort`, `products-serve`, `assets-make-static` — thin - argparse shims over `api.make_products` / `api.sort_products` / - `api.serve_products_to_plotly_dashboard` / `api.make_tables_and_figures`. -- Global `-v`/`-vv` verbosity flag → `logging.basicConfig` writing to both stdout and - `./trendify.log` (relative to CWD, always append mode — this is likely the "multiprocessing - logging broken on Windows" TODO item, since each subprocess calling `basicConfig` again / writing - to the same file without coordination is a known cross-platform hazard). -- `UserMethod.process_argument` resolves `-g/--product-generator` from a string like - `package.module:Class.method` or `/path/to/file.py:function`, supporting dotted attribute access - for `Class.method` — this is the mechanism for pointing the CLI at user code without a plugin - system. -- **Known bug**: `pyproject.toml`'s `[project.scripts]` maps `trendify = "trendify.CLI:trendify"` - but the module is `trendify/cli.py` (lowercase). This only works on case-insensitive filesystems - (macOS default, Windows) and would fail to import on Linux/case-sensitive installs. Worth fixing - outright rather than "preserving" in the rewrite. - -## 6. Other entry points / helper modules - -- **`examples.py`**: `make_example_data(workdir, n_folders=10)` synthesizes noisy multi-frequency - sine-wave CSVs (`results.csv` + `stdin.csv` per numbered subfolder under `workdir/models/`). - `example_data_product_generator(workdir)` is the reference implementation of a `ProductGenerator` - — demonstrates nearly every product type and format option (traces with multiple tags on the - same data, log/linear scale combinations, `AxLine`s, `Point2D` scatter of point-count vs run, - `TableEntry` + `HistogramEntry` + companion mean `AxLine`). Good source of "what must still work" - examples for rewrite acceptance tests. Also exposes a `trendify_make_sample_data` console script - (`make_sample_data`) and a `_main()` used as `python -m trendify.examples`-style smoke test - (generates 100 runs, makes products, sorts, renders tables/figures — no dashboard). -- **`gallery.py`**: independent of `examples.py`; hand-builds a small fixed set of products (sine - wave with peak detection, two Gaussian clusters + centroids, three-distribution histogram - comparison, a distributions×metrics stats table), asserts on `product.py`... actually copies - itself (`gallery.py`) into the output dir for user reference, writes `data_products.json` - directly (skipping the generate step), then **shells out** to the `trendify` console script - (`subprocess.run(["trendify", "products-sort", ...])` then `["trendify", - "assets-make-static", ...])`, with a Windows `shell=True` branch. This is the only place in the - codebase that invokes trendify as a subprocess rather than calling the Python API directly — - worth deciding whether the rewrite keeps that indirection (it does exercise the actual installed - console script, which is a plausible intentional integration-test-like design) or calls the API - functions in-process like everywhere else. -- **`streamlit.py`**: `make_streamlit(trendify_dir)` writes a small launcher script + a - `.streamlit/config.toml` theme file (hardcoded to `trendify` pink `#e92063`). `make_dashboard` - itself does the actual UI: sidebar built by recursively grouping the tag tuples found via - `get_tags` (which walks for files named `index_map`) into nested `st.expander`s with buttons, - `st.session_state.selected_tags` tracks selection, main page uses `@st.cache_data` on - `process_tag` (keyed by tag+dir) to avoid recomputation, then renders one `PlotlyFigure` with a - small form for height/tooltip-mode controls. Only handles the "collection produced a - `PlotlyFigure`" case; anything else shows `st.warning(...)`. Note the docs/issue-tracker URLs and - `st.logo` reference bundled SVG assets (`trendify.assets/logo.svg`, `logo_white_bg.svg`) that - must ship with the package (`importlib.resources`). -- **`production_server.py`**: thin `waitress.serve(app.server, ...)` wrapper; `run_production_server` - accepts either a `dash.Dash` or a `DataProductCollection` (builds the dashboard for you in the - latter case) and requires `waitress`+`dash` be importable or exits via `sys.exit(1)` with a - printed message (not a raised exception — inconsistent with the rest of the codebase's use of - `logger`). -- **`server.py`** / **`local_server.py`**: see §1, candidates for deletion. - -## 7. Cross-cutting behaviors worth preserving deliberately - -- **Parallelism model**: `map_callable` in `api.py` is the single place that decides - sequential-`for`-loop vs `ProcessPoolExecutor`, based on `n_procs`. Every stage (generate, sort, - render) goes through it, so `n_procs=1` is explicitly documented as the "get a real traceback" - debug mode. Any rewrite of the execution model should preserve an easy "run everything in-process - with full tracebacks" switch. -- **Tag-driven nested directories**: a tuple tag `("a", "b")` becomes a two-level directory, a - scalar tag `"a"` becomes one level. This convention is depended on by: the sort step (path - construction), the Streamlit tag discovery (splitting `Path.relative_to(products_dir).parts`), - and `make_include_files`. If tag typing gets stricter in the rewrite, this folder convention is - the main external contract to keep, since users' existing sorted-output directories (and anyone - scripting around them, e.g. MkDocs includes) depend on it. -- **JSON compatibility / discriminator field**: every serialized `DataProduct` JSON object has a - `product_type` string key holding the exact class name. If the rewrite wants old - `data_products.json` files (already generated and sitting on disk from prior runs) to keep - loading, either keep class names identical or write an explicit migration/alias step — don't - rename `Trace2D`/`Point2D`/etc. without a plan for existing data. - [`pydantic_schemas.json`](pydantic_schemas.json) is the field-level source of truth to diff - against for any planned schema change. - Similarly, `HashableBase.__hash__` (`hash((type(self),) + tuple(self.__dict__.values()))`) is - used to de-duplicate `Pen`/`Marker`/`Grid`/`HistogramStyle`/`Format2D` instances via `set()` in - several places (`Trace2D` grouping by pen in `plotly_dashboard`, `Histogrammer` grouping by - style, `Format2D.union_from_iterable`, `Grid.union_from_iterable`). This requires every field on - those classes to itself be hashable — e.g. `bbox_to_anchor` is a `tuple` not a `list`, - `linestyle` accepts a `tuple[int, tuple[int,...]]` not `list[int]`, precisely so instances stay - hashable. Any new field added to these classes must preserve hashability or these `set()`-based - dedup/merge operations will start raising `TypeError: unhashable type`. -- **`extra="forbid"` vs `extra="allow"`**: the `DataProduct` base class allows extra fields (so old - JSON with now-unknown extra keys still loads), but essentially every concrete subclass and every - style class overrides to `extra="forbid"` (stricter, catches typos). Worth keeping this pattern - intentionally rather than picking one globally. -- **Metadata dict** (`dict[str,str]`) on every product is surfaced as a hover tooltip in both the - Streamlit-fed Plotly figures (`add_to_plotly` implementations build an HTML `
`-joined - `hovertemplate`) and was intended for Grafana tooltips (per docstrings) — a real, used feature to - keep, not a documentation artifact. - -## 8. Known bugs / rough edges observed while reading (not exhaustive, worth triaging before porting "as-is") - -1. `Marker.from_pen` (`marker.py`) — `pen.model_dump().pop("linestyle")` discards the whole dict - and returns one value, then splats it as `**kwargs` into `Marker(...)`. Will raise at runtime if - ever actually called with a realistic `Pen`. Only caller: `Trace2D.propagate_format2d_and_pen` - (itself grep-unreferenced elsewhere in `src/`). -2. `Marker.as_scatter_plot_kwargs` has the dict key `"marker"` twice (last wins; harmless but - dead code). -3. `PlotlyFigure.apply_format`'s x/y-range-under-log-scale branches both check - `if format2d.scale_x == AxisScale.LOG` / `elif format2d.scale_x == AxisScale.LOG` (the `elif` - should almost certainly be `AxisScale.LINEAR` given the surrounding log10-transform logic) — the - non-log branch for setting a plain range is currently unreachable. -4. `PlotlyFigure.add_data_product` raises `NotImplementedError()` unconditionally with unreachable - code after it — stub method, never completed, never called (Dash path doesn't use it). -5. `Histogrammer.plotly_histogram` also raises `NotImplementedError()` — histogram Plotly rendering - actually happens via `HistogramEntry.add_to_plotly` instead; this method looks unused/orphaned. -6. `pyproject.toml` console script points at `trendify.CLI:trendify` — wrong case vs. actual - `trendify/cli.py`. -7. `server.py` has a hardcoded absolute path (`/Users/talbotknighton/...`) as `DATABASE_ROOT` — - can't work on any other machine as-is. -8. Grid/Format2D "union" merges silently assume homogeneity (single-element `set()` unpacking - raises a bare `ValueError: too many values to unpack` with no context if two products sharing a - tag disagree on title/label/grid) — a confusing failure mode for end users that a rewrite could - turn into a clear, actionable error. -9. Static asset rendering (`process_collection`) draws XY data and histogram data onto the *same* - `SingleAxisFigure`/axes for a shared tag — if a tag is used for both `Point2D`/`Trace2D` and - `HistogramEntry` simultaneously, they'll overlay on one plot, which is very likely unintended - given the interactive dashboards keep these as separate sections per tag. -10. `TableEntry.value: float | str | bool` but the class docstring only documents `float | str` — - minor doc/type drift. - -## 9. Dependencies (from `pyproject.toml`) - -Runtime: `dash`, `filelock`, `flask`, `grafana-api` (dead weight per §1/§6), `jinja2`, `matplotlib`, -`numpy`, `numpydantic`, `pandas`, `plotly`, `pydantic>=2`, `scipy` (only used, optionally, for KDE -in the Dash histogram section), `streamlit`, `strenum` (redundant with the stdlib `enum.StrEnum` -already used directly in `format2d.py`/`legend.py`/`grid.py` on Python ≥3.11 — the project already -requires `>=3.11`, so the `strenum` dependency in `helpers.py`/`examples.py` is likely vestigial), -`supervisor`, `waitress`. Python `>=3.11`. - -## 10. Suggested reading order if resuming this rewrite later - -1. `api/base/data_product.py` + `api/base/helpers.py` — the polymorphism and tagging foundation. -2. `api/generator/data_product_collection.py` — the central data structure and sort/query/process logic. -3. `pydantic_schemas.json` — exact current wire format, field by field. -4. `api/plotting/*.py` + `api/formats/format2d.py` — the concrete product types and their - `plot_to_ax`/`add_to_plotly` dual implementations. -5. `api/generator/{histogrammer,table_builder,xy_data_plotter}.py` + `api/api.py` — how products - become static assets. -6. `plotly_dashboard.py` vs `streamlit.py` — the two divergent interactive renderers (§4 table). -7. `cli.py` — how it's all wired together for end users. diff --git a/rewrite_reference/dump_schemas.py b/rewrite_reference/dump_schemas.py deleted file mode 100644 index a2c2b76..0000000 --- a/rewrite_reference/dump_schemas.py +++ /dev/null @@ -1,48 +0,0 @@ -""" -Dumps JSON schemas for every pydantic model in trendify's public API surface. -Run with the project's venv: .venv/bin/python dump_schemas.py -""" - -import json -from pathlib import Path - -import trendify as t -from trendify.api.base.data_product import DataProduct, _data_product_subclass_registry -from trendify.api.generator.data_product_collection import ( - DataProductCollection, - ProductEntryMetadata, - ProductIndexMap, -) - -# All pydantic BaseModel classes that make up the public schema surface. -models = { - # base - "DataProduct": DataProduct, - # data product subclasses (auto-registered via __init_subclass__) - **_data_product_subclass_registry, - # collection / bookkeeping - "DataProductCollection": DataProductCollection, - "ProductEntryMetadata": ProductEntryMetadata, - "ProductIndexMap": ProductIndexMap, - # style / format - "Pen": t.Pen, - "Marker": t.Marker, - "Legend": t.Legend, - "Grid": t.Grid, - "GridAxis": t.GridAxis, - "Format2D": t.Format2D, - "HistogramStyle": t.HistogramStyle, -} - -out = {} -for name, cls in sorted(models.items()): - try: - out[name] = cls.model_json_schema() - except Exception as e: - out[name] = {"__error__": str(e)} - -dest = Path("/home/gable/git/trendify/rewrite_reference/pydantic_schemas.json") -dest.write_text(json.dumps(out, indent=2, default=str)) -print(f"Wrote {len(out)} schemas to {dest}") -for name in sorted(out): - print(" -", name) diff --git a/rewrite_reference/pydantic_schemas.json b/rewrite_reference/pydantic_schemas.json deleted file mode 100644 index f432280..0000000 --- a/rewrite_reference/pydantic_schemas.json +++ /dev/null @@ -1,4479 +0,0 @@ -{ - "AxLine": { - "$defs": { - "AxisScale": { - "enum": [ - "linear", - "log" - ], - "title": "AxisScale", - "type": "string" - }, - "Format2D": { - "additionalProperties": false, - "description": "Formatting data for matplotlib figure and axes\n\nAttributes:\n title_fig (Optional[str], optional): Sets [figure title][matplotlib.figure.Figure.suptitle]. Defaults to None.\n legend (Optional[Legend], optional): Sets [legend style][trendify.api.styling.legend.Legend]. Defaults to Legend().\n title_ax (Optional[str], optional): Sets [axis title][matplotlib.axes.Axes.set_title]. Defaults to None.\n label_x (Optional[str], optional): Sets [x-axis label][matplotlib.axes.Axes.set_xlabel]. Defaults to None.\n label_y (Optional[str], optional): Sets [y-axis label][matplotlib.axes.Axes.set_ylabel]. Defaults to None.\n lim_x_min (float | None, optional): Sets [x-axis lower bound][matplotlib.axes.Axes.set_xlim]. Defaults to None.\n lim_x_max (float | None, optional): Sets [x-axis upper bound][matplotlib.axes.Axes.set_xlim]. Defaults to None.\n lim_y_min (float | None, optional): Sets [y-axis lower bound][matplotlib.axes.Axes.set_ylim]. Defaults to None.\n lim_y_max (float | None, optional): Sets [y-axis upper bound][matplotlib.axes.Axes.set_ylim]. Defaults to None.\n grid (Grid | None,optional): Sets the [grid][matplotlib.pyplot.grid]. Defaults to None.\n scale_x (AxisScale, optional): Sets the x axis scale to an option from [AxisScale][trendify.api.formats.format2d.AxisScale]. Defaults to AxisScale.LINEAR\n scale_y (AxisScale, optional): Sets the y axis scale to an option from [AxisScale][trendify.api.formats.format2d.AxisScale]. Defaults to AxisScale.LINEAR\n figure_width (float, optional): Sets the of the width of rendered figure in inches. Defaults to 6.4.\n figure_height (float, optional): Sets the of the height of rendered figure in inches. Defaults to 4.8.", - "properties": { - "title_fig": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Title Fig" - }, - "legend": { - "anyOf": [ - { - "$ref": "#/$defs/Legend" - }, - { - "type": "null" - } - ], - "default": { - "visible": true, - "title": null, - "framealpha": 1.0, - "loc": "best", - "ncol": 1, - "fancybox": true, - "edgecolor": "black", - "zorder": 10, - "bbox_to_anchor": null - } - }, - "title_ax": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Title Ax" - }, - "label_x": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Label X" - }, - "label_y": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Label Y" - }, - "lim_x_min": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Lim X Min" - }, - "lim_x_max": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Lim X Max" - }, - "lim_y_min": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Lim Y Min" - }, - "lim_y_max": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Lim Y Max" - }, - "grid": { - "anyOf": [ - { - "$ref": "#/$defs/Grid" - }, - { - "type": "null" - } - ], - "default": null - }, - "scale_x": { - "$ref": "#/$defs/AxisScale", - "default": "linear" - }, - "scale_y": { - "$ref": "#/$defs/AxisScale", - "default": "linear" - }, - "figure_width": { - "default": 6.4, - "title": "Figure Width", - "type": "number" - }, - "figure_height": { - "default": 4.8, - "title": "Figure Height", - "type": "number" - } - }, - "title": "Format2D", - "type": "object" - }, - "Grid": { - "additionalProperties": false, - "description": "Container for major and minor grid line configuration.\n\nAttributes:\n major (GridAxis): Configuration for major grid lines.\n minor (GridAxis): Configuration for minor grid lines.\n enable_minor_ticks (bool): Whether to enable minor ticks on the axes.", - "properties": { - "major": { - "$ref": "#/$defs/GridAxis", - "default": { - "show": false, - "pen": { - "alpha": 1.0, - "color": "gray", - "label": null, - "linestyle": "-", - "size": 0.75, - "zorder": 0.0 - } - } - }, - "minor": { - "$ref": "#/$defs/GridAxis", - "default": { - "show": false, - "pen": { - "alpha": 1.0, - "color": "gray", - "label": null, - "linestyle": "-", - "size": 0.75, - "zorder": 0.0 - } - } - }, - "enable_minor_ticks": { - "default": false, - "title": "Enable Minor Ticks", - "type": "boolean" - }, - "zorder": { - "default": -1, - "title": "Zorder", - "type": "number" - } - }, - "title": "Grid", - "type": "object" - }, - "GridAxis": { - "additionalProperties": false, - "description": "Controls styling and visibility for one type of grid (major or minor).\n\nAttributes:\n show (bool): Whether to display this grid axis.\n pen (Pen): Style and label information for drawing to matplotlib axes.", - "properties": { - "show": { - "default": false, - "title": "Show", - "type": "boolean" - }, - "pen": { - "$ref": "#/$defs/Pen", - "default": { - "color": "gray", - "size": 0.75, - "alpha": 1.0, - "zorder": 0.0, - "linestyle": "-", - "label": null - } - } - }, - "title": "GridAxis", - "type": "object" - }, - "Legend": { - "description": "Configuration container for Matplotlib legend styling and placement.\n\nThe `Legend` class controls the appearance and position of the plot legend.\nPlacement is governed by a combination of the `loc` and `bbox_to_anchor`\nparameters, mirroring Matplotlib's `Axes.legend()`.\n\nAttributes:\n visible (bool): Whether the legend should be displayed. Defaults to True.\n title (str | None): Title displayed above the legend entries.\n framealpha (float): Opacity of the legend background. 1 = fully opaque, 0 = fully transparent.\n loc (LegendLocation): Anchor point for the legend (e.g., upper right, lower left). See `LegendLocation` enum for options.\n ncol (int): Number of columns to arrange legend entries into.\n fancybox (bool): Whether to draw a rounded (True) or square (False) legend frame.\n edgecolor (str): Color of the legend frame border. Default is \"black\".\n bbox_to_anchor (tuple[float, float] | None): Offset position of the legend in figure or axes coordinates. If None, the legend is placed inside the axes using `loc`.\n\n Good starter values for common placements:\n\n - **Inside (default)**:\n ```python\n bbox_to_anchor=None\n ```\n - **Outside right**:\n ```python\n loc=LegendLocation.CENTER_LEFT\n bbox_to_anchor=(1.02, 0.5)\n ```\n - **Outside left**:\n ```python\n loc=LegendLocation.CENTER_RIGHT\n bbox_to_anchor=(-0.02, 0.5)\n ```\n - **Outside top**:\n ```python\n loc=LegendLocation.LOWER_CENTER\n bbox_to_anchor=(0.5, 1.02)\n ```\n - **Outside bottom**:\n ```python\n loc=LegendLocation.UPPER_CENTER\n bbox_to_anchor=(0.5, -0.02)\n ```", - "properties": { - "visible": { - "default": true, - "title": "Visible", - "type": "boolean" - }, - "title": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Title" - }, - "framealpha": { - "default": 1, - "title": "Framealpha", - "type": "number" - }, - "loc": { - "$ref": "#/$defs/LegendLocation", - "default": "best" - }, - "ncol": { - "default": 1, - "title": "Ncol", - "type": "integer" - }, - "fancybox": { - "default": true, - "title": "Fancybox", - "type": "boolean" - }, - "edgecolor": { - "default": "black", - "title": "Edgecolor", - "type": "string" - }, - "zorder": { - "default": 10, - "title": "Zorder", - "type": "integer" - }, - "bbox_to_anchor": { - "anyOf": [ - { - "maxItems": 2, - "minItems": 2, - "prefixItems": [ - { - "type": "number" - }, - { - "type": "number" - } - ], - "type": "array" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Bbox To Anchor" - } - }, - "title": "Legend", - "type": "object" - }, - "LegendLocation": { - "enum": [ - "best", - "upper right", - "upper left", - "lower left", - "lower right", - "right", - "center left", - "center right", - "lower center", - "upper center", - "center" - ], - "title": "LegendLocation", - "type": "string" - }, - "LineOrientation": { - "description": "Defines orientation for axis lines\n\nAttributes:\n HORIZONTAL (LineOrientation): Horizontal line\n VERTICAL (LineOrientation): Vertical line", - "enum": [ - "horizontal", - "vertical" - ], - "title": "LineOrientation", - "type": "string" - }, - "Pen": { - "additionalProperties": false, - "description": "Defines the pen drawing to matplotlib.\n\nAttributes:\n color (str): Color of line\n size (float): Line width\n alpha (float): Opacity from 0 to 1 (inclusive)\n linestyle (Union[str, Tuple[int, Tuple[int, ...]]]): Linestyle to plot. Supports `str` or `tuple` definition ([matplotlib documentation](https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html)).\n zorder (float): Prioritization\n label (Union[str, None]): Legend label", - "properties": { - "color": { - "anyOf": [ - { - "maxItems": 3, - "minItems": 3, - "prefixItems": [ - { - "type": "number" - }, - { - "type": "number" - }, - { - "type": "number" - } - ], - "type": "array" - }, - { - "maxItems": 4, - "minItems": 4, - "prefixItems": [ - { - "type": "number" - }, - { - "type": "number" - }, - { - "type": "number" - }, - { - "type": "number" - } - ], - "type": "array" - }, - { - "type": "string" - } - ], - "default": "k", - "title": "Color" - }, - "size": { - "default": 1, - "title": "Size", - "type": "number" - }, - "alpha": { - "default": 1, - "title": "Alpha", - "type": "number" - }, - "zorder": { - "default": 0, - "title": "Zorder", - "type": "number" - }, - "linestyle": { - "anyOf": [ - { - "type": "string" - }, - { - "maxItems": 2, - "minItems": 2, - "prefixItems": [ - { - "type": "integer" - }, - { - "items": { - "type": "integer" - }, - "type": "array" - } - ], - "type": "array" - } - ], - "default": "-", - "title": "Linestyle" - }, - "label": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Label" - } - }, - "title": "Pen", - "type": "object" - } - }, - "additionalProperties": false, - "description": "Defines a horizontal or vertical line to be drawn on a plot.\n\nAttributes:\n value (float): Value at which to draw the line (x-value for vertical, y-value for horizontal)\n orientation (LineOrientation): Whether line should be horizontal or vertical\n pen (Pen): Style and label information for drawing to matplotlib axes\n tags (Tags): Tags to be used for sorting data\n metadata (dict[str, str]): A dictionary of metadata", - "properties": { - "tags": { - "items": { - "anyOf": [ - { - "items": {}, - "type": "array" - }, - {} - ] - }, - "title": "Tags", - "type": "array" - }, - "metadata": { - "additionalProperties": { - "type": "string" - }, - "default": {}, - "title": "Metadata", - "type": "object" - }, - "format2d": { - "anyOf": [ - { - "$ref": "#/$defs/Format2D" - }, - { - "type": "null" - } - ], - "default": null - }, - "value": { - "title": "Value", - "type": "number" - }, - "orientation": { - "$ref": "#/$defs/LineOrientation" - }, - "pen": { - "$ref": "#/$defs/Pen", - "default": { - "color": "k", - "size": 1.0, - "alpha": 1.0, - "zorder": 0.0, - "linestyle": "-", - "label": null - } - } - }, - "required": [ - "tags", - "value", - "orientation" - ], - "title": "AxLine", - "type": "object" - }, - "DataProduct": { - "additionalProperties": true, - "description": "Base class for data products to be generated and handled.\n\nAttributes:\n product_type (str): Product type should be the same as the class name.\n The product type is used to search for products from a [DataProductCollection][trendify.api.DataProductCollection].\n tags (Tags): Tags to be used for sorting data.\n metadata (dict[str, str]): A dictionary of metadata to be used as a tool tip for mousover in grafana", - "properties": { - "tags": { - "items": { - "anyOf": [ - { - "items": {}, - "type": "array" - }, - {} - ] - }, - "title": "Tags", - "type": "array" - }, - "metadata": { - "additionalProperties": { - "type": "string" - }, - "default": {}, - "title": "Metadata", - "type": "object" - } - }, - "required": [ - "tags" - ], - "title": "DataProduct", - "type": "object" - }, - "DataProductCollection": { - "$defs": { - "DataProduct": { - "additionalProperties": true, - "description": "Base class for data products to be generated and handled.\n\nAttributes:\n product_type (str): Product type should be the same as the class name.\n The product type is used to search for products from a [DataProductCollection][trendify.api.DataProductCollection].\n tags (Tags): Tags to be used for sorting data.\n metadata (dict[str, str]): A dictionary of metadata to be used as a tool tip for mousover in grafana", - "properties": { - "tags": { - "items": { - "anyOf": [ - { - "items": {}, - "type": "array" - }, - {} - ] - }, - "title": "Tags", - "type": "array" - }, - "metadata": { - "additionalProperties": { - "type": "string" - }, - "default": {}, - "title": "Metadata", - "type": "object" - } - }, - "required": [ - "tags" - ], - "title": "DataProduct", - "type": "object" - } - }, - "description": "A collection of data products.\n\nUse this class to serialize data products to JSON, de-serialized them from JSON, filter the products, etc.\n\nAttributes:\n elements (ProductList): A list of data products.", - "properties": { - "derived_from": { - "anyOf": [ - { - "format": "path", - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Derived From" - }, - "elements": { - "items": { - "$ref": "#/$defs/DataProduct" - }, - "title": "Elements", - "type": "array" - } - }, - "title": "DataProductCollection", - "type": "object" - }, - "Format2D": { - "$defs": { - "AxisScale": { - "enum": [ - "linear", - "log" - ], - "title": "AxisScale", - "type": "string" - }, - "Grid": { - "additionalProperties": false, - "description": "Container for major and minor grid line configuration.\n\nAttributes:\n major (GridAxis): Configuration for major grid lines.\n minor (GridAxis): Configuration for minor grid lines.\n enable_minor_ticks (bool): Whether to enable minor ticks on the axes.", - "properties": { - "major": { - "$ref": "#/$defs/GridAxis", - "default": { - "show": false, - "pen": { - "alpha": 1.0, - "color": "gray", - "label": null, - "linestyle": "-", - "size": 0.75, - "zorder": 0.0 - } - } - }, - "minor": { - "$ref": "#/$defs/GridAxis", - "default": { - "show": false, - "pen": { - "alpha": 1.0, - "color": "gray", - "label": null, - "linestyle": "-", - "size": 0.75, - "zorder": 0.0 - } - } - }, - "enable_minor_ticks": { - "default": false, - "title": "Enable Minor Ticks", - "type": "boolean" - }, - "zorder": { - "default": -1, - "title": "Zorder", - "type": "number" - } - }, - "title": "Grid", - "type": "object" - }, - "GridAxis": { - "additionalProperties": false, - "description": "Controls styling and visibility for one type of grid (major or minor).\n\nAttributes:\n show (bool): Whether to display this grid axis.\n pen (Pen): Style and label information for drawing to matplotlib axes.", - "properties": { - "show": { - "default": false, - "title": "Show", - "type": "boolean" - }, - "pen": { - "$ref": "#/$defs/Pen", - "default": { - "color": "gray", - "size": 0.75, - "alpha": 1.0, - "zorder": 0.0, - "linestyle": "-", - "label": null - } - } - }, - "title": "GridAxis", - "type": "object" - }, - "Legend": { - "description": "Configuration container for Matplotlib legend styling and placement.\n\nThe `Legend` class controls the appearance and position of the plot legend.\nPlacement is governed by a combination of the `loc` and `bbox_to_anchor`\nparameters, mirroring Matplotlib's `Axes.legend()`.\n\nAttributes:\n visible (bool): Whether the legend should be displayed. Defaults to True.\n title (str | None): Title displayed above the legend entries.\n framealpha (float): Opacity of the legend background. 1 = fully opaque, 0 = fully transparent.\n loc (LegendLocation): Anchor point for the legend (e.g., upper right, lower left). See `LegendLocation` enum for options.\n ncol (int): Number of columns to arrange legend entries into.\n fancybox (bool): Whether to draw a rounded (True) or square (False) legend frame.\n edgecolor (str): Color of the legend frame border. Default is \"black\".\n bbox_to_anchor (tuple[float, float] | None): Offset position of the legend in figure or axes coordinates. If None, the legend is placed inside the axes using `loc`.\n\n Good starter values for common placements:\n\n - **Inside (default)**:\n ```python\n bbox_to_anchor=None\n ```\n - **Outside right**:\n ```python\n loc=LegendLocation.CENTER_LEFT\n bbox_to_anchor=(1.02, 0.5)\n ```\n - **Outside left**:\n ```python\n loc=LegendLocation.CENTER_RIGHT\n bbox_to_anchor=(-0.02, 0.5)\n ```\n - **Outside top**:\n ```python\n loc=LegendLocation.LOWER_CENTER\n bbox_to_anchor=(0.5, 1.02)\n ```\n - **Outside bottom**:\n ```python\n loc=LegendLocation.UPPER_CENTER\n bbox_to_anchor=(0.5, -0.02)\n ```", - "properties": { - "visible": { - "default": true, - "title": "Visible", - "type": "boolean" - }, - "title": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Title" - }, - "framealpha": { - "default": 1, - "title": "Framealpha", - "type": "number" - }, - "loc": { - "$ref": "#/$defs/LegendLocation", - "default": "best" - }, - "ncol": { - "default": 1, - "title": "Ncol", - "type": "integer" - }, - "fancybox": { - "default": true, - "title": "Fancybox", - "type": "boolean" - }, - "edgecolor": { - "default": "black", - "title": "Edgecolor", - "type": "string" - }, - "zorder": { - "default": 10, - "title": "Zorder", - "type": "integer" - }, - "bbox_to_anchor": { - "anyOf": [ - { - "maxItems": 2, - "minItems": 2, - "prefixItems": [ - { - "type": "number" - }, - { - "type": "number" - } - ], - "type": "array" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Bbox To Anchor" - } - }, - "title": "Legend", - "type": "object" - }, - "LegendLocation": { - "enum": [ - "best", - "upper right", - "upper left", - "lower left", - "lower right", - "right", - "center left", - "center right", - "lower center", - "upper center", - "center" - ], - "title": "LegendLocation", - "type": "string" - }, - "Pen": { - "additionalProperties": false, - "description": "Defines the pen drawing to matplotlib.\n\nAttributes:\n color (str): Color of line\n size (float): Line width\n alpha (float): Opacity from 0 to 1 (inclusive)\n linestyle (Union[str, Tuple[int, Tuple[int, ...]]]): Linestyle to plot. Supports `str` or `tuple` definition ([matplotlib documentation](https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html)).\n zorder (float): Prioritization\n label (Union[str, None]): Legend label", - "properties": { - "color": { - "anyOf": [ - { - "maxItems": 3, - "minItems": 3, - "prefixItems": [ - { - "type": "number" - }, - { - "type": "number" - }, - { - "type": "number" - } - ], - "type": "array" - }, - { - "maxItems": 4, - "minItems": 4, - "prefixItems": [ - { - "type": "number" - }, - { - "type": "number" - }, - { - "type": "number" - }, - { - "type": "number" - } - ], - "type": "array" - }, - { - "type": "string" - } - ], - "default": "k", - "title": "Color" - }, - "size": { - "default": 1, - "title": "Size", - "type": "number" - }, - "alpha": { - "default": 1, - "title": "Alpha", - "type": "number" - }, - "zorder": { - "default": 0, - "title": "Zorder", - "type": "number" - }, - "linestyle": { - "anyOf": [ - { - "type": "string" - }, - { - "maxItems": 2, - "minItems": 2, - "prefixItems": [ - { - "type": "integer" - }, - { - "items": { - "type": "integer" - }, - "type": "array" - } - ], - "type": "array" - } - ], - "default": "-", - "title": "Linestyle" - }, - "label": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Label" - } - }, - "title": "Pen", - "type": "object" - } - }, - "additionalProperties": false, - "description": "Formatting data for matplotlib figure and axes\n\nAttributes:\n title_fig (Optional[str], optional): Sets [figure title][matplotlib.figure.Figure.suptitle]. Defaults to None.\n legend (Optional[Legend], optional): Sets [legend style][trendify.api.styling.legend.Legend]. Defaults to Legend().\n title_ax (Optional[str], optional): Sets [axis title][matplotlib.axes.Axes.set_title]. Defaults to None.\n label_x (Optional[str], optional): Sets [x-axis label][matplotlib.axes.Axes.set_xlabel]. Defaults to None.\n label_y (Optional[str], optional): Sets [y-axis label][matplotlib.axes.Axes.set_ylabel]. Defaults to None.\n lim_x_min (float | None, optional): Sets [x-axis lower bound][matplotlib.axes.Axes.set_xlim]. Defaults to None.\n lim_x_max (float | None, optional): Sets [x-axis upper bound][matplotlib.axes.Axes.set_xlim]. Defaults to None.\n lim_y_min (float | None, optional): Sets [y-axis lower bound][matplotlib.axes.Axes.set_ylim]. Defaults to None.\n lim_y_max (float | None, optional): Sets [y-axis upper bound][matplotlib.axes.Axes.set_ylim]. Defaults to None.\n grid (Grid | None,optional): Sets the [grid][matplotlib.pyplot.grid]. Defaults to None.\n scale_x (AxisScale, optional): Sets the x axis scale to an option from [AxisScale][trendify.api.formats.format2d.AxisScale]. Defaults to AxisScale.LINEAR\n scale_y (AxisScale, optional): Sets the y axis scale to an option from [AxisScale][trendify.api.formats.format2d.AxisScale]. Defaults to AxisScale.LINEAR\n figure_width (float, optional): Sets the of the width of rendered figure in inches. Defaults to 6.4.\n figure_height (float, optional): Sets the of the height of rendered figure in inches. Defaults to 4.8.", - "properties": { - "title_fig": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Title Fig" - }, - "legend": { - "anyOf": [ - { - "$ref": "#/$defs/Legend" - }, - { - "type": "null" - } - ], - "default": { - "visible": true, - "title": null, - "framealpha": 1.0, - "loc": "best", - "ncol": 1, - "fancybox": true, - "edgecolor": "black", - "zorder": 10, - "bbox_to_anchor": null - } - }, - "title_ax": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Title Ax" - }, - "label_x": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Label X" - }, - "label_y": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Label Y" - }, - "lim_x_min": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Lim X Min" - }, - "lim_x_max": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Lim X Max" - }, - "lim_y_min": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Lim Y Min" - }, - "lim_y_max": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Lim Y Max" - }, - "grid": { - "anyOf": [ - { - "$ref": "#/$defs/Grid" - }, - { - "type": "null" - } - ], - "default": null - }, - "scale_x": { - "$ref": "#/$defs/AxisScale", - "default": "linear" - }, - "scale_y": { - "$ref": "#/$defs/AxisScale", - "default": "linear" - }, - "figure_width": { - "default": 6.4, - "title": "Figure Width", - "type": "number" - }, - "figure_height": { - "default": 4.8, - "title": "Figure Height", - "type": "number" - } - }, - "title": "Format2D", - "type": "object" - }, - "Grid": { - "$defs": { - "GridAxis": { - "additionalProperties": false, - "description": "Controls styling and visibility for one type of grid (major or minor).\n\nAttributes:\n show (bool): Whether to display this grid axis.\n pen (Pen): Style and label information for drawing to matplotlib axes.", - "properties": { - "show": { - "default": false, - "title": "Show", - "type": "boolean" - }, - "pen": { - "$ref": "#/$defs/Pen", - "default": { - "color": "gray", - "size": 0.75, - "alpha": 1.0, - "zorder": 0.0, - "linestyle": "-", - "label": null - } - } - }, - "title": "GridAxis", - "type": "object" - }, - "Pen": { - "additionalProperties": false, - "description": "Defines the pen drawing to matplotlib.\n\nAttributes:\n color (str): Color of line\n size (float): Line width\n alpha (float): Opacity from 0 to 1 (inclusive)\n linestyle (Union[str, Tuple[int, Tuple[int, ...]]]): Linestyle to plot. Supports `str` or `tuple` definition ([matplotlib documentation](https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html)).\n zorder (float): Prioritization\n label (Union[str, None]): Legend label", - "properties": { - "color": { - "anyOf": [ - { - "maxItems": 3, - "minItems": 3, - "prefixItems": [ - { - "type": "number" - }, - { - "type": "number" - }, - { - "type": "number" - } - ], - "type": "array" - }, - { - "maxItems": 4, - "minItems": 4, - "prefixItems": [ - { - "type": "number" - }, - { - "type": "number" - }, - { - "type": "number" - }, - { - "type": "number" - } - ], - "type": "array" - }, - { - "type": "string" - } - ], - "default": "k", - "title": "Color" - }, - "size": { - "default": 1, - "title": "Size", - "type": "number" - }, - "alpha": { - "default": 1, - "title": "Alpha", - "type": "number" - }, - "zorder": { - "default": 0, - "title": "Zorder", - "type": "number" - }, - "linestyle": { - "anyOf": [ - { - "type": "string" - }, - { - "maxItems": 2, - "minItems": 2, - "prefixItems": [ - { - "type": "integer" - }, - { - "items": { - "type": "integer" - }, - "type": "array" - } - ], - "type": "array" - } - ], - "default": "-", - "title": "Linestyle" - }, - "label": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Label" - } - }, - "title": "Pen", - "type": "object" - } - }, - "additionalProperties": false, - "description": "Container for major and minor grid line configuration.\n\nAttributes:\n major (GridAxis): Configuration for major grid lines.\n minor (GridAxis): Configuration for minor grid lines.\n enable_minor_ticks (bool): Whether to enable minor ticks on the axes.", - "properties": { - "major": { - "$ref": "#/$defs/GridAxis", - "default": { - "show": false, - "pen": { - "alpha": 1.0, - "color": "gray", - "label": null, - "linestyle": "-", - "size": 0.75, - "zorder": 0.0 - } - } - }, - "minor": { - "$ref": "#/$defs/GridAxis", - "default": { - "show": false, - "pen": { - "alpha": 1.0, - "color": "gray", - "label": null, - "linestyle": "-", - "size": 0.75, - "zorder": 0.0 - } - } - }, - "enable_minor_ticks": { - "default": false, - "title": "Enable Minor Ticks", - "type": "boolean" - }, - "zorder": { - "default": -1, - "title": "Zorder", - "type": "number" - } - }, - "title": "Grid", - "type": "object" - }, - "GridAxis": { - "$defs": { - "Pen": { - "additionalProperties": false, - "description": "Defines the pen drawing to matplotlib.\n\nAttributes:\n color (str): Color of line\n size (float): Line width\n alpha (float): Opacity from 0 to 1 (inclusive)\n linestyle (Union[str, Tuple[int, Tuple[int, ...]]]): Linestyle to plot. Supports `str` or `tuple` definition ([matplotlib documentation](https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html)).\n zorder (float): Prioritization\n label (Union[str, None]): Legend label", - "properties": { - "color": { - "anyOf": [ - { - "maxItems": 3, - "minItems": 3, - "prefixItems": [ - { - "type": "number" - }, - { - "type": "number" - }, - { - "type": "number" - } - ], - "type": "array" - }, - { - "maxItems": 4, - "minItems": 4, - "prefixItems": [ - { - "type": "number" - }, - { - "type": "number" - }, - { - "type": "number" - }, - { - "type": "number" - } - ], - "type": "array" - }, - { - "type": "string" - } - ], - "default": "k", - "title": "Color" - }, - "size": { - "default": 1, - "title": "Size", - "type": "number" - }, - "alpha": { - "default": 1, - "title": "Alpha", - "type": "number" - }, - "zorder": { - "default": 0, - "title": "Zorder", - "type": "number" - }, - "linestyle": { - "anyOf": [ - { - "type": "string" - }, - { - "maxItems": 2, - "minItems": 2, - "prefixItems": [ - { - "type": "integer" - }, - { - "items": { - "type": "integer" - }, - "type": "array" - } - ], - "type": "array" - } - ], - "default": "-", - "title": "Linestyle" - }, - "label": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Label" - } - }, - "title": "Pen", - "type": "object" - } - }, - "additionalProperties": false, - "description": "Controls styling and visibility for one type of grid (major or minor).\n\nAttributes:\n show (bool): Whether to display this grid axis.\n pen (Pen): Style and label information for drawing to matplotlib axes.", - "properties": { - "show": { - "default": false, - "title": "Show", - "type": "boolean" - }, - "pen": { - "$ref": "#/$defs/Pen", - "default": { - "color": "gray", - "size": 0.75, - "alpha": 1.0, - "zorder": 0.0, - "linestyle": "-", - "label": null - } - } - }, - "title": "GridAxis", - "type": "object" - }, - "HistogramEntry": { - "$defs": { - "AxisScale": { - "enum": [ - "linear", - "log" - ], - "title": "AxisScale", - "type": "string" - }, - "Format2D": { - "additionalProperties": false, - "description": "Formatting data for matplotlib figure and axes\n\nAttributes:\n title_fig (Optional[str], optional): Sets [figure title][matplotlib.figure.Figure.suptitle]. Defaults to None.\n legend (Optional[Legend], optional): Sets [legend style][trendify.api.styling.legend.Legend]. Defaults to Legend().\n title_ax (Optional[str], optional): Sets [axis title][matplotlib.axes.Axes.set_title]. Defaults to None.\n label_x (Optional[str], optional): Sets [x-axis label][matplotlib.axes.Axes.set_xlabel]. Defaults to None.\n label_y (Optional[str], optional): Sets [y-axis label][matplotlib.axes.Axes.set_ylabel]. Defaults to None.\n lim_x_min (float | None, optional): Sets [x-axis lower bound][matplotlib.axes.Axes.set_xlim]. Defaults to None.\n lim_x_max (float | None, optional): Sets [x-axis upper bound][matplotlib.axes.Axes.set_xlim]. Defaults to None.\n lim_y_min (float | None, optional): Sets [y-axis lower bound][matplotlib.axes.Axes.set_ylim]. Defaults to None.\n lim_y_max (float | None, optional): Sets [y-axis upper bound][matplotlib.axes.Axes.set_ylim]. Defaults to None.\n grid (Grid | None,optional): Sets the [grid][matplotlib.pyplot.grid]. Defaults to None.\n scale_x (AxisScale, optional): Sets the x axis scale to an option from [AxisScale][trendify.api.formats.format2d.AxisScale]. Defaults to AxisScale.LINEAR\n scale_y (AxisScale, optional): Sets the y axis scale to an option from [AxisScale][trendify.api.formats.format2d.AxisScale]. Defaults to AxisScale.LINEAR\n figure_width (float, optional): Sets the of the width of rendered figure in inches. Defaults to 6.4.\n figure_height (float, optional): Sets the of the height of rendered figure in inches. Defaults to 4.8.", - "properties": { - "title_fig": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Title Fig" - }, - "legend": { - "anyOf": [ - { - "$ref": "#/$defs/Legend" - }, - { - "type": "null" - } - ], - "default": { - "visible": true, - "title": null, - "framealpha": 1.0, - "loc": "best", - "ncol": 1, - "fancybox": true, - "edgecolor": "black", - "zorder": 10, - "bbox_to_anchor": null - } - }, - "title_ax": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Title Ax" - }, - "label_x": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Label X" - }, - "label_y": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Label Y" - }, - "lim_x_min": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Lim X Min" - }, - "lim_x_max": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Lim X Max" - }, - "lim_y_min": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Lim Y Min" - }, - "lim_y_max": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Lim Y Max" - }, - "grid": { - "anyOf": [ - { - "$ref": "#/$defs/Grid" - }, - { - "type": "null" - } - ], - "default": null - }, - "scale_x": { - "$ref": "#/$defs/AxisScale", - "default": "linear" - }, - "scale_y": { - "$ref": "#/$defs/AxisScale", - "default": "linear" - }, - "figure_width": { - "default": 6.4, - "title": "Figure Width", - "type": "number" - }, - "figure_height": { - "default": 4.8, - "title": "Figure Height", - "type": "number" - } - }, - "title": "Format2D", - "type": "object" - }, - "Grid": { - "additionalProperties": false, - "description": "Container for major and minor grid line configuration.\n\nAttributes:\n major (GridAxis): Configuration for major grid lines.\n minor (GridAxis): Configuration for minor grid lines.\n enable_minor_ticks (bool): Whether to enable minor ticks on the axes.", - "properties": { - "major": { - "$ref": "#/$defs/GridAxis", - "default": { - "show": false, - "pen": { - "alpha": 1.0, - "color": "gray", - "label": null, - "linestyle": "-", - "size": 0.75, - "zorder": 0.0 - } - } - }, - "minor": { - "$ref": "#/$defs/GridAxis", - "default": { - "show": false, - "pen": { - "alpha": 1.0, - "color": "gray", - "label": null, - "linestyle": "-", - "size": 0.75, - "zorder": 0.0 - } - } - }, - "enable_minor_ticks": { - "default": false, - "title": "Enable Minor Ticks", - "type": "boolean" - }, - "zorder": { - "default": -1, - "title": "Zorder", - "type": "number" - } - }, - "title": "Grid", - "type": "object" - }, - "GridAxis": { - "additionalProperties": false, - "description": "Controls styling and visibility for one type of grid (major or minor).\n\nAttributes:\n show (bool): Whether to display this grid axis.\n pen (Pen): Style and label information for drawing to matplotlib axes.", - "properties": { - "show": { - "default": false, - "title": "Show", - "type": "boolean" - }, - "pen": { - "$ref": "#/$defs/Pen", - "default": { - "color": "gray", - "size": 0.75, - "alpha": 1.0, - "zorder": 0.0, - "linestyle": "-", - "label": null - } - } - }, - "title": "GridAxis", - "type": "object" - }, - "HistogramStyle": { - "description": "Label and style data for generating histogram bars\n\nAttributes:\n color (str): Color of bars\n label (str|None): Legend entry\n histtype (str): Histogram type corresponding to matplotlib argument of same name\n alpha_edge (float): Opacity of bar edge\n alpha_face (float): Opacity of bar face\n linewidth (float): Line width of bar outline\n bins (int | list[int] | Tuple[int] | NDArray[Shape[\"*\"], int] | None): Number of bins (see [matplotlib docs][matplotlib.pyplot.hist])", - "properties": { - "color": { - "default": "k", - "title": "Color", - "type": "string" - }, - "label": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Label" - }, - "histtype": { - "default": "stepfilled", - "title": "Histtype", - "type": "string" - }, - "alpha_edge": { - "default": 0, - "title": "Alpha Edge", - "type": "number" - }, - "alpha_face": { - "default": 0.3, - "title": "Alpha Face", - "type": "number" - }, - "linewidth": { - "default": 2, - "title": "Linewidth", - "type": "number" - }, - "zorder": { - "default": 1, - "title": "Zorder", - "type": "integer" - }, - "bins": { - "anyOf": [ - { - "type": "integer" - }, - { - "items": { - "type": "integer" - }, - "type": "array" - }, - { - "maxItems": 1, - "minItems": 1, - "prefixItems": [ - { - "type": "integer" - } - ], - "type": "array" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Bins" - } - }, - "title": "HistogramStyle", - "type": "object" - }, - "Legend": { - "description": "Configuration container for Matplotlib legend styling and placement.\n\nThe `Legend` class controls the appearance and position of the plot legend.\nPlacement is governed by a combination of the `loc` and `bbox_to_anchor`\nparameters, mirroring Matplotlib's `Axes.legend()`.\n\nAttributes:\n visible (bool): Whether the legend should be displayed. Defaults to True.\n title (str | None): Title displayed above the legend entries.\n framealpha (float): Opacity of the legend background. 1 = fully opaque, 0 = fully transparent.\n loc (LegendLocation): Anchor point for the legend (e.g., upper right, lower left). See `LegendLocation` enum for options.\n ncol (int): Number of columns to arrange legend entries into.\n fancybox (bool): Whether to draw a rounded (True) or square (False) legend frame.\n edgecolor (str): Color of the legend frame border. Default is \"black\".\n bbox_to_anchor (tuple[float, float] | None): Offset position of the legend in figure or axes coordinates. If None, the legend is placed inside the axes using `loc`.\n\n Good starter values for common placements:\n\n - **Inside (default)**:\n ```python\n bbox_to_anchor=None\n ```\n - **Outside right**:\n ```python\n loc=LegendLocation.CENTER_LEFT\n bbox_to_anchor=(1.02, 0.5)\n ```\n - **Outside left**:\n ```python\n loc=LegendLocation.CENTER_RIGHT\n bbox_to_anchor=(-0.02, 0.5)\n ```\n - **Outside top**:\n ```python\n loc=LegendLocation.LOWER_CENTER\n bbox_to_anchor=(0.5, 1.02)\n ```\n - **Outside bottom**:\n ```python\n loc=LegendLocation.UPPER_CENTER\n bbox_to_anchor=(0.5, -0.02)\n ```", - "properties": { - "visible": { - "default": true, - "title": "Visible", - "type": "boolean" - }, - "title": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Title" - }, - "framealpha": { - "default": 1, - "title": "Framealpha", - "type": "number" - }, - "loc": { - "$ref": "#/$defs/LegendLocation", - "default": "best" - }, - "ncol": { - "default": 1, - "title": "Ncol", - "type": "integer" - }, - "fancybox": { - "default": true, - "title": "Fancybox", - "type": "boolean" - }, - "edgecolor": { - "default": "black", - "title": "Edgecolor", - "type": "string" - }, - "zorder": { - "default": 10, - "title": "Zorder", - "type": "integer" - }, - "bbox_to_anchor": { - "anyOf": [ - { - "maxItems": 2, - "minItems": 2, - "prefixItems": [ - { - "type": "number" - }, - { - "type": "number" - } - ], - "type": "array" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Bbox To Anchor" - } - }, - "title": "Legend", - "type": "object" - }, - "LegendLocation": { - "enum": [ - "best", - "upper right", - "upper left", - "lower left", - "lower right", - "right", - "center left", - "center right", - "lower center", - "upper center", - "center" - ], - "title": "LegendLocation", - "type": "string" - }, - "Pen": { - "additionalProperties": false, - "description": "Defines the pen drawing to matplotlib.\n\nAttributes:\n color (str): Color of line\n size (float): Line width\n alpha (float): Opacity from 0 to 1 (inclusive)\n linestyle (Union[str, Tuple[int, Tuple[int, ...]]]): Linestyle to plot. Supports `str` or `tuple` definition ([matplotlib documentation](https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html)).\n zorder (float): Prioritization\n label (Union[str, None]): Legend label", - "properties": { - "color": { - "anyOf": [ - { - "maxItems": 3, - "minItems": 3, - "prefixItems": [ - { - "type": "number" - }, - { - "type": "number" - }, - { - "type": "number" - } - ], - "type": "array" - }, - { - "maxItems": 4, - "minItems": 4, - "prefixItems": [ - { - "type": "number" - }, - { - "type": "number" - }, - { - "type": "number" - }, - { - "type": "number" - } - ], - "type": "array" - }, - { - "type": "string" - } - ], - "default": "k", - "title": "Color" - }, - "size": { - "default": 1, - "title": "Size", - "type": "number" - }, - "alpha": { - "default": 1, - "title": "Alpha", - "type": "number" - }, - "zorder": { - "default": 0, - "title": "Zorder", - "type": "number" - }, - "linestyle": { - "anyOf": [ - { - "type": "string" - }, - { - "maxItems": 2, - "minItems": 2, - "prefixItems": [ - { - "type": "integer" - }, - { - "items": { - "type": "integer" - }, - "type": "array" - } - ], - "type": "array" - } - ], - "default": "-", - "title": "Linestyle" - }, - "label": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Label" - } - }, - "title": "Pen", - "type": "object" - } - }, - "additionalProperties": false, - "description": "Use this class to specify a value to be collected into a matplotlib histogram.\n\nAttributes:\n tags (Tags): Tags used to sort data products\n value (float | str): Value to be binned\n style (HistogramStyle): Style of histogram display", - "properties": { - "tags": { - "items": { - "anyOf": [ - { - "items": {}, - "type": "array" - }, - {} - ] - }, - "title": "Tags", - "type": "array" - }, - "metadata": { - "additionalProperties": { - "type": "string" - }, - "default": {}, - "title": "Metadata", - "type": "object" - }, - "format2d": { - "anyOf": [ - { - "$ref": "#/$defs/Format2D" - }, - { - "type": "null" - } - ], - "default": null - }, - "value": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "string" - } - ], - "title": "Value" - }, - "style": { - "anyOf": [ - { - "$ref": "#/$defs/HistogramStyle" - }, - { - "type": "null" - } - ] - } - }, - "required": [ - "tags", - "value" - ], - "title": "HistogramEntry", - "type": "object" - }, - "HistogramStyle": { - "description": "Label and style data for generating histogram bars\n\nAttributes:\n color (str): Color of bars\n label (str|None): Legend entry\n histtype (str): Histogram type corresponding to matplotlib argument of same name\n alpha_edge (float): Opacity of bar edge\n alpha_face (float): Opacity of bar face\n linewidth (float): Line width of bar outline\n bins (int | list[int] | Tuple[int] | NDArray[Shape[\"*\"], int] | None): Number of bins (see [matplotlib docs][matplotlib.pyplot.hist])", - "properties": { - "color": { - "default": "k", - "title": "Color", - "type": "string" - }, - "label": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Label" - }, - "histtype": { - "default": "stepfilled", - "title": "Histtype", - "type": "string" - }, - "alpha_edge": { - "default": 0, - "title": "Alpha Edge", - "type": "number" - }, - "alpha_face": { - "default": 0.3, - "title": "Alpha Face", - "type": "number" - }, - "linewidth": { - "default": 2, - "title": "Linewidth", - "type": "number" - }, - "zorder": { - "default": 1, - "title": "Zorder", - "type": "integer" - }, - "bins": { - "anyOf": [ - { - "type": "integer" - }, - { - "items": { - "type": "integer" - }, - "type": "array" - }, - { - "maxItems": 1, - "minItems": 1, - "prefixItems": [ - { - "type": "integer" - } - ], - "type": "array" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Bins" - } - }, - "title": "HistogramStyle", - "type": "object" - }, - "Legend": { - "$defs": { - "LegendLocation": { - "enum": [ - "best", - "upper right", - "upper left", - "lower left", - "lower right", - "right", - "center left", - "center right", - "lower center", - "upper center", - "center" - ], - "title": "LegendLocation", - "type": "string" - } - }, - "description": "Configuration container for Matplotlib legend styling and placement.\n\nThe `Legend` class controls the appearance and position of the plot legend.\nPlacement is governed by a combination of the `loc` and `bbox_to_anchor`\nparameters, mirroring Matplotlib's `Axes.legend()`.\n\nAttributes:\n visible (bool): Whether the legend should be displayed. Defaults to True.\n title (str | None): Title displayed above the legend entries.\n framealpha (float): Opacity of the legend background. 1 = fully opaque, 0 = fully transparent.\n loc (LegendLocation): Anchor point for the legend (e.g., upper right, lower left). See `LegendLocation` enum for options.\n ncol (int): Number of columns to arrange legend entries into.\n fancybox (bool): Whether to draw a rounded (True) or square (False) legend frame.\n edgecolor (str): Color of the legend frame border. Default is \"black\".\n bbox_to_anchor (tuple[float, float] | None): Offset position of the legend in figure or axes coordinates. If None, the legend is placed inside the axes using `loc`.\n\n Good starter values for common placements:\n\n - **Inside (default)**:\n ```python\n bbox_to_anchor=None\n ```\n - **Outside right**:\n ```python\n loc=LegendLocation.CENTER_LEFT\n bbox_to_anchor=(1.02, 0.5)\n ```\n - **Outside left**:\n ```python\n loc=LegendLocation.CENTER_RIGHT\n bbox_to_anchor=(-0.02, 0.5)\n ```\n - **Outside top**:\n ```python\n loc=LegendLocation.LOWER_CENTER\n bbox_to_anchor=(0.5, 1.02)\n ```\n - **Outside bottom**:\n ```python\n loc=LegendLocation.UPPER_CENTER\n bbox_to_anchor=(0.5, -0.02)\n ```", - "properties": { - "visible": { - "default": true, - "title": "Visible", - "type": "boolean" - }, - "title": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Title" - }, - "framealpha": { - "default": 1, - "title": "Framealpha", - "type": "number" - }, - "loc": { - "$ref": "#/$defs/LegendLocation", - "default": "best" - }, - "ncol": { - "default": 1, - "title": "Ncol", - "type": "integer" - }, - "fancybox": { - "default": true, - "title": "Fancybox", - "type": "boolean" - }, - "edgecolor": { - "default": "black", - "title": "Edgecolor", - "type": "string" - }, - "zorder": { - "default": 10, - "title": "Zorder", - "type": "integer" - }, - "bbox_to_anchor": { - "anyOf": [ - { - "maxItems": 2, - "minItems": 2, - "prefixItems": [ - { - "type": "number" - }, - { - "type": "number" - } - ], - "type": "array" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Bbox To Anchor" - } - }, - "title": "Legend", - "type": "object" - }, - "Marker": { - "additionalProperties": false, - "description": "Defines marker for scattering to matplotlib\n\nAttributes:\n color (str): Color of line\n size (float): Line width\n alpha (float): Opacity from 0 to 1 (inclusive)\n zorder (float): Prioritization\n label (Union[str, None]): Legend label\n symbol (str): Matplotlib symbol string", - "properties": { - "color": { - "default": "k", - "title": "Color", - "type": "string" - }, - "size": { - "default": 5, - "title": "Size", - "type": "number" - }, - "alpha": { - "default": 1, - "title": "Alpha", - "type": "number" - }, - "zorder": { - "default": 0, - "title": "Zorder", - "type": "number" - }, - "label": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Label" - }, - "symbol": { - "default": ".", - "title": "Symbol", - "type": "string" - } - }, - "title": "Marker", - "type": "object" - }, - "Pen": { - "additionalProperties": false, - "description": "Defines the pen drawing to matplotlib.\n\nAttributes:\n color (str): Color of line\n size (float): Line width\n alpha (float): Opacity from 0 to 1 (inclusive)\n linestyle (Union[str, Tuple[int, Tuple[int, ...]]]): Linestyle to plot. Supports `str` or `tuple` definition ([matplotlib documentation](https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html)).\n zorder (float): Prioritization\n label (Union[str, None]): Legend label", - "properties": { - "color": { - "anyOf": [ - { - "maxItems": 3, - "minItems": 3, - "prefixItems": [ - { - "type": "number" - }, - { - "type": "number" - }, - { - "type": "number" - } - ], - "type": "array" - }, - { - "maxItems": 4, - "minItems": 4, - "prefixItems": [ - { - "type": "number" - }, - { - "type": "number" - }, - { - "type": "number" - }, - { - "type": "number" - } - ], - "type": "array" - }, - { - "type": "string" - } - ], - "default": "k", - "title": "Color" - }, - "size": { - "default": 1, - "title": "Size", - "type": "number" - }, - "alpha": { - "default": 1, - "title": "Alpha", - "type": "number" - }, - "zorder": { - "default": 0, - "title": "Zorder", - "type": "number" - }, - "linestyle": { - "anyOf": [ - { - "type": "string" - }, - { - "maxItems": 2, - "minItems": 2, - "prefixItems": [ - { - "type": "integer" - }, - { - "items": { - "type": "integer" - }, - "type": "array" - } - ], - "type": "array" - } - ], - "default": "-", - "title": "Linestyle" - }, - "label": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Label" - } - }, - "title": "Pen", - "type": "object" - }, - "PlottableData2D": { - "$defs": { - "AxisScale": { - "enum": [ - "linear", - "log" - ], - "title": "AxisScale", - "type": "string" - }, - "Format2D": { - "additionalProperties": false, - "description": "Formatting data for matplotlib figure and axes\n\nAttributes:\n title_fig (Optional[str], optional): Sets [figure title][matplotlib.figure.Figure.suptitle]. Defaults to None.\n legend (Optional[Legend], optional): Sets [legend style][trendify.api.styling.legend.Legend]. Defaults to Legend().\n title_ax (Optional[str], optional): Sets [axis title][matplotlib.axes.Axes.set_title]. Defaults to None.\n label_x (Optional[str], optional): Sets [x-axis label][matplotlib.axes.Axes.set_xlabel]. Defaults to None.\n label_y (Optional[str], optional): Sets [y-axis label][matplotlib.axes.Axes.set_ylabel]. Defaults to None.\n lim_x_min (float | None, optional): Sets [x-axis lower bound][matplotlib.axes.Axes.set_xlim]. Defaults to None.\n lim_x_max (float | None, optional): Sets [x-axis upper bound][matplotlib.axes.Axes.set_xlim]. Defaults to None.\n lim_y_min (float | None, optional): Sets [y-axis lower bound][matplotlib.axes.Axes.set_ylim]. Defaults to None.\n lim_y_max (float | None, optional): Sets [y-axis upper bound][matplotlib.axes.Axes.set_ylim]. Defaults to None.\n grid (Grid | None,optional): Sets the [grid][matplotlib.pyplot.grid]. Defaults to None.\n scale_x (AxisScale, optional): Sets the x axis scale to an option from [AxisScale][trendify.api.formats.format2d.AxisScale]. Defaults to AxisScale.LINEAR\n scale_y (AxisScale, optional): Sets the y axis scale to an option from [AxisScale][trendify.api.formats.format2d.AxisScale]. Defaults to AxisScale.LINEAR\n figure_width (float, optional): Sets the of the width of rendered figure in inches. Defaults to 6.4.\n figure_height (float, optional): Sets the of the height of rendered figure in inches. Defaults to 4.8.", - "properties": { - "title_fig": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Title Fig" - }, - "legend": { - "anyOf": [ - { - "$ref": "#/$defs/Legend" - }, - { - "type": "null" - } - ], - "default": { - "visible": true, - "title": null, - "framealpha": 1.0, - "loc": "best", - "ncol": 1, - "fancybox": true, - "edgecolor": "black", - "zorder": 10, - "bbox_to_anchor": null - } - }, - "title_ax": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Title Ax" - }, - "label_x": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Label X" - }, - "label_y": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Label Y" - }, - "lim_x_min": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Lim X Min" - }, - "lim_x_max": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Lim X Max" - }, - "lim_y_min": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Lim Y Min" - }, - "lim_y_max": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Lim Y Max" - }, - "grid": { - "anyOf": [ - { - "$ref": "#/$defs/Grid" - }, - { - "type": "null" - } - ], - "default": null - }, - "scale_x": { - "$ref": "#/$defs/AxisScale", - "default": "linear" - }, - "scale_y": { - "$ref": "#/$defs/AxisScale", - "default": "linear" - }, - "figure_width": { - "default": 6.4, - "title": "Figure Width", - "type": "number" - }, - "figure_height": { - "default": 4.8, - "title": "Figure Height", - "type": "number" - } - }, - "title": "Format2D", - "type": "object" - }, - "Grid": { - "additionalProperties": false, - "description": "Container for major and minor grid line configuration.\n\nAttributes:\n major (GridAxis): Configuration for major grid lines.\n minor (GridAxis): Configuration for minor grid lines.\n enable_minor_ticks (bool): Whether to enable minor ticks on the axes.", - "properties": { - "major": { - "$ref": "#/$defs/GridAxis", - "default": { - "show": false, - "pen": { - "alpha": 1.0, - "color": "gray", - "label": null, - "linestyle": "-", - "size": 0.75, - "zorder": 0.0 - } - } - }, - "minor": { - "$ref": "#/$defs/GridAxis", - "default": { - "show": false, - "pen": { - "alpha": 1.0, - "color": "gray", - "label": null, - "linestyle": "-", - "size": 0.75, - "zorder": 0.0 - } - } - }, - "enable_minor_ticks": { - "default": false, - "title": "Enable Minor Ticks", - "type": "boolean" - }, - "zorder": { - "default": -1, - "title": "Zorder", - "type": "number" - } - }, - "title": "Grid", - "type": "object" - }, - "GridAxis": { - "additionalProperties": false, - "description": "Controls styling and visibility for one type of grid (major or minor).\n\nAttributes:\n show (bool): Whether to display this grid axis.\n pen (Pen): Style and label information for drawing to matplotlib axes.", - "properties": { - "show": { - "default": false, - "title": "Show", - "type": "boolean" - }, - "pen": { - "$ref": "#/$defs/Pen", - "default": { - "color": "gray", - "size": 0.75, - "alpha": 1.0, - "zorder": 0.0, - "linestyle": "-", - "label": null - } - } - }, - "title": "GridAxis", - "type": "object" - }, - "Legend": { - "description": "Configuration container for Matplotlib legend styling and placement.\n\nThe `Legend` class controls the appearance and position of the plot legend.\nPlacement is governed by a combination of the `loc` and `bbox_to_anchor`\nparameters, mirroring Matplotlib's `Axes.legend()`.\n\nAttributes:\n visible (bool): Whether the legend should be displayed. Defaults to True.\n title (str | None): Title displayed above the legend entries.\n framealpha (float): Opacity of the legend background. 1 = fully opaque, 0 = fully transparent.\n loc (LegendLocation): Anchor point for the legend (e.g., upper right, lower left). See `LegendLocation` enum for options.\n ncol (int): Number of columns to arrange legend entries into.\n fancybox (bool): Whether to draw a rounded (True) or square (False) legend frame.\n edgecolor (str): Color of the legend frame border. Default is \"black\".\n bbox_to_anchor (tuple[float, float] | None): Offset position of the legend in figure or axes coordinates. If None, the legend is placed inside the axes using `loc`.\n\n Good starter values for common placements:\n\n - **Inside (default)**:\n ```python\n bbox_to_anchor=None\n ```\n - **Outside right**:\n ```python\n loc=LegendLocation.CENTER_LEFT\n bbox_to_anchor=(1.02, 0.5)\n ```\n - **Outside left**:\n ```python\n loc=LegendLocation.CENTER_RIGHT\n bbox_to_anchor=(-0.02, 0.5)\n ```\n - **Outside top**:\n ```python\n loc=LegendLocation.LOWER_CENTER\n bbox_to_anchor=(0.5, 1.02)\n ```\n - **Outside bottom**:\n ```python\n loc=LegendLocation.UPPER_CENTER\n bbox_to_anchor=(0.5, -0.02)\n ```", - "properties": { - "visible": { - "default": true, - "title": "Visible", - "type": "boolean" - }, - "title": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Title" - }, - "framealpha": { - "default": 1, - "title": "Framealpha", - "type": "number" - }, - "loc": { - "$ref": "#/$defs/LegendLocation", - "default": "best" - }, - "ncol": { - "default": 1, - "title": "Ncol", - "type": "integer" - }, - "fancybox": { - "default": true, - "title": "Fancybox", - "type": "boolean" - }, - "edgecolor": { - "default": "black", - "title": "Edgecolor", - "type": "string" - }, - "zorder": { - "default": 10, - "title": "Zorder", - "type": "integer" - }, - "bbox_to_anchor": { - "anyOf": [ - { - "maxItems": 2, - "minItems": 2, - "prefixItems": [ - { - "type": "number" - }, - { - "type": "number" - } - ], - "type": "array" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Bbox To Anchor" - } - }, - "title": "Legend", - "type": "object" - }, - "LegendLocation": { - "enum": [ - "best", - "upper right", - "upper left", - "lower left", - "lower right", - "right", - "center left", - "center right", - "lower center", - "upper center", - "center" - ], - "title": "LegendLocation", - "type": "string" - }, - "Pen": { - "additionalProperties": false, - "description": "Defines the pen drawing to matplotlib.\n\nAttributes:\n color (str): Color of line\n size (float): Line width\n alpha (float): Opacity from 0 to 1 (inclusive)\n linestyle (Union[str, Tuple[int, Tuple[int, ...]]]): Linestyle to plot. Supports `str` or `tuple` definition ([matplotlib documentation](https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html)).\n zorder (float): Prioritization\n label (Union[str, None]): Legend label", - "properties": { - "color": { - "anyOf": [ - { - "maxItems": 3, - "minItems": 3, - "prefixItems": [ - { - "type": "number" - }, - { - "type": "number" - }, - { - "type": "number" - } - ], - "type": "array" - }, - { - "maxItems": 4, - "minItems": 4, - "prefixItems": [ - { - "type": "number" - }, - { - "type": "number" - }, - { - "type": "number" - }, - { - "type": "number" - } - ], - "type": "array" - }, - { - "type": "string" - } - ], - "default": "k", - "title": "Color" - }, - "size": { - "default": 1, - "title": "Size", - "type": "number" - }, - "alpha": { - "default": 1, - "title": "Alpha", - "type": "number" - }, - "zorder": { - "default": 0, - "title": "Zorder", - "type": "number" - }, - "linestyle": { - "anyOf": [ - { - "type": "string" - }, - { - "maxItems": 2, - "minItems": 2, - "prefixItems": [ - { - "type": "integer" - }, - { - "items": { - "type": "integer" - }, - "type": "array" - } - ], - "type": "array" - } - ], - "default": "-", - "title": "Linestyle" - }, - "label": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Label" - } - }, - "title": "Pen", - "type": "object" - } - }, - "additionalProperties": true, - "description": "Base class for children of DataProduct to be plotted ax xy data on a 2D plot\n\nAttributes:\n format2d (Format2D|None): Format to apply to plot\n tags (Tags): Tags to be used for sorting data.\n metadata (dict[str, str]): A dictionary of metadata to be used as a tool tip for mousover in grafana", - "properties": { - "tags": { - "items": { - "anyOf": [ - { - "items": {}, - "type": "array" - }, - {} - ] - }, - "title": "Tags", - "type": "array" - }, - "metadata": { - "additionalProperties": { - "type": "string" - }, - "default": {}, - "title": "Metadata", - "type": "object" - }, - "format2d": { - "anyOf": [ - { - "$ref": "#/$defs/Format2D" - }, - { - "type": "null" - } - ], - "default": null - } - }, - "required": [ - "tags" - ], - "title": "PlottableData2D", - "type": "object" - }, - "Point2D": { - "$defs": { - "AxisScale": { - "enum": [ - "linear", - "log" - ], - "title": "AxisScale", - "type": "string" - }, - "Format2D": { - "additionalProperties": false, - "description": "Formatting data for matplotlib figure and axes\n\nAttributes:\n title_fig (Optional[str], optional): Sets [figure title][matplotlib.figure.Figure.suptitle]. Defaults to None.