diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index 934eb1f..6bd2490 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -1,95 +1,33 @@ -# # .github/workflows/docs.yml -# name: Deploy Documentation - -# on: -# push: -# tags: -# - 'v*.*.*' -# workflow_dispatch: # Allow manual triggering - -# permissions: -# contents: write # This is the key addition - explicitly grant write permission - -# jobs: -# deploy: -# runs-on: ubuntu-latest -# steps: -# - uses: actions/checkout@v3 -# with: -# fetch-depth: 0 # fetch all history for proper versioning - -# - name: Set up Python -# uses: actions/setup-python@v4 -# with: -# python-version: '3.10' - -# - name: Install dependencies -# run: | -# python -m pip install --upgrade pip -# pip install -e ".[dev]" - -# - name: Set up Git user -# run: | -# git config --local user.email "github-actions[bot]@users.noreply.github.com" -# git config --local user.name "github-actions[bot]" - -# - name: Extract version from tag -# id: get_version -# run: | -# echo "VERSION=${GITHUB_REF#refs/tags/v}" >> $GITHUB_ENV - -# - name: Deploy docs -# run: | -# mike deploy --push --update-aliases ${{ env.VERSION }} latest - -# .github/workflows/docs.yml -name: Deploy Documentation - +name: Documentation on: - workflow_run: - workflows: ["Publish Package"] - types: - - completed + push: branches: + - master - main - workflow_dispatch: # Allow manual triggering - permissions: - contents: write - + contents: read + pages: write + id-token: write jobs: deploy: - # Only run if the package publish was successful - if: ${{ github.event.workflow_run.conclusion == 'success' }} + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 + - uses: actions/configure-pages@v5 + - uses: actions/checkout@v5 + - name: Install uv + uses: astral-sh/setup-uv@v5 with: - fetch-depth: 0 # fetch all history for proper versioning - - - name: Set up Python - uses: actions/setup-python@v4 + enable-cache: true + - name: Install Python 3.14 + run: uv python install 3.14 + - name: Generate API reference pages + run: uv run --python 3.14 --group dev python scripts/gen_ref_pages.py + - run: uv run --python 3.14 --group dev zensical build --clean + - uses: actions/upload-pages-artifact@v4 with: - python-version: '3.11' - - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install -e ".[dev]" - - - name: Set up Git user - run: | - git config --local user.email "github-actions[bot]@users.noreply.github.com" - git config --local user.name "github-actions[bot]" - - - name: Get latest tag - id: get_tag - run: | - # Get the latest tag - TAG=$(git describe --tags `git rev-list --tags --max-count=1`) - echo "TAG=${TAG}" >> $GITHUB_ENV - echo "VERSION=${TAG#v}" >> $GITHUB_ENV - - - name: Deploy docs - run: | - mike deploy --push --update-aliases ${{ env.VERSION }} latest \ No newline at end of file + path: site + - uses: actions/deploy-pages@v4 + id: deployment diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 45ad880..ef2b0b2 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -2,35 +2,97 @@ 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: ["3.12", "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 + + - name: Generate coverage report + if: matrix.python-version == '3.14' && github.event_name == 'push' && github.ref == 'refs/heads/main' + run: uv run --python ${{ matrix.python-version }} --group dev pytest --cov=stochas --cov-report=xml + + - name: Generate coverage badge + if: matrix.python-version == '3.14' && github.event_name == 'push' && github.ref == 'refs/heads/main' + run: uv run --python ${{ matrix.python-version }} --group dev genbadge coverage -i coverage.xml -o docs/assets/coverage-badge.svg + + - name: Commit coverage badge + if: matrix.python-version == '3.14' && github.event_name == 'push' && github.ref == 'refs/heads/main' + uses: stefanzweifel/git-auto-commit-action@v5 + with: + commit_message: "chore: update coverage badge" + file_pattern: docs/assets/coverage-badge.svg + + 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/.github/workflows/test.yml b/.github/workflows/test.yml deleted file mode 100644 index 9e38e4a..0000000 --- a/.github/workflows/test.yml +++ /dev/null @@ -1,30 +0,0 @@ -# .github/workflows/test.yml -name: Run Tests - -on: - push: - branches: [ dev, main ] - pull_request: - branches: [ main ] - -jobs: - test: - runs-on: ubuntu-latest - strategy: - matrix: - python-version: ["3.11", "3.12", "3.13"] - - steps: - - uses: actions/checkout@v3 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v4 - with: - python-version: ${{ matrix.python-version }} - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install pytest pytest-cov - pip install -e ".[dev]" - - name: Run tests - run: | - pytest tests/ \ No newline at end of file diff --git a/.gitignore b/.gitignore index 636636b..2d5e7f3 100644 --- a/.gitignore +++ b/.gitignore @@ -1,19 +1,38 @@ -**/workdir/ -**/__pycache__/**/* -**/*.egg*/ -**/*.egg-info/** -**/*.pyc -**/*.pyo -**/*.pyd -**/*.pyz -**/*.code-workspace -dist/ +# Python-generated files +__pycache__/ +*.py[oc] build/ -trendify.log +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 + +*.prof diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..8d86721 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,39 @@ +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 + rev: v0.15.17 + hooks: + - id: ruff-check + args: [--fix] + - id: ruff-format + - repo: https://github.com/astral-sh/uv-pre-commit + rev: 0.11.21 + hooks: + - id: uv-lock + - repo: https://github.com/astral-sh/uv-pre-commit + rev: 0.11.21 + hooks: + - id: uv-export + - repo: https://github.com/RobertCraigie/pyright-python + rev: v1.1.411 + hooks: + - id: pyright + - id: pyright + name: Pyright (docs examples) + args: [docs] + pass_filenames: false + always_run: true + stages: [pre-commit] + - 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/README.md b/README.md index 19727f0..4032aad 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,65 @@ +

+ Trendify +

+# trendify: Efficient Plotting and Table Building -[![PyPI version](https://img.shields.io/pypi/v/trendify.svg)](https://pypi.org/project/trendify/) -[![Python versions](https://img.shields.io/pypi/pyversions/trendify.svg)](https://pypi.org/project/trendify/) -[![License](https://img.shields.io/github/license/TalbotKnighton/trendify.svg)](https://github.com/TalbotKnighton/trendify/blob/main/LICENSE) -[![Documentation](https://img.shields.io/badge/docs-latest-blue.svg)](https://talbotknighton.github.io/trendify/) -[![GitHub](https://img.shields.io/github/stars/TalbotKnighton/trendify?style=social)](https://github.com/TalbotKnighton/trendify) +

+ + PyPI version + + + Python versions + + + Tests & Release Status + + + Coverage + + + Ruff + + + Pydantic v2 + + + License + + + Documentation + +

+ +--- + +## Installation + +Install the package via your preferred manager: + +```bash linenums="0" +uv add trendify +``` + +or with `pip`: + +```bash linenums="0" +pip install trendify +``` + +--- + +## Core Features + +- **Built to scale**: process thousands of runs without holding every run's data in memory at once. Throughput stays flat whether you have dozens of runs or tens of thousands. +- **Typed records, no migrations**: points, traces, tables, and histograms are validated [Pydantic](https://docs.pydantic.dev/latest/) models. Add your own record types anytime without writing a schema migration. +- **Parallelizable**: `trendify generate` can fan your processing function out across multiple CPU cores with `--n-procs`, with multiprocessing-safe logging that funnels every worker's output through a single queue. +- **Static assets or a live dashboard**: render tagged data straight to Matplotlib images and CSV tables with `trendify render`, or launch an interactive FastAPI dashboard with `trendify viewer` to browse tags, tables, and interactive plots in the browser. + +--- + +## Why use `trendify`? + +- **Scalable.** Throughput stays flat whether you're processing dozens of runs or tens of thousands. +- **Memory efficient.** Each run is processed once and cached on disk, nothing needs to stay open or held in memory for the whole sweep. This is critical for processing large amounts of data with less memory than is available when batch processing. +- **Flexible output.** Render static Matplotlib images and CSV tables for a report, or browse the same data interactively in a live dashboard. diff --git a/alternative_tag_selectors.py b/alternative_tag_selectors.py deleted file mode 100644 index 012701b..0000000 --- a/alternative_tag_selectors.py +++ /dev/null @@ -1,252 +0,0 @@ -""" -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'}) - - @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 \ No newline at end of file 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/custom_handler.py b/custom_handler.py deleted file mode 100644 index e057bfd..0000000 --- a/custom_handler.py +++ /dev/null @@ -1,8 +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/docs/api_and_cli.md b/docs/api_and_cli.md deleted file mode 100644 index 0f3d4ac..0000000 --- a/docs/api_and_cli.md +++ /dev/null @@ -1,133 +0,0 @@ ---- -hide: - - navigation ---- - -# API and CLI - -## Functionality Overview - -The `trendify` package - -- Maps a user-defined function over given directories to produce JSON serialized [Data Products][trendify.api.base.data_product.DataProduct]. -- Sorts [Data Products][trendify.api.base.data_product.DataProduct] according to user-specified [Tags][trendify.api.base.helpers.Tags] -- Writes collected products to CSV files or static images (via [matplotlib][matplotlib] backend) -- Generates nested `include.md` files for importing generated assets into markdown reports (or MkDocs web page) -- _In Progress:_ Generates a Grafana dashboard with panels for each data [Tag][trendify.api.base.helpers.Tag] -- _Future Work:_ Generates nested `include.tex` files for nested assets - -Trendify sorts products and outputs them as CSV and JPG files to an assets directory or prepares them for display in Grafana via the [make_it_trendy][trendify.api.api.make_it_trendy] method. This method is a convenient wrapper on multiple individual steps: - -- [make_products][trendify.api.api.make_products] -- [sort_products][trendify.api.api.sort_products] -- [make_tables_and_figures][trendify.api.api.make_tables_and_figures] -- [make_include_files][trendify.api.api.make_include_files] - -Each step can be mapped in parallel as part of a process pool by providing an integer argument `n_procs` greater than 1. Parllel excecution greatly speeds up processing times for computationally expensive data product generators or for plotting large numbers data products. - - -## API - -The user specifies a function that takes in a `Path` and returns a list holding instances of the following children of -[DataProduct][trendify.DataProduct]: - -- [`Trace2D`][trendify.api.plotting.trace.Trace2D] -- [`Point2D`][trendify.api.plotting.point.Point2D] -- [`TableEntry`][trendify.api.formats.table.TableEntry] -- [`HistogramEntry`][trendify.api.plotting.histogram.HistogramEntry] - -All [Data Products][trendify.DataProduct] inherit type checking and JSON serialization from PyDantic [BaseModel][pydantic.BaseModel]. - -[XYData][trendify.api.formats.format2d.XYData] product inputs include: - -- [Tags][trendify.api.base.helpers.Tags] used to sort and collect the products -- [Pen][trendify.api.base.pen.Pen] defines the line style and legend label for [`Trace2D`][trendify.api.plotting.trace.Trace2D] -- [Marker][trendify.api.styling.marker.Marker] defines the symbol style and legend label for [`Point2D`][trendify.api.plotting.point.Point2D] - -[`TableEntry`][trendify.api.formats.table.TableEntry] inputs include - -- `row` and `column` used to generate a pivot table if possible (so long as the `row`,`col` index pair is not repeated in a collected set) -- `value` -- `units` - -Labels and figure formats are assignable. Trendify will automatically collapse matplotlib legend labels -down to a unique set. Use unique pen label, marker label, histogram style label, or row/col pair as unique identifiers. Make sure that the formatting specified for like-tagged `DataProduct` istances to be the same. - -Trendify is easiest to run from the CLI which is a wrapper on the following methods. These can also be run via a Python script: - -- [make_products][trendify.api.api.make_products] -- [sort_products][trendify.api.api.sort_products] -- [make_tables_and_figures][trendify.api.api.make_tables_and_figures] -- [make_it_trendy][trendify.api.api.make_it_trendy] - - - -## CLI - -The `trendify` command line interface (CLI) allows a user-defined data product generator method to be mapped over raw data. - -### Command Line Arguments - -The `trendify` command line program takes the following sub-commands that run the various steps of the `trendify` framework. - -| Command | Action | -| - | - | -| products-make | Makes products or assets | -| products-sort | Sorts data products by tags | -| products-serve | Serves data products to URL endpoint | -| assets-make-static | Makes static assets | -| assets-make-interactive | Makes interactive assets | - -The `trendify` program also takes the following `make` commands which runs runs the product -`make`, `sort`, and `serve` commands as well as generating a JSON file to define a Grafana dashboard. - -| Command | Action | -| - | - | -| make static | Makes static assets (CSV and JPG files). | -| make grafana | Makes interactive grafana dashboard JSON file. Serves generated products on local host. | -| make all | Makes both static and interactive assets. Serves generated products on the local host. | - -To get a complete list of the input arguments to these commands run them with the `-h` flag to get a list of available arguments. - -The make commands take some of the following arguments. - -| Short Form Flag | Long Form Flag | Input Type | Usage | -| ---- | -------------------------- | ----- | ---------- | -| `-h` | `--help` | | Causes help info to be printed to the Linux terminal | -| `-g` | `--product-generator` | `str` | Specifies the data product generator method to map over raw input data directories. This argument uses a syntax borrowed from the script specification used in pyproject.toml files. See [details][-product-generator] below. | -| `-i` | `--input-directories` | `glob` or `list[str]` | Specifies directories over which the data product generator `method` will be mapped. Use standard bash glob expansion to pass in a list of directories or provide a glob string to run using pythons `glob.glob` method. See [details][-input-directories] below.| -| `-n` | `--n-procs` | `int` | Sets the number of parallel processes to use in each trendify step. Use `-n 1` for full Traceback during debugging and `-n 10` or some integer greater than 1 for parallelization speedup on larger data sets | -| `-o` | `--output-directory` | `str` | Specifies the path to which `trendify` will output sorted products and assets. | -| | `--protocol` | `str` | Defaults to 'http' | -| | `--host` | `str` | Defaults to '0.0.0.0' | -| | `--port` | `int` | Port to serve the products to. Defaults to `8000` | - -#### --product-generator - -The method can be input in any of the following formats: - -- `/global/path/to/module.py` -- `/global/path/to/module.py:method_name` -- `/global/path/to/module.py:ClassName.method_name` -- `./local/path/to/module.py` -- `./local/path/to/module.py:method_name` -- `./local/path/to/module.py:ClassName.method_name` -- `package.module` -- `package.module:method` -- `package.module:ClassName.method` - -#### --input-directories - -The input data directories over which the product generator will be mapped can be entered using standard bash globs - -- `**` expands to any file path -- `*` expands to any characters -- Etc. - -Make sure not to include directories with no results since the generator method will produce an error. - -Globbed results files are replaced with the containing directory (that is, a glob result of `./some/path/results.csv` will result in `./some/path/` being be passed to the product generator method). - -!!! note "Directory Structure" - - The current version requires each results set to be contained in its own sub-directory. There are no restrictions on the locations of the input data directories. diff --git a/docs/assets/gh-social.png b/docs/assets/gh-social.png new file mode 100644 index 0000000..54cfae1 Binary files /dev/null and b/docs/assets/gh-social.png differ diff --git a/docs/assets/logo.svg b/docs/assets/logo.svg new file mode 100644 index 0000000..32e33f8 --- /dev/null +++ b/docs/assets/logo.svg @@ -0,0 +1,803 @@ + + + +TRENDIFY diff --git a/docs/assets/main-logo-white-bg.svg b/docs/assets/main-logo-white-bg.svg new file mode 100644 index 0000000..ea66caf --- /dev/null +++ b/docs/assets/main-logo-white-bg.svg @@ -0,0 +1,75 @@ + + + + diff --git a/docs/assets/main-logo.svg b/docs/assets/main-logo.svg new file mode 100644 index 0000000..d0a26a4 --- /dev/null +++ b/docs/assets/main-logo.svg @@ -0,0 +1,66 @@ + + + + diff --git a/docs/assets/read-me-banner.png b/docs/assets/read-me-banner.png new file mode 100644 index 0000000..998676a Binary files /dev/null and b/docs/assets/read-me-banner.png differ diff --git a/docs/assets/read-me-banner.svg b/docs/assets/read-me-banner.svg new file mode 100644 index 0000000..9bcc3d7 --- /dev/null +++ b/docs/assets/read-me-banner.svg @@ -0,0 +1,157 @@ + + + + diff --git a/docs/css/code_select 2.py b/docs/css/code_select 2.py deleted file mode 100644 index d17ab08..0000000 --- a/docs/css/code_select 2.py +++ /dev/null @@ -1,5 +0,0 @@ -.language-pycon .gp, .language-pycon .go { /* Generic.Prompt, Generic.Output */ - user-select: none; -} - - diff --git a/docs/css/code_select.css b/docs/css/code_select.css deleted file mode 100644 index d17ab08..0000000 --- a/docs/css/code_select.css +++ /dev/null @@ -1,5 +0,0 @@ -.language-pycon .gp, .language-pycon .go { /* Generic.Prompt, Generic.Output */ - user-select: none; -} - - diff --git a/docs/css/extra.css b/docs/css/extra.css new file mode 100644 index 0000000..f1084dd --- /dev/null +++ b/docs/css/extra.css @@ -0,0 +1,122 @@ +:root { + --md-text-font: + "Franklin Gothic", "Segoe UI", Roboto, Helvetica, Arial, sans-serif; + font-weight: 300; + + /* Primary Color: Rose 500 */ + --md-primary-fg-color: #f43f5e; + --md-primary-fg-color--light: #fb7185; + --md-primary-fg-color--dark: #e11d48; + + /* Accent Color: Sky 500 */ + --md-accent-fg-color: #0ea5e9; + --md-accent-fg-color--transparent: rgba(14, 165, 233, 0.1); +} + +.md-typeset h1, +.md-typeset h2, +.md-typeset h3, +.md-typeset h4, +.md-header__topic { + font-family: "Franklin Gothic Heavy", "Franklin Gothic", sans-serif; + font-weight: 700; + /* Ensure the browser picks the heaviest variant */ + text-transform: uppercase; + /* Optional: gives it that industrial/engineering look */ + /* letter-spacing: -0.02em; */ + /* Optional: tightens up the 'Heavy' look */ +} + +/* Light Mode Sync */ +[data-md-color-scheme="default"] { + --md-default-bg-color: #e2e8f0; + /* Slate-200 */ + --md-default-fg-color: #1e293b; + /* Slate-800 */ + --md-footer-bg-color: #cbd5e1; + /* Slate-300 (Nav Bar) */ + --md-footer-bg-color--dark: #e2e8f0; + /* Slate-200 (Meta Bar) */ +} + +/* Dark Mode Sync */ +[data-md-color-scheme="slate"] { + --md-default-bg-color: #0f172a; + /* Slate-900 */ + --md-default-fg-color: #f1f5f9; + /* Slate-100 */ + --md-card-bg-color: #1e293b; + /* Slate-800 */ + --md-header-bg-color: #0f172a; + /* Slate-900 */ + --md-footer-bg-color: #020617; + /* Slate-950 (Nav Bar) */ + --md-footer-bg-color--dark: #0f172a; + /* Slate-900 (Meta Bar) */ +} + +.md-header__button.md-logo img { + height: 2rem; + width: auto; + border-radius: 4px; + transition: + transform 0.3s cubic-bezier(0.4, 0, 0.2, 1), + filter 0.3s ease-in-out; +} + +.md-header__button.md-logo:hover img { + transform: scale(1.1); + filter: drop-shadow(0 0 8px #0ea5e9) drop-shadow(0 0 12px #0ea5e9); +} + +/* Ensure the figure doesn't collapse or overlap */ +.splash, +.fade-in { + display: block; + width: 100%; + margin: 2rem 0; + text-align: center; + overflow: visible; +} + +/* 2. Target the IMAGE for animation, transition, and height */ +.splash img, +.fade-in img { + height: 10em; + width: auto; + display: block; + margin: 0 auto; + + /* Animation */ + opacity: 0; + animation-name: fadeIn; + animation-duration: 2s; + animation-timing-function: ease-in-out; + animation-fill-mode: forwards; + + /* Transition - MUST be on the img for the scale to work */ + transition: transform 0.6s cubic-bezier(0.34, 1.56, 0.64, 1); +} + +.splash img:hover, +.fade-in img:hover { + transform: scale(1.05); +} + +/* Optional: Add a fade-in animation for that "Splash" feel */ +@keyframes fadeIn { + from { + opacity: 0; + transform: translateY(0px); + } + + to { + opacity: 1; + transform: translateY(0); + } +} + +.fade-in { + width: 50%; + height: auto; +} diff --git a/docs/css/logo.css b/docs/css/logo.css deleted file mode 100644 index 39b7c02..0000000 --- a/docs/css/logo.css +++ /dev/null @@ -1,12 +0,0 @@ -.md-header__button.md-logo { - margin-top: 0; - margin-bottom: 0; - padding-top: 0; - padding-bottom: 0; -} - -.md-header__button.md-logo img, -.md-header__button.md-logo svg { - height: 2.4rem; - width: 2.4rem; -} \ No newline at end of file diff --git a/docs/css/mkdocstrings 2.css b/docs/css/mkdocstrings 2.css deleted file mode 100644 index 66ba00e..0000000 --- a/docs/css/mkdocstrings 2.css +++ /dev/null @@ -1,127 +0,0 @@ -/* Indentation. */ -div.doc-contents:not(.first) { - padding-left: 25px; - border-left: .05rem solid var(--md-typeset-table-color); -} - -/* Mark external links as such. */ -a.external::after, -a.autorefs-external::after { - /* https://primer.style/octicons/arrow-up-right-24 */ - mask-image: url('data:image/svg+xml,'); - -webkit-mask-image: url('data:image/svg+xml,'); - content: ' '; - - display: inline-block; - vertical-align: middle; - position: relative; - - height: 1em; - width: 1em; - background-color: currentColor; -} - -a.external:hover::after, -a.autorefs-external:hover::after { - background-color: var(--md-accent-fg-color); -} - - - -/* Fancier color for operators such as * and |. */ -.doc-signature .o { - color: var(--md-code-hl-special-color); -} - -/* Fancier color for constants such as None, True, and False. */ -.doc-signature .kc { - color: var(--md-code-hl-constant-color); -} - -/* Fancier color for built-in types (only useful when cross-references are used). */ -.doc-signature .n > a[href^="https://docs.python.org/"][href*="/functions.html#"], -.doc-signature .n > a[href^="https://docs.python.org/"][href*="/stdtypes.html#"] { - color: var(--md-code-hl-constant-color); -} - -.doc-symbol-parameter::after { -content: "P"; -} - -.doc-symbol-attribute::after { -content: "A"; -} - -.doc-symbol-function::after { -content: "F"; -} - -.doc-symbol-method::after { -content: "M"; -} - -.doc-symbol-class::after { -content: "C"; -} - -.doc-symbol-module::after { -content: "M"; -} - -.doc-section-title { - font-weight: bold; -} - -.doc-label { border-radius: 15px; padding: 2px 8px; font-weight: bold; } -.doc-label-special { background-color: #3330E4; color: white; } -.doc-label-private { background-color: #F637EC; color: white; } -.doc-label-property { background-color: #FBB454; color: black; } -.doc-label-read-only { background-color: #FAEA48; color: black; } - -[data-md-color-scheme="default"] { - --doc-symbol-parameter-fg-color: #df50af; - --doc-symbol-attribute-fg-color: #0079ff; - --doc-symbol-function-fg-color: #00dfa2; - --doc-symbol-method-fg-color: #00dfa2; - --doc-symbol-class-fg-color: #d1b619; - --doc-symbol-module-fg-color: #ff0060; - - --doc-symbol-parameter-bg-color: #df50af1a; - --doc-symbol-attribute-bg-color: #0079ff1a; - --doc-symbol-function-bg-color: #00dfa21a; - --doc-symbol-method-bg-color: #00dfa21a; - --doc-symbol-class-bg-color: #d1b6191a; - --doc-symbol-module-bg-color: #ff00601a; -} - -[data-md-color-scheme="slate"] { - --doc-symbol-parameter-fg-color: #ffa8cc; - --doc-symbol-attribute-fg-color: #963fb8; - --doc-symbol-function-fg-color: #6d67e4; - --doc-symbol-method-fg-color: #6d67e4; - --doc-symbol-class-fg-color: #46c2cb; - --doc-symbol-module-fg-color: #f2f7a1; - - --doc-symbol-parameter-bg-color: #ffa8cc1a; - --doc-symbol-attribute-bg-color: #963fb81a; - --doc-symbol-function-bg-color: #6d67e41a; - --doc-symbol-method-bg-color: #6d67e41a; - --doc-symbol-class-bg-color: #46c2cb1a; - --doc-symbol-module-bg-color: #f2f7a11a; -} - -:root { - --doc-symbol-parameter-fg-color: #df50af; - --doc-symbol-attribute-fg-color: #0079ff; - --doc-symbol-function-fg-color: #00dfa2; - --doc-symbol-method-fg-color: #00dfa2; - --doc-symbol-class-fg-color: #d1b619; - --doc-symbol-module-fg-color: #ff0060; - - --doc-symbol-parameter-bg-color: #df50af1a; - --doc-symbol-attribute-bg-color: #0079ff1a; - --doc-symbol-function-bg-color: #00dfa21a; - --doc-symbol-method-bg-color: #00dfa21a; - --doc-symbol-class-bg-color: #d1b6191a; - --doc-symbol-module-bg-color: #ff00601a; -} \ No newline at end of file diff --git a/docs/css/mkdocstrings.css b/docs/css/mkdocstrings.css deleted file mode 100644 index 85e5a20..0000000 --- a/docs/css/mkdocstrings.css +++ /dev/null @@ -1,122 +0,0 @@ -/* Indentation. */ -div.doc-contents:not(.first) { - padding-left: 25px; - border-left: .05rem solid var(--md-typeset-table-color); -} - -/* Mark external links as such. */ -a.external::after, -a.autorefs-external::after { - /* https://primer.style/octicons/arrow-up-right-24 */ - /* mask-image: url('data:image/svg+xml,'); - -webkit-mask-image: url('data:image/svg+xml,'); */ - /* content: ' '; */ - - display: inline-block; - vertical-align: middle; - position: relative; - - height: 1em; - width: 1em; - background-color: currentColor; -} - -a.external:hover::after, -a.autorefs-external:hover::after { - background-color: var(--md-accent-fg-color); -} - - - -/* Fancier color for operators such as * and |. */ -.doc-signature .o { - color: var(--md-code-hl-special-color); -} - -/* Fancier color for constants such as None, True, and False. */ -.doc-signature .kc { - color: var(--md-code-hl-constant-color); -} - -/* Fancier color for built-in types (only useful when cross-references are used). */ -.doc-signature .n > a[href^="https://docs.python.org/"][href*="/functions.html#"], -.doc-signature .n > a[href^="https://docs.python.org/"][href*="/stdtypes.html#"] { - color: var(--md-code-hl-constant-color); -} - -.doc-symbol-parameter::after { -content: "P"; -} - -.doc-symbol-attribute::after { -content: "A"; -} - -.doc-symbol-function::after { -content: "F"; -} - -.doc-symbol-method::after { -content: "M"; -} - -.doc-symbol-class::after { -content: "C"; -} - -.doc-symbol-module::after { -content: "M"; -} - -.doc-section-title { - font-weight: bold; -} - - -[data-md-color-scheme="default"] { - --doc-symbol-parameter-fg-color: #df50af; - --doc-symbol-attribute-fg-color: #0079ff; - --doc-symbol-function-fg-color: #00dfa2; - --doc-symbol-method-fg-color: #00dfa2; - --doc-symbol-class-fg-color: #d1b619; - --doc-symbol-module-fg-color: #ff0060; - - --doc-symbol-parameter-bg-color: #df50af1a; - --doc-symbol-attribute-bg-color: #0079ff1a; - --doc-symbol-function-bg-color: #00dfa21a; - --doc-symbol-method-bg-color: #00dfa21a; - --doc-symbol-class-bg-color: #d1b6191a; - --doc-symbol-module-bg-color: #ff00601a; -} - -[data-md-color-scheme="slate"] { - --doc-symbol-parameter-fg-color: #ffa8cc; - --doc-symbol-attribute-fg-color: #963fb8; - --doc-symbol-function-fg-color: #6d67e4; - --doc-symbol-method-fg-color: #6d67e4; - --doc-symbol-class-fg-color: #46c2cb; - --doc-symbol-module-fg-color: #f2f7a1; - - --doc-symbol-parameter-bg-color: #ffa8cc1a; - --doc-symbol-attribute-bg-color: #963fb81a; - --doc-symbol-function-bg-color: #6d67e41a; - --doc-symbol-method-bg-color: #6d67e41a; - --doc-symbol-class-bg-color: #46c2cb1a; - --doc-symbol-module-bg-color: #f2f7a11a; -} - -:root { - --doc-symbol-parameter-fg-color: #df50af; - --doc-symbol-attribute-fg-color: #0079ff; - --doc-symbol-function-fg-color: #00dfa2; - --doc-symbol-method-fg-color: #00dfa2; - --doc-symbol-class-fg-color: #d1b619; - --doc-symbol-module-fg-color: #ff0060; - - --doc-symbol-parameter-bg-color: #df50af1a; - --doc-symbol-attribute-bg-color: #0079ff1a; - --doc-symbol-function-bg-color: #00dfa21a; - --doc-symbol-method-bg-color: #00dfa21a; - --doc-symbol-class-bg-color: #d1b6191a; - --doc-symbol-module-bg-color: #ff00601a; -} \ No newline at end of file diff --git a/docs/example.md b/docs/example.md deleted file mode 100644 index 3eb4c40..0000000 --- a/docs/example.md +++ /dev/null @@ -1,22 +0,0 @@ - -# Example - -An example is provided with the `trendify` package to demonstrate functionality. The example commands below genereate sample data and run a pre-defined post-processor to produce and sort products as well as generating assets. - -After pip installing `trendify`, open an terminal and run the following shell commands. - -``` sh -workdir=./workdir -generator=trendify.examples:example_data_product_generator -trendify_make_sample_data -wd $workdir -n 10 -trendify make all -g $generator -i $workdir/models/*/ -o $workdir/trendify_output/ -n 10 --port 8000 -``` - -See the source code and documentation of the methods used in this example: - -- [trendify_make_sample_data][trendify.examples.make_example_data] -- [example_data_product_generator][trendify.examples.example_data_product_generator] - -The static outputs should include the following image: - -![Example Generated Static Asset](assets/static/trace_plot.jpg) diff --git a/docs/example_data/.gitignore b/docs/example_data/.gitignore new file mode 100644 index 0000000..1a3868d --- /dev/null +++ b/docs/example_data/.gitignore @@ -0,0 +1,2 @@ +models +trendify/*.db* diff --git a/docs/example_data/trendify/assets/batch_means_table_melted.csv b/docs/example_data/trendify/assets/batch_means_table_melted.csv new file mode 100644 index 0000000..bbf99bd --- /dev/null +++ b/docs/example_data/trendify/assets/batch_means_table_melted.csv @@ -0,0 +1,61 @@ +row,col,value,unit +0,wave_1,0.033883985963653836,m/s +0,wave_2,0.48140038121346485,m/s +0,wave_3,0.4336854066275722,m/s +1,wave_1,0.05161656541949166,m/s +1,wave_2,0.6081589154837236,m/s +1,wave_3,0.6528083436333808,m/s +2,wave_1,0.03817556976546212,m/s +2,wave_2,0.4354839056467192,m/s +2,wave_3,0.4215395638213238,m/s +3,wave_1,0.03191411004724959,m/s +3,wave_2,0.5481909779187941,m/s +3,wave_3,0.5015457049799089,m/s +4,wave_1,0.04510153669242987,m/s +4,wave_2,0.9405296336822633,m/s +4,wave_3,0.4089418312480353,m/s +5,wave_1,0.04876816481797261,m/s +5,wave_2,0.8475611992612225,m/s +5,wave_3,0.5051412135281409,m/s +6,wave_1,0.04052594748051735,m/s +6,wave_2,0.6145806908005733,m/s +6,wave_3,0.5899010334809793,m/s +7,wave_1,0.04110041047431338,m/s +7,wave_2,0.7120136282579775,m/s +7,wave_3,0.4462688395690813,m/s +8,wave_1,0.06204324280082923,m/s +8,wave_2,0.4062499537520507,m/s +8,wave_3,0.5449963231627638,m/s +9,wave_1,0.028074875608987165,m/s +9,wave_2,0.6631117205081412,m/s +9,wave_3,0.6870289799419732,m/s +10,wave_1,0.03717652980739284,m/s +10,wave_2,0.9880919913726497,m/s +10,wave_3,0.38197132188491134,m/s +11,wave_1,0.02921057552331016,m/s +11,wave_2,0.9716604710179235,m/s +11,wave_3,0.400953489459974,m/s +12,wave_1,0.07294838417865855,m/s +12,wave_2,0.4703146956865868,m/s +12,wave_3,0.3923924896906037,m/s +13,wave_1,0.0392719601796248,m/s +13,wave_2,0.8411339277071775,m/s +13,wave_3,0.4712268141796765,m/s +14,wave_1,0.039863485358342346,m/s +14,wave_2,0.6609797694546892,m/s +14,wave_3,0.6949159088853283,m/s +15,wave_1,0.06667601579010972,m/s +15,wave_2,0.4086098807193371,m/s +15,wave_3,0.33916148335719754,m/s +16,wave_1,0.05425763329417748,m/s +16,wave_2,0.5931435382055393,m/s +16,wave_3,0.4872566970843842,m/s +17,wave_1,0.03044352272994952,m/s +17,wave_2,0.6711403732186891,m/s +17,wave_3,0.42796641093342735,m/s +18,wave_1,0.047171950119329877,m/s +18,wave_2,0.3067258796501125,m/s +18,wave_3,0.4249437627713855,m/s +19,wave_1,0.056628372483964746,m/s +19,wave_2,0.5646274107424921,m/s +19,wave_3,0.471411985190813,m/s diff --git a/docs/example_data/trendify/assets/batch_means_table_melted.md b/docs/example_data/trendify/assets/batch_means_table_melted.md new file mode 100644 index 0000000..10ac2f4 --- /dev/null +++ b/docs/example_data/trendify/assets/batch_means_table_melted.md @@ -0,0 +1,62 @@ +| row | col | value | unit | +|-------|--------|-----------|--------| +| 0 | wave_1 | 0.033884 | m/s | +| 0 | wave_2 | 0.4814 | m/s | +| 0 | wave_3 | 0.433685 | m/s | +| 1 | wave_1 | 0.0516166 | m/s | +| 1 | wave_2 | 0.608159 | m/s | +| 1 | wave_3 | 0.652808 | m/s | +| 2 | wave_1 | 0.0381756 | m/s | +| 2 | wave_2 | 0.435484 | m/s | +| 2 | wave_3 | 0.42154 | m/s | +| 3 | wave_1 | 0.0319141 | m/s | +| 3 | wave_2 | 0.548191 | m/s | +| 3 | wave_3 | 0.501546 | m/s | +| 4 | wave_1 | 0.0451015 | m/s | +| 4 | wave_2 | 0.94053 | m/s | +| 4 | wave_3 | 0.408942 | m/s | +| 5 | wave_1 | 0.0487682 | m/s | +| 5 | wave_2 | 0.847561 | m/s | +| 5 | wave_3 | 0.505141 | m/s | +| 6 | wave_1 | 0.0405259 | m/s | +| 6 | wave_2 | 0.614581 | m/s | +| 6 | wave_3 | 0.589901 | m/s | +| 7 | wave_1 | 0.0411004 | m/s | +| 7 | wave_2 | 0.712014 | m/s | +| 7 | wave_3 | 0.446269 | m/s | +| 8 | wave_1 | 0.0620432 | m/s | +| 8 | wave_2 | 0.40625 | m/s | +| 8 | wave_3 | 0.544996 | m/s | +| 9 | wave_1 | 0.0280749 | m/s | +| 9 | wave_2 | 0.663112 | m/s | +| 9 | wave_3 | 0.687029 | m/s | +| 10 | wave_1 | 0.0371765 | m/s | +| 10 | wave_2 | 0.988092 | m/s | +| 10 | wave_3 | 0.381971 | m/s | +| 11 | wave_1 | 0.0292106 | m/s | +| 11 | wave_2 | 0.97166 | m/s | +| 11 | wave_3 | 0.400953 | m/s | +| 12 | wave_1 | 0.0729484 | m/s | +| 12 | wave_2 | 0.470315 | m/s | +| 12 | wave_3 | 0.392392 | m/s | +| 13 | wave_1 | 0.039272 | m/s | +| 13 | wave_2 | 0.841134 | m/s | +| 13 | wave_3 | 0.471227 | m/s | +| 14 | wave_1 | 0.0398635 | m/s | +| 14 | wave_2 | 0.66098 | m/s | +| 14 | wave_3 | 0.694916 | m/s | +| 15 | wave_1 | 0.066676 | m/s | +| 15 | wave_2 | 0.40861 | m/s | +| 15 | wave_3 | 0.339161 | m/s | +| 16 | wave_1 | 0.0542576 | m/s | +| 16 | wave_2 | 0.593144 | m/s | +| 16 | wave_3 | 0.487257 | m/s | +| 17 | wave_1 | 0.0304435 | m/s | +| 17 | wave_2 | 0.67114 | m/s | +| 17 | wave_3 | 0.427966 | m/s | +| 18 | wave_1 | 0.047172 | m/s | +| 18 | wave_2 | 0.306726 | m/s | +| 18 | wave_3 | 0.424944 | m/s | +| 19 | wave_1 | 0.0566284 | m/s | +| 19 | wave_2 | 0.564627 | m/s | +| 19 | wave_3 | 0.471412 | m/s | diff --git a/docs/example_data/trendify/assets/batch_means_table_pivot.csv b/docs/example_data/trendify/assets/batch_means_table_pivot.csv new file mode 100644 index 0000000..c670f24 --- /dev/null +++ b/docs/example_data/trendify/assets/batch_means_table_pivot.csv @@ -0,0 +1,21 @@ +row,wave_1,wave_2,wave_3 +0,0.033883985963653836,0.48140038121346485,0.4336854066275722 +1,0.05161656541949166,0.6081589154837236,0.6528083436333808 +2,0.03817556976546212,0.4354839056467192,0.4215395638213238 +3,0.03191411004724959,0.5481909779187941,0.5015457049799089 +4,0.04510153669242987,0.9405296336822633,0.4089418312480353 +5,0.04876816481797261,0.8475611992612225,0.5051412135281409 +6,0.04052594748051735,0.6145806908005733,0.5899010334809793 +7,0.04110041047431338,0.7120136282579775,0.4462688395690813 +8,0.06204324280082923,0.4062499537520507,0.5449963231627638 +9,0.028074875608987165,0.6631117205081412,0.6870289799419732 +10,0.03717652980739284,0.9880919913726497,0.38197132188491134 +11,0.02921057552331016,0.9716604710179235,0.400953489459974 +12,0.07294838417865855,0.4703146956865868,0.3923924896906037 +13,0.0392719601796248,0.8411339277071775,0.4712268141796765 +14,0.039863485358342346,0.6609797694546892,0.6949159088853283 +15,0.06667601579010972,0.4086098807193371,0.33916148335719754 +16,0.05425763329417748,0.5931435382055393,0.4872566970843842 +17,0.03044352272994952,0.6711403732186891,0.42796641093342735 +18,0.047171950119329877,0.3067258796501125,0.4249437627713855 +19,0.056628372483964746,0.5646274107424921,0.471411985190813 diff --git a/docs/example_data/trendify/assets/batch_means_table_pivot.md b/docs/example_data/trendify/assets/batch_means_table_pivot.md new file mode 100644 index 0000000..171b298 --- /dev/null +++ b/docs/example_data/trendify/assets/batch_means_table_pivot.md @@ -0,0 +1,22 @@ +| row | wave_1 | wave_2 | wave_3 | +|-------|-----------|----------|----------| +| 0 | 0.033884 | 0.4814 | 0.433685 | +| 1 | 0.0516166 | 0.608159 | 0.652808 | +| 2 | 0.0381756 | 0.435484 | 0.42154 | +| 3 | 0.0319141 | 0.548191 | 0.501546 | +| 4 | 0.0451015 | 0.94053 | 0.408942 | +| 5 | 0.0487682 | 0.847561 | 0.505141 | +| 6 | 0.0405259 | 0.614581 | 0.589901 | +| 7 | 0.0411004 | 0.712014 | 0.446269 | +| 8 | 0.0620432 | 0.40625 | 0.544996 | +| 9 | 0.0280749 | 0.663112 | 0.687029 | +| 10 | 0.0371765 | 0.988092 | 0.381971 | +| 11 | 0.0292106 | 0.97166 | 0.400953 | +| 12 | 0.0729484 | 0.470315 | 0.392392 | +| 13 | 0.039272 | 0.841134 | 0.471227 | +| 14 | 0.0398635 | 0.66098 | 0.694916 | +| 15 | 0.066676 | 0.40861 | 0.339161 | +| 16 | 0.0542576 | 0.593144 | 0.487257 | +| 17 | 0.0304435 | 0.67114 | 0.427966 | +| 18 | 0.047172 | 0.306726 | 0.424944 | +| 19 | 0.0566284 | 0.564627 | 0.471412 | diff --git a/docs/example_data/trendify/assets/batch_means_table_stats.csv b/docs/example_data/trendify/assets/batch_means_table_stats.csv new file mode 100644 index 0000000..1846835 --- /dev/null +++ b/docs/example_data/trendify/assets/batch_means_table_stats.csv @@ -0,0 +1,4 @@ +Name,min,mean,max,sigma3 +wave_1,0.028074875608987165,0.04474264192678835,0.07294838417865855,0.03812416501859012 +wave_2,0.3067258796501125,0.6366854472150065,0.9880919913726497,0.5940186867168052 +wave_3,0.33916148335719754,0.4842028801715431,0.6949159088853283,0.3054908024970264 diff --git a/docs/example_data/trendify/assets/batch_means_table_stats.md b/docs/example_data/trendify/assets/batch_means_table_stats.md new file mode 100644 index 0000000..17469c2 --- /dev/null +++ b/docs/example_data/trendify/assets/batch_means_table_stats.md @@ -0,0 +1,5 @@ +| Name | min | mean | max | sigma3 | +|--------|-----------|-----------|-----------|-----------| +| wave_1 | 0.0280749 | 0.0447426 | 0.0729484 | 0.0381242 | +| wave_2 | 0.306726 | 0.636685 | 0.988092 | 0.594019 | +| wave_3 | 0.339161 | 0.484203 | 0.694916 | 0.305491 | diff --git a/docs/example_data/trendify/assets/harmonic_mean_distribution.svg b/docs/example_data/trendify/assets/harmonic_mean_distribution.svg new file mode 100644 index 0000000..4fbf9b1 --- 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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/example_generator.py b/docs/example_generator.py new file mode 100644 index 0000000..af86c7b --- /dev/null +++ b/docs/example_generator.py @@ -0,0 +1,201 @@ +"""A complete `RecordGenerator` for the "Writing a Record Generator" user guide.""" + +from __future__ import annotations + +from pathlib import Path + +import polars as pl + +# --8<-- [start:generate_records] +# --8<-- [start:handle] +import trendify + + +def generate_records(workdir: Path) -> trendify.RecordList: + """ + Reads this run's `results.csv` and returns one example of every plottable record + type, plus a table. + """ + records: trendify.RecordList = [] + df = pl.read_csv(workdir / "results.csv") + # --8<-- [end:handle] + run_label = workdir.name + + add_scatter_records(records, df) + add_trace_records(records, df) + add_axline_records(records) + add_summary_point_records(records, df, run_label) + add_histogram_records(records, df) + add_table_entries_records(records, df, run_label) + + for record in records: + # render all as SVG instead of JPG + if isinstance(record, trendify.Format2D): + record.renderer = trendify.Vector() + return records + + +# --8<-- [end:generate_records] + + +# --8<-- [start:scatter] +def add_scatter_records(records: trendify.RecordList, df: pl.DataFrame) -> None: + """ + Maps the relationship between two channels for this run. + + `Scatter2D` plots every raw `(wave_1, wave_2)` coordinate pair from this run as an + unconnected point, sharing one marker style. + """ + trendify.Format2D( + tags=["signature_correlation"], label_x="wave_1", label_y="wave_2" + ).append_to_list(records) + + trendify.Scatter2D( + tags=["signature_correlation"], + x=df["wave_1"].to_numpy(), # compatible with numpy arrays + y=df["wave_2"].to_numpy(), + marker=trendify.Marker(label="wave_1 vs. wave_2", symbol="x", size=10), + ).append_to_list(records) + + +# --8<-- [end:scatter] + + +# --8<-- [start:traces] +def add_trace_records(records: trendify.RecordList, df: pl.DataFrame) -> None: + """ + Plots the raw time-series data: one `Trace2D` per channel. + + Because they all share the "time_series" tag, every channel overlays on a single + unified plot for easy visual comparison. + """ + # Define line styles + pens = { + "wave_1": trendify.Pen( + label="wave_1", + color=trendify.Color.ROSE_500, + zorder=10, + ), + "wave_2": trendify.Pen( + label="wave_2", + color=trendify.Color.SKY_500, + zorder=20, + ), + "wave_3": trendify.Pen( + label="wave_3", + color=trendify.Color.AMBER_500, + zorder=30, + ), + } + + # Loop over columns and add lines + for column in ["wave_1", "wave_2", "wave_3"]: + trendify.Trace2D( + tags=["time_series"], + x=df["time"].to_numpy(), + y=df[column].to_numpy(), + pen=pens[column], + ).append_to_list(records) + + +# --8<-- [end:traces] + + +# --8<-- [start:axline] +def add_axline_records(records: trendify.RecordList) -> None: + """ + Draws a zero-amplitude static baseline directly across the "time_series" plot. + + This showcases how static annotations like `AxLine` seamlessly layer on top of + dynamically generated traces sharing the same plot target tag. + """ + # Define a Format2D with a grid + trendify.Format2D( + tags=["time_series"], + label_x="Time (s)", + label_y="Signal Amplitude", + grid=trendify.Grid.from_theme(trendify.GridTheme.MATLAB), + ).append_to_list(records) + + trendify.AxLine( + tags=["time_series"], + value=-1.0, + orientation=trendify.LineOrientation.HORIZONTAL, + pen=trendify.Pen(color="gray", size=3, linestyle="--", label="baseline"), + ).append_to_list(records) + + +# --8<-- [end:axline] + + +# --8<-- [start:point] +def add_summary_point_records( + records: trendify.RecordList, df: pl.DataFrame, run_label: str +) -> None: + """ + Aggregates one scalar summary per run. + + Unlike raw scatter data, each `Point2D` registers as an independent, hoverable entity. + As multiple runs are processed, this plot populates a timeline of each run's `wave_1` + mean across your entire batch history. + """ + trendify.Format2D( + tags=["run_means_tracking"], label_x="Run", label_y="wave_1 mean" + ).append_to_list(records) + + trendify.Point2D( + tags=["run_means_tracking"], + x=run_label, + y=df["wave_1"].to_numpy().mean(), + marker=trendify.Marker(label="wave_1 run mean", size=20), + ).append_to_list(records) + + +# --8<-- [end:point] + + +# --8<-- [start:histogram] +def add_histogram_records(records: trendify.RecordList, df: pl.DataFrame) -> None: + """ + Bins one scalar summary per run into a histogram. + + This extracts the mean value of `wave_1` from the current run and bins it into a + population histogram shared across all runs, letting you monitor its distribution + across the batch. + """ + trendify.Format2D( + tags=["harmonic_mean_distribution"], label_x="wave_1 mean", label_y="Count" + ).append_to_list(records) + + trendify.HistogramEntry( + tags=["harmonic_mean_distribution"], + value=df["wave_1"].to_numpy().mean(), + style=trendify.HistogramStyle(label="wave_1 batch mean", bins=10), + ).append_to_list(records) + + +# --8<-- [end:histogram] + + +# --8<-- [start:table] +def add_table_entries_records( + records: trendify.RecordList, df: pl.DataFrame, run_label: str +) -> None: + """ + Tabulates multi-channel performance parameters into structured comparative matrices. + + One `TableEntry` is constructed per channel column. Assigning a unique, distinct + `row` per execution context (the run directory name) ensures cross-run values map + into explicit comparative rows instead of overwriting a single `(row, col)` storage slot. + """ + for column in ["wave_1", "wave_2", "wave_3"]: + trendify.TableEntry( + tags=["batch_means_table"], + row=run_label, + col=column, + value=df[column].to_numpy().mean(), + unit="m/s", + ).append_to_list(records) + + +# --8<-- [end:table] diff --git a/docs/hooks.py b/docs/hooks.py index e69de29..db554de 100644 --- a/docs/hooks.py +++ b/docs/hooks.py @@ -0,0 +1,25 @@ +"""MkDocs hooks that generate documentation assets before each build.""" + +import csv +from pathlib import Path + +import tabulate + +_EXAMPLE_TABLES_DIR = Path(__file__).parent / "example_data" / "trendify" / "assets" + + +def on_pre_build(config: dict) -> None: + """Regenerate example report tables so docs always reflect the current to_tables output.""" + for csv_path in _EXAMPLE_TABLES_DIR.rglob("*.csv"): + _csv_to_md(csv_path) + + +def _csv_to_md(csv_path: Path) -> None: + """Write a GitHub-flavored markdown table alongside a CSV file.""" + with csv_path.open(newline="") as f: + rows = list(csv.DictReader(f)) + md = tabulate.tabulate(rows, headers="keys", tablefmt="github") + csv_path.with_suffix(".md").write_text(md + "\n") + + +on_pre_build({}) diff --git a/docs/index.md b/docs/index.md index 6fbd714..7836190 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,94 +1,82 @@ --- hide: - navigation +# - toc --- + + +

+ Trendify +

+ +# trendify: Efficient Plotting and Table Building + +

+ + PyPI version + + + Python versions + + + Tests & Release Status + + + Coverage + + + Ruff + + + Pydantic v2 + + + License + + + Documentation + +

-
-![logo](assets/logo_pink.svg){ width="200" } -
- -# Welcome to Trendify - - -[![PyPI Version](https://img.shields.io/pypi/v/trendify.svg)](https://pypi.org/project/trendify/) -[![Python Versions](https://img.shields.io/pypi/pyversions/trendify.svg)](https://pypi.org/project/trendify/) -[![License](https://img.shields.io/github/license/TalbotKnighton/trendify.svg)](https://github.com/TalbotKnighton/trendify/blob/main/LICENSE) -[![GitHub](https://img.shields.io/github/stars/TalbotKnighton/trendify?style=social)](https://github.com/TalbotKnighton/trendify) - - -### Description - -The `trendify` package makes it easy to compare data from multiple runs of a batch process. The core functionality is to generate CSV tables and JPEG images by mapping a user-provided processing function over a user-provided set of input data directories. Parallelization and data serialization are used to speed up processing time and maintain low memory requirements. `trendify` is run via a terminal [command line interface (CLI)][cli] one-liner method or via a Python application programming interface (API). - -See the [Flow Chart][flow-chart] and [Vocabulary][vocabulary] sections below for a visual diagram of the program flow and vocabulary reference. - -The [Motivation][motivation] section discusses the problem this package solves and why it is useful. - -The [Recipe][recipe] section provides a template for users to follow. - -The [Example][example] section provides a minimum working example. - -Available python methods and command line syntax are described in the [API and CLI][api-and-cli] section. - -Planned future work and features are shown in the [Planned Features][planned-features] section. +--- -### Installation +## Installation -Install from PyPI +Install the package via your preferred manager: -```bash -pip install trendify -``` +=== "`uv`" -### Links + ```bash linenums="0" + uv add trendify + ``` -[View on PyPI](https://pypi.org/project/trendify/) +=== "`pip`" -[View source code](https://github.com/TalbotKnighton/trendify?tab=readme-ov-file) + ```bash linenums="0" + pip install trendify + ``` -### Flow Chart +--- -The following flow chart shows how `trendify` generates assets from user inputs. +## Core Features -``` mermaid -graph TD - subgraph "User Inputs" - A[(Input Directories)]@{ shape: lin-cyl }; - AA[Product Generator]; - AAA[Output Directory]; - AAAA[Number of Processes] - end - A --> B[CLI or Script]@{ shape: diamond}; - AA --> B; - AAA --> B; - AAAA --> B; - B --> |Map generator over raw data dirs| CCC[(Tagged Data Products)]@{shape: lin-cyl}; - CCC --> |Sort and process products| CC[(Assets)]@{shape: lin-cyl}; - CC -.-> H[Interactive Displays]; - H -.-> |Grafana API| I[Grafana Dashboard]; - H -.-> K[Etc.]; - CC -.-> D[Static Assets]; - D -.-> |Pandas| E[CSV]; - D -.-> |Matplotlib| F[JPG]; - D -.-> G[Etc.]; -``` +- **Built to scale**: process thousands of runs without holding every run's data in memory at once. Throughput stays flat whether you have dozens of runs or tens of thousands. +- **Typed records, no migrations**: points, traces, tables, and histograms are validated [Pydantic](https://docs.pydantic.dev/latest/) models. Add your own record types anytime without writing a schema migration. +- **Parallelizable**: `trendify generate` can fan your processing function out across multiple CPU cores with `--n-procs`, with multiprocessing-safe logging that funnels every worker's output through a single queue. +- **Static assets or a live dashboard**: render tagged data straight to Matplotlib images and CSV tables with `trendify render`, or launch an interactive FastAPI dashboard with `trendify viewer` to browse tags, tables, and interactive plots in the browser. -### Vocabulary +--- -The following is a table of important trendify objects / vocabulary sorted alphabetically: +## Why use `trendify`? -| Term | Meaning | -| ---- | ------- | -| API | Application programming interface: Definition of valid objects for processing within `trendify` framework | -| Asset | An asset to be used in a report (such as static CSV or JPG files) or interacted with (such as a Grafana dashboard) | -| CLI | Command line interface: `trendify` script installed with package used to run the framework | -| [DataProduct][trendify.api.base.data_product.DataProduct] | Base class for [tagged][trendify.api.base.helpers.Tag] products to be sorted and displayed in static or interactive assets.| -| [DataProductGenerator][trendify.api.generator.data_product_generator.DataProductGenerator] | A [Callable][typing.Callable] to be mapped over raw data directories. Given the [Path][pathlib.Path] to a working directory, the method returns a [ProductList][trendify.api.base.data_product.ProductList] (i.e. a list of instances of [DataProduct][trendify.api.base.data_product.DataProduct] instances): [`Trace2D`][trendify.api.plotting.trace.Trace2D], [`Point2D`][trendify.api.plotting.point.Point2D], [`TableEntry`][trendify.api.formats.table.TableEntry], [`HistogramEntry`][trendify.api.plotting.histogram.HistogramEntry], etc. | -| [HistogramEntry][trendify.api.plotting.histogram.HistogramEntry] | Tagged, labeled data point to be counted and histogrammed | -| [Point2D][trendify.api.plotting.point.Point2D] | Tagged, labeled [XYData][trendify.api.formats.format2d.XYData] defining a point to be scattered on xy graph | -| [Product List][trendify.api.base.data_product.ProductList] | List of [DataProduct][trendify.api.formats.table.TableEntry] instances | -| Raw Data | Data from some batch process or individual runs (with results from each run stored in separate subdirectories) | -| [TableEntry][trendify.api.formats.table.TableEntry] | Tagged data point to be collected into a table, pivoted, and statistically analyzed | -| [Tag][trendify.api.base.helpers.Tag] | Hashable tag used for sorting and collection of [DataProduct][trendify.api.base.data_product.DataProduct] instances | -| [Trace2D][trendify.api.plotting.trace.Trace2D] | Tagged, labeled [XYData][trendify.api.formats.format2d.XYData] defining a line to be plotted on xy graph | -| [XYData][trendify.api.formats.format2d.XYData] | Base class for products to be plotted on an xy graph | +- **Scalable.** Throughput stays flat whether you're processing dozens of runs or tens of thousands. +- **Memory efficient.** Each run is processed once and cached on disk, nothing needs to stay open or held in memory for the whole sweep. This is critical for processing large amounts of data with less memory than is available when batch processing. +- **Flexible output.** Render static Matplotlib images and CSV tables for a report, or browse the same data interactively in a live dashboard. diff --git a/docs/javascripts/katex.js b/docs/javascripts/katex.js new file mode 100644 index 0000000..0946ce0 --- /dev/null +++ b/docs/javascripts/katex.js @@ -0,0 +1,10 @@ +document$.subscribe(({ body }) => { + renderMathInElement(body, { + delimiters: [ + { left: "$$", right: "$$", display: true }, + { left: "$", right: "$", display: false }, + { left: "\\(", right: "\\)", display: false }, + { left: "\\[", right: "\\]", display: true }, + ], + }); +}); diff --git a/docs/javascripts/tablesort.js b/docs/javascripts/tablesort.js new file mode 100644 index 0000000..98717ae --- /dev/null +++ b/docs/javascripts/tablesort.js @@ -0,0 +1,6 @@ +document$.subscribe(function () { + var tables = document.querySelectorAll("article table:not([class])") + tables.forEach(function (table) { + new Tablesort(table) + }) +}) diff --git a/docs/more_examples.md b/docs/more_examples.md deleted file mode 100644 index 408ec13..0000000 --- a/docs/more_examples.md +++ /dev/null @@ -1,204 +0,0 @@ -# Trendify Examples - - -!!! warning - - This particular page was AI-generated and not yet tested. Use it for ideas, but trust the rest of the documentation more until this warning is removed. - -This guide demonstrates how to use the various data products in Trendify. We'll create example data, generate different types of data products, and show how to process them. - -## Setup - -First, let's import the necessary modules and set up our environment: - -```python -import numpy as np -from pathlib import Path -from trendify.API import ( - Trace2D, Point2D, TableEntry, HistogramEntry, AxLine, - Pen, Marker, Format2D, LineOrientation, - DataProductCollection, make_it_trendy -) - -# Create a directory for our example -from pathlib import Path -example_dir = Path("trendify_example") -example_dir.mkdir(exist_ok=True) -``` - -## Creating Data Products - -### 1. Trace2D - Line Plots - -```python -# Create some example data -x = np.linspace(0, 10, 100) -y = np.sin(x) - -# Create a Trace2D product -trace = Trace2D.from_xy( - tags=['example', 'sine_wave'], - x=x, - y=y, - pen=Pen( - color='blue', - label='Sine Wave', - size=2 - ), - format2d=Format2D( - title_fig="Example Sine Wave", - title_ax="Sine Function", - label_x="X", - label_y="Sin(X)", - lim_x_min=0, - lim_x_max=10, - lim_y_min=-1.5, - lim_y_max=1.5 - ) -) -``` - -### 2. Point2D - Scatter Plots - -```python -# Create scattered points -x_scattered = np.random.uniform(0, 10, 20) -y_scattered = np.random.normal(0, 0.5, 20) - -points = [ - Point2D( - tags=['example', 'scattered_points'], - x=x_, - y=y_, - marker=Marker( - color='red', - symbol='o', - size=50, - label='Random Points' - ), - format2d=Format2D( - title_fig="Scattered Points", - label_x="X", - label_y="Y" - ) - ) - for x_, y_ in zip(x_scattered, y_scattered) -] -``` - -### 3. AxLine - Horizontal and Vertical Lines - -```python -# Create axis lines -hline = AxLine( - tags=['example', 'reference_lines'], - value=0.0, - orientation=LineOrientation.HORIZONTAL, - pen=Pen(color='green', label='Zero Line', size=1.5) -) - -vline = AxLine( - tags=['example', 'reference_lines'], - value=5.0, - orientation=LineOrientation.VERTICAL, - pen=Pen(color='red', label='Middle', size=1.5) -) -``` - -### 4. TableEntry - Data Tables - -```python -# Create table entries -table_entries = [ - TableEntry( - tags=['example', 'measurements'], - row='Sample 1', - col='Value', - value=42.0, - unit='meters' - ), - TableEntry( - tags=['example', 'measurements'], - row='Sample 2', - col='Value', - value=37.5, - unit='meters' - ) -] -``` - -### 5. HistogramEntry - Histograms - -```python -# Create histogram data -hist_data = np.random.normal(0, 1, 1000) -hist_entries = [ - HistogramEntry( - tags=['example', 'distribution'], - value=v, - style=HistogramStyle( - color='blue', - label='Normal Distribution', - bins=30 - ) - ) - for v in hist_data -] -``` - -## Combining and Processing Data Products - -Now let's combine all our data products and process them: - -```python -# Create a data product generator function -def example_generator(workdir: Path): - return [ - trace, - *points, - hline, - vline, - *table_entries, - *hist_entries - ] - -# Process everything -make_it_trendy( - data_product_generator=example_generator, - input_dirs=[example_dir], - output_dir=example_dir / "output", - n_procs=1, - dpi_static_plots=300 -) -``` - -After running this code, you'll find: - -- Generated plots in the output directory -- CSV files with table data -- Histogram plots -- A organized structure based on the tags you used - -The plots will include: - -- The sine wave with scattered points -- Reference lines (horizontal at y=0 and vertical at x=5) -- A histogram of the normal distribution -- Tables with the measurement data - -Each plot will be properly formatted with titles, labels, and legends as specified in the Format2D objects. - -## Viewing Results - -You can find your results in the following locations: - -- `example_dir/output/assets/static/` - Static plots and tables -- `example_dir/output/assets/interactive/` - Interactive Grafana dashboard configuration (if enabled) -- `example_dir/output/products/` - Sorted JSON files containing the data products - -## Notes - -- Make sure to adjust tags to organize your data products as needed -- The Format2D objects can be shared between related data products -- You can customize colors, styles, and labels using Pen and Marker objects -- The output directory structure mirrors your tag structure \ No newline at end of file diff --git a/docs/motivation.md b/docs/motivation.md deleted file mode 100644 index a7c74ee..0000000 --- a/docs/motivation.md +++ /dev/null @@ -1,106 +0,0 @@ ---- -hide: - - navigation ---- - -# Motivation - -This page explains the motivation for the Trendify package in hopes that the reader will better understand what benefit it provides, how to use it, and what features to expect in the future. - -## Problem Statement - -Data is commonly conveyed through visual assets such as: - -- Drawings -- __Graphs__ -- __Tables__ - -Graphs and Tables are intended to present data in a digestible way. - -It is easy to write complicted data manipulations for preparing data using a high level language such as Python, but there are several pitfalls when distilling large amounts of data: - -1. Computational expense (long processing times) -2. Limited RAM - -As discussed below, Trendify provides a scalable framework for quickly distilling graphs and tables from large amounts of data. - -### Illustrative Problem Statement - -The following bullets provide an illustrative common use case. Suppose that - -- An engineering model has been run through a Monte Carlo simulation for a large number of runs. -- The output data from each run needs to be processed to evaluate target criteria. -- The collected output data from all runs is significant (possibly larger than the available RAM on a device). -- The computational expense of post-processing each run is significant (for example, data needs to be rotated, transformed, smoothed, etc. in expensive ways). - -### Brute Force Approaches - -#### Nested For Loops, Low Memory Cost - -One way to process the data is to perform the following tasks sequentially: - -- Loop over figures to be created -- Open a [`matplotlib Figure`][matplotlib.figure.Figure] -- Sequentially loop over each output directory -- Process the data to provide the required information -- Plot to the open [`matplotlib Figure`][matplotlib.figure.Figure] -- Save the [`matplotlib Figure`][matplotlib.figure.Figure] -- Repeat the above steps for a new figure. - -This approach prevents a memory overload since only one batch run is loaded and processed at a time. However, this approach is often unacceptably slow since the same data needs to be loaded over and over for each figure or table to be created. - -#### Single For Loop, High Memory Cost - -A variation is to open multiple [`matplotlib Figure`][matplotlib.figure.Figure] instances at the same time (eliminateing the outer for loop). This approach avoids having to redundantly load/process data since the processed results can be added to every relevant figure/table. But, this can lead to a memory overload if many images and tables are being generated. - -### Trendify Approach - -Trendify uses multiple concepts to avoid the memory and computation pitfals of brute-force loops. - -#### Serialization/Deserialization - -JSON serialization is the act of saving Python objects to plain text using the JSON file format. - -JSON deserialization is the reverse process of creating Python objects by loading plain text from a JSON file. - -The [`Pydantic`](https://docs.pydantic.dev/latest/) package provides an integrated framework for serialization/deserialization in Python. -In the [`Pydantic`](https://docs.pydantic.dev/latest/) framework, type hints serve a double purpose: - -- Tells what type of data is expected in each variable (helps with linting hits and auto-completion in IDE) -- Data validation (pydantic closes program if JSON data does not match type hint) - -Trendify defines [`Pydantic`](https://docs.pydantic.dev/latest/) data classes to store 2D traces, table entries, etc. and load them back into memory as needed. -Loading distilled data from a JSON file is orders of magnitude faster than loading and processing raw data. -Serialization/deserialization eliminates the need to hold large amounts of data in memory in order to generate assets. -This allows Trendify to decouple the raw data processing stage from the aggregation stage of generating figures and tables by saving the intermediate data products as JSON files. - -#### Parallelization - -Trendify uses parallel processing to utilize all available machine cores (up to a user specified value) when processing batch data. -This provides a scalable linear speedup based on the number of cores. - -#### Data Products - -At the moment, Trendify is written to accomodate only a few specific types of data products (distilled data to be aggregated into assets). These include Trace2D, TableEntry, HistogramEntry, etc. Trendify can be expanded in the future to allow any arbitrary data type and aggregation step. - -#### Data Product Generators - -Functions are "first-class citizens" of Python, meaning that they can be passed into other functions as arguments. This allows function composition such as shown in the following example: - -```python -def apply_binary_function(some_binary_function, argument_1, argument_2): - return some_binary_function(argument_1, argument_2) - -def add(a, b): - return a + b - -result = apply_some_binary_function(add, 1, 1) # The `add` function is passed as an argument to `apply_binary_function` -print(result) # Prints `2` -``` - -Trendify provides a framework for applying any user-defined processing function to a set of working directories. -Thus, the end-user only needs to define what processing they want to do as a function (with a pre-determined signature) and pass that function to the Trendify framework via the command line or Python script. - -#### Command Line Interface - -The `trendify` command line interface allows users to map a data product generator from a Python source file or an installed Python package as discussed in the [recipe][recipe] and the [CLI docs][cli]. In a terminal (with the Python environment to which `trendify` is installed active) run the the command `trendify --help` for more info. \ No newline at end of file diff --git a/docs/planned_features.md b/docs/planned_features.md deleted file mode 100644 index eb11c25..0000000 --- a/docs/planned_features.md +++ /dev/null @@ -1,11 +0,0 @@ ---- -hide: - - navigation ---- - -# Planned Features - -- S3 bucket interface to push database files to storage -- Server to provide data to Grafana dashboard over network (pathfinder working on local computer) -- User authentication -- Smarter Grafana dashboards (auto populate input/output selector buttons for scatter plots, etc.) \ No newline at end of file diff --git a/docs/recipe.md b/docs/recipe.md deleted file mode 100644 index 801359c..0000000 --- a/docs/recipe.md +++ /dev/null @@ -1,78 +0,0 @@ ---- -hide: - - navigation ---- - -# Recipe - -## Defining a Data Product Generator - -Define a [Data Product Generator][trendify.api.generator.data_product_generator.DataProductGenerator] to ingest data and return a list of `trendify` data products. Valid products are listed in the vocabulary table above and reproduced in the smaller table here. See the code reference for class constructor inputs. The `trendify` framework will map this method over a set of results directories, save and sort the returned products, and produce assets. Each product will need to have a list of [tags][trendify.api.base.helpers.Tag] assigned (the list can be length 1). You can also provide labels to be used for generating a legend. - -| Valid Data Products | Resulting Asseet | -| ---- | ------- | -| [HistogramEntry][trendify.api.plotting.histogram.HistogramEntry] | Tagged, labeled data point to be counted and histogrammed | -| [Point2D][trendify.api.plotting.point.Point2D] | Tagged, labeled [XYData][trendify.api.formats.format2d.XYData] defining a point to be scattered on xy graph | -| [TableEntry][trendify.api.formats.table.TableEntry] | Tagged data point to be collected into a table, pivoted, and statistically analyzed | -| [Trace2D][trendify.api.plotting.trace.Trace2D] | Tagged, labeled [XYData][trendify.api.formats.format2d.XYData] defining a line to be plotted on xy graph | - -```python -from pathlib import Path -import trendify - -def user_defined_data_product_generator(workdir: Path) -> trendify.ProductList: - inputs = ... # load inputs from workdir - results = ... # load results from workdir - products: trendify.ProductList = [] - - # Append products to list - trendify.Trace2D(...).append_to_list(products) - trendify.Point2D(...).append_to_list(products) - trendify.TableEntry(...).append_to_list(products) - trendify.HistogramEntry(...).append_to_list(products) - ... - - # Return the list of valid data products - return products -``` - -## Running the Generator Function - -Run the folling command in a terminal (with trendify installed to the active python environment) [command line interface (CLI)][cli] to - -- [make data products][trendify.api.api.make_products] -- [sort data products][trendify.api.api.sort_products] -- [make static assets][trendify.api.api.make_tables_and_figures] -- [make static asset include files][trendify.api.api.make_include_files] - -``` bash -workdir=./workdir -inputs=$workdir/data_directories/*/ -output=$workdir/output/ -generator=trendify.examples:example_data_product_generator -trendify make all -g $generator -i $inputs -o $output -n 10 --port 800 -``` - -!!! note "Use Parallelization" - - Use `--n-procs` > 1 to parallelize the above steps. Use `--n-procs 1` for debugging your product generator (better error Traceback). - -## Viewing the Results - -### Combined - -`trendify make all` outputs both static and interactive assets. All flavors of the `trendify make` command produce `data_products.json` files in the input directories and sorted products in a user-specified output directory. - -### Static Assets - -`trendify make static` outputs the following assets: - -- Static CSV and JPG files in the `$workdir/trendify_output/static_assets/` directory. - -### Interactive Assets - -`trendify make interactive` produces a JSON file to define an interactive Grafana dashboard that loads and displays the generated data. This functionality has been demonstrated, but is still very much in the early stages and being defined. Benefits include the ability to mouse-over data points and see tracked metadata (such as which run produced a given data point). - -!!! note "To Do" - - Add more documentation for how to start Grafana, serve the data, and view the data. diff --git a/docs/redraw_plots.sh b/docs/redraw_plots.sh new file mode 100644 index 0000000..5438a57 --- /dev/null +++ b/docs/redraw_plots.sh @@ -0,0 +1,17 @@ +#!/usr/bin/env bash + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +workdir="$SCRIPT_DIR/example_data" + +rm -f "$workdir/trendify/trendify.db*" + +trendify example-data -w "$workdir" -n 20 + +trendify run \ + -i "$workdir/models/*" \ + -g "$SCRIPT_DIR/example_generator.py:generate_records" \ + -o "$workdir/trendify" \ + -n 4 + +python "$SCRIPT_DIR/hooks.py" +python "$SCRIPT_DIR/../scripts/gen_ref_pages.py" diff --git a/docs/user-guide.md b/docs/user-guide.md new file mode 100644 index 0000000..1ea125a --- /dev/null +++ b/docs/user-guide.md @@ -0,0 +1,190 @@ +# Writing and Running a Record Generator + +!!! abstract + `trendify` turns a folder of raw run directories into plots and tables by mapping a single Python function, a **generator function**, over each one. This guide covers how to write that function and how to run it through the `trendify` CLI. + +--- + +## Generator Function + +A generator function takes a directory path as an argument and returns a `RecordList`: + +???+ example "Example Generator Function Handle" + ```python + --8<-- "docs/example_generator.py:handle" + ``` + +`trendify` calls this function once per run directory, passing that directory's `Path`. The function processes raw data, and defines and returns a list of `Record` objects (`Point2D`, `Trace2D`, `TableEntry`, `HistogramEntry`, `AxLine`, and so on), each carrying one or more **tags**. + +A tag is a string, an int, or a tuple of strings and ints, for example `"histogram"` or `("an_xy_plot", "trace_plot")`. Records that share a tag get aggregated onto the same plot or table when the data is rendered. + +Because of `trendify`'s unique approach to building plots, the order you define a record has no bearing on the generators execution or the plot's appearnce. + +???+ example + The example functions shown here are small record generators. In the example, it reads the `results.csv` that `trendify example-data` writes into each run directory (a `time` column plus one column per wave) and builds one example of every plottable record type. + + ```bash linenums="0" + trendify example-data --workdir ./data --n-folders 20 + ``` + + +### Scatter2D and Format2D + +A `Scatter2D` plots a bulk collection of `(x, y)` points sharing one `Marker` style, unconnected by a line. Use it for a large array of points from one generation call that all share a style, such as a raw scatter of measurements from one run. + +`Format2D` is itself a record: written once per tag, it configures that tag's axes (scale, limits, legend, grid, labels) for every other record sharing it: + +???+ example + ```python + --8<-- "docs/example_generator.py:scatter" + ``` + +
+ ![Scatter2D Result](./example_data/trendify/assets/signature_correlation.svg){ width="50%" height="auto" } +
A rendered result of `Scatter2D` and `Format2D`.