\n legend (Optional[Legend], optional): Sets [legend style][trendify.api.styling.legend.Legend]. Defaults to Legend().\n title_ax (Optional[str], optional): Sets [axis title][matplotlib.axes.Axes.set_title]. Defaults to None.\n label_x (Optional[str], optional): Sets [x-axis label][matplotlib.axes.Axes.set_xlabel]. Defaults to None.\n label_y (Optional[str], optional): Sets [y-axis label][matplotlib.axes.Axes.set_ylabel]. Defaults to None.\n lim_x_min (float | None, optional): Sets [x-axis lower bound][matplotlib.axes.Axes.set_xlim]. Defaults to None.\n lim_x_max (float | None, optional): Sets [x-axis upper bound][matplotlib.axes.Axes.set_xlim]. Defaults to None.\n lim_y_min (float | None, optional): Sets [y-axis lower bound][matplotlib.axes.Axes.set_ylim]. Defaults to None.\n lim_y_max (float | None, optional): Sets [y-axis upper bound][matplotlib.axes.Axes.set_ylim]. Defaults to None.\n grid (Grid | None,optional): Sets the [grid][matplotlib.pyplot.grid]. Defaults to None.\n scale_x (AxisScale, optional): Sets the x axis scale to an option from [AxisScale][trendify.api.formats.format2d.AxisScale]. Defaults to AxisScale.LINEAR\n scale_y (AxisScale, optional): Sets the y axis scale to an option from [AxisScale][trendify.api.formats.format2d.AxisScale]. Defaults to AxisScale.LINEAR\n figure_width (float, optional): Sets the of the width of rendered figure in inches. Defaults to 6.4.\n figure_height (float, optional): Sets the of the height of rendered figure in inches. Defaults to 4.8.", - "properties": { - "title_fig": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Title Fig" - }, - "legend": { - "anyOf": [ - { - "$ref": "#/$defs/Legend" - }, - { - "type": "null" - } - ], - "default": { - "visible": true, - "title": null, - "framealpha": 1.0, - "loc": "best", - "ncol": 1, - "fancybox": true, - "edgecolor": "black", - "zorder": 10, - "bbox_to_anchor": null - } - }, - "title_ax": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Title Ax" - }, - "label_x": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Label X" - }, - "label_y": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Label Y" - }, - "lim_x_min": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Lim X Min" - }, - "lim_x_max": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Lim X Max" - }, - "lim_y_min": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Lim Y Min" - }, - "lim_y_max": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Lim Y Max" - }, - "grid": { - "anyOf": [ - { - "$ref": "#/$defs/Grid" - }, - { - "type": "null" - } - ], - "default": null - }, - "scale_x": { - "$ref": "#/$defs/AxisScale", - "default": "linear" - }, - "scale_y": { - "$ref": "#/$defs/AxisScale", - "default": "linear" - }, - "figure_width": { - "default": 6.4, - "title": "Figure Width", - "type": "number" - }, - "figure_height": { - "default": 4.8, - "title": "Figure Height", - "type": "number" - } - }, - "title": "Format2D", - "type": "object" - }, - "Grid": { - "additionalProperties": false, - "description": "Container for major and minor grid line configuration.\n\nAttributes:\n major (GridAxis): Configuration for major grid lines.\n minor (GridAxis): Configuration for minor grid lines.\n enable_minor_ticks (bool): Whether to enable minor ticks on the axes.", - "properties": { - "major": { - "$ref": "#/$defs/GridAxis", - "default": { - "show": false, - "pen": { - "alpha": 1.0, - "color": "gray", - "label": null, - "linestyle": "-", - "size": 0.75, - "zorder": 0.0 - } - } - }, - "minor": { - "$ref": "#/$defs/GridAxis", - "default": { - "show": false, - "pen": { - "alpha": 1.0, - "color": "gray", - "label": null, - "linestyle": "-", - "size": 0.75, - "zorder": 0.0 - } - } - }, - "enable_minor_ticks": { - "default": false, - "title": "Enable Minor Ticks", - "type": "boolean" - }, - "zorder": { - "default": -1, - "title": "Zorder", - "type": "number" - } - }, - "title": "Grid", - "type": "object" - }, - "GridAxis": { - "additionalProperties": false, - "description": "Controls styling and visibility for one type of grid (major or minor).\n\nAttributes:\n show (bool): Whether to display this grid axis.\n pen (Pen): Style and label information for drawing to matplotlib axes.", - "properties": { - "show": { - "default": false, - "title": "Show", - "type": "boolean" - }, - "pen": { - "$ref": "#/$defs/Pen", - "default": { - "color": "gray", - "size": 0.75, - "alpha": 1.0, - "zorder": 0.0, - "linestyle": "-", - "label": null - } - } - }, - "title": "GridAxis", - "type": "object" - }, - "Legend": { - "description": "Configuration container for Matplotlib legend styling and placement.\n\nThe `Legend` class controls the appearance and position of the plot legend.\nPlacement is governed by a combination of the `loc` and `bbox_to_anchor`\nparameters, mirroring Matplotlib's `Axes.legend()`.\n\nAttributes:\n visible (bool): Whether the legend should be displayed. Defaults to True.\n title (str | None): Title displayed above the legend entries.\n framealpha (float): Opacity of the legend background. 1 = fully opaque, 0 = fully transparent.\n loc (LegendLocation): Anchor point for the legend (e.g., upper right, lower left). See `LegendLocation` enum for options.\n ncol (int): Number of columns to arrange legend entries into.\n fancybox (bool): Whether to draw a rounded (True) or square (False) legend frame.\n edgecolor (str): Color of the legend frame border. Default is \"black\".\n bbox_to_anchor (tuple[float, float] | None): Offset position of the legend in figure or axes coordinates. If None, the legend is placed inside the axes using `loc`.\n\n Good starter values for common placements:\n\n - **Inside (default)**:\n ```python\n bbox_to_anchor=None\n ```\n - **Outside right**:\n ```python\n loc=LegendLocation.CENTER_LEFT\n bbox_to_anchor=(1.02, 0.5)\n ```\n - **Outside left**:\n ```python\n loc=LegendLocation.CENTER_RIGHT\n bbox_to_anchor=(-0.02, 0.5)\n ```\n - **Outside top**:\n ```python\n loc=LegendLocation.LOWER_CENTER\n bbox_to_anchor=(0.5, 1.02)\n ```\n - **Outside bottom**:\n ```python\n loc=LegendLocation.UPPER_CENTER\n bbox_to_anchor=(0.5, -0.02)\n ```", - "properties": { - "visible": { - "default": true, - "title": "Visible", - "type": "boolean" - }, - "title": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Title" - }, - "framealpha": { - "default": 1, - "title": "Framealpha", - "type": "number" - }, - "loc": { - "$ref": "#/$defs/LegendLocation", - "default": "best" - }, - "ncol": { - "default": 1, - "title": "Ncol", - "type": "integer" - }, - "fancybox": { - "default": true, - "title": "Fancybox", - "type": "boolean" - }, - "edgecolor": { - "default": "black", - "title": "Edgecolor", - "type": "string" - }, - "zorder": { - "default": 10, - "title": "Zorder", - "type": "integer" - }, - "bbox_to_anchor": { - "anyOf": [ - { - "maxItems": 2, - "minItems": 2, - "prefixItems": [ - { - "type": "number" - }, - { - "type": "number" - } - ], - "type": "array" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Bbox To Anchor" - } - }, - "title": "Legend", - "type": "object" - }, - "LegendLocation": { - "enum": [ - "best", - "upper right", - "upper left", - "lower left", - "lower right", - "right", - "center left", - "center right", - "lower center", - "upper center", - "center" - ], - "title": "LegendLocation", - "type": "string" - }, - "Marker": { - "additionalProperties": false, - "description": "Defines marker for scattering to matplotlib\n\nAttributes:\n color (str): Color of line\n size (float): Line width\n alpha (float): Opacity from 0 to 1 (inclusive)\n zorder (float): Prioritization\n label (Union[str, None]): Legend label\n symbol (str): Matplotlib symbol string", - "properties": { - "color": { - "default": "k", - "title": "Color", - "type": "string" - }, - "size": { - "default": 5, - "title": "Size", - "type": "number" - }, - "alpha": { - "default": 1, - "title": "Alpha", - "type": "number" - }, - "zorder": { - "default": 0, - "title": "Zorder", - "type": "number" - }, - "label": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Label" - }, - "symbol": { - "default": ".", - "title": "Symbol", - "type": "string" - } - }, - "title": "Marker", - "type": "object" - }, - "Pen": { - "additionalProperties": false, - "description": "Defines the pen drawing to matplotlib.\n\nAttributes:\n color (str): Color of line\n size (float): Line width\n alpha (float): Opacity from 0 to 1 (inclusive)\n linestyle (Union[str, Tuple[int, Tuple[int, ...]]]): Linestyle to plot. Supports `str` or `tuple` definition ([matplotlib documentation](https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html)).\n zorder (float): Prioritization\n label (Union[str, None]): Legend label", - "properties": { - "color": { - "anyOf": [ - { - "maxItems": 3, - "minItems": 3, - "prefixItems": [ - { - "type": "number" - }, - { - "type": "number" - }, - { - "type": "number" - } - ], - "type": "array" - }, - { - "maxItems": 4, - "minItems": 4, - "prefixItems": [ - { - "type": "number" - }, - { - "type": "number" - }, - { - "type": "number" - }, - { - "type": "number" - } - ], - "type": "array" - }, - { - "type": "string" - } - ], - "default": "k", - "title": "Color" - }, - "size": { - "default": 1, - "title": "Size", - "type": "number" - }, - "alpha": { - "default": 1, - "title": "Alpha", - "type": "number" - }, - "zorder": { - "default": 0, - "title": "Zorder", - "type": "number" - }, - "linestyle": { - "anyOf": [ - { - "type": "string" - }, - { - "maxItems": 2, - "minItems": 2, - "prefixItems": [ - { - "type": "integer" - }, - { - "items": { - "type": "integer" - }, - "type": "array" - } - ], - "type": "array" - } - ], - "default": "-", - "title": "Linestyle" - }, - "label": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Label" - } - }, - "title": "Pen", - "type": "object" - } - }, - "additionalProperties": false, - "description": "Defines a point to be scattered onto xy plot.\n\nAttributes:\n tags (Tags): Tags to be used for sorting data.\n x (float | str): X value for the point.\n y (float | str): Y value for the point.\n marker (Marker | None): Style and label information for scattering points to matplotlib axes.\n Only the label information is used in Grafana.\n Eventually style information will be used in grafana.\n metadata (dict[str, str]): A dictionary of metadata to be used as a tool tip for mousover in grafana", - "properties": { - "tags": { - "items": { - "anyOf": [ - { - "items": {}, - "type": "array" - }, - {} - ] - }, - "title": "Tags", - "type": "array" - }, - "metadata": { - "additionalProperties": { - "type": "string" - }, - "default": {}, - "title": "Metadata", - "type": "object" - }, - "format2d": { - "anyOf": [ - { - "$ref": "#/$defs/Format2D" - }, - { - "type": "null" - } - ], - "default": null - }, - "x": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "string" - } - ], - "title": "X" - }, - "y": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "string" - } - ], - "title": "Y" - }, - "marker": { - "anyOf": [ - { - "$ref": "#/$defs/Marker" - }, - { - "type": "null" - } - ], - "default": { - "color": "k", - "size": 5.0, - "alpha": 1.0, - "zorder": 0.0, - "label": null, - "symbol": "." - } - } - }, - "required": [ - "tags", - "x", - "y" - ], - "title": "Point2D", - "type": "object" - }, - "ProductEntryMetadata": { - "properties": { - "source": { - "format": "path", - "title": "Source", - "type": "string" - } - }, - "required": [ - "source" - ], - "title": "ProductEntryMetadata", - "type": "object" - }, - "ProductIndexMap": { - "$defs": { - "ProductEntryMetadata": { - "properties": { - "source": { - "format": "path", - "title": "Source", - "type": "string" - } - }, - "required": [ - "source" - ], - "title": "ProductEntryMetadata", - "type": "object" - } - }, - "properties": { - "entries": { - "additionalProperties": { - "$ref": "#/$defs/ProductEntryMetadata" - }, - "title": "Entries", - "type": "object" - } - }, - "title": "ProductIndexMap", - "type": "object" - }, - "TableEntry": { - "additionalProperties": false, - "description": "Defines an entry to be collected into a table.\n\nCollected table entries will be printed in three forms when possible: melted, pivot (when possible), and stats (on pivot columns, when possible).\n\nAttributes:\n tags (Tags): Tags used to sort data products\n row (float | str): Row Label\n col (float | str): Column Label\n value (float | str): Value\n unit (str | None): Units for value\n metadata (dict[str, str]): A dictionary of metadata to be used as a tool tip for mousover in grafana", - "properties": { - "tags": { - "items": { - "anyOf": [ - { - "items": {}, - "type": "array" - }, - {} - ] - }, - "title": "Tags", - "type": "array" - }, - "metadata": { - "additionalProperties": { - "type": "string" - }, - "default": {}, - "title": "Metadata", - "type": "object" - }, - "row": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "string" - } - ], - "title": "Row" - }, - "col": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "string" - } - ], - "title": "Col" - }, - "value": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "string" - }, - { - "type": "boolean" - } - ], - "title": "Value" - }, - "unit": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Unit" - } - }, - "required": [ - "tags", - "row", - "col", - "value" - ], - "title": "TableEntry", - "type": "object" - }, - "Trace2D": { - "$defs": { - "AxisScale": { - "enum": [ - "linear", - "log" - ], - "title": "AxisScale", - "type": "string" - }, - "Format2D": { - "additionalProperties": false, - "description": "Formatting data for matplotlib figure and axes\n\nAttributes:\n title_fig (Optional[str], optional): Sets [figure title][matplotlib.figure.Figure.suptitle]. Defaults to None.\n legend (Optional[Legend], optional): Sets [legend style][trendify.api.styling.legend.Legend]. Defaults to Legend().\n title_ax (Optional[str], optional): Sets [axis title][matplotlib.axes.Axes.set_title]. Defaults to None.\n label_x (Optional[str], optional): Sets [x-axis label][matplotlib.axes.Axes.set_xlabel]. Defaults to None.\n label_y (Optional[str], optional): Sets [y-axis label][matplotlib.axes.Axes.set_ylabel]. Defaults to None.\n lim_x_min (float | None, optional): Sets [x-axis lower bound][matplotlib.axes.Axes.set_xlim]. Defaults to None.\n lim_x_max (float | None, optional): Sets [x-axis upper bound][matplotlib.axes.Axes.set_xlim]. Defaults to None.\n lim_y_min (float | None, optional): Sets [y-axis lower bound][matplotlib.axes.Axes.set_ylim]. Defaults to None.\n lim_y_max (float | None, optional): Sets [y-axis upper bound][matplotlib.axes.Axes.set_ylim]. Defaults to None.\n grid (Grid | None,optional): Sets the [grid][matplotlib.pyplot.grid]. Defaults to None.\n scale_x (AxisScale, optional): Sets the x axis scale to an option from [AxisScale][trendify.api.formats.format2d.AxisScale]. Defaults to AxisScale.LINEAR\n scale_y (AxisScale, optional): Sets the y axis scale to an option from [AxisScale][trendify.api.formats.format2d.AxisScale]. Defaults to AxisScale.LINEAR\n figure_width (float, optional): Sets the of the width of rendered figure in inches. Defaults to 6.4.\n figure_height (float, optional): Sets the of the height of rendered figure in inches. Defaults to 4.8.", - "properties": { - "title_fig": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Title Fig" - }, - "legend": { - "anyOf": [ - { - "$ref": "#/$defs/Legend" - }, - { - "type": "null" - } - ], - "default": { - "visible": true, - "title": null, - "framealpha": 1.0, - "loc": "best", - "ncol": 1, - "fancybox": true, - "edgecolor": "black", - "zorder": 10, - "bbox_to_anchor": null - } - }, - "title_ax": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Title Ax" - }, - "label_x": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Label X" - }, - "label_y": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Label Y" - }, - "lim_x_min": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Lim X Min" - }, - "lim_x_max": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Lim X Max" - }, - "lim_y_min": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Lim Y Min" - }, - "lim_y_max": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Lim Y Max" - }, - "grid": { - "anyOf": [ - { - "$ref": "#/$defs/Grid" - }, - { - "type": "null" - } - ], - "default": null - }, - "scale_x": { - "$ref": "#/$defs/AxisScale", - "default": "linear" - }, - "scale_y": { - "$ref": "#/$defs/AxisScale", - "default": "linear" - }, - "figure_width": { - "default": 6.4, - "title": "Figure Width", - "type": "number" - }, - "figure_height": { - "default": 4.8, - "title": "Figure Height", - "type": "number" - } - }, - "title": "Format2D", - "type": "object" - }, - "Grid": { - "additionalProperties": false, - "description": "Container for major and minor grid line configuration.\n\nAttributes:\n major (GridAxis): Configuration for major grid lines.\n minor (GridAxis): Configuration for minor grid lines.\n enable_minor_ticks (bool): Whether to enable minor ticks on the axes.", - "properties": { - "major": { - "$ref": "#/$defs/GridAxis", - "default": { - "show": false, - "pen": { - "alpha": 1.0, - "color": "gray", - "label": null, - "linestyle": "-", - "size": 0.75, - "zorder": 0.0 - } - } - }, - "minor": { - "$ref": "#/$defs/GridAxis", - "default": { - "show": false, - "pen": { - "alpha": 1.0, - "color": "gray", - "label": null, - "linestyle": "-", - "size": 0.75, - "zorder": 0.0 - } - } - }, - "enable_minor_ticks": { - "default": false, - "title": "Enable Minor Ticks", - "type": "boolean" - }, - "zorder": { - "default": -1, - "title": "Zorder", - "type": "number" - } - }, - "title": "Grid", - "type": "object" - }, - "GridAxis": { - "additionalProperties": false, - "description": "Controls styling and visibility for one type of grid (major or minor).\n\nAttributes:\n show (bool): Whether to display this grid axis.\n pen (Pen): Style and label information for drawing to matplotlib axes.", - "properties": { - "show": { - "default": false, - "title": "Show", - "type": "boolean" - }, - "pen": { - "$ref": "#/$defs/Pen", - "default": { - "color": "gray", - "size": 0.75, - "alpha": 1.0, - "zorder": 0.0, - "linestyle": "-", - "label": null - } - } - }, - "title": "GridAxis", - "type": "object" - }, - "Legend": { - "description": "Configuration container for Matplotlib legend styling and placement.\n\nThe `Legend` class controls the appearance and position of the plot legend.\nPlacement is governed by a combination of the `loc` and `bbox_to_anchor`\nparameters, mirroring Matplotlib's `Axes.legend()`.\n\nAttributes:\n visible (bool): Whether the legend should be displayed. Defaults to True.\n title (str | None): Title displayed above the legend entries.\n framealpha (float): Opacity of the legend background. 1 = fully opaque, 0 = fully transparent.\n loc (LegendLocation): Anchor point for the legend (e.g., upper right, lower left). See `LegendLocation` enum for options.\n ncol (int): Number of columns to arrange legend entries into.\n fancybox (bool): Whether to draw a rounded (True) or square (False) legend frame.\n edgecolor (str): Color of the legend frame border. Default is \"black\".\n bbox_to_anchor (tuple[float, float] | None): Offset position of the legend in figure or axes coordinates. If None, the legend is placed inside the axes using `loc`.\n\n Good starter values for common placements:\n\n - **Inside (default)**:\n ```python\n bbox_to_anchor=None\n ```\n - **Outside right**:\n ```python\n loc=LegendLocation.CENTER_LEFT\n bbox_to_anchor=(1.02, 0.5)\n ```\n - **Outside left**:\n ```python\n loc=LegendLocation.CENTER_RIGHT\n bbox_to_anchor=(-0.02, 0.5)\n ```\n - **Outside top**:\n ```python\n loc=LegendLocation.LOWER_CENTER\n bbox_to_anchor=(0.5, 1.02)\n ```\n - **Outside bottom**:\n ```python\n loc=LegendLocation.UPPER_CENTER\n bbox_to_anchor=(0.5, -0.02)\n ```", - "properties": { - "visible": { - "default": true, - "title": "Visible", - "type": "boolean" - }, - "title": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Title" - }, - "framealpha": { - "default": 1, - "title": "Framealpha", - "type": "number" - }, - "loc": { - "$ref": "#/$defs/LegendLocation", - "default": "best" - }, - "ncol": { - "default": 1, - "title": "Ncol", - "type": "integer" - }, - "fancybox": { - "default": true, - "title": "Fancybox", - "type": "boolean" - }, - "edgecolor": { - "default": "black", - "title": "Edgecolor", - "type": "string" - }, - "zorder": { - "default": 10, - "title": "Zorder", - "type": "integer" - }, - "bbox_to_anchor": { - "anyOf": [ - { - "maxItems": 2, - "minItems": 2, - "prefixItems": [ - { - "type": "number" - }, - { - "type": "number" - } - ], - "type": "array" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Bbox To Anchor" - } - }, - "title": "Legend", - "type": "object" - }, - "LegendLocation": { - "enum": [ - "best", - "upper right", - "upper left", - "lower left", - "lower right", - "right", - "center left", - "center right", - "lower center", - "upper center", - "center" - ], - "title": "LegendLocation", - "type": "string" - }, - "Marker": { - "additionalProperties": false, - "description": "Defines marker for scattering to matplotlib\n\nAttributes:\n color (str): Color of line\n size (float): Line width\n alpha (float): Opacity from 0 to 1 (inclusive)\n zorder (float): Prioritization\n label (Union[str, None]): Legend label\n symbol (str): Matplotlib symbol string", - "properties": { - "color": { - "default": "k", - "title": "Color", - "type": "string" - }, - "size": { - "default": 5, - "title": "Size", - "type": "number" - }, - "alpha": { - "default": 1, - "title": "Alpha", - "type": "number" - }, - "zorder": { - "default": 0, - "title": "Zorder", - "type": "number" - }, - "label": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Label" - }, - "symbol": { - "default": ".", - "title": "Symbol", - "type": "string" - } - }, - "title": "Marker", - "type": "object" - }, - "Pen": { - "additionalProperties": false, - "description": "Defines the pen drawing to matplotlib.\n\nAttributes:\n color (str): Color of line\n size (float): Line width\n alpha (float): Opacity from 0 to 1 (inclusive)\n linestyle (Union[str, Tuple[int, Tuple[int, ...]]]): Linestyle to plot. Supports `str` or `tuple` definition ([matplotlib documentation](https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html)).\n zorder (float): Prioritization\n label (Union[str, None]): Legend label", - "properties": { - "color": { - "anyOf": [ - { - "maxItems": 3, - "minItems": 3, - "prefixItems": [ - { - "type": "number" - }, - { - "type": "number" - }, - { - "type": "number" - } - ], - "type": "array" - }, - { - "maxItems": 4, - "minItems": 4, - "prefixItems": [ - { - "type": "number" - }, - { - "type": "number" - }, - { - "type": "number" - }, - { - "type": "number" - } - ], - "type": "array" - }, - { - "type": "string" - } - ], - "default": "k", - "title": "Color" - }, - "size": { - "default": 1, - "title": "Size", - "type": "number" - }, - "alpha": { - "default": 1, - "title": "Alpha", - "type": "number" - }, - "zorder": { - "default": 0, - "title": "Zorder", - "type": "number" - }, - "linestyle": { - "anyOf": [ - { - "type": "string" - }, - { - "maxItems": 2, - "minItems": 2, - "prefixItems": [ - { - "type": "integer" - }, - { - "items": { - "type": "integer" - }, - "type": "array" - } - ], - "type": "array" - } - ], - "default": "-", - "title": "Linestyle" - }, - "label": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Label" - } - }, - "title": "Pen", - "type": "object" - }, - "Point2D": { - "additionalProperties": false, - "description": "Defines a point to be scattered onto xy plot.\n\nAttributes:\n tags (Tags): Tags to be used for sorting data.\n x (float | str): X value for the point.\n y (float | str): Y value for the point.\n marker (Marker | None): Style and label information for scattering points to matplotlib axes.\n Only the label information is used in Grafana.\n Eventually style information will be used in grafana.\n metadata (dict[str, str]): A dictionary of metadata to be used as a tool tip for mousover in grafana", - "properties": { - "tags": { - "items": { - "anyOf": [ - { - "items": {}, - "type": "array" - }, - {} - ] - }, - "title": "Tags", - "type": "array" - }, - "metadata": { - "additionalProperties": { - "type": "string" - }, - "default": {}, - "title": "Metadata", - "type": "object" - }, - "format2d": { - "anyOf": [ - { - "$ref": "#/$defs/Format2D" - }, - { - "type": "null" - } - ], - "default": null - }, - "x": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "string" - } - ], - "title": "X" - }, - "y": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "string" - } - ], - "title": "Y" - }, - "marker": { - "anyOf": [ - { - "$ref": "#/$defs/Marker" - }, - { - "type": "null" - } - ], - "default": { - "color": "k", - "size": 5.0, - "alpha": 1.0, - "zorder": 0.0, - "label": null, - "symbol": "." - } - } - }, - "required": [ - "tags", - "x", - "y" - ], - "title": "Point2D", - "type": "object" - } - }, - "additionalProperties": false, - "description": "A collection of points comprising a trace.\nUse the [Trace2D.from_xy][trendify.api.Trace2D.from_xy] constructor.\n\nAttributes:\n points (List[Point2D]): List of points. Usually the points would have null values\n for `marker` and `format2d` fields to save space.\n pen (Pen): Style and label information for drawing to matplotlib axes.\n Only the label information is used in Grafana.\n Eventually style information will be used in grafana.\n tags (Tags): Tags to be used for sorting data.\n metadata (dict[str, str]): A dictionary of metadata to be used as a tool tip for mousover in grafana", - "properties": { - "tags": { - "items": { - "anyOf": [ - { - "items": {}, - "type": "array" - }, - {} - ] - }, - "title": "Tags", - "type": "array" - }, - "metadata": { - "additionalProperties": { - "type": "string" - }, - "default": {}, - "title": "Metadata", - "type": "object" - }, - "format2d": { - "anyOf": [ - { - "$ref": "#/$defs/Format2D" - }, - { - "type": "null" - } - ], - "default": null - }, - "points": { - "items": { - "$ref": "#/$defs/Point2D" - }, - "title": "Points", - "type": "array" - }, - "pen": { - "$ref": "#/$defs/Pen", - "default": { - "color": "k", - "size": 1.0, - "alpha": 1.0, - "zorder": 0.0, - "linestyle": "-", - "label": null - } - } - }, - "required": [ - "tags", - "points" - ], - "title": "Trace2D", - "type": "object" - }, - "XYData": { - "$defs": { - "AxisScale": { - "enum": [ - "linear", - "log" - ], - "title": "AxisScale", - "type": "string" - }, - "Format2D": { - "additionalProperties": false, - "description": "Formatting data for matplotlib figure and axes\n\nAttributes:\n title_fig (Optional[str], optional): Sets [figure title][matplotlib.figure.Figure.suptitle]. Defaults to None.\n legend (Optional[Legend], optional): Sets [legend style][trendify.api.styling.legend.Legend]. Defaults to Legend().\n title_ax (Optional[str], optional): Sets [axis title][matplotlib.axes.Axes.set_title]. Defaults to None.\n label_x (Optional[str], optional): Sets [x-axis label][matplotlib.axes.Axes.set_xlabel]. Defaults to None.\n label_y (Optional[str], optional): Sets [y-axis label][matplotlib.axes.Axes.set_ylabel]. Defaults to None.\n lim_x_min (float | None, optional): Sets [x-axis lower bound][matplotlib.axes.Axes.set_xlim]. Defaults to None.\n lim_x_max (float | None, optional): Sets [x-axis upper bound][matplotlib.axes.Axes.set_xlim]. Defaults to None.\n lim_y_min (float | None, optional): Sets [y-axis lower bound][matplotlib.axes.Axes.set_ylim]. Defaults to None.\n lim_y_max (float | None, optional): Sets [y-axis upper bound][matplotlib.axes.Axes.set_ylim]. Defaults to None.\n grid (Grid | None,optional): Sets the [grid][matplotlib.pyplot.grid]. Defaults to None.\n scale_x (AxisScale, optional): Sets the x axis scale to an option from [AxisScale][trendify.api.formats.format2d.AxisScale]. Defaults to AxisScale.LINEAR\n scale_y (AxisScale, optional): Sets the y axis scale to an option from [AxisScale][trendify.api.formats.format2d.AxisScale]. Defaults to AxisScale.LINEAR\n figure_width (float, optional): Sets the of the width of rendered figure in inches. Defaults to 6.4.\n figure_height (float, optional): Sets the of the height of rendered figure in inches. Defaults to 4.8.", - "properties": { - "title_fig": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Title Fig" - }, - "legend": { - "anyOf": [ - { - "$ref": "#/$defs/Legend" - }, - { - "type": "null" - } - ], - "default": { - "visible": true, - "title": null, - "framealpha": 1.0, - "loc": "best", - "ncol": 1, - "fancybox": true, - "edgecolor": "black", - "zorder": 10, - "bbox_to_anchor": null - } - }, - "title_ax": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Title Ax" - }, - "label_x": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Label X" - }, - "label_y": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Label Y" - }, - "lim_x_min": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Lim X Min" - }, - "lim_x_max": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Lim X Max" - }, - "lim_y_min": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Lim Y Min" - }, - "lim_y_max": { - "anyOf": [ - { - "type": "number" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Lim Y Max" - }, - "grid": { - "anyOf": [ - { - "$ref": "#/$defs/Grid" - }, - { - "type": "null" - } - ], - "default": null - }, - "scale_x": { - "$ref": "#/$defs/AxisScale", - "default": "linear" - }, - "scale_y": { - "$ref": "#/$defs/AxisScale", - "default": "linear" - }, - "figure_width": { - "default": 6.4, - "title": "Figure Width", - "type": "number" - }, - "figure_height": { - "default": 4.8, - "title": "Figure Height", - "type": "number" - } - }, - "title": "Format2D", - "type": "object" - }, - "Grid": { - "additionalProperties": false, - "description": "Container for major and minor grid line configuration.\n\nAttributes:\n major (GridAxis): Configuration for major grid lines.\n minor (GridAxis): Configuration for minor grid lines.\n enable_minor_ticks (bool): Whether to enable minor ticks on the axes.", - "properties": { - "major": { - "$ref": "#/$defs/GridAxis", - "default": { - "show": false, - "pen": { - "alpha": 1.0, - "color": "gray", - "label": null, - "linestyle": "-", - "size": 0.75, - "zorder": 0.0 - } - } - }, - "minor": { - "$ref": "#/$defs/GridAxis", - "default": { - "show": false, - "pen": { - "alpha": 1.0, - "color": "gray", - "label": null, - "linestyle": "-", - "size": 0.75, - "zorder": 0.0 - } - } - }, - "enable_minor_ticks": { - "default": false, - "title": "Enable Minor Ticks", - "type": "boolean" - }, - "zorder": { - "default": -1, - "title": "Zorder", - "type": "number" - } - }, - "title": "Grid", - "type": "object" - }, - "GridAxis": { - "additionalProperties": false, - "description": "Controls styling and visibility for one type of grid (major or minor).\n\nAttributes:\n show (bool): Whether to display this grid axis.\n pen (Pen): Style and label information for drawing to matplotlib axes.", - "properties": { - "show": { - "default": false, - "title": "Show", - "type": "boolean" - }, - "pen": { - "$ref": "#/$defs/Pen", - "default": { - "color": "gray", - "size": 0.75, - "alpha": 1.0, - "zorder": 0.0, - "linestyle": "-", - "label": null - } - } - }, - "title": "GridAxis", - "type": "object" - }, - "Legend": { - "description": "Configuration container for Matplotlib legend styling and placement.\n\nThe `Legend` class controls the appearance and position of the plot legend.\nPlacement is governed by a combination of the `loc` and `bbox_to_anchor`\nparameters, mirroring Matplotlib's `Axes.legend()`.\n\nAttributes:\n visible (bool): Whether the legend should be displayed. Defaults to True.\n title (str | None): Title displayed above the legend entries.\n framealpha (float): Opacity of the legend background. 1 = fully opaque, 0 = fully transparent.\n loc (LegendLocation): Anchor point for the legend (e.g., upper right, lower left). See `LegendLocation` enum for options.\n ncol (int): Number of columns to arrange legend entries into.\n fancybox (bool): Whether to draw a rounded (True) or square (False) legend frame.\n edgecolor (str): Color of the legend frame border. Default is \"black\".\n bbox_to_anchor (tuple[float, float] | None): Offset position of the legend in figure or axes coordinates. If None, the legend is placed inside the axes using `loc`.\n\n Good starter values for common placements:\n\n - **Inside (default)**:\n ```python\n bbox_to_anchor=None\n ```\n - **Outside right**:\n ```python\n loc=LegendLocation.CENTER_LEFT\n bbox_to_anchor=(1.02, 0.5)\n ```\n - **Outside left**:\n ```python\n loc=LegendLocation.CENTER_RIGHT\n bbox_to_anchor=(-0.02, 0.5)\n ```\n - **Outside top**:\n ```python\n loc=LegendLocation.LOWER_CENTER\n bbox_to_anchor=(0.5, 1.02)\n ```\n - **Outside bottom**:\n ```python\n loc=LegendLocation.UPPER_CENTER\n bbox_to_anchor=(0.5, -0.02)\n ```", - "properties": { - "visible": { - "default": true, - "title": "Visible", - "type": "boolean" - }, - "title": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Title" - }, - "framealpha": { - "default": 1, - "title": "Framealpha", - "type": "number" - }, - "loc": { - "$ref": "#/$defs/LegendLocation", - "default": "best" - }, - "ncol": { - "default": 1, - "title": "Ncol", - "type": "integer" - }, - "fancybox": { - "default": true, - "title": "Fancybox", - "type": "boolean" - }, - "edgecolor": { - "default": "black", - "title": "Edgecolor", - "type": "string" - }, - "zorder": { - "default": 10, - "title": "Zorder", - "type": "integer" - }, - "bbox_to_anchor": { - "anyOf": [ - { - "maxItems": 2, - "minItems": 2, - "prefixItems": [ - { - "type": "number" - }, - { - "type": "number" - } - ], - "type": "array" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Bbox To Anchor" - } - }, - "title": "Legend", - "type": "object" - }, - "LegendLocation": { - "enum": [ - "best", - "upper right", - "upper left", - "lower left", - "lower right", - "right", - "center left", - "center right", - "lower center", - "upper center", - "center" - ], - "title": "LegendLocation", - "type": "string" - }, - "Pen": { - "additionalProperties": false, - "description": "Defines the pen drawing to matplotlib.\n\nAttributes:\n color (str): Color of line\n size (float): Line width\n alpha (float): Opacity from 0 to 1 (inclusive)\n linestyle (Union[str, Tuple[int, Tuple[int, ...]]]): Linestyle to plot. Supports `str` or `tuple` definition ([matplotlib documentation](https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html)).\n zorder (float): Prioritization\n label (Union[str, None]): Legend label", - "properties": { - "color": { - "anyOf": [ - { - "maxItems": 3, - "minItems": 3, - "prefixItems": [ - { - "type": "number" - }, - { - "type": "number" - }, - { - "type": "number" - } - ], - "type": "array" - }, - { - "maxItems": 4, - "minItems": 4, - "prefixItems": [ - { - "type": "number" - }, - { - "type": "number" - }, - { - "type": "number" - }, - { - "type": "number" - } - ], - "type": "array" - }, - { - "type": "string" - } - ], - "default": "k", - "title": "Color" - }, - "size": { - "default": 1, - "title": "Size", - "type": "number" - }, - "alpha": { - "default": 1, - "title": "Alpha", - "type": "number" - }, - "zorder": { - "default": 0, - "title": "Zorder", - "type": "number" - }, - "linestyle": { - "anyOf": [ - { - "type": "string" - }, - { - "maxItems": 2, - "minItems": 2, - "prefixItems": [ - { - "type": "integer" - }, - { - "items": { - "type": "integer" - }, - "type": "array" - } - ], - "type": "array" - } - ], - "default": "-", - "title": "Linestyle" - }, - "label": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Label" - } - }, - "title": "Pen", - "type": "object" - } - }, - "additionalProperties": true, - "description": "Base class for children of DataProduct to be plotted ax xy data on a 2D plot", - "properties": { - "tags": { - "items": { - "anyOf": [ - { - "items": {}, - "type": "array" - }, - {} - ] - }, - "title": "Tags", - "type": "array" - }, - "metadata": { - "additionalProperties": { - "type": "string" - }, - "default": {}, - "title": "Metadata", - "type": "object" - }, - "format2d": { - "anyOf": [ - { - "$ref": "#/$defs/Format2D" - }, - { - "type": "null" - } - ], - "default": null - } - }, - "required": [ - "tags" - ], - "title": "XYData", - "type": "object" - } -} \ No newline at end of file diff --git a/sample_hierarchical_data.py b/sample_hierarchical_data.py deleted file mode 100644 index 6ae0c08..0000000 --- a/sample_hierarchical_data.py +++ /dev/null @@ -1,239 +0,0 @@ -""" -Generate sample data with hierarchical tags for testing the Trendify Plotly dashboard. -This script creates various data products organized under hierarchical tag structures. -""" - -import numpy as np -import sys -from pathlib import Path - -# Add the project to path to allow importing -project_root = Path(__file__).parent -if str(project_root) not in sys.path: - sys.path.insert(0, str(project_root)) - -from trendify.api.api import ( - DataProductCollection, - Point2D, - Trace2D, - TableEntry, - HistogramEntry, - AxLine, - LineOrientation, - Format2D, - Pen, -) - - -def generate_sample_data(): - """Generate a collection of data products with hierarchical tags.""" - # First, create a list of data products - data_products = [] - - # Define hierarchical tag categories - categories = { - "Performance": ["CPU", "Memory", "Disk", "Network"], - "Analytics": ["Users", "Sessions", "Conversions", "Revenue"], - "Infrastructure": ["Servers", "Databases", "Load Balancers", "Cache"], - "Monitoring": ["Errors", "Latency", "Uptime", "Throughput"], - "Finance": ["Revenue", "Costs", "ROI", "Growth"], - } - - # Create Format2D objects for different plot types - time_series_format = Format2D( - title_ax="Time Series Data", - label_x="Time (s)", - label_y="Value", - title_legend="Metrics", - ) - - scatter_format = Format2D( - title_ax="Correlation Analysis", - label_x="X Value", - label_y="Y Value", - title_legend="Data Points", - ) - - histogram_format = Format2D( - title_ax="Distribution Analysis", - label_x="Value", - label_y="Frequency", - title_legend="Distributions", - ) - - # Generate data for each category - for category, subcategories in categories.items(): - for subcategory in subcategories: - # Create a hierarchical tag - tag_str = f"{category}/{subcategory}" - - # Add time series traces (1-3 per subcategory) - for i in range(1, np.random.randint(2, 5)): - x = np.linspace(0, 10, 100) - noise = np.random.normal(0, 0.5, 100) - amplitude = np.random.uniform(1, 5) - frequency = np.random.uniform(0.5, 2) - - y = amplitude * np.sin(frequency * x) + noise - - data_products.append( - Trace2D.from_xy( - x=x, - y=y, - pen=Pen(label=f"{subcategory} Metric {i}"), - format2d=time_series_format, - tags=[tag_str], - ) - ) - - # Add scatter points (for some subcategories) - if np.random.random() > 0.3: # 70% chance of adding points - num_points = np.random.randint(20, 100) - x = np.random.normal(5, 2, num_points) - y = 2 * x + np.random.normal(0, 3, num_points) - - data_products.append( - Trace2D.from_xy( - x=x, - y=y, - pen=Pen(label=f"{subcategory} Data Points"), - format2d=scatter_format, - tags=[tag_str], - ) - ) - - # Add histogram data (for some subcategories) - if np.random.random() > 0.5: # 50% chance of adding histogram - # Generate some sample distribution data - if np.random.random() > 0.5: - # Normal distribution - values = np.random.normal(50, 15, 1000) - dist_type = "Normal" - else: - # Bimodal distribution - values = np.concatenate( - [np.random.normal(30, 10, 500), np.random.normal(70, 10, 500)] - ) - dist_type = "Bimodal" - from trendify import HistogramStyle - - for v in values: - data_products.append( - HistogramEntry( - value=v, - style=HistogramStyle(label=f"{subcategory} {dist_type}"), - format2d=histogram_format, - tags=[tag_str], - ) - ) - - # Add table data (for some subcategories) - if np.random.random() > 0.6: # 40% chance of adding table - # Create a sample table with metrics - metrics = ["Count", "Avg", "Min", "Max", "Std Dev"] - features = [f"Feature {i}" for i in range(1, 4)] - - for metric in metrics: - for feature in features: - value = np.random.uniform(10, 100) - data_products.append( - TableEntry( - row=metric, - col=feature, - value=value, - tags=[tag_str], - unit=None, - ) - ) - - # Add reference lines (for some subcategories) - if np.random.random() > 0.7: # 30% chance of adding reference lines - # Add horizontal threshold line - threshold = np.random.uniform(1, 4) - data_products.append( - AxLine( - value=threshold, - orientation=LineOrientation.HORIZONTAL, - pen=Pen(label=f"{subcategory} Threshold"), - tags=[tag_str], - ) - ) - - # Maybe add vertical reference - if np.random.random() > 0.5: - ref_x = np.random.uniform(3, 7) - data_products.append( - AxLine( - value=ref_x, - orientation=LineOrientation.VERTICAL, - pen=Pen(label=f"{subcategory} Reference"), - tags=[tag_str], - ) - ) - - # Add some flat (non-hierarchical) tags as well for comparison - flat_tags = ["Summary", "Overview", "Highlights", "Alerts"] - - for tag_name in flat_tags: - # Add a couple of traces - x = np.linspace(0, 10, 100) - for i in range(2): - y = np.random.normal(5, 2, 100) + i * 2 - data_products.append( - Trace2D.from_xy( - x=x, - y=y, - pen=Pen(label=f"Metric {i + 1}"), - format2d=time_series_format, - tags=[tag_name], - ) - ) - - # Add a table - for row in ["Total", "Average", "Change"]: - for col in ["Yesterday", "Today", "Weekly"]: - value = np.random.uniform(100, 1000) - data_products.append( - TableEntry(row=row, col=col, value=value, tags=[tag_name]) - ) - - # Create collection from data products - collection = DataProductCollection(elements=data_products) - - return collection - - -def main(): - """Main function to generate and display sample data.""" - print("Generating sample hierarchical data for Trendify Plotly dashboard...") - collection = generate_sample_data() - - # Print summary of what was generated - tags = collection.get_tags() - print(f"Generated {len(collection.elements)} data products across {len(tags)} tags") - - print("\nTag hierarchy:") - for tag in sorted([str(t) for t in tags]): - print(f" - {tag}") - - print("\nData product counts by type:") - trace_count = len(collection.get_products(object_type=Trace2D).elements or []) - point_count = len(collection.get_products(object_type=Point2D).elements or []) - table_count = len(collection.get_products(object_type=TableEntry).elements or []) - hist_count = len(collection.get_products(object_type=HistogramEntry).elements or []) - axline_count = len(collection.get_products(object_type=AxLine).elements or []) - - print(f" - Traces: {trace_count}") - print(f" - Points: {point_count}") - print(f" - Tables: {table_count}") - print(f" - Histograms: {hist_count}") - print(f" - AxLines: {axline_count}") - - # Launch the Plotly dashboard - print("\nLaunching Plotly dashboard...") - app = collection.generate_plotly_dashboard(title="Hierarchical Tag Demo") - app.run_server(debug=True) - - -if __name__ == "__main__": - main() diff --git a/src/trendify/__init__.py b/src/trendify/__init__.py index c7e908e..0dd05b3 100644 --- a/src/trendify/__init__.py +++ b/src/trendify/__init__.py @@ -2,12 +2,20 @@ Provides top-level imports """ -from trendify import base -from trendify import formats -from trendify import plotting -from trendify import store -from trendify import styling - +from trendify import ( + base, + cli, + color, + examples, + formats, + generator, + log, + pipeline, + plotting, + store, + styling, + typing, +) from trendify.base import ( DataProduct, HashableBase, @@ -22,6 +30,16 @@ helpers, pen, ) +from trendify.cli import ( + app, +) +from trendify.color import ( + Color, +) +from trendify.examples import ( + example_data_product_generator, + make_example_data, +) from trendify.formats import ( AxisScale, Format2D, @@ -31,6 +49,30 @@ format2d, table, ) +from trendify.generator import ( + Histogrammer, + TableBuilder, + XYDataPlotter, + generate, + generate_products, + get_sorted_dirs, + histogrammer, + make_include_files, + render, + render_assets, + table_builder, + xy_data_plotter, +) +from trendify.log import ( + create_queue_listener, + get_logger, + set_log_level, + setup_logger, + worker_init, +) +from trendify.pipeline import ( + TrendifyPipeline, +) from trendify.plotting import ( AxLine, HistogramEntry, @@ -47,13 +89,14 @@ trace, ) from trendify.store import ( - ProductStore, SCHEMA_VERSION, + ProductStore, connect, db, decode_tag, encode_tag, product_store, + tag_to_path_parts, tags, ) from trendify.styling import ( @@ -67,11 +110,16 @@ legend, marker, ) +from trendify.typing import ( + MatN, + VecN, +) __all__ = [ "SCHEMA_VERSION", "AxLine", "AxisScale", + "Color", "DataProduct", "Format2D", "Grid", @@ -80,10 +128,12 @@ "HashableBase", "HistogramEntry", "HistogramStyle", + "Histogrammer", "Legend", "LegendLocation", "LineOrientation", "Marker", + "MatN", "Pen", "PlotlyFigure", "PlottableData2D", @@ -94,33 +144,62 @@ "ProductType", "R", "SingleAxisFigure", + "TableBuilder", "TableEntry", "Tag", "Tags", "Trace2D", + "TrendifyPipeline", + "VecN", "XYData", + "XYDataPlotter", + "app", "axline", "base", + "cli", + "color", "connect", + "create_queue_listener", "data_product", "db", "decode_tag", "encode_tag", + "example_data_product_generator", + "examples", "figure", "format2d", "formats", + "generate", + "generate_products", + "generator", + "get_logger", + "get_sorted_dirs", "grid", "helpers", "histogram", + "histogrammer", "legend", + "log", + "make_example_data", + "make_include_files", "marker", "pen", + "pipeline", "plotting", "point", "product_store", + "render", + "render_assets", + "set_log_level", + "setup_logger", "store", "styling", "table", + "table_builder", + "tag_to_path_parts", "tags", "trace", + "typing", + "worker_init", + "xy_data_plotter", ] diff --git a/src/trendify/base/data_product.py b/src/trendify/base/data_product.py index a637daf..dc40ef3 100644 --- a/src/trendify/base/data_product.py +++ b/src/trendify/base/data_product.py @@ -1,9 +1,15 @@ +""" +Base `DataProduct` pydantic model, the `ProductGenerator`/`ProductList` type aliases user code +implements against, and the subclass registry `ProductStore` uses to deserialize a stored +`product_type` string back into its concrete pydantic class. +""" + from __future__ import annotations +import logging +from collections.abc import Callable from pathlib import Path from typing import Any -from collections.abc import Callable -import logging from pydantic import ( BaseModel, @@ -16,7 +22,6 @@ from trendify.base.helpers import Tags - logger = logging.getLogger(__name__) __all__ = ["DataProduct", "ProductGenerator", "ProductList"] @@ -74,21 +79,22 @@ def __init_subclass__(cls, **kwargs: Any) -> None: """ super().__init_subclass__(**kwargs) _data_product_subclass_registry[cls.__name__] = cls + logger.debug(f"Registered DataProduct subclass {cls.__name__!r}") model_config = ConfigDict(extra="allow") - def append_to_list(self, l: list): + def append_to_list(self, to_add_to: list): """ Appends self to list. Args: - l (List): list to which `self` will be appended + to_add_to (List): list to which `self` will be appended Returns: (Self): returns instance of `self` """ - l.append(self) + to_add_to.append(self) return self @classmethod @@ -115,7 +121,15 @@ def deserialize(cls, product_type: str, payload: str) -> DataProduct: (DataProduct): the reconstructed, correctly-typed product instance """ - duck_type = _data_product_subclass_registry[product_type] + try: + duck_type = _data_product_subclass_registry[product_type] + except KeyError: + logger.error( + f"No registered DataProduct subclass named {product_type!r}. Known types: " + f"{sorted(_data_product_subclass_registry)}. This usually means the module " + f"defining that subclass hasn't been imported in this process." + ) + raise return duck_type.model_validate_json(payload) def set_metadata(self, new: dict[str, str]): diff --git a/src/trendify/base/helpers.py b/src/trendify/base/helpers.py index 7ed5e30..ac3b595 100644 --- a/src/trendify/base/helpers.py +++ b/src/trendify/base/helpers.py @@ -1,11 +1,20 @@ +""" +Small shared building blocks used across the rest of the package: the `Tag`/`Tags` type +aliases every `DataProduct` and the store's tag-encoding logic build on, `HashableBase` for +pydantic models that need to go in a `set`, and the `ProductType` enum. +""" + from __future__ import annotations import logging -from enum import StrEnum, auto -from typing import TypeVar +from enum import StrEnum +from typing import TYPE_CHECKING, TypeVar from pydantic import BaseModel +if TYPE_CHECKING: + from trendify.base.data_product import DataProduct + logger = logging.getLogger(__name__) __all__ = [ @@ -16,16 +25,18 @@ "Tags", ] -R = TypeVar("R") +R = TypeVar("R", bound="DataProduct") +"""Bound to `DataProduct`: used by `ProductStore.get_products`/`get_products_of_type` so a +call like `get_products_of_type(Point2D)` returns `list[Point2D]` rather than `list[DataProduct]`. +""" Tag = str | int | tuple[str | int, ...] """ Determines what types can be used to define a tag. -Narrower than v1's ``Union[tuple[Hashable, ...], Hashable]``: tags must be reliably -JSON-encodable (str/int, or tuples thereof) because the store canonicalizes a tag into a -`tag_key` via `json.dumps` for indexed lookup (see `trendify.store.tags`). A bare `Hashable` -can't guarantee a stable, 1:1 JSON encoding. +Tags must be reliably JSON-encodable (str/int, or tuples thereof) because the store +canonicalizes a tag into a `tag_key` via `json.dumps` for indexed lookup (see +`trendify.store.tags`). """ Tags = list[Tag] @@ -60,9 +71,9 @@ class ProductType(StrEnum): """ - DataProduct = auto() - XYData = auto() - Trace2D = auto() - Point2D = auto() - TableEntry = auto() - HistogramEntry = auto() + DATA_PRODUCT = "data_product" + XY_DATA = "xy_data" + TRACE_2D = "trace_2d" + POINT_2D = "point_2d" + TABLE_ENTRY = "table_entry" + HISTOGRAM_ENTRY = "histogram_entry" diff --git a/src/trendify/base/pen.py b/src/trendify/base/pen.py index 7d5a046..25513bd 100644 --- a/src/trendify/base/pen.py +++ b/src/trendify/base/pen.py @@ -1,3 +1,8 @@ +""" +`Pen`: shared line/marker styling (color, size, alpha, linestyle, zorder, label) used by +`Trace2D`, `AxLine`, and anything else that draws a line to matplotlib or Plotly. +""" + from __future__ import annotations diff --git a/src/trendify/cli.py b/src/trendify/cli.py new file mode 100644 index 0000000..a2df73b --- /dev/null +++ b/src/trendify/cli.py @@ -0,0 +1,218 @@ +""" +`trendify` CLI, built on Typer. It is a thin argument-parsing layer over `TrendifyPipeline`: +every command resolves its arguments then calls straight into the same pipeline class a Python +caller would use directly, so the CLI and the Python API can never drift apart. +""" + +from __future__ import annotations + +import glob as glob_module +import importlib +import importlib.util +import logging +import os +import sys +from pathlib import Path +from typing import cast + +import typer + +from trendify.base.data_product import ProductGenerator +from trendify.log import setup_logger +from trendify.pipeline import TrendifyPipeline + +__all__ = ["app"] + +logger = logging.getLogger(__name__) + +app = typer.Typer( + name="trendify", + help="Generate visual data products and static assets from raw data.", + no_args_is_help=True, +) + +InputDirectoriesOption = typer.Option( + ..., + "-i", + "--input-directories", + help="Raw data directories (or glob patterns) to map the product generator over.", +) +ProductGeneratorOption = typer.Option( + ..., + "-g", + "--product-generator", + help=( + "ProductGenerator to map over input directories, given as 'module:function', " + "'module:Class.method', or '/path/to/file.py:function'." + ), +) +OutputDirectoryOption = typer.Option( + ..., "-o", "--output-directory", help="Directory the pipeline reads/writes under." +) +NProcsOption = typer.Option( + 1, "-n", "--n-procs", help="Number of parallel worker processes." +) +VerboseOption = typer.Option( + 0, "-v", "--verbose", count=True, help="Increase log verbosity." +) +DpiOption = typer.Option( + 500, "--dpi", help="Resolution (dots per inch) for saved matplotlib figures." +) +NoTablesOption = typer.Option( + False, "--no-tables", help="Suppress TableEntry CSV output." +) +NoXyPlotsOption = typer.Option( + False, "--no-xy-plots", help="Suppress Point2D/Trace2D/AxLine plot output." +) +NoHistogramsOption = typer.Option( + False, "--no-histograms", help="Suppress HistogramEntry plot output." +) +NoIncludeFilesOption = typer.Option( + False, "--no-include-files", help="Suppress MkDocs include.md generation." +) + + +def _configure_logging(verbose: int) -> None: + """ + Configures the root logger via `trendify.log.setup_logger` (Rich console output plus a + rotating `trendify.log` file) rather than a one-off `logging.basicConfig`. This way a CLI + run gets the same beautiful, concurrent-safe logging setup a Python caller gets by calling + `setup_logger()` themselves, and `generate_products`'s multiprocess path picks up these + same handlers via its `QueueListener` (see `trendify.generator.generate`). + """ + level = logging.DEBUG if verbose >= 1 else logging.INFO + setup_logger(level=level) + + +def _cap_n_procs(n_procs: int) -> int: + """Caps `n_procs` at `5 * os.cpu_count()`, as a precaution against overwhelming the machine.""" + max_procs = 5 * (os.cpu_count() or 1) + if n_procs > max_procs: + logger.info( + f"Requested {n_procs = } exceeds {max_procs = }; capping to {max_procs}" + ) + return max_procs + return n_procs + + +def _resolve_input_directories(patterns: list[str]) -> list[Path]: + """ + Expands glob patterns (and plain paths) into a list of directories. A match on a file + resolves to its parent directory. + """ + dirs: list[Path] = [] + for pattern in patterns: + for match in glob_module.glob(pattern, root_dir=os.getcwd(), recursive=True): + path = Path(match) + dirs.append((path.parent if path.is_file() else path).resolve()) + return dirs + + +def _import_from_path(module_name: str, file_path: Path): + spec = importlib.util.spec_from_file_location(module_name, file_path) + if spec is None or spec.loader is None: + raise typer.BadParameter(f"Could not import {file_path} as a Python module") + module = importlib.util.module_from_spec(spec) + sys.modules[module_name] = module + spec.loader.exec_module(module) + return module + + +def _resolve_product_generator(spec: str) -> ProductGenerator: + """ + Resolves a `"module:function"` / `"module:Class.method"` / `"/path/to/file.py:function"` + spec string into a callable. + """ + module_path, _, attr_path = spec.partition(":") + if not attr_path: + raise typer.BadParameter( + f"{spec!r} must be in the form 'module:function' or 'path/to/file.py:function'" + ) + + if Path(module_path).exists(): + module = _import_from_path(Path(module_path).stem, Path(module_path)) + else: + module = importlib.import_module(module_path) + + obj: object = module + for part in attr_path.split("."): + obj = getattr(obj, part) + return cast(ProductGenerator, obj) + + +@app.command() +def generate( + input_directories: list[str] = InputDirectoriesOption, + product_generator: str = ProductGeneratorOption, + output_directory: Path = OutputDirectoryOption, + n_procs: int = NProcsOption, + verbose: int = VerboseOption, +) -> None: + """Generate tagged data products from raw data directories.""" + _configure_logging(verbose) + pipeline = TrendifyPipeline( + output_dir=output_directory, n_procs=_cap_n_procs(n_procs) + ) + total = pipeline.generate( + product_generator=_resolve_product_generator(product_generator), + data_dirs=_resolve_input_directories(input_directories), + ) + typer.echo(f"Wrote {total} products to {pipeline.db_path}") + + +@app.command() +def render( + output_directory: Path = OutputDirectoryOption, + dpi: int = DpiOption, + no_tables: bool = NoTablesOption, + no_xy_plots: bool = NoXyPlotsOption, + no_histograms: bool = NoHistogramsOption, + no_include_files: bool = NoIncludeFilesOption, + verbose: int = VerboseOption, +) -> None: + """Render CSV tables and matplotlib figures from already-generated products.""" + _configure_logging(verbose) + pipeline = TrendifyPipeline(output_dir=output_directory) + pipeline.render( + dpi=dpi, + no_tables=no_tables, + no_xy_plots=no_xy_plots, + no_histograms=no_histograms, + ) + if not no_include_files: + pipeline.make_include_files() + typer.echo(f"Rendered assets to {pipeline.assets_dir}") + + +@app.command() +def run( + input_directories: list[str] = InputDirectoriesOption, + product_generator: str = ProductGeneratorOption, + output_directory: Path = OutputDirectoryOption, + n_procs: int = NProcsOption, + dpi: int = DpiOption, + no_tables: bool = NoTablesOption, + no_xy_plots: bool = NoXyPlotsOption, + no_histograms: bool = NoHistogramsOption, + no_include_files: bool = NoIncludeFilesOption, + verbose: int = VerboseOption, +) -> None: + """Generate products and render assets in one step.""" + _configure_logging(verbose) + pipeline = TrendifyPipeline( + output_dir=output_directory, n_procs=_cap_n_procs(n_procs) + ) + total = pipeline.run( + product_generator=_resolve_product_generator(product_generator), + data_dirs=_resolve_input_directories(input_directories), + dpi=dpi, + no_tables=no_tables, + no_xy_plots=no_xy_plots, + no_histograms=no_histograms, + no_include_files=no_include_files, + ) + typer.echo(f"Wrote {total} products; assets under {pipeline.assets_dir}") + + +if __name__ == "__main__": + app() diff --git a/src/trendify/color.py b/src/trendify/color.py new file mode 100644 index 0000000..c87dad0 --- /dev/null +++ b/src/trendify/color.py @@ -0,0 +1,260 @@ +""" +`Color`: the Tailwind CSS color palette as a `StrEnum` of hex values, for consistent styling +defaults across `Pen`/`Marker`/`HistogramStyle` and friends. +""" + +import logging +from enum import StrEnum + +logger = logging.getLogger(__name__) + +__all__ = ["Color"] + + +class Color(StrEnum): + """Contains color aliases and converters to go between the various color formats. The color values are defined by the standard [Tailwind CSS](https://tailwindcss.com/docs/colors) color theme.""" + + BLACK = "#000000" + WHITE = "#FFFFFF" + SLATE_50 = "#F8FAFC" + SLATE_100 = "#F1F5F9" + SLATE_200 = "#E2E8F0" + SLATE_300 = "#CBD5E1" + SLATE_400 = "#94A3B8" + SLATE_500 = "#64748B" + SLATE_600 = "#475569" + SLATE_700 = "#334155" + SLATE_800 = "#1E293B" + SLATE_900 = "#0F172A" + SLATE_950 = "#020617" + GRAY_50 = "#F9FAFB" + GRAY_100 = "#F3F4F6" + GRAY_200 = "#E5E7EB" + GRAY_300 = "#D1D5DB" + GRAY_400 = "#9CA3AF" + GRAY_500 = "#6B7280" + GRAY_600 = "#4B5563" + GRAY_700 = "#374151" + GRAY_800 = "#1F2937" + GRAY_900 = "#111827" + GRAY_950 = "#030712" + ZINC_50 = "#FAFAFA" + ZINC_100 = "#F4F4F5" + ZINC_200 = "#E4E4E7" + ZINC_300 = "#D4D4D8" + ZINC_400 = "#A1A1AA" + ZINC_500 = "#71717A" + ZINC_600 = "#52525B" + ZINC_700 = "#3F3F46" + ZINC_800 = "#27272A" + ZINC_900 = "#18181B" + ZINC_950 = "#09090B" + NEUTRAL_50 = "#FAFAFA" + NEUTRAL_100 = "#F5F5F5" + NEUTRAL_200 = "#E5E5E5" + NEUTRAL_300 = "#D4D4D4" + NEUTRAL_400 = "#A3A3A3" + NEUTRAL_500 = "#737373" + NEUTRAL_600 = "#525252" + NEUTRAL_700 = "#404040" + NEUTRAL_800 = "#262626" + NEUTRAL_900 = "#171717" + NEUTRAL_950 = "#0A0A0A" + STONE_50 = "#FAFAF9" + STONE_100 = "#F5F5F4" + STONE_200 = "#E7E5E4" + STONE_300 = "#D6D3D1" + STONE_400 = "#A8A29E" + STONE_500 = "#78716C" + STONE_600 = "#57534E" + STONE_700 = "#44403C" + STONE_800 = "#292524" + STONE_900 = "#1C1917" + STONE_950 = "#0C0A09" + RED_50 = "#FEF2F2" + RED_100 = "#FEE2E2" + RED_200 = "#FECACA" + RED_300 = "#FCA5A5" + RED_400 = "#F87171" + RED_500 = "#EF4444" + RED_600 = "#DC2626" + RED_700 = "#B91C1C" + RED_800 = "#991B1B" + RED_900 = "#7F1D1D" + RED_950 = "#450A0A" + ORANGE_50 = "#FFF7ED" + ORANGE_100 = "#FFEDD5" + ORANGE_200 = "#FED7AA" + ORANGE_300 = "#FDBA74" + ORANGE_400 = "#FB923C" + ORANGE_500 = "#F97316" + ORANGE_600 = "#EA580C" + ORANGE_700 = "#C2410C" + ORANGE_800 = "#9A3412" + ORANGE_900 = "#7C2D12" + ORANGE_950 = "#431407" + AMBER_50 = "#FFFBEB" + AMBER_100 = "#FEF3C7" + AMBER_200 = "#FDE68A" + AMBER_300 = "#FCD34D" + AMBER_400 = "#FBBF24" + AMBER_500 = "#F59E0B" + AMBER_600 = "#D97706" + AMBER_700 = "#B45309" + AMBER_800 = "#92400E" + AMBER_900 = "#78350F" + AMBER_950 = "#451A03" + YELLOW_50 = "#FEFCE8" + YELLOW_100 = "#FEF9C3" + YELLOW_200 = "#FEF08A" + YELLOW_300 = "#FDE047" + YELLOW_400 = "#FACC15" + YELLOW_500 = "#EAB308" + YELLOW_600 = "#CA8A04" + YELLOW_700 = "#A16207" + YELLOW_800 = "#854D0E" + YELLOW_900 = "#713F12" + YELLOW_950 = "#422006" + LIME_50 = "#F7FEE7" + LIME_100 = "#ECFCCB" + LIME_200 = "#D9F99D" + LIME_300 = "#BEF264" + LIME_400 = "#A3E635" + LIME_500 = "#84CC16" + LIME_600 = "#65A30D" + LIME_700 = "#4D7C0F" + LIME_800 = "#3F6212" + LIME_900 = "#365314" + LIME_950 = "#1A2E05" + GREEN_50 = "#F0FDF4" + GREEN_100 = "#DCFCE7" + GREEN_200 = "#BBF7D0" + GREEN_300 = "#86EFAC" + GREEN_400 = "#4ADE80" + GREEN_500 = "#22C55E" + GREEN_600 = "#16A34A" + GREEN_700 = "#15803D" + GREEN_800 = "#166534" + GREEN_900 = "#14532D" + GREEN_950 = "#052E16" + EMERALD_50 = "#ECFDF5" + EMERALD_100 = "#D1FAE5" + EMERALD_200 = "#A7F3D0" + EMERALD_300 = "#6EE7B7" + EMERALD_400 = "#34D399" + EMERALD_500 = "#10B981" + EMERALD_600 = "#059669" + EMERALD_700 = "#047857" + EMERALD_800 = "#065F46" + EMERALD_900 = "#064E3B" + EMERALD_950 = "#022C22" + TEAL_50 = "#F0FDFA" + TEAL_100 = "#CCFBF1" + TEAL_200 = "#99F6E4" + TEAL_300 = "#5EEAD4" + TEAL_400 = "#2DD4BF" + TEAL_500 = "#14B8A6" + TEAL_600 = "#0D9488" + TEAL_700 = "#0F766E" + TEAL_800 = "#115E59" + TEAL_900 = "#134E4A" + TEAL_950 = "#042F2E" + CYAN_50 = "#ECFEFF" + CYAN_100 = "#CFFAFE" + CYAN_200 = "#A5F3FC" + CYAN_300 = "#67E8F9" + CYAN_400 = "#22D3EE" + CYAN_500 = "#06B6D4" + CYAN_600 = "#0891B2" + CYAN_700 = "#0E7490" + CYAN_800 = "#155E75" + CYAN_900 = "#164E63" + CYAN_950 = "#083344" + SKY_50 = "#F0F9FF" + SKY_100 = "#E0F2FE" + SKY_200 = "#BAE6FD" + SKY_300 = "#7DD3FC" + SKY_400 = "#38BDF8" + SKY_500 = "#0EA5E9" + SKY_600 = "#0284C7" + SKY_700 = "#0369A1" + SKY_800 = "#075985" + SKY_900 = "#0C4A6E" + SKY_950 = "#082F49" + BLUE_50 = "#EFF6FF" + BLUE_100 = "#DBEAFE" + BLUE_200 = "#BFDBFE" + BLUE_300 = "#93C5FD" + BLUE_400 = "#60A5FA" + BLUE_500 = "#3B82F6" + BLUE_600 = "#2563EB" + BLUE_700 = "#1D4ED8" + BLUE_800 = "#1E40AF" + BLUE_900 = "#1E3A8A" + BLUE_950 = "#172554" + INDIGO_50 = "#EEF2FF" + INDIGO_100 = "#E0E7FF" + INDIGO_200 = "#C7D2FE" + INDIGO_300 = "#A5B4FC" + INDIGO_400 = "#818CF8" + INDIGO_500 = "#6366F1" + INDIGO_600 = "#4F46E5" + INDIGO_700 = "#4338CA" + INDIGO_800 = "#3730A3" + INDIGO_900 = "#312E81" + INDIGO_950 = "#1E1B4B" + VIOLET_50 = "#F5F3FF" + VIOLET_100 = "#EDE9FE" + VIOLET_200 = "#DDD6FE" + VIOLET_300 = "#C4B5FD" + VIOLET_400 = "#A78BFA" + VIOLET_500 = "#8B5CF6" + VIOLET_600 = "#7C3AED" + VIOLET_700 = "#6D28D9" + VIOLET_800 = "#5B21B6" + VIOLET_900 = "#4C1D95" + VIOLET_950 = "#2E1065" + PURPLE_50 = "#FAF5FF" + PURPLE_100 = "#F3E8FF" + PURPLE_200 = "#E9D5FF" + PURPLE_300 = "#D8B4FE" + PURPLE_400 = "#C084FC" + PURPLE_500 = "#A855F7" + PURPLE_600 = "#9333EA" + PURPLE_700 = "#7E22CE" + PURPLE_800 = "#6B21A8" + PURPLE_900 = "#581C87" + PURPLE_950 = "#3B0764" + FUCHSIA_50 = "#FDF4FF" + FUCHSIA_100 = "#FAE8FF" + FUCHSIA_200 = "#F5D0FE" + FUCHSIA_300 = "#F0ABFC" + FUCHSIA_400 = "#E879F9" + FUCHSIA_500 = "#D946EF" + FUCHSIA_600 = "#C026D3" + FUCHSIA_700 = "#A21CAF" + FUCHSIA_800 = "#86198F" + FUCHSIA_900 = "#701A75" + FUCHSIA_950 = "#4A044E" + PINK_50 = "#FDF2F8" + PINK_100 = "#FCE7F3" + PINK_200 = "#FBCFE8" + PINK_300 = "#F9A8D4" + PINK_400 = "#F472B6" + PINK_500 = "#EC4899" + PINK_600 = "#DB2777" + PINK_700 = "#BE185D" + PINK_800 = "#9D174D" + PINK_900 = "#831843" + PINK_950 = "#500724" + ROSE_50 = "#FFF1F2" + ROSE_100 = "#FFE4E6" + ROSE_200 = "#FECDD3" + ROSE_300 = "#FDA4AF" + ROSE_400 = "#FB7185" + ROSE_500 = "#F43F5E" + ROSE_600 = "#E11D48" + ROSE_700 = "#BE123C" + ROSE_800 = "#9F1239" + ROSE_900 = "#881337" + ROSE_950 = "#4C0519" diff --git a/src/trendify_old/examples.py b/src/trendify/examples.py similarity index 75% rename from src/trendify_old/examples.py rename to src/trendify/examples.py index 14eb9e3..13e5124 100644 --- a/src/trendify_old/examples.py +++ b/src/trendify/examples.py @@ -1,38 +1,26 @@ """ -Module defines a method for making sample data and defines a +Sample data + a `ProductGenerator` used as a fixture for exercising the `trendify` pipeline end to end. """ from __future__ import annotations -# Standard imports +from enum import StrEnum from pathlib import Path -from enum import auto -from strenum import StrEnum +from typing import cast -# Common imports import numpy as np -import pandas as pd +import polars as pl -# Local imports import trendify __all__ = ["example_data_product_generator", "make_example_data"] class Channels(StrEnum): - """ - Attributes: - time (str): `'time'` - wave1 (str): `'wave1'` - wave2 (str): `'wave2'` - wave3 (str): `'wave3'` - - """ - - time = auto() - wave1 = auto() - wave2 = auto() - wave3 = auto() + TIME = "time" + WAVE_1 = "wave_1" + WAVE_2 = "wave_2" + WAVE_3 = "wave_3" def make_example_data(workdir: Path, n_folders: int = 10): @@ -56,31 +44,22 @@ def make_example_data(workdir: Path, n_folders: int = 10): periods = [1, 2, 3] amplitudes = np.random.uniform(low=0.5, high=1.5, size=3) - n_inputs = {"n_samples": n_samples} - p_inputs = {f"p{n}": p for n, p in enumerate(periods)} - a_inputs = {f"a{n}": a for n, a in enumerate(amplitudes)} - inputs = {} - inputs.update(n_inputs) - inputs.update(p_inputs) - inputs.update(a_inputs) - pd.Series(inputs).to_csv(subdir.joinpath("stdin.csv"), header=False) + inputs = {"n_samples": n_samples} + inputs.update({f"p{i}": p for i, p in enumerate(periods)}) + inputs.update({f"a{i}": a for i, a in enumerate(amplitudes)}) + pl.DataFrame( + {"key": list(inputs.keys()), "value": [str(v) for v in inputs.values()]} + ).write_csv(subdir.joinpath("stdin.csv"), include_header=False) rng = np.random.default_rng(seed=42) noise_level = 0.05 - d = [t] + [ + waves = [ a * np.sin(t * (2 * np.pi / p)) + noise_level * rng.normal(size=len(t)) for p, a in zip(periods, amplitudes) ] - df = pd.DataFrame(np.array(d).transpose(), columns=[e.name for e in Channels]) - df.to_csv(subdir.joinpath("results.csv"), index=False) - - csv_files = list(models_dir.glob("**/stdin.csv")) - csv_files.sort() - input_series = [] - for csv in csv_files: - series: pd.Series = pd.read_csv(csv, index_col=0, header=None).squeeze() - series.name = int(csv.parent.stem) - input_series.append(series) + pl.DataFrame(dict(zip([e.name for e in Channels], [t, *waves]))).write_csv( + subdir.joinpath("results.csv") + ) def transform(data: np.ndarray, scale: trendify.AxisScale) -> np.ndarray: @@ -102,10 +81,10 @@ def example_data_product_generator(workdir: Path) -> trendify.ProductList: """ products = [] - df = pd.read_csv(workdir.joinpath("results.csv")) - df = df.set_index(Channels.time.name, drop=True) + df = pl.read_csv(workdir.joinpath("results.csv")) + time = df[Channels.TIME.name].to_numpy() + value_columns = [c for c in df.columns if c != Channels.TIME.name] - # colors = list(plt.rcParams["axes.prop_cycle"].by_key()["color"]) colors = ["#FF0000", "#000B81", "#FFAA00"] alphas = [1.0, 0.3, 1.0] linestyles = ["-", ":", (0, (3, 1, 1, 1))] @@ -114,8 +93,8 @@ def example_data_product_generator(workdir: Path) -> trendify.ProductList: traces = [ trendify.Trace2D.from_xy( - x=df.index, - y=df[col].values, + x=time, + y=df[col].to_numpy(), tags=[("an_xy_plot", "trace_plot")], pen=trendify.Pen( label=col, @@ -131,13 +110,13 @@ def example_data_product_generator(workdir: Path) -> trendify.ProductList: ) .append_to_list(products) .set_metadata({"run_num": run_num}) - for i, col in enumerate(df.columns) + for i, col in enumerate(value_columns) ] traces = [ trendify.Trace2D.from_xy( - x=df.index, - y=df[col].values, + x=time, + y=df[col].to_numpy(), tags=[("an_xy_plot", "another_trace_plot")], pen=trendify.Pen( label=col, @@ -153,13 +132,13 @@ def example_data_product_generator(workdir: Path) -> trendify.ProductList: ) .append_to_list(products) .set_metadata({"run_num": run_num}) - for i, col in enumerate(df.columns) + for i, col in enumerate(value_columns) ] traces = [ trendify.Trace2D.from_xy( - x=df.index, - y=df[col].values, + x=time, + y=df[col].to_numpy(), tags=[("another_xy_plot", "trace_plot")], pen=trendify.Pen( label=col, @@ -178,7 +157,7 @@ def example_data_product_generator(workdir: Path) -> trendify.ProductList: ) .append_to_list(products) .set_metadata({"run_num": run_num}) - for i, col in enumerate(df.columns) + for i, col in enumerate(value_columns) ] trendify.AxLine( tags=[("another_xy_plot", "trace_plot")], @@ -195,8 +174,8 @@ def example_data_product_generator(workdir: Path) -> trendify.ProductList: traces = [ trendify.Trace2D.from_xy( - x=df.index, - y=transform(df[col].values, trendify.AxisScale.LOG), + x=time, + y=transform(df[col].to_numpy(), trendify.AxisScale.LOG), tags=["trace_plot_log_y"], pen=trendify.Pen( label=col, @@ -217,13 +196,13 @@ def example_data_product_generator(workdir: Path) -> trendify.ProductList: ) .append_to_list(products) .set_metadata({"run_num": run_num}) - for i, col in enumerate(df.columns) + for i, col in enumerate(value_columns) ] traces = [ trendify.Trace2D.from_xy( - x=transform(df.index, trendify.AxisScale.LOG), - y=transform(df[col].values, trendify.AxisScale.LOG), + x=transform(time, trendify.AxisScale.LOG), + y=transform(df[col].to_numpy(), trendify.AxisScale.LOG), tags=["trace_plot_log_xy"], pen=trendify.Pen( label=col, @@ -249,7 +228,7 @@ def example_data_product_generator(workdir: Path) -> trendify.ProductList: ) .append_to_list(products) .set_metadata({"run_num": run_num}) - for i, col in