+
+ +### Trace2D + +A `Trace2D` draws an xy line and a `Pen` controls how it looks: + +???+ example + ```python + --8<-- "docs/example_generator.py:traces" + ``` + +### AxLine + +An `AxLine` draws a horizontal or vertical reference line. Giving it the same tag as the traces above (`"time_series"`) puts it on the same plot: + +???+ example + ```python + --8<-- "docs/example_generator.py:axline" + ``` + +
+ ![Trace2D and AxLine Result](./example_data/trendify/assets/time_series.svg){ width="50%" height="auto" } +
A rendered result of `Trace2D` and `AxLine`.
+
+ +### Point2D + +A `Point2D` is a single point, and unlike `Scatter2D` it's its own taggable, hoverable entity. This is the right choice when each point summarizes one run, aggregated across many runs that share a tag: + +???+ example + ```python + --8<-- "docs/example_generator.py:point" + ``` + +
+ ![Point2D Result](./example_data/trendify/assets/run_means_tracking.svg){ width="50%" height="auto" } +
A rendered result of `Point2D`.
+
+ +### Histogram Entry + +A `HistogramEntry` bins one value into a histogram. Every entry sharing a tag (here, one per run) is binned into the same histogram: + +???+ example + ```python + --8<-- "docs/example_generator.py:histogram" + ``` + +
+ ![HistogramEntry Result](./example_data/trendify/assets/harmonic_mean_distribution.svg){ width="50%" height="auto" } +
A rendered result of `HistogramEntry`.
+
+ +### Table Entry + +A `TableEntry` is a single `(row, col, value)` cell, collected into melted, pivot, and stats tables. `row` must be distinct per run, or entries from different runs collide on the same `(row, col)` pair: + +???+ example + ```python + --8<-- "docs/example_generator.py:table" + ``` + +???+ example "`TableEntry` Result" + + === "batch_means_table_stats.csv" + + --8<-- "docs/example_data/trendify/assets/batch_means_table_stats.md" + + === "batch_means_table_pivot.csv" + + --8<-- "docs/example_data/trendify/assets/batch_means_table_pivot.md" + + === "batch_means_table_melted.csv" + + --8<-- "docs/example_data/trendify/assets/batch_means_table_melted.md" + +### Putting it Together + +The generator function itself just calls each of the helpers above and returns the combined list: + +```python +--8<-- "docs/example_generator.py:generate_records" +``` + +A generator you write for real data follows the same shape: read the run directory's contents (CSV, JSON, or whatever format your raw data uses), build up a `RecordList`, tag each record according to how you want it grouped, and return the list. + +--- + +## Running the Generator + +The `trendify` CLI accepts a generator in the form `module:function`, `package.submodule.module:Class.method` (`@classmethod` or `@staticmethod`), or `/path/to/file.py:function`. + +### Generating Records + +`trendify generate` maps the generator over a set of input directories and writes the resulting records into a `trendify.db` under the given output directory: + +```bash linenums="0" +trendify generate \ + --input-directories "./data/models/*" \ + --record-generator "docs/example_generator.py:generate_records" \ + --output-directory "./data/trendify" +``` + +`--input-directories` accepts glob patterns and can be repeated to combine several. Use `--n-procs` to fan the generator call out across multiple worker processes. + +???+ question "But Why?" + This can be helpful if you are still debugging your generator, or you only care about viewing interactive assets (see the later section on [Trendify Viewer](#trendify-viewer)) + +### Rendering Assets + +`trendify render` reads the records already written to `trendify.db` and produces CSV tables and Matplotlib images, one set of assets per tag, in `/assets`: + +```bash linenums="0" +trendify render --output-directory ./data/trendify +``` + +### Running In One Step + +`trendify run` combines the two steps above: + +```bash linenums="0" +trendify run \ + --input-directories "./data/models/*" \ + --record-generator docs/example_generator.py:generate_records \ + --output-directory ./data/trendify +``` + +### Trendify Viewer + +`trendify viewer` launches a local web dashboard for browsing a `trendify.db` file's records interactively, by tag, table, or plot: + +```bash linenums="0" +trendify viewer ./data/trendify/trendify.db +``` + +--- + +!!! success + You can now write a `RecordGenerator` and run it end to end: + + - `generate` to write records + - `render` for static CSV tables and plots + - `run` to do both in one step + - `viewer` to browse the results in a live dashboard. + + Point `--record-generator` at your own function and `--input-directories` at your real raw data to automate record creation in your project. diff --git a/gallery/data/data_products.json b/gallery/data/data_products.json deleted file mode 100644 index 5e84cee..0000000 --- a/gallery/data/data_products.json +++ /dev/null @@ -1 +0,0 @@ -{"derived_from":null,"elements":[{"tags":["waves","sine"],"metadata":{},"format2d":{"title_fig":"Sine Wave with Reference Lines","title_legend":null,"title_ax":"Sine Function with Noise","label_x":"X 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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 6b3886c..0000000 --- a/gallery/gallery.py +++ /dev/null @@ -1,342 +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: {str(e)}") - 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')) \ No newline at end of file diff --git a/gallery/trendify/assets/static/analysis.jpg b/gallery/trendify/assets/static/analysis.jpg deleted file mode 100644 index 72e80d0..0000000 Binary files a/gallery/trendify/assets/static/analysis.jpg and /dev/null differ diff --git a/gallery/trendify/assets/static/clusters.jpg b/gallery/trendify/assets/static/clusters.jpg deleted file mode 100644 index 72e80d0..0000000 Binary files a/gallery/trendify/assets/static/clusters.jpg and /dev/null differ diff --git a/gallery/trendify/assets/static/comparison.jpg b/gallery/trendify/assets/static/comparison.jpg deleted file mode 100644 index 89a8b6b..0000000 Binary files a/gallery/trendify/assets/static/comparison.jpg and /dev/null differ diff --git a/gallery/trendify/assets/static/distributions.jpg b/gallery/trendify/assets/static/distributions.jpg deleted file mode 100644 index 89a8b6b..0000000 Binary files a/gallery/trendify/assets/static/distributions.jpg and /dev/null differ diff --git a/gallery/trendify/assets/static/distributions_melted.csv b/gallery/trendify/assets/static/distributions_melted.csv deleted file mode 100644 index 7b2654e..0000000 --- a/gallery/trendify/assets/static/distributions_melted.csv +++ /dev/null @@ -1,19 +0,0 @@ -row,col,value,unit -Normal,Mean,0.01, -Normal,Std Dev,1.02, -Normal,Skewness,0.05, -Uniform,Mean,0.00, -Uniform,Std Dev,1.15, -Uniform,Skewness,0.00, -Exponential,Mean,1.00, -Exponential,Std Dev,1.00, -Exponential,Skewness,2.00, -Normal,Mean,0.01, -Normal,Std Dev,1.02, -Normal,Skewness,0.05, -Uniform,Mean,0.00, -Uniform,Std Dev,1.15, -Uniform,Skewness,0.00, -Exponential,Mean,1.00, -Exponential,Std Dev,1.00, -Exponential,Skewness,2.00, diff --git a/gallery/trendify/assets/static/distributions_pivot.csv b/gallery/trendify/assets/static/distributions_pivot.csv deleted file mode 100644 index 5c08930..0000000 --- a/gallery/trendify/assets/static/distributions_pivot.csv +++ /dev/null @@ -1,4 +0,0 @@ -row,Mean,Skewness,Std Dev -Exponential,1.00,2.00,1.00 -Normal,0.01,0.05,1.02 -Uniform,0.00,0.00,1.15 diff --git a/gallery/trendify/assets/static/distributions_stats.csv b/gallery/trendify/assets/static/distributions_stats.csv deleted file mode 100644 index af1ce0d..0000000 --- a/gallery/trendify/assets/static/distributions_stats.csv +++ /dev/null @@ -1,4 +0,0 @@ -Name,min,mean,max,sigma3 -Mean,0.0,0.33666666666666667,1.0,1.7234558305915473 -Skewness,0.0,0.6833333333333332,2.0,3.421622422185125 -Std Dev,1.0,1.0566666666666666,1.15,0.2443358344574122 diff --git a/gallery/trendify/assets/static/sine.jpg b/gallery/trendify/assets/static/sine.jpg deleted file mode 100644 index daf8019..0000000 Binary files a/gallery/trendify/assets/static/sine.jpg and /dev/null differ diff --git a/gallery/trendify/assets/static/statistics_melted.csv b/gallery/trendify/assets/static/statistics_melted.csv deleted file mode 100644 index 7b2654e..0000000 --- a/gallery/trendify/assets/static/statistics_melted.csv +++ /dev/null @@ -1,19 +0,0 @@ -row,col,value,unit -Normal,Mean,0.01, -Normal,Std Dev,1.02, -Normal,Skewness,0.05, -Uniform,Mean,0.00, -Uniform,Std Dev,1.15, -Uniform,Skewness,0.00, -Exponential,Mean,1.00, -Exponential,Std Dev,1.00, -Exponential,Skewness,2.00, -Normal,Mean,0.01, -Normal,Std Dev,1.02, -Normal,Skewness,0.05, -Uniform,Mean,0.00, -Uniform,Std Dev,1.15, -Uniform,Skewness,0.00, -Exponential,Mean,1.00, -Exponential,Std Dev,1.00, -Exponential,Skewness,2.00, diff --git a/gallery/trendify/assets/static/statistics_pivot.csv b/gallery/trendify/assets/static/statistics_pivot.csv deleted file mode 100644 index 5c08930..0000000 --- a/gallery/trendify/assets/static/statistics_pivot.csv +++ /dev/null @@ -1,4 +0,0 @@ -row,Mean,Skewness,Std Dev -Exponential,1.00,2.00,1.00 -Normal,0.01,0.05,1.02 -Uniform,0.00,0.00,1.15 diff --git a/gallery/trendify/assets/static/statistics_stats.csv b/gallery/trendify/assets/static/statistics_stats.csv deleted file mode 100644 index af1ce0d..0000000 --- a/gallery/trendify/assets/static/statistics_stats.csv +++ /dev/null @@ -1,4 +0,0 @@ -Name,min,mean,max,sigma3 -Mean,0.0,0.33666666666666667,1.0,1.7234558305915473 -Skewness,0.0,0.6833333333333332,2.0,3.421622422185125 -Std Dev,1.0,1.0566666666666666,1.15,0.2443358344574122 diff --git a/gallery/trendify/assets/static/waves.jpg b/gallery/trendify/assets/static/waves.jpg deleted file mode 100644 index daf8019..0000000 Binary files a/gallery/trendify/assets/static/waves.jpg and /dev/null differ diff --git a/gallery/trendify/products/analysis/0.json b/gallery/trendify/products/analysis/0.json deleted file mode 100644 index 743347a..0000000 --- a/gallery/trendify/products/analysis/0.json +++ /dev/null @@ -1 +0,0 @@ -{"derived_from":null,"elements":[{"tags":["clusters","analysis"],"metadata":{},"format2d":{"title_fig":"Cluster Analysis","title_legend":null,"title_ax":"Two Clusters with Centroids","label_x":"X Position","label_y":"Y 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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/implementation_recommendation.py b/implementation_recommendation.py deleted file mode 100644 index 769f28e..0000000 --- a/implementation_recommendation.py +++ /dev/null @@ -1,74 +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... - # ... \ No newline at end of file diff --git a/improved_tag_selection.py b/improved_tag_selection.py deleted file mode 100644 index 31e6a5c..0000000 --- a/improved_tag_selection.py +++ /dev/null @@ -1,68 +0,0 @@ -""" -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: - """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 \ No newline at end of file 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/mkdocs.yml b/mkdocs.yml index bac9286..f5891ad 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -1,198 +1,220 @@ -site_name: Trendify +site_name: trendify site_url: https://talbotknighton.github.io/trendify/ -repo_url: https://github.com/TalbotKnighton/trendify -repo_name: TalbotKnighton/trendify -site_description: Data visualization for batch processes via data models. -site_author: Talbot Knighton +site_author: Talbot Knighton and David Gable +copyright: Copyright © 2026 Talbot Knighton and David Gable +repo_url: https://github.com/talbotknighton/trendify +repo_name: trendify +remote_branch: gh-pages +remote_name: origin -# edit_uri: blob/master/docs/ +hooks: + - docs/hooks.py + +watch: + - docs + - src + - scripts + +nav: + - Home: index.md + - User Guide: user-guide.md + - Code Reference: + - reference/trendify/index.md + - reference/trendify/cli.md + - reference/trendify/color.md + - reference/trendify/examples.md + - reference/trendify/log.md + - reference/trendify/pipeline.md + - reference/trendify/typing.md + - Base: + - reference/trendify/base/index.md + - reference/trendify/base/helpers.md + - reference/trendify/base/pen.md + - reference/trendify/base/record.md + - Formats: + - reference/trendify/formats/index.md + - reference/trendify/formats/format2d.md + - reference/trendify/formats/table.md + - Generator: + - reference/trendify/generator/index.md + - reference/trendify/generator/generate.md + - reference/trendify/generator/histogrammer.md + - reference/trendify/generator/render.md + - reference/trendify/generator/table_builder.md + - reference/trendify/generator/xy_data_plotter.md + - Plotting: + - reference/trendify/plotting/index.md + - reference/trendify/plotting/axline.md + - reference/trendify/plotting/figure.md + - reference/trendify/plotting/histogram.md + - reference/trendify/plotting/point.md + - reference/trendify/plotting/scatter.md + - reference/trendify/plotting/trace.md + - Store: + - reference/trendify/store/index.md + - reference/trendify/store/db.md + - reference/trendify/store/record_store.md + - reference/trendify/store/tags.md + - Styling: + - reference/trendify/styling/index.md + - reference/trendify/styling/grid.md + - reference/trendify/styling/legend.md + - reference/trendify/styling/marker.md + - Viewer: + - reference/trendify/viewer/index.md + - reference/trendify/viewer/app.md + - reference/trendify/viewer/plot_config.md + - reference/trendify/viewer/tag_tree.md + - Routes: + - reference/trendify/viewer/routes/index.md + - reference/trendify/viewer/routes/api.md + - reference/trendify/viewer/routes/pages.md theme: name: material - logo: assets/logo_pink_white_bg.svg - favicon: assets/logo_pink_white_bg.svg - features: - # - header.autohide - - search.suggest - - search.highlight - - navigation.tabs - - navigation.top - - navigation.tabs - - navigation.tabs.sticky - - navigation.sections - - navigation.tracking - # - navigation.indexes - - content.tabs - - content.tabs.link - - content.code.annotation - - content.code.copy - - content.tooltips - # - toc.integrate - color_mode: auto - user_color_mode_toggle: true - language: en + logo: assets/main-logo.svg + favicon: assets/main-logo-white-bg.svg + font: false + palette: - - scheme: default + - media: "(prefers-color-scheme: light)" + scheme: default + primary: custom + accent: custom toggle: - icon: material/toggle-switch-off-outline + icon: material/lightbulb-on name: Switch to dark mode - primary: pink - accent: blue - - scheme: slate + + - media: "(prefers-color-scheme: dark)" + scheme: slate + primary: custom + accent: custom toggle: - icon: material/toggle-switch + icon: material/lightbulb-outline name: Switch to light mode - primary: pink - accent: blue - highlightjs: true - hljs_languages: - - yaml - - django - -nav: - - Welcome: index.md - - Motivation: motivation.md - - Examples: - - Basic: example.md - - More: more_examples.md - - Recipe: recipe.md - - API and CLI: api_and_cli.md - - Code Reference: reference/ - - Planned Features: planned_features.md -# Add the extra section here -extra: - # Add your custom configurations or variables - disable_ssl_certificate_validation: true - -extra_css: - - css/code_select.css - - css/mkdocstrings.css - - css/logo.css + features: + - navigation.tabs + - navigation.instant + # - navigation.instant.preview + - navigation.instant.prefetch + - navigation.instant.progress + - navigation.top + - navigation.path + - navigation.sections + - navigation.tabs.sticky + - navigation.footer + - content.action.edit + - content.action.view + - content.code.copy + - content.code.select + - content.code.annotate + - content.tooltips + - search.highlight + - search.suggest + - search.share + - content.tabs.link + - content.footnote.tooltips -# exclude_docs: | -# *.py +plugins: + - search + - open-in-new-tab: + add_icon: true + # gen-files and literate-nav are MkDocs-only plugins; zensical ignores them. + # Reference pages are pre-generated by scripts/gen_ref_pages.py instead. + # - gen-files: + # scripts: + # - scripts/gen_ref_pages.py + # - literate-nav: + # nav_file: SUMMARY.md + # - section-index + - mkdocstrings: + handlers: + python: + paths: [src] + options: + modernize_annotations: true + backlinks: tree + docstring_options: + ignore_init_summary: true + merge_init_into_class: true + show_root_heading: true + show_root_full_path: false + show_source: true + show_bases: true + members_order: source + show_symbol_type_heading: true + show_symbol_type_toc: true + show_signature_annotations: true + separate_signature: true + summary: false # set to true to get summary tables + inherited_members: true + # inheritance_diagram_direction: TD + # show_inheritance_diagram: true + show_attribute_values: true + signature_crossrefs: true + extensions: + - pydantic: { schema: true } + inventories: + - https://docs.python.org/3/objects.inv + - https://mkdocstrings.github.io/griffe/objects.inv + - https://docs.pydantic.dev/latest/objects.inv + - https://numpy.org/doc/stable/objects.inv markdown_extensions: - - toc: - permalink: true + - abbr - admonition - - codehilite - - toc: - permalink: true - title: On this page - attr_list + - md_in_html - def_list - - tables + - pymdownx.arithmatex: + generic: true + - pymdownx.tasklist: + custom_checkbox: true + - toc: + permalink: true - pymdownx.highlight: - use_pygments: false + anchor_linenums: true + line_spans: __span pygments_lang_class: true + auto_title: true + linenums: true + - pymdownx.inlinehilite + - pymdownx.details - pymdownx.snippets - - pymdownx.superfences - - pymdownx.tabbed: - alternate_style: true - - callouts - - mdx_gh_links: - user: mkdocs - repo: mkdocs - - mkdocs-click - # - - pymdownx.snippets: - base_path: "docs/" - auto_append: - - includes/abbreviations.md + - pymdownx.blocks.caption - pymdownx.superfences: custom_fences: - name: mermaid class: mermaid format: !!python/name:pymdownx.superfences.fence_code_format - - abbr - - attr_list - - md_in_html - - def_list - - pymdownx.highlight: - anchor_linenums: true - - pymdownx.inlinehilite - - pymdownx.tasklist: - custom_checkbox: true - clickable_checkbox: false - - admonition - - pymdownx.arithmatex: - generic: true - - footnotes - - pymdownx.details - - pymdownx.superfences - pymdownx.tabbed: alternate_style: true + - pymdownx.emoji: + emoji_index: !!python/name:material.extensions.emoji.twemoji + emoji_generator: !!python/name:material.extensions.emoji.to_svg + - tables + - footnotes + - pymdownx.critic + - pymdownx.caret + - pymdownx.keys - pymdownx.mark - - markdown_include.include: - base_path: docs - -copyright: Copyright © 2024 Talbot Knighton, Maintained by the Talbot Knighton. + - pymdownx.tilde + - zensical.extensions.glightbox: + auto_themed: true + - zensical.extensions.macros -hooks: - - docs/hooks.py +extra: + generator: false -plugins: - # - git-revision-date-localized: - # enable_creation_date: true - - search - - glightbox - - gen-files: - scripts: - - scripts/gen_ref_pages.py - - section-index - - autorefs - - literate-nav: - nav_file: SUMMARY.md - implicit_index: true - - mkdocstrings: - handlers: - python: - import: - # https://stackoverflow.com/questions/52805115/certificate-verify-failed-unable-to-get-local-issuer-certificate - - https://docs.python.org/3/objects.inv - - https://matplotlib.org/objects.inv - - https://numpy.org/doc/stable/objects.inv - - https://pandas.pydata.org/docs/objects.inv - - https://docs.scipy.org/doc/scipy/objects.inv - - https://numpydantic.readthedocs.io/en/latest/objects.inv - - https://docs.pydantic.dev/latest/objects.inv - options: - extensions: - - griffe_pydantic: - schema: true - docstring_style: google - docstring_section_style: table - members_order: alphabetical - show_root_heading: true - show_source: true - separate_signature: true - show_signature: true - show_signature_annotations: true - signature_crossrefs: true - # - merge_init_into_class: true - show_if_no_docstring: false - # annotations_path: full - show_docstring_functions: true - heading_level: 2 - line_length: 100 - show_root_toc_entry: false - show_root_full_path: false - inherited_members: false - show_submodules: false - show_docstring_classes: true - docstring_options: - ignore_init_summary: false - paths: [src] - # - - exclude: - glob: - - snippets/* - - snippets +extra_javascript: + - javascripts/katex.js + - https://unpkg.com/katex@0/dist/katex.min.js + - https://unpkg.com/katex@0/dist/contrib/auto-render.min.js + - https://unpkg.com/mermaid@10.9.0/dist/mermaid.min.js + - https://unpkg.com/tablesort@5.3.0/dist/tablesort.min.js + - javascripts/tablesort.js -watch: - - src/ - - scripts/ - - docs/ +extra_css: + - css/extra.css + - https://unpkg.com/katex@0/dist/katex.min.css diff --git a/plotly_examples/histogram_1.py b/plotly_examples/histogram_1.py deleted file mode 100644 index 7c35d28..0000000 --- a/plotly_examples/histogram_1.py +++ /dev/null @@ -1,905 +0,0 @@ -"""Interactive histogram visualization with interactive legend features.""" -import dash -from dash import dcc, html -from dash.dependencies import Input, Output, State, MATCH, ALL -import plotly.graph_objects as go -import pandas as pd -import numpy as np -from typing import Dict, 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) \ No newline at end of file diff --git a/plotly_examples/sensitivity_1.py b/plotly_examples/sensitivity_1.py deleted file mode 100644 index a4a509a..0000000 --- a/plotly_examples/sensitivity_1.py +++ /dev/null @@ -1,733 +0,0 @@ -"""Interactive multi-column scatter plot interface with statistics.""" -import dash -from dash import dcc, html -from dash.dependencies import Input, Output, State, MATCH, 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 - -# # 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) \ No newline at end of file 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 ce0a3a6..0130ac4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,105 +1,154 @@ [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 = ">=3.12" dependencies = [ - "dash", - "filelock", - "flask", - "jinja2", - "pandas", - "plotly", - "pydantic", - "matplotlib", - "numpy", - "numpydantic", - "scipy", - "strenum", - "supervisor", - "waitress", - "grafana_api", - "streamlit", + "fastapi>=0.134.0", + "matplotlib>=3.11.0", + "numpy>=2.4.1", + "numpydantic>=1.7.0", + "plotly>=6.8.0", + "polars>=1.39.0", + "pydantic>=2.12.5", + "rich>=14.3.3", + "typer>=0.24.1", + "uvicorn>=0.41.0", +] + +keywords = [ + "Data", + "Products", + "Process", + "Post", + "Database", + "Visualization", + "Dashboard", + "Plotting", + "Trends", + "Matplotlib", + "Plotly", +] + +classifiers = [ + "Development Status :: 3 - Alpha", + "Intended Audience :: Developers", + "Intended Audience :: Science/Research", + "Topic :: Scientific/Engineering :: Visualization", + "Environment :: Web Environment", + "Environment :: Console", + "Typing :: Typed", + "Operating System :: OS Independent", + "Natural Language :: English", + "License :: OSI Approved :: MIT License", + "Programming Language :: Python :: 3 :: Only", + "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"] +exclude = ["src/trendify/viewer/templates/static/ts/**"] + +[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", + "httpx2>=2.5.0", + "mkinit>=1.2.0", + "pytest>=9.1.1", + "snakeviz>=2.2.2", + "griffe-pydantic>=1.2.0", + "mkdocs-gen-files>=0.6.0", + "mkdocs-glightbox>=0.5.2", + "mkdocs-literate-nav>=0.6.2", + "mkdocs-material>=9.7.1", + "mkdocs-open-in-new-tab>=1.0.8", + "mkdocs-section-index>=0.3.10", + "mkdocstrings[python]>=1.0.2", + "pyright[nodejs]>=1.1.411", + "ruff>=0.15.17", + "tabulate>=0.9.0", + "watchfiles>=1.1.1", + "zensical>=0.0.36", + "genbadge[coverage]>=1.1.3", + "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" +trendify = "trendify.cli:app" diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..6618cc6 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,1265 @@ +# This file was autogenerated by uv via the following command: +# uv export --frozen --output-file=requirements.txt +-e . +annotated-doc==0.0.4 \ + --hash=sha256:571ac1dc6991c450b25a9c2d84a3705e2ae7a53467b5d111c24fa8baabbed320 \ + --hash=sha256:fbcda96e87e9c92ad167c2e53839e57503ecfda18804ea28102353485033faa4 + # 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 + # httpx2 + # starlette + # watchfiles +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 +certifi==2026.6.17 \ + --hash=sha256:024c88eeec92ca068db80f02b8b07c9cef7b9fe261d1d535abfd5abd6f6af432 \ + --hash=sha256:2227dcbaafe0d2f59279d1762ddddc37783ed4354594f194ffc31d20f41fc3db + # via requests +charset-normalizer==3.4.7 \ + --hash=sha256:03853ed82eeebbce3c2abfdbc98c96dc205f32a79627688ac9a27370ea61a49c \ + --hash=sha256:0c96c3b819b5c3e9e165495db84d41914d6894d55181d2d108cc1a69bfc9cce0 \ + --hash=sha256:0ea948db76d31190bf08bd371623927ee1339d5f2a0b4b1b4a4439a65298703c \ + --hash=sha256:0f7eb884681e3938906ed0434f20c63046eacd0111c4ba96f27b76084cd679f5 \ + --hash=sha256:1c2aed2e5e41f24ea8ef1590b8e848a79b56f3a5564a65ceec43c9d692dc7d8a \ + --hash=sha256:203104ed3e428044fd943bc4bf45fa73c0730391f9621e37fe39ecf477b128cb \ + --hash=sha256:2257141f39fe65a3fdf38aeccae4b953e5f3b3324f4ff0daf9f15b8518666a2c \ + --hash=sha256:298930cec56029e05497a76988377cbd7457ba864beeea92ad7e844fe74cd1f1 \ + --hash=sha256:2d6eb928e13016cea4f1f21d1e10c1cebd5a421bc57ddf5b1142ae3f86824fab \ + --hash=sha256:3534e7dcbdcf757da6b85a0bbf5b6868786d5982dd959b065e65481644817a18 \ + --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=sha256:54523e136b8948060c0fa0bc7b1b50c32c186f2fceee897a495406bb6e311d2b \ + --hash=sha256:5649fd1c7bade02f320a462fdefd0b4bd3ce036065836d4f42e0de958038e116 \ + --hash=sha256:56be790f86bfb2c98fb742ce566dfb4816e5a83384616ab59c49e0604d49c51d \ + --hash=sha256:5b77459df20e08151cd6f8b9ef8ef1f961ef73d85c21a555c7eed5b79410ec10 \ + --hash=sha256:5ed6ab538499c8644b8a3e18debabcd7ce684f3fa91cf867521a7a0279cab2d6 \ + --hash=sha256:6178f72c5508bfc5fd446a5905e698c6212932f25bcdd4b47a757a50605a90e2 \ + --hash=sha256:64f02c6841d7d83f832cd97ccf8eb8a906d06eb95d5276069175c696b024b60a \ + --hash=sha256:67f6279d125ca0046a7fd386d01b311c6363844deac3e5b069b514ba3e63c246 \ + --hash=sha256:6c114670c45346afedc0d947faf3c7f701051d2518b943679c8ff88befe14f8e \ + --hash=sha256:708838739abf24b2ceb208d0e22403dd018faeef86ddac04319a62ae884c4f15 \ + --hash=sha256:715479b9a2802ecac752a3b0efa2b0b60285cf962ee38414211abdfccc233b41 \ + --hash=sha256:733784b6d6def852c814bce5f318d25da2ee65dd4839a0718641c696e09a2960 \ + --hash=sha256:752a45dc4a6934060b3b0dab47e04edc3326575f82be64bc4fc293914566503e \ + --hash=sha256:7579e913a5339fb8fa133f6bbcfd8e6749696206cf05acdbdca71a1b436d8e72 \ + --hash=sha256:7804338df6fcc08105c7745f1502ba68d900f45fd770d5bdd5288ddccb8a42d8 \ + --hash=sha256:80d04837f55fc81da168b98de4f4b797ef007fc8a79ab71c6ec9bc4dd662b15b \ + --hash=sha256:8778f0c7a52e56f75d12dae53ae320fae900a8b9b4164b981b9c5ce059cd1fcb \ + --hash=sha256:8d828b6667a32a728a1ad1d93957cdf37489c57b97ae6c4de2860fa749b8fc1e \ + --hash=sha256:92a0a01ead5e668468e952e4238cccd7c537364eb7d851ab144ab6627dbbe12f \ + --hash=sha256:a180c5e59792af262bf263b21a3c49353f25945d8d9f70628e73de370d55e1e1 \ + --hash=sha256:a277ab8928b9f299723bc1a2dabb1265911b1a76341f90a510368ca44ad9ab66 \ + --hash=sha256:a5fe03b42827c13cdccd08e6c0247b6a6d4b5e3cdc53fd1749f5896adcdc2356 \ + --hash=sha256:a89c23ef8d2c6b27fd200a42aa4ac72786e7c60d40efdc76e6011260b6e949c4 \ + --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:d6038d37043bced98a66e68d3aa2b6a35505dc01328cd65217cefe82f25def44 \ + --hash=sha256:e044c39e41b92c845bc815e5ae4230804e8e7bc29e399b0437d64222d92809dd \ + --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 + # genbadge + # mkdocs + # properdocs + # uvicorn + # zensical +colorama==0.4.6 \ + --hash=sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44 \ + --hash=sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6 + # via + # click + # mkdocs + # mkdocs-material + # properdocs + # pytest + # typer +contourpy==1.3.3 \ + --hash=sha256:023b44101dfe49d7d53932be418477dba359649246075c996866106da069af69 \ + --hash=sha256:07ce5ed73ecdc4a03ffe3e1b3e3c1166db35ae7584be76f65dbbe28a7791b0cc \ + --hash=sha256:083e12155b210502d0bca491432bb04d56dc3432f95a979b429f2848c3dbe880 \ + --hash=sha256:13b68d6a62db8eafaebb8039218921399baf6e47bf85006fd8529f2a08ef33fc \ + --hash=sha256:177fb367556747a686509d6fef71d221a4b198a3905fe824430e5ea0fda54eb5 \ + --hash=sha256:1cadd8b8969f060ba45ed7c1b714fe69185812ab43bd6b86a9123fe8f99c3263 \ + --hash=sha256:1fd43c3be4c8e5fd6e4f2baeae35ae18176cf2e5cced681cca908addf1cdd53b \ + --hash=sha256:22e9b1bd7a9b1d652cd77388465dc358dafcd2e217d35552424aa4f996f524f5 \ + --hash=sha256:283edd842a01e3dcd435b1c5116798d661378d83d36d337b8dde1d16a5fc9ba3 \ + --hash=sha256:2a2a8b627d5cc6b7c41a4beff6c5ad5eb848c88255fda4a8745f7e901b32d8e4 \ + --hash=sha256:2b7e9480ffe2b0cd2e787e4df64270e3a0440d9db8dc823312e2c940c167df7e \ + --hash=sha256:33c82d0138c0a062380332c861387650c82e4cf1747aaa6938b9b6516762e772 \ + --hash=sha256:348ac1f5d4f1d66d3322420f01d42e43122f43616e0f194fc1c9f5d830c5b286 \ + --hash=sha256:3c30273eb2a55024ff31ba7d052dde990d7d8e5450f4bbb6e913558b3d6c2301 \ + --hash=sha256:451e71b5a7d597379ef572de31eeb909a87246974d960049a9848c3bc6c41bf7 \ + --hash=sha256:459c1f020cd59fcfe6650180678a9993932d80d44ccde1fa1868977438f0b411 \ + --hash=sha256:4d00e655fcef08aba35ec9610536bfe90267d7ab5ba944f7032549c55a146da1 \ + --hash=sha256:4debd64f124ca62069f313a9cb86656ff087786016d76927ae2cf37846b006c9 \ + --hash=sha256:4feffb6537d64b84877da813a5c30f1422ea5739566abf0bd18065ac040e120a \ + --hash=sha256:50ed930df7289ff2a8d7afeb9603f8289e5704755c7e5c3bbd929c90c817164b \ + --hash=sha256:556dba8fb6f5d8742f2923fe9457dbdd51e1049c4a43fd3986a0b14a1d815fc6 \ + --hash=sha256:626d60935cf668e70a5ce6ff184fd713e9683fb458898e4249b63be9e28286ea \ + --hash=sha256:644a6853d15b2512d67881586bd03f462c7ab755db95f16f14d7e238f2852c67 \ + --hash=sha256:655456777ff65c2c548b7c454af9c6f33f16c8884f11083244b5819cc214f1b5 \ + --hash=sha256:66c8a43a4f7b8df8b71ee1840e4211a3c8d93b214b213f590e18a1beca458f7d \ + --hash=sha256:6afc576f7b33cf00996e5c1102dc2a8f7cc89e39c0b55df93a0b78c1bd992b36 \ + --hash=sha256:6c3d53c796f8647d6deb1abe867daeb66dcc8a97e8455efa729516b997b8ed99 \ + --hash=sha256:8153b8bfc11e1e4d75bcb0bff1db232f9e10b274e0929de9d608027e0d34ff8b \ + --hash=sha256:87acf5963fc2b34825e5b6b048f40e3635dd547f590b04d2ab317c2619ef7ae8 \ + --hash=sha256:88df9880d507169449d434c293467418b9f6cbe82edd19284aa0409e7fdb933d \ + --hash=sha256:92d9abc807cf7d0e047b95ca5d957cf4792fcd04e920ca70d48add15c1a90ea7 \ + --hash=sha256:95b181891b4c71de4bb404c6621e7e2390745f887f2a026b2d99e92c17892339 \ + --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: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:fd914713266421b7536de2bfa8181aa8c699432b6763a0ea64195ebe28bff6a9 \ + --hash=sha256:fde6c716d51c04b1c25d0b90364d0be954624a0ee9d60e23e850e8d48353d07a + # via matplotlib +coverage==7.15.0 \ + --hash=sha256:02adc79a920c73c647c5d117f55747df7f2de94571884758ce8bc58e04f0a796 \ + --hash=sha256:08fb028000ed0aaa0a4cbdfbb98be7cb42f370db973fbbb469733505ab20e13e \ + --hash=sha256:0bf781da64326b677be344df505171435b6f58716108606621d5d27d964fff8b \ + --hash=sha256:0bfc0be1f702042207a93a00523b1065ee1fe951e96edf311581c0bbc2e34888 \ + --hash=sha256:0e4950c9d6d3e39c64c991814ff315e2d0b9cb8152363594212c9e55208c0a8f \ + --hash=sha256:110cbdf8d2e216577312cf06ccf85539c0e5a5420ef747e4a4719b5e483c88cd \ + --hash=sha256:13fb759be317fdc62e0f56bffdf61cfcb45c7761ad6b71e3e583e71a67ae753c \ + --hash=sha256:20c8a976c365c8cb12f0cbd099508772ea41fb5fa80657a8506df0e11bd278c5 \ + --hash=sha256:20d9ccc4ebd0edc434d86dfd2a1dd2a8efa6b6b3073d0485a394fee86459ebb4 \ + --hash=sha256:27f31ecb458da3f859aab3f15ada871eb7a7768807d88df4a9f186bb17737970 \ + --hash=sha256:2bcf9afaf064172c6ec3c58a325a9957ad1178c05dd934e25f253321776e0676 \ + --hash=sha256:2c57a275078ee3fa185f83e400f765bc764a549de66d99b47881645cbd4ea629 \ + --hash=sha256:2c5d4619214f1d9993e7b00a8600d14614b7e9d84e89507460b126aa5e6559e5 \ + --hash=sha256:2c6f0fa473003905c6d5bac328ee4eba9fbea654f15bc24b8a3274b23363fa99 \ + --hash=sha256:31e5c3e70c85307ea35a12964e2e40f56ca2ee4b1c8c721ccf4609d17071080b \ + --hash=sha256:346771144d34f7fa84ec28386f78e0f31653f33cf35e19d253d5b35f9e8201da \ + --hash=sha256:363ab38cc78b615f11c9cac3cf1d7eef950c18b9fdedfb9066f59461dcf84d68 \ + --hash=sha256:3812c61afc6685c7999b39320779ab8f43b7a3081fdb0def39976e56fbdb9a21 \ + --hash=sha256:3bb3040e9f4bbe26fcb0cd7cc85ac63e630d3f3a9c74f027abf4caa27e706663 \ + --hash=sha256:41cb79af843222e11da87127ad0ecbfa878abadd0f770a4a99391a27d3887324 \ + --hash=sha256:52f9a4d2c4c56c8848bc2f524916698354b0211488b38c49ad9ae54f6cafbff6 \ + --hash=sha256:54fd9c53a5fafff509195f1b6a3f9be615d8e8362a3629ff1de23d270c03c86b \ + --hash=sha256:56da6a4cbe8f7e9e80bd072ca9cefe67d7106a440a7ec06519ec6507ac94ad19 \ + --hash=sha256:5be4caf3b28836f078abe700f8944dac4a65d78f16d6c600c89cb624e5535782 \ + --hash=sha256:5f764a3fa339bde6b3aa97657f5a6a3a9451e4a5b4ea98a2892c773a43525f77 \ + --hash=sha256:65a6b6164ee5c39e2f3803f314292d6c61a607ba7fee253d1e03c42dc3903502 \ + --hash=sha256:6c664c5444b1d970b1b2a450e21fb19ee5c9cfdf151ded2dda37260031cca0da \ + --hash=sha256:6cb3602054ccbe9f0d8c2dc04bbeba90d5719236e2cd06e042ddd6d3fc7b6e37 \ + --hash=sha256:6eb7c300fbed667fd6e3588eba71c1904cdb06110ca6fdf908c26bdd88b8e382 \ + --hash=sha256:75128817f95a5c45bb01d65fd2d8b9cb54bbe03d81608fb70e3e14b437ad56c2 \ + --hash=sha256:769e8ece11a596315ebf5aa7ec383aeeed016c091d2bf6363ffb996d41529092 \ + --hash=sha256:781a704516e2d8346fbbd5be6c6f3412dd824785146528b3a01816f26c081007 \ + --hash=sha256:7d2008989ef8fe54188d3f3bfa2e3099b025af11e90a6a1b9e7dc433d04263d8 \ + --hash=sha256:87b47553097ba185ed964866078e7e63adea9f5f51b5f39691c34f30afd21080 \ + --hash=sha256:94c9686bfe8a9a6810297aecbd99beaa3445f9e8dc2f80b1382cca0d86b64461 \ + --hash=sha256:9887bb428fe2d4cd4bee89bac1a6c9932f484afd5b36fbd4ff6ea5f825bb1f5e \ + --hash=sha256:9ac3fe7a1435986463eaa8ee253ae2f2a268709ba4ae5c7dd1f52a05391ad78f \ + --hash=sha256:9bd671c25f9d85f09d7ec481d0e43d5139f486c06a37139847a7ce569788af72 \ + --hash=sha256:a263060f1de0b4b74b4e089c2a70b8003b3781c733329a9c8fd54995328f9950 \ + --hash=sha256:ab282853ed5fbd64bbb162f19cb8fcb7087187508a6374b4f9c34ec1577c4e8f \ + --hash=sha256:aeefb2dd178fe7eee79f0ad25d75855cb35ee9ed472db2c5ea06f5b4fd00cec5 \ + --hash=sha256:b5fb23fa2de9dce1f5c36c09066d8fcda16cd96e8e26686caa2d7cb9b567d65c \ + --hash=sha256:b75ee5e8cb7575636ac598719b4307ac529ec8fcd79608a35c3cd4d4dada812d \ + --hash=sha256:baf06bc987115d6fb938d403f7eab684a057766c490367999a2b71a6883110c6 \ + --hash=sha256:bb25d825d885ca8036795dacfc3924d33091fc76d71ebc99420c6b79e77d96fa \ + --hash=sha256:bbcbb317c2e5ded5b21104af81c29f391be2af98d065693ffbe8d23949b948e5 \ + --hash=sha256:bd4a1b44bcb65ee29e947ac92bbee04956df3a6bfc6143641bb6cae7ede00fc9 \ + --hash=sha256:c48decf16e0dfd5b049c7d5e82200c23c08126719142998d4f172444e3d0529e \ + --hash=sha256:cc78d9843bd576fbe2118248258d485e968dc535f95ed504a7b0867ba9b51389 \ + --hash=sha256:cec79341dbe6281484024979976d0c7f22beae08b4a254655decd25d42cbe766 \ + --hash=sha256:d34a010905fb6401324ba016b5da03d574967f7b21ce48ea41e66f0f1f95f641 \ + --hash=sha256:d5cf007add5ab4bb8fa9f4c77e3732127c9e6cad501d7db43355fbfafca0be84 \ + --hash=sha256:dd58ad1404704303ca8d4f4b8a1095e7cbc7040ef17a66df1e6619aa10176430 \ + --hash=sha256:e4d0bb73455bf97ab243a8f12c37c686ccf1c13bb614b7b85f1d062f06f42b2c \ + --hash=sha256:f00d5ae1dd2fe13fb8186e3e7d37bcbd8b25c0d764ff7d1b32cef9be058510a8 \ + --hash=sha256:f0405f2ff97b1c4c0e782cb32e02f32369bcf2e6b618b591d67e1ea754575dfe \ + --hash=sha256:f58185f06edf6ad68ec9fb155d63ef650c82f3fbd7e1770e2867751fb13158f4 \ + --hash=sha256:f64627d55def5a43282d70e08396672692f77e4da610a5bb8bb4060b432b6859 \ + --hash=sha256:f948fd5ba1b9cbca91f0ae08b4c1ce2b139509149a435e2585d056d57d70bf01 \ + --hash=sha256:fb7dc0c3b7d8a1077abea0b8546ebc5e26d6ef6ecefc2f0f5ad2b8a53bdad837 \ + --hash=sha256:fe9c87ff42e5472d80d21704972e1f96e104a0a599d77c5e35db5a3c562e2571 \ + --hash=sha256:ffb31267816b93b075302248cc1737506081b4f163df4401e9df1a6424aafabe + # via pytest-cov +cycler==0.12.1 \ + --hash=sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30 \ + --hash=sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c + # via matplotlib +deepmerge==2.1.0 \ + --hash=sha256:07ca7a7b8935df596c512fa8161877c0487ac61f691c07766e7d71d2b23bdd2f \ + --hash=sha256:8f148339a91d680a75ecb74ade235d9e759a93df373a0b04e9d31c8666cfeb75 + # via zensical +defusedxml==0.7.1 \ + --hash=sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69 \ + --hash=sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61 + # via genbadge +fastapi==0.139.0 \ + --hash=sha256:99ab7b2d92223c76d6cf10757ab3f89d45b38267fc20b2a136cf02f6beac3145 \ + --hash=sha256:cf15e1e9e667ddb0ad63811e60bd11390d1aac838ca4a7a23f421807b2308189 + # via trendify +fonttools==4.63.0 \ + --hash=sha256:0eac00b9118c3c2f87d272e45341871c5b3066baa3c86897fa634a7c3fb59096 \ + --hash=sha256:1e874792a8212b44583ea02189d9e693906b2f78b261f372f95d6c563210ac1d \ + --hash=sha256:22135da48a348785c5e2d5d2d9d6bec5ed44adacbaeb9db12d9493bf6c6bfa68 \ + --hash=sha256:22693918177bd9ceabec4736d338045f357769416fc6b0b2508eefef75b08616 \ + --hash=sha256:27fdc65af8da6f88b9c6121c47a464cbe359fcfff7ff6fc2d37a1f395d755b78 \ + --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=sha256:7d76edbff9014094dbf03bd2d074709dfa6ec7aba13d838c937a2b33d2d6a86e \ + --hash=sha256:7d782fac32985914c351556f68ac0855391572bcd87de50e05970d3cd4c96fc5 \ + --hash=sha256:85be818f5506e8a7753153def2c9550178f0ecae6a47b5e0e8dbb23f7cc90380 \ + --hash=sha256:948428a275741f0b64b113c955425a953314f4b9ab9997f73a72c83e68e569c8 \ + --hash=sha256:9ced0bd02ac751dd6319b0da88aaef24414e3b0dbc32bb4f24944821a3741a27 \ + --hash=sha256:9e12f105d2b6342c559c298afb674006bb2893afc7102dcf8a1b55b0486b4e40 \ + --hash=sha256:a9faff9e0c1f76f9fd55899d2ce785832efebab37eb8ae13995853aef178bef0 \ + --hash=sha256:af2fd1664d00a397d75f806985ddb36282091c2131a73a6485c23b4a34722263 \ + --hash=sha256:ba04cb5891d4c0c21b6da95eda8d7b090021508a294fff33464fc7d241e0856b \ + --hash=sha256:bf00f21eb5fb721dbaf73d1e9da6d02a1af7768f2ebcf9798be98beab8ba90f6 \ + --hash=sha256:c1aaa4b9c75798400ac043ce04d74e7830376c85095a5a6ed7cba2f17a266bf4 \ + --hash=sha256:c2a2a42198b696a6f48fad91709afb55176e66a5e566131219dba372fb7f8c59 \ + --hash=sha256:caeb583deeb5168e694b65cda8b4ee62abedfa66cf88488734466f2366b9c4e0 \ + --hash=sha256:ccf41f2efdf56994d22d73bef4ced1052161958169428d06ba9724ea9e9a64be \ + --hash=sha256:cd7e9857e5e63738b9d9fd707bc1f59c8b09e5177726d23664db393c59bb08bd \ + --hash=sha256:ee08ebfa58f6e1aeff5697ab9582105bb620008c1caafb681e4c557e7483027b \ + --hash=sha256:ef3048ef05dbb552b89817713d9cac912e00d0fde4a3105c00d29e52e10c89af \ + --hash=sha256:fd1e3094f42d806d3d7c79162fc59e5910fcbe3a7360c385b8da969bc4493745 + # via matplotlib +genbadge==1.1.3 \ + --hash=sha256:2292ea9cc20af4463dfde952c6b15544fdab9d6e50945f63a42cc400c521fa74 \ + --hash=sha256:6e4316c171c6f0f84becae4eb116258340bdc054458632abc622d36b8040655e +ghp-import==2.1.0 \ + --hash=sha256:8337dd7b50877f163d4c0289bc1f1c7f127550241988d568c1db512c4324a619 \ + --hash=sha256:9c535c4c61193c2df8871222567d7fd7e5014d835f97dc7b7439069e2413d343 + # via + # mkdocs + # properdocs +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 + # httpcore2 + # uvicorn +httpcore2==2.5.0 \ + --hash=sha256:5ce35188de461d31e8d000bfb8ef8bf22c6c16587a211e5571deaa5e9bdf842a \ + --hash=sha256:88aa170137c17328d5ac44234f9fd10706466d5fb347f3edac4d39b91137b09d + # via httpx2 +httpx2==2.5.0 \ + --hash=sha256:3d2d4d9cf4b61f1a1f46a95947cfdb47e80cb56a2f91c6256ac8f58e4891df41 \ + --hash=sha256:e2df9cb4611021527ff8a675b1c320b610a2ec397acc8d6fe6e91df2d9b33c29 +idna==3.18 \ + --hash=sha256:7f952cbe720b688055e3f87de14f5c3e5fdaa8bc3928985c4077ca689de849a2 \ + --hash=sha256:ffb385a7e039654cef1ab9ef32c6fafe283c0c0467bba1d9029738ce4a14a848 + # via + # anyio + # httpx2 + # requests +iniconfig==2.3.0 \ + --hash=sha256:c76315c77db068650d49c5b56314774a7804df16fee4402c1f19d6d15d8c4730 \ + --hash=sha256:f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12 + # via pytest +jinja2==3.1.6 \ + --hash=sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d \ + --hash=sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67 + # via + # mkdocs + # mkdocs-material + # mkdocstrings + # properdocs + # zensical +kiwisolver==1.5.0 \ + --hash=sha256:012b1eb16e28718fa782b5e61dc6f2da1f0792ca73bd05d54de6cb9561665fc9 \ + --hash=sha256:0255a027391d52944eae1dbb5d4cc5903f57092f3674e8e544cdd2622826b3f0 \ + --hash=sha256:0b85aad90cea8ac6797a53b5d5f2e967334fa4d1149f031c4537569972596cb8 \ + --hash=sha256:0bf3acf1419fa93064a4c2189ac0b58e3be7872bf6ee6177b0d4c63dc4cea276 \ + --hash=sha256:0c50b89ffd3e1a911c69a1dd3de7173c0cd10b130f56222e57898683841e4f96 \ + --hash=sha256:0cbe94b69b819209a62cb27bdfa5dc2a8977d8de2f89dfd97ba4f53ed3af754e \ + --hash=sha256:0e3aafb33aed7479377e5e9a82e9d4bf87063741fc99fc7ae48b0f16e32bdd6f \ + --hash=sha256:1465387ac63576c3e125e5337a6892b9e99e0627d52317f3ca79e6930d889d15 \ + --hash=sha256:16b85d37c2cbb3253226d26e64663f755d88a03439a9c47df6246b35defbdfb7 \ + --hash=sha256:1b0feb50971481a2cc44d94e88bdb02cdd497618252ae226b8eb1201b957e368 \ + --hash=sha256:1d49a49ac4cbfb7c1375301cd1ec90169dfeae55ff84710d782260ce77a75a02 \ + --hash=sha256:1d9daea4ea6b9be74fe2f01f7fbade8d6ffab263e781274cffca0dba9be9eec9 \ + --hash=sha256:1dd9b0b119a350976a6d781e7278ec7aca0b201e1a9e2d23d9804afecb6ca681 \ + --hash=sha256:1f1489f769582498610e015a8ef2d36f28f505ab3096d0e16b4858a9ec214f57 \ + --hash=sha256:2a075bd7bd19c70cf67c8badfa36cf7c5d8de3c9ddb8420c51e10d9c50e94920 \ + --hash=sha256:332b4f0145c30b5f5ad9374881133e5aa64320428a57c2c2b61e9d891a51c2f3 \ + --hash=sha256:377815a8616074cabbf3f53354e1d040c35815a134e01d7614b7692e4bf8acfa \ + --hash=sha256:38f4a703656f493b0ad185211ccfca7f0386120f022066b018eb5296d8613e23 \ + --hash=sha256:3ac2360e93cb41be81121755c6462cff3beaa9967188c866e5fce5cf13170859 \ + --hash=sha256:3cdcb35dc9d807259c981a85531048ede628eabcffb3239adf3d17463518992d \ + --hash=sha256:413b820229730d358efd838ecbab79902fe97094565fdc80ddb6b0a18c18a581 \ + --hash=sha256:4432b835675f0ea7414aab3d37d119f7226d24869b7a829caeab49ebda407b0c \ + --hash=sha256:4db576bb8c3ef9365f8b40fe0f671644de6736ae2c27a2c62d7d8a1b4329f099 \ + --hash=sha256:4e7f886f47ab881692f278ae901039a234e4025a68e6dfab514263a0b1c4ae05 \ + --hash=sha256:4e9750bc21b886308024f8a54ccb9a2cc38ac9fa813bf4348434e3d54f337ff9 \ + --hash=sha256:5060731cc3ed12ca3a8b57acd4aeca5bbc2f49216dd0bec1650a1acd89486bcd \ + --hash=sha256:5092eb5b1172947f57d6ea7d89b2f29650414e4293c47707eb499ec07a0ac796 \ + --hash=sha256:5124d1ea754509b09e53738ec185584cc609aae4a3b510aaf4ed6aa047ef9303 \ + --hash=sha256:530a3fd64c87cffa844d4b6b9768774763d9caa299e9b75d8eca6a4423b31314 \ + --hash=sha256:56fa888f10d0f367155e76ce849fa1166fc9730d13bd2d65a2aa13b6f5424489 \ + --hash=sha256:58f812017cd2985c21fbffb4864d59174d4903dd66fa23815e74bbc7a0e2dd57 \ + --hash=sha256:5ae8e62c147495b01a0f4765c878e9bfdf843412446a247e28df59936e99e797 \ + --hash=sha256:5b233ea3e165e43e35dba1d2b8ecc21cf070b45b65ae17dd2747d2713d942021 \ + --hash=sha256:6783e069732715ad0c3ce96dbf21dbc2235ab0593f2baf6338101f70371f4028 \ + --hash=sha256:6ab8ba9152203feec73758dad83af9a0bbe05001eb4639e547207c40cfb52083 \ + --hash=sha256:70d593af6a6ca332d1df73d519fddb5148edb15cd90d5f0155e3746a6d4fcc65 \ + --hash=sha256:72ec46b7eba5b395e0a7b63025490d3214c11013f4aacb4f5e8d6c3041829588 \ + --hash=sha256:7a32f72973f0f950c1920475d5c5ea3d971b81b6f0ec53b8d0a956cc965f22e0 \ + --hash=sha256:7a4aa69609f40fce3cbc3f87b2061f042eee32f94b8f11db707b66a26461591a \ + --hash=sha256:7c60d3c9b06fb23bd9c6139281ccbdc384297579ae037f08ae90c69f6845c0b1 \ + --hash=sha256:80aa065ffd378ff784822a6d7c3212f2d5f5e9c3589614b5c228b311fd3063ac \ + --hash=sha256:893ff3a711d1b515ba9da14ee090519bad4610ed1962fbe298a434e8c5f8db53 \ + --hash=sha256:89fc958c702ee9a745e4700378f5d23fddbc46ff89e8fdbf5395c24d5c1452a3 \ + --hash=sha256:8df31fe574b8b3993cc61764f40941111b25c2d9fea13d3ce24a49907cd2d615 \ + --hash=sha256:9027d773c4ff81487181a925945743413f6069634d0b122d0b37684ccf4f1e18 \ + --hash=sha256:940dda65d5e764406b9fb92761cbf462e4e63f712ab60ed98f70552e496f3bf1 \ + --hash=sha256:b0f172dc8ffaccb8522d7c5d899de00133f2f1ca7b0a49b7da98e901de87bf2d \ + --hash=sha256:b2af221f268f5af85e776a73d62b0845fc8baf8ef0abfae79d29c77d0e776aaf \ + --hash=sha256:bb5136fb5352d3f422df33f0c879a1b0c204004324150cc3b5e3c4f310c9049f \ + --hash=sha256:bc4d8e252f532ab46a1de9349e2d27b91fce46736a9eedaa37beaca66f574ed4 \ + --hash=sha256:bdd3e53429ff02aa319ba59dfe4ceeec345bf46cf180ec2cf6fd5b942e7975e9 \ + --hash=sha256:be12f931839a3bdfe28b584db0e640a65a8bcbc24560ae3fdb025a449b3d754e \ + --hash=sha256:c0e1403fd7c26d77c1f03e096dc58a5c726503fa0db0456678b8668f76f521e3 \ + --hash=sha256:c31c13da98624f957b0fb1b5bae5383b2333c2c3f6793d9825dd5ce79b525cb7 \ + --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:db485b3847d182b908b483b2ed133c66d88d49cacf98fd278fadafe11b4478d1 \ + --hash=sha256:dda366d548e89a90d88a86c692377d18d8bd64b39c1fb2b92cb31370e2896bbd \ + --hash=sha256:e315e5ec90d88e140f57696ff85b484ff68bb311e36f2c414aa4286293e6dee0 \ + --hash=sha256:e4415a8db000bf49a6dd1c478bf70062eaacff0f462b92b0ba68791a905861f9 \ + --hash=sha256:e7a116ae737f0000343218c4edf5bd45893bfeaff0993c0b215d7124c9f77646 \ + --hash=sha256:e7c4c09a490dc4d4a7f8cbee56c606a320f9dc28cf92a7157a39d1ce7676a657 \ + --hash=sha256:ed3a984b31da7481b103f68776f7128a89ef26ed40f4dc41a2223cda7fb24819 \ + --hash=sha256:f18c2d9782259a6dc132fdc7a63c168cbc74b35284b6d75c673958982a378384 \ + --hash=sha256:f443b4825c50a51ee68585522ab4a1d1257fac65896f282b4c6763337ac9f5d2 \ + --hash=sha256:f6764a4ccab3078db14a632420930f6186058750df066b8ea2a7106df91d3203 \ + --hash=sha256:f7c7553b13f69c1b29a5bde08ddc6d9d0c8bfb84f9ed01c30db25944aeb852a7 \ + --hash=sha256:fa6248cd194edff41d7ea9425ced8ca3a6f838bfb295f6f1d6e6bb694a8518df \ + --hash=sha256:fa8eb9ecdb7efb0b226acec134e0d709e87a909fa4971a54c0c4f6e88635484c \ + --hash=sha256:fc20894c3d21194d8041a28b65622d5b86db786da6e3cfe73f0c762951a61167 \ + --hash=sha256:fc4d3f1fb9ca0ae9f97b095963bc6326f1dbfd3779d6679a1e016b9baaa153d3 \ + --hash=sha256:fd40bb9cd0891c4c3cb1ddf83f8bbfa15731a248fdc8162669405451e2724b09 + # via matplotlib +markdown==3.10.2 \ + --hash=sha256:994d51325d25ad8aa7ce4ebaec003febcce822c3f8c911e3b17c52f7f589f950 \ + --hash=sha256:e91464b71ae3ee7afd3017d9f358ef0baf158fd9a298db92f1d4761133824c36 + # via + # mkdocs + # mkdocs-autorefs + # mkdocs-material + # mkdocstrings + # properdocs + # pymdown-extensions + # zensical +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 + # mkdocs + # mkdocs-autorefs + # mkdocstrings + # properdocs +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=sha256:41635d7909d19e52e924a521dde6d8f670b0f53ab1d0e8c331fa831554f681d1 \ + --hash=sha256:446307e6b04b57b1f1239e228a1ec2af0d589a1008cebc3dfa3f5441d095cfb6 \ + --hash=sha256:565af866fd63e4bd3f987d580afe27c44c2552a3b3305f4ecbb85133601ea6f3 \ + --hash=sha256:652fb5696271d4c50f196d22a5ff4f8e4444c74f847423570d7dc0aa2bbd0159 \ + --hash=sha256:68c0c7be01b30dcca3638934f7f591df73401235cbdbf0d1ab1c71e7db7f8b57 \ + --hash=sha256:6a98f5476ce784a50ce09998f4ae1e6a9f25043cef8a480c98949902eda74620 \ + --hash=sha256:6ce3b839b34ae1f430b4616893a2945a2999debaa7e94e7e29a2a8bbf286f7b5 \ + --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:d9695457a467ff86d23f35037a43deb6f1134dd6d3e2ac8ce1e2087cff09ffb9 \ + --hash=sha256:dfabef0230d0697aa0d717385194dd41162e00207a68bf4abf94c2bf4c27dca0 \ + --hash=sha256:e6b3e64dea5062c570f04358e2711859f3531b459f29516274fbad889079e4f3 + # via trendify +mdurl==0.1.2 \ + --hash=sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8 \ + --hash=sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba + # via markdown-it-py +mergedeep==1.3.4 \ + --hash=sha256:0096d52e9dad9939c3d975a774666af186eda617e6ca84df4c94dec30004f2a8 \ + --hash=sha256:70775750742b25c0d8f36c55aed03d24c3384d17c951b3175d898bd778ef0307 + # via + # mkdocs + # mkdocs-get-deps +mkdocs==1.6.1 \ + --hash=sha256:7b432f01d928c084353ab39c57282f29f92136665bdd6abf7c1ec8d822ef86f2 \ + --hash=sha256:db91759624d1647f3f34aa0c3f327dd2601beae39a366d6e064c03468d35c20e + # via + # mkdocs-autorefs + # mkdocs-gen-files + # mkdocs-literate-nav + # mkdocs-material + # mkdocs-open-in-new-tab + # mkdocs-section-index + # mkdocstrings +mkdocs-autorefs==1.4.4 \ + --hash=sha256:834ef5408d827071ad1bc69e0f39704fa34c7fc05bc8e1c72b227dfdc5c76089 \ + --hash=sha256:d54a284f27a7346b9c38f1f852177940c222da508e66edc816a0fa55fc6da197 + # via + # mkdocstrings + # mkdocstrings-python +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-open-in-new-tab==1.0.8 \ + --hash=sha256:051d767a4467b12d89827e1fea0ec660b05b027c726317fe4fceee5456e36ad2 \ + --hash=sha256:3e0dad08cc9938b0b13097be8e0aa435919de1eeb2d1a648e66b5dee8d57e048 +mkdocs-section-index==0.3.12 \ + --hash=sha256:285635bf86c643b0fc7a343053d7a818049817bff4408f52b80c4367bd5e7268 \ + --hash=sha256:a1100039546beb4ebef63ce6fc91f3195fb9c0c3763105d4d3d7cd31e0a046eb +mkdocstrings==1.0.4 \ + --hash=sha256:3969a6515b77db65fd097b53c1b7aa4ae840bd71a2ee62a6a3e89503446d7172 \ + --hash=sha256:63464b4b29053514f32a1dbbf604e52876d5e638111b0c295ab7ed3cac73ca9b + # via mkdocstrings-python +mkdocstrings-python==2.0.5 \ + --hash=sha256:30c837bbff016549f659fcba6539ac351303f0fd7e713c89a040611072236e9d \ + --hash=sha256:3a4d92556ad39637e88af94a5374213af9a8e3040c3824ceaed04b486c017594 + # via mkdocstrings +mkinit==1.2.0 \ + --hash=sha256:172755988cdcdc5212d0b76b586ec4765c2bff9ae41266a93f5f4b962c3d2def \ + --hash=sha256:4b776ca991ccedbb202b079bd3e573d753121baa2a4f330324247325c9a1d346 +narwhals==2.23.0 \ + --hash=sha256:13e7ff5b4bb4a2f77b907c2e4d8a76e273dfc1323a3c997440a2f9fd26aed408 \ + --hash=sha256:769e7b9ab102c93d8fa019f6b4cd1a657909b04a20bf6210e5a35aae06814ae9 + # via plotly +nodeenv==1.10.0 \ + --hash=sha256:5bb13e3eed2923615535339b3c620e76779af4cb4c6a90deccc9e36b274d3827 \ + --hash=sha256:996c191ad80897d076bdfba80a41994c2b47c68e224c542b48feba42ba00f8bb + # via pyright +nodejs-wheel-binaries==24.16.0 \ + --hash=sha256:2f3036292811514ba847b3708492644764f88a833ac425c5f55007014308ddfd \ + --hash=sha256:3d0370fe7120ce9697a4f60d40480d2bd8808d9f30131458d5afc0040d4e5a51 \ + --hash=sha256:8308940b5edd0a50dc5267ea36ba21c9f668e83fe0d9f293937174d3a7e31c36 \ + --hash=sha256:85dc92bbb79c851569c5925dcc2a4c915a034efab375f99e4e7e6bbe9cca8342 \ + --hash=sha256:bb136be9944f0662dcf1120f45193a6b75b13fac378971a95cc42c9f879a81aa \ + --hash=sha256:c973cb69dc5fd16e6f6dc6e579e2c3d5534e2a1f57619dddf5ba070efa7dde37 \ + --hash=sha256:d9f8f677dcf30e37ac244f07869726abe043f01eb0f45722b1df31cc2af7093c \ + --hash=sha256:db8a8a76ebd2b28ecbfc9ad464baa3707241b9e050a30e2efdf6f60c0f886502 \ + --hash=sha256:f1a3d8f7b4491cbbd023ba3fc4e901fcca2d9fb80d57f24ba3890de8b1dbac03 + # via pyright +numpy==2.5.0 \ + --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 + # trendify +numpydantic==1.10.0 \ + --hash=sha256:1c62a445d305da69a5fa4510f7e7e1c25218f58fe462e5d59548042f1f769e66 \ + --hash=sha256:a17d5ccc3b893c4a2e539c81c2f18960d70884ad7fcaa09c9eb56a95e8daf4a3 + # via trendify +packaging==26.2 \ + --hash=sha256:5fc45236b9446107ff2415ce77c807cee2862cb6fac22b8a73826d0693b0980e \ + --hash=sha256:ff452ff5a3e828ce110190feff1178bb1f2ea2281fa2075aadb987c2fb221661 + # via + # matplotlib + # mkdocs + # plotly + # properdocs + # pytest +paginate==0.5.7 \ + --hash=sha256:22bd083ab41e1a8b4f3690544afb2c60c25e5c9a63a30fa2f483f6c60c8e5945 \ + --hash=sha256:b885e2af73abcf01d9559fd5216b57ef722f8c42affbb63942377668e35c7591 + # via mkdocs-material +pathspec==1.1.1 \ + --hash=sha256:17db5ecd524104a120e173814c90367a96a98d07c45b2e10c2f3919fff91bf5a \ + --hash=sha256:a00ce642f577bf7f473932318056212bc4f8bfdf53128c78bbd5af0b9b20b189 + # via + # mkdocs + # properdocs +pillow==12.3.0 \ + --hash=sha256:06ff022112bc9cbf83b60f8e028d94ad87b60621706487e65f673de61610ab59 \ + --hash=sha256:0740a512dc522224c77d9aa5a8d70d8b7d73fb91f2c21125d8d025d3b8990e45 \ + --hash=sha256:0847a763afefb695bc912d7c131e7e0632d4edc1d8698f58ddabec8e46b8b6d3 \ + --hash=sha256:0dd2064cbc55aaec028ef5fbb60fa47bb6c3e7918e07ff17935284b227a9d2df \ + --hash=sha256:0feb2e9d6ad6c9e3c06effe9d00f3f1e618a6643273576b016f591e9315a7139 \ + --hash=sha256:1182d52bc2d5e5d7d0949503aa7e36d12f42205dc287e4883f407b1988820d39 \ + --hash=sha256:164b31cd1a0490ab6efae01aa5df49da7061be0af1b30e035b6e9a1bfe34ee6e \ + --hash=sha256:1657923d2d45afb66526e5b933e5b3052e6bdea196c90d3abb2424e18c77dae8 \ + --hash=sha256:186941b6aef820ad110fb01fb06eb925374dc3a21b17e37ec9a53b250c6fe2d1 \ + --hash=sha256:1cca606cd25738df4ed873d5ad46bbdb3d83b5cbca291f6b4ff13a4df6b0bbe8 \ + --hash=sha256:21900ce7ba264168cd50defae43cd75d25c833ad4ad6e73ffc5596d12e25ac89 \ + --hash=sha256:23aceaa007d6172b02c277f0cd359c79492bbb14f7072b4ede9fbcaf20648130 \ + --hash=sha256:24870b09b224f7ae3c39ed07d10e819d06f8720bc551847b1d623832b5b0e28d \ + --hash=sha256:251bf95b67017e27b13d82f5b326234ca62d70f9cf4c2b9032de2358a3b12c7b \ + --hash=sha256:28ce87c5ab450a9dd970b52e5aca5fe63ed432d18a2eaddd1979a00a1ba24ace \ + --hash=sha256:30f2aa603c41533cc25c05acd0da21636e84a315768feb631c937177db558931 \ + --hash=sha256:331b624368d4f1d069149002f25f44bc61c8919ce8ddb3c45bdad8f6e2d89510 \ + --hash=sha256:3b8182a766685eaa002637e28b4ec8d6b18819a0c71f579bf0dbaa5830297cce \ + --hash=sha256:3edce1d53195db527e0191f84b71d02022de0540bf43a16ed734ed7537b07385 \ + --hash=sha256:446c34dcc4324b084a53b705127dc15717b22c5e140ae0a3c38349d4efec071e \ + --hash=sha256:4998562bf62a445225f22e07c896bb04b35b1b1f2eb6d760584c9c51d7a5f78c \ + --hash=sha256:4b0a7fe987b14c31ebda6083f74f22b561fd3739bc0ac51e019622e3d72668c7 \ + --hash=sha256:4e8c2a84d977f50b9daed6eeaf3baef67d00d5d74d932288f02cb94518ee3ace \ + --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=sha256:962864dc93511324d51ddbb5b9f8731bf71675b93ca612a07441896f4688fb8c \ + --hash=sha256:9cf95fe4d0f84c82d282745d9bb08ad9f926efa00be4697e767b814ce40d4330 \ + --hash=sha256:9e881fca225083806662a5c43d627d215f258ff43c890f831966c7d7ba9c7402 \ + --hash=sha256:a2b55dd6b2a4c4b7d87ffa56bdb33fdc5fdb9a462173861a7bc097f17d91cb09 \ + --hash=sha256:a45650e8ce7fafffd731db8550230db6b0d306d181a90b67d3e6bca2f1990930 \ + --hash=sha256:a876864214e136f0eb367788dbd7df045f4806801518e2cfe9e13229cfe06d8f \ + --hash=sha256:ae26d61dfa7a47befdc7572b521024e8745f3d809bd95ca9505a7bba9ef849ec \ + --hash=sha256:af8d94b0db561cf68b88a267c5c44b49e134f525d0dc2cb7ed413a66bc23559a \ + --hash=sha256:b629de27fda84b42cde7edef0d85f13b958b47f6e9bbcbba9b673c562a89bd8b \ + --hash=sha256:ba09209fbe443b4acccebe845d8a138b89a8f4fbaeedd44953490b5315d5e965 \ + --hash=sha256:ba54cfebe86920a559a7c4d6b9050791c20513650a1952ebe3368c7dc70306f8 \ + --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:e91206ee562682b51b98ef4b26a6ef48fd84e15fd4c4bc5ec768eb641d206838 \ + --hash=sha256:e9871b1ffbfa9656b60aeee92ed5136a5742696006fa322b29ea3d8da0ecc9cf \ + --hash=sha256:e9aeb04d6aef139de265b29683e119b638208f88cf73cdd1658aa07221165321 \ + --hash=sha256:f13c32a3abd6079a66d9526e18dad9b6d280384d49d7c54040cd57b6424041d9 \ + --hash=sha256:f7401aebd7f581d7f83a439d87d474999317ee099218e5ad25d125290990ba65 \ + --hash=sha256:fa4ecea169a355be7a3ade2c783e2ed12f0e40d2c5621cda8b3297faf7fbb9f5 \ + --hash=sha256:fdafc9cce40277e0f7a0feabce0ee50dd2fa1800f3b38015e51296b5e814048d \ + --hash=sha256:fe3cca2e4e8a592be0f269a1ca4835c25199d9f3ce815c8491048f785b0a0198 \ + --hash=sha256:ffd0c5368496f41b0944be820fcb7a838aa6e623d250b01acf2643939c3f99d7 + # via + # genbadge + # matplotlib +platformdirs==4.10.0 \ + --hash=sha256:31e761a6a0ca04faf7353ea759bdba55652be214725111e5aac52dfa29d4bef7 \ + --hash=sha256:fb516cdb12eb0d857d0cd85a7c57cea4d060bee4578d6cf5a14dfdf8cbf8784a + # via + # mkdocs-get-deps + # properdocs +plotly==6.8.0 \ + --hash=sha256:13c5c4a0f70b74cab1913eda0de49b826df5931708eb6f9c3010040614700ec8 \ + --hash=sha256:e088e7ddc68d4f70e3d66659224727a45296d71d2b8284181862d3d8f1f0d88f + # via trendify +pluggy==1.6.0 \ + --hash=sha256:7dcc130b76258d33b90f61b658791dede3486c3e6bfb003ee5c9bfb396dd22f3 \ + --hash=sha256:e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746 + # via + # pytest + # pytest-cov +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 +properdocs==1.6.7 \ + --hash=sha256:6fa0cfa2e01bf338f684892c8a506cf70ea88ae7f3479c933b6fa20168101cbd \ + --hash=sha256:adc7b16e562890af0e098a7e5b02e3a81c20894a87d6a28d345c9300de73c26e + # via + # mkdocs-gen-files + # mkdocs-literate-nav + # mkdocs-section-index +pydantic==2.13.4 \ + --hash=sha256:45a282cde31d808236fd7ea9d919b128653c8b38b393d1c4ab335c62924d9aba \ + --hash=sha256:c40756b57adaa8b1efeeced5c196f3f3b7c435f90e84ea7f443901bec8099ef6 + # via + # fastapi + # numpydantic + # trendify +pydantic-core==2.46.4 \ + --hash=sha256:00c603d540afdd6b80eb39f078f33ebd46211f02f33e34a32d9f053bba711de0 \ + --hash=sha256:0186750b482eefa11d7f435892b09c5c606193ef3375bcf94aa00ae6bfb66262 \ + --hash=sha256:0cbe8b01f948de4286c74cdd6c667aceb38f5c1e26f0693b3983d9d74887c65e \ + --hash=sha256:184c081504d17f1c1066e430e117142b2c77d9448a97f7b65c6ac9fd9aee238d \ + --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=sha256:4fc73cb559bdb54b1134a706a2802a4cddd27a0633f5abb7e53056268751ac6a \ + --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=sha256:6b3ace8194b0e5204818c92802dcdca7fc6d88aabbb799d7c795540d9cd6d292 \ + --hash=sha256:7027560ee92211647d0d34e3f7cd6f50da56399d26a9c8ad0da286d3869a53f3 \ + --hash=sha256:7283d57845ecf5a163403eb0702dfc220cc4fbdd18919cb5ccea4f95ee1cdab4 \ + --hash=sha256:7a5f930472650a82629163023e630d160863fce524c616f4e5186e5de9d9a49b \ + --hash=sha256:811ff8e9c313ab425368bcbb36e5c4ebd7108c2bbf4e4089cfbb0b01eff63fac \ + --hash=sha256:8233f2947cf85404441fd7e0085f53b10c93e0ee78611099b5c7237e36aacbf7 \ + --hash=sha256:82cf5301172168103724d49a1444d3378cb20cdee30b116a1bd6031236298a5d \ + --hash=sha256:85bb3611ff1802f3ee7fdd7dbff26b56f343fb432d57a4728fdd49b6ef35e2f4 \ + --hash=sha256:8d0820e8192167f80d88d64038e609c31452eeca865b4e1d9950a27a4609b00b \ + --hash=sha256:8daafc69c93ee8a0204506a3b6b30f586ef54028f52aeeeb5c4cfc5184fd5914 \ + --hash=sha256:926c9541b14b12b1681dca8a0b75feb510b06c6341b70a8e500c2fdcff837cce \ + --hash=sha256:9401557acd873c3a7f3eb9383edef8ac4968f9510e340f4808d427e75667e7b4 \ + --hash=sha256:9551187363ffc0de2a00b2e47c25aeaeb1020b69b668762966df15fc5659dd5a \ + --hash=sha256:962ccbab7b642487b1d8b7df90ef677e03134cf1fd8880bf698649b22a69371f \ + --hash=sha256:97e7cf2be5c77b7d1a9713a05605d49460d02c6078d38d8bef3cbe323c548424 \ + --hash=sha256:9aa768456404a8bf48a4406685ac2bec8e72b62c69313734fa3b73cf33b3a894 \ + --hash=sha256:9d56801be94b86a9da183e5f3766e6310752b99ff647e38b09a9500d88e46e76 \ + --hash=sha256:9fa8ae11da9e2b3126c6426f147e0fba88d96d65921799bb30c6abd1cb2c97fb \ + --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:c94f0688e7b8d0a67abf40e57a7eaaecd17cc9586706a31b76c031f63df052b4 \ + --hash=sha256:cbaf13819775b7f769bf4a1f066cb6df7a28d4480081a589828ef190226881cd \ + --hash=sha256:cd2213145bcc2ba85884d0ac63d222fece9209678f77b9b4d76f054c561adb28 \ + --hash=sha256:ce5c1d2a8b27468f433ca974829c44060b8097eedc39933e3c206a90ee49c4a9 \ + --hash=sha256:e739fee756ba1010f8bcccb534252e85a35fe45ae92c295a06059ce58b74ccd3 \ + --hash=sha256:e846ae7835bf0703ae43f534ab79a867146dadd59dc9ca5c8b53d5c8f7c9ef02 \ + --hash=sha256:e9c26f834c65f5752f3f06cb08cb86a913ceb7274d0db6e267808a708b46bc89 \ + --hash=sha256:f027324c56cd5406ca49c124b0db10e56c69064fec039acc571c29020cc87c76 \ + --hash=sha256:f99626688942fb746e545232e7726926f3be91b5975f8b55327665fafda991c7 \ + --hash=sha256:fbdb89b3e1c94a30cc5edfce477c6e6a5dc4d8f84665b455c27582f211a1c72c \ + --hash=sha256:fc3e9034a63de20e15e8ade85358bc6efc614008cab72898b4b4952bea0509ff + # via pydantic +pygments==2.20.0 \ + --hash=sha256:6757cd03768053ff99f3039c1a36d6c0aa0b263438fcab17520b30a303a82b5f \ + --hash=sha256:81a9e26dd42fd28a23a2d169d86d7ac03b46e2f8b59ed4698fb4785f946d0176 + # via + # mkdocs-material + # pytest + # rich + # zensical +pymdown-extensions==11.0.1 \ + --hash=sha256:db3943a62bab7e03af1364f0c4083e64b91fb097675a4b6cceccfbe9a77e5eb2 \ + --hash=sha256:dd2905ae6fc5b75582fafb139a1266ffc754705efa902aa50067fa7ff4f94ec0 + # via + # mkdocs-material + # mkdocstrings + # zensical +pyparsing==3.3.2 \ + --hash=sha256:850ba148bd908d7e2411587e247a1e4f0327839c40e2e5e6d05a007ecc69911d \ + --hash=sha256:c777f4d763f140633dcb6d8a3eda953bf7a214dc4eff598413c070bcdc117cbc + # via matplotlib +pyright==1.1.411 \ + --hash=sha256:d885a0551f2e763b089a02702174e7f4ba77548cddabc972ab86d1f7f1b0f998 \ + --hash=sha256:dc7c72a8e2700c55baa127554040e067041ea53ccfd50bf96308cc4291c7d5d9 +pytest==9.1.1 \ + --hash=sha256:1088fbde8f2b49d95a549a195707afa7a76a3ce9bcadc26b6d71f0ffda5fe313 \ + --hash=sha256:37a86b45efb9a47a61a36449063e8e18d0cab3161329fc099eb21783169c4f0c + # via pytest-cov +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 +pyyaml==6.0.3 \ + --hash=sha256:00c4bdeba853cc34e7dd471f16b4114f4162dc03e6b7afcc2128711f0eca823c \ + --hash=sha256:02893d100e99e03eda1c8fd5c441d8c60103fd175728e23e431db1b589cf5ab3 \ + --hash=sha256:0f29edc409a6392443abf94b9cf89ce99889a1dd5376d94316ae5145dfedd5d6 \ + --hash=sha256:16249ee61e95f858e83976573de0f5b2893b3677ba71c9dd36b9cf8be9ac6d65 \ + --hash=sha256:2283a07e2c21a2aa78d9c4442724ec1eb15f5e42a723b99cb3d822d48f5f7ad1 \ + --hash=sha256:34d5fcd24b8445fadc33f9cf348c1047101756fd760b4dacb5c3e99755703310 \ + --hash=sha256:41715c910c881bc081f1e8872880d3c650acf13dfa8214bad49ed4cede7c34ea \ + --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=sha256:66e1674c3ef6f541c35191caae2d429b967b99e02040f5ba928632d9a7f0f065 \ + --hash=sha256:6adc77889b628398debc7b65c073bcb99c4a0237b248cacaf3fe8a557563ef6c \ + --hash=sha256:79005a0d97d5ddabfeeea4cf676af11e647e41d81c9a7722a193022accdb6b7c \ + --hash=sha256:7c6610def4f163542a622a73fb39f534f8c101d690126992300bf3207eab9764 \ + --hash=sha256:7f047e29dcae44602496db43be01ad42fc6f1cc0d8cd6c83d342306c32270196 \ + --hash=sha256:8d1fab6bb153a416f9aeb4b8763bc0f22a5586065f86f7664fc23339fc1c1fac \ + --hash=sha256:8da9669d359f02c0b91ccc01cac4a67f16afec0dac22c2ad09f46bee0697eba8 \ + --hash=sha256:8dc52c23056b9ddd46818a57b78404882310fb473d63f17b07d5c40421e47f8e \ + --hash=sha256:9149cad251584d5fb4981be1ecde53a1ca46c891a79788c0df828d2f166bda28 \ + --hash=sha256:93dda82c9c22deb0a405ea4dc5f2d0cda384168e466364dec6255b293923b2f3 \ + --hash=sha256:96b533f0e99f6579b3d4d4995707cf36df9100d67e0c8303a0c55b27b5f99bc5 \ + --hash=sha256:a33284e20b78bd4a18c8c2282d549d10bc8408a2a7ff57653c0cf0b9be0afce5 \ + --hash=sha256:a80cb027f6b349846a3bf6d73b5e95e782175e52f22108cfa17876aaeff93702 \ + --hash=sha256:b3bc83488de33889877a0f2543ade9f70c67d66d9ebb4ac959502e12de895788 \ + --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 + # mkdocs + # mkdocs-get-deps + # properdocs + # pymdown-extensions + # pyyaml-env-tag + # zensical +pyyaml-env-tag==1.1 \ + --hash=sha256:17109e1a528561e32f026364712fee1264bc2ea6715120891174ed1b980d2e04 \ + --hash=sha256:2eb38b75a2d21ee0475d6d97ec19c63287a7e140231e4214969d0eac923cd7ff + # via + # mkdocs + # properdocs +requests==2.34.2 \ + --hash=sha256:2a0d60c172f83ac6ab31e4554906c0f3b3588d37b5cb939b1c061f4907e278e0 \ + --hash=sha256:f288924cae4e29463698d6d60bc6a4da69c89185ad1e0bcc4104f584e960b9ed + # via + # genbadge + # mkdocs-material +rich==15.0.0 \ + --hash=sha256:33bd4ef74232fb73fe9279a257718407f169c09b78a87ad3d296f548e27de0bb \ + --hash=sha256:edd07a4824c6b40189fb7ac9bc4c52536e9780fbbfbddf6f1e2502c31b068c36 + # via + # trendify + # typer +ruff==0.15.20 \ + --hash=sha256:00e188c53e499c3c1637f73c91dcf2fb56d576cab76ce1be50a27c4e80e37078 \ + --hash=sha256:01cc00dd58f0df339d0e902219dd53990ea99996a0344e5d9cc8d45d5307e460 \ + --hash=sha256:1416eb04349192646b54de98f146c4f59afe37d0decfc02c3cbbf396f3a28566 \ + --hash=sha256:2d2374caa2f2c2f9e2b7da0a50802cfb8b79f55a9b5e49379f564544fbf56487 \ + --hash=sha256:2f5b2a6d614e8700388806a14996c40fab2c47b819ef57d790a34878858ed9ca \ + --hash=sha256:309809086c2acb67624950a3c8133e80f32d0d3e27106c0cd60ff26657c9f24b \ + --hash=sha256:3413bb3c3d2ca6a8208f1f4809cd2dca3c6de6d0b491c0e70847672bde6e6efd \ + --hash=sha256:5b9c0c367ad8e5d0d5b5b8537864c469a0a0e55417aadfbeca41fa61333be9f4 \ + --hash=sha256:9ebd1fd9b9c95fc0bd7b2761aebec1f030013d2e193a2901b224af68fe47251b \ + --hash=sha256:a1ed17b65293e0c2f22fc387bc13198a5de94bf4429589b0ff6946b0feaf21a3 \ + --hash=sha256:a525c81c70fb0380344dd1d8745d8cc1c890b7fc94a58d5a07bd8eb9557b8415 \ + --hash=sha256:b6df3b1e4610432f0386dba04d853b5f08cbbc903410c6fcc02f620f05aff53c \ + --hash=sha256:bd7ec42b3bb3da066488db093308a69c4ac5ee6d2af333a86ba6e2eb2e7dd44b \ + --hash=sha256:c5b16cdd67ca108185cd36dce98c576350c03b1660a751de725fb049193a0632 \ + --hash=sha256:e1a36ad0eb77fba9aabfb69ede54de6f376d04ac18ebea022847046d340a8267 \ + --hash=sha256:e89f198a1ea6ef0d727c1cf16088bc91a6cb0ab947dedc966715691647186eae \ + --hash=sha256:ed65ef510e43a137207e0f01cfcf998aeddb1aeeda5c9d35023e910284d7cf21 \ + --hash=sha256:f701305e66b38ea6c91882490eb73459796808e4c6362a1b765255e0cdcd4053 +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=sha256:4b0e1ad8b3d3d11bc173d33bb0fcf9d3ef8c667f1f3debd31ac8e9e3880ee174 \ + --hash=sha256:4d41b7e95ab0e025053efcba71267af12a6c12bc624e3bdf5dd83c6534f3d696 \ + --hash=sha256:5d5b5437ce7548e7bc0b7712114de07dd0e4f94a30b06c968a6344148621df50 \ + --hash=sha256:6517b40e41ae5cc7756f92d88c59f178eb4a3683c7ce39c66bd5617587f628e5 \ + --hash=sha256:67f826152635521e1751665e315f3be82d027fe79d6446bf8434e0f7069e55db \ + --hash=sha256:68e1ef717b47f5cdcd1b151b2176d7184c38cb3772f8964509979840575203ef \ + --hash=sha256:794a10f0c2cb9662ad85c5a970a72ca072057d68f7f0b3cf7b3230e5f2a8f221 \ + --hash=sha256:7c596b424c55ae87003140f55e6aa6f88e060b645781fa69e947ef60691b2bde \ + --hash=sha256:88475ef523fe5426d113e8319eeb741806a51cc025840f337661734c65cf1aa4 \ + --hash=sha256:89764b4d1e32d38e635dfb270a639fc707af4315b863fd161357a517321e5046 \ + --hash=sha256:8b57c64690e3c86b5d07386e2e598f4d3b4990144b1d5717db7038d0b675a97e \ + --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:c1933f53fe7410777b3373b9900010ae8d392a6d245f019d8f011d12f700e389 \ + --hash=sha256:c4b4b7c5d09a20539d369891332a107a869ee170453254048fbc18f893deb4f9 \ + --hash=sha256:d42566a7e649d4e5461e763a241f46542df2613876422e0530bc59999063f36d \ + --hash=sha256:d60cabbf1899916a6389fa36f2908b1a76e00dd044f710ae3dd2d0b14919dfc7 \ + --hash=sha256:ec333fe02c4b7d8a03c0aa58c7c2265edd3312b1ef309f03efd020f595f6dae1 + # via mkdocs-glightbox +setuptools==83.0.0 \ + --hash=sha256:025bccbbf0fa05b6192bc64ae1e7b16e001fd6d6d4d5de03c97b1c1ade523bef \ + --hash=sha256:29b23c360f22f414dc7336bb39178cc7bcbf6021ed2733cde173f09dba19abb3 + # via genbadge +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 +snakeviz==2.2.2 \ + --hash=sha256:08028c6f8e34a032ff14757a38424770abb8662fb2818985aeea0d9bc13a7d83 \ + --hash=sha256:77e7b9c82f6152edc330040319b97612351cd9b48c706434c535c2df31d10ac5 +starlette==1.3.1 \ + --hash=sha256:05d0213193f2fbaae60e2ecb593b4add4262ad4e46536b54abe36f11a71724e0 \ + --hash=sha256:c7372aae11c3c3f26a42df7bd626cec2f47d03483d261d369516a615a53714c6 + # via fastapi +tabulate==0.10.0 \ + --hash=sha256:e2cfde8f79420f6deeffdeda9aaec3b6bc5abce947655d17ac662b126e48a60d \ + --hash=sha256:f0b0622e567335c8fabaaa659f1b33bcb6ddfe2e496071b743aa113f8774f2d3 +tomli==2.4.1 \ + --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=sha256:4b605484e43cdc43f0954ddae319fb75f04cc10dd80d830540060ee7cd0243cd \ + --hash=sha256:504aa796fe0569bb43171066009ead363de03675276d2d121ac1a4572397870f \ + --hash=sha256:51529d40e3ca50046d7606fa99ce3956a617f9b36380da3b7f0dd3dd28e68cb5 \ + --hash=sha256:52c8ef851d9a240f11a88c003eacb03c31fc1c9c4ec64a99a0f922b93874fda9 \ + --hash=sha256:559db847dc486944896521f68d8190be1c9e719fced785720d2216fe7022b662 \ + --hash=sha256:5cb41aa38891e073ee49d55fbc7839cfdb2bc0e600add13874d048c94aadddd1 \ + --hash=sha256:5e262d41726bc187e69af7825504c933b6794dc3fbd5945e41a79bb14c31f585 \ + --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:a120733b01c45e9a0c34aeef92bf0cf1d56cfe81ed9d47d562f9ed591a9828ac \ + --hash=sha256:b1d22e6e9387bf4739fbe23bfa80e93f6b0373a7f1b96c6227c32bef95a4d7a8 \ + --hash=sha256:b8c198f8c1805dc42708689ed6864951fd2494f924149d3e4bce7710f8eb5232 \ + --hash=sha256:c742f741d58a28940ce01d58f0ab2ea3ced8b12402f162f4d534dfe18ba1cd6a \ + --hash=sha256:c7f2c7f2b9ca6bdeef8f0fa897f8e05085923eb091721675170254cbc5b02897 \ + --hash=sha256:d312ef37c91508b0ab2cee7da26ec0b3ed2f03ce12bd87a588d771ae15dcf82d \ + --hash=sha256:d4d8fe59808a54658fcc0160ecfb1b30f9089906c50b23bcb4c69eddc19ec2b4 \ + --hash=sha256:da25dc3563bff5965356133435b757a795a17b17d01dbc0f42fb32447ddfd917 \ + --hash=sha256:eab21f45c7f66c13f2a9e0e1535309cee140182a9cdae1e041d02e47291e8396 \ + --hash=sha256:eb0dc4e38e6a1fd579e5d50369aa2e10acfc9cace504579b2faabb478e76941a \ + --hash=sha256:ede3e6487c5ef5d28634ba3f31f989030ad6af71edfb0055cbbd14189ff240ba \ + --hash=sha256:f3c6818a1a86dd6dca7ddcaaf76947d5ba31aecc28cb1b67009a5877c9a64f3f \ + --hash=sha256:f758f1b9299d059cc3f6546ae2af89670cb1c4d48ea29c3cacc4fe7de3058257 \ + --hash=sha256:fd0409a3653af6c147209d267a0e4243f0ae46b011aa978b1080359fddc9b6cf \ + --hash=sha256:ff18e6a727ee0ab0388507b89d1bc6a22b138d1e2fa56d1ad494586d61d2eae9 + # via zensical +tornado==6.5.7 \ + --hash=sha256:148b2eb15c2c765a50796172c1e499649b35f30d2e3c3d3e15913cfa56bfb163 \ + --hash=sha256:66c513a76cda70d53907bc27cf1447557699c2e95aa48ba27a442ff61c3ddfc2 \ + --hash=sha256:7778b30bef919231265e91c69963ce0f49a1e9c07ac900bbe75b19ce2575ba92 \ + --hash=sha256:8a46347a18f23fb92b396beebe0fb78f61dda0cc302445202c16203d8a18848b \ + --hash=sha256:8d759e71906ee783f8867b93bf26a265743da4c1e2f4a018464c1ba019862972 \ + --hash=sha256:9da38de27f1da3b78a966f0dae12b5a1ea9afe72ca805d84ff06508272ddf100 \ + --hash=sha256:de942f843533a039ef9fa3d9c88c7cd8a7c94553fb5ad0154270989b3d99a2c4 \ + --hash=sha256:e726f0c75da7726eec023aa62751ff8878bd2737e34fbdd33b1ae5897d2200f5 \ + --hash=sha256:f8de3bf12d3efdd0cbe7c8887868198f8a91415e3f29fcf258d9b8eb7b1d9ae4 \ + --hash=sha256:ff934fce95643af5f11efdae618eaa73d469dc588641e5c8d19295a0c65c4796 + # via snakeviz +truststore==0.10.4 \ + --hash=sha256:9d91bd436463ad5e4ee4aba766628dd6cd7010cf3e2461756b3303710eebc301 \ + --hash=sha256:adaeaecf1cbb5f4de3b1959b42d41f6fab57b2b1666adb59e89cb0b53361d981 + # via + # httpcore2 + # httpx2 +typer==0.26.8 \ + --hash=sha256:3512ca79ac5c11113414b36e80281b872884477722440691c89d1112e321a49c \ + --hash=sha256:c244a6bd558886fe3f8780efb6bdd28bb9aff005a94eedebaa5cb32926fe2f7e + # via trendify +typing-extensions==4.16.0 \ + --hash=sha256:481caa481374e813c1b176ada14e97f1f67a4539ce9cfeb3f350d78d6370c2e8 \ + --hash=sha256:dc983d19a509c94dba722ee6abd33940f7c05a89e243c47e907eb4db6f1a43e5 + # via + # anyio + # fastapi + # httpx2 + # pydantic + # pydantic-core + # pyright + # starlette + # typing-inspection +typing-inspection==0.4.2 \ + --hash=sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7 \ + --hash=sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464 + # via + # fastapi + # pydantic +ubelt==1.4.2 \ + --hash=sha256:40322cf3bb59c05b7567222b10bd0d553c6abc463cd480c23ea3dbcf2ef332fb \ + --hash=sha256:6cdbcbba75cd7c3e1ef9556a13b7c0e29a55f67cb454f022a981e42808f2b06e + # via mkinit +urllib3==2.7.0 \ + --hash=sha256:231e0ec3b63ceb14667c67be60f2f2c40a518cb38b03af60abc813da26505f4c \ + --hash=sha256:9fb4c81ebbb1ce9531cce37674bbc6f1360472bc18ca9a553ede278ef7276897 + # via requests +uvicorn==0.49.0 \ + --hash=sha256:ba3d14c3ee7e41c6c654c46c9eb489d33213cdd30aa1696eab1374337c13f68f \ + --hash=sha256:ebf4271aa580d9de97f93192d4595176df6e91f9aae919ca73e4fc07df1e66a3 + # via trendify +watchdog==6.0.0 \ + --hash=sha256:07df1fdd701c5d4c8e55ef6cf55b8f0120fe1aef7ef39a1c6fc6bc2e606d517a \ + --hash=sha256:20ffe5b202af80ab4266dcd3e91aae72bf2da48c0d33bdb15c66658e685e94e2 \ + --hash=sha256:212ac9b8bf1161dc91bd09c048048a95ca3a4c4f5e5d4a7d1b1a7d5752a7f96f \ + --hash=sha256:2cce7cfc2008eb51feb6aab51251fd79b85d9894e98ba847408f662b3395ca3c \ + --hash=sha256:490ab2ef84f11129844c23fb14ecf30ef3d8a6abafd3754a6f75ca1e6654136c \ + --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:bdd4e6f14b8b18c334febb9c4425a878a2ac20efd1e0b231978e7b150f92a948 \ + --hash=sha256:c7c15dda13c4eb00d6fb6fc508b3c0ed88b9d5d374056b239c4ad1611125c860 \ + --hash=sha256:cbafb470cf848d93b5d013e2ecb245d4aa1c8fd0504e863ccefa32445359d680 \ + --hash=sha256:e3df4cbb9a450c6d49318f6d14f4bbc80d763fa587ba46ec86f99f9e6876bb26 + # via + # mkdocs + # properdocs +watchfiles==1.2.0 \ + --hash=sha256:01859b11fd9fbca670f4d5da00fbac282cfea9bd67a2125d8b2833a3b5617ea9 \ + --hash=sha256:01ea8d66f0693b9b60a6541c8d10263091ca9a9060d242f3c1f3143f9aad2c98 \ + --hash=sha256:054dc20fd2e3132b4c3883b4a00d72fd6e1f56fdaf89fccd12e8057d74cd74d7 \ + --hash=sha256:0a105bc2283f67e8fbec74253ec2d94925de92ed72c0393f1206bf326b7b7b69 \ + --hash=sha256:0a37faaed405c67e28e6be45a1fa4f206ef5a2860f27c237db9fa30704c38242 \ + --hash=sha256:0c4997d4e4a55f0d02b6cde327322daf3a0400e5df6c6b15948994bf72497925 \ + --hash=sha256:0cb4d80e212f116474a545c21c912b445f16bb0cef9e6a73a498164223e14e2f \ + --hash=sha256:0e831a271c035d89789cffc386b6aa1375f39f1cd25eb7ca0997e4970d152fc5 \ + --hash=sha256:10d86db20695afe7997ac9e1717637d6714a8d0220458c33f3d2061f54cec427 \ + --hash=sha256:1bc6195825b7dcd217968bb1f801a60fd4c16e8eeab5bedc7fe917d7d5995ab4 \ + --hash=sha256:20aa0e708b920bde876a4aa82dc7dd6ebea228a63a67cda6632c2fc87b787efa \ + --hash=sha256:23282a321c8baf9b3a3c4afff673f9fe65eb7fdc2338d765ccad9d3d1916a5ba \ + --hash=sha256:2581a94056e55d7d0a31a823ea92bf73749c489ca2285bfdc0fbe6b2bb49d50c \ + --hash=sha256:2995c176de7692b86a2e4c58d9ec718f753150a979cb4a754e2b4ffa38e70906 \ + --hash=sha256:2b37d10b5a63bd4d87e18472d80fa525bd670586fae62e5dd580452764879b65 \ + --hash=sha256:2cb93af48550faf1cea04c303107c8b75833de7013e57ce27d3b8d21d8d0f58c \ + --hash=sha256:2d95ddc1eb6914154253d239089900813f6a767e174b8e6a50e7fdacb7e4236c \ + --hash=sha256:3416ff151bb6b5a8d8d11664974fbef4d9305b9b2957839ab5a270468fd8df30 \ + --hash=sha256:3651aa7058595e9cfb75d35dd5ada2bf9f48a5b8a0f3562821d3e210c507e077 \ + --hash=sha256:37a6721cdf3f65dbb13aa9503510ccb4451603ac837e44d265d7992a597e1374 \ + --hash=sha256:41bc1199f7523b3f82843c88cbb979180c949caef0342cf90968f178e5d49b01 \ + --hash=sha256:43d818978d06062d9b22c4fab2ebe44cf5213d42dc8e62bda8c2760cfa2eeb33 \ + --hash=sha256:4429f3b105524a10b72c3a819b091c495d2811d419c1e1e8df773a5a5974f831 \ + --hash=sha256:4543579a9bdb0c9560039b4ffddbdb39545707659fbc430ce4c10f3f68d557f9 \ + --hash=sha256:4c887eba18b7945ac73067a8b4a66f21cd46c2539b2bc68588f7be6c7eb6d26b \ + --hash=sha256:4e4ff8e37f99cf1da89e255e07c9c4b37c214038c4283707bdec308cb1b0ea1f \ + --hash=sha256:4f34e26a19f91f710c08e0183429f0d1d15df734e6bc78c31e77b9ea9c433658 \ + --hash=sha256:5327989a465505f05cfe06f04fa9d0c2fd5432bb243e10e6f012b1bdca3c8579 \ + --hash=sha256:56d8641cf834c2836922899105bd3ce3d0dfc69291d52edf0b4d0436829b34c0 \ + --hash=sha256:63ac26eefbf4af1741247d6fb68b11c49a25b2f7413fbd318a83a12aaa9cf666 \ + --hash=sha256:71283b39fd17e5408eb123bd37aeecfd9d54c81fc184421943208aadb879d103 \ + --hash=sha256:71cd71740ed2c15211ebb237ced4e39a1cdf6f80566e5fe95428da1626f4fde6 \ + --hash=sha256:7571e4464cb6e434958f867f7f730b8ab0b75e3f8e5eac0499168486ab3c33a8 \ + --hash=sha256:7a2cffd17d27d2ecbb310c2b1d8174f222a5495b1a721894afa88ec11e25b898 \ + --hash=sha256:7a7ce236284f002a156f70add88efe5c70879cccbb658be0822c54b1306fc09d \ + --hash=sha256:7ba0480b9a74af058f43b337e937a451e109295c420916d68ad24e3dc02f5e44 \ + --hash=sha256:8520a4ab0e37f770afc34459c4f8f7019e153f9124dc101c15538365875d1ab2 \ + --hash=sha256:86bc13c25a8d1fcd70b51d0ce7c9b65e90de5666fcbfd3e34957cc73ee19aeb5 \ + --hash=sha256:8f200104103feb097de4cab8fe4f5dd18a2026934c7dea98c55a2f5fd6d5a33b \ + --hash=sha256:8f70d8b291ef6e88d19b1f297a6905ddb978888d9272b0d05e6f53309856bcfc \ + --hash=sha256:8fa585ede612ee9f9e91b18bebf9ba11b9ae29a4e3a0d0cf6fca3e382133f0d5 \ + --hash=sha256:922c0e019fe68b3ae392965a766b02a71ba1168c932cebc3733cd52c5fe5b377 \ + --hash=sha256:9649193aa27bd9ff2e80ff29bfaa93085496c7a3a377592823cc58b77ee88add \ + --hash=sha256:9f04b092229ad2c50126dd3c922c8822e51e605993764a33058d4a791ab42281 \ + --hash=sha256:a0f27f01bee51861392bb6b7c4fdb290b27d1eb194e9e28788d68102a0e898d9 \ + --hash=sha256:a204794696ffb8f9b10fba6f7cb5216d42f3b2b71860ccac6b6e42f5f10973b0 \ + --hash=sha256:ae99b14c5f21e026e0e9d96f40e07d8570ebee6cafd9d8fc318354606daa7a28 \ + --hash=sha256:b141a4891c995a039cd89e9a49e62df1dc8a559a5d1a6e4c7106d16c12777a55 \ + --hash=sha256:b4e77f6a55f858504069abd35d336a637555c09bca453dde1ee1e5ada8a6a1fb \ + --hash=sha256:b718bf356bbc15e559bd8ef41782b573b8ae0e3f177ab244b440568d7ea02cfb \ + --hash=sha256:b8c8358484d5fa12ef34f05b7f4168eaf1932f408725ff6d023c33ec17bd79d4 \ + --hash=sha256:b974946a10af379d425e2eef5b62f5c6ebeaccf91d45eaad6f5b27ecd4f91aa0 \ + --hash=sha256:b9909cc2b48468b575eefa944919e1fe8a36c5849d5c7c168f80a8c1db69398e \ + --hash=sha256:b9f732dc58b2dbe69e464ccf8fff7a03b0dd0be439da4c0720d3558527d3d6b4 \ + --hash=sha256:bb7e52ecf68ba46d22df23467b87cffeb2146908aa523ebfe803019618cfda06 \ + --hash=sha256:bc13eb17538be00c874699dc0abe4ee2bc8d50bb1166a6b9e175ef3fd7eb8f26 \ + --hash=sha256:c0db965c5f79aa49fe672d297cf1febc5ad149b658594944f49a54a2b96270a7 \ + --hash=sha256:c525543d91961c6955b2636b308569e84a1d1c5f5f2932041ab9ef46422f43e3 \ + --hash=sha256:c5c19526f4e54a00f2666a6c0e9e40d582c09e865055ea7378bf0009aab857b3 \ + --hash=sha256:c995fba777f1ea992f090f9236e9284cf7a5d1a0130dd5a3d82c598cacd76838 \ + --hash=sha256:ca148d73dea36c9763aaa351e4d7a51780ec1584217c45276f4fe8239c768b71 \ + --hash=sha256:cee9d5efd929efdac5f7e58f72b3376f676b64050a91c5b99a7094c5b2317488 \ + --hash=sha256:d20029a60a71a052a24c4db7673bc4de39ab89adbaccbfb5d67987c5d73f424d \ + --hash=sha256:d413349d565dab74297f2a63e84a097936be69bf8f3b3801f27f380e32040f44 \ + --hash=sha256:d4a4b147f5dca2a5d325a06a832fb43f345751adfbc63204aec30e0d9ca965a2 \ + --hash=sha256:d73a585accffa5ae39c17264c36ec3166d2fad7000c780f5ef83b2722afb9dd2 \ + --hash=sha256:e140ed30ebde76796b686e67c182cff10ea2fbab186fafd1560f74bb5a473a6e \ + --hash=sha256:e53a384f76b631c3ae5334ce6a52f0baa3a911eb94a4eac7f160079868b716d5 \ + --hash=sha256:eb283ee99e21ad6443c8cdb06ac5b34b1308c329cbdf03fa02b445363714c799 \ + --hash=sha256:ecb47f183a8025b2aa18b546725c3657e542112ae9c0613a2af79b4fa8d04ad7 \ + --hash=sha256:f155b3a1b2a5fc89cdc70d47ee5d54e3b75e88efa34982028a35daef9ba00379 \ + --hash=sha256:f22943b7770483f6ea0721c6b11d022947a98eb0acae14694de034f4d0d38925 \ + --hash=sha256:f28b2725eb8cce327b9b3ab02415c853011dc55c95832fe90de6bc56f5315f72 \ + --hash=sha256:f88af53d6ddaf72179ef613ddc905e6f4785f712b49b80b3bef9f3525e6194b4 \ + --hash=sha256:faea288b6f0ab1902ef08f4ca6de005dccf856c4e0c4f21b8c5fce02d90a1b08 \ + --hash=sha256:fff610d7bb2256a317bb1e96f0d7862c7aa8076733ee5df0fd41bbe76a24a4f4 +zensical==0.0.46 \ + --hash=sha256:1543a693a160de60e86ca589592401b584670e7e12c5ae30e3c2ba76786f7ec3 \ + --hash=sha256:26e98fb8ab7ab50cdd20a73e2c7d4d9aae0b46cf2d8691e6bb22f9c261b8a60a \ + --hash=sha256:3ec21f4fb1e78cd7c0d6b07ae336b04770e27ba020dabc457b2790e5d34f1978 \ + --hash=sha256:46fe578f26963f8ee89567983e62737b6fadc9197d4742e1020b522e092d7baa \ + --hash=sha256:85f018f2a7ee76a83915c87ddb12b58cf343fd6154081d33ac95b6751b011dd7 \ + --hash=sha256:9487c147c9cceb50c04d0ad70b024821a6eab1629dafd70ab6d1e86ec841e623 \ + --hash=sha256:aef03fa186a5589148e10b62610500989c6b075a2c08e1554233adbf91b2a3dc \ + --hash=sha256:bbee37801f1ed500f158dc0992c569282950f780ae353c37fe6969f99983d701 \ + --hash=sha256:bc7446cdf97a8dea390f20ed2bd6b030cddc1bd36a8ce113ea3efef6fa61c573 \ + --hash=sha256:d91af81ab058c8693dfd75f2f77b4c73bcba4125681d1d276f38624291820bd2 \ + --hash=sha256:d9221264a9a87409900a47e29985607b0c9245dacb89077e87c8e16e31edc167 \ + --hash=sha256:ec43018d5343ca2e1d71aa352eeddd560fef504effd03025840a5a783abefa4f \ + --hash=sha256:f42a4683c762f026878d19ede4bcf7bfbb84dbecb5ad923949abb77806ed88a5 diff --git a/run.sh b/run.sh new file mode 100644 index 0000000..e5e6b21 --- /dev/null +++ b/run.sh @@ -0,0 +1,14 @@ +set -e + +# rm -rf scripts/example_data/trendify + +# trendify run \ +# -i "scripts/example_data/models/*" \ +# -g scripts/matrix_generator.py:build_configuration_matrix \ +# -o scripts/example_data/trendify \ +# -n 4 + +trendify viewer \ + scripts/example_data/trendify/trendify.db \ + --host 0.0.0.0 \ + --port 8084 diff --git a/sample_data/.gitignore b/sample_data/.gitignore deleted file mode 100644 index 40b4ebd..0000000 --- a/sample_data/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -trendify -good_result \ No newline at end of file diff --git a/sample_data/models/0/data_product.json b/sample_data/models/0/data_product.json deleted file mode 100644 index f30a373..0000000 --- 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-0.8409090909090909,-1.1669170869436671,0.3601564682079411,0.6225741212090208 -0.8636363636363636,-1.0517989512227595,0.30883305755378554,0.6429977042558713 -0.8863636363636364,-0.8431384878776291,0.2160677567810504,0.6201004320318494 -0.9090909090909092,-0.6857725776865154,0.24674243190903822,0.6368730106028997 -0.9318181818181819,-0.5283278541544792,0.1443244918011971,0.5759765906034046 -0.9545454545454546,-0.41000324998960175,0.1129650728247546,0.6635551384654466 -0.9772727272727273,-0.178692804632652,0.08035099715175534,0.60624192466182 -1.0,0.005834290457036083,0.022326558801497302,0.5279697099421288 diff --git a/sample_data/models/9/stdin.csv b/sample_data/models/9/stdin.csv deleted file mode 100644 index e9d38c4..0000000 --- a/sample_data/models/9/stdin.csv +++ /dev/null @@ -1,7 +0,0 @@ -n_samples,45.0 -p0,1.0 -p1,2.0 -p2,3.0 -a0,1.3371822158175504 -a1,0.7893791557605863 -a2,0.6945639493238038 diff --git a/sample_hierarchical_data.py b/sample_hierarchical_data.py deleted file mode 100644 index d310e57..0000000 --- a/sample_hierarchical_data.py +++ /dev/null @@ -1,229 +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 pandas as pd -import os -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, - Tags, - Point2D, - Trace2D, - TableEntry, - HistogramEntry, - AxLine, - 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"] - } - - # 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() \ No newline at end of file diff --git a/scripts/gen_ref_pages.py b/scripts/gen_ref_pages.py index 20302a2..90e717a 100644 --- a/scripts/gen_ref_pages.py +++ b/scripts/gen_ref_pages.py @@ -1,35 +1,65 @@ -"""Generate the code reference pages and navigation.""" +""" +Generate API reference pages in docs/reference/. + +Run this script before building the docs site: + + python scripts/gen_ref_pages.py + +The output is written to docs/reference/ and is consumed by zensical (or +mkdocs) via the ::: autodoc directives. The directory is gitignored; this +script must be run as part of the CI pipeline before zensical build. + +Pages are generated automatically by walking src/trendify/: every +`foo/bar.py` becomes `reference/trendify/foo/bar.md` and every +`foo/__init__.py` becomes `reference/trendify/foo/index.md`, mirroring the +package layout. Packages whose __init__.py is empty (packaging markers for +non-Python assets, e.g. viewer/templates/static/) are skipped since there is +nothing to document. +""" from pathlib import Path -import mkdocs_gen_files +ROOT = Path(__file__).parent.parent +SRC = ROOT / "src" +PACKAGE = "trendify" +DOCS_REF = ROOT / "docs" / "reference" + + +def iter_modules() -> list[tuple[str, Path]]: + """Return (dotted identifier, output path relative to DOCS_REF) for every documentable module.""" + modules: list[tuple[str, Path]] = [] + + for path in sorted((SRC / PACKAGE).rglob("*.py")): + parts = list(path.relative_to(SRC).with_suffix("").parts) + + if parts[-1] == "__main__": + continue -nav = mkdocs_gen_files.Nav() + if parts[-1] == "__init__": + if not path.read_text(encoding="utf-8").strip(): + continue + parts.pop() + rel_path = Path(*parts, "index.md") + else: + rel_path = Path(*parts).with_suffix(".md") -root = Path(__file__).parent.parent -src = root / "src" + modules.append((".".join(parts), rel_path)) -for path in sorted(src.rglob("*.py")): - module_path = path.relative_to(src).with_suffix("") - doc_path = path.relative_to(src).with_suffix(".md") - full_doc_path = Path("reference", doc_path) + return modules - parts = tuple(module_path.parts) - if parts[-1] == "__init__": - parts = parts[:-1] - doc_path = doc_path.with_name("index.md") - full_doc_path = full_doc_path.with_name("index.md") - elif parts[-1] == "__main__": - continue +def main() -> None: + DOCS_REF.mkdir(parents=True, exist_ok=True) - nav[parts] = doc_path.as_posix() + modules = iter_modules() + for identifier, rel_path in modules: + dest = DOCS_REF / rel_path + dest.parent.mkdir(parents=True, exist_ok=True) + dest.write_text(f"::: {identifier}\n", encoding="utf-8") + print(f" {dest.relative_to(ROOT)}") - with mkdocs_gen_files.open(full_doc_path, "w") as fd: - ident = ".".join(parts) - fd.write(f"::: {ident}") + print(f"\nWrote {len(modules)} pages to {DOCS_REF.relative_to(ROOT)}/") - 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 +if __name__ == "__main__": + main() diff --git a/scripts/gen_ts_models.py b/scripts/gen_ts_models.py new file mode 100644 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..3a6cadb --- /dev/null +++ b/scripts/matrix_generator.py @@ -0,0 +1,910 @@ +""" +Not intended for use as a unit test. + +Comprehensive `RecordGenerator`-compatible fixture enumerating configuration combinations +across every `Record` subclass (`Point2D`, `Scatter2D`, `Trace2D`, `AxLine`, +`HistogramEntry`, `TableEntry`) plus tags where several record 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 record-type +combination it needs to support. + +`build_configuration_matrix` is the single entry point. Its signature matches +`RecordGenerator` (`Callable[[Path], RecordList]`), so it can be passed directly to +`generate_records`/`TrendifyPipeline.generate` like any real generator, or called directly +in a test and handed to `RecordStore.write_run`. +""" + +from __future__ import annotations + +from pathlib import Path +from typing import Literal + +import numpy as np + +from trendify.base.pen import Pen +from trendify.base.record import RecordList +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) + + +# --8<-- [start:build_configuration_matrix] +def build_configuration_matrix(workdir: Path) -> RecordList: + """ + Returns one comprehensive `RecordList` 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 and to give every run's + `TableEntry` rows a distinct `row` key. Without that, every run emits the exact + same (row, col) pairs and `RecordStore.get_table_entries` collapses across runs + into one row/col-collision-riddled melted table that can never pivot. + + """ + records: RecordList = [] + run_label = workdir.name + + _add_axis_scale_combinations(records) + _add_grid_theme_combinations(records) + _add_legend_location_combinations(records) + _add_legend_option_combinations(records) + _add_format2d_limit_combinations(records) + _add_format2d_size_and_label_combinations(records) + _add_pen_color_combinations(records) + _add_pen_linestyle_combinations(records) + _add_pen_field_combinations(records) + _add_trace_marker_combinations(records) + _add_marker_symbol_combinations(records) + _add_marker_color_and_field_combinations(records) + _add_scatter_configuration_combinations(records) + _add_point_coordinate_type_combinations(records) + _add_axline_orientation_combinations(records) + _add_histogram_histtype_combinations(records) + _add_histogram_bins_combinations(records) + _add_histogram_alpha_combinations(records) + _add_histogram_style_edge_case_combinations(records) + _add_table_entry_value_type_combinations(records, run_label) + _add_table_many_columns_combination(records, run_label) + _add_tag_shape_combinations(records) + _add_metadata_combinations(records, run_label) + _add_mixed_record_type_combinations(records, run_label) + + return records + + +# --8<-- [end:build_configuration_matrix] + + +# --8<-- [start:axis_scale_combinations] +def _add_axis_scale_combinations(records: RecordList) -> 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}" + tag = ("axis_scale_combinations", tag_name) + Format2D(tags=[tag], scale_x=scale_x, scale_y=scale_y).append_to_list(records) + Trace2D( + tags=[tag], + x=x, + y=y, + pen=Pen(label=tag_name), + ).append_to_list(records) + + +# --8<-- [end:axis_scale_combinations] + + +def _add_grid_theme_combinations(records: RecordList) -> 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(): + tag = ("grid_theme_combinations", tag_name) + Format2D(tags=[tag], grid=grid).append_to_list(records) + Trace2D( + tags=[tag], + x=_TIME, + y=_rising_wave(), + pen=Pen(label=tag_name), + ).append_to_list(records) + + +def _add_legend_location_combinations(records: RecordList) -> None: + for loc in LegendLocation: + tag_name = f"legend_loc_{loc.name.lower()}" + tag = ("legend_location_combinations", tag_name) + Format2D(tags=[tag], legend=Legend(loc=loc)).append_to_list(records) + Trace2D( + tags=[tag], + x=_TIME, + y=_rising_wave(), + pen=Pen(label=tag_name), + ).append_to_list(records) + + +def _add_legend_option_combinations(records: RecordList) -> 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(): + tag = ("legend_option_combinations", tag_name) + Format2D(tags=[tag], legend=legend).append_to_list(records) + Trace2D( + tags=[tag], + x=_TIME, + y=_rising_wave(), + pen=Pen(label=tag_name), + ).append_to_list(records) + + +def _add_format2d_limit_combinations(records: RecordList) -> None: + limit_variants = { + "lim_x_min_set": {"lim_x": (0.3, None)}, + "lim_x_max_set": {"lim_x": (None, 0.7)}, + "lim_y_min_set": {"lim_y": (-0.5, None)}, + "lim_y_max_set": {"lim_y": (None, 0.5)}, + "lim_x_and_y_all_set": {"lim_x": (0.2, 0.8), "lim_y": (-0.8, 0.8)}, + } + for tag_name, limit_kwargs in limit_variants.items(): + tag = ("axis_limit_combinations", tag_name) + Format2D(tags=[tag], **limit_kwargs).append_to_list(records) + Trace2D( + tags=[tag], + x=_TIME, + y=_rising_wave(), + pen=Pen(label=tag_name), + ).append_to_list(records) + + +def _add_format2d_size_and_label_combinations(records: RecordList) -> None: + field_variants = { + "figure_width_custom": {"figure_width": 12.0}, + "figure_height_custom": {"figure_height": 10.0}, + "title_fig_set": {"title_fig": "Figure title"}, + "title_ax_set": {"title_ax": "Axes title"}, + "label_x_set": {"label_x": "X axis label"}, + "label_y_set": {"label_y": "Y axis label"}, + } + for tag_name, format2d_kwargs in field_variants.items(): + tag = ("format2d_field_combinations", tag_name) + Format2D(tags=[tag], **format2d_kwargs).append_to_list(records) + Trace2D( + tags=[tag], + x=_TIME, + y=_rising_wave(), + pen=Pen(label=tag_name), + ).append_to_list(records) + + +def _add_pen_color_combinations(records: RecordList) -> 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( + tags=[("pen_color_combinations", tag_name)], + x=_TIME, + y=_rising_wave(), + pen=Pen(label=tag_name, color=color), + ).append_to_list(records) + + +def _add_pen_linestyle_combinations(records: RecordList) -> 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( + tags=[("pen_linestyle_combinations", tag_name)], + x=_TIME, + y=_rising_wave(), + pen=Pen(label=tag_name, linestyle=linestyle), + ).append_to_list(records) + + +def _add_pen_field_combinations(records: RecordList) -> None: + Trace2D( + tags=[("pen_field_combinations", "pen_alpha_partial")], + x=_TIME, + y=_rising_wave(), + pen=Pen(label="pen_alpha_partial", alpha=0.4), + ).append_to_list(records) + Trace2D( + tags=[("pen_field_combinations", "pen_alpha_zero")], + x=_TIME, + y=_rising_wave(), + pen=Pen(label="pen_alpha_zero", alpha=0.0), + ).append_to_list(records) + Trace2D( + tags=[("pen_field_combinations", "pen_size_thick")], + x=_TIME, + y=_rising_wave(), + pen=Pen(label="pen_size_thick", size=6.0), + ).append_to_list(records) + Trace2D( + tags=[("pen_field_combinations", "pen_label_omitted")], + x=_TIME, + y=_rising_wave(), + pen=Pen(label=None), + ).append_to_list(records) + Trace2D( + 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(records) + Trace2D( + 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(records) + + +def _add_trace_marker_combinations(records: RecordList) -> None: + Trace2D( + tags=[("trace_marker_combinations", "trace_marker_omitted")], + x=_TIME, + y=_rising_wave(), + pen=Pen(label="trace_marker_omitted"), + ).append_to_list(records) + Trace2D( + 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(records) + Trace2D( + 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(records) + Trace2D( + tags=[("trace_marker_combinations", "trace_markers_only_no_line")], + x=_TIME, + y=_rising_wave(), + pen=Pen(label="trace_markers_only_no_line", linestyle=None), + marker=Marker(symbol="D", color="tab:green"), + ).append_to_list(records) + + +def _add_marker_symbol_combinations(records: RecordList) -> 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(records) + Point2D( + tags=[("marker_symbol_combinations", "marker_omitted")], + x=0.0, + y=0.0, + marker=None, + ).append_to_list(records) + + +def _add_marker_color_and_field_combinations(records: RecordList) -> 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(records) + + 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(records) + 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(records) + + +def _add_scatter_configuration_combinations(records: RecordList) -> None: + rng = np.random.default_rng(seed=6) + x = rng.uniform(size=60) + y = rng.uniform(size=60) + + Scatter2D( + tags=[("scatter_configuration_combinations", "scatter_default_marker")], + x=x, + y=y, + ).append_to_list(records) + + 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( + tags=[("scatter_configuration_combinations", tag_name)], + x=x, + y=y, + marker=Marker(label=tag_name, color=color), + ).append_to_list(records) + + Scatter2D( + 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(records) + Scatter2D( + 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(records) + + +def _add_point_coordinate_type_combinations(records: RecordList) -> 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(records) + + 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(records) + + +def _add_axline_orientation_combinations(records: RecordList) -> None: + AxLine( + tags=[("axline_orientation_combinations", "axline_horizontal")], + value=0.5, + orientation=LineOrientation.HORIZONTAL, + pen=Pen(label="axline_horizontal", color="r"), + ).append_to_list(records) + AxLine( + tags=[("axline_orientation_combinations", "axline_vertical")], + value=0.5, + orientation=LineOrientation.VERTICAL, + pen=Pen(label="axline_vertical", color="b"), + ).append_to_list(records) + + +def _add_histogram_histtype_combinations(records: RecordList) -> None: + rng = np.random.default_rng(seed=1) + histtypes: list[Literal["bar", "step", "stepfilled"]] = [ + "bar", + "step", + "stepfilled", + ] + for histtype in histtypes: + 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(records) + + +def _add_histogram_bins_combinations(records: RecordList) -> 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(records) + + +def _add_histogram_alpha_combinations(records: RecordList) -> 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(records) + + +def _add_histogram_style_edge_case_combinations(records: RecordList) -> None: + rng = np.random.default_rng(seed=4) + + 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(records) + 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(records) + + +def _add_table_entry_value_type_combinations( + records: RecordList, run_label: str +) -> None: + """ + Each `row` is keyed off `run_label` (rather than a fixed literal) so that across the many + runs this fixture is called for, every tag accumulates a genuine multi-row table instead + of every run overwriting the same (row, col) pair -- the latter makes + `RecordStore.get_table_entries`'s melted table un-pivotable (a repeated (row, col) key is + a pivot error, see `TableBuilder.pivot_table`). + """ + TableEntry( + tags=[("table_value_type_combinations", "table_value_float")], + row=run_label, + col="col_1", + value=3.14, + ).append_to_list(records) + TableEntry( + tags=[("table_value_type_combinations", "table_value_string")], + row=run_label, + col="col_1", + value="a_string_value", + ).append_to_list(records) + TableEntry( + tags=[("table_value_type_combinations", "table_value_bool")], + row=run_label, + col="col_1", + value=True, + ).append_to_list(records) + TableEntry( + tags=[("table_value_type_combinations", "table_unit_set")], + row=run_label, + col="col_1", + value=2.5, + unit="meters", + ).append_to_list(records) + TableEntry( + tags=[("table_value_type_combinations", "table_row_col_numeric")], + row=float(run_label), + col=2.0, + value=9.9, + ).append_to_list(records) + 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=f"{row}_{run_label}", + col=col, + value=value, + ).append_to_list(records) + + +def _add_table_many_columns_combination(records: RecordList, run_label: str) -> None: + """ + One `TableEntry` tag with 40 columns, to exercise the table viewer with a table too wide + to fit on screen (horizontal scrolling, column resize handles against a real overflow + case) rather than only ever seeing tables that comfortably fit. + """ + for col in range(40): + TableEntry( + tags=[("table_value_type_combinations", "table_many_columns")], + row=run_label, + col=f"metric_{col:02d}", + value=float(run_label) * 100 + col, + ).append_to_list(records) + + +def _add_tag_shape_combinations(records: RecordList) -> None: + Point2D( + tags=["tag_shape_scalar_string"], + x=1.0, + y=1.0, + ).append_to_list(records) + Point2D(tags=[2024], x=1.0, y=1.0).append_to_list(records) + Point2D( + tags=[("tag_shape_tuple_of_strings", "nested_leaf")], + x=1.0, + y=1.0, + ).append_to_list(records) + Point2D( + tags=[("tag_shape_tuple_mixed_str_and_int", 2024)], + x=1.0, + y=1.0, + ).append_to_list(records) + Point2D( + tags=[("tag_shape_deeply_nested", "group_a", "subgroup_b", "leaf")], + x=1.0, + y=1.0, + ).append_to_list(records) + + +def _add_metadata_combinations(records: RecordList, run_label: str) -> None: + Point2D( + tags=[("metadata_combinations", "metadata_set")], + x=1.0, + y=1.0, + ).append_to_list(records).set_metadata( + {"run": run_label, "source": "record_matrix"} + ) + Point2D( + tags=[("metadata_combinations", "metadata_empty")], + x=1.0, + y=1.0, + ).append_to_list(records) + + +def _add_mixed_record_type_combinations(records: RecordList, run_label: str) -> None: + rng = np.random.default_rng(seed=5) + + trace_and_axline_tag = ( + "mixed_record_type_combinations", + "trace_and_axline_same_plot", + ) + Trace2D( + tags=[trace_and_axline_tag], + x=_TIME, + y=_rising_wave(), + pen=Pen(label="trace_and_axline_same_plot"), + ).append_to_list(records) + AxLine( + tags=[trace_and_axline_tag], + value=0.0, + orientation=LineOrientation.HORIZONTAL, + pen=Pen(label="zero_reference_line", color="k"), + ).append_to_list(records) + + point_trace_axline_tag = ( + "mixed_record_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(records) + Trace2D( + tags=[point_trace_axline_tag], + x=_TIME, + y=_rising_wave(0.8), + pen=Pen(label="point_trace_and_axline_same_plot"), + ).append_to_list(records) + AxLine( + tags=[point_trace_axline_tag], + value=0.5, + orientation=LineOrientation.VERTICAL, + pen=Pen(label="midpoint_reference_line", color="g"), + ).append_to_list(records) + + trace_and_histogram_tag = ( + "mixed_record_type_combinations", + "trace_and_histogram_shared_tag", + ) + Trace2D( + tags=[trace_and_histogram_tag], + x=_TIME, + y=_rising_wave(), + pen=Pen(label="trace_and_histogram_shared_tag"), + ).append_to_list(records) + for value in rng.normal(size=30): + HistogramEntry( + tags=[trace_and_histogram_tag], value=float(value) + ).append_to_list(records) + + point_and_histogram_tag = ( + "mixed_record_type_combinations", + "point_and_histogram_shared_tag", + ) + Point2D(tags=[point_and_histogram_tag], x=0.0, y=0.0).append_to_list(records) + for value in rng.normal(size=30): + HistogramEntry( + tags=[point_and_histogram_tag], value=float(value) + ).append_to_list(records) + + scatter_and_trace_tag = ( + "mixed_record_type_combinations", + "scatter_and_trace_same_plot", + ) + Scatter2D( + 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(records) + Trace2D( + tags=[scatter_and_trace_tag], + x=_TIME, + y=_rising_wave(), + pen=Pen(label="scatter_and_trace_same_plot"), + ).append_to_list(records) + + scatter_and_histogram_tag = ( + "mixed_record_type_combinations", + "scatter_and_histogram_shared_tag", + ) + Scatter2D( + tags=[scatter_and_histogram_tag], + x=rng.uniform(size=30), + y=rng.uniform(size=30), + ).append_to_list(records) + for value in rng.normal(size=30): + HistogramEntry( + tags=[scatter_and_histogram_tag], value=float(value) + ).append_to_list(records) + + axline_and_histogram_tag = ( + "mixed_record_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(records) + 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(records) + + all_xy_types_and_histogram_tag = ( + "mixed_record_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(records) + Trace2D( + tags=[all_xy_types_and_histogram_tag], + x=_TIME, + y=_rising_wave(0.6), + pen=Pen(label="sample_trace"), + ).append_to_list(records) + 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(records) + for value in rng.normal(size=30): + HistogramEntry( + tags=[all_xy_types_and_histogram_tag], value=float(value) + ).append_to_list(records) + + format2d_written_twice_tag = ( + "mixed_record_type_combinations", + "format2d_written_twice_shared_tag", + ) + Point2D(tags=[format2d_written_twice_tag], x=0.0, y=0.0).append_to_list(records) + # Format2D is a singleton per tag: writing it twice for the same tag (redundant + # across runs, or even within one run's own record list) must not accumulate rows. + Format2D( + tags=[format2d_written_twice_tag], grid=Grid.from_theme(GridTheme.MATLAB) + ).append_to_list(records) + Format2D( + tags=[format2d_written_twice_tag], grid=Grid.from_theme(GridTheme.MATLAB) + ).append_to_list(records) + Trace2D( + tags=[format2d_written_twice_tag], + x=_TIME, + y=_rising_wave(), + pen=Pen(label="format2d_written_twice_shared_tag"), + ).append_to_list(records) + + table_and_point_tag = ( + "mixed_record_type_combinations", + "table_and_point_shared_tag", + ) + TableEntry( + tags=[table_and_point_tag], row=run_label, col="col_1", value=1.0 + ).append_to_list(records) + Point2D(tags=[table_and_point_tag], x=1.0, y=1.0).append_to_list(records) + + table_point_and_histogram_tag = ( + "mixed_record_type_combinations", + "table_point_and_histogram_shared_tag", + ) + TableEntry( + tags=[table_point_and_histogram_tag], row=run_label, col="col_1", value=1.0 + ).append_to_list(records) + Point2D(tags=[table_point_and_histogram_tag], x=1.0, y=1.0).append_to_list(records) + for value in rng.normal(size=30): + HistogramEntry( + tags=[table_point_and_histogram_tag], value=float(value) + ).append_to_list(records) + + every_record_type_tag = ( + "mixed_record_type_combinations", + "table_point_scatter_trace_axline_and_histogram_shared_tag", + ) + TableEntry( + tags=[every_record_type_tag], row=run_label, col="col_1", value=1.0 + ).append_to_list(records) + Point2D( + tags=[every_record_type_tag], + x=0.2, + y=0.2, + marker=Marker(label="sample_point"), + ).append_to_list(records) + Scatter2D( + tags=[every_record_type_tag], + x=rng.uniform(size=30), + y=rng.uniform(size=30), + marker=Marker(label="scattered_samples", color="tab:cyan"), + ).append_to_list(records) + Trace2D( + tags=[every_record_type_tag], + x=_TIME, + y=_rising_wave(0.6), + pen=Pen(label="sample_trace"), + ).append_to_list(records) + AxLine( + tags=[every_record_type_tag], + value=0.0, + orientation=LineOrientation.HORIZONTAL, + pen=Pen(label="zero_reference_line", color="k"), + ).append_to_list(records) + for value in rng.normal(size=30): + HistogramEntry(tags=[every_record_type_tag], value=float(value)).append_to_list( + records + ) + + +if __name__ == "__main__": + import datetime + import shutil + + from trendify.log import setup_logger + from trendify.pipeline import TrendifyPipeline + + setup_logger() + + output_directory = Path(__file__).parent / "example_data" / "trendify" + + n_proc_sweep: dict[int, datetime.datetime | None] = {i: None for i in range(1, 2)} + # NPROC: 1, Delta t = 00m 28s + # NPROC: 2, Delta t = 00m 20s + # NPROC: 3, Delta t = 00m 20s + # NPROC: 4, Delta t = 00m 22s + # NPROC: 5, Delta t = 00m 25s + # NPROC: 6, Delta t = 00m 28s + # NPROC: 7, Delta t = 00m 32s + # NPROC: 8, Delta t = 00m 36s + # NPROC: 9, Delta t = 00m 40s + + dirs: list[Path] = [] + root = Path.cwd() + for pattern in ["scripts/example_data/models/*"]: + matches = root.rglob(pattern) if "**" in pattern else root.glob(pattern) + + for path in matches: + if path.name.startswith("."): + continue + dirs.append((path.parent if path.is_file() else path).resolve()) + + for n_proc in n_proc_sweep.keys(): + try: + shutil.rmtree(output_directory) + except FileNotFoundError: + pass + + def run(): + pipeline = TrendifyPipeline(output_dir=output_directory, n_procs=n_proc) + pipeline.run( + record_generator=build_configuration_matrix, + data_dirs=dirs, + ) + + start = datetime.datetime.now() + run() + # with cProfile.Profile() as profiler: + # assert n_proc == 1 + # run() + # profiler.dump_stats("program.prof") + stop = datetime.datetime.now() + n_proc_sweep[n_proc] = datetime.datetime(1, 1, 1) + (stop - start) + + for n_proc, delta_time in n_proc_sweep.items(): + assert delta_time + print(f"NPROC: {n_proc}, Delta t = {delta_time.strftime('%Mm %Ss')}") diff --git a/src/.DS_Store b/src/.DS_Store deleted file mode 100644 index e5417ba..0000000 Binary files a/src/.DS_Store and /dev/null differ diff --git a/src/trendify/CLI.py b/src/trendify/CLI.py deleted file mode 100644 index 5163fdd..0000000 --- a/src/trendify/CLI.py +++ /dev/null @@ -1,593 +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 typing import List, 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.local_server import TrendifyProductServerLocal -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/__init__.py b/src/trendify/__init__.py index 3ab8434..87ad56f 100644 --- a/src/trendify/__init__.py +++ b/src/trendify/__init__.py @@ -2,5 +2,246 @@ Provides top-level imports """ -from trendify.api import * -from trendify.server import * +from trendify import ( + base, + cli, + color, + examples, + formats, + generator, + log, + pipeline, + plotting, + store, + styling, + typing, + viewer, +) +from trendify.base import ( + HashableBase, + Pen, + R, + Record, + RecordGenerator, + RecordList, + RecordType, + Tag, + Tags, + helpers, + pen, + record, +) +from trendify.cli import ( + app as cli_app, +) +from trendify.color import ( + Color, +) +from trendify.examples import ( + example_record_generator, + make_example_data, +) +from trendify.formats import ( + AxisScale, + Format2D, + PlottableData2D, + Rastered, + TableEntry, + Vector, + XYData, + format2d, + table, +) +from trendify.generator import ( + Histogrammer, + TableBuilder, + XYDataPlotter, + generate, + generate_records, + get_sorted_dirs, + histogrammer, + 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, + HistogramStyle, + LineOrientation, + PlotlyFigure, + Point2D, + Scatter2D, + SingleAxisFigure, + Trace2D, + axline, + figure, + histogram, + point, + scatter, + trace, +) +from trendify.store import ( + SCHEMA_VERSION, + RecordStore, + connect, + db, + decode_tag, + encode_tag, + record_store, + tag_to_path_parts, + tags, +) +from trendify.styling import ( + Grid, + GridAxis, + GridTheme, + Legend, + LegendLocation, + Marker, + grid, + legend, + marker, +) +from trendify.typing import ( + MatN, + VecN, +) +from trendify.viewer import ( + HoverMode, + InterpMode, + LineMode, + PlotConfig, + TagNode, + api, + build_tag_tree, + camel_case_dict, + create_app, + create_app_from_env, + pages, + plot_config, + router, + routes, + tag_tree, + viewer_app, +) + +__all__ = [ + "SCHEMA_VERSION", + "AxLine", + "AxisScale", + "Color", + "Format2D", + "Grid", + "GridAxis", + "GridTheme", + "HashableBase", + "HistogramEntry", + "HistogramStyle", + "Histogrammer", + "HoverMode", + "InterpMode", + "Legend", + "LegendLocation", + "LineMode", + "LineOrientation", + "Marker", + "MatN", + "Pen", + "PlotConfig", + "PlotlyFigure", + "PlottableData2D", + "Point2D", + "R", + "Rastered", + "Record", + "RecordGenerator", + "RecordList", + "RecordStore", + "RecordType", + "Scatter2D", + "SingleAxisFigure", + "TableBuilder", + "TableEntry", + "Tag", + "TagNode", + "Tags", + "Trace2D", + "TrendifyPipeline", + "VecN", + "Vector", + "XYData", + "XYDataPlotter", + "api", + "axline", + "base", + "build_tag_tree", + "camel_case_dict", + "cli", + "cli_app", + "color", + "connect", + "create_app", + "create_app_from_env", + "create_queue_listener", + "db", + "decode_tag", + "encode_tag", + "example_record_generator", + "examples", + "figure", + "format2d", + "formats", + "generate", + "generate_records", + "generator", + "get_logger", + "get_sorted_dirs", + "grid", + "helpers", + "histogram", + "histogrammer", + "legend", + "log", + "make_example_data", + "marker", + "pages", + "pen", + "pipeline", + "plot_config", + "plotting", + "point", + "record", + "record_store", + "render", + "render_assets", + "router", + "routes", + "scatter", + "set_log_level", + "setup_logger", + "store", + "styling", + "table", + "table_builder", + "tag_to_path_parts", + "tag_tree", + "tags", + "trace", + "typing", + "viewer", + "viewer_app", + "worker_init", + "xy_data_plotter", +] diff --git a/src/trendify/api/__init__.py b/src/trendify/api/__init__.py deleted file mode 100644 index f916345..0000000 --- a/src/trendify/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/api/api.py b/src/trendify/api/api.py deleted file mode 100644 index 1d94e05..0000000 --- a/src/trendify/api/api.py +++ /dev/null @@ -1,487 +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 typing import List, 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(f"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(f"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/api/base/__init__.py b/src/trendify/api/base/__init__.py deleted file mode 100644 index cde36fd..0000000 --- a/src/trendify/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/api/base/data_product.py b/src/trendify/api/base/data_product.py deleted file mode 100644 index ce56666..0000000 --- a/src/trendify/api/base/data_product.py +++ /dev/null @@ -1,126 +0,0 @@ -from __future__ import annotations - -from pathlib import Path -from typing import Callable, List, Any -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", "ProductList", "ProductGenerator"] - -_data_product_subclass_registry: dict[str, 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/api/base/helpers.py b/src/trendify/api/base/helpers.py deleted file mode 100644 index 3fe8005..0000000 --- a/src/trendify/api/base/helpers.py +++ /dev/null @@ -1,69 +0,0 @@ -from __future__ import annotations - -from enum import auto -from strenum import StrEnum -from typing import Union, List, Tuple, TypeVar, Hashable -import logging - -from pydantic import BaseModel - -logger = logging.getLogger(__name__) - -__all__ = [ - "DATA_PRODUCTS_FNAME_DEFAULT", - "R", - "Tag", - "Tags", - "HashableBase", - "ProductType", -] - -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/api/formats/__init__.py b/src/trendify/api/formats/__init__.py deleted file mode 100644 index 61a4447..0000000 --- a/src/trendify/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/api/formats/format2d.py b/src/trendify/api/formats/format2d.py deleted file mode 100644 index 81ecc1b..0000000 --- a/src/trendify/api/formats/format2d.py +++ /dev/null @@ -1,146 +0,0 @@ -from __future__ import annotations - -from enum import StrEnum -from typing import TYPE_CHECKING, Iterable, Optional -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__ = ["Format2D", "PlottableData2D", "XYData", "AxisScale"] - - -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: 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 - 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/api/formats/table.py b/src/trendify/api/formats/table.py deleted file mode 100644 index db3ae51..0000000 --- a/src/trendify/api/formats/table.py +++ /dev/null @@ -1,84 +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 as e: - 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/api/generator/__init__.py b/src/trendify/api/generator/__init__.py deleted file mode 100644 index ada3dc8..0000000 --- a/src/trendify/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/api/generator/data_product_collection.py b/src/trendify/api/generator/data_product_collection.py deleted file mode 100644 index bdf7131..0000000 --- a/src/trendify/api/generator/data_product_collection.py +++ /dev/null @@ -1,853 +0,0 @@ -from __future__ import annotations - -import os -from itertools import chain -from pathlib import Path -from typing import TYPE_CHECKING, Union, List, Iterable, Any, Callable, Tuple, Type -import logging - -import pandas as pd - -try: - from typing import Self -except: - from typing_extensions 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", - "flatten", - "ProductEntryMetadata", - "ProductIndexMap", -] - - -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[Path, int]: - return {v: k for k, v in self.entries.items()} - - @classmethod - @validate_call - def get_index( - cls, - save_dir: Path, - metadata: ProductEntryMetadata, - ) -> Self: - 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): - """ - 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: Union[Iterable, 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 | None = None - - def __init__(self, **kwargs: Any): - DataProduct.deserialize_child_classes(key="elements", **kwargs) - super().__init__(**kwargs) - - @classmethod - def from_iterable(cls, *products: Tuple[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[R]: - """ - 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 self.elements 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 not tag in e.tags] - ) - case (False, False): - # assert self.elements 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[R]: - """ - 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 self.elements 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 - 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") - - @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 | None = "*.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: List[TableEntry] = collection.get_products( - tag=tag, - object_type=TableEntry, - ).elements - - 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: List[Trace2D] = collection.get_products( - tag=tag, - object_type=Trace2D, - ).elements - points: List[Point2D] = collection.get_products( - tag=tag, - object_type=Point2D, - ).elements - axlines: List[AxLine] = collection.get_products( - tag=tag, - object_type=AxLine, - ).elements - - 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: List[HistogramEntry] = collection.get_products( - tag=tag, - object_type=HistogramEntry, - ).elements - - 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.build_tables(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(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( - 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 - - 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/api/generator/data_product_generator.py b/src/trendify/api/generator/data_product_generator.py deleted file mode 100644 index 66595a2..0000000 --- a/src/trendify/api/generator/data_product_generator.py +++ /dev/null @@ -1,46 +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/api/generator/histogrammer.py b/src/trendify/api/generator/histogrammer.py deleted file mode 100644 index 4846aa6..0000000 --- a/src/trendify/api/generator/histogrammer.py +++ /dev/null @@ -1,125 +0,0 @@ -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.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/api/generator/table_builder.py b/src/trendify/api/generator/table_builder.py deleted file mode 100644 index 6dc5db0..0000000 --- a/src/trendify/api/generator/table_builder.py +++ /dev/null @@ -1,150 +0,0 @@ -from __future__ import annotations - -from pathlib import Path -from typing import List -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(tag=tag, object_type=TableEntry).elements - ) - - 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 {str(save_path_partial)}_*.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: {str(e)}" - ) - - @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/api/generator/xy_data_plotter.py b/src/trendify/api/generator/xy_data_plotter.py deleted file mode 100644 index bb47a16..0000000 --- a/src/trendify/api/generator/xy_data_plotter.py +++ /dev/null @@ -1,192 +0,0 @@ -from __future__ import annotations - -from pathlib import Path -from typing import List -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: 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: - 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] - ) - - {None} - ) - 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: - saf.ax.scatter(x, y, **marker.as_scatter_plot_kwargs()) - - 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/api/plotting/__init__.py b/src/trendify/api/plotting/__init__.py deleted file mode 100644 index 88b062a..0000000 --- a/src/trendify/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/api/plotting/trace.py b/src/trendify/api/plotting/trace.py deleted file mode 100644 index 5b90af3..0000000 --- a/src/trendify/api/plotting/trace.py +++ /dev/null @@ -1,189 +0,0 @@ -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 -except: - from typing_extensions import Self, 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/api/styling/__init__.py b/src/trendify/api/styling/__init__.py deleted file mode 100644 index fd03478..0000000 --- a/src/trendify/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/assets/logo.svg b/src/trendify/assets/logo.svg deleted file mode 100644 index 3fa2c70..0000000 --- a/src/trendify/assets/logo.svg +++ /dev/null @@ -1,3 +0,0 @@ - - - diff --git a/src/trendify/assets/logo_white_bg.svg b/src/trendify/assets/logo_white_bg.svg deleted file mode 100644 index c6a97bc..0000000 --- a/src/trendify/assets/logo_white_bg.svg +++ /dev/null @@ -1,11 +0,0 @@ - - - - - - - - - - - diff --git a/src/trendify/base/__init__.py b/src/trendify/base/__init__.py new file mode 100644 index 0000000..4b5a0d2 --- /dev/null +++ b/src/trendify/base/__init__.py @@ -0,0 +1,34 @@ +from trendify.base import helpers +from trendify.base import pen +from trendify.base import record + +from trendify.base.helpers import ( + HashableBase, + R, + RecordType, + Tag, + Tags, +) +from trendify.base.pen import ( + Pen, +) +from trendify.base.record import ( + Record, + RecordGenerator, + RecordList, +) + +__all__ = [ + "HashableBase", + "Pen", + "R", + "Record", + "RecordGenerator", + "RecordList", + "RecordType", + "Tag", + "Tags", + "helpers", + "pen", + "record", +] diff --git a/src/trendify/base/helpers.py b/src/trendify/base/helpers.py new file mode 100644 index 0000000..f306f4b --- /dev/null +++ b/src/trendify/base/helpers.py @@ -0,0 +1,76 @@ +""" +Small shared building blocks used across the rest of the package: the `Tag`/`Tags` type +aliases every `Record` and the store's tag-encoding logic build on, `HashableBase` for +pydantic models that need to go in a `set`, and the `RecordType` enum. +""" + +from __future__ import annotations + +import logging +from enum import StrEnum +from typing import TYPE_CHECKING, TypeVar + +from pydantic import BaseModel + +if TYPE_CHECKING: + from trendify.base.record import Record + +logger = logging.getLogger(__name__) + +__all__ = [ + "HashableBase", + "R", + "RecordType", + "Tag", + "Tags", +] + +R = TypeVar("R", bound="Record") +"""Bound to `Record`: used by `RecordStore.get_records`/`get_records_of_type` so a +call like `get_records_of_type(Point2D)` returns `list[Point2D]` rather than `list[Record]`. +""" + +Tag = str | int | tuple[str | int, ...] +""" +Determines what types can be used to define a tag. + +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] +""" +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 RecordType(StrEnum): + """Defines all record types. Used to type-cast URL info in server to validate.""" + + XY_DATA = "xy_data" + """XY_DATA name""" + + TRACE_2D = "trace_2d" + """TRACE_2D name""" + + POINT_2D = "point_2d" + """POINT_2D name""" + + TABLE_ENTRY = "table_entry" + """TABLE_ENTRY name""" + + HISTOGRAM_ENTRY = "histogram_entry" + """HISTOGRAM_ENTRY name""" diff --git a/src/trendify/api/base/pen.py b/src/trendify/base/pen.py similarity index 67% rename from src/trendify/api/base/pen.py rename to src/trendify/base/pen.py index dc44fef..660d824 100644 --- a/src/trendify/api/base/pen.py +++ b/src/trendify/base/pen.py @@ -1,35 +1,39 @@ -from __future__ import annotations +""" +`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 typing import Optional, Tuple, Union +from __future__ import annotations +from matplotlib.colors import to_rgba from pydantic import ConfigDict -from matplotlib.colors import to_rgba, to_rgb -import numpy as np -from trendify.api.base.helpers import HashableBase +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" + """Defines the pen drawing style.""" + + color: tuple[float, float, float] | tuple[float, float, float, float] | str = "k" + """Color of line""" + size: float = 1 + """Line width""" + alpha: float = 1 + """Opacity from 0 to 1 (inclusive)""" + zorder: float = 0 - linestyle: Union[str, Tuple[int, Tuple[int, ...]]] = "-" - label: Union[str, None] = None + """Prioritization of trace line relative to other plotted data""" + + linestyle: str | tuple[int, tuple[int, ...]] | None = "-" + """Linestyle to plot. Supports `str` or `tuple` definition ([matplotlib documentation](https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html)). + Use `None` to draw no line at all, for a markers-only trace.""" + + label: str | None = None + """Legend label""" model_config = ConfigDict(extra="forbid") @@ -40,14 +44,32 @@ def as_scatter_plot_kwargs(self): return { "color": self.color, "linewidth": self.size, - "linestyle": self.linestyle, + # matplotlib's `Line2D` treats `linestyle=None` as "use the default style", not + # "no line" (only the string "none" means that), so `None` is translated here to + # keep `Pen.linestyle = None` meaning "no line" from `has_line`'s point of view. + "linestyle": self.linestyle if self.linestyle is not None else "none", "alpha": self.alpha, "zorder": self.zorder, "label": self.label, } + @property + def has_line(self) -> bool: + """ + Whether this pen draws a visible line. `False` when `linestyle` is `None`, which + callers like `Trace2D.add_to_plotly` use to render markers only, with no connecting + line. + """ + return self.linestyle is not None + def _convert_linestyle_to_plotly(self) -> str: """Convert matplotlib linestyle to plotly dash style""" + # `None` means "no line" (see `has_line`); callers exclude "lines" from the Plotly + # trace's `mode` in that case, so this value is never actually rendered, but return + # something valid regardless. + if self.linestyle is None: + return "solid" + # Handle string styles style_map = { "-": "solid", @@ -68,6 +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): @@ -101,6 +124,7 @@ 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/base/record.py b/src/trendify/base/record.py new file mode 100644 index 0000000..5b82abc --- /dev/null +++ b/src/trendify/base/record.py @@ -0,0 +1,148 @@ +""" +Base `Record` pydantic model, the `RecordGenerator`/`RecordList` type aliases user code +implements against, and the subclass registry `RecordStore` uses to deserialize a stored +`record_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 pydantic import ( + BaseModel, + ConfigDict, + InstanceOf, + SerializeAsAny, + computed_field, + model_validator, +) + +from trendify.base.helpers import Tags + +logger = logging.getLogger(__name__) + +__all__ = ["Record", "RecordGenerator", "RecordList"] + +_record_subclass_registry: dict[str, type[Record]] = {} + + +class Record(BaseModel): + """ + Base class for records to be generated and handled. + """ + + 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 mouseover in Grafana.""" + + @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 record_type(self) -> str: + """ + Returns: + (str): Record type should be the same as the class name. + The record type is used to search for records 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.record.Record.deserialize] method + """ + super().__init_subclass__(**kwargs) + _record_subclass_registry[cls.__name__] = cls + logger.debug(f"Registered Record subclass {cls.__name__!r}") + + model_config = ConfigDict(extra="allow") + + def append_to_list(self, to_add_to: list): + """ + Appends self to list. + + Args: + to_add_to (List): list to which `self` will be appended + + Returns: + (Self): returns instance of `self` + + """ + to_add_to.append(self) + return self + + @classmethod + def registry(cls) -> dict[str, type[Record]]: + """ + Returns: + (dict[str, type[Record]]): a copy of the record_type -> class registry, + used by the store to resolve which leaf `record_type` names correspond to a + given (possibly non-leaf) type when filtering by `object_type`. + + """ + return dict(_record_subclass_registry) + + @classmethod + def deserialize(cls, record_type: str, payload: str) -> Record: + """ + Loads a stored JSON payload into the pydantic dataclass registered under `record_type`. + + Args: + record_type (str): the `record_type` discriminator string (same as the class name) + payload (str): the raw JSON text (e.g. from the store's `records.payload` column) + + Returns: + (Record): the reconstructed, correctly-typed record instance + + """ + try: + duck_type = _record_subclass_registry[record_type] + except KeyError: + logger.error( + f"No registered Record subclass named {record_type!r}. Known types: " + f"{sorted(_record_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]): + self.metadata = new + return self + + +RecordList = list[SerializeAsAny[InstanceOf[Record]]] +"""List of serializable [Record][trendify.base.record.Record] or child classes thereof""" + +RecordGenerator = Callable[[Path], RecordList] +""" +Callable method type. Users must provide a `RecordGenerator` to map over raw data. + +Args: + path (Path): Workdir holding raw data (Should be one per run from a batch) + +Returns: + (RecordList): List of records to be sorted and used to produce assets +""" diff --git a/src/trendify/cli.py b/src/trendify/cli.py new file mode 100644 index 0000000..fd92d4e --- /dev/null +++ b/src/trendify/cli.py @@ -0,0 +1,401 @@ +""" +`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 +import importlib +import importlib.util +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.record import RecordGenerator +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.record_store import RecordStore + +__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 records 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 records and static assets from raw data. + """ + + +InputDirectoriesOption = typer.Option( + ..., + "-i", + "--input-directories", + help="Raw data directories (or glob patterns) to map the record generator over.", +) +RecordGeneratorOption = typer.Option( + ..., + "-g", + "--record-generator", + help=( + "RecordGenerator 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.", + 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." +) +SkipTablesOption = typer.Option( + False, "--skip-tables", help="Suppress TableEntry CSV output." +) +SkipXyPlotsOption = typer.Option( + False, + "--skip-xy-plots", + help="Suppress Point2D/Scatter2D/Trace2D/AxLine plot output.", +) +SkipHistogramsOption = typer.Option( + False, + "--skip-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.", +) +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, quiet: int, logo: bool = True) -> logging.Logger: + """ + 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_records`'s multiprocess path picks up these + same handlers via its `QueueListener` (see `trendify.generator.generate`). + """ + if logo: + print_logo() + level = logging.INFO - (verbose * 10) + (quiet * 10) + return setup_logger(level=level) + + +def _resolve_input_directories(patterns: list[str], ctx: typer.Context) -> list[Path]: + """ + Expands glob patterns (and plain paths) into a list of directories. A match on a file + resolves to its parent directory. + + `ctx.args` picks up bare paths that Click couldn't bind to `-i`/`--input-directories` + directly. Git Bash/MSYS2 glob-expands wildcard arguments to *native* (non-MSYS) + executables at process-spawn time, even when the argument was quoted in bash (bash + itself correctly leaves a quoted `*` alone, but MSYS's runtime re-expands it before the + native `trendify` executable ever sees argv). That turns `-i "data/*"` into + `-i data/1 data/2 data/3 ...