enumerate(df.columns) + for i, col in enumerate(value_columns) ] trendify.AxLine( tags=["trace_plot_log_xy"], @@ -262,8 +241,8 @@ def example_data_product_generator(workdir: Path) -> trendify.ProductList: traces = [ trendify.Trace2D.from_xy( - x=transform(df.index, trendify.AxisScale.LOG), - y=df[col].values, + x=transform(time, trendify.AxisScale.LOG), + y=df[col].to_numpy(), tags=["trace_plot_log_x"], pen=trendify.Pen( label=col, @@ -280,13 +259,13 @@ def example_data_product_generator(workdir: Path) -> trendify.ProductList: ) .append_to_list(products) .set_metadata({"run_num": run_num}) - for i, col in enumerate(df.columns) + for i, col in enumerate(value_columns) ] for i, trace in enumerate(traces): trendify.Point2D( x=workdir.name, - y=len(trace.y), + y=len(trace.points), marker=trendify.Marker( size=10, label=trace.pen.label, @@ -297,18 +276,20 @@ def example_data_product_generator(workdir: Path) -> trendify.ProductList: tags=["scatter_plot"], ).append_to_list(products).set_metadata({"run_num": run_num}) - for name, series in df.items(): + for col in value_columns: + series = df[col] trendify.TableEntry( row=workdir.name, - col=name, - value=len(series), + col=col, + value=series.len(), tags=["table"], unit=None, ).append_to_list(products) + mean = cast(float, series.mean()) trendify.HistogramEntry( tags=["histogram"], - value=series.mean(), + value=mean, format2d=trendify.Format2D( title_ax="Idk lol", title_fig="Idk lol2", @@ -330,7 +311,7 @@ def example_data_product_generator(workdir: Path) -> trendify.ProductList: trendify.AxLine( tags=["histogram"], - value=series.mean(), + value=mean, orientation=trendify.LineOrientation.VERTICAL, pen=trendify.Pen(color="r", label="mean", zorder=2), ).append_to_list(products) @@ -342,7 +323,6 @@ def make_sample_data(): """ Generates sample data to run the trendify code on """ - from trendify.examples import make_example_data import argparse parser = argparse.ArgumentParser( @@ -370,7 +350,7 @@ def make_sample_data(): def _main(): """ - Makes sample data, processes it, and serves it for importing into Grafana + Makes sample data and runs the generate pipeline against it. """ here = Path(__file__).parent workdir = here.joinpath("workdir") @@ -378,35 +358,15 @@ def _main(): make_example_data(workdir=workdir, n_folders=100) process_dirs = list(workdir.joinpath("models").glob("*/")) - products_dir = workdir.joinpath("products") - outputs_dir = workdir.joinpath("outputs") - grafana_dir = workdir.joinpath("grafana") + db_path = workdir.joinpath("trendify.db") n_procs = 30 - trendify.make_products( + trendify.generate_products( product_generator=example_data_product_generator, data_dirs=process_dirs, + db_path=db_path, n_procs=n_procs, ) - trendify.sort_products( - data_dirs=process_dirs, - output_dir=products_dir, - ) - # trendify.make_grafana_dashboard( - # products_dir=products_dir, - # output_dir=grafana_dir, - # n_procs=n_procs, - # ) - trendify.make_tables_and_figures( - products_dir=products_dir, - output_dir=outputs_dir, - dpi=500, - n_procs=n_procs, - ) - # trendify.make_include_files( - # root_dir=outputs_dir, - # heading_level=2, - # ) if __name__ == "__main__": diff --git a/src/trendify/formats/format2d.py b/src/trendify/formats/format2d.py index e7a6091..b6142b9 100644 --- a/src/trendify/formats/format2d.py +++ b/src/trendify/formats/format2d.py @@ -1,3 +1,9 @@ +""" +`Format2D` (axis/legend/grid/scale/limit settings shared by every 2D plot type) and the +`PlottableData2D`/`XYData` base classes that carry an optional `Format2D` and know how to draw +themselves onto a Plotly figure. +""" + from __future__ import annotations import logging @@ -81,13 +87,21 @@ def union_from_iterable(cls, format2ds: Iterable[Format2D]): """ formats = list(set(format2ds) - {None}) - [title_fig] = set(i.title_fig for i in formats if i is not None) - [legend] = set(i.legend for i in formats if i is not None) - [title_ax] = set(i.title_ax for i in formats if i is not None) - [label_x] = set(i.label_x for i in formats if i is not None) - [label_y] = set(i.label_y for i in formats if i is not None) - [figure_width] = set(i.figure_width for i in formats) - [figure_height] = set(i.figure_height for i in formats) + try: + [title_fig] = set(i.title_fig for i in formats if i is not None) + [legend] = set(i.legend for i in formats if i is not None) + [title_ax] = set(i.title_ax for i in formats if i is not None) + [label_x] = set(i.label_x for i in formats if i is not None) + [label_y] = set(i.label_y for i in formats if i is not None) + [figure_width] = set(i.figure_width for i in formats) + [figure_height] = set(i.figure_height for i in formats) + except ValueError: + logger.error( + "Format2D.union_from_iterable: products sharing a tag disagree on a field " + "that must be identical across all of them (title_fig, legend, title_ax, " + f"label_x, label_y, figure_width, or figure_height). {formats = }" + ) + raise x_min = [i.lim_x_min for i in formats if i.lim_x_min is not None] x_max = [i.lim_x_max for i in formats if i.lim_x_max is not None] @@ -101,8 +115,15 @@ def union_from_iterable(cls, format2ds: Iterable[Format2D]): grid = Grid.union_from_iterable(f.grid for f in formats if f.grid is not None) - [scale_x] = set(i.scale_x for i in formats) - [scale_y] = set(i.scale_y for i in formats) + try: + [scale_x] = set(i.scale_x for i in formats) + [scale_y] = set(i.scale_y for i in formats) + except ValueError: + logger.error( + f"Format2D.union_from_iterable: products sharing a tag disagree on " + f"scale_x/scale_y. {formats = }" + ) + raise return cls( title_fig=title_fig, diff --git a/src/trendify/formats/table.py b/src/trendify/formats/table.py index 22c5c21..af3870d 100644 --- a/src/trendify/formats/table.py +++ b/src/trendify/formats/table.py @@ -1,3 +1,9 @@ +""" +`TableEntry`: the one `DataProduct` subclass the store gives real SQL columns instead of an +opaque JSON payload, since its whole purpose (pivoting into a wide table, then computing +per-column stats) is naturally SQL-shaped. See `trendify.generator.table_builder`. +""" + from __future__ import annotations import logging diff --git a/src/trendify/generator/__init__.py b/src/trendify/generator/__init__.py new file mode 100644 index 0000000..b10f601 --- /dev/null +++ b/src/trendify/generator/__init__.py @@ -0,0 +1,38 @@ +from trendify.generator import generate +from trendify.generator import histogrammer +from trendify.generator import render +from trendify.generator import table_builder +from trendify.generator import xy_data_plotter + +from trendify.generator.generate import ( + generate_products, + get_sorted_dirs, +) +from trendify.generator.histogrammer import ( + Histogrammer, +) +from trendify.generator.render import ( + make_include_files, + render_assets, +) +from trendify.generator.table_builder import ( + TableBuilder, +) +from trendify.generator.xy_data_plotter import ( + XYDataPlotter, +) + +__all__ = [ + "Histogrammer", + "TableBuilder", + "XYDataPlotter", + "generate", + "generate_products", + "get_sorted_dirs", + "histogrammer", + "make_include_files", + "render", + "render_assets", + "table_builder", + "xy_data_plotter", +] diff --git a/src/trendify/generator/generate.py b/src/trendify/generator/generate.py new file mode 100644 index 0000000..293490a --- /dev/null +++ b/src/trendify/generator/generate.py @@ -0,0 +1,140 @@ +""" +Generate pipeline: maps a user-supplied `ProductGenerator` over raw-data run directories and +writes the resulting products straight to a `ProductStore`-backed `.db` file. + +There is no separate "sort" step: tags are indexed at write time, so retrieving products by +tag is an indexed query against `ProductStore`, not a physical grouping step that has to run +before rendering can start. +""" + +from __future__ import annotations + +import logging +from concurrent.futures import ProcessPoolExecutor +from pathlib import Path +from typing import Any + +from trendify.base.data_product import ProductGenerator +from trendify.log import create_queue_listener +from trendify.log import worker_init as _init_worker_logging +from trendify.store.product_store import ProductStore + +__all__ = ["generate_products", "get_sorted_dirs"] + +logger = logging.getLogger(__name__) + +# Per-process globals set by `_init_worker`, one `ProductStore` connection per worker process +# (WAL mode lets every worker write directly to the same `.db` file, see `store/db.py`). +_worker_store: ProductStore | None = None +_worker_generator: ProductGenerator | None = None + + +def get_sorted_dirs(dirs: list[Path]) -> list[Path]: + """ + Sorts dirs numerically if possible, else alphabetically. + + Args: + dirs (list[Path]): Directories to sort + + Returns: + (list[Path]): Sorted list of directories + + """ + dirs = list(dirs) + try: + dirs.sort(key=lambda p: int(p.name)) + except ValueError: + dirs.sort() + return dirs + + +def _init_worker(db_path: str, product_generator: ProductGenerator) -> None: + global _worker_store, _worker_generator + _worker_store = ProductStore.open(Path(db_path)) + _worker_generator = product_generator + + +def _init_worker_with_logging( + db_path: str, + product_generator: ProductGenerator, + log_queue: Any, + log_level: int, +) -> None: + # Route this worker's logging through the parent's QueueListener before anything else + # runs, so no worker ever opens a file/console handler directly. See trendify.log for why + # that matters (Windows spawn semantics, and fork-inherited-handler races on POSIX). + _init_worker_logging(log_queue, log_level) + _init_worker(db_path, product_generator) + + +def _generate_and_write(run_dir: Path) -> int: + assert _worker_store is not None and _worker_generator is not None + logger.info(f"Processing {run_dir = }") + products = _worker_generator(run_dir) + return _worker_store.write_run(run_dir, products) + + +def generate_products( + product_generator: ProductGenerator, + data_dirs: list[Path], + db_path: Path, + n_procs: int = 1, +) -> int: + """ + Maps `product_generator` over `data_dirs`, writing each directory's products directly to + the `ProductStore` at `db_path` (one `write_run` transaction per directory). There is no + intermediate file and no separate sort step, since tag-based retrieval from `ProductStore` + is an indexed query. + + Args: + product_generator (ProductGenerator): A callable that returns a list of data products + given a working directory. Must be picklable (e.g. a module-level function) when + `n_procs > 1`, since it is sent to worker processes. + data_dirs (list[Path]): Directories over which to map `product_generator`. + db_path (Path): Path to the trendify output directory's `.db` file. Created if it + doesn't already exist. + n_procs (int): Number of worker processes. `n_procs == 1` runs sequentially in this + process (easier to debug with full tracebacks). `n_procs > 1` uses a + `ProcessPoolExecutor`, with one `ProductStore` connection opened per worker. + + Returns: + (int): total number of products written across all directories. + + """ + sorted_dirs = get_sorted_dirs(data_dirs) + db_path = Path(db_path) + + # Bootstrap the schema up front, from this process, before workers race to open it. + ProductStore.open(db_path).close() + + logger.info(f"Generating and writing DataProducts for {len(sorted_dirs)} run(s)...") + if n_procs > 1: + root_logger = logging.getLogger() + log_queue, listener = create_queue_listener(*root_logger.handlers) + listener.start() + try: + with ProcessPoolExecutor( + max_workers=n_procs, + initializer=_init_worker_with_logging, + initargs=( + str(db_path), + product_generator, + log_queue, + root_logger.level, + ), + ) as executor: + results = list(executor.map(_generate_and_write, sorted_dirs)) + finally: + # Blocks until every already-queued log record has been dispatched, so worker + # log output isn't lost or interleaved with what follows. + listener.stop() + else: + _init_worker(str(db_path), product_generator) + results = [_generate_and_write(d) for d in sorted_dirs] + + total = sum(results) + logger.info( + f"Finished generating DataProducts: {total} products written across " + f"{len(sorted_dirs)} run(s)" + ) + return total diff --git a/src/trendify/generator/histogrammer.py b/src/trendify/generator/histogrammer.py new file mode 100644 index 0000000..e8c79e2 --- /dev/null +++ b/src/trendify/generator/histogrammer.py @@ -0,0 +1,62 @@ +""" +Draws `HistogramEntry` products onto a matplotlib figure. Fed directly by `ProductStore` query +results, so there's no directory-loading state to manage. +""" + +from __future__ import annotations + +import logging + +from pydantic import BaseModel + +from trendify.base.helpers import Tag +from trendify.plotting.figure import SingleAxisFigure +from trendify.plotting.histogram import HistogramEntry + +__all__ = ["Histogrammer"] + +logger = logging.getLogger(__name__) + + +class Histogrammer(BaseModel): + """ + Draws `HistogramEntry` products sharing a tag onto a matplotlib axes, grouped by style. + """ + + @classmethod + def handle_histogram_entries( + cls, + tag: Tag, + histogram_entries: list[HistogramEntry], + saf: SingleAxisFigure | None = None, + ) -> SingleAxisFigure: + """ + Histograms the provided entries onto `saf` (creating a new figure if not given). + + Args: + tag (Tag): tag these entries belong to (used only if a new figure is created) + histogram_entries (list[HistogramEntry]): entries to histogram, grouped by + `HistogramStyle` (each distinct style becomes one `ax.hist` call/series) + saf (SingleAxisFigure | None): figure to draw onto; a new one is created if `None` + + Returns: + (SingleAxisFigure): the figure drawn onto + + """ + if saf is None: + saf = SingleAxisFigure.new(tag=tag) + + histogram_styles = set(h.style for h in histogram_entries) + logger.debug( + f"Histogramming {len(histogram_entries)} entries into {len(histogram_styles)} " + f"style(s) for {tag = }" + ) + for style in histogram_styles: + matching_entries = [e for e in histogram_entries if e.style == style] + values = [e.value for e in matching_entries] + if style is not None: + saf.ax.hist(values, **style.as_plot_kwargs()) + else: + saf.ax.hist(values) + + return saf diff --git a/src/trendify/generator/render.py b/src/trendify/generator/render.py new file mode 100644 index 0000000..88559e0 --- /dev/null +++ b/src/trendify/generator/render.py @@ -0,0 +1,193 @@ +""" +Static rendering pipeline: renders CSV tables and matplotlib figures directly from a +`ProductStore`. + +Design note: XY data (`Point2D`/`Trace2D`/`AxLine`) and histogram data (`HistogramEntry`) +sharing a tag are always rendered to separate figures/files (`.jpg` for XY, +`_histogram.jpg` for histograms), never overlaid onto one shared axes, so a tag used for +both product kinds gets two clean plots instead of a histogram silently drawn on top of an XY +plot. +""" + +from __future__ import annotations + +import logging +from pathlib import Path + +from trendify.formats.format2d import Format2D +from trendify.generator.histogrammer import Histogrammer +from trendify.generator.table_builder import TableBuilder +from trendify.generator.xy_data_plotter import XYDataPlotter +from trendify.plotting.axline import AxLine +from trendify.plotting.histogram import HistogramEntry +from trendify.plotting.point import Point2D +from trendify.plotting.trace import Trace2D +from trendify.store.product_store import ProductStore +from trendify.store.tags import tag_to_path_parts + +__all__ = ["make_include_files", "render_assets"] + +logger = logging.getLogger(__name__) + +INCLUDE_FILENAME = "include.md" + + +def render_assets( + store: ProductStore, + output_dir: Path, + dpi: int = 500, + no_tables: bool = False, + no_xy_plots: bool = False, + no_histograms: bool = False, +) -> None: + """ + Renders CSV tables and matplotlib figures for every tag in `store`, writing them under + `output_dir` (nested per tag). + + Args: + store (ProductStore): store to read products from (a read-only connection is fine, + since `render_assets` never writes; see `ProductStore.open(..., readonly=True)`) + output_dir (Path): directory tables/figures are written under + dpi (int): resolution for saved matplotlib figures + no_tables (bool): suppress `TableEntry` -> CSV output + no_xy_plots (bool): suppress `Point2D`/`Trace2D`/`AxLine` -> matplotlib XY plot output + no_histograms (bool): suppress `HistogramEntry` -> matplotlib histogram output + + """ + output_dir = Path(output_dir) + + for tag in store.tag_tree(): + if not no_tables: + melted = store.get_table_entries(tag) + if melted.height > 0: + logger.info(f"Making tables for {tag = }") + TableBuilder.process_table_entries( + tag=tag, melted=melted, out_dir=output_dir + ) + logger.info(f"Finished tables for {tag = }") + + if not no_xy_plots: + points = store.get_products_of_type(Point2D, tag=tag) + traces = store.get_products_of_type(Trace2D, tag=tag) + axlines = store.get_products_of_type(AxLine, tag=tag) + + if points or traces or axlines: + logger.info(f"Making xy plot for {tag = }") + saf = XYDataPlotter.handle_points_and_traces( + tag=tag, points=points, traces=traces, axlines=axlines + ) + format2ds = [ + p.format2d + for p in (*points, *traces, *axlines) + if p.format2d is not None + ] + if format2ds: + saf.apply_format(Format2D.union_from_iterable(format2ds)) + + save_path = output_dir.joinpath(*tag_to_path_parts(tag)).with_suffix( + ".jpg" + ) + save_path.parent.mkdir(parents=True, exist_ok=True) + logger.info(f"Saving to {save_path}") + saf.savefig(save_path, dpi=dpi) + logger.info(f"Finished xy plot for {tag = }") + + if not no_histograms: + histogram_entries = store.get_products_of_type(HistogramEntry, tag=tag) + + if histogram_entries: + logger.info(f"Making histogram for {tag = }") + saf = Histogrammer.handle_histogram_entries( + tag=tag, histogram_entries=histogram_entries + ) + format2ds = [ + h.format2d for h in histogram_entries if h.format2d is not None + ] + if format2ds: + saf.apply_format(Format2D.union_from_iterable(format2ds)) + + *parents, stem = tag_to_path_parts(tag) + save_path = output_dir.joinpath(*parents, f"{stem}_histogram.jpg") + save_path.parent.mkdir(parents=True, exist_ok=True) + logger.info(f"Saving to {save_path}") + saf.savefig(save_path, dpi=dpi) + logger.info(f"Finished histogram for {tag = }") + + +def make_include_files( + root_dir: Path, + local_server_path: str | Path | None = None, + mkdocs_include_dir: str | Path | None = None, + heading_level: int | None = None, +) -> None: + """ + Makes nested `include.md` files (MkDocs `{% include %}`/`{{ read_csv(...) }}` snippets) for + the figures/tables/subdirectories under `root_dir`. This is a pure filesystem walk over + already-rendered assets and doesn't touch `ProductStore` at all. + + Args: + root_dir (Path): directory for which include files should be recursively generated + local_server_path (str | Path | None): prefix to use for figure paths, for serving + figures from a local dev server alongside `mkdocs serve` + mkdocs_include_dir (str | Path | None): path matching mkdocs.yml's `include_dir`, used + to make included paths relative to it + heading_level (int | None): base Markdown heading level for per-directory headers + + """ + root_dir = Path(root_dir) + dirs = sorted(root_dir.glob("**/")) + if not dirs: + return + + min_len = min(len(list(p.parents)) for p in dirs) + for s in dirs: + child_dirs = sorted(s.glob("*/")) + tables_to_include = [ + x + for pattern in ("*pivot.csv", "*stats.csv") + for x in s.glob(pattern, case_sensitive=False) + ] + figures_to_include = [ + x + for pattern in ("*.jpg", "*.png") + for x in s.glob(pattern, case_sensitive=False) + ] + children_to_include = [ + c.resolve().joinpath(INCLUDE_FILENAME) for c in child_dirs + ] + + if local_server_path is not None: + figures_to_include = [ + Path(local_server_path).joinpath(x.relative_to(root_dir)) + for x in figures_to_include + ] + if mkdocs_include_dir is not None: + mkdocs_include_dir = Path(mkdocs_include_dir) + tables_to_include = [ + x.relative_to(mkdocs_include_dir.parent) for x in tables_to_include + ] + children_to_include = [ + x.relative_to(mkdocs_include_dir) for x in children_to_include + ] + + fig_inclusion_statements = sorted(f"![]({x})" for x in figures_to_include) + table_inclusion_statements = sorted( + f"{{{{ read_csv('{x}', disable_numparse=True) }}}}" + for x in tables_to_include + ) + child_inclusion_statements = sorted( + "{% include '" + str(x) + "' %}" for x in children_to_include + ) + inclusions = ( + table_inclusion_statements + + fig_inclusion_statements + + child_inclusion_statements + ) + + header = "" + if heading_level is not None and len(inclusions) > 1: + depth = len(list(s.parents)) - min_len + header = "#" * (depth + heading_level) + s.name + + text = "\n\n".join([header, *inclusions]) + s.joinpath(INCLUDE_FILENAME).write_text(text) diff --git a/src/trendify/generator/table_builder.py b/src/trendify/generator/table_builder.py new file mode 100644 index 0000000..32609e4 --- /dev/null +++ b/src/trendify/generator/table_builder.py @@ -0,0 +1,173 @@ +""" +Builds melted/pivot/stats CSV tables for `TableEntry` products, from `ProductStore` query +results, using Polars. `ProductStore.get_table_entries` already hands back the melted +row/col/value/unit shape as a `pl.DataFrame` via one indexed SQL query, so this module only +needs to pivot and compute statistics, not reparse or reshape raw records. +""" + +from __future__ import annotations + +import logging +from pathlib import Path + +import polars as pl +from pydantic import BaseModel + +from trendify.base.helpers import Tag +from trendify.store.tags import tag_to_path_parts + +__all__ = ["TableBuilder"] + +logger = logging.getLogger(__name__) + + +class TableBuilder(BaseModel): + """ + Builds tables (melted, pivot, and stats) from `TableEntry` products for a given tag. + """ + + @classmethod + def process_table_entries( + cls, tag: Tag, melted: pl.DataFrame, out_dir: Path + ) -> None: + """ + Saves CSV files for the melted data frame, pivot dataframe, and pivot dataframe stats. + + File names all use the tag with different suffixes: `_melted.csv`, + `_pivot.csv`, `_stats.csv`. + + Args: + tag (Tag): product tag these entries belong to + melted (pl.DataFrame): row/col/value/unit table, as returned by + `ProductStore.get_table_entries` + out_dir (Path): directory under which the CSVs are saved (nested per tag) + + """ + if melted.height == 0: + return + + *parents, stem = tag_to_path_parts(tag) + save_dir = out_dir.joinpath(*parents) + save_dir.mkdir(parents=True, exist_ok=True) + logger.info(f"Saving tables for {tag = } to {save_dir}/{stem}_*.csv") + + _write_csv(melted, save_dir / f"{stem}_melted.csv") + + pivot = cls.pivot_table(melted) + if pivot is not None: + _write_csv(pivot, save_dir / f"{stem}_pivot.csv") + + try: + stats = cls.get_stats_table(pivot) + if stats is not None: + stats.write_csv(save_dir / f"{stem}_stats.csv") + else: + logger.debug( + f"No stats table for {tag = }: no numeric pivoted columns" + ) + except Exception as e: + logger.error( + f"Could not generate stats table for {tag = }. Error: {e!s}" + ) + else: + logger.debug( + f"No pivot table for {tag = }: a (row, col) pair repeats in the melted table" + ) + + @classmethod + def pivot_table(cls, melted: pl.DataFrame) -> pl.DataFrame | None: + """ + Attempts to pivot a melted row/col/value DataFrame into wide form. + + Args: + melted (pl.DataFrame): DataFrame with `row`/`col`/`value` columns + + Returns: + (pl.DataFrame | None): pivoted DataFrame, or `None` if the pivot fails because a + `(row, col)` pair repeats. + + """ + try: + return melted.pivot(on="col", index="row", values="value") + except pl.exceptions.ComputeError: + return None + + @classmethod + def get_stats_table(cls, pivot: pl.DataFrame) -> pl.DataFrame | None: + """ + Computes min/mean/max/3-sigma statistics for each pivoted column. + + Non-numeric values are coerced to null rather than causing the whole column to be + skipped. + + Args: + pivot (pl.DataFrame): pivoted table (as returned by `pivot_table`), with a `"row"` + index column plus one column per pivoted `col` value. + + Returns: + (pl.DataFrame | None): stats table with a `"Name"` column (one row per pivoted + column) plus `min`/`mean`/`max`/`sigma3` columns, or `None` if there are no + value columns or every computed statistic is null. + + """ + value_columns = [c for c in pivot.columns if c != "row"] + if not value_columns: + return None + + rows = [] + any_stat = False + for col in value_columns: + numeric = _coerce_numeric(pivot[col]) + mn, mean, mx, std = ( + numeric.min(), + numeric.mean(), + numeric.max(), + numeric.std(), + ) + if any(v is not None for v in (mn, mean, mx, std)): + any_stat = True + rows.append( + { + "Name": col, + "min": mn, + "mean": mean, + "max": mx, + "sigma3": None if std is None else std * 3, + } + ) + if not any_stat: + return None + return pl.DataFrame(rows) + + +def _coerce_numeric(series: pl.Series) -> pl.Series: + """Best-effort element-wise cast to float, `None` for values that don't convert.""" + if series.dtype != pl.Object: + return series.cast(pl.Float64, strict=False) + + def _to_float(v): + if v is None: + return None + try: + return float(v) + except (TypeError, ValueError): + return None + + return pl.Series( + series.name, [_to_float(v) for v in series.to_list()], dtype=pl.Float64 + ) + + +def _write_csv(df: pl.DataFrame, path: Path) -> None: + """ + Writes `df` to CSV, stringifying any `Object`-dtype columns first. Polars' CSV writer + doesn't support `Object` directly, and `TableEntry.value`'s heterogeneous float/str/bool + union is stored as `Object` by `ProductStore.get_table_entries`. + """ + object_cols = [c for c, dtype in df.schema.items() if dtype == pl.Object] + if object_cols: + df = df.with_columns( + pl.Series(c, [None if v is None else str(v) for v in df[c].to_list()]) + for c in object_cols + ) + df.write_csv(path) diff --git a/src/trendify/generator/xy_data_plotter.py b/src/trendify/generator/xy_data_plotter.py new file mode 100644 index 0000000..963dc13 --- /dev/null +++ b/src/trendify/generator/xy_data_plotter.py @@ -0,0 +1,77 @@ +""" +Draws `Point2D`/`Trace2D`/`AxLine` products onto a matplotlib figure. Fed directly by +`ProductStore` query results, so there's no directory-loading state to manage. +""" + +from __future__ import annotations + +import logging + +from pydantic import BaseModel + +from trendify.base.helpers import Tag +from trendify.plotting.axline import AxLine +from trendify.plotting.figure import SingleAxisFigure +from trendify.plotting.point import Point2D +from trendify.plotting.trace import Trace2D + +__all__ = ["XYDataPlotter"] + +logger = logging.getLogger(__name__) + + +class XYDataPlotter(BaseModel): + """ + Draws `Point2D`/`Trace2D`/`AxLine` products sharing a tag onto a matplotlib axes. + """ + + @classmethod + def handle_points_and_traces( + cls, + tag: Tag, + points: list[Point2D], + traces: list[Trace2D], + axlines: list[AxLine], + saf: SingleAxisFigure | None = None, + ) -> SingleAxisFigure: + """ + Plots points (scattered, grouped by marker), traces, and axlines onto `saf`. + + Args: + tag (Tag): tag these products belong to (used only if a new figure is created) + points (list[Point2D]): points to scatter, grouped by `Marker` (each distinct + marker becomes one `ax.scatter` call/series) + traces (list[Trace2D]): traces to plot + axlines (list[AxLine]): axis lines to plot + saf (SingleAxisFigure | None): figure to draw onto; a new one is created if `None` + + Returns: + (SingleAxisFigure): the figure drawn onto + + """ + if saf is None: + saf = SingleAxisFigure.new(tag=tag) + + logger.debug( + f"Plotting {len(points)} point(s), {len(traces)} trace(s), {len(axlines)} " + f"axline(s) for {tag = }" + ) + if points: + markers = set(p.marker for p in points) + for marker in markers: + matching_points = [p for p in points if p.marker == marker] + x = [p.x for p in matching_points] + y = [p.y for p in matching_points] + if x and y: + if marker is not None: + saf.ax.scatter(x, y, **marker.as_scatter_plot_kwargs()) + else: + saf.ax.scatter(x, y) + + for trace in traces: + trace.plot_to_ax(saf.ax) + + for axline in axlines: + axline.plot_to_ax(saf.ax) + + return saf diff --git a/src/trendify/log.py b/src/trendify/log.py new file mode 100644 index 0000000..19cbc6c --- /dev/null +++ b/src/trendify/log.py @@ -0,0 +1,195 @@ +""" +Trendify's logging setup: a Rich-formatted console handler plus a rotating log file +(`setup_logger`), a multiprocessing-safe queue/listener pair for routing worker-process log +records back through those same handlers (`create_queue_listener`/`worker_init`), and +`set_log_level` for a Python caller who just wants to control verbosity. +""" + +from __future__ import annotations + +import logging +import multiprocessing +from logging.handlers import QueueHandler, QueueListener, RotatingFileHandler +from pathlib import Path +from typing import Any + +from rich.console import Console +from rich.logging import RichHandler +from rich.theme import Theme + +__all__ = [ + "create_queue_listener", + "get_logger", + "set_log_level", + "setup_logger", + "worker_init", +] + +# Library-standard courtesy: don't let logging print "No handlers could be found for logger +# trendify.*" if the consuming application never configures logging at all. This only affects +# the "nothing configured anywhere" case. An application that calls `logging.basicConfig()` +# or `setup_logger()` still sees everything, since a `NullHandler` doesn't stop propagation. +logging.getLogger("trendify").addHandler(logging.NullHandler()) + +theme = Theme( + { + "logging.level.debug": "bold dodger_blue1", + "logging.level.info": "bold green", + "logging.level.warning": "bold yellow", + "logging.level.error": "bold red", + "path": "dim white", + "message": "white", + } +) + + +class TerminalFilter(logging.Filter): + def filter(self, record: logging.LogRecord): + # Block if 'file_only' is True + return not getattr(record, "file_only", False) + + +class FileFilter(logging.Filter): + def filter(self, record: logging.LogRecord): + # Block if 'terminal_only' is True + return not getattr(record, "terminal_only", False) + + +def get_logger(name: str): + logger = logging.getLogger(name) + + if not logger.hasHandlers(): + logger.addHandler(logging.NullHandler()) + return logger + + +def setup_logger( + level=logging.INFO, + terminal: bool = True, + log_file: Path | str | None = Path("trendify.log"), + max_bytes: int = 10 * 1024 * 1024, # 10MB + backup_count: int = 5, +) -> logging.Logger: + """Shortcut to a sensible trendify logger using Rich for the terminal.""" + logger = logging.getLogger() + logger.setLevel(level) + + # avoid double logging by preventing logs from being sent to the root logger + logger.propagate = False + + if not terminal and log_file is None: + return logger + + # clear existing handlers + if logger.hasHandlers(): + logger.handlers.clear() + + # --- RICH HANDLER FOR TERMINAL --- + if terminal: + console = Console(theme=theme) + rich_handler = RichHandler( + console=console, + show_time=True, + log_time_format="[%Y-%m-%d %H:%M:%S]", + show_path=True, + enable_link_path=False, + # highlighter=None, + markup=True, # allows color in the log message itself currently disabled in case an array is logged. Square brackets will break logging. + rich_tracebacks=True, + ) + rich_handler.addFilter(TerminalFilter()) + logger.addHandler(rich_handler) + + # --- PLAIN TEXT HANDLER --- + if log_file is not None: + log_file = Path(log_file) + log_file.parent.mkdir(parents=True, exist_ok=True) + + file_format = ( + "%(asctime)s [%(levelname)s] %(pathname)s:%(lineno)d - %(message)s" + ) + file_handler = RotatingFileHandler( + log_file, maxBytes=max_bytes, backupCount=backup_count + ) + file_handler.setFormatter(logging.Formatter(file_format)) + file_handler.addFilter(FileFilter()) + logger.addHandler(file_handler) + + return logger + + +def create_queue_listener( + *handlers: logging.Handler, +) -> tuple[multiprocessing.Queue[Any], QueueListener]: + """ + Creates a multiprocessing-safe log queue plus a `QueueListener` that drains it in the + calling process and dispatches records to `handlers`. + + This is what makes multiprocess logging safe, including on Windows: pair it with + `worker_init` in each worker process, so that no worker ever opens the real console/file + handlers directly. Only the listener, running in the parent process, does that. On Windows, + the default `spawn` start method means workers don't inherit the parent's configured + handlers at all; on POSIX with `fork`, workers *do* inherit them, which is its own hazard + (multiple processes writing to the same file concurrently). Routing everything through one + queue and one listener avoids both failure modes uniformly. + + Caller owns the listener's lifecycle: call `.start()` before spinning up worker processes + and `.stop()` once they've finished (a `try`/`finally` around a `ProcessPoolExecutor` block + is the natural place, since `.stop()` blocks until every already-queued record has been + dispatched, so no log messages are lost when the pool shuts down). + + Args: + *handlers (logging.Handler): the real handlers worker log records should ultimately + reach (e.g. the handlers already attached to the root logger by `setup_logger`) + + Returns: + (tuple[multiprocessing.Queue, QueueListener]): the queue to hand to worker processes + (via `ProcessPoolExecutor`'s `initializer`/`initargs`, calling `worker_init` in + each) and the not-yet-started listener draining it + + """ + log_queue: multiprocessing.Queue[Any] = multiprocessing.Queue(-1) + listener = QueueListener(log_queue, *handlers, respect_handler_level=True) + return log_queue, listener + + +def worker_init(queue: multiprocessing.Queue[Any], level: int) -> None: + """ + Initializes a worker process's root logger to ship every record through `queue` instead of + touching a real handler directly. Pair with `create_queue_listener`, called once in the + parent process before workers start. + + Args: + queue (multiprocessing.Queue): queue created by `create_queue_listener` + level (int): log level to apply to this worker's root logger + + """ + root = logging.getLogger() + + # Clear any handlers inherited via fork (POSIX default) so this worker never writes + # directly to a file/console shared with the parent or sibling workers. Only the + # QueueHandler below is installed, so everything funnels through the parent's + # QueueListener. + root.handlers.clear() + + handler = QueueHandler(queue) + root.addHandler(handler) + + root.setLevel(level) + + +def set_log_level(level: int | str, logger_name: str = "trendify") -> None: + """ + Sets the log level for `logger_name` (defaults to the top-level `"trendify"` logger, i.e. + every `trendify.*` module logger, since none of them set an explicit level of their own + and so inherit whatever their nearest ancestor has set). This is the supported way for + a Python caller to control trendify's log verbosity without needing to call `setup_logger` + or otherwise touch logging internals directly. + + Args: + level (int | str): a `logging` level (e.g. `logging.DEBUG`) or level name (e.g. + `"DEBUG"`) + logger_name (str): logger to set the level on; defaults to `"trendify"` + + """ + logging.getLogger(logger_name).setLevel(level) diff --git a/src/trendify/pipeline.py b/src/trendify/pipeline.py new file mode 100644 index 0000000..03de6b7 --- /dev/null +++ b/src/trendify/pipeline.py @@ -0,0 +1,150 @@ +""" +Top-level pipeline API: generate -> render -> include files, backed by one `ProductStore` +`.db` file per output directory. `TrendifyPipeline` is the single implementation both the +`trendify` CLI (`trendify.cli`) and direct Python callers use. The CLI is a thin +argument-parsing layer over this class, not a separate code path. +""" + +from __future__ import annotations + +import logging +from pathlib import Path + +from pydantic import BaseModel, Field + +from trendify.base.data_product import ProductGenerator +from trendify.generator.generate import generate_products +from trendify.generator.render import make_include_files, render_assets +from trendify.store.product_store import ProductStore + +__all__ = ["TrendifyPipeline"] + +logger = logging.getLogger(__name__) + + +class TrendifyPipeline(BaseModel): + """ + Runs the trendify pipeline (generate -> render -> include files) against one output + directory, backed by a single `ProductStore` `.db` file. + + Args: + output_dir (Path): directory the pipeline reads/writes under. Holds `trendify.db` + (the `ProductStore`) and an `assets/` subdirectory (rendered CSVs/figures). + n_procs (int): number of worker processes used by `generate`. + + """ + + output_dir: Path + n_procs: int = Field(default=1, ge=1) + + @property + def db_path(self) -> Path: + return self.output_dir / "trendify.db" + + @property + def assets_dir(self) -> Path: + return self.output_dir / "assets" + + def generate( + self, + product_generator: ProductGenerator, + data_dirs: list[Path], + ) -> int: + """ + Maps `product_generator` over `data_dirs`, writing products directly to this + pipeline's `ProductStore`. See `trendify.generator.generate.generate_products`. + + Returns: + (int): total number of products written + + """ + logger.info( + f"Generating products for {len(data_dirs)} directory(ies) into {self.db_path} " + f"({self.n_procs} worker process(es))" + ) + total = generate_products( + product_generator=product_generator, + data_dirs=data_dirs, + db_path=self.db_path, + n_procs=self.n_procs, + ) + logger.info(f"Generated {total} product(s)") + return total + + def render( + self, + dpi: int = 500, + no_tables: bool = False, + no_xy_plots: bool = False, + no_histograms: bool = False, + ) -> None: + """ + Renders CSV tables and matplotlib figures for every tag into `self.assets_dir`. See + `trendify.generator.render.render_assets`. + + """ + logger.info(f"Rendering assets from {self.db_path} into {self.assets_dir}") + with ProductStore.open(self.db_path, readonly=True) as store: + render_assets( + store, + self.assets_dir, + dpi=dpi, + no_tables=no_tables, + no_xy_plots=no_xy_plots, + no_histograms=no_histograms, + ) + logger.info(f"Finished rendering assets into {self.assets_dir}") + + def make_include_files( + self, + heading_level: int | None = 2, + local_server_path: str | Path | None = None, + mkdocs_include_dir: str | Path | None = None, + ) -> None: + """ + Writes nested MkDocs `include.md` files for `self.assets_dir`. See + `trendify.generator.render.make_include_files`. + + """ + logger.info(f"Writing include.md files under {self.assets_dir}") + make_include_files( + root_dir=self.assets_dir, + local_server_path=local_server_path, + mkdocs_include_dir=mkdocs_include_dir, + heading_level=heading_level, + ) + + def run( + self, + product_generator: ProductGenerator, + data_dirs: list[Path], + dpi: int = 500, + no_tables: bool = False, + no_xy_plots: bool = False, + no_histograms: bool = False, + no_include_files: bool = False, + ) -> int: + """ + Runs the full pipeline: generate, then render (unless all three asset kinds are + suppressed), then write include files (unless suppressed). + + Returns: + (int): total number of products written by `generate` + + """ + logger.info(f"Running full pipeline (generate -> render) for {self.output_dir}") + total = self.generate(product_generator=product_generator, data_dirs=data_dirs) + + no_assets = no_tables and no_xy_plots and no_histograms + if not no_assets: + self.render( + dpi=dpi, + no_tables=no_tables, + no_xy_plots=no_xy_plots, + no_histograms=no_histograms, + ) + if not no_include_files: + self.make_include_files() + + logger.info(f"Finished full pipeline for {self.output_dir}") + return total diff --git a/src/trendify/plotting/axline.py b/src/trendify/plotting/axline.py index b8c2ea4..3297ec0 100644 --- a/src/trendify/plotting/axline.py +++ b/src/trendify/plotting/axline.py @@ -1,7 +1,11 @@ +""" +`AxLine`: a horizontal or vertical reference line drawn on a plot, styled with a `Pen`. +""" + from __future__ import annotations import logging -from enum import Enum +from enum import StrEnum from matplotlib.axes import Axes from pydantic import ConfigDict @@ -15,7 +19,7 @@ logger = logging.getLogger(__name__) -class LineOrientation(Enum): +class LineOrientation(StrEnum): """ Defines orientation for axis lines diff --git a/src/trendify/plotting/figure.py b/src/trendify/plotting/figure.py index 8bf76d4..d1a31e7 100644 --- a/src/trendify/plotting/figure.py +++ b/src/trendify/plotting/figure.py @@ -1,3 +1,10 @@ +""" +`SingleAxisFigure` (matplotlib) and `PlotlyFigure` (Plotly) wrap a figure/axes pair and know how +to apply a `Format2D`/`Grid` to it and save it to disk. These are the two rendering targets +every `PlottableData2D` subclass draws itself onto (`plot_to_ax` for matplotlib, `add_to_plotly` +for Plotly). +""" + from __future__ import annotations from dataclasses import dataclass, field @@ -6,6 +13,8 @@ import warnings import logging +from typing import Any, cast + import numpy as np import plotly.graph_objects as go @@ -163,6 +172,7 @@ def savefig(self, path: Path, dpi: int = 500): (Self): Returns self """ + logger.debug(f"Saving matplotlib figure for {self.tag = } to {path} ({dpi = })") self.fig.savefig(path, dpi=dpi) return self @@ -203,7 +213,7 @@ def apply_format(self, format2d: Format2D): format2d (Format2D): format information to apply to the figure """ - layout_updates = {} + layout_updates: dict[str, Any] = {} # Set titles if format2d.title_fig is not None and format2d.title_ax is not None: @@ -215,9 +225,9 @@ def apply_format(self, format2d: Format2D): # Set axis labels if format2d.label_x is not None: - layout_updates["xaxis"] = {"title": format2d.label_x} + layout_updates["xaxis"] = cast(dict[str, Any], {"title": format2d.label_x}) if format2d.label_y is not None: - layout_updates["yaxis"] = {"title": format2d.label_y} + layout_updates["yaxis"] = cast(dict[str, Any], {"title": format2d.label_y}) # Set axis ranges def _log10(v: float | None): @@ -225,7 +235,7 @@ def _log10(v: float | None): if format2d.lim_x_min is not None or format2d.lim_x_max is not None: if "xaxis" not in layout_updates: - layout_updates["xaxis"] = {} + layout_updates["xaxis"] = cast(dict[str, Any], {}) if format2d.scale_x == AxisScale.LOG: layout_updates["xaxis"]["range"] = [ @@ -233,8 +243,9 @@ def _log10(v: float | None): _log10(format2d.lim_x_max), ] elif format2d.scale_x == AxisScale.LINEAR: - # v1 bugfix: this branch previously re-checked `== AxisScale.LOG`, - # making the plain (non-log) range-setting path unreachable. + # Deliberately checks LINEAR explicitly rather than falling through an + # unconditional else, so a third AxisScale value can't silently reuse the + # log-scaled range below. layout_updates["xaxis"]["range"] = [ format2d.lim_x_min, format2d.lim_x_max, @@ -242,14 +253,14 @@ def _log10(v: float | None): if format2d.lim_y_min is not None or format2d.lim_y_max is not None: if "yaxis" not in layout_updates: - layout_updates["yaxis"] = {} + layout_updates["yaxis"] = cast(dict[str, Any], {}) if format2d.scale_y == AxisScale.LOG: layout_updates["yaxis"]["range"] = [ _log10(format2d.lim_y_min), _log10(format2d.lim_y_max), ] elif format2d.scale_y == AxisScale.LINEAR: - # same fix as above, for the y axis + # Same reasoning as the x-axis branch above. layout_updates["yaxis"]["range"] = [ format2d.lim_y_min, format2d.lim_y_max, @@ -258,12 +269,12 @@ def _log10(v: float | None): # Set axis scales if format2d.scale_x is not None: if "xaxis" not in layout_updates: - layout_updates["xaxis"] = {} + layout_updates["xaxis"] = cast(dict[str, Any], {}) layout_updates["xaxis"]["type"] = format2d.scale_x.value if format2d.scale_y is not None: if "yaxis" not in layout_updates: - layout_updates["yaxis"] = {} + layout_updates["yaxis"] = cast(dict[str, Any], {}) layout_updates["yaxis"]["type"] = format2d.scale_y.value # Set legend diff --git a/src/trendify/plotting/histogram.py b/src/trendify/plotting/histogram.py index cbe8b22..bdc2669 100644 --- a/src/trendify/plotting/histogram.py +++ b/src/trendify/plotting/histogram.py @@ -1,3 +1,9 @@ +""" +`HistogramEntry` (a single value to be binned into a matplotlib/Plotly histogram) and its +`HistogramStyle` (bar color, opacity, bin count, and the derived RGBA/contrast-color helpers +used by both renderers). +""" + from __future__ import annotations import logging @@ -5,12 +11,12 @@ import plotly.graph_objects as go from matplotlib.colors import to_rgba -from numpydantic import NDArray, Shape from pydantic import ConfigDict, Field from trendify.base.helpers import HashableBase, Tags from trendify.formats.format2d import PlottableData2D from trendify.plotting.figure import PlotlyFigure +from trendify.typing import VecN __all__ = ["HistogramEntry", "HistogramStyle"] @@ -28,7 +34,7 @@ class HistogramStyle(HashableBase): alpha_edge (float): Opacity of bar edge alpha_face (float): Opacity of bar face linewidth (float): Line width of bar outline - bins (int | list[int] | Tuple[int] | NDArray[Shape["*"], int] | None): Number of bins (see [matplotlib docs][matplotlib.pyplot.hist]) + bins (int | list[int] | Tuple[int] | None): Number of bins (see [matplotlib docs][matplotlib.pyplot.hist]) """ @@ -39,7 +45,7 @@ class HistogramStyle(HashableBase): alpha_face: float = 0.3 linewidth: float = 2 zorder: int = 1 - bins: int | list[int] | tuple[int] | NDArray[Shape["*"], int] | None = None + bins: int | list[int] | tuple[int] | VecN | None = None def as_plot_kwargs(self): """ @@ -197,7 +203,7 @@ def add_to_plotly(self, plotly_figure: PlotlyFigure): for trace in traces: if trace.legendgroup == legend_key: # Append the value to the existing trace's x data - trace.x = list(trace.x) + [self.value] + trace.x = [*(trace.x or ()), self.value] return plotly_figure metadata_html = ( diff --git a/src/trendify/plotting/point.py b/src/trendify/plotting/point.py index ee7a01f..15c01af 100644 --- a/src/trendify/plotting/point.py +++ b/src/trendify/plotting/point.py @@ -1,3 +1,7 @@ +""" +`Point2D`: a single scattered (x, y) point, styled with a `Marker`. +""" + from __future__ import annotations import logging diff --git a/src/trendify/plotting/trace.py b/src/trendify/plotting/trace.py index 5970c32..7845a65 100644 --- a/src/trendify/plotting/trace.py +++ b/src/trendify/plotting/trace.py @@ -1,3 +1,8 @@ +""" +`Trace2D`: a line/scatter trace built from a list of `Point2D`s, styled with a `Pen`. Use the +`Trace2D.from_xy` constructor rather than building the `points` list by hand. +""" + from __future__ import annotations import logging @@ -5,7 +10,6 @@ import numpy as np import plotly.graph_objects as go -from numpydantic import NDArray, Shape from pydantic import ConfigDict from trendify.base.helpers import Tags @@ -14,6 +18,7 @@ from trendify.plotting.figure import PlotlyFigure from trendify.plotting.point import Point2D from trendify.styling.marker import Marker +from trendify.typing import VecN if TYPE_CHECKING: from matplotlib.axes import Axes @@ -47,23 +52,23 @@ class Trace2D(XYData): pen: Pen = Pen() @property - def x(self) -> NDArray[Shape["*"], float]: + def x(self) -> VecN: """ Returns an array of x values from `self.points` Returns: - (NDArray[Shape["*"], float]): array of x values from `self.points` + (VecN): array of x values from `self.points` """ return np.array([p.x for p in self.points]) @property - def y(self) -> NDArray[Shape["*"], float]: + def y(self) -> VecN: """ Returns an array of y values from `self.points` Returns: - (NDArray[Shape["*"], float]): array of y values from `self.points` + (VecN): array of y values from `self.points` """ return np.array([p.y for p in self.points]) @@ -91,8 +96,8 @@ def propagate_format2d_and_pen(self, marker_symbol: str = ".") -> None: def from_xy( cls, tags: Tags, - x: NDArray[Shape["*"], float], - y: NDArray[Shape["*"], float], + x: VecN, + y: VecN, pen: Pen = Pen(), format2d: Format2D | None = None, ): @@ -101,8 +106,8 @@ def from_xy( Args: tags (Tags): Tags used to sort data products - x (NDArray[Shape["*"], float]): x values - y (NDArray[Shape["*"], float]): y values + x (VecN): x values + y (VecN): y values pen (Pen): Style and label for trace format2d (Format2D | None): Format to apply to plot diff --git a/src/trendify/store/__init__.py b/src/trendify/store/__init__.py index 61d9609..858138c 100644 --- a/src/trendify/store/__init__.py +++ b/src/trendify/store/__init__.py @@ -12,6 +12,7 @@ from trendify.store.tags import ( decode_tag, encode_tag, + tag_to_path_parts, ) __all__ = [ @@ -22,5 +23,6 @@ "decode_tag", "encode_tag", "product_store", + "tag_to_path_parts", "tags", ] diff --git a/src/trendify/store/db.py b/src/trendify/store/db.py index d0ce3f0..8396672 100644 --- a/src/trendify/store/db.py +++ b/src/trendify/store/db.py @@ -1,22 +1,23 @@ """ -SQLite connection factory, pragmas, and schema management for the trendify v2 store. - -Design (see rewrite_reference/OVERVIEW.md and the v2 architecture plan for full context): +SQLite connection factory, pragmas, and schema management for the trendify store. - One `.db` file per trendify output directory, opened by every worker process independently. -- WAL mode gives concurrent readers + one writer without the readers blocking on the writer; - `busy_timeout` replaces v1's hand-rolled `FileLock` for the writer-vs-writer case. +- WAL mode gives concurrent readers plus one writer without the readers blocking on the + writer; `busy_timeout` handles the writer-vs-writer case without any external file locking. - Schema versioning via `PRAGMA user_version`, checked/applied on every `connect()` call so opening a fresh `.db` file bootstraps it and opening an existing one verifies compatibility. """ from __future__ import annotations +import logging import sqlite3 from pathlib import Path __all__ = ["SCHEMA_VERSION", "connect"] +logger = logging.getLogger(__name__) + SCHEMA_VERSION = 1 _SCHEMA_DDL = """ @@ -64,14 +65,15 @@ def connect(db_path: Path, *, readonly: bool = False) -> sqlite3.Connection: Args: db_path (Path): path to the trendify output directory's `.db` file - readonly (bool): open in read-only mode (`mode=ro` URI) — used by render workers that - only ever query, never write, so they can safely run concurrently with an + readonly (bool): open in read-only mode (`mode=ro` URI), for render workers that only + ever query and never write, so they can safely run concurrently with an in-progress writer under WAL. Returns: (sqlite3.Connection): a configured connection, with row access by column name. """ + logger.debug(f"Connecting to {db_path = } ({readonly = })") if readonly: uri = f"file:{db_path}?mode=ro" conn = sqlite3.connect(uri, uri=True) @@ -94,16 +96,23 @@ def connect(db_path: Path, *, readonly: bool = False) -> sqlite3.Connection: def _ensure_schema(conn: sqlite3.Connection) -> None: """ Bootstraps the schema on a fresh database, or verifies the on-disk schema version matches - `SCHEMA_VERSION` on an existing one. There is deliberately no migration logic yet (v2 has - only ever had one schema version) — this is the hook future migrations attach to. + `SCHEMA_VERSION` on an existing one. There is deliberately no migration logic yet, since + there has only ever been one schema version; this is the hook future migrations attach to. """ (version,) = conn.execute("PRAGMA user_version").fetchone() if version == 0: + logger.info(f"Bootstrapping fresh schema (version {SCHEMA_VERSION})") conn.executescript(_SCHEMA_DDL) conn.execute(f"PRAGMA user_version={SCHEMA_VERSION}") conn.commit() elif version != SCHEMA_VERSION: + logger.error( + f"Database schema version {version} does not match expected " + f"{SCHEMA_VERSION}, and no migration path exists yet." + ) raise RuntimeError( f"Database schema version {version} does not match expected " f"{SCHEMA_VERSION}, and no migration path exists yet." ) + else: + logger.debug(f"Schema version {version} verified") diff --git a/src/trendify/store/product_store.py b/src/trendify/store/product_store.py index 282a04a..f6a67d1 100644 --- a/src/trendify/store/product_store.py +++ b/src/trendify/store/product_store.py @@ -1,33 +1,42 @@ """ -ProductStore: the SQLite-backed replacement for v1's DataProductCollection file-glob methods. - -Pydantic objects remain what flows in and out everywhere -- only the storage/query mechanics -change. See rewrite_reference/OVERVIEW.md and the v2 architecture plan for the full design -rationale (why SQLite/WAL, why this schema shape, why no merge phase). +`ProductStore`: the SQLite-backed store for tagged `DataProduct`s. + +Pydantic objects are what flow in and out everywhere; storage and querying happen entirely +underneath that interface. Every product's full JSON payload is stored opaquely, so new +`DataProduct` subclasses are storable and queryable the moment they're registered, with no +schema migration required. Tags are normalized into an indexed join table, so a product with N +tags costs one payload row plus N small index rows rather than N duplicated payloads. `TableEntry` +is the one exception: it gets real SQL columns (row/col/value/unit) instead of an opaque payload, +since pivoting and computing per-column statistics is naturally SQL-shaped. """ from __future__ import annotations import datetime +import logging import sqlite3 from collections.abc import Iterable, Iterator +from dataclasses import dataclass from pathlib import Path +from typing import cast import polars as pl from trendify.base.data_product import DataProduct -from trendify.base.helpers import Tag +from trendify.base.helpers import R, Tag from trendify.formats.table import TableEntry from trendify.store import db from trendify.store.tags import decode_tag, encode_tag __all__ = ["ProductStore"] +logger = logging.getLogger(__name__) + def _leaf_type_names(object_type: type[DataProduct]) -> list[str]: """ Expands a (possibly non-leaf) `DataProduct` type into the list of registered leaf - `product_type` names that are instances of it -- e.g. `PlottableData2D` expands to + `product_type` names that are instances of it: `PlottableData2D`, for example, expands to `["Point2D", "Trace2D", "AxLine", "HistogramEntry"]`. This lets tag/type filtering happen as a plain SQL `product_type IN (...)` clause instead of deserializing every tag-matched row just to run an `isinstance` check in Python. @@ -44,6 +53,7 @@ def _tag_sort_key(tag: Tag): return (len(as_tuple), as_tuple) +@dataclass class ProductStore: """ SQLite-backed store for `DataProduct`s, grouped by run and queryable by tag/type. @@ -54,8 +64,7 @@ class ProductStore: traces = store.get_products_of_type(Trace2D, tag="my_tag") """ - def __init__(self, conn: sqlite3.Connection): - self._conn = conn + _conn: sqlite3.Connection @classmethod def open(cls, db_path: Path, *, readonly: bool = False) -> ProductStore: @@ -68,15 +77,14 @@ def __exit__(self, *exc_info) -> None: self.close() def close(self) -> None: + logger.debug("Closing ProductStore connection") self._conn.close() def write_run(self, run_path: Path, products: Iterable[DataProduct]) -> int: """ Replaces all products for `run_path` in a single transaction. Idempotent: calling this again for the same `run_path` (e.g. re-running a generator on the same input directory) - deletes and replaces that run's rows rather than accumulating duplicates -- this is what - replaces v1's `ProductIndexMap`/`FileLock` stable-indexing dance, since there's no - per-tag file to keep numbered anymore. + deletes and replaces that run's rows rather than accumulating duplicates. Args: run_path (Path): the raw-data run directory these products were generated from @@ -89,6 +97,7 @@ def write_run(self, run_path: Path, products: Iterable[DataProduct]) -> int: products = list(products) run_path_str = str(Path(run_path).resolve()) now = datetime.datetime.now(datetime.UTC).isoformat() + logger.debug(f"Writing {len(products)} product(s) for run {run_path_str}") conn = self._conn with conn: # transaction: commits on success, rolls back on exception @@ -153,6 +162,9 @@ def write_run(self, run_path: Path, products: Iterable[DataProduct]) -> int: ], ) + logger.debug( + f"Wrote {len(products)} product(s) for run {run_path_str} (run_id={run_id})" + ) return len(products) def get_tags(self, object_type: type[DataProduct] | None = None) -> set[Tag]: @@ -182,10 +194,8 @@ def get_tags(self, object_type: type[DataProduct] | None = None) -> set[Tag]: def tag_tree(self, object_type: type[DataProduct] | None = None) -> list[Tag]: """ Returns: - (list[Tag]): tags sorted the way v1's Streamlit sidebar expects (shallow tags - first, then lexicographically) -- direct replacement for v1's - `streamlit.get_tags()`, which discovered this list by globbing for files - literally named `index_map` in the sorted-products directory tree. + (list[Tag]): tags sorted for display in a nested sidebar or tree view: shallow + tags first, then lexicographically within each depth. """ return sorted(self.get_tags(object_type=object_type), key=_tag_sort_key) @@ -193,24 +203,24 @@ def tag_tree(self, object_type: type[DataProduct] | None = None) -> list[Tag]: def get_products( self, tag: Tag | None = None, - object_type: type[DataProduct] | None = None, - ) -> Iterator[DataProduct]: + object_type: type[R] | None = None, + ) -> Iterator[R]: """ Streams matching products, deserialized to their concrete pydantic type. Filtering by `tag` and/or `object_type` happens in SQL (indexed tag lookup, `product_type IN (...)` for the type hierarchy) before anything is deserialized, so this only ever parses JSON - for rows that actually match -- unlike v1, which reparsed every JSON in a tag directory - before filtering in Python. + for rows that actually match. Args: tag (Tag | None): tag to filter by; `None` matches every tag - object_type (type[DataProduct] | None): type (or base type) to filter by; - `None` matches every type + object_type (type[R] | None): type (or base type) to filter by; `None` matches + every type Yields: - (DataProduct): matching products, in insertion order + (R): matching products, in insertion order """ + logger.debug(f"Querying products ({tag = }, {object_type = })") select = "SELECT p.product_type, p.payload FROM products p" joins = [] where = [] @@ -234,27 +244,25 @@ def get_products( cursor = self._conn.execute(query, params) for row in cursor: - yield DataProduct.deserialize(row["product_type"], row["payload"]) + yield cast(R, DataProduct.deserialize(row["product_type"], row["payload"])) def get_products_of_type( - self, object_type: type[DataProduct], tag: Tag | None = None - ) -> list[DataProduct]: + self, object_type: type[R], tag: Tag | None = None + ) -> list[R]: """ - Same as `get_products`, but eager (a `list`) -- convenience for callers that don't need - streaming, mirroring v1's `DataProductCollection.get_products_of_type`. + Same as `get_products`, but eager (a `list`), for callers that don't need streaming. """ return list(self.get_products(tag=tag, object_type=object_type)) def get_table_entries(self, tag: Tag) -> pl.DataFrame: """ - Fetches `TableEntry` rows for `tag` directly from the `table_entries` table -- skips - deserializing full `DataProduct` payloads entirely, since row/col/value/unit are already - real columns. This is the direct replacement for `TableBuilder.load_table`'s "reparse - every JSON in every input dir" step. + Fetches `TableEntry` rows for `tag` directly from the `table_entries` table, skipping + full `DataProduct` payload deserialization entirely since row/col/value/unit are already + real columns. Note: `value` is resolved to a single Python value per row (whichever of `value_num`/`value_text`/`value_bool` is populated) before handing rows to Polars, - rather than asking Polars/SQLite to reconcile a mixed-type SQL column -- Polars expects + rather than asking Polars/SQLite to reconcile a mixed-type SQL column. Polars expects one dtype per column, so resolving the union type in Python first is the well-defined choice. @@ -262,8 +270,8 @@ def get_table_entries(self, tag: Tag) -> pl.DataFrame: tag (Tag): tag to filter by Returns: - (pl.DataFrame): columns `row`, `col`, `value`, `unit` -- same shape as v1's - `TableEntry.get_entry_dict()`. + (pl.DataFrame): a "melted" table with columns `row`, `col`, `value`, `unit`, one + row per `TableEntry` product matching `tag`. """ rows = self._conn.execute( @@ -271,6 +279,7 @@ def get_table_entries(self, tag: Tag) -> pl.DataFrame: "FROM table_entries WHERE tag_key = ?", (encode_tag(tag),), ).fetchall() + logger.debug(f"Fetched {len(rows)} table_entries row(s) for {tag = }") records = [] for r in rows: diff --git a/src/trendify/store/tags.py b/src/trendify/store/tags.py index 911fb34..727b41e 100644 --- a/src/trendify/store/tags.py +++ b/src/trendify/store/tags.py @@ -2,10 +2,8 @@ Canonical encoding between the pydantic-facing `Tag` type and the `tag_key` string stored/indexed in the `product_tags` and `table_entries` SQL tables. -This is the direct successor to v1's tuple-tag -> nested-folder-path convention: instead of -turning a tag into a filesystem path, we turn it into a single indexed string key. The same -`encode_tag` function must be used at write time and query time so lookups are exact-match -index seeks. +The same `encode_tag` function must be used at write time and query time so lookups are +exact-match index seeks. """ from __future__ import annotations @@ -14,7 +12,7 @@ from trendify.base.helpers import Tag -__all__ = ["decode_tag", "encode_tag"] +__all__ = ["decode_tag", "encode_tag", "tag_to_path_parts"] def encode_tag(tag: Tag) -> str: @@ -49,3 +47,19 @@ def decode_tag(tag_key: str) -> Tag: if isinstance(decoded, list): return tuple(decoded) return decoded + + +def tag_to_path_parts(tag: Tag) -> tuple[str, ...]: + """ + Converts a `Tag` into output-path parts: a tuple tag `("a", "b")` becomes nested path + segments `("a", "b")`, and a scalar tag `"a"` becomes a single segment `("a",)`. + + Args: + tag (Tag): tag to convert + + Returns: + (tuple[str, ...]): path parts, last one being the file stem + + """ + parts = tag if isinstance(tag, tuple) else (tag,) + return tuple(str(p) for p in parts) diff --git a/src/trendify/styling/grid.py b/src/trendify/styling/grid.py index 7926a54..596f0fc 100644 --- a/src/trendify/styling/grid.py +++ b/src/trendify/styling/grid.py @@ -1,24 +1,28 @@ +""" +`Grid` (major/minor gridline styling for a plot axes) and its `GridTheme` presets, selectable +via `Grid.from_theme`, plus the per-axis `GridAxis` settings a `Grid` is built from. +""" + from __future__ import annotations -from enum import Enum, auto -from collections.abc import Iterable import logging +from collections.abc import Iterable +from enum import StrEnum from pydantic import ConfigDict from trendify.base.helpers import HashableBase from trendify.base.pen import Pen - logger = logging.getLogger(__name__) __all__ = ["Grid", "GridAxis", "GridTheme"] -class GridTheme(Enum): - MATLAB = auto() - LIGHT = auto() - DARK = auto() +class GridTheme(StrEnum): + MATLAB = "matlab" + LIGHT = "light" + DARK = "dark" class GridAxis(HashableBase): @@ -136,10 +140,17 @@ def union_from_iterable(cls, grids: Iterable[Grid]) -> Grid: return cls() # Enforce consistent GridAxis settings - [major] = set(g.major for g in grids) - [minor] = set(g.minor for g in grids) - [enable_minor_ticks] = set(g.enable_minor_ticks for g in grids) - [zorder] = set(g.zorder for g in grids) + try: + [major] = set(g.major for g in grids) + [minor] = set(g.minor for g in grids) + [enable_minor_ticks] = set(g.enable_minor_ticks for g in grids) + [zorder] = set(g.zorder for g in grids) + except ValueError: + logger.error( + f"Grid.union_from_iterable: products sharing a tag disagree on " + f"major/minor/enable_minor_ticks/zorder. {grids = }" + ) + raise return cls( major=major, diff --git a/src/trendify/styling/legend.py b/src/trendify/styling/legend.py index dfd325f..3fcce83 100644 --- a/src/trendify/styling/legend.py +++ b/src/trendify/styling/legend.py @@ -1,3 +1,8 @@ +""" +`Legend` (matplotlib/Plotly legend styling: location, title, opacity, border) and the +`LegendLocation` enum of matplotlib-recognized location strings it validates against. +""" + from __future__ import annotations from enum import StrEnum diff --git a/src/trendify/styling/marker.py b/src/trendify/styling/marker.py index cadbc18..10948f9 100644 --- a/src/trendify/styling/marker.py +++ b/src/trendify/styling/marker.py @@ -1,3 +1,8 @@ +""" +`Marker`: scatter-point styling (color, size, alpha, symbol) for `Point2D`, including the +`from_pen` conversion used when a `Trace2D`'s `Pen` needs to render as scattered markers. +""" + from __future__ import annotations import logging @@ -18,7 +23,7 @@ class Marker(HashableBase): Defines marker for scattering to matplotlib Attributes: - color (str): Color of line + color (tuple[float, float, float] | tuple[float, float, float, float] | str): Color of line size (float): Line width alpha (float): Opacity from 0 to 1 (inclusive) zorder (float): Prioritization @@ -27,7 +32,7 @@ class Marker(HashableBase): """ - color: str = "k" + color: tuple[float, float, float] | tuple[float, float, float, float] | str = "k" size: float = 5 alpha: float = 1 zorder: float = 0 @@ -43,12 +48,9 @@ def from_pen( """ Converts Pen to marker with the option to specify a symbol. - Note (v1 bugfix): v1 did `pen.model_dump().pop("linestyle")`, which pops and - returns a single value (discarding the rest of the dict) and then tries to - splat that scalar as `**kwargs` -- guaranteed `TypeError` on any real call. - The fix is to dump the dict, drop the one field (`linestyle`) that `Pen` has - and `Marker` doesn't, and splat what's left (color/size/alpha/zorder/label, - which line up 1:1 with `Marker`'s fields). + `Pen` has one field (`linestyle`) that `Marker` doesn't, so its dumped dict can't be + splatted directly as `**kwargs` into `Marker`. This drops that one field and splats + what's left (color/size/alpha/zorder/label, which line up 1:1 with `Marker`'s fields). """ data = pen.model_dump() data.pop("linestyle", None) diff --git a/src/trendify/typing.py b/src/trendify/typing.py new file mode 100644 index 0000000..85c2652 --- /dev/null +++ b/src/trendify/typing.py @@ -0,0 +1,26 @@ +""" +Shared numeric array type aliases (`VecN`, `MatN`) used across `trendify.plotting`/ +`trendify.styling` for fields like `Trace2D.x`/`Trace2D.y` and `HistogramStyle.bins`. Backed by +`numpydantic.NDArray` for runtime pydantic validation, with a plain `TypeAlias` swapped in under +`TYPE_CHECKING` so static type checkers see an ordinary array/list/tuple union instead of +`numpydantic`'s shape-annotated generic. +""" + +from typing import TYPE_CHECKING, Annotated + +import numpy as np +from numpydantic import NDArray, Shape + +__all__ = ["MatN", "VecN"] + +if TYPE_CHECKING: + type _VecBase = np.ndarray | list[float | int] | tuple[float | int, ...] + + type VecN = _VecBase + """An N-element numeric array of arbitrary length.""" + + type MatN = _VecBase + """An NxN numeric matrix of arbitrary length.""" +else: + VecN = Annotated[NDArray[Shape["*"], float | int], ...] # type: ignore # noqa: F722 + MatN = Annotated[NDArray[Shape["*, *"], float | int], ...] # type: ignore # noqa: F722 diff --git a/src/trendify_old/assets/logo.svg b/src/trendify/viewer/templates/static/assets/logo.svg similarity index 100% rename from src/trendify_old/assets/logo.svg rename to src/trendify/viewer/templates/static/assets/logo.svg diff --git a/src/trendify_old/assets/logo_white_bg.svg b/src/trendify/viewer/templates/static/assets/logo_white_bg.svg similarity index 100% rename from src/trendify_old/assets/logo_white_bg.svg rename to src/trendify/viewer/templates/static/assets/logo_white_bg.svg diff --git a/src/trendify_old/__init__.py b/src/trendify_old/__init__.py deleted file mode 100644 index 3ab8434..0000000 --- a/src/trendify_old/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -""" -Provides top-level imports -""" - -from trendify.api import * -from trendify.server import * diff --git a/src/trendify_old/api/__init__.py b/src/trendify_old/api/__init__.py deleted file mode 100644 index f916345..0000000 --- a/src/trendify_old/api/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -from trendify.api.generator import * -from trendify.api.plotting import * -from trendify.api.base import * -from trendify.api.formats import * -from trendify.api.styling import * - -from trendify.api.api import * diff --git a/src/trendify_old/api/api.py b/src/trendify_old/api/api.py deleted file mode 100644 index eade765..0000000 --- a/src/trendify_old/api/api.py +++ /dev/null @@ -1,491 +0,0 @@ -""" -Module for generating, sorting, and plotting data products. -This uses pydantic dataclasses for JSON serialization to avoid overloading system memory. - -Some important learning material for pydantic classes and JSON (de)serialization: - -- [Nested Pydantic Models](https://bugbytes.io/posts/pydantic-nested-models-and-json-schemas/) -- [Deserializing Child Classes](https://blog.devgenius.io/deserialize-child-classes-with-pydantic-that-gonna-work-784230e1cf83) - -Attributes: - DATA_PRODUCTS_FNAME_DEFAULT (str): Hard-coded json file name 'data_products.json' - -""" - -from __future__ import annotations - -from concurrent.futures import ProcessPoolExecutor -from pathlib import Path -import time -from collections.abc import Callable -import logging - -import numpy as np - -from trendify.api.base.data_product import ProductGenerator -from trendify.api.generator.data_product_collection import ( - DataProductCollection, - flatten, -) -from trendify.api.base.helpers import DATA_PRODUCTS_FNAME_DEFAULT -from trendify.api.generator.data_product_generator import DataProductGenerator - - -logger = logging.getLogger(__name__) - -__all__ = [ - # process directories - "make_products", - "sort_products", - # "make_grafana_dashboard", - "make_tables_and_figures", - "make_include_files", - # combined process - "make_it_trendy", - "serve_products_to_plotly_dashboard", -] - - -### Runners - - -def make_include_files( - root_dir: Path, - local_server_path: str | Path | None = None, - mkdocs_include_dir: str | Path | None = None, - # products_dir_replacement_path: str | Path = None, - heading_level: int | None = None, -): - """ - Makes nested include files for inclusion into an MkDocs site. - - Note: - I recommend to create a Grafana panel and link to that from the MkDocs site instead. - - Args: - root_dir (Path): Directory for which the include files should be recursively generated - local_server_path (str|Path|None): What should the beginning of the path look like? - Use `//localhost:8001/...` something like that to work with `python -m mkdocs serve` - while running `python -m http.server 8001` in order to have interactive updates. - Use my python `convert_links.py` script to update after running `python -m mkdocs build` - in order to fix the links for the MkDocs site. See this repo for an example. - mkdocs_include_dir (str|Path|None): Path to be used for mkdocs includes. - This path should correspond to includ dir in `mkdocs.yml` file. (See `vulcan_srb_sep` repo for example). - - Note: - Here is how to setup `mkdocs.yml` file to have an `include_dir` that can be used to - include generated markdown files (and the images/CSVs that they reference). - - ``` - plugins: - - macros: - include_dir: run_for_record - ``` - - """ - INCLUDE = "include.md" - dirs = list(root_dir.glob("**/")) - dirs.sort() - if dirs: - min_len = np.min([len(list(p.parents)) for p in dirs]) - for s in dirs: - child_dirs = list(s.glob("*/")) - child_dirs.sort() - tables_to_include: list[Path] = [ - x - for x in flatten( - [ - list(s.glob(p, case_sensitive=False)) - for p in ["*pivot.csv", "*stats.csv"] - ] - ) - ] - figures_to_include: list[Path] = [ - x - for x in flatten( - [list(s.glob(p, case_sensitive=False)) for p in ["*.jpg", "*.png"]] - ) - ] - children_to_include: list[Path] = [ - c.resolve().joinpath(INCLUDE) for c in child_dirs - ] - if local_server_path is not None: - figures_to_include = [ - Path(local_server_path).joinpath(x.relative_to(root_dir)) - for x in figures_to_include - ] - if mkdocs_include_dir is not None: - mkdocs_include_dir = Path(mkdocs_include_dir) - - tables_to_include = [ - x.relative_to(mkdocs_include_dir.parent) for x in tables_to_include - ] - children_to_include = [ - x.relative_to(mkdocs_include_dir) for x in children_to_include - ] - - bb_open = r"{{" - bb_close = r"}}" - fig_inclusion_statements = [f"![]({x})" for x in figures_to_include] - table_inclusion_statements = [ - f"{bb_open} read_csv('{x}', disable_numparse=True) {bb_close}" - for x in tables_to_include - ] - child_inclusion_statments = [ - "{% include '" + str(x) + "' %}" for x in children_to_include - ] - fig_inclusion_statements.sort() - table_inclusion_statements.sort() - child_inclusion_statments.sort() - inclusions = ( - table_inclusion_statements - + fig_inclusion_statements - + child_inclusion_statments - ) - - header = ( - "".join(["#"] * ((len(list(s.parents)) - min_len) + heading_level)) - + s.name - if heading_level is not None and len(inclusions) > 1 - else "" - ) - text = "\n\n".join([header] + inclusions) - - s.joinpath(INCLUDE).write_text(text) - - -def map_callable( - f: Callable[[Path], DataProductCollection], - *iterables, - n_procs: int = 1, - mp_context=None, -): - """ - Args: - f (Callable[[Path], DataProductCollection]): Function to be mapped - iterables (Tuple[Iterable, ...]): iterables of arguments for mapped function `f` - n_procs (int): Number of parallel processes to run - mp_context (str): Context to use for creating new processes (see `multiprocessing` package documentation) - - """ - if n_procs > 1: - with ProcessPoolExecutor( - max_workers=n_procs, mp_context=mp_context - ) as executor: - result = list(executor.map(f, *iterables)) - else: - result = [f(*arg_tuple) for arg_tuple in zip(*iterables)] - - return result - - -def get_sorted_dirs(dirs: list[Path]): - """ - Sorts dirs numerically if possible, else alphabetically - - Args: - dirs (List[Path]): Directories to sort - - Returns: - (List[Path]): Sorted list of directories - - """ - dirs = list(dirs) - try: - dirs.sort(key=lambda p: int(p.name)) - except ValueError: - dirs.sort() - return dirs - - -def make_products( - product_generator: Callable[[Path], DataProductCollection] | None, - data_dirs: list[Path], - n_procs: int = 1, - data_products_fname: str = DATA_PRODUCTS_FNAME_DEFAULT, -): - """ - Maps `product_generator` over `dirs_in` to produce data product JSON files in those directories. - Sorts the generated data products into a nested file structure starting from `dir_products`. - Nested folders are generated for tags that are Tuples. Sorted data files are named according to the - directory from which they were loaded. - - Args: - product_generator (ProductGenerator | None): A callable function that returns - a list of data products given a working directory. - data_dirs (List[Path]): Directories over which to map the `product_generator` - n_procs (int = 1): Number of processes to run in parallel. If `n_procs==1`, directories will be - processed sequentially (easier for debugging since the full traceback will be provided). - If `n_procs > 1`, a [ProcessPoolExecutor][concurrent.futures.ProcessPoolExecutor] will - be used to load and process directories and/or tags in parallel. - data_products_fname (str): File name to be used for storing generated data products - - """ - sorted_dirs = get_sorted_dirs(dirs=data_dirs) - - if product_generator is None: - logger.error("No data product generator provided") - else: - logger.info("Generating tagged DataProducts and writing to JSON files...") - map_callable( - DataProductGenerator(processor=product_generator).process_and_save, - sorted_dirs, - [data_products_fname] * len(sorted_dirs), - n_procs=n_procs, - ) - logger.info("Finished generating tagged DataProducts and writing to JSON files") - - -def sort_products( - data_dirs: list[Path], - output_dir: Path, - n_procs: int = 1, - data_products_fname: str = DATA_PRODUCTS_FNAME_DEFAULT, -): - """ - Loads the tagged data products from `data_dirs` and sorts them (by tag) into a nested folder structure rooted at `output_dir`. - - Args: - data_dirs (List[Path]): Directories containing JSON data product files - output_dir (Path): Directory to which sorted products will be written - data_products_fname (str): File name in which the data products to be sorted are stored - - """ - sorted_data_dirs = get_sorted_dirs(dirs=data_dirs) - - logger.info("Sorting data by tags") - output_dir.mkdir(parents=True, exist_ok=True) - - map_callable( - DataProductCollection.sort_by_tags_single_directory, - sorted_data_dirs, - [output_dir] * len(sorted_data_dirs), - [data_products_fname] * len(sorted_data_dirs), - n_procs=n_procs, - ) - - logger.info("Finished sorting by tags") - - -# def make_grafana_dashboard( -# products_dir: Path, -# output_dir: Path, -# protocol: str, -# host: str, -# port: int, -# n_procs: int = 1, -# ): -# import grafana_api as gapi - -# """ -# Makes a JSON file to import to Grafana for displaying tagged data tables, histograms and XY plots. - -# Args: -# products_dir (Path): Root directory into which products have been sorted by tag -# output_dir (Path): Root directory into which Grafana dashboard and panal definitions will be written -# n_procs (int): Number of parallel tasks used for processing data product tags -# protocol (str): Communication protocol for data server -# host (str): Sever address for providing data to interactive dashboard -# n_procs (int): Number of parallel processes -# """ -# print( -# f"\n\n\nGenerating Grafana Dashboard JSON Spec in {output_dir} based on products in {products_dir}" -# ) -# output_dir.mkdir(parents=True, exist_ok=True) - -# product_dirs = list(products_dir.glob("**/*/")) -# panel_dir = output_dir.joinpath("panels") -# map_callable( -# DataProductCollection.make_grafana_panels, -# product_dirs, -# [panel_dir] * len(product_dirs), -# [f"{protocol}://{host}:{port}"] * len(product_dirs), -# n_procs=n_procs, -# ) - -# panels = [ -# gapi.Panel.model_validate_json(p.read_text()) for p in panel_dir.glob("*.json") -# ] -# dashboard = gapi.Dashboard(panels=panels) -# output_dir.joinpath("dashboard.json").write_text(dashboard.model_dump_json()) -# print("\nFinished Generating Grafana Dashboard JSON Spec") - - -def make_tables_and_figures( - products_dir: Path, - output_dir: Path, - dpi: int = 500, - n_procs: int = 1, - no_tables: bool = False, - no_xy_plots: bool = False, - no_histograms: bool = False, -): - """ - Makes CSV tables and creates plots (using matplotlib). - - Tags will be processed in parallel and output in nested directory structure under `output_dir`. - - Args: - products_dir (Path): Directory to which the sorted data products will be written - output_dir (Path): Directory to which tables and matplotlib histograms and plots will be written if - the appropriate boolean variables `make_tables`, `make_xy_plots`, `make_histograms` are true. - n_procs (int = 1): Number of processes to run in parallel. If `n_procs==1`, directories will be - processed sequentially (easier for debugging since the full traceback will be provided). - If `n_procs > 1`, a [ProcessPoolExecutor][concurrent.futures.ProcessPoolExecutor] will - be used to load and process directories and/or tags in parallel. - dpi (int = 500): Resolution of output plots when using matplotlib - (for `make_xy_plots==True` and/or `make_histograms==True`) - no_tables (bool): Whether or not to collect the - [`TableEntry`][trendify.api.TableEntry] products and write them - to CSV files (`_melted.csv` with `_pivot.csv` and `_stats.csv` when possible). - no_xy_plots (bool): Whether or not to plot the [`XYData`][trendify.api.XYData] products using matplotlib - no_histograms (bool): Whether or not to generate histograms of the - [`HistogramEntry`][trendify.api.HistogramEntry] products - using matplotlib. - - """ - if not (no_tables and no_xy_plots and no_histograms): - product_dirs = list(products_dir.glob("**/*/")) - map_callable( - DataProductCollection.process_collection, - product_dirs, # dir_in - [output_dir] * len(product_dirs), # dir_out - [no_tables] * len(product_dirs), # no_tables - [no_xy_plots] * len(product_dirs), # no_xy_plots - [no_histograms] * len(product_dirs), # no_histograms - [dpi] * len(product_dirs), # dpi - n_procs=n_procs, - ) - - -def _mkdir(p: Path): - p.mkdir(exist_ok=True, parents=True) - return p - - -def make_it_trendy( - data_product_generator: ProductGenerator | None, - input_dirs: list[Path], - output_dir: Path, - n_procs: int = 1, - dpi_static_plots: int = 500, - no_static_tables: bool = False, - no_static_xy_plots: bool = False, - no_static_histograms: bool = False, - no_grafana_dashboard: bool = False, - no_include_files: bool = False, - protocol: str = "http", - server: str = "0.0.0.0", - port: int = 8000, - data_products_fname: str = DATA_PRODUCTS_FNAME_DEFAULT, -): - """ - Maps `data_product_generator` over `dirs_in` to produce data product JSON files in those directories. - Sorts the generated data products into a nested file structure starting from `dir_products`. - Nested folders are generated for tags that are Tuples. Sorted data files are named according to the - directory from which they were loaded. - - Args: - data_product_generator (ProductGenerator | None): A callable function that returns - a list of data products given a working directory. - input_dirs (List[Path]): Directories over which to map the `product_generator` - output_dir (Path): Directory to which the trendify products and assets will be written. - n_procs (int = 1): Number of processes to run in parallel. If `n_procs==1`, directories will be - processed sequentially (easier for debugging since the full traceback will be provided). - If `n_procs > 1`, a [ProcessPoolExecutor][concurrent.futures.ProcessPoolExecutor] will - be used to load and process directories and/or tags in parallel. - dpi_static_plots (int = 500): Resolution of output plots when using matplotlib - (for `make_xy_plots==True` and/or `make_histograms==True`) - no_static_tables (bool): Suppresses static assets from the [`TableEntry`][trendify.api.TableEntry] products - no_static_xy_plots (bool): Suppresses static assets from the - [`XYData`][trendify.api.XYData] - ([Trace2D][trendify.api.Trace2D] and [Point2D][trendify.api.Point2D]) products - no_static_histograms (bool): Suppresses static assets from the [`HistogramEntry`][trendify.api.HistogramEntry] products - no_grafana_dashboard (bool): Suppresses generation of Grafana dashboard JSON definition file - no_include_files (bool): Suppresses generation of include files for importing static assets to markdown or LaTeX reports - data_products_fname (str): File name to be used for storing generated data products - - """ - input_dirs = [ - Path(p).parent if Path(p).is_file() else Path(p) for p in list(input_dirs) - ] - output_dir = Path(output_dir) - - make_products( - product_generator=data_product_generator, - data_dirs=input_dirs, - n_procs=n_procs, - data_products_fname=data_products_fname, - ) - - products_dir = _mkdir(output_dir.joinpath("products")) - - # Sort products - start = time.time() - sort_products( - data_dirs=input_dirs, - output_dir=products_dir, - n_procs=n_procs, - data_products_fname=data_products_fname, - ) - end = time.time() - logger.info(f"Time to sort = {end - start}") - - no_static_assets = no_static_tables and no_static_histograms and no_static_xy_plots - no_interactive_assets = no_grafana_dashboard - no_assets = no_static_assets and no_interactive_assets - - if not no_assets: - assets_dir = output_dir.joinpath("assets") - if not no_interactive_assets: - interactive_assets_dir = _mkdir(assets_dir.joinpath("interactive")) - if not no_grafana_dashboard: - grafana_dir = _mkdir(interactive_assets_dir.joinpath("grafana")) - # make_grafana_dashboard( - # products_dir=products_dir, - # output_dir=grafana_dir, - # n_procs=n_procs, - # protocol=protocol, - # server=server, - # port=port, - # ) - - if not no_static_assets: - static_assets_dir = _mkdir(assets_dir.joinpath("static")) - make_tables_and_figures( - products_dir=products_dir, - output_dir=static_assets_dir, - dpi=dpi_static_plots, - n_procs=n_procs, - no_tables=no_static_tables, - no_xy_plots=no_static_xy_plots, - no_histograms=no_static_histograms, - ) - - if not no_include_files: - make_include_files( - root_dir=static_assets_dir, - heading_level=2, - ) - - -def serve_products_to_plotly_dashboard( - *dirs: Path, - title: str = "Trendify Autodash", - host: str = "127.0.0.1", - port: int = 8000, - debug: bool = False, - data_products_filename: str = DATA_PRODUCTS_FNAME_DEFAULT, -): - """ """ - collection = DataProductCollection.collect_from_all_jsons( - *dirs, data_products_filename=data_products_filename - ) - - assert isinstance(collection, DataProductCollection) - collection.serve_plotly_dashboard( - title=title, - debug=debug, - host=host, - port=port, - ) diff --git a/src/trendify_old/api/base/__init__.py b/src/trendify_old/api/base/__init__.py deleted file mode 100644 index cde36fd..0000000 --- a/src/trendify_old/api/base/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -# from trendify.api.data_product_collection import * -from trendify.api.base.data_product import * -from trendify.api.base.helpers import * -from trendify.api.base.pen import * diff --git a/src/trendify_old/api/base/data_product.py b/src/trendify_old/api/base/data_product.py deleted file mode 100644 index 4381082..0000000 --- a/src/trendify_old/api/base/data_product.py +++ /dev/null @@ -1,132 +0,0 @@ -from __future__ import annotations - -from pathlib import Path -from typing import Any -from collections.abc import Callable -import logging - -from pydantic import ( - BaseModel, - ConfigDict, - InstanceOf, - SerializeAsAny, - computed_field, - model_validator, -) - -from trendify.api.base.helpers import Tags - - -logger = logging.getLogger(__name__) - -__all__ = ["DataProduct", "ProductGenerator", "ProductList"] - -_data_product_subclass_registry: dict[str, type[DataProduct]] = {} - - -class DataProduct(BaseModel): - """ - Base class for data products to be generated and handled. - - Attributes: - product_type (str): Product type should be the same as the class name. - The product type is used to search for products from a [DataProductCollection][trendify.api.DataProductCollection]. - tags (Tags): Tags to be used for sorting data. - metadata (dict[str, str]): A dictionary of metadata to be used as a tool tip for mousover in grafana - - """ - - tags: Tags - metadata: dict[str, str] = {} - - @model_validator(mode="before") - @classmethod - def _remove_computed_fields(cls, data: dict[str, Any]) -> dict[str, Any]: - """ - Removes computed fields before passing data to constructor. - - Args: - data (dict[str, Any]): Raw data to be validated before passing to pydantic class constructor. - - Returns: - (dict[str, Any]): Sanitized data to be passed to class constructor. - - """ - for f in cls.model_computed_fields: - data.pop(f, None) - return data - - @computed_field - @property - def product_type(self) -> str: - """ - Returns: - (str): Product type should be the same as the class name. - The product type is used to search for products from a - [DataProductCollection][trendify.api.DataProductCollection]. - - """ - return type(self).__name__ - - def __init_subclass__(cls, **kwargs: Any) -> None: - """ - Registers child subclasses to be able to parse them from JSON file using the - [deserialize_child_classes][trendify.api.DataProduct.deserialize_child_classes] method - """ - super().__init_subclass__(**kwargs) - _data_product_subclass_registry[cls.__name__] = cls - - model_config = ConfigDict(extra="allow") - - def append_to_list(self, l: list): - """ - Appends self to list. - - Args: - l (List): list to which `self` will be appended - - Returns: - (Self): returns instance of `self` - - """ - l.append(self) - return self - - @classmethod - def deserialize_child_classes(cls, key: str, **kwargs): - """ - Loads json data to pydandic dataclass of whatever DataProduct child time is appropriate - - Args: - key (str): json key - kwargs (dict): json entries stored under given key - - """ - type_key = "product_type" - elements = kwargs.get(key, None) - if elements: - for index in range(len(kwargs[key])): - duck_info = kwargs[key][index] - if isinstance(duck_info, dict): - product_type = duck_info.pop(type_key) - duck_type = _data_product_subclass_registry[product_type] - kwargs[key][index] = duck_type(**duck_info) - - def set_metadata(self, new: dict[str, str]): - self.metadata = new - return self - - -ProductList = list[SerializeAsAny[InstanceOf[DataProduct]]] -"""List of serializable [DataProduct][trendify.api.DataProduct] or child classes thereof""" - -ProductGenerator = Callable[[Path], ProductList] -""" -Callable method type. Users must provide a `ProductGenerator` to map over raw data. - -Args: - path (Path): Workdir holding raw data (Should be one per run from a batch) - -Returns: - (ProductList): List of data products to be sorted and used to produce assets -""" diff --git a/src/trendify_old/api/base/helpers.py b/src/trendify_old/api/base/helpers.py deleted file mode 100644 index c167742..0000000 --- a/src/trendify_old/api/base/helpers.py +++ /dev/null @@ -1,71 +0,0 @@ -from __future__ import annotations - -from enum import auto -from strenum import StrEnum -from typing import Union, TypeVar -from collections.abc import Hashable -import logging - -from pydantic import BaseModel - -logger = logging.getLogger(__name__) - -__all__ = [ - "DATA_PRODUCTS_FNAME_DEFAULT", - "HashableBase", - "ProductType", - "R", - "Tag", - "Tags", -] - -DATA_PRODUCTS_FNAME_DEFAULT = "data_products.json" -""" -Hard-coded file name for storing data products in batch-processed input directories. -""" - -R = TypeVar("R") - -Tag = Union[tuple[Hashable, ...], Hashable] -""" -Determines what types can be used to define a tag -""" - -Tags = list[Tag] -""" -List of tags -""" - - -class HashableBase(BaseModel): - """ - Defines a base for hashable pydantic data classes so that they can be reduced to a minimal set through type-casting. - """ - - def __hash__(self): - """ - Defines hash function - """ - return hash((type(self),) + tuple(self.__dict__.values())) - - -class ProductType(StrEnum): - """ - Defines all product types. Used to type-cast URL info in server to validate. - - Attributes: - DataProduct (str): class name - XYData (str): class name - Trace2D (str): class name - Point2D (str): class name - TableEntry (str): class name - HistogramEntry (str): class name - - """ - - DataProduct = auto() - XYData = auto() - Trace2D = auto() - Point2D = auto() - TableEntry = auto() - HistogramEntry = auto() diff --git a/src/trendify_old/api/base/pen.py b/src/trendify_old/api/base/pen.py deleted file mode 100644 index 132c6e5..0000000 --- a/src/trendify_old/api/base/pen.py +++ /dev/null @@ -1,136 +0,0 @@ -from __future__ import annotations - - -from pydantic import ConfigDict -from matplotlib.colors import to_rgba - -from trendify.api.base.helpers import HashableBase - -__all__ = ["Pen"] - - -class Pen(HashableBase): - """ - Defines the pen drawing to matplotlib. - - Attributes: - color (str): Color of line - size (float): Line width - alpha (float): Opacity from 0 to 1 (inclusive) - linestyle (Union[str, Tuple[int, Tuple[int, ...]]]): Linestyle to plot. Supports `str` or `tuple` definition ([matplotlib documentation](https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html)). - zorder (float): Prioritization - label (Union[str, None]): Legend label - - """ - - color: tuple[float, float, float] | tuple[float, float, float, float] | str = "k" - size: float = 1 - alpha: float = 1 - zorder: float = 0 - linestyle: str | tuple[int, tuple[int, ...]] = "-" - label: str | None = None - - model_config = ConfigDict(extra="forbid") - - def as_scatter_plot_kwargs(self): - """ - Returns kwargs dictionary for passing to [matplotlib plot][matplotlib.axes.Axes.plot] method - """ - return { - "color": self.color, - "linewidth": self.size, - "linestyle": self.linestyle, - "alpha": self.alpha, - "zorder": self.zorder, - "label": self.label, - } - - def _convert_linestyle_to_plotly(self) -> str: - """Convert matplotlib linestyle to plotly dash style""" - # Handle string styles - style_map = { - "-": "solid", - "--": "dash", - ":": "dot", - "-.": "dashdot", - } - if isinstance(self.linestyle, str): - return style_map.get(self.linestyle, "solid") - - # Handle tuple styles - convert to 'dash' as approximation - return "dash" - - @property - def rgba(self) -> str: - """ - Convert the pen's color to rgba string format. - - Returns: - str: Color in 'rgba(r,g,b,a)' format where r,g,b are 0-255 and a is 0-1 - - """ - # Handle different color input formats - if isinstance(self.color, tuple): - if len(self.color) == 3: # RGB tuple - r, g, b = self.color - a = self.alpha - else: # RGBA tuple - r, g, b, a = self.color - # Convert 0-1 range to 0-255 for RGB - r, g, b = int(r * 255), int(g * 255), int(b * 255) - else: # String color (name or hex) - # Use matplotlib's color converter - rgba_vals = to_rgba(self.color, self.alpha) - # Convert 0-1 range to 0-255 for RGB - r, g, b = [int(x * 255) for x in rgba_vals[:3]] - a = rgba_vals[3] - - return f"rgba({r}, {g}, {b}, {a})" - - @property - def rgb(self) -> str: - return ", ".join(self.rgba.split(",")[0:-1]) + ")" - - def get_contrast_color(self, background_luminance: float = 1.0) -> str: - """ - Returns 'white' or 'black' to provide the best contrast against the pen's color, - taking into account the alpha (transparency) value of the line. - - Args: - background_luminance (float): The luminance of the background (default is 1.0 for white). - - Returns: - str: 'white' or 'black' - - """ - # Convert the pen's color to RGB (0-255 range) and get alpha - if isinstance(self.color, tuple): - if len(self.color) == 3: # RGB tuple - r, g, b = self.color - a = self.alpha - else: # RGBA tuple - r, g, b, a = self.color - r, g, b = int(r * 255), int(g * 255), int(b * 255) - else: # String color (name or hex) - rgba_vals = to_rgba(self.color, self.alpha) - r, g, b = [int(x * 255) for x in rgba_vals[:3]] - a = rgba_vals[3] - - # Calculate relative luminance of the pen's color - def luminance(channel): - channel /= 255.0 - return ( - channel / 12.92 - if channel <= 0.03928 - else ((channel + 0.055) / 1.055) ** 2.4 - ) - - color_luminance = ( - 0.2126 * luminance(r) + 0.7152 * luminance(g) + 0.0722 * luminance(b) - ) - - # Blend the color luminance with the background luminance based on alpha - blended_luminance = (1 - a) * background_luminance + a * color_luminance - - # Return white for dark blended colors, black for light blended colors - return "white" if blended_luminance < 0.5 else "black" diff --git a/src/trendify_old/api/formats/__init__.py b/src/trendify_old/api/formats/__init__.py deleted file mode 100644 index 61a4447..0000000 --- a/src/trendify_old/api/formats/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from trendify.api.formats.format2d import * -from trendify.api.formats.table import * diff --git a/src/trendify_old/api/formats/format2d.py b/src/trendify_old/api/formats/format2d.py deleted file mode 100644 index 28bbde8..0000000 --- a/src/trendify_old/api/formats/format2d.py +++ /dev/null @@ -1,152 +0,0 @@ -from __future__ import annotations - -from enum import StrEnum -from typing import TYPE_CHECKING -from collections.abc import Iterable -import logging - -import numpy as np -from pydantic import ConfigDict - -from trendify.api.base.data_product import DataProduct -from trendify.api.base.helpers import HashableBase -from trendify.api.styling.grid import Grid -from trendify.api.styling.legend import Legend - -if TYPE_CHECKING: - from trendify.api.plotting.plotting import PlotlyFigure -logger = logging.getLogger(__name__) - -__all__ = ["AxisScale", "Format2D", "PlottableData2D", "XYData"] - - -class AxisScale(StrEnum): - LINEAR = "linear" - """Format axis as linear""" - LOG = "log" - """Format axis with log base 10""" - - -class Format2D(HashableBase): - """ - Formatting data for matplotlib figure and axes - - Attributes: - title_fig (Optional[str], optional): Sets [figure title][matplotlib.figure.Figure.suptitle]. Defaults to None. - legend (Optional[Legend], optional): Sets [legend style][trendify.api.styling.legend.Legend]. Defaults to Legend(). - title_ax (Optional[str], optional): Sets [axis title][matplotlib.axes.Axes.set_title]. Defaults to None. - label_x (Optional[str], optional): Sets [x-axis label][matplotlib.axes.Axes.set_xlabel]. Defaults to None. - label_y (Optional[str], optional): Sets [y-axis label][matplotlib.axes.Axes.set_ylabel]. Defaults to None. - lim_x_min (float | None, optional): Sets [x-axis lower bound][matplotlib.axes.Axes.set_xlim]. Defaults to None. - lim_x_max (float | None, optional): Sets [x-axis upper bound][matplotlib.axes.Axes.set_xlim]. Defaults to None. - lim_y_min (float | None, optional): Sets [y-axis lower bound][matplotlib.axes.Axes.set_ylim]. Defaults to None. - lim_y_max (float | None, optional): Sets [y-axis upper bound][matplotlib.axes.Axes.set_ylim]. Defaults to None. - grid (Grid | None,optional): Sets the [grid][matplotlib.pyplot.grid]. Defaults to None. - scale_x (AxisScale, optional): Sets the x axis scale to an option from [AxisScale][trendify.api.formats.format2d.AxisScale]. Defaults to AxisScale.LINEAR - scale_y (AxisScale, optional): Sets the y axis scale to an option from [AxisScale][trendify.api.formats.format2d.AxisScale]. Defaults to AxisScale.LINEAR - figure_width (float, optional): Sets the of the width of rendered figure in inches. Defaults to 6.4. - figure_height (float, optional): Sets the of the height of rendered figure in inches. Defaults to 4.8. - - """ - - title_fig: str | None | None = None - legend: Legend | None = Legend() - title_ax: str | None | None = None - label_x: str | None | None = None - label_y: str | None | None = None - lim_x_min: float | None = None - lim_x_max: float | None = None - lim_y_min: float | None = None - lim_y_max: float | None = None - grid: Grid | None = None - scale_x: AxisScale = AxisScale.LINEAR - scale_y: AxisScale = AxisScale.LINEAR - figure_width: float = 6.4 - figure_height: float = 4.8 - - model_config = ConfigDict(extra="forbid") - - @classmethod - def union_from_iterable(cls, format2ds: Iterable[Format2D]): - """ - Gets the most inclusive format object (in terms of limits) from a list of `Format2D` objects. - Requires that the label and title fields are identical for all format objects in the list. - - Args: - format2ds (Iterable[Format2D]): Iterable of `Format2D` objects. - - Returns: - (Format2D): Single format object from list of objects. - - """ - formats = list(set(format2ds) - {None}) - - [title_fig] = set(i.title_fig for i in formats if i is not None) - [legend] = set(i.legend for i in formats if i is not None) - [title_ax] = set(i.title_ax for i in formats if i is not None) - [label_x] = set(i.label_x for i in formats if i is not None) - [label_y] = set(i.label_y for i in formats if i is not None) - [figure_width] = set(i.figure_width for i in formats) - [figure_height] = set(i.figure_height for i in formats) - - x_min = [i.lim_x_min for i in formats if i.lim_x_min is not None] - x_max = [i.lim_x_max for i in formats if i.lim_x_max is not None] - y_min = [i.lim_y_min for i in formats if i.lim_y_min is not None] - y_max = [i.lim_y_max for i in formats if i.lim_y_max is not None] - - lim_x_min = np.min(x_min) if len(x_min) > 0 else None - lim_x_max = np.max(x_max) if len(x_max) > 0 else None - lim_y_min = np.min(y_min) if len(y_min) > 0 else None - lim_y_max = np.max(y_max) if len(y_max) > 0 else None - - grid = Grid.union_from_iterable(f.grid for f in formats if f.grid is not None) - - [scale_x] = set(i.scale_x for i in formats) - [scale_y] = set(i.scale_y for i in formats) - - return cls( - title_fig=title_fig, - legend=legend, - title_ax=title_ax, - label_x=label_x, - label_y=label_y, - lim_x_min=lim_x_min, - lim_x_max=lim_x_max, - lim_y_min=lim_y_min, - lim_y_max=lim_y_max, - grid=grid, - scale_x=scale_x, - scale_y=scale_y, - figure_width=figure_width, - figure_height=figure_height, - ) - - -class PlottableData2D(DataProduct): - """ - Base class for children of DataProduct to be plotted ax xy data on a 2D plot - - Attributes: - format2d (Format2D|None): Format to apply to plot - tags (Tags): Tags to be used for sorting data. - metadata (dict[str, str]): A dictionary of metadata to be used as a tool tip for mousover in grafana - - """ - - format2d: Format2D | None = None - - def add_to_plotly(self, plotly_figure: PlotlyFigure): - """ - Add this data product to a plotly figure - - Args: - plotly_figure (PlotlyFigure): Plotly figure to add data to - - """ - raise NotImplementedError("Subclasses must implement add_to_plotly") - - -class XYData(PlottableData2D): - """ - Base class for children of DataProduct to be plotted ax xy data on a 2D plot - """ diff --git a/src/trendify_old/api/formats/table.py b/src/trendify_old/api/formats/table.py deleted file mode 100644 index 877b296..0000000 --- a/src/trendify_old/api/formats/table.py +++ /dev/null @@ -1,88 +0,0 @@ -from __future__ import annotations - -from pathlib import Path -import traceback -import logging - -import pandas as pd -from pydantic import ConfigDict - -from trendify.api.base.data_product import DataProduct - -logger = logging.getLogger(__name__) - -__all__ = ["TableEntry"] - - -class TableEntry(DataProduct): - """ - Defines an entry to be collected into a table. - - Collected table entries will be printed in three forms when possible: melted, pivot (when possible), and stats (on pivot columns, when possible). - - Attributes: - tags (Tags): Tags used to sort data products - row (float | str): Row Label - col (float | str): Column Label - value (float | str): Value - unit (str | None): Units for value - metadata (dict[str, str]): A dictionary of metadata to be used as a tool tip for mousover in grafana - - """ - - row: float | str - col: float | str - value: float | str | bool - unit: str | None = None - - model_config = ConfigDict(extra="forbid") - - def get_entry_dict(self): - """ - Returns a dictionary of entries to be used in creating a table. - - Returns: - (dict[str, str | float]): Dictionary of entries to be used in creating a melted [DataFrame][pandas.DataFrame] - - """ - return { - "row": self.row, - "col": self.col, - "value": self.value, - "unit": self.unit, - } - - @classmethod - def pivot_table(cls, melted: pd.DataFrame): - """ - Attempts to pivot melted row, col, value DataFrame into a wide form DataFrame - - Args: - melted (pd.DataFrame): Melted data frame having columns named `'row'`, `'col'`, `'value'`. - - Returns: - (pd.DataFrame | None): pivoted DataFrame if pivot works else `None`. Pivot operation fails if - row or column index pairs are repeated. - - """ - try: - result = melted.pivot(index="row", columns="col", values="value") - except ValueError: - logger.debug(traceback.format_exc()) - result = None - return result - - @classmethod - def load_and_pivot(cls, path: Path): - """ - Loads melted table from csv and pivots to wide form. - csv should have columns named `'row'`, `'col'`, and `'value'`. - - Args: - path (Path): path to CSV file - - Returns: - (pd.DataFrame | None): Pivoted data frame or elese `None` if pivot operation fails. - - """ - return cls.pivot_table(melted=pd.read_csv(path)) diff --git a/src/trendify_old/api/generator/__init__.py b/src/trendify_old/api/generator/__init__.py deleted file mode 100644 index ada3dc8..0000000 --- a/src/trendify_old/api/generator/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from trendify.api.generator.data_product_collection import * -from trendify.api.generator.data_product_generator import * -from trendify.api.generator.histogrammer import * -from trendify.api.generator.table_builder import * -from trendify.api.generator.xy_data_plotter import * diff --git a/src/trendify_old/api/generator/data_product_collection.py b/src/trendify_old/api/generator/data_product_collection.py deleted file mode 100644 index eb2145f..0000000 --- a/src/trendify_old/api/generator/data_product_collection.py +++ /dev/null @@ -1,872 +0,0 @@ -from __future__ import annotations - -import os -from itertools import chain -from pathlib import Path -from typing import TYPE_CHECKING, Any -from collections.abc import Iterable, Iterator -import logging - - -try: - from typing import Self -except: - from typing import Self - -import dash -from filelock import FileLock -from pydantic import BaseModel, Field, validate_call - -from trendify.api.formats.format2d import Format2D -from trendify.api.generator.histogrammer import Histogrammer -from trendify.api.base.helpers import Tag, R, DATA_PRODUCTS_FNAME_DEFAULT -from trendify.api.base.data_product import DataProduct, ProductList -from trendify.api.plotting.plotting import SingleAxisFigure, PlotlyFigure -from trendify.api.plotting.histogram import HistogramEntry -from trendify.api.plotting.point import Point2D -from trendify.api.plotting.trace import Trace2D -from trendify.api.plotting.axline import AxLine -from trendify.api.formats.table import TableEntry - -if TYPE_CHECKING: - from trendify.api.generator.table_builder import TableBuilder - -logger = logging.getLogger(__name__) - -__all__ = [ - "DataProductCollection", - "ProductEntryMetadata", - "ProductIndexMap", - "flatten", -] - - -UQL_TableEntry = r""" -parse-json -| project "elements" -| project "row", "col", "value", "unit", "metadata" -""" # .replace('\n', r'\n').replace('"', r'\"') + '"' - -UQL_Point2D = r""" -parse-json -| project "elements" -| extend "label"="marker.label" -""" # .replace('\n', r'\n').replace('"', r'\"') + '"' - -UQL_Trace2D = r""" -parse-json -| project "elements" -| extend "label"="pen.label" -| mv-expand "points" -| extend "x"="points.x", "y"="points.y" -| project "label", "x", "y", "metadata" -""" # .replace('\n', r'\n').replace('"', r'\"') + '"' - - -class ProductEntryMetadata(BaseModel): - source: Path - - def __hash__(self): - return hash(self.model_dump_json()) - - -class ProductIndexMap(BaseModel): - entries: dict[int, ProductEntryMetadata] = Field(default_factory=dict) - - @property - def inverse(self) -> dict[ProductEntryMetadata, int]: - return {v: k for k, v in self.entries.items()} - - @classmethod - @validate_call - def get_index( - cls, - save_dir: Path, - metadata: ProductEntryMetadata, - ) -> int: - if not save_dir.is_dir(): - raise ValueError(f"{save_dir} is not a directory") - lock_file = save_dir.joinpath("reserving_index.lock") - with FileLock(lock_file): - index_map_file = save_dir.joinpath("index_map") - if not index_map_file.exists(): - index_map = ProductIndexMap() - else: - index_map = ProductIndexMap.model_validate_json( - index_map_file.read_text() - ) - if metadata in index_map.entries.values(): - index_to_use = index_map.inverse[metadata] - else: - index_to_use = max(index_map.entries.keys(), default=-1) + 1 - index_map.entries[index_to_use] = metadata - index_map_file.write_text(index_map.model_dump_json()) - return index_to_use - - -def _should_be_flattened(obj: Any): - """ - Checks if object is an iterable container that should be flattened. - `DataProduct`s will not be flattened. Strings will not be flattened. - Everything else will be flattened. - - Args: - obj (Any): Object to be tested - - Returns: - (bool): Whether or not to flatten object - - """ - return isinstance(obj, Iterable) and not isinstance(obj, (str, bytes, DataProduct)) - - -def flatten(obj: Iterable[Any]) -> Iterator[Any]: - """ - Recursively flattens iterable up to a point (leaves `str`, `bytes`, and `DataProduct` unflattened) - - Args: - obj (Iterable): Object to be flattened - - Returns: - (Iterable): Flattned iterable - - """ - if not _should_be_flattened(obj): - yield obj - else: - for sublist in obj: - yield from flatten(sublist) - - -def atleast_1d(obj: Any) -> Iterable: - """ - Converts scalar objec to a list of length 1 or leaves an iterable object unchanged. - - Args: - obj (Any): Object that needs to be at least 1d - - Returns: - (Iterable): Returns an iterable - - """ - if not _should_be_flattened(obj): - return [obj] - else: - return obj - - -def _squeeze(obj: Any) -> Any: - """ - Returns a scalar if object is iterable of length 1 else returns object. - - Args: - obj (Union[Iterable, Any]): An object to be squeezed if possible - - Returns: - (Any): Either iterable or scalar if possible - - """ - if _should_be_flattened(obj) and len(obj) == 1: - return obj[0] - else: - return obj - - -class DataProductCollection(BaseModel): - """ - A collection of data products. - - Use this class to serialize data products to JSON, de-serialized them from JSON, filter the products, etc. - - Attributes: - elements (ProductList): A list of data products. - - """ - - derived_from: Path | None = None - elements: ProductList = Field(default_factory=list) - - def __init__(self, **kwargs: Any): - DataProduct.deserialize_child_classes(key="elements", **kwargs) - super().__init__(**kwargs) - - @classmethod - def from_iterable(cls, *products: ProductList): - """ - Returns a new instance containing all of the products provided in the `*products` argument. - - Args: - products (Tuple[ProductList, ...]): Lists of data products to combine into a collection - - Returns: - (cls): A data product collection containing all of the provided products in the `*products` argument. - - """ - return cls(elements=list(flatten(products))) - - def get_tags(self, data_product_type: type[DataProduct] | None = None) -> set: - """ - Gets the tags related to a given type of `DataProduct`. Parent classes will match all child class types. - - Args: - data_product_type (Type[DataProduct] | None): type for which you want to get the list of tags - - Returns: - (set): set of tags applying to the given `data_product_type`. - - """ - tags = [] - for e in flatten(self.elements): - if data_product_type is None or isinstance(e, data_product_type): - for t in e.tags: - tags.append(t) - return set(tags) - - def add_products(self, *products: DataProduct): - """ - Args: - products (Tuple[DataProduct|ProductList, ...]): Products or lists of products to be - appended to collection elements. - - """ - self.elements.extend(flatten(products)) - - def generate_plotly_dashboard( - self, - title: str = "Trendify Autodash", - debug: bool = False, - ) -> dash.Dash: - from trendify.plotly_dashboard import generate_plotly_dashboard - - return generate_plotly_dashboard( - collection=self, - title=title, - debug=debug, - ) - - def serve_plotly_dashboard( - self, - title: str = "Trendify Autodash", - host: str = "127.0.0.1", - port: int = 8000, - debug: bool = False, - ): - app = self.generate_plotly_dashboard( - title=title, - debug=debug, - ) - if not debug: - try: - import waitress - - # Try to use waitress if available (a production WSGI server) - os.environ["FLASK_ENV"] = ( - "production" # This reduces some of the output - ) - logger.info( - f"Starting production server with waitress on http://{host}:{port}" - ) - waitress.serve(app.server, host=host, port=port) - except ImportError: - # Fall back to development server with a message about installing waitress - logger.warning( - "'waitress' package not found. For production use, install it with:\npip install waitress" - ) - logger.info(f"Starting development server on {host}:{port}") - app.run_server(debug=debug, host=host, port=port) - else: - # Use Flask development server - app.run_server(debug=debug, host=host, port=port) - - @classmethod - def collect_and_serve_plotly_dashboard( - cls, - *dirs: Path, - recursive: bool = False, - title: str = "Trendify Autodash", - host: str = "127.0.0.1", - port: int = 8000, - debug: bool = False, - data_products_filename: str = DATA_PRODUCTS_FNAME_DEFAULT, - ) -> tuple[DataProductCollection, dash.Dash]: - collection = cls.collect_from_all_jsons( - *dirs, recursive=recursive, data_products_filename=data_products_filename - ) - assert isinstance(collection, DataProductCollection) - plotly = collection.generate_plotly_dashboard(title=title, debug=debug) - plotly.run(debug=debug, host=host, port=port) - - return collection, plotly - - def drop_products( - self, - tag: Tag | None = None, - object_type: type[R] | None = None, - ) -> Self: - """ - Removes products matching `tag` and/or `object_type` from collection elements. - - Args: - tag (Tag | None): Tag for which data products should be dropped - object_type (Type | None): Type of data product to drop - - Returns: - (DataProductCollection): A new collection from which matching elements have been dropped. - - """ - match_key = tag is None, object_type is None - match match_key: - case (True, True): - return type(self)(elements=self.elements) - case (True, False): - assert object_type is not None - return type(self)( - elements=[ - e for e in self.elements if not isinstance(e, object_type) - ] - ) - case (False, True): - return type(self)( - elements=[e for e in self.elements if tag not in e.tags] - ) - case (False, False): - assert object_type is not None - return type(self)( - elements=[ - e - for e in self.elements - if not (tag in e.tags and isinstance(e, object_type)) - ] - ) - case _: - raise ValueError("Something is wrong with match statement") - - def get_products( - self, tag: Tag | None = None, object_type: type[R] | None = None - ) -> Self: - """ - Returns a new collection containing products matching `tag` and/or `object_type`. - Both `tag` and `object_type` default to `None` which matches all products. - - Args: - tag (Tag | None): Tag of data products to be kept. `None` matches all products. - object_type (Type | None): Type of data product to keep. `None` matches all products. - - Returns: - (DataProductCollection): A new collection containing matching elements. - - """ - match_key = tag is None, object_type is None - match match_key: - case (True, True): - return type(self)(elements=self.elements) - case (True, False): - assert object_type is not None - return type(self)( - elements=[e for e in self.elements if isinstance(e, object_type)] - ) - case (False, True): - return type(self)(elements=[e for e in self.elements if tag in e.tags]) - case (False, False): - assert object_type is not None - return type(self)( - elements=[ - e - for e in self.elements - if tag in e.tags and isinstance(e, object_type) - ] - ) - case _: - raise ValueError("Something is wrong with match statement") - - def get_products_of_type( - self, object_type: type[R], tag: Tag | None = None - ) -> list[R]: - """ - Returns the elements matching `object_type` (and `tag`, if given) as a list typed to `object_type`. - - Args: - object_type (Type[R]): Type of data product to keep. - tag (Tag | None): Tag of data products to be kept. `None` matches all products. - - Returns: - (list[R]): List of matching elements. - - """ - if tag is None: - return [e for e in self.elements if isinstance(e, object_type)] - return [ - e for e in self.elements if tag in e.tags and isinstance(e, object_type) - ] - - @classmethod - def union(cls, *collections: DataProductCollection): - """ - Aggregates all of the products from multiple collections into a new larger collection. - - Args: - collections (Tuple[DataProductCollection, ...]): Data product collections - for which the products should be combined into a new collection. - - Returns: - (Type[Self]): A new data product collection containing all products from - the provided `*collections`. - - """ - return cls(elements=list(flatten(chain(c.elements for c in collections)))) - - @classmethod - def collect_from_all_jsons( - cls, - *dirs: Path, - recursive: bool = False, - data_products_filename: str = "*.json", - ): - """ - Loads all products from JSONs in the given list of directories. - If recursive is set to `True`, the directories will be searched recursively - (this could lead to double counting if you pass in subdirectories of a parent). - - Args: - dirs (Tuple[Path, ...]): Directories from which to load data product JSON files. - recursive (bool): whether or not to search each of the provided directories recursively for - data product json files. - - Returns: - (Type[Self] | None): Data product collection if JSON files are found. - Otherwise, returns None if no product JSON files were found. - - """ - if not recursive: - jsons: list[Path] = list( - flatten(chain(list(d.glob(data_products_filename)) for d in dirs)) - ) - else: - jsons: list[Path] = list( - flatten( - chain(list(d.glob(f"**/{data_products_filename}")) for d in dirs) - ) - ) - if jsons: - return cls.union( - *tuple([cls.model_validate_json(p.read_text()) for p in jsons]) - ) - else: - return None - - @classmethod - def sort_by_tags( - cls, - dirs_in: list[Path], - dir_out: Path, - data_products_fname: str = DATA_PRODUCTS_FNAME_DEFAULT, - ): - """ - Loads the data product JSON files from `dirs_in` sorts the products. - Sorted products are written to smaller files in a nested directory structure under `dir_out`. - A nested directory structure is generated according to the data tags. - Resulting product files are named according to the directory from which they were originally loaded. - - Args: - dirs_in (List[Path]): Directories from which the data product JSON files are to be loaded. - dir_out (Path): Directory to which the sorted data products will be written into a - nested folder structure generated according to the data tags. - data_products_fname (str): Name of data products file - - """ - dirs_in = list(dirs_in) - dirs_in.sort() - len_dirs = len(dirs_in) - for n, dir_in in enumerate(dirs_in): - logger.info(f"Sorting tagged data from dir {n}/{len_dirs}") # , end=f"\r") - cls.sort_by_tags_single_directory( - dir_in=dir_in, dir_out=dir_out, data_products_fname=data_products_fname - ) - - @classmethod - def sort_by_tags_single_directory( - cls, - dir_in: Path, - dir_out: Path, - data_products_fname: str = DATA_PRODUCTS_FNAME_DEFAULT, - ): - """ - Loads the data product JSON files from `dir_in` and sorts the products. - Sorted products are written to smaller files in a nested directory structure under `dir_out`. - A nested directory structure is generated according to the data tags. - Resulting product files are named according to the directory from which they were originally loaded. - - Args: - dir_in (List[Path]): Directories from which the data product JSON files are to be loaded. - dir_out (Path): Directory to which the sorted data products will be written into a - nested folder structure generated according to the data tags. - data_products_fname (str): Name of data products file - - """ - products_file = dir_in.joinpath(data_products_fname) - if products_file.exists(): - logger.info(f"Sorting results from {dir_in = }") - collection = DataProductCollection.model_validate_json( - products_file.read_text() - ) - collection.derived_from = dir_in - tags = collection.get_tags() - for tag in tags: - sub_collection = collection.get_products(tag=tag) - save_dir = dir_out.joinpath(*atleast_1d(tag)) - save_dir.mkdir(parents=True, exist_ok=True) - # next_index = _get_and_reserve_index(save_dir=save_dir, dir_in=dir_in) - next_index = ProductIndexMap.get_index( - save_dir=save_dir, - metadata=ProductEntryMetadata( - source=products_file.resolve(), - ), - ) - file = save_dir.joinpath(str(next_index)).with_suffix(".json") - file.write_text(sub_collection.model_dump_json()) - else: - logger.info(f"No results found in {dir_in = }") - - @classmethod - def process_collection( - cls, - dir_in: Path, - dir_out: Path, - no_tables: bool, - no_xy_plots: bool, - no_histograms: bool, - dpi: int, - ): - """ - Processes collection of elements corresponding to a single tag. - This method should be called on a directory containing jsons for which the products have been - sorted. - - Args: - dir_in (Path): Input directory for loading assets - dir_out (Path): Output directory for assets - no_tables (bool): Suppresses table asset creation - no_xy_plots (bool): Suppresses xy plot asset creation - no_histograms (bool): Suppresses histogram asset creation - dpi (int): Sets resolution of asset output - - """ - collection = cls.collect_from_all_jsons(dir_in) - - if collection is not None: - for tag in collection.get_tags(): - # tags = collection.get_tags() - # try: - # [tag] = collection.get_tags() - # except: - # breakpoint() - saf: SingleAxisFigure | None = None - format_2ds: list[Format2D] = [] - - if not no_tables: - table_entries = collection.get_products_of_type( - tag=tag, - object_type=TableEntry, - ) - - if table_entries: - from trendify.api.generator.table_builder import TableBuilder - - logger.info(f"Making tables for {tag = }") - TableBuilder.process_table_entries( - tag=tag, - table_entries=table_entries, - out_dir=dir_out, - ) - logger.info(f"Finished tables for {tag = }") - - if not no_xy_plots: - traces = collection.get_products_of_type( - tag=tag, - object_type=Trace2D, - ) - points = collection.get_products_of_type( - tag=tag, - object_type=Point2D, - ) - axlines = collection.get_products_of_type( - tag=tag, - object_type=AxLine, - ) - - if points or traces or axlines: # Update condition - from trendify.api.generator.xy_data_plotter import XYDataPlotter - - logger.info(f"Making xy plot for {tag = }") - saf = XYDataPlotter.handle_points_and_traces( - tag=tag, - points=points, - traces=traces, - axlines=axlines, # Add this parameter - dir_out=dir_out, - dpi=dpi, - saf=saf, - ) - - format_2ds += [ - p.format2d - for p in points - if isinstance(p.format2d, Format2D) - ] - format_2ds += [ - t.format2d - for t in traces - if isinstance(t.format2d, Format2D) - ] - format_2ds += [ - a.format2d - for a in axlines - if isinstance(a.format2d, Format2D) - ] - logger.info(f"Finished xy plot for {tag = }") - - if not no_histograms: - histogram_entries = collection.get_products_of_type( - tag=tag, - object_type=HistogramEntry, - ) - - if histogram_entries: - logger.info(f"Making histogram for {tag = }") - saf = Histogrammer.handle_histogram_entries( - tag=tag, - histogram_entries=histogram_entries, - dir_out=dir_out, - dpi=dpi, - saf=saf, - ) - - format_2ds += [ - h.format2d - for h in histogram_entries - if isinstance(h.format2d, Format2D) - ] - logger.info(f"Finished histogram for {tag = }") - - if isinstance(saf, SingleAxisFigure): - formats = list(set(format_2ds)) - format2d = Format2D.union_from_iterable(formats) - saf.apply_format(format2d) - - save_path = dir_out.joinpath(*tuple(atleast_1d(tag))).with_suffix( - ".jpg" - ) - save_path.parent.mkdir(exist_ok=True, parents=True) - logger.info(f"Saving to {save_path}") - saf.savefig(save_path, dpi=dpi) - del saf - - def process_tables_for_tag( - self, - tag: Tag, - in_dirs: list[Path], - out_dir: Path, - data_products_fname: str = DATA_PRODUCTS_FNAME_DEFAULT, - ) -> TableBuilder: - """ - Process tables for a given tag using TableBuilder. - - Args: - tag (Tag): The tag for which to process tables. - in_dirs (List[Path]): Input directories containing data products. - out_dir (Path): Output directory for saving tables. - data_products_fname (str): Name of the data products file. - - Returns: - TableBuilder: The TableBuilder instance with generated tables. - - """ - from trendify.api.generator.table_builder import TableBuilder - - logger.info(f"Processing tables for {tag = }") - table_builder = TableBuilder(in_dirs=in_dirs, out_dir=out_dir) - table_builder.load_table(tag=tag, data_products_fname=data_products_fname) - return table_builder - - @classmethod - def process_tag_for_streamlit( - cls, jsons: list[Path], tag: Tag - ) -> PlotlyFigure | None: - collection = cls.union( - *tuple([cls.model_validate_json(p.read_text()) for p in jsons]) - ) - - if collection is not None: - plotly_figure: PlotlyFigure | None = None - format_2ds: list[Format2D] = [] - - traces = collection.get_products_of_type(tag=tag, object_type=Trace2D) - points = collection.get_products_of_type(tag=tag, object_type=Point2D) - axlines = collection.get_products_of_type(tag=tag, object_type=AxLine) - hist_entries = collection.get_products_of_type( - tag=tag, object_type=HistogramEntry - ) - - if points or traces or axlines or hist_entries: - from trendify.api.generator.xy_data_plotter import XYDataPlotter - - logger.info(f"Making xy plot for {tag = }") - plotly_figure = XYDataPlotter.plotly_handle_points_and_traces( - tag=tag, - points=points, - traces=traces, - axlines=axlines, - hist_entries=hist_entries, - plotly_figure=plotly_figure, - ) - - format_2ds += [ - p.format2d for p in points if isinstance(p.format2d, Format2D) - ] - format_2ds += [ - t.format2d for t in traces if isinstance(t.format2d, Format2D) - ] - format_2ds += [ - a.format2d for a in axlines if isinstance(a.format2d, Format2D) - ] - - format_2ds += [ - h.format2d for h in hist_entries if isinstance(h.format2d, Format2D) - ] - - if format_2ds: - combined_format = Format2D.union_from_iterable(format_2ds) - plotly_figure.apply_format(combined_format) - - logger.info(f"Finished plotly for {tag = }") - - return plotly_figure - - return None - msg = f"Tag {tag} does not have a generator implemented" - logger.critical(msg) - raise NotImplementedError(msg) - - # @classmethod - # def make_grafana_panels( - # cls, - # dir_in: Path, - # panel_dir: Path, - # server_path: str, - # ): - # """ - # Processes collection of elements corresponding to a single tag. - # This method should be called on a directory containing jsons for which the products have been - # sorted. - - # Args: - # dir_in (Path): Directory from which to read data products (should be sorted first) - # panel_dir (Path): Where to put the panel information - # """ - # import grafana_api as gapi - - # collection = cls.collect_from_all_jsons(dir_in) - # panel_dir.mkdir(parents=True, exist_ok=True) - - # if collection is not None: - # for tag in collection.get_tags(): - # dot_tag = ( - # ".".join([str(t) for t in tag]) if should_be_flattened(tag) else tag - # ) - # underscore_tag = ( - # "_".join([str(t) for t in tag]) if should_be_flattened(tag) else tag - # ) - - # table_entries: List[TableEntry] = collection.get_products( - # tag=tag, object_type=TableEntry - # ).elements - - # if table_entries: - # print(f"\n\nMaking tables for {tag = }\n") - # panel = gapi.Panel( - # title=( - # str(tag).capitalize() - # if isinstance(tag, str) - # else " ".join([str(t).title() for t in tag]) - # ), - # targets=[ - # gapi.Target( - # datasource=gapi.DataSource(), - # url="/".join( - # [server_path.strip("/"), dot_tag, "TableEntry"] - # ), - # uql=UQL_TableEntry, - # ) - # ], - # type="table", - # ) - # panel_dir.joinpath(underscore_tag + "_table_panel.json").write_text( - # panel.model_dump_json() - # ) - # print(f"\nFinished tables for {tag = }\n") - - # traces: List[Trace2D] = collection.get_products( - # tag=tag, object_type=Trace2D - # ).elements - # points: List[Point2D] = collection.get_products( - # tag=tag, object_type=Point2D - # ).elements - - # if points or traces: - # print(f"\n\nMaking xy chart for {tag = }\n") - # panel = gapi.Panel( - # targets=[ - # gapi.Target( - # datasource=gapi.DataSource(), - # url="/".join( - # [server_path.strip("/"), dot_tag, "Point2D"] - # ), - # uql=UQL_Point2D, - # refId="A", - # ), - # gapi.Target( - # datasource=gapi.DataSource(), - # url="/".join( - # [server_path.strip("/"), dot_tag, "Trace2D"] - # ), - # uql=UQL_Trace2D, - # refId="B", - # ), - # ], - # transformations=[ - # gapi.Merge(), - # gapi.PartitionByValues.from_fields( - # fields="label", - # keep_fields=False, - # fields_as_labels=False, - # ), - # ], - # type="xychart", - # ) - # panel_dir.joinpath(underscore_tag + "_xy_panel.json").write_text( - # panel.model_dump_json() - # ) - # print(f"\nFinished xy plot for {tag = }\n") - - # # histogram_entries: List[HistogramEntry] = collection.get_products(tag=tag, object_type=HistogramEntry).elements - # # if histogram_entries: - # # print(f'\n\nMaking histogram for {tag = }\n') - # # panel = gapi.Panel( - # # targets=[ - # # gapi.Target( - # # datasource=gapi.DataSource(), - # # url=server_path.joinpath(dot_tag, 'Point2D'), - # # uql=UQL_Point2D, - # # refId='A', - # # ), - # # gapi.Target( - # # datasource=gapi.DataSource(), - # # url=server_path.joinpath(dot_tag, 'Trace2D'), - # # uql=UQL_Trace2D, - # # refId='B', - # # ) - # # ], - # # type='xychart', - # # ) - # # panel.model_dump_json(dir_out.joinpath(underscore_tag + '_xy_panel.json'), indent=4) - # # print(f'\nFinished histogram for {tag = }\n') diff --git a/src/trendify_old/api/generator/data_product_generator.py b/src/trendify_old/api/generator/data_product_generator.py deleted file mode 100644 index 507fb71..0000000 --- a/src/trendify_old/api/generator/data_product_generator.py +++ /dev/null @@ -1,47 +0,0 @@ -from __future__ import annotations - -from pathlib import Path -import logging - -from trendify.api.base.data_product import ProductGenerator -from trendify.api.generator.data_product_collection import DataProductCollection -from trendify.api.base.helpers import DATA_PRODUCTS_FNAME_DEFAULT - -__all__ = ["DataProductGenerator"] - -logger = logging.getLogger(__name__) - - -class DataProductGenerator: - """ - A wrapper for saving the data products generated by a user defined function - - Args: - processor (ProductGenerator): A callable that receives a working directory - and returns a list of data products. - - """ - - def __init__(self, processor: ProductGenerator): - self._processor = processor - - def process_and_save( - self, workdir: Path, data_products_fname: str = DATA_PRODUCTS_FNAME_DEFAULT - ): - """ - Runs the user-defined processor method stored at instantiation. - - Saves the returned products to a JSON file in the same directory. - - Args: - workdir (Path): working directory on which to run the processor method. - data_products_fname (str): Name of data products file - - """ - logger.info(f"Processing {workdir = } with {self._processor = }") - collection = DataProductCollection.from_iterable(self._processor(workdir)) - if collection.elements: - workdir.mkdir(exist_ok=True, parents=True) - workdir.joinpath(data_products_fname).write_text( - collection.model_dump_json() - ) diff --git a/src/trendify_old/api/generator/histogrammer.py b/src/trendify_old/api/generator/histogrammer.py deleted file mode 100644 index aaea04f..0000000 --- a/src/trendify_old/api/generator/histogrammer.py +++ /dev/null @@ -1,126 +0,0 @@ -from __future__ import annotations - -from pathlib import Path -import logging - -# from trendify.api.data_product_collection import DataProductCollection -from trendify.api.base.helpers import Tag -from trendify.api.plotting.histogram import HistogramEntry -from trendify.api.plotting.plotting import SingleAxisFigure, PlotlyFigure - -__all__ = ["Histogrammer"] - -logger = logging.getLogger(__name__) - - -class Histogrammer: - """ - Class for loading data products and histogramming the [`HistogramEntry`][trendify.api.HistogramEntry]s - - Args: - in_dirs (List[Path]): Directories from which the data products are to be loaded. - out_dir (Path): Directory to which the generated histogram will be stored - dpi (int): resolution of plot - - """ - - def __init__( - self, - in_dirs: list[Path], - out_dir: Path, - dpi: int, - ): - self.in_dirs = in_dirs - self.out_dir = out_dir - self.dpi = dpi - - # def plot( - # self, - # tag: Tag, - # data_products_fname: str = DATA_PRODUCTS_FNAME_DEFAULT, - # ): - # """ - # Generates a histogram by loading data from stored `in_dirs` and saves the plot to `out_dir` directory. - # A nested folder structure will be created if the provided `tag` is a tuple. - # In that case, the last tag item (with an appropriate suffix) will be used for the file name. - - # Args: - # tag (Tag): Tag used to filter the loaded data products - # """ - # print(f"Making histogram plot for {tag = }") - - # histogram_entries: List[HistogramEntry] = [] - # for directory in self.in_dirs: - # collection = DataProductCollection.model_validate_json( - # directory.joinpath(data_products_fname).read_text() - # ) - # histogram_entries.extend( - # collection.get_products(tag=tag, object_type=HistogramEntry).elements - # ) - - # self.handle_histogram_entries( - # tag=tag, - # histogram_entries=histogram_entries, - # dir_out=self.out_dir, - # dpi=self.dpi, - # ) - - @classmethod - def handle_histogram_entries( - cls, - tag: Tag, - histogram_entries: list[HistogramEntry], - dir_out: Path, - dpi: int, - saf: SingleAxisFigure | None = None, - ) -> SingleAxisFigure: - """ - Histograms the provided entries. Formats and saves the figure. Closes the figure. - - Args: - tag (Tag): Tag used to filter the loaded data products - histogram_entries (List[HistogramEntry]): A list of [`HistogramEntry`][trendify.api.HistogramEntry]s - dir_out (Path): Directory to which the generated histogram will be stored - dpi (int): resolution of plot - - """ - if saf is None: - saf = SingleAxisFigure.new(tag=tag) - - histogram_styles = set([h.style for h in histogram_entries]) - for s in histogram_styles: - matching_entries = [e for e in histogram_entries if e.style == s] - values = [e.value for e in matching_entries] - if s is not None: - saf.ax.hist(values, **s.as_plot_kwargs()) - else: - saf.ax.hist(values) - - # save_path = dir_out.joinpath(*tuple(atleast_1d(tag))).with_suffix(".jpg") - # try: - # format2d_set = set([h.format2d for h in histogram_entries]) - {None} - # [format2d] = format2d_set - # saf.apply_format(format2d=format2d) - # except: - # print( - # f"Format not applied to {save_path = } multiple entries conflict for given tag:\n\t{format2d_set = }" - # ) - # save_path = dir_out.joinpath(*tuple(atleast_1d(tag))).with_suffix(".jpg") - # save_path.parent.mkdir(exist_ok=True, parents=True) - # print(f"Saving to {save_path}") - # saf.savefig(save_path, dpi=dpi) - # del saf - - return saf - - @classmethod - def plotly_histogram( - cls, - tag: Tag, - histogram_entries: list[HistogramEntry], - plotly_figure: PlotlyFigure | None = None, - ) -> PlotlyFigure: - if plotly_figure is None: - plotly_figure = PlotlyFigure.new(tag=tag) - raise NotImplementedError() - return plotly_figure diff --git a/src/trendify_old/api/generator/table_builder.py b/src/trendify_old/api/generator/table_builder.py deleted file mode 100644 index eb78ae8..0000000 --- a/src/trendify_old/api/generator/table_builder.py +++ /dev/null @@ -1,153 +0,0 @@ -from __future__ import annotations - -from pathlib import Path -import logging - -import pandas as pd - -from trendify.api.generator.data_product_collection import ( - DataProductCollection, - atleast_1d, -) -from trendify.api.base.helpers import Tag, DATA_PRODUCTS_FNAME_DEFAULT -from trendify.api.formats.table import TableEntry - -__all__ = ["TableBuilder"] - -logger = logging.getLogger(__name__) - - -class TableBuilder: - """ - Builds tables (melted, pivot, and stats) for histogramming and including in a report or Grafana dashboard. - - Args: - in_dirs (List[Path]): directories from which to load data products - out_dir (Path): directory in which tables should be saved - - """ - - def __init__( - self, - in_dirs: list[Path], - out_dir: Path, - ): - self.in_dirs = in_dirs - self.out_dir = out_dir - - def load_table( - self, - tag: Tag, - data_products_fname: str = DATA_PRODUCTS_FNAME_DEFAULT, - ): - """ - Collects table entries from JSON files corresponding to given tag and processes them. - - Saves CSV files for the melted data frame, pivot dataframe, and pivot dataframe stats. - - File names will all use the tag with different suffixes - `'tag_melted.csv'`, `'tag_pivot.csv'`, `'name_stats.csv'`. - - Args: - tag (Tag): product tag for which to collect and process. - - """ - logger.info(f"Making table for {tag = }") - - table_entries: list[TableEntry] = [] - for subdir in self.in_dirs: - collection = DataProductCollection.model_validate_json( - subdir.joinpath(data_products_fname).read_text() - ) - table_entries.extend( - collection.get_products_of_type(tag=tag, object_type=TableEntry) - ) - - self.process_table_entries( - tag=tag, table_entries=table_entries, out_dir=self.out_dir - ) - - @classmethod - def process_table_entries( - cls, - tag: Tag, - table_entries: list[TableEntry], - out_dir: Path, - ): - """ - - Saves CSV files for the melted data frame, pivot dataframe, and pivot dataframe stats. - - File names will all use the tag with different suffixes - `'tag_melted.csv'`, `'tag_pivot.csv'`, `'name_stats.csv'`. - - Args: - tag (Tag): product tag for which to collect and process. - table_entries (List[TableEntry]): List of table entries - out_dir (Path): Directory to which table CSV files should be saved - - """ - melted = pd.DataFrame([t.get_entry_dict() for t in table_entries]) - pivot = TableEntry.pivot_table(melted=melted) - - save_path_partial = out_dir.joinpath(*tuple(atleast_1d(tag))) - save_path_partial.parent.mkdir(exist_ok=True, parents=True) - logger.info(f"Saving to {save_path_partial!s}_*.csv") - - melted.to_csv( - save_path_partial.with_stem(save_path_partial.stem + "_melted").with_suffix( - ".csv" - ), - index=False, - ) - - if pivot is not None: - pivot.to_csv( - save_path_partial.with_stem( - save_path_partial.stem + "_pivot" - ).with_suffix(".csv"), - index=True, - ) - - try: - stats = cls.get_stats_table(df=pivot) - if not stats.empty and not stats.isna().all().all(): - stats.to_csv( - save_path_partial.with_stem( - save_path_partial.stem + "_stats" - ).with_suffix(".csv"), - index=True, - ) - except Exception as e: - logger.error( - f"Could not generate pivot table for {tag = }. Error: {e!s}" - ) - - @classmethod - def get_stats_table( - cls, - df: pd.DataFrame, - ): - """ - Computes multiple statistics for each column - - Args: - df (pd.DataFrame): DataFrame for which the column statistics are to be calculated. - - Returns: - (pd.DataFrame): Dataframe having statistics (column headers) for each of the columns - of the input `df`. The columns of `df` will be the row indices of the stats table. - - """ - # Try to convert to numeric, coerce errors to NaN - numeric_df = df.apply(pd.to_numeric, errors="coerce") - - stats = { - "min": numeric_df.min(axis=0), - "mean": numeric_df.mean(axis=0), - "max": numeric_df.max(axis=0), - "sigma3": numeric_df.std(axis=0) * 3, - } - df_stats = pd.DataFrame(stats, index=df.columns) - df_stats.index.name = "Name" - return df_stats diff --git a/src/trendify_old/api/generator/xy_data_plotter.py b/src/trendify_old/api/generator/xy_data_plotter.py deleted file mode 100644 index 77600a9..0000000 --- a/src/trendify_old/api/generator/xy_data_plotter.py +++ /dev/null @@ -1,191 +0,0 @@ -from __future__ import annotations - -from pathlib import Path -import logging - -from trendify.api.generator.data_product_collection import ( - DataProductCollection, - atleast_1d, -) -from trendify.api.formats.format2d import Format2D -from trendify.api.base.helpers import Tag, DATA_PRODUCTS_FNAME_DEFAULT -from trendify.api.plotting.histogram import HistogramEntry -from trendify.api.plotting.plotting import PlotlyFigure, SingleAxisFigure -from trendify.api.plotting.point import Point2D -from trendify.api.plotting.trace import Trace2D -from trendify.api.plotting.axline import AxLine - -__all__ = ["XYDataPlotter"] - -logger = logging.getLogger(__name__) - - -class XYDataPlotter: - """ - Plots xy data from user-specified directories to a single axis figure - - Args: - in_dirs (List[Path]): Directories in which to search for data products from JSON files - out_dir (Path): directory to which figure will be output - dpi (int): Saved image resolution - - """ - - def __init__( - self, - in_dirs: list[Path], - out_dir: Path, - dpi: int = 500, - ): - self.in_dirs = in_dirs - self.out_dir = out_dir - self.dpi = dpi - - def plot( - self, - tag: Tag, - data_products_fname: str = DATA_PRODUCTS_FNAME_DEFAULT, - ): - """ - - Collects data from json files in stored `self.in_dirs`, - - plots the relevant products, - - applies labels and formatting, - - saves the figure - - closes matplotlib figure - - Args: - tag (Tag): data tag for which products are to be collected and plotted. - data_products_fname (str): Data products file name - - """ - logger.info(f"Making xy plot for {tag = }") - saf = SingleAxisFigure.new(tag=tag) - - for subdir in self.in_dirs: - collection = DataProductCollection.model_validate_json( - subdir.joinpath(data_products_fname).read_text() - ) - traces = collection.get_products_of_type(tag=tag, object_type=Trace2D) - points = collection.get_products_of_type(tag=tag, object_type=Point2D) - - if points or traces: - if points: - markers = set([p.marker for p in points]) - for marker in markers: - matching_points = [p for p in points if p.marker == marker] - x = [p.x for p in matching_points] - y = [p.y for p in matching_points] - if x and y: - if marker is not None: - saf.ax.scatter(x, y, **marker.as_scatter_plot_kwargs()) - else: - saf.ax.scatter(x, y) - - for trace in traces: - trace.plot_to_ax(saf.ax) - - formats = list( - set( - [p.format2d for p in points if p.format2d] - + [t.format2d for t in traces if t.format2d] - ) - ) - format2d = Format2D.union_from_iterable(formats) - saf.apply_format(format2d) - # saf.ax.autoscale(enable=True, axis='both', tight=True) - - save_path = self.out_dir.joinpath(*tuple(atleast_1d(tag))).with_suffix(".jpg") - save_path.parent.mkdir(exist_ok=True, parents=True) - logger.info(f"Saving to {save_path = }") - saf.savefig(path=save_path, dpi=self.dpi) - del saf - - @classmethod - def handle_points_and_traces( - cls, - tag: Tag, - points: list[Point2D], - traces: list[Trace2D], - axlines: list[AxLine], # Add this parameter - dir_out: Path, - dpi: int, - saf: SingleAxisFigure | None = None, - ): - """ - Plots points, traces, and axlines, formats figure, saves figure, and closes matplotlinb figure. - - Args: - tag (Tag): Tag corresponding to the provided points and traces - points (List[Point2D]): Points to be scattered - traces (List[Trace2D]): List of traces to be plotted - axlines (List[AxLine]): List of axis lines to be plotted - dir_out (Path): directory to output the plot - dpi (int): resolution of plot - - """ - if saf is None: - saf = SingleAxisFigure.new(tag=tag) - - if points: - markers = set([p.marker for p in points]) - for marker in markers: - matching_points = [p for p in points if p.marker == marker] - x = [p.x for p in matching_points] - y = [p.y for p in matching_points] - if x and y: - if marker is not None: - saf.ax.scatter(x, y, **marker.as_scatter_plot_kwargs()) - else: - saf.ax.scatter(x, y) - - for trace in traces: - trace.plot_to_ax(saf.ax) - - # Add plotting of axlines - for axline in axlines: - axline.plot_to_ax(saf.ax) - - # formats = list( - # set( - # [p.format2d for p in points] - # + [t.format2d for t in traces] - # + [a.format2d for a in axlines] - # ) - # ) - - # format2d = Format2D.union_from_iterable(formats) - # saf.apply_format(format2d) - # saf.ax.autoscale(enable=True, axis='both', tight=True) - - # save_path = dir_out.joinpath(*tuple(atleast_1d(tag))).with_suffix(".jpg") - # save_path.parent.mkdir(exist_ok=True, parents=True) - # print(f"Saving to {save_path = }") - # saf.savefig(path=save_path, dpi=dpi) - # del saf - - return saf - - @classmethod - def plotly_handle_points_and_traces( - cls, - tag: Tag, - points: list[Point2D], - traces: list[Trace2D], - axlines: list[AxLine], - hist_entries: list[HistogramEntry], - plotly_figure: PlotlyFigure | None = None, - ) -> PlotlyFigure: - if plotly_figure is None: - plotly_figure = PlotlyFigure.new(tag=tag) - - # Add all data products - for point in points: - point.add_to_plotly(plotly_figure=plotly_figure) - for trace in traces: - trace.add_to_plotly(plotly_figure=plotly_figure) - for axline in axlines: - axline.add_to_plotly(plotly_figure=plotly_figure) - for h in hist_entries: - h.add_to_plotly(plotly_figure=plotly_figure) - - return plotly_figure diff --git a/src/trendify_old/api/plotting/__init__.py b/src/trendify_old/api/plotting/__init__.py deleted file mode 100644 index 88b062a..0000000 --- a/src/trendify_old/api/plotting/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from trendify.api.plotting.axline import * -from trendify.api.plotting.histogram import * -from trendify.api.plotting.plotting import * -from trendify.api.plotting.point import * -from trendify.api.plotting.trace import * diff --git a/src/trendify_old/api/plotting/axline.py b/src/trendify_old/api/plotting/axline.py deleted file mode 100644 index 6bac333..0000000 --- a/src/trendify_old/api/plotting/axline.py +++ /dev/null @@ -1,123 +0,0 @@ -from __future__ import annotations - -from enum import Enum -import logging - -from matplotlib.axes import Axes -from pydantic import ConfigDict - -from trendify.api.formats.format2d import PlottableData2D -from trendify.api.base.pen import Pen -from trendify.api.plotting.plotting import PlotlyFigure - -__all__ = ["AxLine", "LineOrientation"] - -logger = logging.getLogger(__name__) - - -class LineOrientation(Enum): - """ - Defines orientation for axis lines - - Attributes: - HORIZONTAL (LineOrientation): Horizontal line - VERTICAL (LineOrientation): Vertical line - - """ - - HORIZONTAL = "horizontal" - VERTICAL = "vertical" - - -class AxLine(PlottableData2D): - """ - Defines a horizontal or vertical line to be drawn on a plot. - - Attributes: - value (float): Value at which to draw the line (x-value for vertical, y-value for horizontal) - orientation (LineOrientation): Whether line should be horizontal or vertical - pen (Pen): Style and label information for drawing to matplotlib axes - tags (Tags): Tags to be used for sorting data - metadata (dict[str, str]): A dictionary of metadata - - """ - - value: float - orientation: LineOrientation - pen: Pen = Pen() - - model_config = ConfigDict(extra="forbid") - - def plot_to_ax(self, ax: Axes): - """ - Plots line to matplotlib axes object. - - Args: - ax (Axes): axes to which line should be plotted - - """ - match self.orientation: - case LineOrientation.HORIZONTAL: - ax.axhline(y=self.value, **self.pen.as_scatter_plot_kwargs()) - case LineOrientation.VERTICAL: - ax.axvline(x=self.value, **self.pen.as_scatter_plot_kwargs()) - case _: - logger.error(f"Unrecognized line orientation {self.orientation}") - - def add_to_plotly(self, plotly_figure: PlotlyFigure) -> PlotlyFigure: - """Add axis line to plotly figure without showing it in the legend""" - match self.orientation: - case LineOrientation.HORIZONTAL: - plotly_figure.fig.add_hline( - y=self.value, - line=dict( - color=self.pen.rgba if self.pen else None, - width=self.pen.size if self.pen else None, - dash=( - self.pen._convert_linestyle_to_plotly() - if self.pen - else None - ), - ), - showlegend=False, # Do not show in the legend - opacity=self.pen.alpha if self.pen else None, - ) - plotly_figure.fig.add_annotation( - x=1.0, - y=self.value, - xref="paper", # Reference to the entire plotting area - yref="y", - text=self.pen.label, - showarrow=False, # Hide the arrow if not needed - xanchor="left", - yanchor="middle", # Center the text horizontally with the vline - ) - case LineOrientation.VERTICAL: - plotly_figure.fig.add_vline( - x=self.value, - line=dict( - color=self.pen.rgba if self.pen else None, - width=self.pen.size if self.pen else None, - dash=( - self.pen._convert_linestyle_to_plotly() - if self.pen - else None - ), - ), - showlegend=False, # Do not show in the legend - opacity=self.pen.alpha if self.pen else None, - ) - plotly_figure.fig.add_annotation( - x=self.value, - y=1.0, # Position slightly above the plot area - yref="paper", # Reference to the entire plotting area - text=self.pen.label, - showarrow=False, # Hide the arrow if not needed - xanchor="center", # Center the text horizontally with the vline - yanchor="bottom", - ) - case _: - logger.error(f"Unrecognized line orientation {self.orientation}") - return plotly_figure - - return plotly_figure diff --git a/src/trendify_old/api/plotting/histogram.py b/src/trendify_old/api/plotting/histogram.py deleted file mode 100644 index 46a779c..0000000 --- a/src/trendify_old/api/plotting/histogram.py +++ /dev/null @@ -1,249 +0,0 @@ -from __future__ import annotations - -import logging -from typing import cast -from matplotlib.colors import to_rgba -import plotly.graph_objects as go - -from numpydantic import NDArray, Shape -from pydantic import ConfigDict, Field - -from trendify.api.formats.format2d import PlottableData2D -from trendify.api.base.helpers import HashableBase, Tags -from trendify.api.plotting.plotting import PlotlyFigure - -__all__ = ["HistogramEntry", "HistogramStyle"] - -logger = logging.getLogger(__name__) - - -class HistogramStyle(HashableBase): - """ - Label and style data for generating histogram bars - - Attributes: - color (str): Color of bars - label (str|None): Legend entry - histtype (str): Histogram type corresponding to matplotlib argument of same name - alpha_edge (float): Opacity of bar edge - alpha_face (float): Opacity of bar face - linewidth (float): Line width of bar outline - bins (int | list[int] | Tuple[int] | NDArray[Shape["*"], int] | None): Number of bins (see [matplotlib docs][matplotlib.pyplot.hist]) - - """ - - color: str = "k" - label: str | None = None - histtype: str = "stepfilled" - alpha_edge: float = 0 - alpha_face: float = 0.3 - linewidth: float = 2 - zorder: int = 1 - bins: int | list[int] | tuple[int] | NDArray[Shape["*"], int] | None = None - - def as_plot_kwargs(self): - """ - Returns: - (dict): kwargs for matplotlib `hist` method - - """ - return { - "facecolor": (self.color, self.alpha_face), - "edgecolor": (self.color, self.alpha_edge), - "linewidth": self.linewidth, - "label": self.label, - "histtype": self.histtype, - "bins": self.bins, - "zorder": self.zorder, - } - - @property - def rgba_face(self) -> str: - """ - Convert the pen's color to rgba string format. - - Returns: - str: Color in 'rgba(r,g,b,a)' format where r,g,b are 0-255 and a is 0-1 - - """ - # Handle different color input formats - if isinstance(self.color, tuple): - if len(self.color) == 3: # RGB tuple - r, g, b = self.color - a = self.alpha_face - else: # RGBA tuple - r, g, b, a = self.color - # Convert 0-1 range to 0-255 for RGB - r, g, b = int(r * 255), int(g * 255), int(b * 255) - else: # String color (name or hex) - # Use matplotlib's color converter - rgba_vals = to_rgba(self.color, self.alpha_face) - # Convert 0-1 range to 0-255 for RGB - r, g, b = [int(x * 255) for x in rgba_vals[:3]] - a = rgba_vals[3] - - return f"rgba({r}, {g}, {b}, {a})" - - @property - def rgba_edge(self) -> str: - """ - Convert the pen's color to rgba string format. - - Returns: - str: Color in 'rgba(r,g,b,a)' format where r,g,b are 0-255 and a is 0-1 - - """ - # Handle different color input formats - if isinstance(self.color, tuple): - if len(self.color) == 3: # RGB tuple - r, g, b = self.color - a = self.alpha_edge - else: # RGBA tuple - r, g, b, a = self.color - # Convert 0-1 range to 0-255 for RGB - r, g, b = int(r * 255), int(g * 255), int(b * 255) - else: # String color (name or hex) - # Use matplotlib's color converter - rgba_vals = to_rgba(self.color, self.alpha_edge) - # Convert 0-1 range to 0-255 for RGB - r, g, b = [int(x * 255) for x in rgba_vals[:3]] - a = rgba_vals[3] - - return f"rgba({r}, {g}, {b}, {a})" - - @property - def rgb_face(self) -> str: - return ", ".join(self.rgba_face.split(",")[0:-1]) + ")" - - @property - def rgb_edge(self) -> str: - return ", ".join(self.rgba_edge.split(",")[0:-1]) + ")" - - def get_face_contrast_color(self, background_luminance: float = 1.0) -> str: - """ - Returns 'white' or 'black' to provide the best contrast against the pen's color, - taking into account the alpha (transparency) value of the line. - - Args: - background_luminance (float): The luminance of the background (default is 1.0 for white). - - Returns: - str: 'white' or 'black' - - """ - # Convert the pen's color to RGB (0-255 range) and get alpha - if isinstance(self.color, tuple): - if len(self.color) == 3: # RGB tuple - r, g, b = self.color - a = self.alpha_face - else: # RGBA tuple - r, g, b, a = self.color - r, g, b = int(r * 255), int(g * 255), int(b * 255) - else: # String color (name or hex) - rgba_vals = to_rgba(self.color, self.alpha_face) - r, g, b = [int(x * 255) for x in rgba_vals[:3]] - a = rgba_vals[3] - - # Calculate relative luminance of the pen's color - def luminance(channel): - channel /= 255.0 - return ( - channel / 12.92 - if channel <= 0.03928 - else ((channel + 0.055) / 1.055) ** 2.4 - ) - - color_luminance = ( - 0.2126 * luminance(r) + 0.7152 * luminance(g) + 0.0722 * luminance(b) - ) - - # Blend the color luminance with the background luminance based on alpha - blended_luminance = (1 - a) * background_luminance + a * color_luminance - - # Return white for dark blended colors, black for light blended colors - return "white" if blended_luminance < 0.5 else "black" - - -class HistogramEntry(PlottableData2D): - """ - Use this class to specify a value to be collected into a matplotlib histogram. - - Attributes: - tags (Tags): Tags used to sort data products - value (float | str): Value to be binned - style (HistogramStyle): Style of histogram display - - """ - - value: float | str - tags: Tags - style: HistogramStyle | None = Field(default_factory=HistogramStyle) - - model_config = ConfigDict(extra="forbid") - - def add_to_plotly(self, plotly_figure: PlotlyFigure): - """Add histogram entry to plotly figure, merging with existing traces if possible""" - if not self.style: - logger.error("HistogramEntry style is not defined.") - return plotly_figure - - # Create legend group key based on the label and color - legend_key = ( - f"{self.style.label}_{self.style.rgba_face}" if self.style.label else None - ) - - # Check if a trace with the same legend group already exists - traces = cast("tuple[go.Histogram, ...]", plotly_figure.fig.data) - for trace in traces: - if trace.legendgroup == legend_key: - # Append the value to the existing trace's x data - trace.x = list(trace.x) + [self.value] - return plotly_figure - - metadata_html = ( - "
".join([f"{key}: {value}" for key, value in self.metadata.items()]) - if self.metadata - else "" - ) - - hovertemplate = ( - f"{self.style.label if self.style.label else ''}
" - "x: %{x}
" - "y: %{y}
" - f"{metadata_html}" - ) - - # If no existing trace, add a new one - plotly_figure.fig.add_trace( - go.Histogram( - x=[self.value], - name=self.style.label, # Legend label - marker=dict( - color=self.style.rgba_face, - line=dict( - color=self.style.rgba_edge, - width=self.style.linewidth, - ), - ), - zorder=self.style.zorder, - nbinsx=self.style.bins if isinstance(self.style.bins, int) else None, - legendgroup=legend_key, # Group histograms with the same label and color - hovertemplate=hovertemplate, - hoverlabel=dict( - bgcolor=self.style.rgba_face, - bordercolor=self.style.rgba_edge, - font=dict( - color=self.style.get_face_contrast_color() # Optionally, set the font color as well - ), - ), - showlegend=( - True if self.style.label is not None else False - ), # Always show legend for the first trace - ) - ) - - # Track the legend group to avoid duplicate legend entries - if legend_key and legend_key not in plotly_figure.legend_groups: - plotly_figure.legend_groups.add(legend_key) - - return plotly_figure diff --git a/src/trendify_old/api/plotting/plotting.py b/src/trendify_old/api/plotting/plotting.py deleted file mode 100644 index 3955f60..0000000 --- a/src/trendify_old/api/plotting/plotting.py +++ /dev/null @@ -1,343 +0,0 @@ -from __future__ import annotations - -from dataclasses import dataclass, field -from pathlib import Path -import matplotlib.pyplot as plt -import warnings -import logging - -import numpy as np -import plotly.graph_objects as go - -from trendify.api.formats.format2d import AxisScale, PlottableData2D - -try: - from typing import Self, TYPE_CHECKING -except: - from typing import Self, TYPE_CHECKING - -from pydantic import ConfigDict - -from trendify.api.base.helpers import Tag - -if TYPE_CHECKING: - from trendify.api.formats.format2d import Format2D, Grid - from matplotlib.axes import Axes - from matplotlib.figure import Figure - - -__all__ = ["PlotlyFigure", "SingleAxisFigure"] - -logger = logging.getLogger(__name__) - - -@dataclass -class SingleAxisFigure: - """ - Data class storing a matlab figure and axis. The stored tag data in this class is so-far unused. - - Attributes: - ax (Axes): Matplotlib axis to which data will be plotted - fig (Figure): Matplotlib figure. - tag (Tag): Figure tag. Not yet used. - - """ - - model_config = ConfigDict(arbitrary_types_allowed=True) - tag: Tag - fig: Figure - ax: Axes - - @classmethod - def new(cls, tag: Tag): - """ - Creates new figure and axis. Returns new instance of this class. - - Args: - tag (Tag): tag (not yet used) - - Returns: - (Type[Self]): New single axis figure - - """ - fig = plt.figure() - ax = fig.add_subplot(1, 1, 1) - return cls( - tag=tag, - fig=fig, - ax=ax, - ) - - def apply_format(self, format2d: Format2D): - """ - Applies format to figure and axes labels and limits - - Args: - format2d (Format2D): format information to apply to the single axis figure - - """ - if format2d.title_ax is not None: - self.ax.set_title(format2d.title_ax) - if format2d.title_fig is not None: - self.fig.suptitle(format2d.title_fig) - - leg = None - if format2d.legend is not None: - with warnings.catch_warnings(action="ignore", category=UserWarning): - handles, labels = self.ax.get_legend_handles_labels() - by_label = dict(zip(labels, handles)) - if by_label: - sorted_items = sorted(by_label.items(), key=lambda item: item[0]) - labels_sorted, handles_sorted = zip(*sorted_items) - - kwargs = format2d.legend.to_kwargs() - - leg = self.ax.legend( - handles=handles_sorted, - labels=labels_sorted, - bbox_to_anchor=format2d.legend.bbox_to_anchor, - **kwargs, - ) - leg.set_zorder(level=format2d.legend.zorder) - - if leg is not None and format2d.legend.edgecolor: - leg.get_frame().set_edgecolor(format2d.legend.edgecolor) - - if format2d.label_x is not None: - self.ax.set_xlabel(xlabel=format2d.label_x) - if format2d.label_y is not None: - self.ax.set_ylabel(ylabel=format2d.label_y) - - self.ax.set_xlim(left=format2d.lim_x_min, right=format2d.lim_x_max) - self.ax.set_ylim(bottom=format2d.lim_y_min, top=format2d.lim_y_max) - - self.ax.set_xscale(format2d.scale_x.value) - self.ax.set_yscale(format2d.scale_y.value) - - if format2d.grid is not None: - self.apply_grid(format2d.grid) - - self.fig.set_size_inches(format2d.figure_width, format2d.figure_height) - self.fig.tight_layout(rect=(0, 0.03, 1, 0.95)) - return self - - def apply_grid(self, grid: Grid): - self.ax.set_axisbelow(True) - - # Major grid - if grid.major.show: - self.ax.grid( - visible=True, - which="major", - color=grid.major.pen.color, - linestyle=grid.major.pen.linestyle, - linewidth=grid.major.pen.size, - alpha=grid.major.pen.alpha, - zorder=grid.zorder, - ) - else: - self.ax.grid(visible=False, which="major") - - # Minor ticks and grid - if grid.enable_minor_ticks: - self.ax.minorticks_on() - else: - self.ax.minorticks_off() - - if grid.minor.show: - self.ax.grid( - visible=True, - which="minor", - color=grid.minor.pen.color, - linestyle=grid.minor.pen.linestyle, - linewidth=grid.minor.pen.size, - alpha=grid.minor.pen.alpha, - zorder=grid.zorder, - ) - else: - self.ax.grid(visible=False, which="minor") - - def savefig(self, path: Path, dpi: int = 500): - """ - Wrapper on matplotlib savefig method. Saves figure to given path with given dpi resolution. - - Returns: - (Self): Returns self - - """ - self.fig.savefig(path, dpi=dpi) - return self - - def __del__(self): - """ - Closes stored matplotlib figure before deleting reference to object. - """ - plt.close(self.fig) - - -@dataclass -class PlotlyFigure: - model_config = ConfigDict(arbitrary_types_allowed=True) - tag: Tag - fig: go.Figure - legend_groups: set[str] = field(default_factory=set) - - @classmethod - def new(cls, tag: Tag): - """ - Creates new figure and axis. Returns new instance of this class. - - Args: - tag (Tag): tag (not yet used) - - Returns: - (Type[Self]): New single axis figure - - """ - fig = go.Figure() - return cls(tag=tag, fig=fig) - - def apply_format(self, format2d: Format2D): - """ - Applies format to Plotly figure layout including axes labels and limits - - Args: - format2d (Format2D): format information to apply to the figure - - """ - layout_updates = {} - - # Set titles - if format2d.title_fig is not None and format2d.title_ax is not None: - layout_updates["title"] = f"{format2d.title_fig} | {format2d.title_ax}" - elif format2d.title_fig is None and format2d.title_ax is not None: - layout_updates["title"] = f"{format2d.title_ax}" - elif format2d.title_fig is not None and format2d.title_ax is None: - layout_updates["title"] = f"{format2d.title_fig}" - - # Set axis labels - if format2d.label_x is not None: - layout_updates["xaxis"] = {"title": format2d.label_x} - if format2d.label_y is not None: - layout_updates["yaxis"] = {"title": format2d.label_y} - - # Set axis ranges - def _log10(v: float | None): - return None if v is None else np.log10(v) - - if format2d.lim_x_min is not None or format2d.lim_x_max is not None: - if "xaxis" not in layout_updates: - layout_updates["xaxis"] = {} - - if format2d.scale_x == AxisScale.LOG: - layout_updates["xaxis"]["range"] = [ - _log10(format2d.lim_x_min), - _log10(format2d.lim_x_max), - ] - elif format2d.scale_x == AxisScale.LOG: - layout_updates["xaxis"]["range"] = [ - format2d.lim_x_min, - format2d.lim_x_max, - ] - - if format2d.lim_y_min is not None or format2d.lim_y_max is not None: - if "yaxis" not in layout_updates: - layout_updates["yaxis"] = {} - if format2d.scale_y == AxisScale.LOG: - layout_updates["yaxis"]["range"] = [ - _log10(format2d.lim_y_min), - _log10(format2d.lim_y_max), - ] - elif format2d.scale_y == AxisScale.LOG: - layout_updates["yaxis"]["range"] = [ - format2d.lim_y_min, - format2d.lim_y_max, - ] - - # Set axis scales - if format2d.scale_x is not None: - if "xaxis" not in layout_updates: - layout_updates["xaxis"] = {} - layout_updates["xaxis"]["type"] = format2d.scale_x.value - - if format2d.scale_y is not None: - if "yaxis" not in layout_updates: - layout_updates["yaxis"] = {} - layout_updates["yaxis"]["type"] = format2d.scale_y.value - - # Set legend - if format2d.legend is not None: - layout_updates["showlegend"] = format2d.legend.visible - layout_updates["legend"] = dict( - title=format2d.legend.title, - bordercolor=format2d.legend.edgecolor, - **format2d.legend.plotly_location, - ) - - # Apply grid if specified - if format2d.grid is not None: - self.apply_grid(format2d.grid) - - # Update layout - self.fig.update_layout(**layout_updates) - return self - - def apply_grid(self, grid: Grid): - """ - Applies grid settings to the Plotly figure - - Args: - grid (Grid): Grid configuration to apply - - """ - # Major grid - - xaxis_updates = { - "showgrid": grid.major.show, - "gridcolor": grid.major.pen.rgba if grid.major.show else None, - "gridwidth": grid.major.pen.size if grid.major.show else None, - "griddash": "solid" if grid.major.pen.linestyle == "-" else "dash", - "gridwidth": grid.major.pen.size if grid.major.show else None, - } - - yaxis_updates = { - "showgrid": grid.major.show, - "gridcolor": grid.major.pen.rgba if grid.major.show else None, - "gridwidth": grid.major.pen.size if grid.major.show else None, - "griddash": "solid" if grid.major.pen.linestyle == "-" else "dash", - "gridwidth": grid.major.pen.size if grid.major.show else None, - } - - # Minor ticks and grid - if grid.enable_minor_ticks: - xaxis_updates["minor"] = { - "showgrid": grid.minor.show, - "gridcolor": grid.minor.pen.rgba if grid.minor.show else None, - "gridwidth": grid.minor.pen.size if grid.minor.show else None, - "griddash": "solid" if grid.minor.pen.linestyle == "-" else "dash", - } - yaxis_updates["minor"] = { - "showgrid": grid.minor.show, - "gridcolor": grid.minor.pen.rgba if grid.minor.show else None, - "gridwidth": grid.minor.pen.size if grid.minor.show else None, - "griddash": "solid" if grid.minor.pen.linestyle == "-" else "dash", - } - - self.fig.update_xaxes(**xaxis_updates) - self.fig.update_yaxes(**yaxis_updates) - - def add_data_product(self, product: PlottableData2D) -> Self: - """ - Add a data product to the figure - - Args: - product (PlottableData2D): Data product to add to figure - - Returns: - Self: Returns self for method chaining - - """ - raise NotImplementedError() - - product.add_to_plotly(self.fig) - return self diff --git a/src/trendify_old/api/plotting/point.py b/src/trendify_old/api/plotting/point.py deleted file mode 100644 index 8979722..0000000 --- a/src/trendify_old/api/plotting/point.py +++ /dev/null @@ -1,91 +0,0 @@ -from __future__ import annotations - -import logging - -from pydantic import ConfigDict -import plotly.graph_objects as go - -from trendify.api.plotting.plotting import PlotlyFigure -from trendify.api.styling.marker import Marker - -# from trendify.api.plotting.plotting import XYData -from trendify.api.formats.format2d import XYData - -__all__ = ["Point2D"] - -logger = logging.getLogger(__name__) - - -class Point2D(XYData): - """ - Defines a point to be scattered onto xy plot. - - Attributes: - tags (Tags): Tags to be used for sorting data. - x (float | str): X value for the point. - y (float | str): Y value for the point. - marker (Marker | None): Style and label information for scattering points to matplotlib axes. - Only the label information is used in Grafana. - Eventually style information will be used in grafana. - metadata (dict[str, str]): A dictionary of metadata to be used as a tool tip for mousover in grafana - - """ - - x: float | str - y: float | str - marker: Marker | None = Marker() - - model_config = ConfigDict(extra="forbid") - - def add_to_plotly(self, plotly_figure: PlotlyFigure) -> PlotlyFigure: - """Add point to plotly figure with legendgroup support""" - legend_key = ( - f"{self.marker.label}_{self.marker.color}_{self.marker.symbol}" - if self.marker - else None - ) - # Prepare metadata for the tooltip - metadata_html = ( - "
".join([f"{key}: {value}" for key, value in self.metadata.items()]) - if self.metadata - else "" - ) - - # Define hovertemplate for the tooltip - hovertemplate = ( - f"{self.marker.label if self.marker else ''}
" - "x: %{x}
" - "y: %{y}
" - f"{metadata_html}" - ) - - plotly_figure.fig.add_trace( - go.Scatter( - x=[self.x], - y=[self.y], - name=self.marker.label if self.marker else None, - mode="markers", - marker=dict( - color=self.marker.rgba if self.marker else None, - size=self.marker.size if self.marker else None, - symbol=self.marker.plotly_symbol if self.marker else None, - ), - zorder=int(self.marker.zorder) if self.marker else None, - legendgroup=legend_key, - hovertemplate=hovertemplate, - hoverlabel=dict( - bgcolor=(self.marker.rgba if self.marker else None), - font=dict( - color=self.marker.get_contrast_color() if self.marker else None - ), - ), - showlegend=( - True if legend_key not in plotly_figure.legend_groups else False - ), - ) - ) - - if legend_key and legend_key not in plotly_figure.legend_groups: - plotly_figure.legend_groups.add(legend_key) - - return plotly_figure diff --git a/src/trendify_old/api/plotting/trace.py b/src/trendify_old/api/plotting/trace.py deleted file mode 100644 index 51f86a5..0000000 --- a/src/trendify_old/api/plotting/trace.py +++ /dev/null @@ -1,194 +0,0 @@ -from __future__ import annotations - -import logging - -import plotly.graph_objects as go -from trendify.api.plotting.plotting import PlotlyFigure - -try: - from typing import TYPE_CHECKING -except: - from typing import TYPE_CHECKING - -import numpy as np -from numpydantic import NDArray, Shape -from pydantic import ConfigDict - -# from trendify.api.plotting.plotting import XYData -from trendify.api.formats.format2d import XYData -from trendify.api.base.pen import Pen -from trendify.api.base.helpers import Tags -from trendify.api.styling.marker import Marker -from trendify.api.plotting.point import Point2D - -if TYPE_CHECKING: - from trendify.api.formats.format2d import Format2D - from matplotlib.axes import Axes - -__all__ = ["Trace2D"] - -logger = logging.getLogger(__name__) - - -class Trace2D(XYData): - """ - A collection of points comprising a trace. - Use the [Trace2D.from_xy][trendify.api.Trace2D.from_xy] constructor. - - Attributes: - points (List[Point2D]): List of points. Usually the points would have null values - for `marker` and `format2d` fields to save space. - pen (Pen): Style and label information for drawing to matplotlib axes. - Only the label information is used in Grafana. - Eventually style information will be used in grafana. - tags (Tags): Tags to be used for sorting data. - metadata (dict[str, str]): A dictionary of metadata to be used as a tool tip for mousover in grafana - - """ - - model_config = ConfigDict(extra="forbid") - - points: list[Point2D] - pen: Pen = Pen() - - @property - def x(self) -> NDArray[Shape["*"], float]: - """ - Returns an array of x values from `self.points` - - Returns: - (NDArray[Shape["*"], float]): array of x values from `self.points` - ' - - """ - return np.array([p.x for p in self.points]) - - @property - def y(self) -> NDArray[Shape["*"], float]: - """ - Returns an array of y values from `self.points` - - Returns: - (NDArray[Shape["*"], float]): array of y values from `self.points` - - """ - return np.array([p.y for p in self.points]) - - def propagate_format2d_and_pen(self, marker_symbol: str = ".") -> None: - """ - Propagates format and style info to all `self.points` (in-place). - I thought this would be useful for grafana before I learned better methods for propagating the data. - It still may end up being useful if my plotting method changes. Keeping for potential future use case. - - Args: - marker_symbol (str): Valid matplotlib marker symbol - - """ - self.points = [ - p.model_copy( - update={ - "tags": self.tags, - "format2d": self.format2d, - "marker": Marker.from_pen(self.pen, symbol=marker_symbol), - } - ) - for p in self.points - ] - - @classmethod - def from_xy( - cls, - tags: Tags, - x: NDArray[Shape["*"], float], - y: NDArray[Shape["*"], float], - pen: Pen = Pen(), - format2d: Format2D | None = None, - ): - """ - Creates a list of [Point2D][trendify.api.plotting.point.Point2D]s from xy data and returns a new [Trace2D][trendify.api.plotting.Trace2D] product. - - Args: - tags (Tags): Tags used to sort data products - x (NDArray[Shape["*"], float]): x values - y (NDArray[Shape["*"], float]): y values - pen (Pen): Style and label for trace - format2d (Format2D | None): Format to apply to plot - - """ - return cls( - tags=tags, - points=[ - Point2D( - tags=[None], - x=x_, - y=y_, - marker=None, - format2d=None, - ) - for x_, y_ in zip(x, y) - ], - pen=pen, - format2d=format2d, - ) - - def plot_to_ax(self, ax: Axes): - """ - Plots xy data from trace to a matplotlib axes object. - - Args: - ax (Axes): axes to which xy data should be plotted - - """ - ax.plot(self.x, self.y, **self.pen.as_scatter_plot_kwargs()) - - def add_to_plotly(self, plotly_figure: PlotlyFigure) -> PlotlyFigure: - legend_key = ( - f"{self.pen.label}_{self.pen.color}_{self.pen._convert_linestyle_to_plotly()}" - if self.pen - else None - ) - # Prepare metadata for the tooltip - metadata_html = ( - "
".join([f"{key}: {value}" for key, value in self.metadata.items()]) - if self.metadata - else "" - ) - - # Define hovertemplate for the tooltip - hovertemplate = ( - f"{self.pen.label if self.pen else ''}
" - "x: %{x}
" - "y: %{y}
" - f"{metadata_html}" - ) - - plotly_figure.fig.add_trace( - go.Scatter( - x=[p.x for p in self.points], - y=[p.y for p in self.points], - name=self.pen.label if self.pen else None, - mode="lines", - line=dict( - color=self.pen.rgba if self.pen else None, - width=self.pen.size if self.pen else None, - dash=self.pen._convert_linestyle_to_plotly() if self.pen else None, - ), - marker=dict( - color=self.pen.rgba if self.pen else None, # Marker color - ), - zorder=int(self.pen.zorder), - hovertemplate=hovertemplate, - hoverlabel=dict( - bgcolor=(self.pen.rgba if self.pen else None), - font=dict(color=self.pen.get_contrast_color()), - ), - legendgroup=legend_key, - showlegend=( - True if legend_key not in plotly_figure.legend_groups else False - ), - ) - ) - - if legend_key and legend_key not in plotly_figure.legend_groups: - plotly_figure.legend_groups.add(legend_key) - return plotly_figure diff --git a/src/trendify_old/api/styling/__init__.py b/src/trendify_old/api/styling/__init__.py deleted file mode 100644 index fd03478..0000000 --- a/src/trendify_old/api/styling/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from trendify.api.styling.grid import * -from trendify.api.styling.marker import * -from trendify.api.styling.legend import * diff --git a/src/trendify_old/api/styling/grid.py b/src/trendify_old/api/styling/grid.py deleted file mode 100644 index 593f092..0000000 --- a/src/trendify_old/api/styling/grid.py +++ /dev/null @@ -1,149 +0,0 @@ -from __future__ import annotations - -from enum import Enum, auto -from collections.abc import Iterable -import logging - -from pydantic import ConfigDict - -from trendify.api.base.helpers import HashableBase -from trendify.api.base.pen import Pen - - -logger = logging.getLogger(__name__) - -__all__ = ["Grid", "GridAxis", "GridTheme"] - - -class GridTheme(Enum): - MATLAB = auto() - LIGHT = auto() - DARK = auto() - - -class GridAxis(HashableBase): - """ - Controls styling and visibility for one type of grid (major or minor). - - Attributes: - show (bool): Whether to display this grid axis. - pen (Pen): Style and label information for drawing to matplotlib axes. - - """ - - show: bool = False - pen: Pen = Pen( - color="gray", - alpha=1.0, - size=0.75, - linestyle="-", - label=None, - ) - - model_config = ConfigDict(extra="forbid") - - -class Grid(HashableBase): - """ - Container for major and minor grid line configuration. - - Attributes: - major (GridAxis): Configuration for major grid lines. - minor (GridAxis): Configuration for minor grid lines. - enable_minor_ticks (bool): Whether to enable minor ticks on the axes. - - """ - - major: GridAxis = GridAxis(show=False) - minor: GridAxis = GridAxis(show=False) - enable_minor_ticks: bool = False - zorder: float = -1 - - model_config = ConfigDict(extra="forbid") - - @classmethod - def from_theme(cls, name: GridTheme) -> Grid: - """ - Predefined themes for common grid styles. - """ - themes = { - GridTheme.MATLAB: cls( - major=GridAxis( - show=True, - pen=Pen( - color="#b0b0b0", - linestyle="-", - size=0.8, - alpha=0.35, - label=None, - ), - ), - minor=GridAxis( - show=True, - pen=Pen( - color="#b0b0b0", - linestyle=(0, (3, 1, 1, 1)), - size=0.6, - alpha=0.25, - label=None, - ), - ), - enable_minor_ticks=True, - ), - GridTheme.LIGHT: cls( - major=GridAxis( - show=True, - pen=Pen( - color="#E0E0E0", - linestyle="--", - size=0.7, - alpha=0.9, - label=None, - ), - ), - minor=GridAxis(show=False), - enable_minor_ticks=False, - ), - GridTheme.DARK: cls( - major=GridAxis( - show=True, - pen=Pen( - color="#444444", - linestyle="--", - size=0.7, - alpha=0.5, - label=None, - ), - ), - minor=GridAxis(show=False), - enable_minor_ticks=False, - ), - } - - try: - return themes[name] - except KeyError: - raise ValueError(f"Unknown grid theme: {name!r}") - - @classmethod - def union_from_iterable(cls, grids: Iterable[Grid]) -> Grid: - """ - Gets the most inclusive grid format from a list of Grid objects. - Requires that all GridAxis fields (major/minor) are consistent across the objects. - """ - grids = list(set(grids) - {None}) - if not grids: - return cls() - - # Enforce consistent GridAxis settings - [major] = set(g.major for g in grids) - [minor] = set(g.minor for g in grids) - [enable_minor_ticks] = set(g.enable_minor_ticks for g in grids) - [zorder] = set(g.zorder for g in grids) - - return cls( - major=major, - minor=minor, - enable_minor_ticks=enable_minor_ticks, - zorder=zorder, - ) diff --git a/src/trendify_old/api/styling/legend.py b/src/trendify_old/api/styling/legend.py deleted file mode 100644 index f51a5bb..0000000 --- a/src/trendify_old/api/styling/legend.py +++ /dev/null @@ -1,206 +0,0 @@ -from __future__ import annotations - -from enum import StrEnum - -from trendify.api.base.helpers import HashableBase - -__all__ = ["Legend", "LegendLocation"] - - -class LegendLocation(StrEnum): - BEST = "best" - UPPER_RIGHT = "upper right" - UPPER_LEFT = "upper left" - LOWER_LEFT = "lower left" - LOWER_RIGHT = "lower right" - RIGHT = "right" - CENTER_LEFT = "center left" - CENTER_RIGHT = "center right" - LOWER_CENTER = "lower center" - UPPER_CENTER = "upper center" - CENTER = "center" - - -class Legend(HashableBase): - """ - Configuration container for Matplotlib legend styling and placement. - - The `Legend` class controls the appearance and position of the plot legend. - Placement is governed by a combination of the `loc` and `bbox_to_anchor` - parameters, mirroring Matplotlib's `Axes.legend()`. - - Attributes: - visible (bool): Whether the legend should be displayed. Defaults to True. - title (str | None): Title displayed above the legend entries. - framealpha (float): Opacity of the legend background. 1 = fully opaque, 0 = fully transparent. - loc (LegendLocation): Anchor point for the legend (e.g., upper right, lower left). See `LegendLocation` enum for options. - ncol (int): Number of columns to arrange legend entries into. - fancybox (bool): Whether to draw a rounded (True) or square (False) legend frame. - edgecolor (str): Color of the legend frame border. Default is "black". - bbox_to_anchor (tuple[float, float] | None): Offset position of the legend in figure or axes coordinates. If None, the legend is placed inside the axes using `loc`. - - Good starter values for common placements: - - - **Inside (default)**: - ```python - bbox_to_anchor=None - ``` - - **Outside right**: - ```python - loc=LegendLocation.CENTER_LEFT - bbox_to_anchor=(1.02, 0.5) - ``` - - **Outside left**: - ```python - loc=LegendLocation.CENTER_RIGHT - bbox_to_anchor=(-0.02, 0.5) - ``` - - **Outside top**: - ```python - loc=LegendLocation.LOWER_CENTER - bbox_to_anchor=(0.5, 1.02) - ``` - - **Outside bottom**: - ```python - loc=LegendLocation.UPPER_CENTER - bbox_to_anchor=(0.5, -0.02) - ``` - - """ - - visible: bool = True - title: str | None = None - framealpha: float = 1 - loc: LegendLocation = LegendLocation.BEST - ncol: int = 1 - fancybox: bool = True - edgecolor: str = "black" - zorder: int = 10 - bbox_to_anchor: tuple[float, float] | None = None - - def to_kwargs(self): - return { - "title": self.title, - "framealpha": self.framealpha, - "loc": self.loc, - "ncol": self.ncol, - "fancybox": self.fancybox, - } - - @property - def plotly_location(self) -> dict: - """ - Convert matplotlib legend location to Plotly legend position parameters. - - Returns: - dict: Dictionary containing Plotly legend position parameters (x, y, xanchor, yanchor) - - """ - # Default position mappings for standard locations - location_map = { - LegendLocation.BEST: { - "x": 1.02, - "y": 1, - "xanchor": "left", - "yanchor": "top", - }, - LegendLocation.UPPER_RIGHT: { - "x": 0.98, - "y": 0.98, - "xanchor": "right", - "yanchor": "top", - }, - LegendLocation.UPPER_LEFT: { - "x": 0.02, - "y": 0.98, - "xanchor": "left", - "yanchor": "top", - }, - LegendLocation.LOWER_LEFT: { - "x": 0.02, - "y": 0.02, - "xanchor": "left", - "yanchor": "bottom", - }, - LegendLocation.LOWER_RIGHT: { - "x": 0.98, - "y": 0.02, - "xanchor": "right", - "yanchor": "bottom", - }, - LegendLocation.RIGHT: { - "x": 1.02, - "y": 0.5, - "xanchor": "left", - "yanchor": "middle", - }, - LegendLocation.CENTER_LEFT: { - "x": -0.02, - "y": 0.5, - "xanchor": "right", - "yanchor": "middle", - }, - LegendLocation.CENTER_RIGHT: { - "x": 1.02, - "y": 0.5, - "xanchor": "left", - "yanchor": "middle", - }, - LegendLocation.LOWER_CENTER: { - "x": 0.5, - "y": 0.02, - "xanchor": "center", - "yanchor": "bottom", - }, - LegendLocation.UPPER_CENTER: { - "x": 0.5, - "y": 0.98, - "xanchor": "center", - "yanchor": "top", - }, - LegendLocation.CENTER: { - "x": 0.5, - "y": 0.5, - "xanchor": "center", - "yanchor": "middle", - }, - } - - # If bbox_to_anchor is provided, use it to override the position - if self.bbox_to_anchor is not None: - x, y = self.bbox_to_anchor - # Determine anchors based on location - if self.loc in [ - LegendLocation.CENTER_LEFT, - LegendLocation.UPPER_LEFT, - LegendLocation.LOWER_LEFT, - ]: - xanchor = "right" - elif self.loc in [ - LegendLocation.CENTER_RIGHT, - LegendLocation.UPPER_RIGHT, - LegendLocation.LOWER_RIGHT, - ]: - xanchor = "left" - else: - xanchor = "center" - - if self.loc in [ - LegendLocation.UPPER_RIGHT, - LegendLocation.UPPER_LEFT, - LegendLocation.UPPER_CENTER, - ]: - yanchor = "top" - elif self.loc in [ - LegendLocation.LOWER_RIGHT, - LegendLocation.LOWER_LEFT, - LegendLocation.LOWER_CENTER, - ]: - yanchor = "bottom" - else: - yanchor = "middle" - - return {"x": x, "y": y, "xanchor": xanchor, "yanchor": yanchor} - - # Use predefined mapping if no bbox_to_anchor - return location_map[self.loc] diff --git a/src/trendify_old/api/styling/marker.py b/src/trendify_old/api/styling/marker.py deleted file mode 100644 index f0c7b76..0000000 --- a/src/trendify_old/api/styling/marker.py +++ /dev/null @@ -1,155 +0,0 @@ -from __future__ import annotations - -import logging - -from matplotlib.colors import to_rgba -from pydantic import ConfigDict - -from trendify.api.base.helpers import HashableBase -from trendify.api.base.pen import Pen - -__all__ = ["Marker"] - -logger = logging.getLogger(__name__) - - -class Marker(HashableBase): - """ - Defines marker for scattering to matplotlib - - Attributes: - color (str): Color of line - size (float): Line width - alpha (float): Opacity from 0 to 1 (inclusive) - zorder (float): Prioritization - label (Union[str, None]): Legend label - symbol (str): Matplotlib symbol string - - """ - - color: str = "k" - size: float = 5 - alpha: float = 1 - zorder: float = 0 - label: str | None = None - symbol: str = "." - - @classmethod - def from_pen( - cls, - pen: Pen, - symbol: str = ".", - ): - """ - Converts Pen to marker with the option to specify a symbol - """ - return cls(symbol=symbol, **pen.model_dump().pop("linestyle")) - - model_config = ConfigDict(extra="forbid") - - def as_scatter_plot_kwargs(self): - """ - Returns: - (dict): dictionary of `kwargs` for [matplotlib scatter][matplotlib.axes.Axes.scatter] - - """ - return { - "marker": self.symbol, - "c": self.color, - "s": self.size, - "alpha": self.alpha, - "zorder": self.zorder, - "label": self.label, - "marker": self.symbol, - } - - @property - def plotly_symbol(self) -> str: - """Convert matplotlib marker symbol to plotly symbol""" - symbol_map = { - ".": "circle", - "o": "circle", - "v": "triangle-down", - "^": "triangle-up", - "<": "triangle-left", - ">": "triangle-right", - "s": "square", - "p": "pentagon", - "*": "star", - "h": "hexagon", - "+": "cross", - "x": "x", - "D": "diamond", - } - return symbol_map.get(self.symbol, "circle") - - @property - def rgba(self) -> str: - """ - Convert the pen's color to rgba string format. - - Returns: - str: Color in 'rgba(r,g,b,a)' format where r,g,b are 0-255 and a is 0-1 - - """ - # Handle different color input formats - if isinstance(self.color, tuple): - if len(self.color) == 3: # RGB tuple - r, g, b = self.color - a = self.alpha - else: # RGBA tuple - r, g, b, a = self.color - # Convert 0-1 range to 0-255 for RGB - r, g, b = int(r * 255), int(g * 255), int(b * 255) - else: # String color (name or hex) - # Use matplotlib's color converter - rgba_vals = to_rgba(self.color, self.alpha) - # Convert 0-1 range to 0-255 for RGB - r, g, b = [int(x * 255) for x in rgba_vals[:3]] - a = rgba_vals[3] - - return f"rgba({r}, {g}, {b}, {a})" - - def get_contrast_color(self, background_luminance: float = 1.0) -> str: - """ - Returns 'white' or 'black' to provide the best contrast against the pen's color, - taking into account the alpha (transparency) value of the line. - - Args: - background_luminance (float): The luminance of the background (default is 1.0 for white). - - Returns: - str: 'white' or 'black' - - """ - # Convert the pen's color to RGB (0-255 range) and get alpha - if isinstance(self.color, tuple): - if len(self.color) == 3: # RGB tuple - r, g, b = self.color - a = self.alpha - else: # RGBA tuple - r, g, b, a = self.color - r, g, b = int(r * 255), int(g * 255), int(b * 255) - else: # String color (name or hex) - rgba_vals = to_rgba(self.color, self.alpha) - r, g, b = [int(x * 255) for x in rgba_vals[:3]] - a = rgba_vals[3] - - # Calculate relative luminance of the pen's color - def luminance(channel): - channel /= 255.0 - return ( - channel / 12.92 - if channel <= 0.03928 - else ((channel + 0.055) / 1.055) ** 2.4 - ) - - color_luminance = ( - 0.2126 * luminance(r) + 0.7152 * luminance(g) + 0.0722 * luminance(b) - ) - - # Blend the color luminance with the background luminance based on alpha - blended_luminance = (1 - a) * background_luminance + a * color_luminance - - # Return white for dark blended colors, black for light blended colors - return "white" if blended_luminance < 0.5 else "black" diff --git a/src/trendify_old/cli.py b/src/trendify_old/cli.py deleted file mode 100644 index b325ebf..0000000 --- a/src/trendify_old/cli.py +++ /dev/null @@ -1,597 +0,0 @@ -""" -The `Trendify` CLI allows the code to be run from a commandline interface. -""" - -from __future__ import annotations - -# Standard - -import argparse -from dataclasses import dataclass -from glob import glob -import importlib -import importlib.util -import os -from pathlib import Path -import sys -from collections.abc import Iterable -import logging - -import matplotlib.pyplot as plt - -logger = logging.getLogger(__name__) - -# Local - -from trendify.api import api -from trendify.api.base.helpers import DATA_PRODUCTS_FNAME_DEFAULT -from trendify.streamlit import make_streamlit - -__all__ = [] - - -def _import_from_path(module_name, file_path): - """ - Imports user-provided module from path - """ - spec = importlib.util.spec_from_file_location(module_name, file_path) - module = importlib.util.module_from_spec(spec) - sys.modules[module_name] = module - spec.loader.exec_module(module) - return module - - -@dataclass -class FileManager: - """ - Determines the folder setup for `trendify` directory - """ - - output_dir: Path - - def __post_init__(self): - self.output_dir = Path(self.output_dir) - - @property - def products_dir(self) -> Path: - path = self.output_dir.joinpath("products") - path.mkdir(exist_ok=True, parents=True) - return path - - @property - def assets_dir(self) -> Path: - path = self.output_dir.joinpath("assets") - path.mkdir(exist_ok=True, parents=True) - return path - - @property - def static_assets_dir(self) -> Path: - path = self.assets_dir.joinpath("static") - path.mkdir(exist_ok=True, parents=True) - return path - - @property - def interactive_assets_dir(self) -> Path: - path = self.assets_dir.joinpath("interactive") - path.mkdir(exist_ok=True, parents=True) - return path - - @property - def grafana_dir(self) -> Path: - path = self.interactive_assets_dir.joinpath("grafana") - path.mkdir(exist_ok=True, parents=True) - return path - - -class NProcs: - """ - Determines the number of processors to use in parallel for running `trendify` commands - """ - - _NAME = "n-procs" - - @classmethod - def get_from_namespace(cls, namespace: argparse.Namespace) -> int: - return cls.process_argument(getattr(namespace, cls._NAME.replace("-", "_"))) - - @classmethod - def add_argument(cls, parser: argparse.ArgumentParser): - """Defines the argument parsing from command line""" - parser.add_argument( - "-n", - f"--{cls._NAME}", - default=1, - help=( - "Specify the number of parallel processes to use for product generation, product sorting, and asset creation." - "\nParallelization reduces wall time for computationally expensive processes." - "\nThe number of parallel processes will be limited to a maximum of 5 times the number of available cores" - "as a precaution not to crash the machine." - ), - ) - - @staticmethod - def process_argument(arg: str): - """ - Type-casts input to `int` and caps value at `5*os.cpu_count()`. - - Args: - arg (int): Desired number of processes - - Returns; - (int): Number of processes capped to `5*os.cpu_count()` - - """ - n_proc = int(arg) - - cpu_count = os.cpu_count() - if cpu_count is None: - cpu_count = 1 - - max_processes = 5 * cpu_count - if n_proc > max_processes: - logger.info( - f"User-specified ({n_proc = }) exceeds ({max_processes = })." - f"Process count will be set to ({max_processes = })" - ) - # print( - # f'User-specified ({arg = }) exceeds ({max_processes = }).' - # f'Process count will be set to ({max_processes = })' - # ) - return min(n_proc, max_processes) - - -class UserMethod: - """ - Defines arguments parsed from command line - """ - - _NAME = "product-generator" - - @classmethod - def get_from_namespace(cls, namespace: argparse.Namespace) -> api.ProductGenerator: - return cls.process_argument(getattr(namespace, cls._NAME.replace("-", "_"))) - - @classmethod - def add_argument(cls, parser: argparse.ArgumentParser): - """Defines the argument parsing from command line""" - parser.add_argument( - "-g", - f"--{cls._NAME}", - required=True, - help=( - "Sepcify method `product_generator(workdir: Path) -> List[DataProduct]` method to map over input directories." - "\n\t\tUse the following formats:" - "\n\t\tpackage.module," - "\n\t\tpackage.module:method," - "\n\t\tpackage.module:Class.method," - "\n\t\t/absolute/path/to/module.py," - "\n\t\t/absolute/path/to/module.py:method," - "\n\t\t/absolute/path/to/module.py:Class.method," - "\n\t\t./relative/path/to/module.py," - "\n\t\t./relative/path/to/module:method," - "\n\t\t./relative/path/to/module:Class.method" - ), - ) - - @staticmethod - def process_argument(arg: str) -> api.ProductGenerator: - """ - Imports python method based on user CLI input - - Args: - arg (str): Method to be imported in the form `package.module:method` or `file/path.py:method`. - `method` can be replaced be `Class.method`. File path can be either relative or absolute. - - Returns: - (Callable): User-specified method to be mapped over raw data directories. - - """ - msplit = arg.split(":") - assert 1 <= len(msplit) <= 2 - module_path = msplit[0] - method_name = msplit[1] if len(msplit) == 2 else None - - if Path(module_path).exists(): - module = _import_from_path(Path(module_path).name, Path(module_path)) - else: - module = importlib.import_module(name=module_path) - - obj = module - - function_handle = None - - assert isinstance(method_name, str) - - function_handle = obj - for arg in method_name.split("."): - function_handle = getattr(function_handle, arg) - - assert function_handle is not None - return function_handle - - -class DataProductsFileName: - """ - Defines arguments parsed from command line - """ - - _NAME = "data-products-file-name" - - @classmethod - def get_from_namespace(cls, namespace: argparse.Namespace) -> str: - return cls.process_argument(getattr(namespace, cls._NAME.replace("-", "_"))) - - @classmethod - def add_argument(cls, parser: argparse.ArgumentParser): - """Defines the argument parsing from command line""" - parser.add_argument( - "-f", - f"--{cls._NAME}", - type=str, - default=DATA_PRODUCTS_FNAME_DEFAULT, - help=( - f"Sepcify the data file name to be used (defaults to {DATA_PRODUCTS_FNAME_DEFAULT})" - ), - ) - - @staticmethod - def process_argument(arg: str) -> str: - """ - Processes input data from command line flag value - - Args: - arg (str): File name to be type-cast to string - - Returns: - (str): String (file name to be used for generated data products) - - """ - return str(arg) - - -class InputDirectories: - """ - Parses the `--input-directories` argument from CLI - """ - - _NAME = "input-directories" - - @classmethod - def get_from_namespace(cls, namespace: argparse.Namespace) -> list[Path]: - return cls.process_argument(getattr(namespace, cls._NAME.replace("-", "_"))) - - @classmethod - def add_argument(cls, parser: argparse.ArgumentParser): - parser.add_argument( - "-i", - f"--{cls._NAME}", - required=True, - help=( - "Specify raw data input directories over which the `product_generator` method will be mapped." - "\nAccepts glob expression (using **, *, regex, etc. for python glob.glob) or list of directories" - ), - nargs="+", - ) - - @staticmethod - def process_argument(arg: str) -> list[Path]: - """ - Converts CLI input to list of directories over which user-specified data product generator method will be mapped. - - Args: - arg (str): Directories or glob string from CLI - - Returns: - (List[Path]): List of directories over which to map the user-specified product generator - - """ - if isinstance(arg, str): - return [ - Path(p).parent.resolve() if Path(p).is_file() else Path(p).resolve() - for p in glob(arg, root_dir=os.getcwd(), recursive=True) - ] - else: - assert isinstance(arg, Iterable) and not isinstance(arg, str) - paths = [] - for i in arg: - for p in glob(i, root_dir=os.getcwd(), recursive=True): - paths.append( - Path(p).parent.resolve() - if Path(p).is_file() - else Path(p).resolve() - ) - return paths - - -class TrendifyDirectory: - """ - Parses the `--trendify-directory` argument from CLI - """ - - def __init__(self, short_flag: str, full_flag: str): - self._short_flag = short_flag - self._full_flag = full_flag - - def get_from_namespace(self, namespace: argparse.Namespace) -> FileManager: - return self.process_argument( - getattr(namespace, self._full_flag.replace("-", "_")) - ) - - def add_argument(self, parser: argparse.ArgumentParser): - parser.add_argument( - f"-{self._short_flag}", - f"--{self._full_flag}", - required=True, - help=( - "Sepcify output root directory to which the generated products and assets will be written." - "\nSubdirectories will be generated inside of the output root directory as needed for differnt product tags and types." - ), - ) - - def process_argument(self, arg: str) -> FileManager: - """ - Converts CLI input to list of directories over which user-specified data product generator method will be mapped. - - Args: - arg (str): Directories or glob string from CLI - - Returns: - (FileManager): List of directories over which to map the user-specified product generator - - """ - return FileManager(output_dir=Path(arg).resolve()) - - -def trendify(*pargs): - """ - Defines the command line interface script installed with python package. - - Run the help via `trendify -h`. - - Args: - *pargs (list[Any]): List of flags and arguments to pass to commandline. - Simulates running from commandline in pythong script. - - """ - # Main parser - parser = argparse.ArgumentParser( - prog="trendify", - usage="Generate visual data products and static/interactives assets from raw data", - ) - actions = parser.add_subparsers(title="Sub Commands", dest="command", metavar="") - - parser.add_argument( - "-v", - "--verbose", - action="count", - default=0, - help="Increase verbosity (-v, -vv for more detail)", - ) - - short_flag = "o" - full_flag = "output-directory" - output_dir = TrendifyDirectory(short_flag, full_flag) - - """ Useful Functions """ - """ Make """ - make = actions.add_parser( - "make", - help="Generates products and assets. Run with -h flag for info on subcommands.", - ) - make_actions = make.add_subparsers(title="Targets", dest="target", metavar="") - # Make static assets (after making products and sorting them) - make_static = make_actions.add_parser( - "static", help="Makes products, sorts them, and generates static assets" - ) - InputDirectories.add_argument(make_static) - UserMethod.add_argument(make_static) - NProcs.add_argument(make_static) - output_dir.add_argument(make_static) - DataProductsFileName.add_argument(make_static) - # Dashboard (after making products and sorting them) - make_dashboard = make_actions.add_parser( - "dashboard", - help="Generates products and prepares the Streamlit dashboard", - ) - output_dir.add_argument(make_dashboard) - # Gallery - make_gallery = make_actions.add_parser( - "gallery", help="Generates a gallery of examples" - ) - make_gallery.add_argument( - f"-{short_flag}", - f"--{full_flag}", - type=Path, - required=False, - help=( - "Sepcify output root directory to which the gallery data, products, and assets will be written. " - "Defaults to current working directory" - ), - default="./gallery/", - ) - - """ Serve """ - serve = actions.add_parser( - "serve", - help="Generates products and serves them to dashboard. Run with -h flag for info on subcommands.", - ) - serve_actions = serve.add_subparsers(title="Targets", dest="target", metavar="") - # Plotly - # Serve plotly dashboard (after making products) - serve_plotly = serve_actions.add_parser( - "plotly", help="Makes products and serves them to interactive dashboards" - ) - InputDirectories.add_argument(serve_plotly) - UserMethod.add_argument(serve_plotly) - NProcs.add_argument(serve_plotly) - DataProductsFileName.add_argument(serve_plotly) - serve_plotly.add_argument( - "--title", type=str, help="Dashboard title", default="Trendify Autodash" - ) - serve_plotly.add_argument( - "--host", type=str, help="What addres to serve the data to", default="0.0.0.0" - ) - serve_plotly.add_argument( - "--port", type=int, help="What port to serve the data on", default=8000 - ) - - """ 1-1 API Wrappers """ - """ Products """ - ### Products Make ### - products_make = actions.add_parser("products-make", help="Makes products or assets") - InputDirectories.add_argument(products_make) - UserMethod.add_argument(products_make) - NProcs.add_argument(products_make) - DataProductsFileName.add_argument(products_make) - ### Products Sort ### - products_sort = actions.add_parser( - "products-sort", help="Sorts data products by tags" - ) - InputDirectories.add_argument(products_sort) - output_dir.add_argument(products_sort) - NProcs.add_argument(products_sort) - DataProductsFileName.add_argument(products_sort) - ### Products Serve ### - products_serve = actions.add_parser( - "products-serve", help="Serves data products to URL endpoint at 127.0.0.1" - ) - InputDirectories.add_argument(products_serve) - DataProductsFileName.add_argument(products_serve) - products_serve.add_argument( - "--title", type=str, help="Dashboard title", default="Trendify Autodash" - ) - products_serve.add_argument( - "--host", type=str, help="What addres to serve the data to", default="127.0.0.1" - ) - products_serve.add_argument( - "--port", type=int, help="What port to serve the data on", default=8000 - ) - - """ Assets """ - ### Assets Make Static - assets_make_static = actions.add_parser( - "assets-make-static", help="Makes static assets" - ) - assets_make_static.add_argument("trendify_output_directory") - NProcs.add_argument(assets_make_static) - - # Test - if pargs: - args = parser.parse_args(*pargs) - else: - args = parser.parse_args() - - # Map verbosity to logging level - level = logging.INFO # default - if args.verbose == 1: - level = logging.DEBUG - elif args.verbose >= 2: - level = logging.DEBUG - - logging.basicConfig( - level=level, - format="%(asctime)s [%(levelname)s] %(name)s: %(message)s", - datefmt="%Y-%m-%d %H:%M:%S", - handlers=[ - logging.StreamHandler(sys.stdout), - logging.FileHandler("trendify.log", mode="a"), - ], - ) - plt.set_loglevel("WARNING") - - logger = logging.getLogger(__name__) - - logger.info(f"Running `trendify` with {args = }") - - match args.command: - case "products-make": - api.make_products( - product_generator=UserMethod.get_from_namespace(args), - data_dirs=InputDirectories.get_from_namespace(args), - n_procs=NProcs.get_from_namespace(args), - data_products_fname=DataProductsFileName.get_from_namespace(args), - ) - case "products-sort": - api.sort_products( - data_dirs=InputDirectories.get_from_namespace(args), - output_dir=output_dir.get_from_namespace(args).products_dir, - n_procs=NProcs.get_from_namespace(args), - data_products_fname=DataProductsFileName.get_from_namespace(args), - ) - case "products-serve": - api.serve_products_to_plotly_dashboard( - *tuple(InputDirectories.get_from_namespace(args)), - title=args.title, - host=args.host, - port=args.port, - data_products_filename=DataProductsFileName.get_from_namespace(args), - ) - case "assets-make-static": - api.make_tables_and_figures( - products_dir=FileManager(args.trendify_output_directory).products_dir, - output_dir=FileManager( - args.trendify_output_directory - ).static_assets_dir, - n_procs=NProcs.get_from_namespace(args), - ) - case "make": - match args.target: - case "gallery": - from trendify.gallery import make_gallery - - make_gallery(Path(getattr(args, full_flag.replace("-", "_"), "."))) - case "static": - um = UserMethod.get_from_namespace(args) - ip = InputDirectories.get_from_namespace(args) - np = NProcs.get_from_namespace(args) - td = output_dir.get_from_namespace(args) - fn = DataProductsFileName.get_from_namespace(args) - api.make_products( - product_generator=um, - data_dirs=ip, - n_procs=np, - data_products_fname=fn, - ) - api.sort_products( - data_dirs=ip, - output_dir=td.products_dir, - n_procs=np, - data_products_fname=fn, - ) - api.make_tables_and_figures( - products_dir=td.products_dir, - output_dir=td.static_assets_dir, - n_procs=np, - ) - case "dashboard": - # Get the output directory from the CLI arguments - td = output_dir.get_from_namespace(args) - - # Call the make_streamlit function with the output directory - make_streamlit(trendify_dir=td.output_dir) - - logger.info(f"Streamlit dashboard prepared in {td.output_dir}") - case _: - raise NotImplementedError() - case "serve": - match args.target: - case "plotly": - um = UserMethod.get_from_namespace(args) - ip = InputDirectories.get_from_namespace(args) - np = NProcs.get_from_namespace(args) - # td = output_dir.get_from_namespace(args) - fn = DataProductsFileName.get_from_namespace(args) - api.make_products( - product_generator=um, - data_dirs=ip, - n_procs=np, - data_products_fname=fn, - ) - api.serve_products_to_plotly_dashboard( - *tuple(ip), - title=args.title, - host=args.host, - port=args.port, - ) - case _: - raise NotImplementedError() diff --git a/src/trendify_old/gallery.py b/src/trendify_old/gallery.py deleted file mode 100644 index e66b6f7..0000000 --- a/src/trendify_old/gallery.py +++ /dev/null @@ -1,327 +0,0 @@ -""" -Example script demonstrating trendify data products. -Run with: python example_trendify_static.py -Then run: trendify make static example_output example_results -""" - -import platform -import subprocess -import sys -import numpy as np -from pathlib import Path -from trendify import ( - Trace2D, - Point2D, - TableEntry, - HistogramEntry, - AxLine, - Pen, - Marker, - Format2D, - LineOrientation, - HistogramStyle, - DataProductCollection, -) - - -def generate_sine_wave_example(): - # Create sine wave with noise - x = np.linspace(0, 4 * np.pi, 100) - y = np.sin(x) + np.random.normal(0, 0.1, len(x)) - - # Create main trace - trace = Trace2D.from_xy( - tags=("waves", "sine"), - x=x, - y=y, - pen=Pen(color="blue", label="Noisy Sine", size=2), - format2d=Format2D( - title_fig="Sine Wave with Reference Lines", - title_ax="Sine Function with Noise", - label_x="X (radians)", - label_y="Amplitude", - lim_x_min=0, - lim_x_max=4 * np.pi, - lim_y_min=-1.5, - lim_y_max=1.5, - ), - ) - - # Add reference lines - hline_upper = AxLine( - tags=("waves", "sine"), - value=1.0, - orientation=LineOrientation.HORIZONTAL, - pen=Pen(color="red", label="Upper Bound", size=1.5), - ) - - hline_lower = AxLine( - tags=("waves", "sine"), - value=-1.0, - orientation=LineOrientation.HORIZONTAL, - pen=Pen(color="red", label="Lower Bound", size=1.5), - ) - - # Add some peak points - peaks = [] - for i, (x_val, y_val) in enumerate(zip(x[1:-1], y[1:-1])): - if y_val > y[i] and y_val > y[i + 2]: # Simple peak detection - peaks.append( - Point2D( - tags=("waves", "sine"), - x=x_val, - y=y_val, - marker=Marker(color="green", symbol="o", size=100, label="Peak"), - ) - ) - - return [trace, hline_upper, hline_lower] + peaks - - -def generate_scatter_plot_example(): - # Create two clusters of points - n_points = 50 - - # Cluster 1 - x1 = np.random.normal(2, 0.5, n_points) - y1 = np.random.normal(2, 0.5, n_points) - - # Cluster 2 - x2 = np.random.normal(4, 0.5, n_points) - y2 = np.random.normal(4, 0.5, n_points) - - format2d = Format2D( - title_fig="Cluster Analysis", - title_ax="Two Clusters with Centroids", - label_x="X Position", - label_y="Y Position", - lim_x_min=0, - lim_x_max=6, - lim_y_min=0, - lim_y_max=6, - ) - - # Create points for each cluster - cluster1 = [ - Point2D( - tags=("clusters", "analysis"), - x=x, - y=y, - marker=Marker(color="blue", symbol="o", size=50, label="Cluster 1"), - format2d=format2d, - ) - for x, y in zip(x1, y1) - ] - - cluster2 = [ - Point2D( - tags=("clusters", "analysis"), - x=x, - y=y, - marker=Marker(color="red", symbol="o", size=50, label="Cluster 2"), - format2d=format2d, - ) - for x, y in zip(x2, y2) - ] - - # Add centroids - centroids = [ - Point2D( - tags=("clusters", "analysis"), - x=np.mean(x1), - y=np.mean(y1), - marker=Marker(color="darkblue", symbol="*", size=200, label="Centroid 1"), - format2d=format2d, - ), - Point2D( - tags=("clusters", "analysis"), - x=np.mean(x2), - y=np.mean(y2), - marker=Marker(color="darkred", symbol="*", size=200, label="Centroid 2"), - format2d=format2d, - ), - ] - - return cluster1 + cluster2 + centroids - - -def generate_histogram_example(): - # Generate three different distributions - n_samples = 1000 - - # Normal distribution - normal_data = np.random.normal(0, 1, n_samples) - normal_entries = [ - HistogramEntry( - tags=("distributions", "comparison"), - value=v, - style=HistogramStyle(color="blue", label="Normal", alpha_face=0.3, bins=30), - format2d=Format2D( - title_fig="Distribution Comparison", - title_ax="Multiple Distributions", - label_x="Value", - label_y="Count", - ), - ) - for v in normal_data - ] - - # Uniform distribution - uniform_data = np.random.uniform(-2, 2, n_samples) - uniform_entries = [ - HistogramEntry( - tags=("distributions", "comparison"), - value=v, - style=HistogramStyle(color="red", label="Uniform", alpha_face=0.3, bins=30), - ) - for v in uniform_data - ] - - # Exponential distribution - exp_data = np.random.exponential(1, n_samples) - exp_entries = [ - HistogramEntry( - tags=("distributions", "comparison"), - value=v, - style=HistogramStyle( - color="green", label="Exponential", alpha_face=0.3, bins=30 - ), - ) - for v in exp_data - ] - - return normal_entries + uniform_entries + exp_entries - - -def generate_table_example(): - # Create a comparison table of distribution statistics - distributions = ["Normal", "Uniform", "Exponential"] - metrics = ["Mean", "Std Dev", "Skewness"] - - # Generate some example statistics - stats = { - "Normal": [0.01, 1.02, 0.05], - "Uniform": [0.0, 1.15, 0.0], - "Exponential": [1.0, 1.0, 2.0], - } - - table_entries = [] - for dist in distributions: - for i, metric in enumerate(metrics): - table_entries.append( - TableEntry( - tags=("distributions", "statistics"), - row=dist, - col=metric, - value=f"{stats[dist][i]:.2f}", - unit=None, - ) - ) - - return table_entries - - -def run_trendify_commands(input_dir: str | Path, output_dir: str | Path): - """ - Runs trendify commands with proper OS handling - - Args: - input_dir (str | Path): Input directory containing data products - output_dir (str | Path): Output directory for sorted products and assets - - """ - # Convert to Path objects - input_dir = Path(input_dir).resolve() - output_dir = Path(output_dir).resolve() - - # Determine if we need shell=True based on OS - use_shell = platform.system() == "Windows" - - commands = [ - [ - "trendify", - "products-sort", - "-i", - str(input_dir), - "-o", - str(output_dir), - "-n", - "1", - ], - ["trendify", "assets-make-static", str(output_dir), "-n", "1"], - ] - - for cmd in commands: - print(f"\nExecuting command: {' '.join(cmd)}") - try: - # Run command and capture output - result = subprocess.run( - cmd, - shell=use_shell, - check=True, - text=True, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - ) - print("Command output:") - print(result.stdout) - - # Print any stderr output if it exists - if result.stderr: - print("Warnings/Errors:") - print(result.stderr) - - except subprocess.CalledProcessError as e: - print(f"Error executing command: {' '.join(cmd)}") - print(f"Exit code: {e.returncode}") - print("Error output:") - print(e.stderr) - sys.exit(1) - except Exception as e: - print(f"Unexpected error executing command: {' '.join(cmd)}") - print(f"Error: {e!s}") - sys.exit(1) - - -def make_gallery(output_dir: Path): - """ - Generates the gallery - """ - # Create output directory - output_dir.mkdir(exist_ok=True, parents=True) - data_dir = output_dir.joinpath("data") - data_dir.mkdir(exist_ok=True, parents=True) - trendify_dir = output_dir.joinpath("trendify") - - try: - from importlib.resources import files - from shutil import copy - - copy( - str(files("trendify").joinpath("gallery.py").resolve()), - str(output_dir.joinpath("gallery.py")), - ) - except Exception as e: - print(e) - - # Generate all examples - products = ( - generate_sine_wave_example() - + generate_scatter_plot_example() - + generate_histogram_example() - + generate_table_example() - ) - - # Save to JSON file - collection = DataProductCollection(elements=products) - data_dir.joinpath("data_products.json").write_text(collection.model_dump_json()) - - # Run trendify commands - run_trendify_commands( - input_dir=data_dir, - output_dir=trendify_dir, - ) - - -if __name__ == "__main__": - make_gallery(Path("gallery")) diff --git a/src/trendify_old/local_server.py b/src/trendify_old/local_server.py deleted file mode 100644 index 133d85d..0000000 --- a/src/trendify_old/local_server.py +++ /dev/null @@ -1,169 +0,0 @@ -""" -Defines Flask server for providing data products on local system -""" - -# Standard impots -from dataclasses import dataclass -from pathlib import Path - -# External imports -from flask import Flask -from waitress import serve - -# Local imports -from trendify.api import api - - -__all__ = ["TrendifyProductServerLocal"] - - -def _to_product_type(type_name: str) -> type: - """ - Converts type name str to trendify type for searching data collection - - Args: - type_name (str): trendify data product type name - - Returns: - (Type): Class type corresponding to given `type_name` - - """ - # breakpoint() - product_type = api.ProductType[type_name] - return getattr(api, product_type.name, None) - - -def _status_check(): - return "Server is working" - - -@dataclass -class ProductGetter: - """ - Attributes: - trendy_dir (Path): Path for trendify - - """ - - trendy_dir: Path - - def _get_data_products( - self, - tag: str, - product_type_name: str = "DataProduct", - ): - """ - Returns data products corresponding to tag and product_type_name - - Args: - tag (str): Product tag to for which to return products. Use the format `a.b.c.d` for a multi-component tag - product_type_name (str): Name of trendify product class. - - Returns: - (Type): Type of product to return - - Example: "URL" - Serve on port `8000` and the URL `0.0.0.0:8000/tag/Trace2D` - where `tag` can be in the format `a.b.c...