`, but `-i` only consumes the one value immediately + following it, so `data/2`/`data/3`/etc. would otherwise be rejected as unexpected extra + arguments (the commands using this opt into that via + `context_settings={"allow_extra_args": True}`). + """ + dirs: list[Path] = [] + for pattern in [*patterns, *ctx.args]: + for match in glob.glob(pattern, root_dir=Path.cwd(), 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_record_generator(spec: str) -> RecordGenerator: + """ + Resolves a `"module:function"` / `"module:Class.method"` / `"/path/to/file.py:function"` + spec string into a callable. + """ + # `rpartition` (not `partition`): a Windows absolute path's drive letter is itself a + # colon (`C:\...\gen.py:generate`), so splitting on the *first* colon mistakes the + # drive letter for the module/function separator. The attr path never contains a + # colon, so the *last* colon is always the real separator. + module_path, _, attr_path = spec.rpartition(":") + 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(): + file_path = Path(module_path).resolve() + # `n_procs > 1` sends `record_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) + + obj: object = module + for part in attr_path.split("."): + obj = getattr(obj, part) + return cast(RecordGenerator, obj) + + +@app.command(name="generate", context_settings={"allow_extra_args": True}) +def generate( + ctx: typer.Context, + input_directories: list[str] = InputDirectoriesOption, + record_generator: str = RecordGeneratorOption, + output_directory: Path = OutputDirectoryOption, + n_procs: int = NProcsOption, + verbose: int = VerboseOption, + quiet: int = QuietOption, +) -> None: + """Generate tagged records from raw data directories.""" + _configure_logging(verbose, quiet) + pipeline = TrendifyPipeline(output_dir=output_directory, n_procs=n_procs) + _total = pipeline.generate( + record_generator=_resolve_record_generator(record_generator), + data_dirs=_resolve_input_directories(input_directories, ctx), + ) + + +@app.command(name="render") +def render( + ctx: typer.Context, + output_directory: Path = OutputDirectoryOption, + verbose: int = VerboseOption, + quiet: int = QuietOption, +) -> None: + """Render CSV tables and matplotlib figures from already-generated records.""" + _configure_logging(verbose, quiet) + pipeline = TrendifyPipeline(output_dir=output_directory) + pipeline.render() + typer.echo(f"Rendered assets to {pipeline.assets_dir}") + + +@app.command(name="run", context_settings={"allow_extra_args": True}) +def run( + ctx: typer.Context, + input_directories: list[str] = InputDirectoriesOption, + record_generator: str = RecordGeneratorOption, + output_directory: Path = OutputDirectoryOption, + n_procs: int = NProcsOption, + verbose: int = VerboseOption, + quiet: int = QuietOption, +) -> None: + """Generate records and render assets in one step.""" + logger = _configure_logging(verbose, quiet) + pipeline = TrendifyPipeline(output_dir=output_directory, n_procs=n_procs) + total = pipeline.run( + record_generator=_resolve_record_generator(record_generator), + data_dirs=_resolve_input_directories(input_directories, ctx), + ) + logger.info(f"Wrote {total} records; 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.""" + logger = _configure_logging(verbose, quiet) + make_example_data(workdir=workdir, n_folders=n_folders) + logger.info(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="viewer") +def viewer( + ctx: typer.Context, + db_path: Path = DbPathArgument, + host: str = HostOption, + port: int = PortOption, + verbose: int = VerboseOption, + quiet: int = QuietOption, +) -> None: + """Launch a local web dashboard for browsing a trendify.db's records.""" + _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 RecordStore.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 + + 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]" + + console.print( + Panel( + f"""\n{connection_info}\n\n[yellow]Press CTRL+C to stop[/yellow]""", + border_style="indian_red1", + expand=False, + title="Trendify Viewer is Live!", + ) + ) + + uvicorn.run(create_app(db_path), host=host, port=port, log_level="critical") + + +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/examples.py b/src/trendify/examples.py index b84eef1..1df70ef 100644 --- a/src/trendify/examples.py +++ b/src/trendify/examples.py @@ -1,85 +1,95 @@ """ -Module defines a method for making sample data and defines a +Sample data + a `RecordGenerator` 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 matplotlib.pyplot as plt +import polars as pl -# Local imports import trendify -__all__ = ["make_example_data", "example_data_product_generator"] +__all__ = ["example_record_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() +class ColumnName(StrEnum): + TIME = "time" + WAVE_1 = "wave_1" + WAVE_2 = "wave_2" + WAVE_3 = "wave_3" def make_example_data(workdir: Path, n_folders: int = 10): """ - Makes some sample data from which to generate products + Makes a highly diverse, varied sample dataset featuring distinct waveform signatures. 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) + 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=60, high=80 + ) # Bumped slightly to resolve sharp shapes beautifully t = np.linspace(0, 1, n_samples) - 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) - - rng = np.random.default_rng(seed=42) - noise_level = 0.05 - d = [t] + [ - 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) + amplitudes = rng.uniform(low=0.8, high=1.5, size=3) + noise_level = 0.03 + + # --- 1. WAVE_1: Damped Harmonic Oscillation --- + decay_rate = rng.uniform(1.5, 3.5) + freq = rng.uniform(3.0, 5.0) + wave_1 = amplitudes[0] * np.sin(2 * np.pi * freq * t) * np.exp( + -decay_rate * t + ) + noise_level * rng.normal(size=len(t)) + + # --- 2. WAVE_2: Sharp Step / Discontinuity --- + step_time = rng.uniform(0.3, 0.6) + base_level = rng.uniform(-0.2, 0.2) + wave_2 = np.where( + t < step_time, base_level, base_level + amplitudes[1] + ) + noise_level * rng.normal(size=len(t)) + + # --- 3. WAVE_3: Logistic Saturation (S-Curve) --- + midpoint = rng.uniform(0.4, 0.6) + steepness = rng.uniform(10.0, 16.0) + wave_3 = amplitudes[2] / ( + 1.0 + np.exp(-steepness * (t - midpoint)) + ) + noise_level * rng.normal(size=len(t)) + + # Collect metadata parameters uniquely for this run to write to stdin.csv + inputs: dict[str, int | float] = { + "n_samples": int(n_samples), + "w1_decay": float(decay_rate), + "w1_freq": float(freq), + "w2_step_time": float(step_time), + "w3_midpoint": float(midpoint), + } + + pl.DataFrame( + {"key": list(inputs.keys()), "value": [str(v) for v in inputs.values()]} + ).write_csv(subdir.joinpath("stdin.csv"), include_header=False) + + # Package channels cleanly matching your original structure + data_channels = [t, wave_1, wave_2, wave_3] + pl.DataFrame(dict(zip([str(e) for e in ColumnName], data_channels))).write_csv( + subdir.joinpath("results.csv") + ) def transform(data: np.ndarray, scale: trendify.AxisScale) -> np.ndarray: @@ -91,29 +101,36 @@ def transform(data: np.ndarray, scale: trendify.AxisScale) -> np.ndarray: raise ValueError(f"Unsupported scale: {scale}") -def example_data_product_generator(workdir: Path) -> trendify.ProductList: +def example_record_generator(workdir: Path) -> trendify.RecordList: """ - Processes the generated sample data in given workdir returning several types of data products. + Processes the generated sample data in given workdir returning several types of records. Args: workdir (Path): Directory containing sample data. + """ - products = [] + records = [] - 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[ColumnName.TIME].to_numpy() + value_columns = [c for c in df.columns if c != ColumnName.TIME] - # 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))] run_num = workdir.name + trendify.Format2D( + tags=[("an_xy_plot", "trace_plot")], + grid=trendify.Grid.from_theme(trendify.GridTheme.MATLAB), + scale_x=trendify.AxisScale.LINEAR, + scale_y=trendify.AxisScale.LINEAR, + ).append_to_list(records) traces = [ - trendify.Trace2D.from_xy( - x=df.index, - y=df[col].values, + trendify.Trace2D( + x=time, + y=df[col].to_numpy(), tags=[("an_xy_plot", "trace_plot")], pen=trendify.Pen( label=col, @@ -121,21 +138,22 @@ def example_data_product_generator(workdir: Path) -> trendify.ProductList: linestyle=linestyles[i % len(linestyles)], alpha=alphas[i], ), - format2d=trendify.Format2D( - grid=trendify.Grid.from_theme(trendify.GridTheme.MATLAB), - scale_x=trendify.AxisScale.LINEAR, - scale_y=trendify.AxisScale.LINEAR, - ), ) - .append_to_list(products) + .append_to_list(records) .set_metadata({"run_num": run_num}) - for i, col in enumerate(df.columns) + for i, col in enumerate(value_columns) ] + trendify.Format2D( + tags=[("an_xy_plot", "another_trace_plot")], + grid=trendify.Grid.from_theme(trendify.GridTheme.MATLAB), + scale_x=trendify.AxisScale.LINEAR, + scale_y=trendify.AxisScale.LINEAR, + ).append_to_list(records) traces = [ - trendify.Trace2D.from_xy( - x=df.index, - y=df[col].values, + trendify.Trace2D( + x=time, + y=df[col].to_numpy(), tags=[("an_xy_plot", "another_trace_plot")], pen=trendify.Pen( label=col, @@ -143,21 +161,25 @@ def example_data_product_generator(workdir: Path) -> trendify.ProductList: linestyle=linestyles[i % len(linestyles)], alpha=alphas[i], ), - format2d=trendify.Format2D( - grid=trendify.Grid.from_theme(trendify.GridTheme.MATLAB), - scale_x=trendify.AxisScale.LINEAR, - scale_y=trendify.AxisScale.LINEAR, - ), ) - .append_to_list(products) + .append_to_list(records) .set_metadata({"run_num": run_num}) - for i, col in enumerate(df.columns) + for i, col in enumerate(value_columns) ] + trendify.Format2D( + tags=[("another_xy_plot", "trace_plot")], + grid=trendify.Grid.from_theme(trendify.GridTheme.MATLAB), + scale_x=trendify.AxisScale.LINEAR, + scale_y=trendify.AxisScale.LINEAR, + legend=trendify.Legend(loc=trendify.LegendLocation.LOWER_CENTER), + figure_width=8, + figure_height=4, + ).append_to_list(records) traces = [ - trendify.Trace2D.from_xy( - x=df.index, - y=df[col].values, + trendify.Trace2D( + x=time, + y=df[col].to_numpy(), tags=[("another_xy_plot", "trace_plot")], pen=trendify.Pen( label=col, @@ -165,36 +187,39 @@ def example_data_product_generator(workdir: Path) -> trendify.ProductList: linestyle=linestyles[i % len(linestyles)], alpha=alphas[i], ), - format2d=trendify.Format2D( - grid=trendify.Grid.from_theme(trendify.GridTheme.MATLAB), - scale_x=trendify.AxisScale.LINEAR, - scale_y=trendify.AxisScale.LINEAR, - legend=trendify.Legend(loc=trendify.LegendLocation.LOWER_CENTER), - figure_width=8, - figure_height=4, - ), ) - .append_to_list(products) + .append_to_list(records) .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")], value=0.5, orientation=trendify.LineOrientation.VERTICAL, pen=trendify.Pen(zorder=11, color="k"), - ).append_to_list(products) + ).append_to_list(records) trendify.AxLine( tags=[("another_xy_plot", "trace_plot")], value=0.45, orientation=trendify.LineOrientation.VERTICAL, pen=trendify.Pen(zorder=9, color="r"), - ).append_to_list(products) - + ).append_to_list(records) + + trendify.Format2D( + tags=["trace_plot_log_y"], + legend=trendify.Legend( + title="example", + loc=trendify.LegendLocation.CENTER_RIGHT, + framealpha=0, + ), + grid=trendify.Grid.from_theme(trendify.GridTheme.MATLAB), + scale_x=trendify.AxisScale.LINEAR, + scale_y=trendify.AxisScale.LOG, + ).append_to_list(records) traces = [ - trendify.Trace2D.from_xy( - x=df.index, - y=transform(df[col].values, trendify.AxisScale.LOG), + trendify.Trace2D( + x=time, + y=transform(df[col].to_numpy(), trendify.AxisScale.LOG), tags=["trace_plot_log_y"], pen=trendify.Pen( label=col, @@ -202,26 +227,29 @@ def example_data_product_generator(workdir: Path) -> trendify.ProductList: linestyle=linestyles[i % len(linestyles)], alpha=alphas[i], ), - format2d=trendify.Format2D( - legend=trendify.Legend( - title="example", - loc=trendify.LegendLocation.CENTER_RIGHT, - framealpha=0, - ), - grid=trendify.Grid.from_theme(trendify.GridTheme.MATLAB), - scale_x=trendify.AxisScale.LINEAR, - scale_y=trendify.AxisScale.LOG, - ), ) - .append_to_list(products) + .append_to_list(records) .set_metadata({"run_num": run_num}) - for i, col in enumerate(df.columns) + for i, col in enumerate(value_columns) ] + trendify.Format2D( + tags=["trace_plot_log_xy"], + legend=trendify.Legend( + fancybox=False, + loc=trendify.LegendLocation.CENTER, + edgecolor="red", + framealpha=1, + ), + lim_y=(0.1, 10), + grid=trendify.Grid.from_theme(trendify.GridTheme.MATLAB), + scale_x=trendify.AxisScale.LOG, + scale_y=trendify.AxisScale.LOG, + ).append_to_list(records) traces = [ - trendify.Trace2D.from_xy( - x=transform(df.index, trendify.AxisScale.LOG), - y=transform(df[col].values, trendify.AxisScale.LOG), + trendify.Trace2D( + 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, @@ -231,23 +259,10 @@ def example_data_product_generator(workdir: Path) -> trendify.ProductList: zorder=[1, 1, 2][i], size=5, ), - format2d=trendify.Format2D( - legend=trendify.Legend( - fancybox=False, - loc=trendify.LegendLocation.CENTER, - edgecolor="red", - framealpha=1, - ), - lim_y_min=0.1, - lim_y_max=10, - grid=trendify.Grid.from_theme(trendify.GridTheme.MATLAB), - scale_x=trendify.AxisScale.LOG, - scale_y=trendify.AxisScale.LOG, - ), ) - .append_to_list(products) + .append_to_list(records) .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"], @@ -256,12 +271,19 @@ def example_data_product_generator(workdir: Path) -> trendify.ProductList: pen=trendify.Pen( alpha=0.5, color="r", linestyle="-", label="test line", zorder=1 ), - ).append_to_list(products) - + ).append_to_list(records) + + trendify.Format2D( + tags=["trace_plot_log_x"], + legend=trendify.Legend(visible=False), + grid=trendify.Grid.from_theme(trendify.GridTheme.MATLAB), + scale_x=trendify.AxisScale.LOG, + scale_y=trendify.AxisScale.LINEAR, + ).append_to_list(records) traces = [ - trendify.Trace2D.from_xy( - x=transform(df.index, trendify.AxisScale.LOG), - y=df[col].values, + trendify.Trace2D( + x=transform(time, trendify.AxisScale.LOG), + y=df[col].to_numpy(), tags=["trace_plot_log_x"], pen=trendify.Pen( label=col, @@ -269,78 +291,126 @@ def example_data_product_generator(workdir: Path) -> trendify.ProductList: linestyle=linestyles[i % len(linestyles)], alpha=alphas[i], ), - format2d=trendify.Format2D( - legend=trendify.Legend(visible=False), - grid=trendify.Grid.from_theme(trendify.GridTheme.MATLAB), - scale_x=trendify.AxisScale.LOG, - scale_y=trendify.AxisScale.LINEAR, - ), ) - .append_to_list(products) + .append_to_list(records) .set_metadata({"run_num": run_num}) - for i, col in enumerate(df.columns) + for i, col in enumerate(value_columns) ] + trendify.Format2D(tags=["scatter_plot"], title_fig="N Points").append_to_list( + records + ) for i, trace in enumerate(traces): trendify.Point2D( x=workdir.name, - y=len(trace.y), + y=len(trace.x), marker=trendify.Marker( size=10, label=trace.pen.label, color=trace.pen.color, alpha=alphas[i], ), - format2d=trendify.Format2D(title_fig="N Points"), tags=["scatter_plot"], - ).append_to_list(products).set_metadata({"run_num": run_num}) + ).append_to_list(records).set_metadata({"run_num": run_num}) + + trendify.Format2D( + tags=[("nested_plots", "group_a", "deep_trace")], + grid=trendify.Grid.from_theme(trendify.GridTheme.MATLAB), + ).append_to_list(records) + trendify.Trace2D( + 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]), + ).append_to_list(records).set_metadata({"run_num": run_num}) + trendify.Format2D( + tags=[("nested_plots", "group_b", "deep_trace")], + grid=trendify.Grid.from_theme(trendify.GridTheme.MATLAB), + ).append_to_list(records) + trendify.Trace2D( + 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]), + ).append_to_list(records).set_metadata({"run_num": run_num}) + + trendify.Format2D( + tags=["histogram"], + title_ax="Idk lol", + title_fig="Idk lol2", + legend=trendify.Legend( + loc=trendify.LegendLocation.UPPER_LEFT, + bbox_to_anchor=(1.05, 1), + ), + label_x="Series value", + label_y="Counts", + grid=trendify.Grid.from_theme(trendify.GridTheme.MATLAB), + ).append_to_list(records) - for name, series in df.items(): + for col in value_columns: + series = df[col] + trendify.TableEntry( + row=workdir.name, + col=col, + value=series.len(), + tags=[("tables", "lengths")], + unit=None, + ).append_to_list(records) trendify.TableEntry( row=workdir.name, - col=name, - value=len(series), - tags=["table"], + col=col, + value=cast(float, series.mean()), + tags=[("tables", "means")], unit=None, - ).append_to_list(products) + ).append_to_list(records) + trendify.TableEntry( + row=workdir.name, + col=col, + value=cast(float, series.std()), + tags=[("tables", "std_devs")], + unit=None, + ).append_to_list(records) + trendify.TableEntry( + row=workdir.name, + col=col, + value=cast(float, series.max()), + tags=[("extrema", "max")], + unit=None, + ).append_to_list(records) + trendify.TableEntry( + row=workdir.name, + col=col, + value=cast(float, series.min()), + tags=[("extrema", "min")], + unit=None, + ).append_to_list(records) + mean = cast(float, series.mean()) trendify.HistogramEntry( tags=["histogram"], - value=series.mean(), - format2d=trendify.Format2D( - title_ax="Idk lol", - title_fig="Idk lol2", - legend=trendify.Legend( - loc=trendify.LegendLocation.UPPER_LEFT, - bbox_to_anchor=(1.05, 1), - ), - label_x="Series value", - label_y="Counts", - grid=trendify.Grid.from_theme(trendify.GridTheme.MATLAB), - ), + value=mean, style=trendify.HistogramStyle( alpha_face=0.75, alpha_edge=1, bins=6, label="A histogram entry", ), - ).append_to_list(products) + ).append_to_list(records) 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) + ).append_to_list(records) - return products + return records 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( @@ -368,7 +438,7 @@ 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") @@ -376,35 +446,15 @@ 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( - product_generator=example_data_product_generator, + trendify.generate_records( + record_generator=example_record_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/__init__.py b/src/trendify/formats/__init__.py new file mode 100644 index 0000000..6ebc1b4 --- /dev/null +++ b/src/trendify/formats/__init__.py @@ -0,0 +1,26 @@ +from trendify.formats import format2d +from trendify.formats import table + +from trendify.formats.format2d import ( + AxisScale, + Format2D, + PlottableData2D, + Rastered, + Vector, + XYData, +) +from trendify.formats.table import ( + TableEntry, +) + +__all__ = [ + "AxisScale", + "Format2D", + "PlottableData2D", + "Rastered", + "TableEntry", + "Vector", + "XYData", + "format2d", + "table", +] diff --git a/src/trendify/formats/format2d.py b/src/trendify/formats/format2d.py new file mode 100644 index 0000000..6e10c67 --- /dev/null +++ b/src/trendify/formats/format2d.py @@ -0,0 +1,128 @@ +""" +`Format2D` (axis/legend/grid/scale/limit settings for a plot) and the `PlottableData2D`/ +`XYData` base classes that know how to draw themselves onto a Plotly figure. + +`Format2D` is a `Record` in its own right, written once per tag (like any other +record) rather than embedded in every plotted record; see `RecordStore.write_run`'s +upsert-by-tag handling for it, and `render.py`'s single per-tag lookup. +""" + +from __future__ import annotations + +import logging +from abc import ABC, abstractmethod +from enum import StrEnum +from typing import TYPE_CHECKING, Literal + +from pydantic import BaseModel, ConfigDict, Field + +from trendify.base.record import Record +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", "Rastered", "Vector", "XYData"] + + +class AxisScale(StrEnum): + LINEAR = "linear" + """Format axis as linear""" + LOG = "log" + """Format axis with log base 10""" + + +class Rastered(BaseModel): + """Renders to a raster image format (matplotlib's `dpi` setting applies).""" + + type: Literal["rastered"] = "rastered" + """Discriminator identifying this renderer variant.""" + + filetype: str = ".jpg" + """File extension used when saving the rendered image.""" + + dpi: int = 500 + """Resolution (dots per inch) used when saving the rendered image.""" + + +class Vector(BaseModel): + """Renders to a vector image format (matplotlib's `dpi` setting doesn't apply).""" + + type: Literal["vector"] = "vector" + """Discriminator identifying this renderer variant.""" + + filetype: str = ".svg" + """File extension used when saving the rendered image.""" + + +class Format2D(Record): + """ + Formatting data for matplotlib figure and axes. Written once per tag. + """ + + title_fig: str | None = None + """Sets the [figure title][matplotlib.figure.Figure.suptitle].""" + + legend: Legend | None = Legend() + """Sets the [legend style][trendify.styling.legend.Legend].""" + + title_ax: str | None = None + """Sets the [axis title][matplotlib.axes.Axes.set_title].""" + + label_x: str | None = None + """Sets the [x-axis label][matplotlib.axes.Axes.set_xlabel].""" + + label_y: str | None = None + """Sets the [y-axis label][matplotlib.axes.Axes.set_ylabel].""" + + lim_x: tuple[float | None, float | None] = (None, None) + """x-axis (lower, upper) bound. Either side `None` means autofit that side to whatever's + plotted.""" + + lim_y: tuple[float | None, float | None] = (None, None) + """y-axis (lower, upper) bound, same semantics as `lim_x`.""" + + grid: Grid | None = None + """Sets the [grid][matplotlib.pyplot.grid].""" + + scale_x: AxisScale = AxisScale.LINEAR + """Sets the x axis scale to an option from [AxisScale][trendify.formats.format2d.AxisScale].""" + + scale_y: AxisScale = AxisScale.LINEAR + """Sets the y axis scale to an option from [AxisScale][trendify.formats.format2d.AxisScale].""" + + figure_width: float = 6.4 + """Sets the width of the rendered figure in inches.""" + + figure_height: float = 4.8 + """Sets the height of the rendered figure in inches.""" + + renderer: Rastered | Vector = Field(default_factory=Rastered, discriminator="type") + """Chooses the saved file format: `Rastered` for a raster image at a configurable `dpi`, + or `Vector` for a vector image.""" + + model_config = ConfigDict(extra="forbid") + + +class PlottableData2D(Record, ABC): + """ + Base class for children of Record to be plotted ax xy data on a 2D plot + """ + + @abstractmethod + def add_to_plotly(self, plotly_figure: PlotlyFigure) -> PlotlyFigure: + """ + Add this record to a plotly figure + + Args: + plotly_figure (PlotlyFigure): Plotly figure to add data to + + """ + + +class XYData(PlottableData2D): + """ + Base class for children of Record 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..220ccb6 --- /dev/null +++ b/src/trendify/formats/table.py @@ -0,0 +1,55 @@ +""" +`TableEntry`: the one `Record` 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 + +from pydantic import ConfigDict + +from trendify.base.record import Record + +logger = logging.getLogger(__name__) + +__all__ = ["TableEntry"] + + +class TableEntry(Record): + """ + 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). + """ + + row: float | str + """Row label""" + + col: float | str + """Column label""" + + value: float | str | bool + """Value""" + + unit: str | None = None + """Units for value""" + + 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/gallery.py b/src/trendify/gallery.py deleted file mode 100644 index e7f496f..0000000 --- a/src/trendify/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: {str(e)}") - 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/generator/__init__.py b/src/trendify/generator/__init__.py new file mode 100644 index 0000000..0780f0d --- /dev/null +++ b/src/trendify/generator/__init__.py @@ -0,0 +1,36 @@ +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_records, + get_sorted_dirs, +) +from trendify.generator.histogrammer import ( + Histogrammer, +) +from trendify.generator.render import ( + render_assets, +) +from trendify.generator.table_builder import ( + TableBuilder, +) +from trendify.generator.xy_data_plotter import ( + XYDataPlotter, +) + +__all__ = [ + "Histogrammer", + "TableBuilder", + "XYDataPlotter", + "generate", + "generate_records", + "get_sorted_dirs", + "histogrammer", + "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..6d6ef9d --- /dev/null +++ b/src/trendify/generator/generate.py @@ -0,0 +1,167 @@ +""" +Generate pipeline: maps a user-supplied `RecordGenerator` over raw-data run directories and +writes the resulting records straight to a `RecordStore`-backed `.db` file. + +There is no separate "sort" step: tags are indexed at write time, so retrieving records by +tag is an indexed query against `RecordStore`, not a physical grouping step that has to run +before rendering can start. + +Worker processes only ever *compute* records; 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 `record_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, as_completed +from pathlib import Path +from typing import Any + +from trendify.base.record import RecordGenerator, RecordList +from trendify.log import create_queue_listener +from trendify.log import worker_init as _init_worker_logging +from trendify.progress import ProgressCallback, ProgressEvent +from trendify.store.record_store import RecordStore + +__all__ = ["generate_records", "get_sorted_dirs"] + +logger = logging.getLogger(__name__) + +# Per-process global set by `_init_worker`: the one thing a compute worker needs. Workers +# never open a `RecordStore`; only the caller of `generate_records` writes. +_worker_generator: RecordGenerator | 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(record_generator: RecordGenerator) -> None: + global _worker_generator + _worker_generator = record_generator + + +def _init_worker_with_logging( + record_generator: RecordGenerator, + 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(record_generator) + + +def _compute(run_dir: Path) -> tuple[Path, RecordList]: + assert _worker_generator is not None + logger.info(f"Processing run_dir = '{run_dir}'") + return run_dir, _worker_generator(run_dir) + + +def generate_records( + record_generator: RecordGenerator, + data_dirs: list[Path], + db_path: Path, + n_procs: int = 1, + on_progress: ProgressCallback | None = None, +) -> int: + """ + Maps `record_generator` over `data_dirs`, writing each directory's records to the + `RecordStore` at `db_path` (one `write_run` transaction per directory). There is no + intermediate file and no separate sort step, since tag-based retrieval from `RecordStore` + is an indexed query. + + Args: + record_generator (RecordGenerator): A callable that returns a list of records + 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 `record_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 computing records in parallel. + `n_procs == 1` runs sequentially in this process (easier to debug with full + tracebacks). `n_procs > 1` uses a `ProcessPoolExecutor` for computing records + 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. + on_progress (ProgressCallback | None): called once per run directory finished + (after its records are written), always from this process -- never a worker, so + it never needs to be picklable. Left to raise: a failing callback aborts + generation rather than being silently swallowed. + + Returns: + (int): total number of records written across all directories. + + """ + sorted_dirs = get_sorted_dirs(data_dirs) + db_path = Path(db_path) + total_dirs = len(sorted_dirs) + + def _report(run_dir: Path, completed: int) -> None: + if on_progress is not None: + on_progress( + ProgressEvent( + stage="generate", + completed=completed, + total=total_dirs, + detail=str(run_dir), + ) + ) + + logger.info(f"Generating and writing Records for {total_dirs} run(s)...") + total = 0 + with RecordStore.