` for multiple components. - - Example: "Grafana parsing command" - parse-json - | project "elements" - | extend "label"="pen.label" - | mv-expand "points" - | extend "x"="points.x", "y"="points.y" - | project "label", "x", "y" - | pivot sum("y"), "x", "label" - | project "label", "x", "y" - - """ - # Interpret Product Type - product_type = _to_product_type(type_name=str(product_type_name)) - if product_type is None: - return f"{product_type_name = } is invalid. Should be one of {[type_name.value for type_name in api.ProductType]}." - - # Interpret Tag - tag_path_components = str(tag).split(".") if "." in str(tag) else [tag] - formatted_tag = ( - tag_path_components[0] - if len(tag_path_components) == 1 - else tuple(tag_path_components) - ) - collection_dir = self.trendy_dir.joinpath(*tuple(tag_path_components)).resolve() - - # Load product collection - product_collection: api.DataProductCollection = ( - api.DataProductCollection.collect_from_all_jsons(collection_dir) - ) - if product_collection is None: - return f"Did not find data product jsons in {collection_dir}" - - # Filter product collection - filtered_data = product_collection.get_products( - tag=formatted_tag, - object_type=product_type, - ) - - # Return filtered products as JSON data - return filtered_data.model_dump_json() - - -@dataclass -class TrendifyProductServerLocal: - app: Flask - product_getter: ProductGetter - - @classmethod - def get_new(cls, products_dir: Path, name: str): - app = Flask(name) - product_getter = ProductGetter(trendy_dir=products_dir) - - app.add_url_rule("/", "/", _status_check) - - app.add_url_rule( - "/", "/get_data_products", product_getter._get_data_products - ) - app.add_url_rule( - "//", "/get_data_products", product_getter._get_data_products - ) - app.add_url_rule( - "//", - "/get_data_products", - product_getter._get_data_products, - ) - app.add_url_rule( - "///", - "/get_data_products", - product_getter._get_data_products, - ) - - return cls( - app=app, - product_getter=product_getter, - ) - - def run(self, host: str = "0.0.0.0", port: int = 8000): - # self.product_getter.trendy_dir.write_text( - # f''' - # [program:myapp] - # command=waitress-serve --port=8080 myapp:app - # directory=/path/to/your/app - # autostart=true - # autorestart=true - # ''' - # ) - print(f"Starting Server on http://{host}:{port}") - serve(self.app, host=host, port=port) - - -def main(host="0.0.0.0", port=8000): - trendy_dir = Path( - "/Users/talbotknighton/Documents/trendify/workdir/trendify_output/products" - ) - TrendifyProductServerLocal.get_new(products_dir=trendy_dir, name=__name__).run( - host=host, port=port - ) - - -if __name__ == "__main__": - main(host="0.0.0.0", port=8000) diff --git a/src/trendify_old/plotly_dashboard.py b/src/trendify_old/plotly_dashboard.py deleted file mode 100644 index c30f89a..0000000 --- a/src/trendify_old/plotly_dashboard.py +++ /dev/null @@ -1,1000 +0,0 @@ -""" -Module for generating interactive Plotly dashboards from data products. -""" - -from typing import Any, cast - -import dash -from dash import dcc, html, dash_table -from dash.dependencies import Input, Output, State -import plotly.graph_objects as go -import pandas as pd -import numpy as np -from collections import defaultdict - -# Import from trendify -from trendify import ( - DataProductCollection, - Point2D, - Trace2D, - TableEntry, - HistogramEntry, - AxLine, - LineOrientation, - Format2D, -) - -# Constants -COLORS = [ - "#1f77b4", - "#ff7f0e", - "#2ca02c", - "#d62728", - "#9467bd", - "#8c564b", - "#e377c2", - "#7f7f7f", - "#bcbd22", - "#17becf", - "#aec7e8", - "#ffbb78", - "#98df8a", - "#ff9896", - "#c5b0d5", - "#c49c94", - "#f7b6d2", - "#c7c7c7", - "#dbdb8d", - "#9edae5", -] - - -class PlotlyDashboardGenerator: - """ - Class for generating interactive Plotly dashboards from data products. - - This class provides a way to create interactive dashboards from trendify data products, - similar to how the static matplotlib plots are generated, but with interactive features. - """ - - def __init__(self, debug=False): - """ - Initialize the dashboard generator. - - Args: - debug (bool): Whether to enable debug logging - - """ - self.debug = debug - self.app = dash.Dash(__name__, suppress_callback_exceptions=True) - self.tag_data = {} - self.color_maps = {} - - def debug_log(self, message: str) -> None: - """Log debug messages if debug mode is enabled.""" - if self.debug: - print(f"[PY] {message}") - - def process_collection( - self, collection: DataProductCollection, title: str = "Trendify Dashboard" - ) -> dash.Dash: - """ - Process collection of data products and generate a Plotly dashboard. - - Args: - collection (DataProductCollection): Collection of data products to visualize - title (str): Title for the dashboard - - Returns: - dash.Dash: The configured Dash application - - """ - self.debug_log( - f"Processing collection with {len(collection.elements or [])} elements" - ) - - # Get all tags in the collection - all_tags = collection.get_tags() - self.debug_log(f"Found {len(all_tags)} tags") - - # Process each tag's data products - tab_contents = [] - for tag in all_tags: - tag_str = str(tag) - self.tag_data[tag_str] = { - "tag": tag, - "traces": collection.get_products( - tag=tag, object_type=Trace2D - ).elements, - "points": collection.get_products( - tag=tag, object_type=Point2D - ).elements, - "axlines": collection.get_products( - tag=tag, object_type=AxLine - ).elements, - "tables": collection.get_products( - tag=tag, object_type=TableEntry - ).elements, - "histograms": collection.get_products( - tag=tag, object_type=HistogramEntry - ).elements, - } - - # Create tab content for this tag - tab_content = self._create_tab_content(tag_str) - if tab_content: - tab_contents.append( - {"label": tag_str, "value": tag_str, "content": tab_content} - ) - - # Create the app layout - self.app.layout = self._create_app_layout(title, tab_contents) - - # Register callbacks - self._register_callbacks() - - return self.app - - def _create_tab_content(self, tag_str: str) -> list[html.Div]: - """Create content for a single tab.""" - data = self.tag_data[tag_str] - content = [] - - # Create XY Plot section if traces or points exist - if data["traces"] or data["points"] or data["axlines"]: - content.append(self._create_xy_plot_section(tag_str)) - - # Create Table section if table entries exist - if data["tables"]: - content.append(self._create_table_section(tag_str)) - - # Create Histogram section if histogram entries exist - if data["histograms"]: - content.append(self._create_histogram_section(tag_str)) - - return content - - def _create_xy_plot_section(self, tag_str: str) -> html.Div: - """Create the XY plot section.""" - data = self.tag_data[tag_str] - - # Initialize color map for this tag if needed - if tag_str not in self.color_maps: - self.color_maps[tag_str] = {} - color_idx = 0 - - # Assign colors to traces - for trace in data["traces"]: - series_name = trace.pen.label or f"Trace {color_idx}" - self.color_maps[tag_str][series_name] = COLORS[color_idx % len(COLORS)] - color_idx += 1 - - # Assign colors to points (grouped by marker.label) - point_labels = set() - for point in data["points"]: - if point.marker and point.marker.label: - point_labels.add(point.marker.label) - - for label in point_labels: - self.color_maps[tag_str][label] = COLORS[color_idx % len(COLORS)] - color_idx += 1 - - # Assign colors to axlines - for axline in data["axlines"]: - series_name = axline.pen.label or f"Line {color_idx}" - self.color_maps[tag_str][series_name] = COLORS[color_idx % len(COLORS)] - color_idx += 1 - - # Get format from first trace or point with format - format2d = None - for item in data["traces"] + data["points"]: - if item.format2d: - format2d = item.format2d - break - - return html.Div( - [ - html.H2("XY Plot"), - html.Div( - [ - html.Div( - [ - dcc.Checklist( - id=f"xy-options-{tag_str}", - options=[ - { - "label": " Show legend", - "value": "show_legend", - }, - {"label": " Show grid", "value": "show_grid"}, - {"label": " Auto range", "value": "auto_range"}, - ], - value=["show_legend", "show_grid", "auto_range"], - ) - ], - style={ - "width": "20%", - "display": "inline-block", - "vertical-align": "top", - }, - ), - html.Div( - [ - dcc.Graph( - id=f"xy-plot-{tag_str}", - style={"height": "600px"}, - figure=self._create_xy_figure(tag_str, format2d), - ) - ], - style={"width": "80%", "display": "inline-block"}, - ), - ] - ), - ] - ) - - def _create_table_section(self, tag_str: str) -> html.Div: - """Create the table section.""" - data = self.tag_data[tag_str] - - # Create melted dataframe from table entries - melted_df = pd.DataFrame([t.get_entry_dict() for t in data["tables"]]) - - # Try to create pivot table - pivot_df = None - if len(melted_df) > 0: - try: - pivot_df = TableEntry.pivot_table(melted_df) - except Exception as e: - self.debug_log(f"Failed to create pivot table: {e}") - - return html.Div( - [ - html.H2("Tables"), - html.Div( - [ - dcc.Tabs( - [ - dcc.Tab( - label="Melted Table", - children=[ - dash_table.DataTable( - id=f"melted-table-{tag_str}", - columns=[ - {"name": col, "id": col} - for col in melted_df.columns - ], - data=melted_df.to_dict("records"), - filter_action="native", - sort_action="native", - sort_mode="multi", - page_size=15, - style_table={"overflowX": "auto"}, - style_cell={ - "minWidth": "100px", - "maxWidth": "300px", - "whiteSpace": "normal", - "textAlign": "left", - }, - style_header={ - "backgroundColor": "lightgrey", - "fontWeight": "bold", - }, - ) - ], - ), - dcc.Tab( - label="Pivot Table", - disabled=pivot_df is None, - children=[ - ( - dash_table.DataTable( - id=f"pivot-table-{tag_str}", - columns=cast( - "list[dash_table.DataTable.Columns]", - [{"name": "row", "id": "row"}] - + [ - { - "name": str(col), - "id": str(col), - } - for col in ( - pivot_df.columns - if pivot_df is not None - else [] - ) - ], - ), - data=( - pivot_df.reset_index().to_dict( - "records" - ) - if pivot_df is not None - else [] - ), - filter_action="native", - sort_action="native", - sort_mode="multi", - page_size=15, - style_table={"overflowX": "auto"}, - style_cell={ - "minWidth": "100px", - "maxWidth": "300px", - "whiteSpace": "normal", - "textAlign": "left", - }, - style_header={ - "backgroundColor": "lightgrey", - "fontWeight": "bold", - }, - ) - if pivot_df is not None - else html.Div( - "Unable to create pivot table" - ) - ) - ], - ), - ] - ), - ] - ), - ] - ) - - def _create_histogram_section(self, tag_str: str) -> html.Div: - """Create the histogram section.""" - data = self.tag_data[tag_str] - - # Group histogram entries by style.label - histogram_groups = defaultdict(list) - for hist in data["histograms"]: - label = hist.style.label if hist.style and hist.style.label else "Default" - histogram_groups[label].append(hist) - - # Initialize color map for histograms if needed - if f"{tag_str}-hist" not in self.color_maps: - self.color_maps[f"{tag_str}-hist"] = {} - for i, label in enumerate(histogram_groups.keys()): - self.color_maps[f"{tag_str}-hist"][label] = COLORS[i % len(COLORS)] - - return html.Div( - [ - html.H2("Histogram"), - html.Div( - [ - html.Div( - [ - html.Label("Number of Bins:"), - dcc.Slider( - id=f"hist-bins-{tag_str}", - min=5, - max=100, - step=5, - value=20, - marks={i: str(i) for i in range(5, 101, 10)}, - ), - html.Br(), - dcc.Checklist( - id=f"hist-options-{tag_str}", - options=[ - {"label": " Show KDE", "value": "show_kde"}, - {"label": " Normalize", "value": "normalize"}, - { - "label": " Show legend", - "value": "show_legend", - }, - ], - value=["show_kde", "show_legend"], - ), - ], - style={ - "width": "20%", - "display": "inline-block", - "vertical-align": "top", - }, - ), - html.Div( - [ - dcc.Graph( - id=f"histogram-{tag_str}", - style={"height": "600px"}, - figure=self._create_histogram_figure( - tag_str, - histogram_groups, - 20, - ["show_kde", "show_legend"], - ), - ) - ], - style={"width": "80%", "display": "inline-block"}, - ), - ] - ), - ] - ) - - def _create_xy_figure( - self, tag_str: str, format2d: Format2D | None = None - ) -> go.Figure: - """Create the XY plot figure.""" - data = self.tag_data[tag_str] - fig = go.Figure() - - # Keep track of added legend entries to avoid duplicates - added_legend_entries = set() - - # Add traces - for trace in data["traces"]: - label = trace.pen.label or "Trace" - color = self.color_maps[tag_str].get(label, COLORS[0]) - - # Create a unique legend entry key - legend_key = f"{label}:{color}:line" - - # Check if this legend entry has already been added - showlegend = legend_key not in added_legend_entries - if showlegend: - added_legend_entries.add(legend_key) - - fig.add_trace( - go.Scatter( - x=trace.x, - y=trace.y, - mode="lines", - name=label, - line=dict(color=color, width=trace.pen.size), - opacity=trace.pen.alpha, - showlegend=showlegend, - ) - ) - - # Group points by marker label - point_groups = defaultdict(list) - for point in data["points"]: - label = ( - point.marker.label if point.marker and point.marker.label else "Points" - ) - point_groups[label].append(point) - - # Add point groups - for label, points in point_groups.items(): - color = self.color_maps[tag_str].get(label, COLORS[0]) - - x_values = [p.x for p in points] - y_values = [p.y for p in points] - - # Get marker properties from the first point - marker_props = points[0].marker - - # Create a unique legend entry key - legend_key = f"{label}:{color}:point" - - # Check if this legend entry has already been added - showlegend = legend_key not in added_legend_entries - if showlegend: - added_legend_entries.add(legend_key) - - fig.add_trace( - go.Scatter( - x=x_values, - y=y_values, - mode="markers", - name=label, - marker=dict( - color=color, - size=marker_props.size / 10 if marker_props else 8, - opacity=marker_props.alpha if marker_props else 1, - symbol="circle", - ), - showlegend=showlegend, - ) - ) - - # Group axlines by label to minimize legend entries - axline_groups = defaultdict(list) - for axline in data["axlines"]: - label = axline.pen.label or "Line" - axline_groups[label].append(axline) - - # Add shapes for axlines - for label, axlines in axline_groups.items(): - color = self.color_maps[tag_str].get(label, COLORS[0]) - - # Get pen properties from the first axline - pen = axlines[0].pen - - # First, add all the shapes (which don't show in legend) - for axline in axlines: - if axline.orientation == LineOrientation.HORIZONTAL: - fig.add_shape( - type="line", - x0=0, - y0=axline.value, - x1=1, - y1=axline.value, - xref="paper", - line=dict( - color=color, - width=pen.size, - dash="solid", - ), - opacity=pen.alpha, - ) - else: # VERTICAL - fig.add_shape( - type="line", - x0=axline.value, - y0=0, - x1=axline.value, - y1=1, - yref="paper", - line=dict( - color=color, - width=pen.size, - dash="solid", - ), - opacity=pen.alpha, - ) - - # Create a unique legend entry key - legend_key = f"{label}:{color}:axline" - - # Check if we need to add a legend entry for this axline group - if legend_key not in added_legend_entries: - added_legend_entries.add(legend_key) - - # Add an invisible trace for the legend (only once per unique label/color) - fig.add_trace( - go.Scatter( - x=[None], - y=[None], - mode="lines", - name=label, - line=dict(color=color, width=pen.size), - opacity=pen.alpha, - showlegend=True, - ) - ) - - # Update layout with format information - layout_args: dict[str, Any] = dict( - title=( - format2d.title_ax - if format2d and format2d.title_ax - else str(data["tag"]) - ), - xaxis=dict( - title=format2d.label_x if format2d and format2d.label_x else None, - showgrid=True, - gridwidth=1, - gridcolor="LightGray", - ), - yaxis=dict( - title=format2d.label_y if format2d and format2d.label_y else None, - showgrid=True, - gridwidth=1, - gridcolor="LightGray", - ), - plot_bgcolor="white", - legend_title=( - format2d.title_legend if format2d and format2d.title_legend else None - ), - showlegend=True, - margin=dict(t=50, b=50, l=50, r=50), - ) - - # Set axis limits if provided in format - if format2d: - if format2d.lim_x_min is not None: - layout_args["xaxis"]["range"] = [ - format2d.lim_x_min, - layout_args["xaxis"].get("range", [None, None])[1], - ] - if format2d.lim_x_max is not None: - layout_args["xaxis"]["range"] = [ - layout_args["xaxis"].get("range", [None, None])[0], - format2d.lim_x_max, - ] - if format2d.lim_y_min is not None: - layout_args["yaxis"]["range"] = [ - format2d.lim_y_min, - layout_args["yaxis"].get("range", [None, None])[1], - ] - if format2d.lim_y_max is not None: - layout_args["yaxis"]["range"] = [ - layout_args["yaxis"].get("range", [None, None])[0], - format2d.lim_y_max, - ] - - fig.update_layout(**layout_args) - return fig - - def _create_histogram_figure( - self, - tag_str: str, - histogram_groups: dict[str, list[HistogramEntry]], - num_bins: int, - options: list[str], - ) -> go.Figure: - """Create the histogram figure.""" - fig = go.Figure() - added_legend_entries = set() - - show_kde = "show_kde" in options - normalize = "normalize" in options - - for label, entries in histogram_groups.items(): - values = [entry.value for entry in entries] - color = self.color_maps.get(f"{tag_str}-hist", {}).get(label, COLORS[0]) - - # Create unique legend key for histogram - hist_legend_key = f"{label}:{color}:histogram" - showlegend = hist_legend_key not in added_legend_entries - - if showlegend: - added_legend_entries.add(hist_legend_key) - - # Add histogram - histnorm = "probability" if normalize else None - fig.add_trace( - go.Histogram( - x=values, - nbinsx=num_bins, - name=label, - histnorm=histnorm, - marker_color=color, - opacity=0.7, - showlegend=showlegend, - ) - ) - - # Add KDE if requested and if scipy is available - if show_kde and len(values) > 1: - try: - from scipy import stats as scipy_stats - - # Create unique legend key for KDE - kde_legend_key = f"{label}:{color}:kde" - kde_showlegend = kde_legend_key not in added_legend_entries - - if kde_showlegend: - added_legend_entries.add(kde_legend_key) - - # Calculate KDE - kde = scipy_stats.gaussian_kde(values) - x_range = np.linspace(min(values), max(values), 200) - y_range = kde(x_range) - - # Scale KDE to match histogram height if not normalized - if not normalize: - hist, edges = np.histogram(values, bins=num_bins) - max_hist_height = np.max(hist) - max_kde_height = np.max(y_range) - scale_factor = ( - max_hist_height / max_kde_height - if max_kde_height > 0 - else 1 - ) - y_range = y_range * scale_factor - - fig.add_trace( - go.Scatter( - x=x_range, - y=y_range, - mode="lines", - name=f"KDE of {label}", - line=dict(color=color, width=2), - showlegend=kde_showlegend, - ) - ) - except ImportError: - self.debug_log("SciPy not available for KDE calculation") - except Exception as e: - self.debug_log(f"Error computing KDE: {e}") - - # Get format from first histogram entry with format - format2d = None - for entries in histogram_groups.values(): - for entry in entries: - if entry.format2d: - format2d = entry.format2d - break - if format2d: - break - - # Update layout - layout_args: dict[str, Any] = dict( - title=( - format2d.title_ax - if format2d and format2d.title_ax - else f"Histogram - {tag_str}" - ), - barmode="overlay", - xaxis=dict( - title=format2d.label_x if format2d and format2d.label_x else "Value", - showgrid=True, - gridwidth=1, - gridcolor="LightGray", - ), - yaxis=dict( - title="Probability" if normalize else "Frequency", - showgrid=True, - gridwidth=1, - gridcolor="LightGray", - ), - plot_bgcolor="white", - paper_bgcolor="white", - legend_title=( - format2d.title_legend if format2d and format2d.title_legend else None - ), - showlegend="show_legend" in options, - margin=dict(t=50, b=50, l=50, r=50), - ) - - # Set axis limits if provided in format - if format2d: - if format2d.lim_x_min is not None: - layout_args["xaxis"]["range"] = [ - format2d.lim_x_min, - layout_args["xaxis"].get("range", [None, None])[1], - ] - if format2d.lim_x_max is not None: - layout_args["xaxis"]["range"] = [ - layout_args["xaxis"].get("range", [None, None])[0], - format2d.lim_x_max, - ] - if format2d.lim_y_min is not None: - layout_args["yaxis"]["range"] = [ - format2d.lim_y_min, - layout_args["yaxis"].get("range", [None, None])[1], - ] - if format2d.lim_y_max is not None: - layout_args["yaxis"]["range"] = [ - layout_args["yaxis"].get("range", [None, None])[0], - format2d.lim_y_max, - ] - - fig.update_layout(**layout_args) - return fig - - def _create_app_layout(self, title: str, tab_contents: list[dict]) -> html.Div: - """Create the main application layout with category-based tag selection.""" - if not tab_contents: - return html.Div( - [html.H1(title), html.Div("No data products found to visualize.")] - ) - - # Extract categories from tag names - # Assuming tag structure might be like 'category/tag_name' or just 'tag_name' - categories = set() - tag_categories = {} - - for tab in tab_contents: - tag_name = tab["label"] - parts = tag_name.split("/") - - if len(parts) > 1: - # Tag has a category prefix - category = parts[0] - tag_display = "/".join( - parts[1:] - ) # Use remaining parts as the display name - else: - # Tag has no category, put it in "General" - category = "General" - tag_display = tag_name - - categories.add(category) - if category not in tag_categories: - tag_categories[category] = [] - - tag_categories[category].append( - {"label": tag_display, "value": tab["value"], "content": tab["content"]} - ) - - # Sort categories - sorted_categories = sorted(list(categories)) - - # Create dropdown options for categories - category_options = [{"label": cat, "value": cat} for cat in sorted_categories] - - # Default selections - default_category = sorted_categories[0] if sorted_categories else None - default_tag = ( - tag_categories[default_category][0]["value"] - if default_category and tag_categories[default_category] - else None - ) - - # Create content divs for each tag - properly set the style during creation - content_divs = [] - for i, tab in enumerate(tab_contents): - # Set the first one to be visible, all others hidden - display_style = {"display": "block"} if i == 0 else {"display": "none"} - - content_divs.append( - html.Div( - tab["content"], - id=f"content-{tab['value']}", - style=display_style, # Set style directly during creation - ) - ) - - return html.Div( - [ - html.H1(title), - html.Div( - [ - # Category selection - html.Div( - [ - html.Label("Category:"), - dcc.Dropdown( - id="category-selector", - options=category_options, - value=default_category, - clearable=False, - style={"width": "100%"}, - ), - ], - style={ - "width": "45%", - "display": "inline-block", - "marginRight": "5%", - }, - ), - # Tag selection - html.Div( - [ - html.Label("Tag:"), - dcc.Dropdown( - id="tag-selector", - # Options will be set by callback - clearable=False, - style={"width": "100%"}, - ), - ], - style={"width": "45%", "display": "inline-block"}, - ), - ], - style={"margin": "10px 0", "width": "100%"}, - ), - html.Div(id="tag-content-container", children=content_divs), - # Stores - dcc.Store(id="active-tag", data=default_tag), - dcc.Store(id="tag-categories", data=tag_categories), - dcc.Store(id="color-maps"), - ] - ) - - def _register_callbacks(self): - """Register all the callbacks for the dashboard.""" - - # Category change callback - updates the tag dropdown options - @self.app.callback( - [Output("tag-selector", "options"), Output("tag-selector", "value")], - [Input("category-selector", "value")], - [State("tag-categories", "data")], - ) - def update_tag_options(selected_category, tag_categories): - if not selected_category or selected_category not in tag_categories: - return [], None - - tag_options = [ - {"label": tag["label"], "value": tag["value"]} - for tag in tag_categories[selected_category] - ] - - # Set default tag to the first one in the category - default_tag = tag_options[0]["value"] if tag_options else None - - return tag_options, default_tag - - # Tag selection callback - updates which content is displayed - @self.app.callback( - [Output("active-tag", "data")] - + [Output(f"content-{tag}", "style") for tag in self.tag_data.keys()], - [Input("tag-selector", "value")], - ) - def switch_tag(tag_value): - styles = [] - for tag in self.tag_data.keys(): - if tag == tag_value: - styles.append({"display": "block"}) - else: - styles.append({"display": "none"}) - return [tag_value] + styles - - # Register callbacks for each tag's components - for tag_str in self.tag_data.keys(): - # XY Plot options callback - if ( - self.tag_data[tag_str]["traces"] - or self.tag_data[tag_str]["points"] - or self.tag_data[tag_str]["axlines"] - ): - - @self.app.callback( - Output(f"xy-plot-{tag_str}", "figure"), - [Input(f"xy-options-{tag_str}", "value")], - ) - def update_xy_plot(options, tag_str=tag_str): - format2d = None - data = self.tag_data[tag_str] - - # Get format from first trace or point with format - for item in data["traces"] + data["points"]: - if item.format2d: - format2d = item.format2d - break - - fig = self._create_xy_figure(tag_str, format2d) - - # Update based on options - fig.update_layout( - showlegend="show_legend" in options, - xaxis=dict( - showgrid="show_grid" in options, - autorange="auto_range" in options, - ), - yaxis=dict( - showgrid="show_grid" in options, - autorange="auto_range" in options, - ), - ) - - return fig - - # Histogram options callback - if self.tag_data[tag_str]["histograms"]: - - @self.app.callback( - Output(f"histogram-{tag_str}", "figure"), - [ - Input(f"hist-bins-{tag_str}", "value"), - Input(f"hist-options-{tag_str}", "value"), - ], - ) - def update_histogram(bins, options, tag_str=tag_str): - data = self.tag_data[tag_str] - - # Group histogram entries by style.label - histogram_groups = defaultdict(list) - for hist in data["histograms"]: - label = ( - hist.style.label - if hist.style and hist.style.label - else "Default" - ) - histogram_groups[label].append(hist) - - fig = self._create_histogram_figure( - tag_str, histogram_groups, bins, options - ) - return fig - - -def generate_plotly_dashboard( - collection: DataProductCollection, - title: str = "Trendify Dashboard", - debug: bool = False, -) -> dash.Dash: - """ - Generate a Plotly dashboard from a data product collection. - - This function creates an interactive dashboard that visualizes the data products - in the collection, similar to how DataProductCollection.process_collection works - with matplotlib, but with interactive Plotly features. - - Args: - collection (DataProductCollection): Collection of data products to visualize - title (str): Title for the dashboard - debug (bool): Whether to enable debug logging - - Returns: - dash.Dash: The configured Dash application ready to run - - """ - generator = PlotlyDashboardGenerator(debug=debug) - return generator.process_collection(collection, title) diff --git a/src/trendify_old/production_server.py b/src/trendify_old/production_server.py deleted file mode 100644 index 0f9ca12..0000000 --- a/src/trendify_old/production_server.py +++ /dev/null @@ -1,127 +0,0 @@ -""" -Production server for trendify dashboards. - -This module provides functions to run Dash applications in a production environment -using the Waitress WSGI server. -""" - -import sys -from pathlib import Path -from typing import Union - -from trendify.api.api import DataProductCollection - - -def run_production_server( - app_or_collection: Union["dash.Dash", DataProductCollection], - host: str = "0.0.0.0", - port: int = 8000, - title: str = "Trendify Dashboard", - debug: bool = False, -) -> None: - """ - Run a Dash application using a production WSGI server (Waitress). - - Args: - app_or_collection: Either a Dash application or a DataProductCollection - host: Host address to listen on - port: Port to listen on - title: Title for the dashboard (used only if app_or_collection is a DataProductCollection) - debug: Enable debug mode (has no effect on Waitress but affects Dash) - - Raises: - ImportError: If Waitress is not installed - - """ - try: - import waitress - except ImportError: - print("Error: The 'waitress' package is required for production deployment.") - print("Please install it with: pip install waitress") - sys.exit(1) - - # Check if input is a Dash app or a DataProductCollection - try: - import dash - - if isinstance(app_or_collection, DataProductCollection): - from trendify.plotly_dashboard import generate_plotly_dashboard - - app = generate_plotly_dashboard(app_or_collection, title, debug) - elif isinstance(app_or_collection, dash.Dash): - app = app_or_collection - else: - raise TypeError( - "app_or_collection must be either a Dash application or a DataProductCollection" - ) - except ImportError: - print("Error: Dash is not installed. Please install it with: pip install dash") - sys.exit(1) - - print(f"Starting production server with Waitress on {host}:{port}") - waitress.serve(app.server, host=host, port=port) - - -def serve_from_data_dir( - data_dir: str | Path, - host: str = "0.0.0.0", - port: int = 8000, - title: str | None = None, - tag: str | None = None, -) -> None: - """ - Serve a dashboard from a directory containing data products. - - Args: - data_dir: Path to directory containing data products - host: Host address to listen on - port: Port to listen on - title: Optional title for the dashboard. If None, will use the directory name - tag: Optional tag to filter data products by - - """ - from trendify.api.api import DataProductCollection - - data_dir = Path(data_dir) - if not data_dir.exists(): - print(f"Error: Directory {data_dir} does not exist") - sys.exit(1) - - if title is None: - title = f"Trendify: {data_dir.name}" - - print(f"Loading data products from {data_dir}") - try: - collection = DataProductCollection.collect_from_all_jsons( - data_dir, recursive=True - ) - print(f"Loaded {len(collection.elements or [])} data products") - - if tag: - filtered = collection.get_products(tag=tag) - print( - f"Filtered to {len(filtered.elements or [])} data products with tag '{tag}'" - ) - collection = filtered - except Exception as e: - print(f"Error loading data products: {e}") - sys.exit(1) - - run_production_server(collection, host, port, title) - - -if __name__ == "__main__": - import argparse - - parser = argparse.ArgumentParser( - description="Run a trendify dashboard in production mode" - ) - parser.add_argument("data_dir", type=str, help="Directory containing data products") - parser.add_argument("--host", type=str, default="0.0.0.0", help="Host to listen on") - parser.add_argument("--port", type=int, default=8000, help="Port to listen on") - parser.add_argument("--title", type=str, help="Dashboard title") - parser.add_argument("--tag", type=str, help="Filter by tag") - - args = parser.parse_args() - - serve_from_data_dir(args.data_dir, args.host, args.port, args.title, args.tag) diff --git a/src/trendify_old/server.py b/src/trendify_old/server.py deleted file mode 100644 index b114dab..0000000 --- a/src/trendify_old/server.py +++ /dev/null @@ -1,170 +0,0 @@ -""" -Defines database server for DataProductCollection from analyses -""" - -# Standard impots -from pathlib import Path -import pandas as pd - -# External imports -from flask import Flask -from waitress import serve - -# Local imports -# from trendify.api.API import ( -# # Collection -# DataProductCollection, -# # DataProduct types -# # DataProduct, -# # XYData, -# # Trace2D, -# # Point2D, -# # TableEntry, -# # HistogramEntry, -# ) -from trendify.api.base.data_product import DataProduct -from trendify.api.generator.data_product_collection import DataProductCollection -from trendify.api.plotting.histogram import HistogramEntry -from trendify.api.formats.format2d import XYData -from trendify.api.plotting.point import Point2D -from trendify.api.plotting.trace import Trace2D -from trendify.api.formats.table import TableEntry - -valid_product_types = [ - DataProduct, - XYData, - Trace2D, - Point2D, - TableEntry, - HistogramEntry, -] -valid_types_names_list = [t.__name__ for t in valid_product_types] - -# App -app = Flask(__name__) - -DATABASE_ROOT = Path( - "/Users/talbotknighton/Documents/trendify/workdir/trendify_output/products/" -) - - -@app.route("/data_products/server-status/") -def get_status(): - return "Data Products server is working..." - - -@app.route("/data_products/aggregate//") -@app.route("/data_products/aggregate///") -def get_aggregate_data_products( - analysis: str = "workdir", - file: str = "trace_plots", -): - FAILED_RETURN_VALUE = None - query_return = FAILED_RETURN_VALUE - - analysis = str(analysis) - analysis_path_components = analysis.split(".") if "." in analysis else [analysis] - assert not any(("." in x) for x in analysis_path_components) - file = str(file) - assert len(file.split(".")) <= 2 - collection_path_components = analysis_path_components + [file] - load_path = DATABASE_ROOT.joinpath(*tuple(collection_path_components)) - assert load_path.is_relative_to(DATABASE_ROOT) - - df = pd.read_csv(load_path, index_col=0) - query_return = df.to_csv() - return query_return - - -@app.route("/data_products//") -@app.route("/data_products///") -@app.route("/data_products///") -@app.route("/data_products////") -def get_data_products( - analysis: str = "workdir.products", - tag: str = "trace_plots", - product_type: str = "DataProduct", -): - """ - Example: Traces - parse-json - | project "elements" - | extend "label"="pen.label" - | mv-expand "points" - | extend "x"="points.x", "y"="points.y" - | project "label", "x", "y" - | pivot sum("y"), "x", "label" - | project "label", "x", "y" - """ - FAILED_RETURN_VALUE = None - query_return = FAILED_RETURN_VALUE - product_type = str(product_type) - - match product_type: - case DataProduct.__name__: - filter_type = DataProduct - case XYData.__name__: - filter_type = XYData - case Trace2D.__name__: - filter_type = Trace2D - case Point2D.__name__: - filter_type = Point2D - case TableEntry.__name__: - filter_type = TableEntry - case HistogramEntry.__name__: - filter_type = HistogramEntry - case _: - query_return = f"{product_type = } should be in {valid_types_names_list}" - return query_return - - try: - analysis = str(analysis) - analysis_path_components = ( - analysis.split(".") if "." in analysis else [analysis] - ) - tag = str(tag) - tag_path_components = tag.split(".") if "." in tag else [tag] - collection_path_components = analysis_path_components + tag_path_components - data_dir = DATABASE_ROOT.joinpath(*tuple(analysis_path_components)) - collection_dir = data_dir.joinpath(*tuple(tag_path_components)) - assert not any(("." in x) for x in collection_path_components) - assert collection_dir.is_relative_to(data_dir) - except AssertionError: - query_return = f"Do not try to access stuff outside of {data_dir = }" - print(f"Do not try to access stuff outside of {data_dir = }") - return query_return - - data: DataProductCollection = DataProductCollection.collect_from_all_jsons( - collection_dir - ) - if data is None: - return f"Did not find data product jsons in {collection_dir}" - formatted_tag = ( - tag_path_components[0] - if len(tag_path_components) == 1 - else tuple(tag_path_components) - ) - filtered_data = data.get_products( - tag=formatted_tag, - object_type=filter_type, - ) - query_return = filtered_data.model_dump_json() - return query_return - - -if __name__ == "__main__": - # print( - # get_data_products( - # analysis='workdir.products', - # tag='tables', - # product_type='DataProduct', - # ) - # ) - # print( - # get_aggregate_data_products( - # analysis='workdir.aggregate', - # file='stdin.csv', - # ) - # ) - print("Starting Server") - serve(app, host="0.0.0.0", port=8000) diff --git a/src/trendify_old/streamlit.py b/src/trendify_old/streamlit.py deleted file mode 100644 index a560a2f..0000000 --- a/src/trendify_old/streamlit.py +++ /dev/null @@ -1,302 +0,0 @@ -import importlib.resources -from importlib.metadata import version -from pathlib import Path -import time -from collections.abc import Sequence - -import streamlit as st - -from trendify.api.generator.data_product_collection import DataProductCollection -from trendify.api.plotting.plotting import PlotlyFigure - -# from trendify.api.generator.data_product_collection import ( -# ProductIndexMap, -# ) - - -def make_theme(): - theme_dir = Path(".streamlit").resolve() - theme_dir.mkdir(parents=True, exist_ok=True) - theme_dir.joinpath(".gitignore").write_text("*") - - toml = """[theme] -base="light" -primaryColor="#e92063" - -[browser] -gatherUsageStats = false -""" - theme_dir.joinpath("config.toml").write_text(toml) - - -def make_streamlit(trendify_dir: Path): - trendify_dir = trendify_dir.resolve() - save_location = trendify_dir.joinpath("assets", "dashboard", "streamlit_run.py") - run_command = f"streamlit run {save_location.as_posix()}" - to_write = f'''"""To run use: - -{run_command} -""" - -import trendify.streamlit as trendy_stream -from pathlib import Path - -trendify_dir = Path("{trendify_dir.as_posix()}") - -trendy_stream.make_dashboard(trendify_dir=trendify_dir) -''' - make_theme() - save_location.parent.mkdir(parents=True, exist_ok=True) - save_location.write_text(to_write) - print( - f"""To run use - -{run_command} -""" - ) - - -def get_index_map_path(trendify_dir: Path, tag: tuple[str, ...]) -> Path: - products_dir = trendify_dir.joinpath("products") - return products_dir.joinpath(*tag, "index_map") - - -def get_tags(trendify_dir: Path) -> Sequence[tuple[str, ...]]: - products_dir = trendify_dir.joinpath("products") - tags = [ - p.parent.relative_to(products_dir).parts - for p in products_dir.rglob("*") - if p.name == "index_map" and p.is_file() - ] - return sorted(tags, key=lambda x: (len(x), x)) - - -def create_nested_expanders( - tags: Sequence[tuple[str, ...]], current_level: int = 0 -) -> dict: - # Group tags by their current level - level_groups = {} - for tag in tags: - if len(tag) > current_level: - if tag[current_level] not in level_groups: - level_groups[tag[current_level]] = { - "subtags": [], # Tags that continue deeper - "complete": False, # Whether this level is a complete tag - } - if len(tag) == current_level + 1: - level_groups[tag[current_level]]["complete"] = True - else: - level_groups[tag[current_level]]["subtags"].append(tag) - - return level_groups - - -def render_nested_expanders( - tags: Sequence[tuple[str, ...]], - current_level: int = 0, - selected_tags: tuple[str, ...] | None = None, -): - if selected_tags is None: - selected_tags = None - - level_groups = create_nested_expanders(tags, current_level) - - # Get currently selected tag (if any) - selected_tag = st.session_state.get("selected_tags", None) - - for tag_name, group_info in level_groups.items(): - # Create the full tag tuple up to this level - current_tag = tuple( - t[: current_level + 1] - for t in tags - if len(t) > current_level and t[current_level] == tag_name - ) - if current_tag: - # Take the first one since they're all the same at this level - current_tag = current_tag[0] - - # Check if this tag is part of the currently selected path - is_selected = selected_tag == current_tag - button_text = f"{tag_name}" - button_type = "primary" if is_selected else "secondary" - - # Only create expander if there are subtags, otherwise just show button - if group_info["subtags"]: - with st.expander(f"📁 {tag_name}", expanded=False): - if group_info["complete"]: - if st.button( - button_text, - key=f"btn_{'_'.join(current_tag)}", # Use the full tag tuple as the key - type=button_type, - ): - st.session_state.selected_tags = current_tag - st.rerun() - - render_nested_expanders( - group_info["subtags"], current_level + 1, selected_tags - ) - else: - # For leaf nodes (no subtags), just show the button - if group_info["complete"]: - if st.button( - button_text, - key=f"btn_{'_'.join(current_tag)}", # Use the full tag tuple as the key - type=button_type, - use_container_width=True, - ): - st.session_state.selected_tags = current_tag - st.rerun() - - return st.session_state.get("selected_tags", None) - - -def make_sidebar(trendify_dir: Path) -> tuple[str, ...] | None: - st.title(f"Trendify (v{version('trendify')})") - - tags = get_tags(trendify_dir=trendify_dir) - st.caption(f"Viewing {len(tags)} assets for {trendify_dir}") - - if "selected_tags" not in st.session_state: - st.session_state.selected_tags = None - - selected_tags = st.session_state.selected_tags - selected_tags = render_nested_expanders(tags=tags, selected_tags=selected_tags) - - return selected_tags - - -@st.cache_data(show_time=True) -def process_tag(tag: tuple[str, ...], trendify_dir: Path): - products_paths = list( - trendify_dir.joinpath("products").joinpath(*tag).glob("*.json") - ) - - return DataProductCollection.process_tag_for_streamlit(products_paths, tag=tag) - - -def make_main_page(tag: tuple[str, ...], trendify_dir: Path): - """Display the main page content for the selected tag""" - st.title(f"{' | '.join(tag)}") - - # Process the tag for tables and plots - proccessed_tag = process_tag(tag=tag, trendify_dir=trendify_dir) - - # Display Plotly figures if available - if isinstance(proccessed_tag, PlotlyFigure): - col1, col2, col3 = st.columns([1, 1, 0.3], vertical_alignment="bottom") - with st.form("plot_form"): - with col1: - if "height" not in st.session_state: - st.session_state.height = 600 - - height = st.slider( - label="Figure Height", - min_value=100, - value=st.session_state.height, - max_value=2000, - step=100, - help="Set the height of the rendered plot (in pixels)", - ) - with col2: - opts = ["closest", "x unified", "y unified", "x", "y"] - if "tooltip_selected" not in st.session_state: - st.session_state.tooltip_selected = "closest" - - index = opts.index(st.session_state.tooltip_selected) - - tooltip = st.selectbox( - key="tooltip", - label="Tooltip", - options=opts, - index=index, - accept_new_options=False, - help="Select tooltip option", - ) - - with col3: - if st.button( - "🔄", - key="refresh_button", - help="Refresh the plot area with the selections made.", - ): - st.session_state.height = height - st.session_state.tooltip_selected = tooltip - st.rerun() - - proccessed_tag.fig.update_layout( - hovermode=st.session_state.tooltip_selected, - height=st.session_state.height, - margin=dict(pad=4, t=25, b=25), - ) - st.plotly_chart(proccessed_tag.fig) - - else: - msg = f"Product with {tag=} does not have a display method for streamlit" - st.warning(msg) - - -def make_dashboard(trendify_dir: str | Path): - start = time.perf_counter() - - trendify_dir = Path(trendify_dir).resolve() - - with importlib.resources.path("trendify.assets", "logo.svg") as data_path: - logo = data_path - - with importlib.resources.path("trendify.assets", "logo_white_bg.svg") as data_path: - logo_white_bg = data_path - - docs = "https://talbotknighton.github.io/trendify/" - - st.set_page_config( - page_title="Trendify UI", - page_icon=logo_white_bg, - layout="wide", - menu_items={ - "Get help": docs, - "Report a bug": "https://github.com/TalbotKnighton/trendify/issues", - "About": "Trendify", - }, - ) - - st.markdown( - """ - - """, - unsafe_allow_html=True, - ) - - st.logo( - image=f"{logo}", - size="large", - link=docs, - ) - - with st.sidebar: - selected_tag = make_sidebar(trendify_dir=trendify_dir) - - if selected_tag is None: - st.info("Select an Asset to Display") - else: - make_main_page(tag=selected_tag, trendify_dir=trendify_dir) - - with st.sidebar: - st.caption(f"Site built in {time.perf_counter() - start:.2f} seconds") - - -def main(): - """ - To run use - - streamlit run src/trendify/streamlit.py - """ - make_theme() - make_dashboard(trendify_dir=Path("sample_data/trendify")) - - -if __name__ == "__main__": - main() diff --git a/test.py b/test.py deleted file mode 100644 index 7af5c38..0000000 --- a/test.py +++ /dev/null @@ -1,39 +0,0 @@ -import matplotlib.pyplot as plt - -from dataclasses import dataclass - - -@dataclass -class Coordinate: - xyz = tuple[float, float, float] - - -def main(): - coords = [ - [(-1681, 52, -1274), (-2377, 62, -967), (2996, 67, 2797), (-238, 69, -211)], - [(2295, 73, -514), (1490, 70, -1091), (265, 60, -836), (-1061, 63, 217)], - [(-70, 63, -926), (2107, 60, 2934), (1223, 61, 1574), (-954, 74, 579)], - [(-2196, 58, -1390), (917, 77, -907), (1167, 70, -2741), (49, 51, 758)], - [(251, 54, -1118), (1858, 75, -2807), (-128, 52, 1605), (-2144, 60, 971)], - ] - flattened_list = [] - for sublist in coords: - for item in sublist: - flattened_list.append(item) - coords = flattened_list - - fig, ax = plt.subplots() - - for coord in coords: - ax.scatter(x=coord[0], y=coord[2]) - - min_x = min([x[0] for x in coords]) - max_x = max([x[0] for x in coords]) - min_z = min([x[2] for x in coords]) - max_z = max([x[2] for x in coords]) - fig.show() - breakpoint() - - -if __name__ == "__main__": - main() diff --git a/tests/test_cli.py b/tests/test_cli.py new file mode 100644 index 0000000..67fee79 --- /dev/null +++ b/tests/test_cli.py @@ -0,0 +1,152 @@ +"""Tests for CLI""" + +import textwrap +from pathlib import Path + +from typer.testing import CliRunner + +from trendify.cli import app +from trendify.plotting.point import Point2D +from trendify.store.product_store import ProductStore + +runner = CliRunner() + + +def _write_generator_module(path: Path) -> Path: + path.write_text( + textwrap.dedent( + """ + from trendify.plotting.point import Point2D + + def generate(workdir): + n = int(workdir.name) + return [Point2D(tags=["scatter"], x=float(n), y=float(n) ** 2)] + """ + ) + ) + return path + + +def _make_run_dirs(tmp_path: Path, n: int) -> Path: + for i in range(n): + (tmp_path / "raw" / str(i)).mkdir(parents=True) + return tmp_path / "raw" / "*" + + +class TestGenerateCommand: + def test_writes_products_to_store(self, tmp_path: Path): + glob_pattern = _make_run_dirs(tmp_path, 3) + gen_module = _write_generator_module(tmp_path / "gen.py") + out_dir = tmp_path / "out" + + result = runner.invoke( + app, + [ + "generate", + "-i", + str(glob_pattern), + "-g", + f"{gen_module}:generate", + "-o", + str(out_dir), + ], + ) + + assert result.exit_code == 0, result.output + with ProductStore.open(out_dir / "trendify.db", readonly=True) as store: + assert len(store.get_products_of_type(Point2D)) == 3 + + def test_bad_product_generator_spec_errors(self, tmp_path: Path): + glob_pattern = _make_run_dirs(tmp_path, 1) + out_dir = tmp_path / "out" + + result = runner.invoke( + app, + [ + "generate", + "-i", + str(glob_pattern), + "-g", + "not-a-valid-spec", + "-o", + str(out_dir), + ], + ) + + assert result.exit_code != 0 + + +class TestRenderCommand: + def test_renders_after_generate(self, tmp_path: Path): + glob_pattern = _make_run_dirs(tmp_path, 2) + gen_module = _write_generator_module(tmp_path / "gen.py") + out_dir = tmp_path / "out" + + runner.invoke( + app, + [ + "generate", + "-i", + str(glob_pattern), + "-g", + f"{gen_module}:generate", + "-o", + str(out_dir), + ], + ) + result = runner.invoke( + app, ["render", "-o", str(out_dir), "--dpi", "50", "--no-tables"] + ) + + assert result.exit_code == 0, result.output + assert (out_dir / "assets" / "scatter.jpg").exists() + assert (out_dir / "assets" / "include.md").exists() + + +class TestRunCommand: + def test_generates_and_renders_in_one_step(self, tmp_path: Path): + glob_pattern = _make_run_dirs(tmp_path, 2) + gen_module = _write_generator_module(tmp_path / "gen.py") + out_dir = tmp_path / "out" + + result = runner.invoke( + app, + [ + "run", + "-i", + str(glob_pattern), + "-g", + f"{gen_module}:generate", + "-o", + str(out_dir), + "--dpi", + "50", + ], + ) + + assert result.exit_code == 0, result.output + assert (out_dir / "assets" / "scatter.jpg").exists() + + def test_no_include_files_flag(self, tmp_path: Path): + glob_pattern = _make_run_dirs(tmp_path, 2) + gen_module = _write_generator_module(tmp_path / "gen.py") + out_dir = tmp_path / "out" + + result = runner.invoke( + app, + [ + "run", + "-i", + str(glob_pattern), + "-g", + f"{gen_module}:generate", + "-o", + str(out_dir), + "--dpi", + "50", + "--no-include-files", + ], + ) + + assert result.exit_code == 0, result.output + assert not (out_dir / "assets" / "include.md").exists() diff --git a/tests/test_generate.py b/tests/test_generate.py new file mode 100644 index 0000000..659c748 --- /dev/null +++ b/tests/test_generate.py @@ -0,0 +1,81 @@ +"""Tests for data product generators""" + +from pathlib import Path + +from trendify.base.pen import Pen +from trendify.generator.generate import generate_products, get_sorted_dirs +from trendify.plotting.point import Point2D +from trendify.plotting.trace import Trace2D +from trendify.store.product_store import ProductStore + + +def _generator(run_dir: Path): + n = int(run_dir.name) + return [ + Point2D(tags=["points"], x=float(n), y=float(n) ** 2), + Trace2D.from_xy( + tags=[("traces", "run")], + x=[0, 1, 2], + y=[n, n, n], + pen=Pen(label=run_dir.name), + ), + ] + + +def _make_run_dirs(tmp_path: Path, n: int) -> list[Path]: + dirs = [] + for i in range(n): + d = tmp_path / "runs" / str(i) + d.mkdir(parents=True) + dirs.append(d) + return dirs + + +class TestGetSortedDirs: + def test_sorts_numeric_dirs_numerically(self, tmp_path: Path): + dirs = [tmp_path / n for n in ["10", "2", "1"]] + assert get_sorted_dirs(dirs) == [ + tmp_path / "1", + tmp_path / "2", + tmp_path / "10", + ] + + def test_falls_back_to_alphabetical(self, tmp_path: Path): + dirs = [tmp_path / n for n in ["b", "a", "c"]] + assert get_sorted_dirs(dirs) == [tmp_path / n for n in ["a", "b", "c"]] + + +class TestGenerateProductsSequential: + def test_writes_all_runs_to_store(self, tmp_path: Path): + run_dirs = _make_run_dirs(tmp_path, 5) + db_path = tmp_path / "trendify.db" + + total = generate_products(_generator, run_dirs, db_path, n_procs=1) + + assert total == 10 # 2 products per run x 5 runs + with ProductStore.open(db_path, readonly=True) as store: + assert len(store.get_products_of_type(Point2D)) == 5 + assert len(store.get_products_of_type(Trace2D, tag=("traces", "run"))) == 5 + + def test_rerunning_is_idempotent(self, tmp_path: Path): + run_dirs = _make_run_dirs(tmp_path, 3) + db_path = tmp_path / "trendify.db" + + generate_products(_generator, run_dirs, db_path, n_procs=1) + generate_products(_generator, run_dirs, db_path, n_procs=1) + + with ProductStore.open(db_path, readonly=True) as store: + assert len(store.get_products_of_type(Point2D)) == 3 + + +class TestGenerateProductsParallel: + def test_matches_sequential_output(self, tmp_path: Path): + run_dirs = _make_run_dirs(tmp_path, 8) + db_path = tmp_path / "trendify.db" + + total = generate_products(_generator, run_dirs, db_path, n_procs=2) + + assert total == 16 + with ProductStore.open(db_path, readonly=True) as store: + points = store.get_products_of_type(Point2D) + assert {p.x for p in points} == {float(i) for i in range(8)} diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py new file mode 100644 index 0000000..3c45b36 --- /dev/null +++ b/tests/test_pipeline.py @@ -0,0 +1,103 @@ +"""Tests for full pipeline""" + +from pathlib import Path + +from trendify.base.data_product import DataProduct +from trendify.pipeline import TrendifyPipeline +from trendify.plotting.point import Point2D +from trendify.store.product_store import ProductStore + + +def _generator(run_dir: Path) -> list[DataProduct]: + n = int(run_dir.name) + return [Point2D(tags=["scatter"], x=float(n), y=float(n) ** 2)] + + +def _make_run_dirs(tmp_path: Path, n: int) -> list[Path]: + dirs = [] + for i in range(n): + d = tmp_path / "raw" / str(i) + d.mkdir(parents=True) + dirs.append(d) + return dirs + + +class TestTrendifyPipelinePaths: + def test_db_path_and_assets_dir(self, tmp_path: Path): + pipeline = TrendifyPipeline(output_dir=tmp_path) + assert pipeline.db_path == tmp_path / "trendify.db" + assert pipeline.assets_dir == tmp_path / "assets" + + +class TestGenerate: + def test_writes_products(self, tmp_path: Path): + dirs = _make_run_dirs(tmp_path, 3) + pipeline = TrendifyPipeline(output_dir=tmp_path / "out") + + total = pipeline.generate(_generator, dirs) + + assert total == 3 + with ProductStore.open(pipeline.db_path, readonly=True) as store: + assert len(store.get_products_of_type(Point2D)) == 3 + + +class TestRender: + def test_writes_figure_for_tag(self, tmp_path: Path): + dirs = _make_run_dirs(tmp_path, 2) + pipeline = TrendifyPipeline(output_dir=tmp_path / "out") + pipeline.generate(_generator, dirs) + + pipeline.render(dpi=50) + + assert (pipeline.assets_dir / "scatter.jpg").exists() + + def test_no_flags_suppress_output(self, tmp_path: Path): + dirs = _make_run_dirs(tmp_path, 2) + pipeline = TrendifyPipeline(output_dir=tmp_path / "out") + pipeline.generate(_generator, dirs) + + pipeline.render(no_xy_plots=True) + + assert not (pipeline.assets_dir / "scatter.jpg").exists() + + +class TestMakeIncludeFiles: + def test_writes_include_md(self, tmp_path: Path): + dirs = _make_run_dirs(tmp_path, 2) + pipeline = TrendifyPipeline(output_dir=tmp_path / "out") + pipeline.generate(_generator, dirs) + pipeline.render(dpi=50) + + pipeline.make_include_files() + + assert (pipeline.assets_dir / "include.md").exists() + + +class TestRun: + def test_generates_and_renders_end_to_end(self, tmp_path: Path): + dirs = _make_run_dirs(tmp_path, 3) + pipeline = TrendifyPipeline(output_dir=tmp_path / "out", n_procs=2) + + total = pipeline.run(_generator, dirs, dpi=50) + + assert total == 3 + assert (pipeline.assets_dir / "scatter.jpg").exists() + assert (pipeline.assets_dir / "include.md").exists() + + def test_no_include_files_suppresses_include_md(self, tmp_path: Path): + dirs = _make_run_dirs(tmp_path, 2) + pipeline = TrendifyPipeline(output_dir=tmp_path / "out") + + pipeline.run(_generator, dirs, dpi=50, no_include_files=True) + + assert not (pipeline.assets_dir / "include.md").exists() + + def test_all_assets_suppressed_skips_render_and_include(self, tmp_path: Path): + dirs = _make_run_dirs(tmp_path, 2) + pipeline = TrendifyPipeline(output_dir=tmp_path / "out") + + pipeline.run( + _generator, dirs, no_tables=True, no_xy_plots=True, no_histograms=True + ) + + assert not pipeline.assets_dir.exists() diff --git a/tests/test_product_store.py b/tests/test_product_store.py index 0aee658..c1da9aa 100644 --- a/tests/test_product_store.py +++ b/tests/test_product_store.py @@ -1,3 +1,5 @@ +"""Tests for data product store""" + from pathlib import Path import polars as pl @@ -5,12 +7,12 @@ from trendify.base.data_product import DataProduct from trendify.base.pen import Pen +from trendify.formats.format2d import XYData from trendify.formats.table import TableEntry from trendify.plotting.axline import AxLine, LineOrientation from trendify.plotting.histogram import HistogramEntry from trendify.plotting.point import Point2D from trendify.plotting.trace import Trace2D -from trendify.formats.format2d import XYData from trendify.store.product_store import ProductStore from trendify.store.tags import decode_tag, encode_tag @@ -121,7 +123,7 @@ def test_no_filters_returns_everything(self, store: ProductStore, tmp_path: Path def test_multi_tag_product_is_not_duplicated_in_storage( self, store: ProductStore, tmp_path: Path ): - # Point2D above carries two tags ("a" and ("a","b")) -- this is precisely the + # Point2D above carries two tags ("a" and ("a","b")), which is precisely the # v1 disk-duplication complaint: assert there's exactly one `products` row for # it, with two matching `product_tags` index rows, not two payload copies. store.write_run(tmp_path / "run1", _sample_products()) diff --git a/tests/test_render.py b/tests/test_render.py new file mode 100644 index 0000000..c85e055 --- /dev/null +++ b/tests/test_render.py @@ -0,0 +1,131 @@ +"""Tests for rendering assets (figures)""" + +from pathlib import Path + +import pytest + +from trendify.base.pen import Pen +from trendify.formats.table import TableEntry +from trendify.generator.render import make_include_files, render_assets +from trendify.plotting.histogram import HistogramEntry +from trendify.plotting.point import Point2D +from trendify.plotting.trace import Trace2D +from trendify.store.product_store import ProductStore + + +@pytest.fixture +def store(tmp_path: Path): + with ProductStore.open(tmp_path / "trendify.db") as s: + yield s + + +class TestRenderAssets: + def test_xy_plot_and_histogram_write_separate_files_for_shared_tag( + self, store: ProductStore, tmp_path: Path + ): + # A tag used for both XY and histogram products is precisely the v1 bug + # (rewrite_reference/OVERVIEW.md #8 item 9): they must not overlay on one figure/file. + products = [ + Trace2D.from_xy( + tags=["shared"], x=[0, 1, 2], y=[0, 1, 4], pen=Pen(label="trace") + ), + HistogramEntry(tags=["shared"], value=1.0), + HistogramEntry(tags=["shared"], value=2.0), + ] + store.write_run(tmp_path / "run1", products) + + out_dir = tmp_path / "out" + render_assets(store, out_dir) + + xy_path = out_dir / "shared.jpg" + hist_path = out_dir / "shared_histogram.jpg" + assert xy_path.exists() + assert hist_path.exists() + assert xy_path != hist_path + + def test_renders_table_xy_and_histogram_for_distinct_tags( + self, store: ProductStore, tmp_path: Path + ): + products = [ + TableEntry(tags=["tbl"], row="r1", col="c1", value=1.0), + Point2D(tags=["scatter"], x=1.0, y=2.0), + HistogramEntry(tags=["hist"], value=1.0), + ] + store.write_run(tmp_path / "run1", products) + + out_dir = tmp_path / "out" + render_assets(store, out_dir) + + assert (out_dir / "tbl_melted.csv").exists() + assert (out_dir / "scatter.jpg").exists() + assert (out_dir / "hist_histogram.jpg").exists() + + def test_tuple_tag_nests_output_path(self, store: ProductStore, tmp_path: Path): + store.write_run( + tmp_path / "run1", [Point2D(tags=[("group", "scatter")], x=1.0, y=2.0)] + ) + + out_dir = tmp_path / "out" + render_assets(store, out_dir) + + assert (out_dir / "group" / "scatter.jpg").exists() + + def test_no_flags_suppress_corresponding_output( + self, store: ProductStore, tmp_path: Path + ): + products = [ + TableEntry(tags=["tag"], row="r1", col="c1", value=1.0), + Point2D(tags=["tag"], x=1.0, y=2.0), + HistogramEntry(tags=["tag"], value=1.0), + ] + store.write_run(tmp_path / "run1", products) + + out_dir = tmp_path / "out" + render_assets( + store, out_dir, no_tables=True, no_xy_plots=True, no_histograms=True + ) + + assert list(out_dir.glob("**/*")) == [] + + def test_mismatched_format2d_across_products_does_not_crash( + self, store: ProductStore, tmp_path: Path + ): + # Products sharing a tag with no explicit format2d (all None) previously crashed + # Format2D.union_from_iterable with "not enough values to unpack" in v1; render_assets + # should just skip applying a format rather than error. + store.write_run( + tmp_path / "run1", + [Point2D(tags=["tag"], x=1.0, y=2.0), Point2D(tags=["tag"], x=2.0, y=3.0)], + ) + out_dir = tmp_path / "out" + render_assets(store, out_dir) + assert (out_dir / "tag.jpg").exists() + + +class TestMakeIncludeFiles: + def test_writes_include_md_referencing_figures_and_tables(self, tmp_path: Path): + (tmp_path / "fig.jpg").touch() + (tmp_path / "table_pivot.csv").touch() + + make_include_files(tmp_path) + + include_text = (tmp_path / "include.md").read_text() + assert "fig.jpg" in include_text + assert "table_pivot.csv" in include_text + + def test_nested_directories_get_child_includes(self, tmp_path: Path): + child = tmp_path / "child" + child.mkdir() + (child / "fig.jpg").touch() + + make_include_files(tmp_path) + + parent_include = (tmp_path / "include.md").read_text() + assert "child" in parent_include + assert (child / "include.md").exists() + + def test_empty_tree_is_a_noop(self, tmp_path: Path): + empty = tmp_path / "empty" + empty.mkdir() + make_include_files(empty) + assert (empty / "include.md").exists() diff --git a/tests/test_table_builder.py b/tests/test_table_builder.py new file mode 100644 index 0000000..003c9e4 --- /dev/null +++ b/tests/test_table_builder.py @@ -0,0 +1,135 @@ +"""Tests for table building""" + +from pathlib import Path + +import polars as pl +import pytest + +from trendify.formats.table import TableEntry +from trendify.generator.table_builder import TableBuilder +from trendify.store.product_store import ProductStore + + +@pytest.fixture +def store(tmp_path: Path): + with ProductStore.open(tmp_path / "trendify.db") as s: + yield s + + +class TestPivotTable: + def test_pivots_row_col_value(self): + melted = pl.DataFrame( + { + "row": ["r1", "r1", "r2"], + "col": ["c1", "c2", "c1"], + "value": [1.0, 2.0, 3.0], + } + ) + pivot = TableBuilder.pivot_table(melted) + assert pivot is not None + assert set(pivot.columns) == {"row", "c1", "c2"} + assert pivot.height == 2 + + def test_duplicate_row_col_pair_returns_none(self): + melted = pl.DataFrame( + {"row": ["r1", "r1"], "col": ["c1", "c1"], "value": [1.0, 2.0]} + ) + assert TableBuilder.pivot_table(melted) is None + + +class TestGetStatsTable: + def test_computes_stats_for_numeric_columns(self): + pivot = pl.DataFrame({"row": ["r1", "r2", "r3"], "c1": [1.0, 2.0, 3.0]}) + stats = TableBuilder.get_stats_table(pivot) + assert stats is not None + row = stats.to_dicts()[0] + assert row["Name"] == "c1" + assert row["min"] == 1.0 + assert row["mean"] == 2.0 + assert row["max"] == 3.0 + + def test_coerces_non_numeric_to_null(self): + pivot = pl.DataFrame({"row": ["r1", "r2"], "c1": ["hello", "world"]}) + stats = TableBuilder.get_stats_table(pivot) + assert stats is None + + def test_no_value_columns_returns_none(self): + pivot = pl.DataFrame({"row": ["r1", "r2"]}) + assert TableBuilder.get_stats_table(pivot) is None + + +class TestProcessTableEntries: + def test_writes_melted_pivot_and_stats_csvs(self, tmp_path: Path): + melted = pl.DataFrame( + { + "row": ["r1", "r1", "r2", "r2"], + "col": ["c1", "c2", "c1", "c2"], + "value": [1.0, 2.0, 3.0, 4.0], + "unit": [None, None, None, None], + } + ) + TableBuilder.process_table_entries(tag="mytag", melted=melted, out_dir=tmp_path) + + assert (tmp_path / "mytag_melted.csv").exists() + assert (tmp_path / "mytag_pivot.csv").exists() + assert (tmp_path / "mytag_stats.csv").exists() + + def test_tuple_tag_nests_output_directory(self, tmp_path: Path): + melted = pl.DataFrame( + {"row": ["r1"], "col": ["c1"], "value": [1.0], "unit": [None]} + ) + TableBuilder.process_table_entries( + tag=("group", "mytag"), melted=melted, out_dir=tmp_path + ) + assert (tmp_path / "group" / "mytag_melted.csv").exists() + + def test_empty_melted_writes_nothing(self, tmp_path: Path): + melted = pl.DataFrame( + schema={"row": pl.Utf8, "col": pl.Utf8, "value": pl.Float64} + ) + TableBuilder.process_table_entries(tag="empty", melted=melted, out_dir=tmp_path) + assert list(tmp_path.iterdir()) == [] + + def test_heterogeneous_value_types_write_csv_without_error(self, tmp_path: Path): + # Object-dtype "value" column, as returned by ProductStore.get_table_entries. + melted = pl.DataFrame( + { + "row": ["r1", "r1", "r2"], + "col": ["c1", "c2", "c1"], + "value": [1.5, "hello", True], + "unit": [None, None, None], + }, + schema={ + "row": pl.Utf8, + "col": pl.Utf8, + "value": pl.Object, + "unit": pl.Utf8, + }, + ) + TableBuilder.process_table_entries(tag="mixed", melted=melted, out_dir=tmp_path) + + melted_csv = (tmp_path / "mixed_melted.csv").read_text() + assert "1.5" in melted_csv + assert "hello" in melted_csv + assert "True" in melted_csv + + +class TestProcessTableEntriesFromStore: + def test_round_trips_through_product_store( + self, store: ProductStore, tmp_path: Path + ): + products = [ + TableEntry(tags=["tbl"], row="r1", col="c1", value=1.0), + TableEntry(tags=["tbl"], row="r1", col="c2", value=2.0), + TableEntry(tags=["tbl"], row="r2", col="c1", value=3.0), + TableEntry(tags=["tbl"], row="r2", col="c2", value=4.0), + ] + store.write_run(tmp_path / "run1", products) + + melted = store.get_table_entries("tbl") + out_dir = tmp_path / "out" + TableBuilder.process_table_entries(tag="tbl", melted=melted, out_dir=out_dir) + + assert (out_dir / "tbl_pivot.csv").exists() + stats_csv = (out_dir / "tbl_stats.csv").read_text() + assert "mean" in stats_csv diff --git a/update_docs_and_push_changes.sh b/update_docs_and_push_changes.sh deleted file mode 100755 index b96b33f..0000000 --- a/update_docs_and_push_changes.sh +++ /dev/null @@ -1,17 +0,0 @@ -#! /bin/bash - -git add src -git commit -m 'updates' - -python -m mkdocs build - -git add --all -git commit -m 'updated docs and site' - -python -m mkdocs gh-deploy - -git push - -rm -r dist/ -python -m build -twine upload dist/* diff --git a/uv.lock b/uv.lock index 98fcc5f..2e418f1 100644 --- a/uv.lock +++ b/uv.lock @@ -1,17 +1,6 @@ version = 1 revision = 3 -requires-python = ">=3.11" -resolution-markers = [ - "python_full_version >= '3.14' and sys_platform == 'win32'", - "python_full_version >= '3.14' and sys_platform == 'emscripten'", - "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'win32'", - "python_full_version >= '3.12' and python_full_version < '3.14' and 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"python_full_version >= '3.12'" }, + { name = "numpy" }, { name = "numpydantic" }, { name = "plotly" }, { name = "polars" }, From 9b2b572250b2c6decb0dc955b2619ee182d5846b Mon Sep 17 00:00:00 2001 From: David Gable Date: Sat, 4 Jul 2026 10:36:12 -0700 Subject: [PATCH 05/19] Building new dashboard and expanding the tests. Breaking changes incoming. --- gallery/data/data_products.json | 1 - gallery/gallery.py | 327 ------ gallery/trendify/assets/static/analysis.jpg | Bin 312163 -> 0 bytes gallery/trendify/assets/static/clusters.jpg | Bin 312163 -> 0 bytes gallery/trendify/assets/static/comparison.jpg | Bin 254491 -> 0 bytes .../trendify/assets/static/distributions.jpg | Bin 254491 -> 0 bytes .../assets/static/distributions_melted.csv | 19 - .../assets/static/distributions_pivot.csv | 4 - .../assets/static/distributions_stats.csv | 4 - gallery/trendify/assets/static/sine.jpg | Bin 358688 -> 0 bytes .../assets/static/statistics_melted.csv | 19 - .../assets/static/statistics_pivot.csv | 4 - .../assets/static/statistics_stats.csv | 4 - gallery/trendify/assets/static/waves.jpg | Bin 358688 -> 0 bytes gallery/trendify/products/analysis/0.json | 1 - gallery/trendify/products/analysis/1.json | 1 - .../trendify/products/analysis/index_map.csv | 2 - gallery/trendify/products/clusters/0.json | 1 - gallery/trendify/products/clusters/1.json | 1 - .../trendify/products/clusters/index_map.csv | 2 - gallery/trendify/products/comparison/0.json | 1 - gallery/trendify/products/comparison/1.json | 1 - .../products/comparison/index_map.csv | 2 - .../trendify/products/distributions/0.json | 1 - .../trendify/products/distributions/1.json | 1 - .../products/distributions/index_map.csv | 2 - gallery/trendify/products/sine/0.json | 1 - gallery/trendify/products/sine/1.json | 1 - gallery/trendify/products/sine/index_map.csv | 2 - gallery/trendify/products/statistics/0.json | 1 - gallery/trendify/products/statistics/1.json | 1 - .../products/statistics/index_map.csv | 2 - .../products/statistics/reserving_index.lock | 0 gallery/trendify/products/waves/0.json | 1 - gallery/trendify/products/waves/1.json | 1 - gallery/trendify/products/waves/index_map.csv | 2 - .../products/waves/reserving_index.lock | 0 pyproject.toml | 6 +- requirements.txt | 129 ++- sample_data/.gitignore | 2 - sample_data/models/0/data_product.json | 1 - sample_data/models/0/results.csv | 47 - sample_data/models/0/stdin.csv | 7 - sample_data/models/1/data_product.json | 1 - sample_data/models/1/results.csv | 49 - sample_data/models/1/stdin.csv | 7 - sample_data/models/2/data_product.json | 1 - sample_data/models/2/results.csv | 42 - sample_data/models/2/stdin.csv | 7 - sample_data/models/3/data_product.json | 1 - sample_data/models/3/results.csv | 44 - sample_data/models/3/stdin.csv | 7 - sample_data/models/4/data_product.json | 1 - sample_data/models/4/results.csv | 42 - sample_data/models/4/stdin.csv | 7 - sample_data/models/5/data_product.json | 1 - sample_data/models/5/results.csv | 50 - sample_data/models/5/stdin.csv | 7 - sample_data/models/6/data_product.json | 1 - sample_data/models/6/results.csv | 46 - sample_data/models/6/stdin.csv | 7 - sample_data/models/7/data_product.json | 1 - sample_data/models/7/results.csv | 47 - sample_data/models/7/stdin.csv | 7 - sample_data/models/8/data_product.json | 1 - sample_data/models/8/results.csv | 44 - sample_data/models/8/stdin.csv | 7 - sample_data/models/9/data_product.json | 1 - sample_data/models/9/results.csv | 46 - sample_data/models/9/stdin.csv | 7 - scripts/gen_ts_models.py | 243 +++++ scripts/matrix_generator.py | 792 +++++++++++++++ src/trendify/__init__.py | 6 +- src/trendify/cli.py | 301 +++++- src/trendify/examples.py | 65 +- src/trendify/formats/format2d.py | 7 +- src/trendify/generator/__init__.py | 15 +- src/trendify/generator/generate.py | 102 +- src/trendify/generator/render.py | 100 +- src/trendify/generator/xy_data_plotter.py | 14 +- src/trendify/pipeline.py | 28 +- src/trendify/plotting/__init__.py | 6 + src/trendify/plotting/figure.py | 22 +- src/trendify/plotting/histogram.py | 19 +- src/trendify/plotting/scatter.py | 130 +++ src/trendify/plotting/trace.py | 105 +- src/trendify/store/product_store.py | 5 +- .../trendify/viewer/__init__.py | 0 src/trendify/viewer/app.py | 108 ++ src/trendify/viewer/plot_config.py | 100 ++ src/trendify/viewer/routes/__init__.py | 7 + src/trendify/viewer/routes/api.py | 133 +++ src/trendify/viewer/routes/pages.py | 33 + src/trendify/viewer/tag_tree.py | 128 +++ src/trendify/viewer/templates/base.html | 48 + src/trendify/viewer/templates/index.html | 75 ++ .../viewer/templates/partials/__init__.py | 0 .../viewer/templates/partials/footer.html | 15 + .../viewer/templates/partials/nav.html | 148 +++ .../viewer/templates/partials/sidebar.html | 156 +++ .../viewer/templates/partials/table_view.html | 26 + .../viewer/templates/static/__init__.py | 0 .../templates/static/assets/__init__.py | 0 .../viewer/templates/static/assets/logo.svg | 11 +- .../templates/static/assets/logo_white_bg.svg | 33 +- .../viewer/templates/static/css/app.css | 69 ++ .../viewer/templates/static/js/main.js | 2 + .../viewer/templates/static/ts/build.mjs | 23 + .../templates/static/ts/package-lock.json | 537 ++++++++++ .../viewer/templates/static/ts/package.json | 15 + .../viewer/templates/static/ts/src/lib/api.ts | 45 + .../templates/static/ts/src/lib/clipboard.ts | 31 + .../templates/static/ts/src/lib/connection.ts | 56 ++ .../templates/static/ts/src/lib/fullscreen.ts | 33 + .../static/ts/src/lib/local-storage.ts | 14 + .../templates/static/ts/src/lib/resize.ts | 57 ++ .../static/ts/src/lib/sidebar-node.ts | 49 + .../templates/static/ts/src/lib/table-view.ts | 169 ++++ .../templates/static/ts/src/lib/theme.ts | 47 + .../templates/static/ts/src/lib/toast.ts | 69 ++ .../templates/static/ts/src/lib/url-state.ts | 55 + .../viewer/templates/static/ts/src/main.ts | 79 ++ .../templates/static/ts/src/types/global.d.ts | 17 + .../viewer/templates/static/ts/tsconfig.json | 16 + .../templates/static/vendored/__init__.py | 0 .../static/vendored/alpine-3.15.12.min.js | 5 + .../static/vendored/alpine-LICENSE.md | 21 + .../static/vendored/dataTables-LICENSE.txt | 3 + .../vendored/dataTables.dataTables.min.css | 1 + .../static/vendored/dataTables.min.js | 4 + .../static/vendored/jquery-3.7.1.min.js | 2 + .../static/vendored/jquery-LICENSE.txt | 3 + .../static/vendored/tailwind-4.3.2.global.js | 947 ++++++++++++++++++ .../static/vendored/tailwind-LICENSE | 21 + tests/test_cli.py | 25 - tests/test_pipeline.py | 21 - tests/test_render.py | 31 +- tests/test_viewer_api.py | 144 +++ uv.lock | 220 ++++ 139 files changed, 5550 insertions(+), 1346 deletions(-) delete mode 100644 gallery/data/data_products.json delete mode 100644 gallery/gallery.py delete mode 100644 gallery/trendify/assets/static/analysis.jpg delete mode 100644 gallery/trendify/assets/static/clusters.jpg delete mode 100644 gallery/trendify/assets/static/comparison.jpg delete mode 100644 gallery/trendify/assets/static/distributions.jpg delete mode 100644 gallery/trendify/assets/static/distributions_melted.csv delete mode 100644 gallery/trendify/assets/static/distributions_pivot.csv delete mode 100644 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src/trendify/viewer/routes/api.py create mode 100644 src/trendify/viewer/routes/pages.py create mode 100644 src/trendify/viewer/tag_tree.py create mode 100644 src/trendify/viewer/templates/base.html create mode 100644 src/trendify/viewer/templates/index.html rename gallery/trendify/products/clusters/reserving_index.lock => src/trendify/viewer/templates/partials/__init__.py (100%) create mode 100644 src/trendify/viewer/templates/partials/footer.html create mode 100644 src/trendify/viewer/templates/partials/nav.html create mode 100644 src/trendify/viewer/templates/partials/sidebar.html create mode 100644 src/trendify/viewer/templates/partials/table_view.html rename gallery/trendify/products/comparison/reserving_index.lock => src/trendify/viewer/templates/static/__init__.py (100%) rename gallery/trendify/products/distributions/reserving_index.lock => src/trendify/viewer/templates/static/assets/__init__.py (100%) create mode 100644 src/trendify/viewer/templates/static/css/app.css create mode 100644 src/trendify/viewer/templates/static/js/main.js create mode 100644 src/trendify/viewer/templates/static/ts/build.mjs create mode 100644 src/trendify/viewer/templates/static/ts/package-lock.json create mode 100644 src/trendify/viewer/templates/static/ts/package.json create mode 100644 src/trendify/viewer/templates/static/ts/src/lib/api.ts create mode 100644 src/trendify/viewer/templates/static/ts/src/lib/clipboard.ts create mode 100644 src/trendify/viewer/templates/static/ts/src/lib/connection.ts create mode 100644 src/trendify/viewer/templates/static/ts/src/lib/fullscreen.ts create mode 100644 src/trendify/viewer/templates/static/ts/src/lib/local-storage.ts create mode 100644 src/trendify/viewer/templates/static/ts/src/lib/resize.ts create mode 100644 src/trendify/viewer/templates/static/ts/src/lib/sidebar-node.ts create mode 100644 src/trendify/viewer/templates/static/ts/src/lib/table-view.ts create mode 100644 src/trendify/viewer/templates/static/ts/src/lib/theme.ts create mode 100644 src/trendify/viewer/templates/static/ts/src/lib/toast.ts create mode 100644 src/trendify/viewer/templates/static/ts/src/lib/url-state.ts create mode 100644 src/trendify/viewer/templates/static/ts/src/main.ts create mode 100644 src/trendify/viewer/templates/static/ts/src/types/global.d.ts create mode 100644 src/trendify/viewer/templates/static/ts/tsconfig.json rename gallery/trendify/products/sine/reserving_index.lock => src/trendify/viewer/templates/static/vendored/__init__.py (100%) create mode 100644 src/trendify/viewer/templates/static/vendored/alpine-3.15.12.min.js create mode 100644 src/trendify/viewer/templates/static/vendored/alpine-LICENSE.md create mode 100644 src/trendify/viewer/templates/static/vendored/dataTables-LICENSE.txt create mode 100644 src/trendify/viewer/templates/static/vendored/dataTables.dataTables.min.css create mode 100644 src/trendify/viewer/templates/static/vendored/dataTables.min.js create mode 100644 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Dev","value":"1.00","unit":null,"product_type":"TableEntry"},{"tags":["distributions","statistics"],"metadata":{},"row":"Exponential","col":"Skewness","value":"2.00","unit":null,"product_type":"TableEntry"}]} \ No newline at end of file diff --git a/gallery/gallery.py b/gallery/gallery.py deleted file mode 100644 index 90a824f..0000000 --- a/gallery/gallery.py +++ /dev/null @@ -1,327 +0,0 @@ -""" -Example script demonstrating trendify data products. -Run with: python example_trendify_static.py -Then run: trendify make static example_output example_results -""" - -import platform -import subprocess -import sys -import numpy as np -from pathlib import Path -from trendify.api.api import ( - Trace2D, - Point2D, - TableEntry, - HistogramEntry, - AxLine, - Pen, - Marker, - Format2D, - LineOrientation, - HistogramStyle, - DataProductCollection, -) - - -def generate_sine_wave_example(): - # Create sine wave with noise - x = np.linspace(0, 4 * np.pi, 100) - y = np.sin(x) + np.random.normal(0, 0.1, len(x)) - - # Create main trace - trace = Trace2D.from_xy( - tags=("waves", "sine"), - x=x, - y=y, - pen=Pen(color="blue", label="Noisy Sine", size=2), - format2d=Format2D( - title_fig="Sine Wave with Reference Lines", - title_ax="Sine Function with Noise", - label_x="X (radians)", - label_y="Amplitude", - lim_x_min=0, - lim_x_max=4 * np.pi, - lim_y_min=-1.5, - lim_y_max=1.5, - ), - ) - - # Add reference lines - hline_upper = AxLine( - tags=("waves", "sine"), - value=1.0, - orientation=LineOrientation.HORIZONTAL, - pen=Pen(color="red", label="Upper Bound", size=1.5), - ) - - hline_lower = AxLine( - tags=("waves", "sine"), - value=-1.0, - orientation=LineOrientation.HORIZONTAL, - pen=Pen(color="red", label="Lower Bound", size=1.5), - ) - - # Add some peak points - peaks = [] - for i, (x_val, y_val) in enumerate(zip(x[1:-1], y[1:-1])): - if y_val > y[i] and y_val > y[i + 2]: # Simple peak detection - peaks.append( - Point2D( - tags=("waves", "sine"), - x=x_val, - y=y_val, - marker=Marker(color="green", symbol="o", size=100, label="Peak"), - ) - ) - - return [trace, hline_upper, hline_lower] + peaks - - -def generate_scatter_plot_example(): - # Create two clusters of points - n_points = 50 - - # Cluster 1 - x1 = np.random.normal(2, 0.5, n_points) - y1 = np.random.normal(2, 0.5, n_points) - - # Cluster 2 - x2 = np.random.normal(4, 0.5, n_points) - y2 = np.random.normal(4, 0.5, n_points) - - format2d = Format2D( - title_fig="Cluster Analysis", - title_ax="Two Clusters with Centroids", - label_x="X Position", - label_y="Y Position", - lim_x_min=0, - lim_x_max=6, - lim_y_min=0, - lim_y_max=6, - ) - - # Create points for each cluster - cluster1 = [ - Point2D( - tags=("clusters", "analysis"), - x=x, - y=y, - marker=Marker(color="blue", symbol="o", size=50, label="Cluster 1"), - format2d=format2d, - ) - for x, y in zip(x1, y1) - ] - - cluster2 = [ - Point2D( - tags=("clusters", "analysis"), - x=x, - y=y, - marker=Marker(color="red", symbol="o", size=50, label="Cluster 2"), - format2d=format2d, - ) - for x, y in zip(x2, y2) - ] - - # Add centroids - centroids = [ - Point2D( - tags=("clusters", "analysis"), - x=np.mean(x1), - y=np.mean(y1), - marker=Marker(color="darkblue", symbol="*", size=200, label="Centroid 1"), - format2d=format2d, - ), - Point2D( - tags=("clusters", "analysis"), - x=np.mean(x2), - y=np.mean(y2), - marker=Marker(color="darkred", symbol="*", size=200, label="Centroid 2"), - format2d=format2d, - ), - ] - - return cluster1 + cluster2 + centroids - - -def generate_histogram_example(): - # Generate three different distributions - n_samples = 1000 - - # Normal distribution - normal_data = np.random.normal(0, 1, n_samples) - normal_entries = [ - HistogramEntry( - tags=("distributions", "comparison"), - value=v, - style=HistogramStyle(color="blue", label="Normal", alpha_face=0.3, bins=30), - format2d=Format2D( - title_fig="Distribution Comparison", - title_ax="Multiple Distributions", - label_x="Value", - label_y="Count", - ), - ) - for v in normal_data - ] - - # Uniform distribution - uniform_data = np.random.uniform(-2, 2, n_samples) - uniform_entries = [ - HistogramEntry( - tags=("distributions", "comparison"), - value=v, - style=HistogramStyle(color="red", label="Uniform", alpha_face=0.3, bins=30), - ) - for v in uniform_data - ] - - # Exponential distribution - exp_data = np.random.exponential(1, n_samples) - exp_entries = [ - HistogramEntry( - tags=("distributions", "comparison"), - value=v, - style=HistogramStyle( - color="green", label="Exponential", alpha_face=0.3, bins=30 - ), - ) - for v in exp_data - ] - - return normal_entries + uniform_entries + exp_entries - - -def generate_table_example(): - # Create a comparison table of distribution statistics - distributions = ["Normal", "Uniform", "Exponential"] - metrics = ["Mean", "Std Dev", "Skewness"] - - # Generate some example statistics - stats = { - "Normal": [0.01, 1.02, 0.05], - "Uniform": [0.0, 1.15, 0.0], - "Exponential": [1.0, 1.0, 2.0], - } - - table_entries = [] - for dist in distributions: - for i, metric in enumerate(metrics): - table_entries.append( - TableEntry( - tags=("distributions", "statistics"), - row=dist, - col=metric, - value=f"{stats[dist][i]:.2f}", - unit=None, - ) - ) - - return table_entries - - -def run_trendify_commands(input_dir: str | Path, output_dir: str | Path): - """ - Runs trendify commands with proper OS handling - - Args: - input_dir (str | Path): Input directory containing data products - output_dir (str | Path): Output directory for sorted products and assets - - """ - # Convert to Path objects - input_dir = Path(input_dir).resolve() - output_dir = Path(output_dir).resolve() - - # Determine if we need shell=True based on OS - use_shell = platform.system() == "Windows" - - commands = [ - [ - "trendify", - "products-sort", - "-i", - str(input_dir), - "-o", - str(output_dir), - "-n", - "1", - ], - ["trendify", "assets-make-static", str(output_dir), "-n", "1"], - ] - - for cmd in commands: - print(f"\nExecuting command: {' '.join(cmd)}") - try: - # Run command and capture output - result = subprocess.run( - cmd, - shell=use_shell, - check=True, - text=True, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - ) - print("Command output:") - print(result.stdout) - - # Print any stderr output if it exists - if result.stderr: - print("Warnings/Errors:") - print(result.stderr) - - except subprocess.CalledProcessError as e: - print(f"Error executing command: {' '.join(cmd)}") - print(f"Exit code: {e.returncode}") - print("Error output:") - print(e.stderr) - sys.exit(1) - except Exception as e: - print(f"Unexpected error executing command: {' '.join(cmd)}") - print(f"Error: {e!s}") - sys.exit(1) - - -def make_gallery(output_dir: Path): - """ - Generates the gallery - """ - # Create output directory - output_dir.mkdir(exist_ok=True, parents=True) - data_dir = output_dir.joinpath("data") - data_dir.mkdir(exist_ok=True, parents=True) - trendify_dir = output_dir.joinpath("trendify") - - try: - from importlib.resources import files - from shutil import copy - - copy( - str(files("trendify").joinpath("gallery.py").resolve()), - str(output_dir.joinpath("gallery.py")), - ) - except Exception as e: - print(e) - - # Generate all examples - products = ( - generate_sine_wave_example() - + generate_scatter_plot_example() - + generate_histogram_example() - + generate_table_example() - ) - - # Save to JSON file - collection = DataProductCollection(elements=products) - data_dir.joinpath("data_products.json").write_text(collection.model_dump_json()) - - # Run trendify commands - run_trendify_commands( - input_dir=data_dir, - output_dir=trendify_dir, - ) - - -if __name__ == "__main__": - make_gallery(Path("gallery")) diff --git a/gallery/trendify/assets/static/analysis.jpg b/gallery/trendify/assets/static/analysis.jpg deleted file mode 100644 index 72e80d045d702cfe0f0c0655123e89d1a5c4e8e1..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 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\ No newline at end of file diff --git a/gallery/trendify/products/waves/index_map.csv b/gallery/trendify/products/waves/index_map.csv deleted file mode 100644 index 2931929..0000000 --- a/gallery/trendify/products/waves/index_map.csv +++ /dev/null @@ -1,2 +0,0 @@ -0,/Users/talbotknighton/Documents/trendify/gallery/data -1,/Users/talbotknighton/Documents/trendify/gallery/data \ No newline at end of file diff --git a/gallery/trendify/products/waves/reserving_index.lock b/gallery/trendify/products/waves/reserving_index.lock deleted file mode 100644 index e69de29..0000000 diff --git a/pyproject.toml b/pyproject.toml index c36540d..427c566 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -9,6 +9,8 @@ description = "Tools for generating data products, storing, and interacting with readme = "README.md" requires-python = ">=3.12" dependencies = [ + "fastapi>=0.139.0", + "jinja2>=3.1.6", "matplotlib>=3.11.0", "numpy>=2.4.6", "numpydantic>=1.10.0", @@ -16,7 +18,9 @@ dependencies = [ "polars>=1.42.1", "pydantic>=2.13.4", "rich>=15.0.0", + "sse-starlette>=3.4.5", "typer>=0.26.8", + "uvicorn>=0.49.0", ] keywords = ["Data", "Products", "Process", "Post", "Database"] @@ -102,7 +106,7 @@ ignore = ["typings"] exclude_also = ["if __name__ == .__main__.:", "@(abc\\.)?abstractmethod"] [dependency-groups] -dev = ["mkinit>=1.2.0", "pytest>=9.1.1"] +dev = ["httpx>=0.28.1", "mkinit>=1.2.0", "pytest>=9.1.1"] [project.urls] "Homepage" = "https://github.com/talbotknighton/trendify" diff --git a/requirements.txt b/requirements.txt index 7dad0b6..afbdb96 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,15 +4,35 @@ annotated-doc==0.0.4 \ --hash=sha256:571ac1dc6991c450b25a9c2d84a3705e2ae7a53467b5d111c24fa8baabbed320 \ --hash=sha256:fbcda96e87e9c92ad167c2e53839e57503ecfda18804ea28102353485033faa4 - # via typer + # via + # fastapi + # typer annotated-types==0.7.0 \ --hash=sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53 \ --hash=sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89 # via pydantic +anyio==4.14.1 \ + --hash=sha256:4e5533c5b8ff0a24f5d7a176cbe6877129cd183893f66b537f8f227d10527d72 \ + --hash=sha256:8d648a3544c1a700e3ff78615cd679e4c5c3f149904287e73687b2596963629e + # via + # httpx + # sse-starlette + # starlette +certifi==2026.6.17 \ + --hash=sha256:024c88eeec92ca068db80f02b8b07c9cef7b9fe261d1d535abfd5abd6f6af432 \ + --hash=sha256:2227dcbaafe0d2f59279d1762ddddc37783ed4354594f194ffc31d20f41fc3db + # via + # httpcore + # httpx +click==8.4.2 \ + --hash=sha256:9a6cea6e60b17ebe0a44c5cc636d94f09bd66142c1cd7d8b4cd731c4917a15f6 \ + --hash=sha256:e6f9f66136c816745b9d65817da91d61d957fb16e02e4dcd0552553c5a197b76 + # via uvicorn colorama==0.4.6 ; sys_platform == 'win32' \ --hash=sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44 \ --hash=sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6 # via + # click # pytest # typer contourpy==1.3.3 \ @@ -77,6 +97,10 @@ cycler==0.12.1 \ --hash=sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30 \ --hash=sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c # via matplotlib +fastapi==0.139.0 \ + --hash=sha256:99ab7b2d92223c76d6cf10757ab3f89d45b38267fc20b2a136cf02f6beac3145 \ + --hash=sha256:cf15e1e9e667ddb0ad63811e60bd11390d1aac838ca4a7a23f421807b2308189 + # via trendify fonttools==4.63.0 \ --hash=sha256:0eac00b9118c3c2f87d272e45341871c5b3066baa3c86897fa634a7c3fb59096 \ --hash=sha256:1e874792a8212b44583ea02189d9e693906b2f78b261f372f95d6c563210ac1d \ @@ -113,10 +137,33 @@ fonttools==4.63.0 \ --hash=sha256:ef3048ef05dbb552b89817713d9cac912e00d0fde4a3105c00d29e52e10c89af \ --hash=sha256:fd1e3094f42d806d3d7c79162fc59e5910fcbe3a7360c385b8da969bc4493745 # via matplotlib +h11==0.16.0 \ + --hash=sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1 \ + --hash=sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86 + # via + # httpcore + # uvicorn +httpcore==1.0.9 \ + --hash=sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55 \ + --hash=sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8 + # via httpx +httpx==0.28.1 \ + --hash=sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc \ + --hash=sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad +idna==3.18 \ + --hash=sha256:7f952cbe720b688055e3f87de14f5c3e5fdaa8bc3928985c4077ca689de849a2 \ + --hash=sha256:ffb385a7e039654cef1ab9ef32c6fafe283c0c0467bba1d9029738ce4a14a848 + # via + # anyio + # httpx iniconfig==2.3.0 \ --hash=sha256:c76315c77db068650d49c5b56314774a7804df16fee4402c1f19d6d15d8c4730 \ --hash=sha256:f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12 # via pytest +jinja2==3.1.6 \ + --hash=sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d \ + --hash=sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67 + # via trendify kiwisolver==1.5.0 \ --hash=sha256:012b1eb16e28718fa782b5e61dc6f2da1f0792ca73bd05d54de6cb9561665fc9 \ --hash=sha256:0255a027391d52944eae1dbb5d4cc5903f57092f3674e8e544cdd2622826b3f0 \ @@ -202,6 +249,64 @@ markdown-it-py==4.2.0 \ --hash=sha256:04a21681d6fbb623de53f6f364d352309d4094dd4194040a10fd51833e418d49 \ --hash=sha256:9f7ebbcd14fe59494226453aed97c1070d83f8d24b6fc3a3bcf9a38092641c4a # via rich +markupsafe==3.0.3 \ + --hash=sha256:0eb9ff8191e8498cca014656ae6b8d61f39da5f95b488805da4bb029cccbfbaf \ + --hash=sha256:1085e7fbddd3be5f89cc898938f42c0b3c711fdcb37d75221de2666af647c175 \ + --hash=sha256:116bb52f642a37c115f517494ea5feb03889e04df47eeff5b130b1808ce7c219 \ + --hash=sha256:12c63dfb4a98206f045aa9563db46507995f7ef6d83b2f68eda65c307c6829eb \ + --hash=sha256:133a43e73a802c5562be9bbcd03d090aa5a1fe899db609c29e8c8d815c5f6de6 \ + --hash=sha256:1353ef0c1b138e1907ae78e2f6c63ff67501122006b0f9abad68fda5f4ffc6ab \ + --hash=sha256:1872df69a4de6aead3491198eaf13810b565bdbeec3ae2dc8780f14458ec73ce \ + --hash=sha256:1b4b79e8ebf6b55351f0d91fe80f893b4743f104bff22e90697db1590e47a218 \ + --hash=sha256:1b52b4fb9df4eb9ae465f8d0c228a00624de2334f216f178a995ccdcf82c4634 \ + --hash=sha256:218551f6df4868a8d527e3062d0fb968682fe92054e89978594c28e642c43a73 \ + --hash=sha256:26a5784ded40c9e318cfc2bdb30fe164bdb8665ded9cd64d500a34fb42067b1c \ + --hash=sha256:2713baf880df847f2bece4230d4d094280f4e67b1e813eec43b4c0e144a34ffe \ + --hash=sha256:32001d6a8fc98c8cb5c947787c5d08b0a50663d139f1305bac5885d98d9b40fa \ + --hash=sha256:3524b778fe5cfb3452a09d31e7b5adefeea8c5be1d43c4f810ba09f2ceb29d37 \ + --hash=sha256:35add3b638a5d900e807944a078b51922212fb3dedb01633a8defc4b01a3c85f \ + --hash=sha256:3a7e8ae81ae39e62a41ec302f972ba6ae23a5c5396c8e60113e9066ef893da0d \ + --hash=sha256:457a69a9577064c05a97c41f4e65148652db078a3a509039e64d3467b9e7ef97 \ + --hash=sha256:4e885a3d1efa2eadc93c894a21770e4bc67899e3543680313b09f139e149ab19 \ + --hash=sha256:4faffd047e07c38848ce017e8725090413cd80cbc23d86e55c587bf979e579c9 \ + --hash=sha256:509fa21c6deb7a7a273d629cf5ec029bc209d1a51178615ddf718f5918992ab9 \ + --hash=sha256:5678211cb9333a6468fb8d8be0305520aa073f50d17f089b5b4b477ea6e67fdc \ + --hash=sha256:5a7d5dc5140555cf21a6fefbdbf8723f06fcd2f63ef108f2854de715e4422cb4 \ + --hash=sha256:69c0b73548bc525c8cb9a251cddf1931d1db4d2258e9599c28c07ef3580ef354 \ + --hash=sha256:722695808f4b6457b320fdc131280796bdceb04ab50fe1795cd540799ebe1698 \ + --hash=sha256:729586769a26dbceff69f7a7dbbf59ab6572b99d94576a5592625d5b411576b9 \ + --hash=sha256:77f0643abe7495da77fb436f50f8dab76dbc6e5fd25d39589a0f1fe6548bfa2b \ + --hash=sha256:795e7751525cae078558e679d646ae45574b47ed6e7771863fcc079a6171a0fc \ + --hash=sha256:7e68f88e5b8799aa49c85cd116c932a1ac15caaa3f5db09087854d218359e485 \ + --hash=sha256:83891d0e9fb81a825d9a6d61e3f07550ca70a076484292a70fde82c4b807286f \ + --hash=sha256:8485f406a96febb5140bfeca44a73e3ce5116b2501ac54fe953e488fb1d03b12 \ + --hash=sha256:8709b08f4a89aa7586de0aadc8da56180242ee0ada3999749b183aa23df95025 \ + --hash=sha256:8f71bc33915be5186016f675cd83a1e08523649b0e33efdb898db577ef5bb009 \ + --hash=sha256:915c04ba3851909ce68ccc2b8e2cd691618c4dc4c4232fb7982bca3f41fd8c3d \ + --hash=sha256:94c6f0bb423f739146aec64595853541634bde58b2135f27f61c1ffd1cd4d16a \ + --hash=sha256:9a1abfdc021a164803f4d485104931fb8f8c1efd55bc6b748d2f5774e78b62c5 \ + --hash=sha256:9b79b7a16f7fedff2495d684f2b59b0457c3b493778c9eed31111be64d58279f \ + --hash=sha256:a4afe79fb3de0b7097d81da19090f4df4f8d3a2b3adaa8764138aac2e44f3af1 \ + --hash=sha256:ad2cf8aa28b8c020ab2fc8287b0f823d0a7d8630784c31e9ee5edea20f406287 \ + --hash=sha256:b8512a91625c9b3da6f127803b166b629725e68af71f8184ae7e7d54686a56d6 \ + --hash=sha256:bdc919ead48f234740ad807933cdf545180bfbe9342c2bb451556db2ed958581 \ + --hash=sha256:bdd37121970bfd8be76c5fb069c7751683bdf373db1ed6c010162b2a130248ed \ + --hash=sha256:be8813b57049a7dc738189df53d69395eba14fb99345e0a5994914a3864c8a4b \ + --hash=sha256:c47a551199eb8eb2121d4f0f15ae0f923d31350ab9280078d1e5f12b249e0026 \ + --hash=sha256:ccfcd093f13f0f0b7fdd0f198b90053bf7b2f02a3927a30e63f3ccc9df56b676 \ + --hash=sha256:d53197da72cc091b024dd97249dfc7794d6a56530370992a5e1a08983ad9230e \ + --hash=sha256:d6dd0be5b5b189d31db7cda48b91d7e0a9795f31430b7f271219ab30f1d3ac9d \ + --hash=sha256:d88b440e37a16e651bda4c7c2b930eb586fd15ca7406cb39e211fcff3bf3017d \ + --hash=sha256:e1cf1972137e83c5d4c136c43ced9ac51d0e124706ee1c8aa8532c1287fa8795 \ + --hash=sha256:e56b7d45a839a697b5eb268c82a71bd8c7f6c94d6fd50c3d577fa39a9f1409f5 \ + --hash=sha256:e8afc3f2ccfa24215f8cb28dcf43f0113ac3c37c2f0f0806d8c70e4228c5cf4d \ + --hash=sha256:eaa9599de571d72e2daf60164784109f19978b327a3910d3e9de8c97b5b70cfe \ + --hash=sha256:ec15a59cf5af7be74194f7ab02d0f59a62bdcf1a537677ce67a2537c9b87fcda \ + --hash=sha256:f190daf01f13c72eac4efd5c430a8de82489d9cff23c364c3ea822545032993e \ + --hash=sha256:f34c41761022dd093b4b6896d4810782ffbabe30f2d443ff5f083e0cbbb8c737 \ + --hash=sha256:f3e98bb3798ead92273dc0e5fd0f31ade220f59a266ffd8a4f6065e0a3ce0523 \ + --hash=sha256:fed51ac40f757d41b7c48425901843666a6677e3e8eb0abcff09e4ba6e664f50 + # via jinja2 matplotlib==3.11.0 \ --hash=sha256:03acfeddf87b0dddb11b081ef7740ad445a3ca8bcb6b8e3011b08f2cf802b75c \ --hash=sha256:0515d495124be3124340e59f164d901ed4484e2246a5b74cfa483cac3b80bd97 \ @@ -405,6 +510,7 @@ pydantic==2.13.4 \ --hash=sha256:45a282cde31d808236fd7ea9d919b128653c8b38b393d1c4ab335c62924d9aba \ --hash=sha256:c40756b57adaa8b1efeeced5c196f3f3b7c435f90e84ea7f443901bec8099ef6 # via + # fastapi # numpydantic # trendify pydantic-core==2.46.4 \ @@ -505,6 +611,16 @@ six==1.17.0 \ --hash=sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274 \ --hash=sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81 # via python-dateutil +sse-starlette==3.4.5 \ + --hash=sha256:83072538bc211a2f68b7b0422226c4af3e9b62e106e07034664b832ca019842a \ + --hash=sha256:e71bad53323f65573c3864a6c3bd0c1eb6e5f092b2e48082b0c35927d19ca296 + # via trendify +starlette==1.3.1 \ + --hash=sha256:05d0213193f2fbaae60e2ecb593b4add4262ad4e46536b54abe36f11a71724e0 \ + --hash=sha256:c7372aae11c3c3f26a42df7bd626cec2f47d03483d261d369516a615a53714c6 + # via + # fastapi + # sse-starlette typer==0.26.8 \ --hash=sha256:3512ca79ac5c11113414b36e80281b872884477722440691c89d1112e321a49c \ --hash=sha256:c244a6bd558886fe3f8780efb6bdd28bb9aff005a94eedebaa5cb32926fe2f7e @@ -513,14 +629,23 @@ typing-extensions==4.16.0 \ --hash=sha256:481caa481374e813c1b176ada14e97f1f67a4539ce9cfeb3f350d78d6370c2e8 \ --hash=sha256:dc983d19a509c94dba722ee6abd33940f7c05a89e243c47e907eb4db6f1a43e5 # via + # anyio + # fastapi # pydantic # pydantic-core + # starlette # typing-inspection typing-inspection==0.4.2 \ --hash=sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7 \ --hash=sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464 - # via pydantic + # via + # fastapi + # pydantic ubelt==1.4.2 \ 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index 0000000..54ecf9f --- /dev/null +++ b/scripts/gen_ts_models.py @@ -0,0 +1,243 @@ +""" +Generate TypeScript types from Pydantic models in plot_config.py. + +Run from the repo root: + + python scripts/gen_ts_models.py + +Writes: + src/trendify/viewer/templates/static/ts/src/lib/plot-config.generated.ts +""" + +from __future__ import annotations + +import json +import re +import sys +import textwrap +from pathlib import Path + +ROOT = Path(__file__).parent.parent +TS_OUT = ( + ROOT + / "src/trendify/viewer/templates/static/ts/src/lib" + / "plot-config.generated.ts" +) + +# --------------------------------------------------------------------------- +# JSON Schema → TypeScript converter +# --------------------------------------------------------------------------- + + +def _ref_name(ref: str) -> str: + """'#/$defs/FooBar' → 'FooBar'""" + return ref.rsplit("/", 1)[-1] + + +def _schema_to_ts(node: dict, defs: dict, extra_unions: dict[str, str]) -> str: + """Recursively convert a JSON Schema node to a TypeScript type string.""" + if "$ref" in node: + return _ref_name(node["$ref"]) + + if "anyOf" in node: + parts = [_schema_to_ts(s, defs, extra_unions) for s in node["anyOf"]] + return " | ".join(parts) + + if "const" in node: + return f'"{node["const"]}"' + + if "enum" in node: + return " | ".join(f'"{v}"' for v in node["enum"]) + + t = node.get("type") + + if t == "string": + return "string" + if t in ("number", "integer"): + return "number" + if t == "boolean": + return "boolean" + if t == "null": + return "null" + + if t == "array": + if "prefixItems" in node: + items = [_schema_to_ts(s, defs, extra_unions) for s in node["prefixItems"]] + return f"[{', '.join(items)}]" + if "items" in node: + items_node = node["items"] + if "oneOf" in items_node and "discriminator" in items_node: + refs = [ + _ref_name(s["$ref"]) for s in items_node["oneOf"] if "$ref" in s + ] + # the open-ended filter union uses the FilterEntry alias; other + # discriminated unions (e.g. Shape) get an explicit union type + if refs and all(r.endswith("Filter") for r in refs): + return "FilterEntry[]" + if refs: + return f"({' | '.join(refs)})[]" + return f"{_schema_to_ts(items_node, defs, extra_unions)}[]" + return "unknown[]" + + if t == "object": + add_props = node.get("additionalProperties") + props = node.get("properties", {}) + required = set(node.get("required", [])) + + if add_props and not props: + # Pure dict / Record + if add_props is True or add_props == {}: + return "Record" + return f"Record" + + # Inline object (rare — models surface as $defs, not inline) + lines = _props_to_ts_lines(props, required, defs, extra_unions) + if add_props is True: + lines.append(" [key: string]: unknown;") + body = "\n".join(lines) + return "{\n" + body + "\n}" + + return "unknown" + + +def _to_const_name(name: str) -> str: + """'DashStyle' -> 'DASH_STYLE'.""" + return re.sub(r"(? str: + """'shapes' -> 'Shape'.""" + base = name[:-1] if name.endswith("s") else name + return base[0].upper() + base[1:] + + +def _props_to_ts_lines( + props: dict, + required: set[str], + defs: dict, + extra_unions: dict[str, str], +) -> list[str]: + """Return one ' field?: Type;' string per property.""" + lines: list[str] = [] + for name, schema in props.items(): + # array of a non-filter discriminated union (e.g. Shape) -> named alias + items_node = schema.get("items") if schema.get("type") == "array" else None + if items_node and "oneOf" in items_node and "discriminator" in items_node: + refs = [_ref_name(s["$ref"]) for s in items_node["oneOf"] if "$ref" in s] + if refs and not all(r.endswith("Filter") for r in refs): + union_name = _singularize(name) + extra_unions[union_name] = " | ".join(refs) + ts_type = f"{union_name}[]" + else: + ts_type = _schema_to_ts(schema, defs, extra_unions) + else: + ts_type = _schema_to_ts(schema, defs, extra_unions) + opt = "" if name in required else "?" + lines.append(f" {name}{opt}: {ts_type};") + return lines + + +# --------------------------------------------------------------------------- +# $defs → top-level TypeScript declarations +# --------------------------------------------------------------------------- + + +def _def_to_ts( + name: str, schema: dict, defs: dict, extra_unions: dict[str, str] +) -> str: + """Convert one $defs entry to a TypeScript type or interface block.""" + # StrEnum / Literal → export type Name = "a" | "b"; + runtime values array + if "enum" in schema: + values = " | ".join(f'"{v}"' for v in schema["enum"]) + values_arr = ", ".join(f'"{v}"' for v in schema["enum"]) + const_name = _to_const_name(name) + return ( + f"export type {name} = {values};\n" + f"export const {const_name}_VALUES: {name}[] = [{values_arr}];\n" + ) + + # BaseModel → export interface Name { ... } + if schema.get("type") == "object": + props = schema.get("properties", {}) + required = set(schema.get("required", [])) + lines = _props_to_ts_lines(props, required, defs, extra_unions) + if schema.get("additionalProperties") is True: + lines.append(" [key: string]: unknown;") + body = "\n".join(lines) if lines else " [key: string]: unknown;" + return f"export interface {name} {{\n{body}\n}}\n" + + return f"// Unhandled $def: {name}\n" + + +def _top_level_interface(schema: dict, defs: dict, extra_unions: dict[str, str]) -> str: + """Generate the top-level model (PlotConfig) as an interface.""" + name = schema.get("title", "PlotConfig") + desc = schema.get("description", "") + props = schema.get("properties", {}) + required = set(schema.get("required", [])) + lines = _props_to_ts_lines(props, required, defs, extra_unions) + body = "\n".join(lines) + comment = f"/** {desc} */\n" if desc else "" + return f"{comment}export interface {name} {{\n{body}\n}}\n" + + +# --------------------------------------------------------------------------- +# Entry point +# --------------------------------------------------------------------------- + + +def main() -> None: + # Import inside main so sys.path manipulation is localised + sys.path.insert(0, str(ROOT / "src")) + from trendify.viewer.plot_config import PlotConfig + + schema = PlotConfig.model_json_schema() + defs: dict = schema.get("$defs", {}) + + blocks: list[str] = [] + extra_unions: dict[str, str] = {} + + # FilterEntry is the structural base for every discriminated filter object; + # emitted first so XAxisConfig/YAxisConfig can reference it. + blocks.append( + "/** a single filter in a filter stack — type-discriminated, open-ended properties. */\n" + "export type FilterEntry = { type: string; enabled?: boolean; [key: string]: unknown };\n" + ) + + # Emit each $def in definition order + for def_name, def_schema in defs.items(): + blocks.append(_def_to_ts(def_name, def_schema, defs, extra_unions)) + + # Emit the top-level PlotConfig interface + blocks.append(_top_level_interface(schema, defs, extra_unions)) + + # Emit named unions discovered while walking properties (e.g. Shape) + for union_name, union_body in extra_unions.items(): + blocks.append(f"export type {union_name} = {union_body};\n") + + # Emit the full JSON Schema document for additive client-side validation + # (see lib/schema-validate.ts). + blocks.append( + "/** Full JSON Schema for PlotConfig - used for additive client-side validation. */\n" + f"export const PLOT_CONFIG_SCHEMA: JsonSchemaNode = {json.dumps(schema, indent=2)};\n" + ) + + header = textwrap.dedent("""\ + // ============================================================ + // AUTO-GENERATED - do not edit manually. + // Source: src/trendify/viewer/plot_config.py + // Regenerate: python scripts/gen_ts_models.py + // ============================================================ + + import type { JsonSchemaNode } from "./schema-validate"; + + """) + + output = header + "\n".join(blocks) + TS_OUT.parent.mkdir(parents=True, exist_ok=True) + TS_OUT.write_text(output, encoding="utf-8") + print(f"Written → {TS_OUT.relative_to(ROOT)}") + + +if __name__ == "__main__": + main() diff --git a/scripts/matrix_generator.py b/scripts/matrix_generator.py new file mode 100644 index 0000000..5ce23c4 --- /dev/null +++ b/scripts/matrix_generator.py @@ -0,0 +1,792 @@ +""" +Not intended for use as a unit test. + +Comprehensive `ProductGenerator`-compatible fixture enumerating configuration combinations +across every `DataProduct` subclass (`Point2D`, `Scatter2D`, `Trace2D`, `AxLine`, +`HistogramEntry`, `TableEntry`) plus tags where several product types are deliberately mixed +together (a `Trace2D` sharing a tag with an `AxLine`, a `Trace2D` sharing a tag with a +`HistogramEntry`, a `TableEntry` sharing a tag with a `Point2D`, etc.), so the rendering +pipeline gets exercised against every styling axis and every same-tag product-type +combination it needs to support. + +`build_configuration_matrix` is the single entry point. Its signature matches +`ProductGenerator` (`Callable[[Path], ProductList]`), so it can be passed directly to +`generate_products`/`TrendifyPipeline.generate` like any real generator, or called directly +in a test and handed to `ProductStore.write_run`. +""" + +from __future__ import annotations + +from pathlib import Path + +import numpy as np + +from trendify.base.data_product import ProductList +from trendify.base.pen import Pen +from trendify.formats.format2d import AxisScale, Format2D +from trendify.formats.table import TableEntry +from trendify.plotting.axline import AxLine, LineOrientation +from trendify.plotting.histogram import HistogramEntry, HistogramStyle +from trendify.plotting.point import Point2D +from trendify.plotting.scatter import Scatter2D +from trendify.plotting.trace import Trace2D +from trendify.styling.grid import Grid, GridTheme +from trendify.styling.legend import Legend, LegendLocation +from trendify.styling.marker import Marker + +__all__ = ["build_configuration_matrix"] + +_TIME = np.linspace(0.1, 1.0, 40) # starts above 0 so log-scale x is always valid + +_MARKER_SYMBOL_NAMES = { + ".": "point", + "o": "circle", + "v": "triangle_down", + "^": "triangle_up", + "<": "triangle_left", + ">": "triangle_right", + "s": "square", + "p": "pentagon", + "*": "star", + "h": "hexagon", + "+": "plus", + "x": "x_mark", + "D": "diamond", +} + + +def _rising_wave(amplitude: float = 1.0) -> np.ndarray: + return amplitude * np.sin(_TIME * 2 * np.pi) + + +def _positive_wave(amplitude: float = 1.0) -> np.ndarray: + return amplitude * np.exp(_TIME) + + +def build_configuration_matrix(workdir: Path) -> ProductList: + """ + Returns one comprehensive `ProductList` covering the styling/config matrix described in + the module docstring. + + Args: + workdir (Path): unused for data generation (this fixture doesn't read from disk); + only `workdir.name` is used, to label metadata the way a real `ProductGenerator` + would when distinguishing runs. + + """ + products: ProductList = [] + run_label = workdir.name + + _add_axis_scale_combinations(products) + _add_grid_theme_combinations(products) + _add_legend_location_combinations(products) + _add_legend_option_combinations(products) + _add_format2d_limit_combinations(products) + _add_format2d_size_and_label_combinations(products) + _add_pen_color_combinations(products) + _add_pen_linestyle_combinations(products) + _add_pen_field_combinations(products) + _add_trace_marker_combinations(products) + _add_marker_symbol_combinations(products) + _add_marker_color_and_field_combinations(products) + _add_scatter_configuration_combinations(products) + _add_point_coordinate_type_combinations(products) + _add_axline_orientation_combinations(products) + _add_histogram_histtype_combinations(products) + _add_histogram_bins_combinations(products) + _add_histogram_alpha_combinations(products) + _add_histogram_style_edge_case_combinations(products) + _add_table_entry_value_type_combinations(products) + _add_tag_shape_combinations(products) + _add_metadata_combinations(products, run_label) + _add_mixed_product_type_combinations(products) + + return products + + +def _add_axis_scale_combinations(products: ProductList) -> None: + for scale_x, scale_y in [ + (AxisScale.LINEAR, AxisScale.LINEAR), + (AxisScale.LINEAR, AxisScale.LOG), + (AxisScale.LOG, AxisScale.LINEAR), + (AxisScale.LOG, AxisScale.LOG), + ]: + x = _TIME if scale_x == AxisScale.LINEAR else _positive_wave(1.0) + y = _rising_wave(1.0) if scale_y == AxisScale.LINEAR else _positive_wave(2.0) + tag_name = f"scale_x_{scale_x.value}_scale_y_{scale_y.value}" + Trace2D.from_xy( + tags=[("axis_scale_combinations", tag_name)], + x=x, + y=y, + pen=Pen(label=tag_name), + format2d=Format2D(scale_x=scale_x, scale_y=scale_y), + ).append_to_list(products) + + +def _add_grid_theme_combinations(products: ProductList) -> None: + grid_variants = { + "grid_omitted": None, + "grid_theme_matlab": Grid.from_theme(GridTheme.MATLAB), + "grid_theme_light": Grid.from_theme(GridTheme.LIGHT), + "grid_theme_dark": Grid.from_theme(GridTheme.DARK), + } + for tag_name, grid in grid_variants.items(): + Trace2D.from_xy( + tags=[("grid_theme_combinations", tag_name)], + x=_TIME, + y=_rising_wave(), + pen=Pen(label=tag_name), + format2d=Format2D(grid=grid), + ).append_to_list(products) + + +def _add_legend_location_combinations(products: ProductList) -> None: + for loc in LegendLocation: + tag_name = f"legend_loc_{loc.name.lower()}" + Trace2D.from_xy( + tags=[("legend_location_combinations", tag_name)], + x=_TIME, + y=_rising_wave(), + pen=Pen(label=tag_name), + format2d=Format2D(legend=Legend(loc=loc)), + ).append_to_list(products) + + +def _add_legend_option_combinations(products: ProductList) -> None: + legend_variants = { + "legend_visible_false": Legend(visible=False), + "legend_bbox_to_anchor_set": Legend(bbox_to_anchor=(1.05, 1)), + "legend_ncol_multiple": Legend(ncol=3), + "legend_fancybox_false": Legend(fancybox=False), + "legend_edgecolor_custom": Legend(edgecolor="blue"), + "legend_framealpha_zero": Legend(framealpha=0), + "legend_title_set": Legend(title="Legend title"), + } + for tag_name, legend in legend_variants.items(): + Trace2D.from_xy( + tags=[("legend_option_combinations", tag_name)], + x=_TIME, + y=_rising_wave(), + pen=Pen(label=tag_name), + format2d=Format2D(legend=legend), + ).append_to_list(products) + + +def _add_format2d_limit_combinations(products: ProductList) -> None: + limit_variants = { + "lim_x_min_set": Format2D(lim_x_min=0.3), + "lim_x_max_set": Format2D(lim_x_max=0.7), + "lim_y_min_set": Format2D(lim_y_min=-0.5), + "lim_y_max_set": Format2D(lim_y_max=0.5), + "lim_x_and_y_all_set": Format2D( + lim_x_min=0.2, lim_x_max=0.8, lim_y_min=-0.8, lim_y_max=0.8 + ), + } + for tag_name, format2d in limit_variants.items(): + Trace2D.from_xy( + tags=[("axis_limit_combinations", tag_name)], + x=_TIME, + y=_rising_wave(), + pen=Pen(label=tag_name), + format2d=format2d, + ).append_to_list(products) + + +def _add_format2d_size_and_label_combinations(products: ProductList) -> None: + field_variants = { + "figure_width_custom": Format2D(figure_width=12.0), + "figure_height_custom": Format2D(figure_height=10.0), + "title_fig_set": Format2D(title_fig="Figure title"), + "title_ax_set": Format2D(title_ax="Axes title"), + "label_x_set": Format2D(label_x="X axis label"), + "label_y_set": Format2D(label_y="Y axis label"), + } + for tag_name, format2d in field_variants.items(): + Trace2D.from_xy( + tags=[("format2d_field_combinations", tag_name)], + x=_TIME, + y=_rising_wave(), + pen=Pen(label=tag_name), + format2d=format2d, + ).append_to_list(products) + + +def _add_pen_color_combinations(products: ProductList) -> None: + color_variants = { + "pen_color_named_string": "tab:blue", + "pen_color_hex_string": "#2ca02c", + "pen_color_rgb_tuple": (0.8, 0.2, 0.4), + "pen_color_rgba_tuple": (0.8, 0.2, 0.4, 0.5), + } + for tag_name, color in color_variants.items(): + Trace2D.from_xy( + tags=[("pen_color_combinations", tag_name)], + x=_TIME, + y=_rising_wave(), + pen=Pen(label=tag_name, color=color), + ).append_to_list(products) + + +def _add_pen_linestyle_combinations(products: ProductList) -> None: + linestyle_variants = { + "pen_linestyle_solid": "-", + "pen_linestyle_dashed": "--", + "pen_linestyle_dotted": ":", + "pen_linestyle_dashdot": "-.", + "pen_linestyle_custom_dash_pattern": (0, (3, 1, 1, 1)), + } + for tag_name, linestyle in linestyle_variants.items(): + Trace2D.from_xy( + tags=[("pen_linestyle_combinations", tag_name)], + x=_TIME, + y=_rising_wave(), + pen=Pen(label=tag_name, linestyle=linestyle), + ).append_to_list(products) + + +def _add_pen_field_combinations(products: ProductList) -> None: + Trace2D.from_xy( + tags=[("pen_field_combinations", "pen_alpha_partial")], + x=_TIME, + y=_rising_wave(), + pen=Pen(label="pen_alpha_partial", alpha=0.4), + ).append_to_list(products) + Trace2D.from_xy( + tags=[("pen_field_combinations", "pen_alpha_zero")], + x=_TIME, + y=_rising_wave(), + pen=Pen(label="pen_alpha_zero", alpha=0.0), + ).append_to_list(products) + Trace2D.from_xy( + tags=[("pen_field_combinations", "pen_size_thick")], + x=_TIME, + y=_rising_wave(), + pen=Pen(label="pen_size_thick", size=6.0), + ).append_to_list(products) + Trace2D.from_xy( + tags=[("pen_field_combinations", "pen_label_omitted")], + x=_TIME, + y=_rising_wave(), + pen=Pen(label=None), + ).append_to_list(products) + Trace2D.from_xy( + tags=[("pen_field_combinations", "pen_zorder_layering")], + x=_TIME, + y=_rising_wave(1.0), + pen=Pen(label="pen_zorder_layering_back", zorder=1), + ).append_to_list(products) + Trace2D.from_xy( + tags=[("pen_field_combinations", "pen_zorder_layering")], + x=_TIME, + y=_rising_wave(1.2), + pen=Pen(label="pen_zorder_layering_front", zorder=5), + ).append_to_list(products) + + +def _add_trace_marker_combinations(products: ProductList) -> None: + Trace2D.from_xy( + tags=[("trace_marker_combinations", "trace_marker_omitted")], + x=_TIME, + y=_rising_wave(), + pen=Pen(label="trace_marker_omitted"), + ).append_to_list(products) + Trace2D.from_xy( + tags=[("trace_marker_combinations", "trace_marker_every_point")], + x=_TIME, + y=_rising_wave(), + pen=Pen(label="trace_marker_every_point"), + marker=Marker(symbol="o", color="tab:blue"), + ).append_to_list(products) + Trace2D.from_xy( + tags=[("trace_marker_combinations", "trace_markevery_5")], + x=_TIME, + y=_rising_wave(), + pen=Pen(label="trace_markevery_5"), + marker=Marker(symbol="^", color="tab:red", size=8.0), + markevery=5, + ).append_to_list(products) + + +def _add_marker_symbol_combinations(products: ProductList) -> None: + for index, (symbol, name) in enumerate(_MARKER_SYMBOL_NAMES.items()): + Point2D( + tags=[("marker_symbol_combinations", f"marker_symbol_{name}")], + x=float(index), + y=float(index), + marker=Marker(symbol=symbol, label=f"marker_symbol_{name}"), + ).append_to_list(products) + Point2D( + tags=[("marker_symbol_combinations", "marker_omitted")], + x=0.0, + y=0.0, + marker=None, + ).append_to_list(products) + + +def _add_marker_color_and_field_combinations(products: ProductList) -> None: + color_variants = { + "marker_color_named_string": "tab:orange", + "marker_color_hex_string": "#d62728", + "marker_color_rgb_tuple": (0.1, 0.6, 0.9), + "marker_color_rgba_tuple": (0.1, 0.6, 0.9, 0.4), + } + for index, (tag_name, color) in enumerate(color_variants.items()): + Point2D( + tags=[("marker_color_combinations", tag_name)], + x=float(index), + y=float(index) ** 2, + marker=Marker(label=tag_name, color=color), + ).append_to_list(products) + + Point2D( + tags=[("marker_field_combinations", "marker_size_large")], + x=1.0, + y=1.0, + marker=Marker(label="marker_size_large", size=20.0), + ).append_to_list(products) + Point2D( + tags=[("marker_field_combinations", "marker_alpha_partial")], + x=1.0, + y=1.0, + marker=Marker(label="marker_alpha_partial", alpha=0.3), + ).append_to_list(products) + + +def _add_scatter_configuration_combinations(products: ProductList) -> None: + rng = np.random.default_rng(seed=6) + x = rng.uniform(size=60) + y = rng.uniform(size=60) + + Scatter2D.from_xy( + tags=[("scatter_configuration_combinations", "scatter_default_marker")], + x=x, + y=y, + ).append_to_list(products) + + color_variants = { + "scatter_marker_color_named_string": "tab:green", + "scatter_marker_color_hex_string": "#9467bd", + "scatter_marker_color_rgb_tuple": (0.3, 0.3, 0.9), + "scatter_marker_color_rgba_tuple": (0.3, 0.3, 0.9, 0.5), + } + for tag_name, color in color_variants.items(): + Scatter2D.from_xy( + tags=[("scatter_configuration_combinations", tag_name)], + x=x, + y=y, + marker=Marker(label=tag_name, color=color), + ).append_to_list(products) + + Scatter2D.from_xy( + tags=[("scatter_configuration_combinations", "scatter_marker_symbol_star")], + x=x, + y=y, + marker=Marker(symbol="*", label="scatter_marker_symbol_star", size=15.0), + ).append_to_list(products) + Scatter2D.from_xy( + tags=[("scatter_configuration_combinations", "scatter_marker_alpha_partial")], + x=x, + y=y, + marker=Marker(label="scatter_marker_alpha_partial", alpha=0.25), + ).append_to_list(products) + + +def _add_point_coordinate_type_combinations(products: ProductList) -> None: + Point2D( + tags=[("point_coordinate_type_combinations", "x_and_y_numeric")], + x=1.0, + y=2.0, + marker=Marker(label="x_and_y_numeric"), + ).append_to_list(products) + + category_values = {"low": 1.0, "medium": 2.0, "high": 3.0} + for category, value in category_values.items(): + Point2D( + tags=[("point_coordinate_type_combinations", "x_categorical_string")], + x=category, + y=value, + marker=Marker(label=category), + ).append_to_list(products) + + +def _add_axline_orientation_combinations(products: ProductList) -> None: + AxLine( + tags=[("axline_orientation_combinations", "axline_horizontal")], + value=0.5, + orientation=LineOrientation.HORIZONTAL, + pen=Pen(label="axline_horizontal", color="r"), + ).append_to_list(products) + AxLine( + tags=[("axline_orientation_combinations", "axline_vertical")], + value=0.5, + orientation=LineOrientation.VERTICAL, + pen=Pen(label="axline_vertical", color="b"), + ).append_to_list(products) + + +def _add_histogram_histtype_combinations(products: ProductList) -> None: + rng = np.random.default_rng(seed=1) + for histtype in ["bar", "barstacked", "step", "stepfilled"]: + tag_name = f"histogram_histtype_{histtype}" + for value in rng.normal(size=30): + HistogramEntry( + tags=[("histogram_histtype_combinations", tag_name)], + value=float(value), + style=HistogramStyle(histtype=histtype, label=tag_name), + ).append_to_list(products) + + +def _add_histogram_bins_combinations(products: ProductList) -> None: + rng = np.random.default_rng(seed=2) + bins_variants = { + "histogram_bins_int": 10, + "histogram_bins_list": [-3, -2, -1, 0, 1, 2, 3], + "histogram_bins_tuple": (-3, 0, 3), + } + for tag_name, bins in bins_variants.items(): + for value in rng.normal(size=30): + HistogramEntry( + tags=[("histogram_bins_combinations", tag_name)], + value=float(value), + style=HistogramStyle(bins=bins, label=tag_name), + ).append_to_list(products) + + +def _add_histogram_alpha_combinations(products: ProductList) -> None: + rng = np.random.default_rng(seed=3) + alpha_variants = { + "histogram_alpha_face_low": HistogramStyle( + alpha_face=0.15, label="histogram_alpha_face_low" + ), + "histogram_alpha_face_high": HistogramStyle( + alpha_face=0.9, label="histogram_alpha_face_high" + ), + "histogram_alpha_edge_set": HistogramStyle( + alpha_edge=1.0, label="histogram_alpha_edge_set" + ), + } + for tag_name, style in alpha_variants.items(): + for value in rng.normal(size=30): + HistogramEntry( + tags=[("histogram_alpha_combinations", tag_name)], + value=float(value), + style=style, + ).append_to_list(products) + + +def _add_histogram_style_edge_case_combinations(products: ProductList) -> None: + rng = np.random.default_rng(seed=4) + for value in rng.normal(size=30): + HistogramEntry( + tags=[("histogram_style_edge_cases", "histogram_style_omitted")], + value=float(value), + style=None, + ).append_to_list(products) + + for value in rng.normal(loc=-1, scale=0.5, size=20): + HistogramEntry( + tags=[("histogram_style_edge_cases", "histogram_multiple_styles_grouped")], + value=float(value), + style=HistogramStyle(color="tab:blue", label="group_a"), + ).append_to_list(products) + for value in rng.normal(loc=1, scale=0.5, size=20): + HistogramEntry( + tags=[("histogram_style_edge_cases", "histogram_multiple_styles_grouped")], + value=float(value), + style=HistogramStyle(color="tab:red", label="group_b"), + ).append_to_list(products) + + +def _add_table_entry_value_type_combinations(products: ProductList) -> None: + TableEntry( + tags=[("table_value_type_combinations", "table_value_float")], + row="row_1", + col="col_1", + value=3.14, + ).append_to_list(products) + TableEntry( + tags=[("table_value_type_combinations", "table_value_string")], + row="row_1", + col="col_1", + value="a_string_value", + ).append_to_list(products) + TableEntry( + tags=[("table_value_type_combinations", "table_value_bool")], + row="row_1", + col="col_1", + value=True, + ).append_to_list(products) + TableEntry( + tags=[("table_value_type_combinations", "table_unit_set")], + row="row_1", + col="col_1", + value=2.5, + unit="meters", + ).append_to_list(products) + TableEntry( + tags=[("table_value_type_combinations", "table_row_col_numeric")], + row=1.0, + col=2.0, + value=9.9, + ).append_to_list(products) + for row, col, value in [ + ("row_1", "col_1", 1.0), + ("row_1", "col_2", 2.0), + ("row_2", "col_1", 3.0), + ("row_2", "col_2", 4.0), + ]: + TableEntry( + tags=[("table_value_type_combinations", "table_pivotable_grid")], + row=row, + col=col, + value=value, + ).append_to_list(products) + + +def _add_tag_shape_combinations(products: ProductList) -> None: + Point2D(tags=["tag_shape_scalar_string"], x=1.0, y=1.0).append_to_list(products) + Point2D(tags=[2024], x=1.0, y=1.0).append_to_list(products) + Point2D( + tags=[("tag_shape_tuple_of_strings", "nested_leaf")], x=1.0, y=1.0 + ).append_to_list(products) + Point2D( + tags=[("tag_shape_tuple_mixed_str_and_int", 2024)], x=1.0, y=1.0 + ).append_to_list(products) + Point2D( + tags=[("tag_shape_deeply_nested", "group_a", "subgroup_b", "leaf")], + x=1.0, + y=1.0, + ).append_to_list(products) + + +def _add_metadata_combinations(products: ProductList, run_label: str) -> None: + Point2D( + tags=[("metadata_combinations", "metadata_set")], + x=1.0, + y=1.0, + ).append_to_list(products).set_metadata( + {"run": run_label, "source": "product_matrix"} + ) + Point2D( + tags=[("metadata_combinations", "metadata_empty")], + x=1.0, + y=1.0, + ).append_to_list(products) + + +def _add_mixed_product_type_combinations(products: ProductList) -> None: + rng = np.random.default_rng(seed=5) + + trace_and_axline_tag = ( + "mixed_product_type_combinations", + "trace_and_axline_same_plot", + ) + Trace2D.from_xy( + tags=[trace_and_axline_tag], + x=_TIME, + y=_rising_wave(), + pen=Pen(label="trace_and_axline_same_plot"), + ).append_to_list(products) + AxLine( + tags=[trace_and_axline_tag], + value=0.0, + orientation=LineOrientation.HORIZONTAL, + pen=Pen(label="zero_reference_line", color="k"), + ).append_to_list(products) + + point_trace_axline_tag = ( + "mixed_product_type_combinations", + "point_trace_and_axline_same_plot", + ) + Point2D( + tags=[point_trace_axline_tag], + x=0.5, + y=0.5, + marker=Marker(label="marked_sample_point", color="tab:purple"), + ).append_to_list(products) + Trace2D.from_xy( + tags=[point_trace_axline_tag], + x=_TIME, + y=_rising_wave(0.8), + pen=Pen(label="point_trace_and_axline_same_plot"), + ).append_to_list(products) + AxLine( + tags=[point_trace_axline_tag], + value=0.5, + orientation=LineOrientation.VERTICAL, + pen=Pen(label="midpoint_reference_line", color="g"), + ).append_to_list(products) + + trace_and_histogram_tag = ( + "mixed_product_type_combinations", + "trace_and_histogram_shared_tag", + ) + Trace2D.from_xy( + tags=[trace_and_histogram_tag], + x=_TIME, + y=_rising_wave(), + pen=Pen(label="trace_and_histogram_shared_tag"), + ).append_to_list(products) + for value in rng.normal(size=30): + HistogramEntry( + tags=[trace_and_histogram_tag], value=float(value) + ).append_to_list(products) + + point_and_histogram_tag = ( + "mixed_product_type_combinations", + "point_and_histogram_shared_tag", + ) + Point2D(tags=[point_and_histogram_tag], x=0.0, y=0.0).append_to_list(products) + for value in rng.normal(size=30): + HistogramEntry( + tags=[point_and_histogram_tag], value=float(value) + ).append_to_list(products) + + scatter_and_trace_tag = ( + "mixed_product_type_combinations", + "scatter_and_trace_same_plot", + ) + Scatter2D.from_xy( + tags=[scatter_and_trace_tag], + x=rng.uniform(size=30), + y=rng.uniform(size=30), + marker=Marker(label="scattered_samples", color="tab:purple"), + ).append_to_list(products) + Trace2D.from_xy( + tags=[scatter_and_trace_tag], + x=_TIME, + y=_rising_wave(), + pen=Pen(label="scatter_and_trace_same_plot"), + ).append_to_list(products) + + scatter_and_histogram_tag = ( + "mixed_product_type_combinations", + "scatter_and_histogram_shared_tag", + ) + Scatter2D.from_xy( + tags=[scatter_and_histogram_tag], + x=rng.uniform(size=30), + y=rng.uniform(size=30), + ).append_to_list(products) + for value in rng.normal(size=30): + HistogramEntry( + tags=[scatter_and_histogram_tag], value=float(value) + ).append_to_list(products) + + axline_and_histogram_tag = ( + "mixed_product_type_combinations", + "axline_and_histogram_shared_tag", + ) + histogram_values = rng.normal(loc=2.0, scale=1.0, size=30) + for value in histogram_values: + HistogramEntry( + tags=[axline_and_histogram_tag], + value=float(value), + style=HistogramStyle(label="axline_and_histogram_shared_tag"), + ).append_to_list(products) + AxLine( + tags=[axline_and_histogram_tag], + value=float(np.mean(histogram_values)), + orientation=LineOrientation.VERTICAL, + pen=Pen(label="histogram_mean_reference_line", color="r", zorder=2), + ).append_to_list(products) + + all_xy_types_and_histogram_tag = ( + "mixed_product_type_combinations", + "point_trace_axline_and_histogram_shared_tag", + ) + Point2D( + tags=[all_xy_types_and_histogram_tag], + x=0.2, + y=0.2, + marker=Marker(label="sample_point"), + ).append_to_list(products) + Trace2D.from_xy( + tags=[all_xy_types_and_histogram_tag], + x=_TIME, + y=_rising_wave(0.6), + pen=Pen(label="sample_trace"), + ).append_to_list(products) + AxLine( + tags=[all_xy_types_and_histogram_tag], + value=0.0, + orientation=LineOrientation.HORIZONTAL, + pen=Pen(label="zero_reference_line", color="k"), + ).append_to_list(products) + for value in rng.normal(size=30): + HistogramEntry( + tags=[all_xy_types_and_histogram_tag], value=float(value) + ).append_to_list(products) + + format2d_present_and_omitted_tag = ( + "mixed_product_type_combinations", + "format2d_present_and_omitted_shared_tag", + ) + Point2D(tags=[format2d_present_and_omitted_tag], x=0.0, y=0.0).append_to_list( + products + ) + Trace2D.from_xy( + tags=[format2d_present_and_omitted_tag], + x=_TIME, + y=_rising_wave(), + pen=Pen(label="format2d_present_and_omitted_shared_tag"), + format2d=Format2D(grid=Grid.from_theme(GridTheme.MATLAB)), + ).append_to_list(products) + + table_and_point_tag = ( + "mixed_product_type_combinations", + "table_and_point_shared_tag", + ) + TableEntry( + tags=[table_and_point_tag], row="row_1", col="col_1", value=1.0 + ).append_to_list(products) + Point2D(tags=[table_and_point_tag], x=1.0, y=1.0).append_to_list(products) + + table_point_and_histogram_tag = ( + "mixed_product_type_combinations", + "table_point_and_histogram_shared_tag", + ) + TableEntry( + tags=[table_point_and_histogram_tag], row="row_1", col="col_1", value=1.0 + ).append_to_list(products) + Point2D(tags=[table_point_and_histogram_tag], x=1.0, y=1.0).append_to_list(products) + for value in rng.normal(size=30): + HistogramEntry( + tags=[table_point_and_histogram_tag], value=float(value) + ).append_to_list(products) + + every_product_type_tag = ( + "mixed_product_type_combinations", + "table_point_scatter_trace_axline_and_histogram_shared_tag", + ) + TableEntry( + tags=[every_product_type_tag], row="row_1", col="col_1", value=1.0 + ).append_to_list(products) + Point2D( + tags=[every_product_type_tag], + x=0.2, + y=0.2, + marker=Marker(label="sample_point"), + ).append_to_list(products) + Scatter2D.from_xy( + tags=[every_product_type_tag], + x=rng.uniform(size=30), + y=rng.uniform(size=30), + marker=Marker(label="scattered_samples", color="tab:cyan"), + ).append_to_list(products) + Trace2D.from_xy( + tags=[every_product_type_tag], + x=_TIME, + y=_rising_wave(0.6), + pen=Pen(label="sample_trace"), + ).append_to_list(products) + AxLine( + tags=[every_product_type_tag], + value=0.0, + orientation=LineOrientation.HORIZONTAL, + pen=Pen(label="zero_reference_line", color="k"), + ).append_to_list(products) + for value in rng.normal(size=30): + HistogramEntry( + tags=[every_product_type_tag], value=float(value) + ).append_to_list(products) diff --git a/src/trendify/__init__.py b/src/trendify/__init__.py index 0dd05b3..a7112ee 100644 --- a/src/trendify/__init__.py +++ b/src/trendify/__init__.py @@ -57,7 +57,6 @@ generate_products, get_sorted_dirs, histogrammer, - make_include_files, render, render_assets, table_builder, @@ -80,12 +79,14 @@ LineOrientation, PlotlyFigure, Point2D, + Scatter2D, SingleAxisFigure, Trace2D, axline, figure, histogram, point, + scatter, trace, ) from trendify.store import ( @@ -143,6 +144,7 @@ "ProductStore", "ProductType", "R", + "Scatter2D", "SingleAxisFigure", "TableBuilder", "TableEntry", @@ -181,7 +183,6 @@ "legend", "log", "make_example_data", - "make_include_files", "marker", "pen", "pipeline", @@ -190,6 +191,7 @@ "product_store", "render", "render_assets", + "scatter", "set_log_level", "setup_logger", "store", diff --git a/src/trendify/cli.py b/src/trendify/cli.py index a2df73b..2560f04 100644 --- a/src/trendify/cli.py +++ b/src/trendify/cli.py @@ -12,25 +12,98 @@ import logging import os import sys +from importlib.metadata import version from pathlib import Path from typing import cast import typer +from rich.align import Align +from rich.console import Console +from rich.panel import Panel +from rich.text import Text from trendify.base.data_product import ProductGenerator +from trendify.color import Color +from trendify.examples import make_example_data from trendify.log import setup_logger from trendify.pipeline import TrendifyPipeline +from trendify.store.product_store import ProductStore __all__ = ["app"] logger = logging.getLogger(__name__) +console = Console() +VERSION = version("trendify") + +_LOGO_LINES = [ + "████████╗██████╗ ███████╗███╗ ██╗██████╗ ██╗███████╗██╗ ██╗", + "╚══██╔══╝██╔══██╗██╔════╝████╗ ██║██╔══██╗██║██╔════╝╚██╗ ██╔╝", + " ██║ ██████╔╝█████╗ ██╔██╗ ██║██║ ██║██║█████╗ ╚████╔╝ ", + " ██║ ██╔══██╗██╔══╝ ██║╚██╗██║██║ ██║██║██╔══╝ ╚██╔╝ ", + " ██║ ██║ ██║███████╗██║ ╚████║██████╔╝██║██║ ██║ ", + " ╚═╝ ╚═╝ ╚═╝╚══════╝╚═╝ ╚═══╝╚═════╝ ╚═╝╚═╝ ╚═╝ ", +] +_SHADES = [ + Color.ROSE_300, + Color.ROSE_400, + Color.ROSE_500, + Color.ROSE_600, + Color.ROSE_700, + Color.ROSE_800, +] + + +def print_logo(): + body = Text() + for i, (line, shade) in enumerate(zip(_LOGO_LINES, _SHADES)): + body.append(line, style=f"bold {shade}") + if i != len(_LOGO_LINES) - 1: + body.append("\n") + + console.print( + Panel( + Align.center(body), + expand=False, + border_style="indian_red1", + subtitle=f"[dim]v{VERSION}[/dim]", + ) + ) + app = typer.Typer( name="trendify", help="Generate visual data products and static assets from raw data.", + rich_markup_mode="rich", no_args_is_help=True, ) + +def version_callback(value: bool): + if value: + console.print( + f"[bold indian_red1]trendify[/bold indian_red1] [bold white]{VERSION}" + ) + raise typer.Exit() + + +@app.callback( + epilog="Check out the [link=https://github.com/talbotknighton/trendify/]full documentation[/link] for more info." +) +def main( + ctx: typer.Context, + version: bool = typer.Option( + False, + "--version", + callback=version_callback, + is_eager=True, + help="Show version and exit.", + ), +): + """ + [bold indian_red1]Trendify:[/bold indian_red1] Generate visual data products and static assets from raw data. + """ + + InputDirectoriesOption = typer.Option( ..., "-i", @@ -47,32 +120,73 @@ ), ) OutputDirectoryOption = typer.Option( - ..., "-o", "--output-directory", help="Directory the pipeline reads/writes under." + ..., + "-o", + "--output-directory", + help="Directory the pipeline reads/writes under.", ) NProcsOption = typer.Option( - 1, "-n", "--n-procs", help="Number of parallel worker processes." + 1, + "-n", + "--n-procs", + help="Number of parallel worker processes.", + min=1, + max=os.cpu_count(), ) VerboseOption = typer.Option( 0, "-v", "--verbose", count=True, help="Increase log verbosity." ) +QuietOption = typer.Option( + 0, "--quiet", "-q", count=True, help="Decrease log verbosity." +) DpiOption = typer.Option( - 500, "--dpi", help="Resolution (dots per inch) for saved matplotlib figures." + 500, + "--dpi", + help="Resolution (dots per inch) for saved matplotlib figures.", + min=1, ) NoTablesOption = typer.Option( False, "--no-tables", help="Suppress TableEntry CSV output." ) NoXyPlotsOption = typer.Option( - False, "--no-xy-plots", help="Suppress Point2D/Trace2D/AxLine plot output." + False, + "--no-xy-plots", + help="Suppress Point2D/Scatter2D/Trace2D/AxLine plot output.", ) NoHistogramsOption = typer.Option( - False, "--no-histograms", help="Suppress HistogramEntry plot output." + False, + "--no-histograms", + help="Suppress HistogramEntry plot output.", +) +DbPathArgument = typer.Argument( + ..., + help="Path to a trendify.db file.", +) +ExampleWorkdirOption = typer.Option( + ..., + "-w", + "--workdir", + help="Directory in which to generate sample data.", +) +ExampleNFoldersOption = typer.Option( + 10, + "-n", + "--n-folders", + help="Number of sample data sets (subfolders) to generate.", ) -NoIncludeFilesOption = typer.Option( - False, "--no-include-files", help="Suppress MkDocs include.md generation." +HostOption = typer.Option( + "127.0.0.1", + "--host", + help="Interface to bind the dashboard server to.", +) +PortOption = typer.Option( + 8000, + "--port", + help="Port to bind the dashboard server to.", ) -def _configure_logging(verbose: int) -> None: +def _configure_logging(verbose: int, quiet: int, logo: bool = True) -> None: """ Configures the root logger via `trendify.log.setup_logger` (Rich console output plus a rotating `trendify.log` file) rather than a one-off `logging.basicConfig`. This way a CLI @@ -80,21 +194,12 @@ def _configure_logging(verbose: int) -> None: `setup_logger()` themselves, and `generate_products`'s multiprocess path picks up these same handlers via its `QueueListener` (see `trendify.generator.generate`). """ - level = logging.DEBUG if verbose >= 1 else logging.INFO + if logo: + print_logo() + level = logging.INFO - (verbose * 10) + (quiet * 10) setup_logger(level=level) -def _cap_n_procs(n_procs: int) -> int: - """Caps `n_procs` at `5 * os.cpu_count()`, as a precaution against overwhelming the machine.""" - max_procs = 5 * (os.cpu_count() or 1) - if n_procs > max_procs: - logger.info( - f"Requested {n_procs = } exceeds {max_procs = }; capping to {max_procs}" - ) - return max_procs - return n_procs - - def _resolve_input_directories(patterns: list[str]) -> list[Path]: """ Expands glob patterns (and plain paths) into a list of directories. A match on a file @@ -102,7 +207,7 @@ def _resolve_input_directories(patterns: list[str]) -> list[Path]: """ dirs: list[Path] = [] for pattern in patterns: - for match in glob_module.glob(pattern, root_dir=os.getcwd(), recursive=True): + for match in glob_module.glob(pattern, root_dir=Path.cwd(), recursive=True): path = Path(match) dirs.append((path.parent if path.is_file() else path).resolve()) return dirs @@ -130,7 +235,17 @@ def _resolve_product_generator(spec: str) -> ProductGenerator: ) if Path(module_path).exists(): - module = _import_from_path(Path(module_path).stem, Path(module_path)) + file_path = Path(module_path).resolve() + # `n_procs > 1` sends `product_generator` to worker processes via pickle, which + # serializes a module-level function as a `(module_name, qualname)` reference, not + # the code itself. Worker processes (forkserver/spawn) re-import that module by name + # using the `sys.path` snapshotted when the process pool starts, so the file's parent + # directory must be on `sys.path` *before* that happens, not just registered in this + # process's `sys.modules` (which `_import_from_path` alone would do). + parent_dir = str(file_path.parent) + if parent_dir not in sys.path: + sys.path.insert(0, parent_dir) + module = _import_from_path(file_path.stem, file_path) else: module = importlib.import_module(module_path) @@ -140,38 +255,38 @@ def _resolve_product_generator(spec: str) -> ProductGenerator: return cast(ProductGenerator, obj) -@app.command() +@app.command(name="generate") def generate( + ctx: typer.Context, input_directories: list[str] = InputDirectoriesOption, product_generator: str = ProductGeneratorOption, output_directory: Path = OutputDirectoryOption, n_procs: int = NProcsOption, verbose: int = VerboseOption, + quiet: int = QuietOption, ) -> None: """Generate tagged data products from raw data directories.""" - _configure_logging(verbose) - pipeline = TrendifyPipeline( - output_dir=output_directory, n_procs=_cap_n_procs(n_procs) - ) - total = pipeline.generate( + _configure_logging(verbose, quiet) + pipeline = TrendifyPipeline(output_dir=output_directory, n_procs=n_procs) + _total = pipeline.generate( product_generator=_resolve_product_generator(product_generator), data_dirs=_resolve_input_directories(input_directories), ) - typer.echo(f"Wrote {total} products to {pipeline.db_path}") -@app.command() +@app.command(name="render") def render( + ctx: typer.Context, output_directory: Path = OutputDirectoryOption, dpi: int = DpiOption, no_tables: bool = NoTablesOption, no_xy_plots: bool = NoXyPlotsOption, no_histograms: bool = NoHistogramsOption, - no_include_files: bool = NoIncludeFilesOption, verbose: int = VerboseOption, + quiet: int = QuietOption, ) -> None: """Render CSV tables and matplotlib figures from already-generated products.""" - _configure_logging(verbose) + _configure_logging(verbose, quiet) pipeline = TrendifyPipeline(output_dir=output_directory) pipeline.render( dpi=dpi, @@ -179,13 +294,12 @@ def render( no_xy_plots=no_xy_plots, no_histograms=no_histograms, ) - if not no_include_files: - pipeline.make_include_files() typer.echo(f"Rendered assets to {pipeline.assets_dir}") -@app.command() +@app.command(name="run") def run( + ctx: typer.Context, input_directories: list[str] = InputDirectoriesOption, product_generator: str = ProductGeneratorOption, output_directory: Path = OutputDirectoryOption, @@ -194,14 +308,12 @@ def run( no_tables: bool = NoTablesOption, no_xy_plots: bool = NoXyPlotsOption, no_histograms: bool = NoHistogramsOption, - no_include_files: bool = NoIncludeFilesOption, verbose: int = VerboseOption, + quiet: int = QuietOption, ) -> None: """Generate products and render assets in one step.""" - _configure_logging(verbose) - pipeline = TrendifyPipeline( - output_dir=output_directory, n_procs=_cap_n_procs(n_procs) - ) + _configure_logging(verbose, quiet) + pipeline = TrendifyPipeline(output_dir=output_directory, n_procs=n_procs) total = pipeline.run( product_generator=_resolve_product_generator(product_generator), data_dirs=_resolve_input_directories(input_directories), @@ -209,10 +321,117 @@ def run( no_tables=no_tables, no_xy_plots=no_xy_plots, no_histograms=no_histograms, - no_include_files=no_include_files, ) typer.echo(f"Wrote {total} products; assets under {pipeline.assets_dir}") +@app.command(name="example-data") +def example_data( + ctx: typer.Context, + workdir: Path = ExampleWorkdirOption, + n_folders: int = ExampleNFoldersOption, + verbose: int = VerboseOption, + quiet: int = QuietOption, +) -> None: + """Generate sample raw-data directories for exercising the trendify pipeline.""" + _configure_logging(verbose, quiet) + make_example_data(workdir=workdir, n_folders=n_folders) + typer.echo(f"Wrote {n_folders} sample data folders under {workdir / 'models'}") + + +def get_local_ip(): + """Returns the actual local IP address of this machine.""" + import socket + + s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) + try: + # Does not actually need to connect to 8.8.8.8 to work + s.connect(("8.8.8.8", 1)) + ip = s.getsockname()[0] + except Exception: + ip = "127.0.0.1" + finally: + s.close() + return ip + + +@app.command(name="serve") +def serve( + ctx: typer.Context, + db_path: Path = DbPathArgument, + host: str = HostOption, + port: int = PortOption, + reload: bool = typer.Option( + False, + "--reload", + help="Auto-restart the server when trendify's Python source changes (development only).", + ), + verbose: int = VerboseOption, + quiet: int = QuietOption, +) -> None: + """Launch a local web dashboard for browsing a trendify.db's data products.""" + _configure_logging(verbose, quiet, logo=False) + db_path = db_path.resolve() + if not db_path.exists(): + raise typer.BadParameter(f"No such database file: {db_path}") + + # Fail fast on a bad/corrupt db before uvicorn even starts, rather than a 500 on first request. + with ProductStore.open(db_path, readonly=True): + pass + + # Deferred import: keeps `trendify.cli` import light for callers of generate/render/run + # who never touch the dashboard (FastAPI/Jinja2/uvicorn stay unimported until needed). + import uvicorn + + from trendify.viewer.app import create_app + + if reload: + connection_info_suffix = "\n\n[yellow]--reload is on: editing trendify's Python source will restart the server.[/yellow]" + else: + connection_info_suffix = "" + + password = None # not supported yet + if password: + connection_info = "[yellow]Auth enabled. Use [u]any[/u] username and your provided password.[/yellow]\n\n" + else: + connection_info = "" + + connection_info += ( + f"Local: [bold indian_red1 u]http://127.0.0.1:{port}[/bold indian_red1 u]" + ) + if host == "0.0.0.0": + connection_info += f"\nMobile: [bold indian_red1 u]http://{get_local_ip()}:{port}[/bold indian_red1 u]" + else: + connection_info += "\n\n[dim]Tip: To view on other devices (and to make pages shareable), run with[/dim] [yellow]--host 0.0.0.0[/yellow]" + connection_info += connection_info_suffix + + console.print( + Panel( + f"""[bold green]Trendify Viewer is Live![/bold green]\n\n{connection_info}\n\n[yellow]Press CTRL+C to stop[/yellow]""", + border_style="indian_red1", + expand=False, + title="Trendify Viewer", + ) + ) + + if reload: + # uvicorn's reload mode re-imports the app in a fresh subprocess on every source change, + # so it needs an importable factory string rather than the live `create_app(db_path)` + # instance -- the db_path is handed across that boundary via an env var instead + # (`create_app_from_env` reads it back on the reloaded side). + os.environ["TRENDIFY_DB_PATH"] = str(db_path) + uvicorn.run( + "trendify.viewer.app:create_app_from_env", + factory=True, + host=host, + port=port, + reload=True, + reload_dirs=[str(Path(__file__).parent)], + log_level="critical", + ) + else: + uvicorn.run(create_app(db_path), host=host, port=port, log_level="critical") + + if __name__ == "__main__": app() diff --git a/src/trendify/examples.py b/src/trendify/examples.py index 13e5124..c3af34d 100644 --- a/src/trendify/examples.py +++ b/src/trendify/examples.py @@ -35,23 +35,27 @@ def make_example_data(workdir: Path, n_folders: int = 10): models_dir = workdir.joinpath("models") models_dir.mkdir(parents=True, exist_ok=True) + if not (models_dir / ".gitignore").exists(): + (models_dir / ".gitignore").write_text("*") + for n in range(n_folders): subdir = models_dir.joinpath(str(n)) subdir.mkdir(exist_ok=True, parents=True) - n_samples = np.random.randint(low=40, high=50) + rng = np.random.default_rng(seed=n) + + n_samples = rng.integers(low=40, high=50) t = np.linspace(0, 1, n_samples) periods = [1, 2, 3] - amplitudes = np.random.uniform(low=0.5, high=1.5, size=3) + amplitudes = rng.uniform(low=0.5, high=1.5, size=3) - inputs = {"n_samples": n_samples} + inputs: dict[str, int | float] = {"n_samples": int(n_samples)} inputs.update({f"p{i}": p for i, p in enumerate(periods)}) - inputs.update({f"a{i}": a for i, a in enumerate(amplitudes)}) + inputs.update({f"a{i}": float(a) for i, a in enumerate(amplitudes)}) pl.DataFrame( {"key": list(inputs.keys()), "value": [str(v) for v in inputs.values()]} ).write_csv(subdir.joinpath("stdin.csv"), include_header=False) - rng = np.random.default_rng(seed=42) noise_level = 0.05 waves = [ a * np.sin(t * (2 * np.pi / p)) + noise_level * rng.normal(size=len(t)) @@ -265,7 +269,7 @@ def example_data_product_generator(workdir: Path) -> trendify.ProductList: for i, trace in enumerate(traces): trendify.Point2D( x=workdir.name, - y=len(trace.points), + y=len(trace.x), marker=trendify.Marker( size=10, label=trace.pen.label, @@ -276,13 +280,60 @@ def example_data_product_generator(workdir: Path) -> trendify.ProductList: tags=["scatter_plot"], ).append_to_list(products).set_metadata({"run_num": run_num}) + trendify.Trace2D.from_xy( + x=time, + y=df[value_columns[0]].to_numpy(), + tags=[("nested_plots", "group_a", "deep_trace")], + pen=trendify.Pen(label=value_columns[0], color=colors[0]), + format2d=trendify.Format2D( + grid=trendify.Grid.from_theme(trendify.GridTheme.MATLAB), + ), + ).append_to_list(products).set_metadata({"run_num": run_num}) + trendify.Trace2D.from_xy( + x=time, + y=df[value_columns[-1]].to_numpy(), + tags=[("nested_plots", "group_b", "deep_trace")], + pen=trendify.Pen(label=value_columns[-1], color=colors[-1]), + format2d=trendify.Format2D( + grid=trendify.Grid.from_theme(trendify.GridTheme.MATLAB), + ), + ).append_to_list(products).set_metadata({"run_num": run_num}) + for col in value_columns: series = df[col] trendify.TableEntry( row=workdir.name, col=col, value=series.len(), - tags=["table"], + tags=[("tables", "lengths")], + unit=None, + ).append_to_list(products) + trendify.TableEntry( + row=workdir.name, + col=col, + value=cast(float, series.mean()), + tags=[("tables", "means")], + unit=None, + ).append_to_list(products) + trendify.TableEntry( + row=workdir.name, + col=col, + value=cast(float, series.std()), + tags=[("tables", "std_devs")], + unit=None, + ).append_to_list(products) + trendify.TableEntry( + row=workdir.name, + col=col, + value=cast(float, series.max()), + tags=[("extrema", "max")], + unit=None, + ).append_to_list(products) + trendify.TableEntry( + row=workdir.name, + col=col, + value=cast(float, series.min()), + tags=[("extrema", "min")], unit=None, ).append_to_list(products) diff --git a/src/trendify/formats/format2d.py b/src/trendify/formats/format2d.py index b6142b9..82fc1f1 100644 --- a/src/trendify/formats/format2d.py +++ b/src/trendify/formats/format2d.py @@ -7,6 +7,7 @@ from __future__ import annotations import logging +from abc import ABC, abstractmethod from collections.abc import Iterable from enum import StrEnum from typing import TYPE_CHECKING @@ -143,7 +144,7 @@ def union_from_iterable(cls, format2ds: Iterable[Format2D]): ) -class PlottableData2D(DataProduct): +class PlottableData2D(DataProduct, ABC): """ Base class for children of DataProduct to be plotted ax xy data on a 2D plot @@ -156,7 +157,8 @@ class PlottableData2D(DataProduct): format2d: Format2D | None = None - def add_to_plotly(self, plotly_figure: PlotlyFigure): + @abstractmethod + def add_to_plotly(self, plotly_figure: PlotlyFigure) -> PlotlyFigure: """ Add this data product to a plotly figure @@ -164,7 +166,6 @@ def add_to_plotly(self, plotly_figure: PlotlyFigure): plotly_figure (PlotlyFigure): Plotly figure to add data to """ - raise NotImplementedError("Subclasses must implement add_to_plotly") class XYData(PlottableData2D): diff --git a/src/trendify/generator/__init__.py b/src/trendify/generator/__init__.py index b10f601..34a9c0a 100644 --- a/src/trendify/generator/__init__.py +++ b/src/trendify/generator/__init__.py @@ -1,9 +1,10 @@ -from trendify.generator import generate -from trendify.generator import histogrammer -from trendify.generator import render -from trendify.generator import table_builder -from trendify.generator import xy_data_plotter - +from trendify.generator import ( + generate, + histogrammer, + render, + table_builder, + xy_data_plotter, +) from trendify.generator.generate import ( generate_products, get_sorted_dirs, @@ -12,7 +13,6 @@ Histogrammer, ) from trendify.generator.render import ( - make_include_files, render_assets, ) from trendify.generator.table_builder import ( @@ -30,7 +30,6 @@ "generate_products", "get_sorted_dirs", "histogrammer", - "make_include_files", "render", "render_assets", "table_builder", diff --git a/src/trendify/generator/generate.py b/src/trendify/generator/generate.py index 293490a..e0d257d 100644 --- a/src/trendify/generator/generate.py +++ b/src/trendify/generator/generate.py @@ -5,16 +5,24 @@ There is no separate "sort" step: tags are indexed at write time, so retrieving products by tag is an indexed query against `ProductStore`, not a physical grouping step that has to run before rendering can start. + +Worker processes only ever *compute* products; exactly one connection (in this process) ever +writes. SQLite allows a single writer at a time no matter how many connections are open, so +having every worker process open its own writing connection (the earlier design) doesn't buy +any write throughput -- it only adds lock contention, which gets worse, not better, as +`n_procs` goes up. Splitting the pool into N compute workers feeding one writer lets +`n_procs` control the part that actually parallelizes (running `product_generator`) without +touching the part that can't (writing to the shared `.db` file). """ from __future__ import annotations import logging -from concurrent.futures import ProcessPoolExecutor +from concurrent.futures import ProcessPoolExecutor, as_completed from pathlib import Path from typing import Any -from trendify.base.data_product import ProductGenerator +from trendify.base.data_product import ProductGenerator, ProductList from trendify.log import create_queue_listener from trendify.log import worker_init as _init_worker_logging from trendify.store.product_store import ProductStore @@ -23,9 +31,8 @@ logger = logging.getLogger(__name__) -# Per-process globals set by `_init_worker`, one `ProductStore` connection per worker process -# (WAL mode lets every worker write directly to the same `.db` file, see `store/db.py`). -_worker_store: ProductStore | None = None +# Per-process global set by `_init_worker`: the one thing a compute worker needs. Workers +# never open a `ProductStore` -- only the caller of `generate_products` writes. _worker_generator: ProductGenerator | None = None @@ -48,14 +55,12 @@ def get_sorted_dirs(dirs: list[Path]) -> list[Path]: return dirs -def _init_worker(db_path: str, product_generator: ProductGenerator) -> None: - global _worker_store, _worker_generator - _worker_store = ProductStore.open(Path(db_path)) +def _init_worker(product_generator: ProductGenerator) -> None: + global _worker_generator _worker_generator = product_generator def _init_worker_with_logging( - db_path: str, product_generator: ProductGenerator, log_queue: Any, log_level: int, @@ -64,14 +69,13 @@ def _init_worker_with_logging( # runs, so no worker ever opens a file/console handler directly. See trendify.log for why # that matters (Windows spawn semantics, and fork-inherited-handler races on POSIX). _init_worker_logging(log_queue, log_level) - _init_worker(db_path, product_generator) + _init_worker(product_generator) -def _generate_and_write(run_dir: Path) -> int: - assert _worker_store is not None and _worker_generator is not None - logger.info(f"Processing {run_dir = }") - products = _worker_generator(run_dir) - return _worker_store.write_run(run_dir, products) +def _compute(run_dir: Path) -> tuple[Path, ProductList]: + assert _worker_generator is not None + logger.info(f"Processing run_dir = {run_dir}") + return run_dir, _worker_generator(run_dir) def generate_products( @@ -81,8 +85,8 @@ def generate_products( n_procs: int = 1, ) -> int: """ - Maps `product_generator` over `data_dirs`, writing each directory's products directly to - the `ProductStore` at `db_path` (one `write_run` transaction per directory). There is no + Maps `product_generator` over `data_dirs`, writing each directory's products to the + `ProductStore` at `db_path` (one `write_run` transaction per directory). There is no intermediate file and no separate sort step, since tag-based retrieval from `ProductStore` is an indexed query. @@ -93,9 +97,11 @@ def generate_products( data_dirs (list[Path]): Directories over which to map `product_generator`. db_path (Path): Path to the trendify output directory's `.db` file. Created if it doesn't already exist. - n_procs (int): Number of worker processes. `n_procs == 1` runs sequentially in this - process (easier to debug with full tracebacks). `n_procs > 1` uses a - `ProcessPoolExecutor`, with one `ProductStore` connection opened per worker. + n_procs (int): Number of worker processes computing products in parallel. + `n_procs == 1` runs sequentially in this process (easier to debug with full + tracebacks). `n_procs > 1` uses a `ProcessPoolExecutor` for computing products + only; writing always happens through the single connection this function opens, + regardless of `n_procs`, since SQLite only ever allows one writer at a time. Returns: (int): total number of products written across all directories. @@ -104,35 +110,35 @@ def generate_products( sorted_dirs = get_sorted_dirs(data_dirs) db_path = Path(db_path) - # Bootstrap the schema up front, from this process, before workers race to open it. - ProductStore.open(db_path).close() - logger.info(f"Generating and writing DataProducts for {len(sorted_dirs)} run(s)...") - if n_procs > 1: - root_logger = logging.getLogger() - log_queue, listener = create_queue_listener(*root_logger.handlers) - listener.start() - try: - with ProcessPoolExecutor( - max_workers=n_procs, - initializer=_init_worker_with_logging, - initargs=( - str(db_path), - product_generator, - log_queue, - root_logger.level, - ), - ) as executor: - results = list(executor.map(_generate_and_write, sorted_dirs)) - finally: - # Blocks until every already-queued log record has been dispatched, so worker - # log output isn't lost or interleaved with what follows. - listener.stop() - else: - _init_worker(str(db_path), product_generator) - results = [_generate_and_write(d) for d in sorted_dirs] - - total = sum(results) + total = 0 + with ProductStore.open(db_path) as store: + if n_procs > 1: + root_logger = logging.getLogger() + log_queue, listener = create_queue_listener(*root_logger.handlers) + listener.start() + try: + with ProcessPoolExecutor( + max_workers=n_procs, + initializer=_init_worker_with_logging, + initargs=(product_generator, log_queue, root_logger.level), + ) as executor: + futures = [ + executor.submit(_compute, run_dir) for run_dir in sorted_dirs + ] + for future in as_completed(futures): + run_dir, products = future.result() + total += store.write_run(run_dir, products) + finally: + # Blocks until every already-queued log record has been dispatched, so worker + # log output isn't lost or interleaved with what follows. + listener.stop() + else: + _init_worker(product_generator) + for run_dir in sorted_dirs: + _, products = _compute(run_dir) + total += store.write_run(run_dir, products) + logger.info( f"Finished generating DataProducts: {total} products written across " f"{len(sorted_dirs)} run(s)" diff --git a/src/trendify/generator/render.py b/src/trendify/generator/render.py index 88559e0..61ff240 100644 --- a/src/trendify/generator/render.py +++ b/src/trendify/generator/render.py @@ -2,7 +2,7 @@ Static rendering pipeline: renders CSV tables and matplotlib figures directly from a `ProductStore`. -Design note: XY data (`Point2D`/`Trace2D`/`AxLine`) and histogram data (`HistogramEntry`) +Design note: XY data (`Point2D`/`Scatter2D`/`Trace2D`/`AxLine`) and histogram data (`HistogramEntry`) sharing a tag are always rendered to separate figures/files (`.jpg` for XY, `_histogram.jpg` for histograms), never overlaid onto one shared axes, so a tag used for both product kinds gets two clean plots instead of a histogram silently drawn on top of an XY @@ -21,16 +21,15 @@ from trendify.plotting.axline import AxLine from trendify.plotting.histogram import HistogramEntry from trendify.plotting.point import Point2D +from trendify.plotting.scatter import Scatter2D from trendify.plotting.trace import Trace2D from trendify.store.product_store import ProductStore from trendify.store.tags import tag_to_path_parts -__all__ = ["make_include_files", "render_assets"] +__all__ = ["render_assets"] logger = logging.getLogger(__name__) -INCLUDE_FILENAME = "include.md" - def render_assets( store: ProductStore, @@ -50,7 +49,8 @@ def render_assets( output_dir (Path): directory tables/figures are written under dpi (int): resolution for saved matplotlib figures no_tables (bool): suppress `TableEntry` -> CSV output - no_xy_plots (bool): suppress `Point2D`/`Trace2D`/`AxLine` -> matplotlib XY plot output + no_xy_plots (bool): suppress `Point2D`/`Scatter2D`/`Trace2D`/`AxLine` -> matplotlib + XY plot output no_histograms (bool): suppress `HistogramEntry` -> matplotlib histogram output """ @@ -70,15 +70,20 @@ def render_assets( points = store.get_products_of_type(Point2D, tag=tag) traces = store.get_products_of_type(Trace2D, tag=tag) axlines = store.get_products_of_type(AxLine, tag=tag) + scatters = store.get_products_of_type(Scatter2D, tag=tag) - if points or traces or axlines: + if points or traces or axlines or scatters: logger.info(f"Making xy plot for {tag = }") saf = XYDataPlotter.handle_points_and_traces( - tag=tag, points=points, traces=traces, axlines=axlines + tag=tag, + points=points, + traces=traces, + axlines=axlines, + scatters=scatters, ) format2ds = [ p.format2d - for p in (*points, *traces, *axlines) + for p in (*points, *traces, *axlines, *scatters) if p.format2d is not None ] if format2ds: @@ -112,82 +117,3 @@ def render_assets( logger.info(f"Saving to {save_path}") saf.savefig(save_path, dpi=dpi) logger.info(f"Finished histogram for {tag = }") - - -def make_include_files( - root_dir: Path, - local_server_path: str | Path | None = None, - mkdocs_include_dir: str | Path | None = None, - heading_level: int | None = None, -) -> None: - """ - Makes nested `include.md` files (MkDocs `{% include %}`/`{{ read_csv(...) }}` snippets) for - the figures/tables/subdirectories under `root_dir`. This is a pure filesystem walk over - already-rendered assets and doesn't touch `ProductStore` at all. - - Args: - root_dir (Path): directory for which include files should be recursively generated - local_server_path (str | Path | None): prefix to use for figure paths, for serving - figures from a local dev server alongside `mkdocs serve` - mkdocs_include_dir (str | Path | None): path matching mkdocs.yml's `include_dir`, used - to make included paths relative to it - heading_level (int | None): base Markdown heading level for per-directory headers - - """ - root_dir = Path(root_dir) - dirs = sorted(root_dir.glob("**/")) - if not dirs: - return - - min_len = min(len(list(p.parents)) for p in dirs) - for s in dirs: - child_dirs = sorted(s.glob("*/")) - tables_to_include = [ - x - for pattern in ("*pivot.csv", "*stats.csv") - for x in s.glob(pattern, case_sensitive=False) - ] - figures_to_include = [ - x - for pattern in ("*.jpg", "*.png") - for x in s.glob(pattern, case_sensitive=False) - ] - children_to_include = [ - c.resolve().joinpath(INCLUDE_FILENAME) for c in child_dirs - ] - - if local_server_path is not None: - figures_to_include = [ - Path(local_server_path).joinpath(x.relative_to(root_dir)) - for x in figures_to_include - ] - if mkdocs_include_dir is not None: - mkdocs_include_dir = Path(mkdocs_include_dir) - tables_to_include = [ - x.relative_to(mkdocs_include_dir.parent) for x in tables_to_include - ] - children_to_include = [ - x.relative_to(mkdocs_include_dir) for x in children_to_include - ] - - fig_inclusion_statements = sorted(f"![]({x})" for x in figures_to_include) - table_inclusion_statements = sorted( - f"{{{{ read_csv('{x}', disable_numparse=True) }}}}" - for x in tables_to_include - ) - child_inclusion_statements = sorted( - "{% include '" + str(x) + "' %}" for x in children_to_include - ) - inclusions = ( - table_inclusion_statements - + fig_inclusion_statements - + child_inclusion_statements - ) - - header = "" - if heading_level is not None and len(inclusions) > 1: - depth = len(list(s.parents)) - min_len - header = "#" * (depth + heading_level) + s.name - - text = "\n\n".join([header, *inclusions]) - s.joinpath(INCLUDE_FILENAME).write_text(text) diff --git a/src/trendify/generator/xy_data_plotter.py b/src/trendify/generator/xy_data_plotter.py index 963dc13..8fac9c4 100644 --- a/src/trendify/generator/xy_data_plotter.py +++ b/src/trendify/generator/xy_data_plotter.py @@ -13,6 +13,7 @@ from trendify.plotting.axline import AxLine from trendify.plotting.figure import SingleAxisFigure from trendify.plotting.point import Point2D +from trendify.plotting.scatter import Scatter2D from trendify.plotting.trace import Trace2D __all__ = ["XYDataPlotter"] @@ -22,7 +23,8 @@ class XYDataPlotter(BaseModel): """ - Draws `Point2D`/`Trace2D`/`AxLine` products sharing a tag onto a matplotlib axes. + Draws `Point2D`/`Scatter2D`/`Trace2D`/`AxLine` products sharing a tag onto a matplotlib + axes. """ @classmethod @@ -32,10 +34,11 @@ def handle_points_and_traces( points: list[Point2D], traces: list[Trace2D], axlines: list[AxLine], + scatters: list[Scatter2D], saf: SingleAxisFigure | None = None, ) -> SingleAxisFigure: """ - Plots points (scattered, grouped by marker), traces, and axlines onto `saf`. + Plots points (scattered, grouped by marker), scatters, traces, and axlines onto `saf`. Args: tag (Tag): tag these products belong to (used only if a new figure is created) @@ -43,6 +46,8 @@ def handle_points_and_traces( marker becomes one `ax.scatter` call/series) traces (list[Trace2D]): traces to plot axlines (list[AxLine]): axis lines to plot + scatters (list[Scatter2D]): bulk scatter arrays to plot, each already carrying + its own single shared `Marker` saf (SingleAxisFigure | None): figure to draw onto; a new one is created if `None` Returns: @@ -54,7 +59,7 @@ def handle_points_and_traces( logger.debug( f"Plotting {len(points)} point(s), {len(traces)} trace(s), {len(axlines)} " - f"axline(s) for {tag = }" + f"axline(s), {len(scatters)} scatter(s) for {tag = }" ) if points: markers = set(p.marker for p in points) @@ -68,6 +73,9 @@ def handle_points_and_traces( else: saf.ax.scatter(x, y) + for scatter in scatters: + scatter.plot_to_ax(saf.ax) + for trace in traces: trace.plot_to_ax(saf.ax) diff --git a/src/trendify/pipeline.py b/src/trendify/pipeline.py index 03de6b7..d0c3444 100644 --- a/src/trendify/pipeline.py +++ b/src/trendify/pipeline.py @@ -14,7 +14,7 @@ from trendify.base.data_product import ProductGenerator from trendify.generator.generate import generate_products -from trendify.generator.render import make_include_files, render_assets +from trendify.generator.render import render_assets from trendify.store.product_store import ProductStore __all__ = ["TrendifyPipeline"] @@ -60,7 +60,7 @@ def generate( """ logger.info( f"Generating products for {len(data_dirs)} directory(ies) into {self.db_path} " - f"({self.n_procs} worker process(es))" + f"({self.n_procs} worker process{'es' if self.n_procs > 1 else ''})" ) total = generate_products( product_generator=product_generator, @@ -95,25 +95,6 @@ def render( ) logger.info(f"Finished rendering assets into {self.assets_dir}") - def make_include_files( - self, - heading_level: int | None = 2, - local_server_path: str | Path | None = None, - mkdocs_include_dir: str | Path | None = None, - ) -> None: - """ - Writes nested MkDocs `include.md` files for `self.assets_dir`. See - `trendify.generator.render.make_include_files`. - - """ - logger.info(f"Writing include.md files under {self.assets_dir}") - make_include_files( - root_dir=self.assets_dir, - local_server_path=local_server_path, - mkdocs_include_dir=mkdocs_include_dir, - heading_level=heading_level, - ) - def run( self, product_generator: ProductGenerator, @@ -122,11 +103,10 @@ def run( no_tables: bool = False, no_xy_plots: bool = False, no_histograms: bool = False, - no_include_files: bool = False, ) -> int: """ Runs the full pipeline: generate, then render (unless all three asset kinds are - suppressed), then write include files (unless suppressed). + suppressed). Returns: (int): total number of products written by `generate` @@ -143,8 +123,6 @@ def run( no_xy_plots=no_xy_plots, no_histograms=no_histograms, ) - if not no_include_files: - self.make_include_files() logger.info(f"Finished full pipeline for {self.output_dir}") return total diff --git a/src/trendify/plotting/__init__.py b/src/trendify/plotting/__init__.py index 8da7c3b..aebd901 100644 --- a/src/trendify/plotting/__init__.py +++ b/src/trendify/plotting/__init__.py @@ -2,6 +2,7 @@ from trendify.plotting import figure from trendify.plotting import histogram from trendify.plotting import point +from trendify.plotting import scatter from trendify.plotting import trace from trendify.plotting.axline import ( @@ -19,6 +20,9 @@ from trendify.plotting.point import ( Point2D, ) +from trendify.plotting.scatter import ( + Scatter2D, +) from trendify.plotting.trace import ( Trace2D, ) @@ -30,11 +34,13 @@ "LineOrientation", "PlotlyFigure", "Point2D", + "Scatter2D", "SingleAxisFigure", "Trace2D", "axline", "figure", "histogram", "point", + "scatter", "trace", ] diff --git a/src/trendify/plotting/figure.py b/src/trendify/plotting/figure.py index d1a31e7..858dcd3 100644 --- a/src/trendify/plotting/figure.py +++ b/src/trendify/plotting/figure.py @@ -7,31 +7,27 @@ from __future__ import annotations +import logging +import warnings from dataclasses import dataclass, field from pathlib import Path -import matplotlib.pyplot as plt -import warnings -import logging - -from typing import Any, cast +from typing import TYPE_CHECKING, Any, cast +import matplotlib.pyplot as plt import numpy as np import plotly.graph_objects as go - -from trendify.formats.format2d import AxisScale, PlottableData2D - -from typing import Self, TYPE_CHECKING - from pydantic import ConfigDict from trendify.base.helpers import Tag +from trendify.formats.format2d import AxisScale, PlottableData2D if TYPE_CHECKING: - from trendify.formats.format2d import Format2D - from trendify.styling.grid import Grid from matplotlib.axes import Axes from matplotlib.figure import Figure + from trendify.formats.format2d import Format2D + from trendify.styling.grid import Grid + __all__ = ["PlotlyFigure", "SingleAxisFigure"] @@ -336,7 +332,7 @@ def apply_grid(self, grid: Grid): self.fig.update_xaxes(**xaxis_updates) self.fig.update_yaxes(**yaxis_updates) - def add_data_product(self, product: PlottableData2D) -> Self: + def add_data_product(self, product: PlottableData2D) -> PlotlyFigure: """ Add a data product to the figure diff --git a/src/trendify/plotting/histogram.py b/src/trendify/plotting/histogram.py index bdc2669..ea684b7 100644 --- a/src/trendify/plotting/histogram.py +++ b/src/trendify/plotting/histogram.py @@ -9,9 +9,10 @@ import logging from typing import cast +import numpy as np import plotly.graph_objects as go from matplotlib.colors import to_rgba -from pydantic import ConfigDict, Field +from pydantic import ConfigDict, Field, field_validator from trendify.base.helpers import HashableBase, Tags from trendify.formats.format2d import PlottableData2D @@ -45,7 +46,19 @@ class HistogramStyle(HashableBase): alpha_face: float = 0.3 linewidth: float = 2 zorder: int = 1 - bins: int | list[int] | tuple[int] | VecN | None = None + bins: int | tuple[int, ...] | None = None + + @field_validator("bins", mode="before") + @classmethod + def _coerce_bins_to_hashable( + cls, value: int | list[int] | tuple[int] | VecN | None + ): + # `HistogramStyle` is a `HashableBase` (its instances are deduplicated via `set()` in + # `Histogrammer.handle_histogram_entries`), so `bins` can't be stored as a `list` or + # `np.ndarray`: both are unhashable and would break `HashableBase.__hash__`. + if isinstance(value, (list, np.ndarray)): + return tuple(int(v) for v in value) + return value def as_plot_kwargs(self): """ @@ -187,7 +200,7 @@ class HistogramEntry(PlottableData2D): model_config = ConfigDict(extra="forbid") - def add_to_plotly(self, plotly_figure: PlotlyFigure): + def add_to_plotly(self, plotly_figure: PlotlyFigure) -> PlotlyFigure: """Add histogram entry to plotly figure, merging with existing traces if possible""" if not self.style: logger.error("HistogramEntry style is not defined.") diff --git a/src/trendify/plotting/scatter.py b/src/trendify/plotting/scatter.py new file mode 100644 index 0000000..f023aa6 --- /dev/null +++ b/src/trendify/plotting/scatter.py @@ -0,0 +1,130 @@ +""" +`Scatter2D`: a bulk collection of (x, y) points sharing one `Marker`, drawn as unconnected +scatter points. + +Use this instead of many individual `Point2D` products when a single generation call +produces a large array of points that all share one style and don't need distinct per-point +tags/metadata (e.g. a raw scatter of measurements from one run). `Point2D` remains the right +choice when each point is its own taggable/hoverable entity, such as one point summarizing +one run, aggregated across many runs that share a tag. +""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING + +import plotly.graph_objects as go +from pydantic import ConfigDict + +from trendify.base.helpers import Tags +from trendify.formats.format2d import XYData +from trendify.plotting.figure import PlotlyFigure +from trendify.styling.marker import Marker +from trendify.typing import VecN + +if TYPE_CHECKING: + from matplotlib.axes import Axes + + from trendify.formats.format2d import Format2D + +__all__ = ["Scatter2D"] + +logger = logging.getLogger(__name__) + + +class Scatter2D(XYData): + """ + A collection of unconnected scattered points sharing one `Marker`. + Use the [Scatter2D.from_xy][trendify.plotting.scatter.Scatter2D.from_xy] constructor. + + Attributes: + x (VecN): x values + y (VecN): y values + marker (Marker): Style and label information shared by every point in this scatter. + tags (Tags): Tags to be used for sorting data. + metadata (dict[str, str]): A dictionary of metadata to be used as a tool tip for mousover in grafana + + """ + + model_config = ConfigDict(extra="forbid") + + x: VecN + y: VecN + marker: Marker = Marker() + + @classmethod + def from_xy( + cls, + tags: Tags, + x: VecN, + y: VecN, + marker: Marker = Marker(), + format2d: Format2D | None = None, + ): + """ + Creates a new [Scatter2D][trendify.plotting.scatter.Scatter2D] product from xy data + sharing one marker style. + + Args: + tags (Tags): Tags used to sort data products + x (VecN): x values + y (VecN): y values + marker (Marker): Style and label shared by every point + format2d (Format2D | None): Format to apply to plot + + """ + return cls(tags=tags, x=x, y=y, marker=marker, format2d=format2d) + + def plot_to_ax(self, ax: Axes): + """ + Scatters xy data to a matplotlib axes object. + + Args: + ax (Axes): axes to which xy data should be plotted + + """ + ax.scatter(self.x, self.y, **self.marker.as_scatter_plot_kwargs()) + + def add_to_plotly(self, plotly_figure: PlotlyFigure) -> PlotlyFigure: + legend_key = f"{self.marker.label}_{self.marker.color}_{self.marker.symbol}" + + metadata_html = ( + "
".join(f"{key}: {value}" for key, value in self.metadata.items()) + if self.metadata + else "" + ) + hovertemplate = ( + f"{self.marker.label or ''}
" + "x: %{x}
" + "y: %{y}
" + f"{metadata_html}" + ) + + plotly_figure.fig.add_trace( + go.Scatter( + x=self.x, + y=self.y, + name=self.marker.label, + mode="markers", + marker=dict( + color=self.marker.rgba, + size=self.marker.size, + symbol=self.marker.plotly_symbol, + ), + zorder=int(self.marker.zorder), + legendgroup=legend_key, + hovertemplate=hovertemplate, + hoverlabel=dict( + bgcolor=self.marker.rgba, + font=dict(color=self.marker.get_contrast_color()), + ), + showlegend=( + True if legend_key not in plotly_figure.legend_groups else False + ), + ) + ) + + if legend_key not in plotly_figure.legend_groups: + plotly_figure.legend_groups.add(legend_key) + return plotly_figure diff --git a/src/trendify/plotting/trace.py b/src/trendify/plotting/trace.py index 7845a65..12b4b13 100644 --- a/src/trendify/plotting/trace.py +++ b/src/trendify/plotting/trace.py @@ -1,6 +1,7 @@ """ -`Trace2D`: a line/scatter trace built from a list of `Point2D`s, styled with a `Pen`. Use the -`Trace2D.from_xy` constructor rather than building the `points` list by hand. +`Trace2D`: an xy line built from flat `x`/`y` arrays, styled with a `Pen`, and optionally +marked at intervals with a single shared `Marker` (mirroring matplotlib's native +`markevery`). Use the `Trace2D.from_xy` constructor. """ from __future__ import annotations @@ -8,7 +9,6 @@ import logging from typing import TYPE_CHECKING -import numpy as np import plotly.graph_objects as go from pydantic import ConfigDict @@ -16,7 +16,6 @@ from trendify.base.pen import Pen from trendify.formats.format2d import XYData from trendify.plotting.figure import PlotlyFigure -from trendify.plotting.point import Point2D from trendify.styling.marker import Marker from trendify.typing import VecN @@ -32,15 +31,20 @@ class Trace2D(XYData): """ - A collection of points comprising a trace. + An xy line, optionally marked at intervals. Use the [Trace2D.from_xy][trendify.plotting.trace.Trace2D.from_xy] constructor. Attributes: - points (List[Point2D]): List of points. Usually the points would have null values - for `marker` and `format2d` fields to save space. + x (VecN): x values + y (VecN): y values pen (Pen): Style and label information for drawing to matplotlib axes. Only the label information is used in Grafana. Eventually style information will be used in grafana. + marker (Marker | None): Shared marker style drawn along the line (e.g. one marker + every `markevery` points). `None` draws a plain line with no markers. + markevery (int | None): Draw a marker every Nth point when `marker` is set (passed + straight through to matplotlib's `Axes.plot(markevery=...)`); `None` marks every + point. tags (Tags): Tags to be used for sorting data. metadata (dict[str, str]): A dictionary of metadata to be used as a tool tip for mousover in grafana @@ -48,49 +52,11 @@ class Trace2D(XYData): model_config = ConfigDict(extra="forbid") - points: list[Point2D] + x: VecN + y: VecN pen: Pen = Pen() - - @property - def x(self) -> VecN: - """ - Returns an array of x values from `self.points` - - Returns: - (VecN): array of x values from `self.points` - - """ - return np.array([p.x for p in self.points]) - - @property - def y(self) -> VecN: - """ - Returns an array of y values from `self.points` - - Returns: - (VecN): array of y values from `self.points` - - """ - return np.array([p.y for p in self.points]) - - def propagate_format2d_and_pen(self, marker_symbol: str = ".") -> None: - """ - Propagates format and style info to all `self.points` (in-place). - - Args: - marker_symbol (str): Valid matplotlib marker symbol - - """ - self.points = [ - p.model_copy( - update={ - "tags": self.tags, - "format2d": self.format2d, - "marker": Marker.from_pen(self.pen, symbol=marker_symbol), - } - ) - for p in self.points - ] + marker: Marker | None = None + markevery: int | None = None @classmethod def from_xy( @@ -100,9 +66,11 @@ def from_xy( y: VecN, pen: Pen = Pen(), format2d: Format2D | None = None, + marker: Marker | None = None, + markevery: int | None = None, ): """ - Creates a list of [Point2D][trendify.plotting.point.Point2D]s from xy data and returns a new [Trace2D][trendify.plotting.trace.Trace2D] product. + Creates a new [Trace2D][trendify.plotting.trace.Trace2D] product from xy data. Args: tags (Tags): Tags used to sort data products @@ -110,22 +78,18 @@ def from_xy( y (VecN): y values pen (Pen): Style and label for trace format2d (Format2D | None): Format to apply to plot + marker (Marker | None): Shared marker style drawn along the line + markevery (int | None): Draw a marker every Nth point when `marker` is set """ return cls( tags=tags, - points=[ - Point2D( - tags=[], - x=x_, - y=y_, - marker=None, - format2d=None, - ) - for x_, y_ in zip(x, y) - ], + x=x, + y=y, pen=pen, format2d=format2d, + marker=marker, + markevery=markevery, ) def plot_to_ax(self, ax: Axes): @@ -136,7 +100,14 @@ def plot_to_ax(self, ax: Axes): ax (Axes): axes to which xy data should be plotted """ - ax.plot(self.x, self.y, **self.pen.as_scatter_plot_kwargs()) + kwargs = self.pen.as_scatter_plot_kwargs() + if self.marker is not None: + kwargs["marker"] = self.marker.symbol + kwargs["markersize"] = self.marker.size + kwargs["markerfacecolor"] = self.marker.color + kwargs["markeredgecolor"] = self.marker.color + kwargs["markevery"] = self.markevery if self.markevery is not None else 1 + ax.plot(self.x, self.y, **kwargs) def add_to_plotly(self, plotly_figure: PlotlyFigure) -> PlotlyFigure: legend_key = ( @@ -159,19 +130,23 @@ def add_to_plotly(self, plotly_figure: PlotlyFigure) -> PlotlyFigure: f"{metadata_html}" ) + # `markevery` (subsampling markers along the line) has no direct Plotly equivalent + # for a single trace, so Plotly renders a marker at every point when `marker` is set. plotly_figure.fig.add_trace( go.Scatter( - x=[p.x for p in self.points], - y=[p.y for p in self.points], + x=self.x, + y=self.y, name=self.pen.label if self.pen else None, - mode="lines", + mode="lines+markers" if self.marker is not None else "lines", line=dict( color=self.pen.rgba if self.pen else None, width=self.pen.size if self.pen else None, dash=self.pen._convert_linestyle_to_plotly() if self.pen else None, ), marker=dict( - color=self.pen.rgba if self.pen else None, # Marker color + color=self.marker.rgba if self.marker else self.pen.rgba, + size=self.marker.size if self.marker else None, + symbol=self.marker.plotly_symbol if self.marker else None, ), zorder=int(self.pen.zorder), hovertemplate=hovertemplate, diff --git a/src/trendify/store/product_store.py b/src/trendify/store/product_store.py index f6a67d1..ee9151f 100644 --- a/src/trendify/store/product_store.py +++ b/src/trendify/store/product_store.py @@ -49,8 +49,11 @@ def _leaf_type_names(object_type: type[DataProduct]) -> list[str]: def _tag_sort_key(tag: Tag): + # `Tag` elements may be `str` or `int`, which aren't comparable to each other; sorting + # each element on `(is_int, value)` keeps same-type elements ordered by value while + # elements of different types compare on the `is_int` flag instead of colliding. as_tuple = tag if isinstance(tag, tuple) else (tag,) - return (len(as_tuple), as_tuple) + return (len(as_tuple), tuple((isinstance(x, int), x) for x in as_tuple)) @dataclass diff --git a/gallery/trendify/products/analysis/reserving_index.lock b/src/trendify/viewer/__init__.py similarity index 100% rename from gallery/trendify/products/analysis/reserving_index.lock rename to src/trendify/viewer/__init__.py diff --git a/src/trendify/viewer/app.py b/src/trendify/viewer/app.py new file mode 100644 index 0000000..ffd8a2b --- /dev/null +++ b/src/trendify/viewer/app.py @@ -0,0 +1,108 @@ +""" +FastAPI app factory for the trendify dashboard. See `trendify.cli`'s `serve` command for how +this gets launched. +""" + +from __future__ import annotations + +import datetime +import logging +import os +from collections.abc import AsyncIterator +from contextlib import asynccontextmanager +from importlib.metadata import version +from pathlib import Path + +from fastapi import FastAPI +from fastapi.staticfiles import StaticFiles +from fastapi.templating import Jinja2Templates + +from trendify.store.product_store import ProductStore +from trendify.viewer.routes import api, pages + +__all__ = ["create_app", "create_app_from_env"] + +_DB_PATH_ENV_VAR = "TRENDIFY_DB_PATH" + +logger = logging.getLogger(__name__) + +_VIEWER_DIR = Path(__file__).parent +_TEMPLATES_DIR = _VIEWER_DIR / "templates" +_STATIC_DIR = _TEMPLATES_DIR / "static" + + +def _get_version() -> str: + try: + return version("trendify") + except Exception: + return "0.0.0" + + +def create_app(db_path: Path) -> FastAPI: + """ + Builds the dashboard FastAPI app for `db_path`. + + Opens one read-only `ProductStore` connection, held for the app's lifetime and closed on + shutdown. This app is only ever meant to be run with a single uvicorn worker process: a + local, single-user dashboard has no reason for more, and multiple worker processes would + each need their own store connection (`fork`/`spawn` doesn't share a live sqlite3 + connection across processes). + + That single connection is also thread-affine to whatever thread opens it. It is + deliberately opened inside the `lifespan` handler below, rather than here in `create_app`, + so it's always created on the same thread that ends up running the app's event loop -- + which is the thread that called `uvicorn.run()` for a real server, but is a *different*, + dedicated thread for `starlette.testclient.TestClient` (it runs the ASGI app through an + anyio blocking portal on its own thread). Opening eagerly in `create_app` would bind the + connection to whichever of those threads happens to call this function, which is wrong for + the other one. Every route handler that touches `app.state.store` must likewise be declared + `async def`, not `def`: FastAPI dispatches plain `def` handlers to a worker threadpool, + which would hand the connection to yet another thread and raise `sqlite3.ProgrammingError`. + + Args: + db_path (Path): path to the `.db` file to serve. + + Returns: + (FastAPI): the configured app, ready for `uvicorn.run` (or a `TestClient`). + + """ + + @asynccontextmanager + async def lifespan(app: FastAPI) -> AsyncIterator[None]: + app.state.store = ProductStore.open(db_path, readonly=True) + try: + yield + finally: + app.state.store.close() + + app = FastAPI(title="trendify", version=_get_version(), lifespan=lifespan) + app.state.response_cache = {} + app.state.db_path = db_path + app.state.db_mtime = None + + templates = Jinja2Templates(directory=_TEMPLATES_DIR) + templates.env.globals["current_year"] = datetime.datetime.now().year + templates.env.globals["app_version"] = _get_version() + templates.env.globals["db_path"] = str(db_path) + app.state.templates = templates + + app.mount("/static", StaticFiles(directory=_STATIC_DIR), name="static") + app.include_router(pages.router) + app.include_router(api.router, prefix="/api") + + return app + + +def create_app_from_env() -> FastAPI: + """ + `create_app`, reading `db_path` from the `TRENDIFY_DB_PATH` environment variable instead of + a direct argument. + + This exists solely as a uvicorn ASGI factory target (`uvicorn.run(..., factory=True)`) for + `trendify serve --reload`: uvicorn's reload mode re-imports the app in a fresh subprocess on + every file change, so it needs an importable `module:attribute` string rather than a live app + instance -- there is no other way to hand it a runtime value like `db_path` across that + re-import boundary. + """ + db_path = os.environ[_DB_PATH_ENV_VAR] + return create_app(Path(db_path)) diff --git a/src/trendify/viewer/plot_config.py b/src/trendify/viewer/plot_config.py new file mode 100644 index 0000000..0319e2f --- /dev/null +++ b/src/trendify/viewer/plot_config.py @@ -0,0 +1,100 @@ +""" +Pydantic models for the Dojo trial-viewer PlotConfig. + +These are the single source of truth for both server-side validation (profile save/load) and the generated TypeScript types in `lib/plot-config.generated.ts`. + +To regenerate TypeScript types after changing this file: + + python scripts/gen_ts_models.py +""" + +from __future__ import annotations + +from enum import StrEnum + +from pydantic import BaseModel, ConfigDict, Field +from pydantic.alias_generators import to_camel + +camel_case_dict = ConfigDict( + alias_generator=to_camel, + populate_by_name=True, + serialize_by_alias=True, +) + + +class LineMode(StrEnum): + """Controls whether traces are drawn as lines, markers, or both.""" + + LINES = "lines" + """Connected line only.""" + + MARKERS = "markers" + """Individual point markers only.""" + + LINES_AND_MARKERS = "lines+markers" + """Connected line with point markers.""" + + +class InterpMode(StrEnum): + """Line interpolation method between data points.""" + + LINEAR = "linear" + """Straight segments between points.""" + + SPLINE = "spline" + """Smooth cubic spline.""" + + HV = "hv" + """Horizontal then vertical step.""" + + VH = "vh" + """Vertical then horizontal step.""" + + HVH = "hvh" + """Horizontal-vertical-horizontal step.""" + + VHV = "vhv" + """Vertical-horizontal-vertical step.""" + + +class HoverMode(StrEnum): + """Tooltip behavior when hovering over the plot.""" + + X_UNIFIED = "x unified" + """Single tooltip showing all series at the hovered x value.""" + + Y_UNIFIED = "y unified" + """Single tooltip showing all series at the hovered y value.""" + + CLOSEST = "closest" + """Tooltip for the nearest individual data point.""" + + X = "x" + """Per-series tooltips triggered by x proximity.""" + + Y = "y" + """Per-series tooltips triggered by y proximity.""" + + NONE = "none" + """Tooltips disabled.""" + + +class PlotConfig(BaseModel): + """Complete serialisable state of a trial-viewer plot.""" + + model_config = camel_case_dict + + line_mode: LineMode + """Whether traces render as lines, markers, or both.""" + + interp: InterpMode + """Interpolation method drawn between data points.""" + + hover: HoverMode + """Tooltip behavior on hover.""" + + show_spike: bool + """Whether to draw spike lines from the hovered point to each axis.""" + + max_points: int | None = Field(default=None, gt=0) + """Maximum number of data points per trace returned by the server. When the raw data exceeds this limit the server downsamples using uniform time-domain buckets (equal coverage across the time range regardless of variable timestep). `None` disables downsampling and returns all points.""" diff --git a/src/trendify/viewer/routes/__init__.py b/src/trendify/viewer/routes/__init__.py new file mode 100644 index 0000000..30fe592 --- /dev/null +++ b/src/trendify/viewer/routes/__init__.py @@ -0,0 +1,7 @@ +from trendify.viewer.routes import api +from trendify.viewer.routes import pages + +__all__ = [ + "api", + "pages", +] diff --git a/src/trendify/viewer/routes/api.py b/src/trendify/viewer/routes/api.py new file mode 100644 index 0000000..c986115 --- /dev/null +++ b/src/trendify/viewer/routes/api.py @@ -0,0 +1,133 @@ +""" +JSON API for the dashboard frontend. Currently just the tag tree (the actual sidebar is +server-rendered from the same data, see `routes.pages` -- this endpoint exists for any future +client-side refresh/polling use) and a liveness/db-change ping. Plot/table data endpoints land +in later milestones. + +Every handler reads from the process-lifetime, read-only `ProductStore` on +`request.app.state.store` and caches its response in `request.app.state.response_cache`. The +`.db` file can be regenerated out from under a running `serve` process (e.g. someone re-runs +`trendify generate`/`run`); `/ping` detects that via the file's mtime and clears the cache, so +this is a cache invalidation concern rather than something that makes the cache unsafe to use. +""" + +from __future__ import annotations + +import logging +from collections.abc import Callable +from pathlib import Path +from typing import Any, Literal, cast + +from fastapi import APIRouter, Request +from pydantic import BaseModel + +from trendify.generator.table_builder import TableBuilder +from trendify.store.product_store import ProductStore +from trendify.store.tags import decode_tag +from trendify.viewer.tag_tree import TagNode, build_tag_tree + +TableView = Literal["melted", "pivot", "stats"] + +__all__ = ["router"] + +logger = logging.getLogger(__name__) + +router = APIRouter() + + +class TableResponse(BaseModel): + available: bool + columns: list[str] + rows: list[dict[str, Any]] + + +def _get_store(request: Request) -> ProductStore: + return request.app.state.store + + +def _cached[T](request: Request, cache_key: tuple, build: Callable[[], T]) -> T: + """ + Process-lifetime response cache: `.db` files are static for the life of a `serve` process + (no write path exists in this feature), so a handler's expensive work only ever needs to + run once per distinct cache key. + """ + cache: dict[tuple, object] = request.app.state.response_cache + if cache_key not in cache: + cache[cache_key] = build() + return cast(T, cache[cache_key]) + + +@router.get("/tags", response_model=list[TagNode]) +async def get_tags(request: Request) -> list[TagNode]: + # async def, not def -- see routes.pages.index's comment: keeps this on the event loop's + # thread, matching the ProductStore connection's thread affinity. + store = _get_store(request) + return _cached(request, ("tags",), lambda: build_tag_tree(store)) + + +@router.get("/ping") +async def ping(request: Request) -> dict[str, bool | float | None]: + """ + Cheap liveness check the frontend polls to show a connected/disconnected indicator. + + Also reports the `.db` file's mtime, so the client can detect that someone regenerated it + (e.g. re-ran `trendify generate`/`run` while this server is still up) and prompt a refresh. + The underlying `ProductStore` connection itself stays valid across a regeneration (the + generate pipeline writes into the same file/inode via WAL, it doesn't replace it) -- only + this process's *response cache* goes stale, so an mtime change clears it here. + """ + db_path: Path = request.app.state.db_path + try: + mtime = db_path.stat().st_mtime + except OSError: + mtime = None + + if mtime is not None and mtime != request.app.state.db_mtime: + request.app.state.db_mtime = mtime + request.app.state.response_cache.clear() + + return {"ok": True, "db_updated_at": mtime} + + +@router.get("/table", response_model=TableResponse) +async def get_table(tag: str, view: TableView, request: Request) -> TableResponse: + """ + Table data for the table viewer's Melted/Pivot/Statistics tabs, as DataTables-ready + `{available, columns, rows}` JSON. + + `tag` is the tag's `encode_tag` (JSON) form -- the same string the sidebar's `tojson`- + rendered `tag-selected` payload already produces client-side -- since a tuple tag has no + single unambiguous plain-string/path encoding to put directly in a URL path segment. + + `pivot`/`stats` report `available: false` (not a 500) rather than raising when the pivot + fails (a repeated `(row, col)` pair) or there are no numeric pivoted columns -- both are + normal, expected shapes for some tags' data, not error conditions. + """ + store = _get_store(request) + decoded_tag = decode_tag(tag) + + def build() -> TableResponse: + melted = store.get_table_entries(decoded_tag) + if view == "melted": + return TableResponse( + available=melted.height > 0, + columns=melted.columns, + rows=melted.to_dicts(), + ) + + pivot = TableBuilder.pivot_table(melted) if melted.height > 0 else None + if view == "pivot": + if pivot is None: + return TableResponse(available=False, columns=[], rows=[]) + return TableResponse( + available=True, columns=pivot.columns, rows=pivot.to_dicts() + ) + + stats = TableBuilder.get_stats_table(pivot) if pivot is not None else None + if stats is None: + return TableResponse(available=False, columns=[], rows=[]) + return TableResponse( + available=True, columns=stats.columns, rows=stats.to_dicts() + ) + + return _cached(request, ("table", view, tag), build) diff --git a/src/trendify/viewer/routes/pages.py b/src/trendify/viewer/routes/pages.py new file mode 100644 index 0000000..1eabe50 --- /dev/null +++ b/src/trendify/viewer/routes/pages.py @@ -0,0 +1,33 @@ +""" +HTML page routes for the dashboard. The index page server-renders the full tag tree (see +`trendify.viewer.tag_tree`) directly into the sidebar via a recursive Jinja2 macro -- simpler +and more robust than fetching it client-side and building the recursive structure in Alpine, +which has no solid native support for recursive components. +""" + +from __future__ import annotations + +import logging + +from fastapi import APIRouter, Request +from fastapi.responses import HTMLResponse + +from trendify.viewer.tag_tree import build_tag_tree + +__all__ = ["router"] + +logger = logging.getLogger(__name__) + +router = APIRouter() + + +@router.get("/", response_class=HTMLResponse) +async def index(request: Request) -> HTMLResponse: + # `async def`, not `def`: FastAPI runs sync route handlers in a threadpool, but the + # ProductStore's sqlite3 connection is thread-affine to whatever thread opened it (the + # main thread, in create_app). An `async def` handler that never awaits stays on the + # event loop's thread instead, matching the connection's affinity. + store = request.app.state.store + tag_tree = build_tag_tree(store) + templates = request.app.state.templates + return templates.TemplateResponse(request, "index.html", {"tag_tree": tag_tree}) diff --git a/src/trendify/viewer/tag_tree.py b/src/trendify/viewer/tag_tree.py new file mode 100644 index 0000000..a75d40d --- /dev/null +++ b/src/trendify/viewer/tag_tree.py @@ -0,0 +1,128 @@ +""" +Builds the nested tag hierarchy the sidebar renders, shared by the server-rendered page +(`routes.pages`) and the JSON API (`routes.api`). +""" + +from __future__ import annotations + +import logging +from typing import Literal + +from pydantic import BaseModel + +from trendify.base.helpers import Tag +from trendify.formats.format2d import PlottableData2D +from trendify.store.product_store import ProductStore +from trendify.store.tags import tag_to_path_parts + +__all__ = ["TagNode", "build_tag_tree"] + +logger = logging.getLogger(__name__) + + +class TagNode(BaseModel): + key: Tag + label: str + children: list[TagNode] + has_products: bool + product_kinds: list[Literal["plot", "table"]] + + def search_blob(self) -> str: + """ + Lowercase text of this node's label and every descendant's label, for substring + search matching without needing to walk the tree again client-side. + """ + parts = [self.label] + for child in self.children: + parts.append(child.search_blob()) + return " ".join(parts).lower() + + def subtree_kinds(self) -> list[Literal["plot", "table"]]: + """ + Union of this node's own `product_kinds` and every descendant's, so a folder can be + filtered by product type without needing to walk the tree again client-side (a folder + should stay visible under a "table" filter if any product anywhere inside it is a + table, even if the folder itself has no products of its own). + """ + kinds = set(self.product_kinds) + for child in self.children: + kinds.update(child.subtree_kinds()) + return sorted(kinds) + + def product_count(self) -> int: + """ + Recursive count of self-and-descendant nodes with `has_products`, shown as a + subtle badge next to folder labels in the sidebar. + """ + count = 1 if self.has_products else 0 + for child in self.children: + count += child.product_count() + return count + + +class _TrieNode: + def __init__(self) -> None: + self.children: dict[str, _TrieNode] = {} + self.tag: Tag | None = None + + +def _product_kinds(store: ProductStore, tag: Tag) -> list[Literal["plot", "table"]]: + kinds: list[Literal["plot", "table"]] = [] + if store.get_products_of_type(PlottableData2D, tag=tag): + kinds.append("plot") + if store.get_table_entries(tag).height > 0: + kinds.append("table") + return kinds + + +def _category_rank(node: TagNode) -> int: + """Folders sort before plot leaves, which sort before table leaves.""" + if node.children: + return 0 + if "plot" in node.product_kinds: + return 1 + if "table" in node.product_kinds: + return 2 + return 3 + + +def build_tag_tree(store: ProductStore) -> list[TagNode]: + """ + Walks `store.tag_tree()`'s flat, tuple-encoded tags into a nested `TagNode` hierarchy: a + tag `("a", "b")` becomes a folder `"a"` containing a leaf `"b"`. A tuple prefix (e.g. `"a"` + from that same tag) may never itself have been used as a real tag, so `has_products` is + tracked separately from `children` being non-empty -- a node can be a pure folder, a pure + leaf, or both at once. + """ + root: dict[str, _TrieNode] = {} + + for tag in store.tag_tree(): + parts = tag_to_path_parts(tag) + level = root + node: _TrieNode | None = None + for part in parts: + node = level.setdefault(part, _TrieNode()) + level = node.children + if node is not None: + node.tag = tag + + def to_nodes(level: dict[str, _TrieNode]) -> list[TagNode]: + nodes = [] + for label, node in level.items(): + tag = node.tag + nodes.append( + TagNode( + key=tag if tag is not None else label, + label=label, + children=to_nodes(node.children), + has_products=tag is not None, + product_kinds=_product_kinds(store, tag) if tag is not None else [], + ) + ) + # Two stable sorts: alphabetical first, then by category -- the second sort's stability + # preserves the alphabetical order within each category group. + nodes.sort(key=lambda n: n.label.lower()) + nodes.sort(key=_category_rank) + return nodes + + return to_nodes(root) diff --git a/src/trendify/viewer/templates/base.html b/src/trendify/viewer/templates/base.html new file mode 100644 index 0000000..d19ccb6 --- /dev/null +++ b/src/trendify/viewer/templates/base.html @@ -0,0 +1,48 @@ + + + + + + + {% block title %}trendify{% endblock %} + + + + + + + + + + + + + + + {% block extra_head %}{% endblock %} + + + + {% block content %}{% endblock %} + + + + + diff --git a/src/trendify/viewer/templates/index.html b/src/trendify/viewer/templates/index.html new file mode 100644 index 0000000..43559af --- /dev/null +++ b/src/trendify/viewer/templates/index.html @@ -0,0 +1,75 @@ +{% extends "base.html" %} + +{% block content %} +
+ {% include "partials/nav.html" %} + +
+ {% include "partials/sidebar.html" %} + +
+ +
+ + + +
+
+ + {% include "partials/footer.html" %} + +
+ +
+
+{% endblock %} diff --git a/gallery/trendify/products/clusters/reserving_index.lock b/src/trendify/viewer/templates/partials/__init__.py similarity index 100% rename from gallery/trendify/products/clusters/reserving_index.lock rename to src/trendify/viewer/templates/partials/__init__.py diff --git a/src/trendify/viewer/templates/partials/footer.html b/src/trendify/viewer/templates/partials/footer.html new file mode 100644 index 0000000..a71ff22 --- /dev/null +++ b/src/trendify/viewer/templates/partials/footer.html @@ -0,0 +1,15 @@ + diff --git a/src/trendify/viewer/templates/partials/nav.html b/src/trendify/viewer/templates/partials/nav.html new file mode 100644 index 0000000..982ad9d --- /dev/null +++ b/src/trendify/viewer/templates/partials/nav.html @@ -0,0 +1,148 @@ + diff --git a/src/trendify/viewer/templates/partials/sidebar.html b/src/trendify/viewer/templates/partials/sidebar.html new file mode 100644 index 0000000..d21bd20 --- /dev/null +++ b/src/trendify/viewer/templates/partials/sidebar.html @@ -0,0 +1,156 @@ +{% macro render_node(node, path=[]) %} +{% set full_path = path + [node.label] %} +
+
+ {% if node.children %} + + + + + + {% else %} + + {% endif %} + + {% if node.has_products %} + + {% if "plot" in node.product_kinds %} + + + + + {% endif %} + {% if "table" in node.product_kinds %} + + + + + {% endif %} + {{ node.label }} + + {% else %} + {{ node.label }} + {% endif %} + + {% if node.children %} + {{ node.product_count() }} + {% endif %} +
+ + {% if node.children %} +
+ {% for child in node.children %} + {{ render_node(child, full_path) }} + {% endfor %} +
+ {% endif %} +
+{% endmacro %} + + diff --git a/src/trendify/viewer/templates/partials/table_view.html b/src/trendify/viewer/templates/partials/table_view.html new file mode 100644 index 0000000..3d4b031 --- /dev/null +++ b/src/trendify/viewer/templates/partials/table_view.html @@ -0,0 +1,26 @@ +
+
+ + + +
+

No data available for this view.

+
+
+
+
diff --git a/gallery/trendify/products/comparison/reserving_index.lock b/src/trendify/viewer/templates/static/__init__.py similarity index 100% rename from gallery/trendify/products/comparison/reserving_index.lock rename to src/trendify/viewer/templates/static/__init__.py diff --git a/gallery/trendify/products/distributions/reserving_index.lock b/src/trendify/viewer/templates/static/assets/__init__.py similarity index 100% rename from gallery/trendify/products/distributions/reserving_index.lock rename to src/trendify/viewer/templates/static/assets/__init__.py diff --git a/src/trendify/viewer/templates/static/assets/logo.svg b/src/trendify/viewer/templates/static/assets/logo.svg index 3fa2c70..72fc738 100644 --- a/src/trendify/viewer/templates/static/assets/logo.svg +++ b/src/trendify/viewer/templates/static/assets/logo.svg @@ -1,3 +1,10 @@ - - + + diff --git a/src/trendify/viewer/templates/static/assets/logo_white_bg.svg b/src/trendify/viewer/templates/static/assets/logo_white_bg.svg index c6a97bc..f532d08 100644 --- a/src/trendify/viewer/templates/static/assets/logo_white_bg.svg +++ b/src/trendify/viewer/templates/static/assets/logo_white_bg.svg @@ -1,11 +1,24 @@ - - - - - - - - - - + + + + + + + + + + diff --git a/src/trendify/viewer/templates/static/css/app.css b/src/trendify/viewer/templates/static/css/app.css new file mode 100644 index 0000000..a247db2 --- /dev/null +++ b/src/trendify/viewer/templates/static/css/app.css @@ -0,0 +1,69 @@ +/* Hand-authored styles that don't fit cleanly as inline Tailwind utility classes. */ + +[x-cloak] { + display: none !important; +} + +/* Generic bordered/elevated surface for grouped controls (e.g. the nav bar's theme + selector "pill"). Matches the surrounding chrome background so it reads as a subtle, + bordered inset rather than a jarring color block; child elements provide their own + contrast (e.g. a selected button's lighter background). */ +.card { + border-radius: 0.5rem; + border: 1px solid rgb(148 163 184 / 0.3); /* slate-400/30 */ + background-color: rgb(226 232 240); /* slate-200 */ +} + +.dark .card { + background-color: rgb(30 41 59); /* slate-800 */ +} + +::-webkit-scrollbar { + width: 10px; + height: 10px; +} + +::-webkit-scrollbar-track { + background: transparent; +} + +::-webkit-scrollbar-thumb { + background-color: rgb(100 116 139 / 0.4); /* slate-500/40 */ + border-radius: 9999px; +} + +/* DataTables (table viewer): fixed layout so manually-resized column widths stick instead of + being recalculated on every redraw. */ +table.dataTable { + table-layout: fixed; +} + +.dt-col-resize-handle { + position: absolute; + top: 0; + right: 0; + bottom: 0; + width: 6px; + cursor: col-resize; + user-select: none; +} + +.dt-col-resize-handle:hover { + background-color: rgb(244 63 94 / 0.4); /* rose-500/40 */ +} + +.dt-column-filter { + display: block; + width: 100%; + margin-top: 0.25rem; + border: 1px solid rgb(148 163 184 / 0.4); /* slate-400/40 */ + border-radius: 0.25rem; + padding: 0.125rem 0.25rem; + font-size: 0.875rem; + font-weight: normal; + background-color: transparent; +} + +.dark .dt-column-filter { + color: rgb(226 232 240); /* slate-200 */ +} diff --git a/src/trendify/viewer/templates/static/js/main.js b/src/trendify/viewer/templates/static/js/main.js new file mode 100644 index 0000000..4d323ab --- /dev/null +++ b/src/trendify/viewer/templates/static/js/main.js @@ -0,0 +1,2 @@ +"use strict";(()=>{var g="trendify:theme";function K(){return window.matchMedia("(prefers-color-scheme: dark)").matches}function P(){let e=localStorage.getItem(g);return e==="light"||e==="dark"?e:"system"}function U(e){let t=e==="dark"||e==="system"&&K();document.documentElement.classList.toggle("dark",t)}function _(e){e==="system"?localStorage.removeItem(g):localStorage.setItem(g,e),U(e)}function w(){return{theme:P(),setTheme(e){this.theme=e,_(e)}}}function b(){return{isFullscreen:!1,init(){document.addEventListener("fullscreenchange",()=>{this.isFullscreen=document.fullscreenElement!==null,window.dispatchEvent(new Event("trendify:layout-changed"))})},toggle(){document.fullscreenElement?document.exitFullscreen():document.documentElement.requestFullscreen()}}}function T({storageKey:e,elementId:t,defaultWidth:n,minWidth:o,maxWidth:r}){let a=Number(localStorage.getItem(e));return{width:Number.isFinite(a)&&a>0?a:n,dragging:!1,startDrag(i){i.preventDefault(),this.dragging=!0;let s=i.clientX,c=this.width,d=p=>{let O=c+(p.clientX-s);this.width=Math.min(r,Math.max(o,O))},u=()=>{this.dragging=!1,localStorage.setItem(e,String(this.width)),window.removeEventListener("mousemove",d),window.removeEventListener("mouseup",u)};window.addEventListener("mousemove",d),window.addEventListener("mouseup",u)},fitToContent(){let i=document.getElementById(t);if(!i)return;let s=i.style.width;i.style.width="max-content";let c=i.scrollWidth;i.style.width=s,this.width=Math.min(r,Math.max(o,c)),localStorage.setItem(e,String(this.width))}}}var D=1;function v(){return{toasts:[],push(e,t="info",n=4e3){let o=D++;this.toasts.push({id:o,message:e,kind:t,timestamp:Date.now(),read:!1,visible:!0}),n>0&&setTimeout(()=>{let r=this.toasts.find(a=>a.id===o);r&&(r.visible=!1)},n)},dismiss(e){let t=this.toasts.find(n=>n.id===e);t&&(t.visible=!1)},clearAll(){this.toasts=[]},markAllRead(){this.toasts.forEach(e=>{e.read=!0})},get unreadCount(){return this.toasts.filter(e=>!e.read).length},timeAgo(e){let t=Math.floor((Date.now()-e)/1e3);if(t<5)return"just now";if(t<60)return`${t}s ago`;let n=Math.floor(t/60);return n<60?`${n}m ago`:`${Math.floor(n/60)}h ago`}}}function y({pingUrl:e,intervalMs:t,timeoutMs:n,onConnectionChange:o,onDbChanged:r}){let a=!0,l=null;async function i(){let c=new AbortController,d=setTimeout(()=>c.abort(),n);try{let u=await fetch(e,{signal:c.signal,cache:"no-store"});if(!u.ok)throw new Error(`ping failed: ${u.status}`);let p=await u.json();a||(a=!0,o(!0)),p.db_updated_at!==null&&(l!==null&&p.db_updated_at!==l&&r(),l=p.db_updated_at)}catch{a&&(a=!1,o(!1))}finally{clearTimeout(d)}}let s=setInterval(i,t);return i(),()=>clearInterval(s)}async function S(e){if(navigator.clipboard?.writeText)try{return await navigator.clipboard.writeText(e),!0}catch{}try{let t=document.createElement("textarea");t.value=e,t.style.position="fixed",t.style.opacity="0",document.body.appendChild(t),t.focus(),t.select();let n=document.execCommand("copy");return document.body.removeChild(t),n}catch{return!1}}var f=new Map;async function F(e){let t=f.get(e);if(t!==void 0)return t;let n=await fetch(e);if(!n.ok)throw new Error(`Request failed: ${e} (${n.status})`);let o=await n.json();return f.set(e,o),o}function x(e,t){let n=`/api/table?tag=${encodeURIComponent(JSON.stringify(e))}&view=${t}`;return F(n)}window.addEventListener("trendify:db-changed",()=>f.clear());var E="tag",C="kinds",M="view",W=["melted","pivot","stats"];function V(){let e=new URLSearchParams(window.location.search),t=e.get(E);if(!t)return null;try{let n=JSON.parse(t),o=e.get(C),r=o?o.split(","):[];return{tag:n,productKinds:r}}catch{return null}}function A(e,t){let n=new URLSearchParams(window.location.search);n.set(E,JSON.stringify(e)),n.set(C,t.join(","));let o=`${window.location.pathname}?${n.toString()}`;window.history.replaceState(null,"",o)}function L(){let e=new URLSearchParams(window.location.search).get(M);return W.includes(e??"")?e:null}function N(e){let t=new URLSearchParams(window.location.search);t.set(M,e);let n=`${window.location.pathname}?${t.toString()}`;window.history.replaceState(null,"",n)}function m(e){let t=localStorage.getItem(e);if(t===null)return null;try{return JSON.parse(t)}catch{return null}}function h(e,t){localStorage.setItem(e,JSON.stringify(t))}var J=60,R="trendify:table-page-length",H=10;function z(e,t){return`trendify:table-filters:${JSON.stringify(e)}:${t}`}function q(e){e.querySelectorAll("thead th").forEach(n=>{n.style.position="relative";let o=document.createElement("span");o.className="dt-col-resize-handle",o.addEventListener("mousedown",r=>{r.preventDefault(),r.stopPropagation();let a=r.clientX,l=n.offsetWidth,i=c=>{let d=Math.max(J,l+(c.clientX-a));n.style.width=`${d}px`},s=()=>{window.removeEventListener("mousemove",i),window.removeEventListener("mouseup",s)};window.addEventListener("mousemove",i),window.addEventListener("mouseup",s)}),n.appendChild(o)})}function G(e){let t=m(e)??{},n=this;n.columns().every(function(){let o=this,r=String(o.dataSrc()),a=t[r]??"",l=$(o.header());$("` is available. Did you forget to add `defer` in Alpine's ` + + diff --git a/src/trendify/viewer/templates/index.html b/src/trendify/viewer/templates/index.html index 99769fb..c097afe 100644 --- a/src/trendify/viewer/templates/index.html +++ b/src/trendify/viewer/templates/index.html @@ -24,11 +24,53 @@ Select a tag from the sidebar to view its data.

- -