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=(record_generator, log_queue, root_logger.level), + ) as executor: + futures = [ + executor.submit(_compute, run_dir) for run_dir in sorted_dirs + ] + for completed, future in enumerate(as_completed(futures), start=1): + run_dir, records = future.result() + total += store.write_run(run_dir, records) + logger.info(f"Wrote records for run_dir = '{run_dir}'") + _report(run_dir, completed) + 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(record_generator) + for completed, run_dir in enumerate(sorted_dirs, start=1): + _, records = _compute(run_dir) + total += store.write_run(run_dir, records) + logger.info(f"Wrote records for run_dir = '{run_dir}'") + _report(run_dir, completed) + + logger.info( + f"Finished generating Records: {total} records written across {total_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..42680ae --- /dev/null +++ b/src/trendify/generator/histogrammer.py @@ -0,0 +1,62 @@ +""" +Draws `HistogramEntry` records onto a matplotlib figure. Fed directly by `RecordStore` 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` records 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..9275a4f --- /dev/null +++ b/src/trendify/generator/render.py @@ -0,0 +1,195 @@ +""" +Static rendering pipeline: renders CSV tables and matplotlib figures directly from a +`RecordStore`. + +Design note: every record type sharing a tag (`Point2D`/`Scatter2D`/`Trace2D`/`AxLine`/ +`HistogramEntry`) is drawn onto one shared `SingleAxisFigure` and saved once to `.jpg`, +since histogram data isn't special-cased onto its own figure/file; a tag mixing record types +renders the same way any other tag does. + +Rendering only ever reads, so worker processes each open their own read-only `RecordStore` +connection and render disjoint tags in parallel with zero write-lock contention, since SQLite's +WAL mode allows any number of concurrent readers, unlike `generate_records`, where only one +process can ever write at a time. +""" + +from __future__ import annotations + +import logging +from concurrent.futures import ProcessPoolExecutor, as_completed +from pathlib import Path +from typing import Any + +import matplotlib.pyplot as plt + +from trendify.base.helpers import Tag +from trendify.formats.format2d import Format2D, Rastered +from trendify.generator.histogrammer import Histogrammer +from trendify.generator.table_builder import TableBuilder +from trendify.generator.xy_data_plotter import XYDataPlotter +from trendify.log import create_queue_listener +from trendify.log import worker_init as _init_worker_logging +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.progress import ProgressCallback, ProgressEvent +from trendify.store.record_store import RecordStore +from trendify.store.tags import tag_to_path_parts + +__all__ = ["render_assets"] + +logger = logging.getLogger(__name__) + +# Per-process global set by `_init_worker`: the one read-only connection a render worker needs. +_worker_store: RecordStore | None = None + + +def _init_worker(db_path: str) -> None: + global _worker_store + _worker_store = RecordStore.open(Path(db_path), readonly=True) + + +def _init_worker_with_logging( + db_path: str, + log_queue: Any, + log_level: int, +) -> None: + _init_worker_logging(log_queue, log_level) + _init_worker(db_path) + + +def _render_tag( + tag: Tag, + output_dir: str, +) -> Tag: + # Returns `tag` (not just None) so the parent process's as_completed loop knows which tag + # a given future was for, to report progress -- `future.result()` is the only thing that + # survives the process boundary back to the parent. + assert _worker_store is not None + _render_tag_assets( + _worker_store, + tag, + Path(output_dir), + ) + return tag + + +def _render_tag_assets( + store: RecordStore, + tag: Tag, + output_dir: Path, +) -> None: + 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 = }") + + format2d_records = store.get_records_of_type(Format2D, tag=tag) + format2d = format2d_records[0] if format2d_records else None + + points = store.get_records_of_type(Point2D, tag=tag) + traces = store.get_records_of_type(Trace2D, tag=tag) + scatters = store.get_records_of_type(Scatter2D, tag=tag) + axlines = store.get_records_of_type(AxLine, tag=tag) + histogram_entries = store.get_records_of_type(HistogramEntry, tag=tag) + + if not (points or traces or scatters or axlines or histogram_entries): + return + + # Points/traces/scatters/axlines and histogram entries all draw onto the same + # `SingleAxisFigure`/axes and get saved once, rather than forcing histogram data onto a + # separate figure: a tag mixing record types is treated the same as any other tag. + logger.info(f"Making plot for {tag = }") + saf = XYDataPlotter.handle_points_and_traces( + tag=tag, + points=points, + traces=traces, + axlines=axlines, + scatters=scatters, + ) + if histogram_entries: + Histogrammer.handle_histogram_entries( + tag=tag, histogram_entries=histogram_entries, saf=saf + ) + if format2d is not None: + saf.apply_format(format2d) + + renderer = format2d.renderer if format2d is not None else Rastered() + save_path = output_dir.joinpath(*tag_to_path_parts(tag)).with_suffix( + renderer.filetype + ) + save_path.parent.mkdir(parents=True, exist_ok=True) + logger.info(f"Saving to '{save_path}'") + saf.savefig(save_path, dpi=renderer.dpi if isinstance(renderer, Rastered) else None) + plt.close(saf.fig) + logger.info(f"Finished plot for {tag = }") + + +def render_assets( + db_path: Path, + output_dir: Path, + n_procs: int = 1, + on_progress: ProgressCallback | None = None, +) -> None: + """ + Renders CSV tables and matplotlib figures for every tag in the `RecordStore` at + `db_path`, writing them under `output_dir` (nested per tag). + + Args: + db_path (Path): path to the trendify output directory's `.db` file + output_dir (Path): directory tables/figures are written under + n_procs (int): number of worker processes rendering tags in parallel. `n_procs == 1` runs sequentially in this process (easier to debug with full tracebacks). `n_procs > 1` uses a `ProcessPoolExecutor`, with one read-only `RecordStore` connection opened per worker, which is safe and contention-free since rendering never writes. + on_progress (ProgressCallback | None): called once per tag finished, always from this + process -- never a worker, so it never needs to be picklable. Left to raise: a + failing callback aborts rendering rather than being silently swallowed. + + """ + db_path = Path(db_path) + output_dir = Path(output_dir) + + with RecordStore.open(db_path, readonly=True) as store: + tags = store.tag_tree() + total_tags = len(tags) + + def _report(tag: Tag, completed: int) -> None: + if on_progress is not None: + on_progress( + ProgressEvent( + stage="render", + completed=completed, + total=total_tags, + detail=str(tag), + ) + ) + + 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), log_queue, root_logger.level), + ) as executor: + futures = [ + executor.submit( + _render_tag, + tag, + str(output_dir), + ) + for tag in tags + ] + for completed, future in enumerate(as_completed(futures), start=1): + finished_tag = future.result() + _report(finished_tag, completed) + finally: + listener.stop() + else: + with RecordStore.open(db_path, readonly=True) as store: + for completed, tag in enumerate(tags, start=1): + _render_tag_assets(store, tag, output_dir) + _report(tag, completed) diff --git a/src/trendify/generator/table_builder.py b/src/trendify/generator/table_builder.py new file mode 100644 index 0000000..5fbbff9 --- /dev/null +++ b/src/trendify/generator/table_builder.py @@ -0,0 +1,194 @@ +""" +Builds melted/pivot/stats CSV tables for `TableEntry` records, from `RecordStore` query +results, using Polars. `RecordStore.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 +import re +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__) + +_DIGIT_RUN = re.compile(r"\d+") + + +def _natural_sort_key(value: str) -> str: + """ + Zero-pads every run of digits in `value` so a plain lexicographic sort orders numeric + substrings numerically (e.g. "row_2" before "row_10") instead of by leading digit + ("row_10" before "row_2"). Row labels that aren't numeric at all still compare as + ordinary strings. + """ + return _DIGIT_RUN.sub(lambda m: m.group().zfill(20), value) + + +class TableBuilder(BaseModel): + """ + Builds tables (melted, pivot, and stats) from `TableEntry` records 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): record tag these entries belong to + melted (pl.DataFrame): row/col/value/unit table, as returned by + `RecordStore.get_table_entries` + out_dir (Path): directory under which the CSVs are saved (nested per tag) + + """ + if melted.height == 0: + return + + melted = ( + melted.with_columns( + _sort_key=pl.Series([_natural_sort_key(r) for r in melted["row"]]) + ) + .sort("_sort_key") + .drop("_sort_key") + ) + + *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 `RecordStore.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..9804d44 --- /dev/null +++ b/src/trendify/generator/xy_data_plotter.py @@ -0,0 +1,85 @@ +""" +Draws `Point2D`/`Trace2D`/`AxLine` records onto a matplotlib figure. Fed directly by +`RecordStore` 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.scatter import Scatter2D +from trendify.plotting.trace import Trace2D + +__all__ = ["XYDataPlotter"] + +logger = logging.getLogger(__name__) + + +class XYDataPlotter(BaseModel): + """ + Draws `Point2D`/`Scatter2D`/`Trace2D`/`AxLine` records 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], + scatters: list[Scatter2D], + saf: SingleAxisFigure | None = None, + ) -> SingleAxisFigure: + """ + Plots points (scattered, grouped by marker), scatters, traces, and axlines onto `saf`. + + Args: + tag (Tag): tag these records 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 + 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: + (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), {len(scatters)} scatter(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 scatter in scatters: + scatter.plot_to_ax(saf.ax) + + 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/local_server.py b/src/trendify/local_server.py deleted file mode 100644 index c83fca6..0000000 --- a/src/trendify/local_server.py +++ /dev/null @@ -1,170 +0,0 @@ -""" -Defines Flask server for providing data products on local system -""" - -# Standard impots -from dataclasses import dataclass -from pathlib import Path -import pandas as pd -from typing import Type - -# External imports -from flask import Flask -from waitress import serve - -# Local imports -from trendify.api import api - -from flask import Flask - -__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/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..faa67ac --- /dev/null +++ b/src/trendify/pipeline.py @@ -0,0 +1,136 @@ +""" +Top-level pipeline API: generate -> render -> include files, backed by one `RecordStore` +`.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.record import RecordGenerator +from trendify.generator.generate import generate_records +from trendify.generator.render import render_assets +from trendify.progress import ProgressCallback + +__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 `RecordStore` `.db` file. + """ + + output_dir: Path + """Directory the pipeline reads/writes under. Holds `trendify.db` (the `RecordStore`) and + an `assets/` subdirectory (rendered CSVs/figures).""" + + n_procs: int = Field(default=1, ge=1) + """Number of worker processes used by `generate`.""" + + @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, + record_generator: RecordGenerator, + data_dirs: list[Path], + on_progress: ProgressCallback | None = None, + ) -> int: + """ + Maps `record_generator` over `data_dirs`, writing records directly to this + pipeline's `RecordStore`. See `trendify.generator.generate.generate_records`. + + Args: + record_generator (RecordGenerator): callable mapped over `data_dirs`. + data_dirs (list[Path]): directories to map `record_generator` over. + on_progress (ProgressCallback | None): called once per directory finished, e.g. + to report status from a containerized deployment back to an end user. + + Returns: + (int): total number of records written + + """ + logger.info( + f"Generating records for {len(data_dirs)} directory(ies) into {self.db_path} " + f"({self.n_procs} worker process{'es' if self.n_procs > 1 else ''})" + ) + total = generate_records( + record_generator=record_generator, + data_dirs=data_dirs, + db_path=self.db_path, + n_procs=self.n_procs, + on_progress=on_progress, + ) + logger.info(f"Generated {total} record(s)") + return total + + def render(self, on_progress: ProgressCallback | None = None) -> None: + """ + Renders CSV tables and matplotlib figures for every tag into `self.assets_dir`. See + `trendify.generator.render.render_assets`. + + Args: + on_progress (ProgressCallback | None): called once per tag finished, e.g. to + report status from a containerized deployment back to an end user. + + """ + logger.info( + f"Rendering assets from '{self.db_path}' into '{self.assets_dir}' " + f"({self.n_procs} worker process{'es' if self.n_procs > 1 else ''})" + ) + render_assets( + self.db_path, + self.assets_dir, + n_procs=self.n_procs, + on_progress=on_progress, + ) + logger.info(f"Finished rendering assets into '{self.assets_dir}'") + + def run( + self, + record_generator: RecordGenerator, + data_dirs: list[Path], + on_progress: ProgressCallback | None = None, + ) -> int: + """ + Runs the full pipeline: generate, then render (unless all three asset kinds are + suppressed). + + Args: + record_generator (RecordGenerator): callable mapped over `data_dirs`. + data_dirs (list[Path]): directories to map `record_generator` over. + on_progress (ProgressCallback | None): called once per unit of work finished in + *both* stages -- `ProgressEvent.stage` distinguishes "generate" from "render" + for a single handler covering the whole run. + + Returns: + (int): total number of records written by `generate` + + """ + logger.info( + f"Running full pipeline (generate -> render) for '{self.output_dir}'" + ) + + total = self.generate( + record_generator=record_generator, + data_dirs=data_dirs, + on_progress=on_progress, + ) + + self.render(on_progress=on_progress) + + logger.info(f"Finished full pipeline for '{self.output_dir}'") + return total diff --git a/src/trendify/plotly_dashboard.py b/src/trendify/plotly_dashboard.py deleted file mode 100644 index 63bb4f4..0000000 --- a/src/trendify/plotly_dashboard.py +++ /dev/null @@ -1,998 +0,0 @@ -""" -Module for generating interactive Plotly dashboards from data products. -""" - -import dash -from dash import dcc, html, dash_table -from dash.dependencies import Input, Output, State, ALL -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 ( -# DataProductCollection, -# Tag, -# Tags, -# Point2D, -# Trace2D, -# TableEntry, -# HistogramEntry, -# AxLine, -# LineOrientation, -# Format2D, -# flatten, -# atleast_1d, -# ) - -# 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=[{"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: Optional[Format2D] = 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( - 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( - 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/plotting/__init__.py b/src/trendify/plotting/__init__.py new file mode 100644 index 0000000..aebd901 --- /dev/null +++ b/src/trendify/plotting/__init__.py @@ -0,0 +1,46 @@ +from trendify.plotting import axline +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 ( + 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.scatter import ( + Scatter2D, +) +from trendify.plotting.trace import ( + Trace2D, +) + +__all__ = [ + "AxLine", + "HistogramEntry", + "HistogramStyle", + "LineOrientation", + "PlotlyFigure", + "Point2D", + "Scatter2D", + "SingleAxisFigure", + "Trace2D", + "axline", + "figure", + "histogram", + "point", + "scatter", + "trace", +] diff --git a/src/trendify/api/plotting/axline.py b/src/trendify/plotting/axline.py similarity index 82% rename from src/trendify/api/plotting/axline.py rename to src/trendify/plotting/axline.py index e2a7429..6e3b750 100644 --- a/src/trendify/api/plotting/axline.py +++ b/src/trendify/plotting/axline.py @@ -1,47 +1,49 @@ +""" +`AxLine`: a horizontal or vertical reference line drawn on a plot, styled with a `Pen`. +""" + from __future__ import annotations -from enum import Enum import logging +from enum import StrEnum 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 +from trendify.base.pen import Pen +from trendify.formats.format2d import PlottableData2D +from trendify.plotting.figure import PlotlyFigure -__all__ = ["LineOrientation", "AxLine"] +__all__ = ["AxLine", "LineOrientation"] logger = logging.getLogger(__name__) -class LineOrientation(Enum): - """Defines orientation for axis lines - - Attributes: - HORIZONTAL (LineOrientation): Horizontal line - VERTICAL (LineOrientation): Vertical line +class LineOrientation(StrEnum): + """ + Defines orientation for axis lines. """ HORIZONTAL = "horizontal" + """Horizontal line""" + VERTICAL = "vertical" + """Vertical line""" 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 + """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 = Pen() + """Style and label information for drawing to matplotlib axes.""" model_config = ConfigDict(extra="forbid") @@ -51,6 +53,7 @@ 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/plotting.py b/src/trendify/plotting/figure.py similarity index 60% rename from src/trendify/api/plotting/plotting.py rename to src/trendify/plotting/figure.py index 8818a64..ebc584e 100644 --- a/src/trendify/api/plotting/plotting.py +++ b/src/trendify/plotting/figure.py @@ -1,53 +1,63 @@ +""" +`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 +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 TYPE_CHECKING, Any, cast +import matplotlib +import matplotlib.pyplot as plt 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 pydantic import ConfigDict -from trendify.api.base.data_product import DataProduct -from trendify.api.base.helpers import HashableBase, Tag +from trendify.base.helpers import Tag +from trendify.formats.format2d import AxisScale, PlottableData2D if TYPE_CHECKING: - from trendify.api.formats.format2d import Format2D, Grid from matplotlib.axes import Axes from matplotlib.figure import Figure + from trendify.formats.format2d import Format2D + from trendify.styling.grid import Grid -__all__ = ["SingleAxisFigure", "PlotlyFigure"] + +__all__ = ["PlotlyFigure", "SingleAxisFigure"] logger = logging.getLogger(__name__) +# This pipeline only ever renders headlessly (batch CLI/worker processes, no interactive +# window), so pin the backend explicitly before the first figure is ever created. Left +# unset, pyplot's lazy backend auto-selection probes for a live GUI display on the very +# first `plt.figure()` call (`matplotlib._c_internal_utils.display_is_valid`), which is a +# multi-second stall on some systems for a check whose answer we don't care about anyway. +matplotlib.use("Agg") + @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 + """Figure tag. Not yet used.""" + fig: Figure + """Matplotlib figure.""" + ax: Axes + """Matplotlib axis to which data will be plotted.""" @classmethod def new(cls, tag: Tag): @@ -59,6 +69,7 @@ def new(cls, tag: Tag): Returns: (Type[Self]): New single axis figure + """ fig = plt.figure() ax = fig.add_subplot(1, 1, 1) @@ -74,6 +85,7 @@ 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) @@ -107,8 +119,8 @@ def apply_format(self, format2d: Format2D): 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_xlim(left=format2d.lim_x[0], right=format2d.lim_x[1]) + self.ax.set_ylim(bottom=format2d.lim_y[0], top=format2d.lim_y[1]) self.ax.set_xscale(format2d.scale_x.value) self.ax.set_yscale(format2d.scale_y.value) @@ -156,14 +168,21 @@ def apply_grid(self, grid: Grid): else: self.ax.grid(visible=False, which="minor") - def savefig(self, path: Path, dpi: int = 500): + def savefig(self, path: Path, dpi: int | None = None): """ - Wrapper on matplotlib savefig method. Saves figure to given path with given dpi resolution. + Wrapper on matplotlib savefig method. Saves figure to given path, at the given `dpi` + for raster output. `dpi` is meaningless for vector output (e.g. `.svg`), so `None` + leaves it unset and matplotlib infers the format from `path`'s suffix either way. Returns: (Self): Returns self + """ - self.fig.savefig(path, dpi=dpi) + logger.debug(f"Saving matplotlib figure for {self.tag = } to {path} ({dpi = })") + if dpi is not None: + self.fig.savefig(path, dpi=dpi) + else: + self.fig.savefig(path) return self def __del__(self): @@ -175,10 +194,21 @@ def __del__(self): @dataclass class PlotlyFigure: + """ + Data class storing a Plotly figure alongside the legend groups added to it so far. + """ + model_config = ConfigDict(arbitrary_types_allowed=True) + tag: Tag + """Figure tag. Not yet used.""" + fig: go.Figure + """Plotly figure.""" + legend_groups: set[str] = field(default_factory=set) + """Legend group keys already added to `fig`, used to avoid duplicate legend entries when + multiple records share a style.""" @classmethod def new(cls, tag: Tag): @@ -190,6 +220,7 @@ def new(cls, tag: Tag): Returns: (Type[Self]): New single axis figure + """ fig = go.Figure() return cls(tag=tag, fig=fig) @@ -200,70 +231,78 @@ def apply_format(self, format2d: Format2D): Args: 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: - 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}" + # `title_fig` maps to Plotly's main `title.text` (matches matplotlib's + # `fig.suptitle`); `title_ax` maps to Plotly's `title.subtitle.text` (matches + # matplotlib's `ax.set_title` sitting just below it), rather than concatenating + # both into one string. + title: dict[str, Any] = {} + if format2d.title_fig is not None: + title["text"] = format2d.title_fig + if format2d.title_ax is not None: + title["subtitle"] = {"text": format2d.title_ax} + if title: + layout_updates["title"] = title # 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): 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 format2d.lim_x[0] is not None or format2d.lim_x[1] 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"] = [ - _log10(format2d.lim_x_min), - _log10(format2d.lim_x_max), + _log10(format2d.lim_x[0]), + _log10(format2d.lim_x[1]), ] - elif format2d.scale_x == AxisScale.LOG: + elif format2d.scale_x == AxisScale.LINEAR: + # 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, + format2d.lim_x[0], + format2d.lim_x[1], ] - if format2d.lim_y_min is not None or format2d.lim_y_max is not None: + if format2d.lim_y[0] is not None or format2d.lim_y[1] 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), + _log10(format2d.lim_y[0]), + _log10(format2d.lim_y[1]), ] - elif format2d.scale_y == AxisScale.LOG: + elif format2d.scale_y == AxisScale.LINEAR: + # Same reasoning as the x-axis branch above. layout_updates["yaxis"]["range"] = [ - format2d.lim_y_min, - format2d.lim_y_max, + format2d.lim_y[0], + format2d.lim_y[1], ] # 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 if format2d.legend is not None: - layout_updates["showlegend"] = format2d.legend.visible layout_updates["legend"] = dict( title=format2d.legend.title, @@ -285,6 +324,7 @@ def apply_grid(self, grid: Grid): Args: grid (Grid): Grid configuration to apply + """ # Major grid @@ -293,7 +333,6 @@ def apply_grid(self, grid: Grid): "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 = { @@ -301,7 +340,6 @@ def apply_grid(self, grid: Grid): "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 @@ -322,16 +360,30 @@ 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: - """Add a data product to the figure + def add_record(self, record: PlottableData2D) -> PlotlyFigure: + """ + Add a record to the figure. Every trace `record.add_to_plotly` adds (there's always at + most one -- `AxLine` adds none, since it renders as a layout shape instead) gets + `record.metadata` attached as its Plotly `meta` attribute: not rendered by Plotly + itself, but present verbatim in `to_plotly_json()`'s output, which is how the viewer's + `/api/plot` response lets the dashboard filter traces by metadata client-side without + a separate endpoint or a schema change (see `viewer/templates/.../metadata-filter.ts`). Args: - product (PlottableData2D): Data product to add to figure + record (PlottableData2D): Record to add to figure Returns: Self: Returns self for method chaining - """ - raise NotImplementedError() - product.add_to_plotly(self.fig) + """ + # `fig.data`'s dynamically-generated attributes defeat pyright's static analysis here + # the same way `fig.layout`'s do in test_figure.py's `_layout` helper -- `Any` is a + # deliberate, narrow escape hatch at this one boundary, not a blanket opt-out. Read + # fresh (not cached across `add_to_plotly`): `fig.data` is an immutable tuple, so + # `add_trace` rebinds it to a new, longer one rather than mutating in place. + n_before = len(cast(Any, self.fig.data)) + record.add_to_plotly(self) + if record.metadata: + for trace in cast(Any, self.fig.data)[n_before:]: + trace.meta = record.metadata return self diff --git a/src/trendify/api/plotting/histogram.py b/src/trendify/plotting/histogram.py similarity index 71% rename from src/trendify/api/plotting/histogram.py rename to src/trendify/plotting/histogram.py index f4e0dbe..5a547a8 100644 --- a/src/trendify/api/plotting/histogram.py +++ b/src/trendify/plotting/histogram.py @@ -1,18 +1,25 @@ +""" +`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 -from typing import Tuple import logging -from matplotlib.colors import to_rgba -import plotly.graph_objects as go +from typing import Literal, cast -from numpydantic import NDArray, Shape -from pydantic import ConfigDict, Field +import numpy as np +import plotly.graph_objects as go +from matplotlib.colors import to_rgba +from pydantic import ConfigDict, Field, field_validator, model_validator -from trendify.api.formats.format2d import PlottableData2D -from trendify.api.base.helpers import HashableBase, Tags -from trendify.api.plotting.plotting import PlotlyFigure +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__ = ["HistogramStyle", "HistogramEntry"] +__all__ = ["HistogramEntry", "HistogramStyle"] logger = logging.getLogger(__name__) @@ -20,30 +27,63 @@ 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" + """Color of bars""" + label: str | None = None - histtype: str = "stepfilled" - alpha_edge: float = 0 + """Legend entry""" + + histtype: Literal["bar", "step", "stepfilled"] = "stepfilled" + """Histogram type corresponding to matplotlib argument of same name""" + + alpha_edge: float = 1.0 + """Opacity of bar edge""" + alpha_face: float = 0.3 - linewidth: float = 2 + """Opacity of bar face""" + + linewidth: float = 2.0 + """Line width of bar outline""" + zorder: int = 1 - bins: int | list[int] | Tuple[int] | NDArray[Shape["*"], int] | None = None + """Prioritization""" + + bins: int | tuple[int, ...] | None = None + """Number of bins (see [matplotlib docs][matplotlib.pyplot.hist])""" + + @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 + + @model_validator(mode="after") + def _visible_edge_for_step_histtype(self) -> HistogramStyle: + # `histtype="step"` draws an edge-only patch (`fill=False`), so `alpha_edge` is the + # only thing that can make it visible at all, unlike "bar"/"stepfilled" where the + # face fill carries the color and a zero-alpha edge is just borderless. + # `alpha_edge`'s class default of 0 is right for those other histtypes but leaves a + # "step" histogram completely invisible, so bump it to opaque if left at zero. + if self.histtype == "step" and self.alpha_edge == 0: + logger.warning( + "Histogram type was set to 'step' but alpha_edge was 0, coercing edge transparency to 1" + ) + self.alpha_edge = 1 + return self def as_plot_kwargs(self): """ Returns: (dict): kwargs for matplotlib `hist` method + """ return { "facecolor": (self.color, self.alpha_face), @@ -62,6 +102,7 @@ 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 +129,7 @@ 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 +167,7 @@ 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): @@ -162,35 +205,32 @@ def luminance(channel): 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 + """Value to be binned""" + tags: Tags - style: HistogramStyle | None = Field(default_factory=HistogramStyle) + """Tags used to sort records""" + + style: HistogramStyle = Field(default_factory=HistogramStyle) + """Style of histogram display""" 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.") - 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 - 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] + trace.x = [*(trace.x or ()), self.value] return plotly_figure metadata_html = ( diff --git a/src/trendify/api/plotting/point.py b/src/trendify/plotting/point.py similarity index 77% rename from src/trendify/api/plotting/point.py rename to src/trendify/plotting/point.py index 1c50486..1d1e759 100644 --- a/src/trendify/api/plotting/point.py +++ b/src/trendify/plotting/point.py @@ -1,15 +1,17 @@ +""" +`Point2D`: a single scattered (x, y) point, styled with a `Marker`. +""" + from __future__ import annotations import logging -from pydantic import ConfigDict import plotly.graph_objects as go +from pydantic import ConfigDict -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 +from trendify.formats.format2d import XYData +from trendify.plotting.figure import PlotlyFigure +from trendify.styling.marker import Marker __all__ = ["Point2D"] @@ -19,20 +21,17 @@ 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 + """X value for the point.""" + y: float | str + """Y value for the point.""" + marker: Marker | None = Marker() + """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.""" model_config = ConfigDict(extra="forbid") diff --git a/src/trendify/plotting/scatter.py b/src/trendify/plotting/scatter.py new file mode 100644 index 0000000..4e54b50 --- /dev/null +++ b/src/trendify/plotting/scatter.py @@ -0,0 +1,100 @@ +""" +`Scatter2D`: a bulk collection of (x, y) points sharing one `Marker`, drawn as unconnected +scatter points. + +Use this instead of many individual `Point2D` records 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.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 + +__all__ = ["Scatter2D"] + +logger = logging.getLogger(__name__) + + +class Scatter2D(XYData): + """ + A collection of unconnected scattered points sharing one `Marker`. + """ + + model_config = ConfigDict(extra="forbid") + + x: VecN + """x values""" + + y: VecN + """y values""" + + marker: Marker = Marker() + """Style and label information shared by every point in this scatter.""" + + 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 new file mode 100644 index 0000000..8708c15 --- /dev/null +++ b/src/trendify/plotting/trace.py @@ -0,0 +1,129 @@ +""" +`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`). +""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING + +import plotly.graph_objects as go +from pydantic import ConfigDict + +from trendify.base.pen import Pen +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 + +__all__ = ["Trace2D"] + +logger = logging.getLogger(__name__) + + +class Trace2D(XYData): + """An xy line, optionally marked at intervals.""" + + model_config = ConfigDict(extra="forbid") + + x: VecN + """x values""" + + y: VecN + """y values""" + + pen: 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 = 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 = None + """Draw a marker every Nth point when `marker` is set (passed straight through to + matplotlib's `Axes.plot(markevery=...)`); `None` marks every point.""" + + 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 + + """ + 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 = ( + 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}" + ) + + # `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. + mode_parts = [] + if self.pen.linestyle is not None: + mode_parts.append("lines") + if self.marker is not None: + mode_parts.append("markers") + + plotly_figure.fig.add_trace( + go.Scatter( + x=self.x, + y=self.y, + name=self.pen.label if self.pen else None, + mode="+".join(mode_parts) if mode_parts 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.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, + 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/production_server.py b/src/trendify/production_server.py deleted file mode 100644 index a467bd9..0000000 --- a/src/trendify/production_server.py +++ /dev/null @@ -1,126 +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 os -import sys -from pathlib import Path -from typing import Optional, 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: Union[str, Path], - host: str = "0.0.0.0", - port: int = 8000, - title: Optional[str] = None, - tag: Optional[str] = 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/progress.py b/src/trendify/progress.py new file mode 100644 index 0000000..ee1976a --- /dev/null +++ b/src/trendify/progress.py @@ -0,0 +1,43 @@ +""" +Progress reporting for the generate/render pipeline: a lightweight event type plus a callback +type alias, so a caller embedding trendify (e.g. running it inside a container and feeding +status back to their own UI/API) can observe progress without needing logging configured or +parsing log lines. + +`on_progress` is always invoked from the main process, never a worker: `generate_records` +reports after each `write_run` (the parent process is the only one that ever writes) and +`render_assets` reports after each tag's `future.result()` (or, sequentially, after each +`_render_tag_assets` call) -- so the callback itself never needs to be picklable/sent across a +process boundary, unlike `record_generator` itself. +""" + +from __future__ import annotations + +from collections.abc import Callable +from dataclasses import dataclass +from typing import Literal + +__all__ = ["ProgressCallback", "ProgressEvent"] + + +@dataclass(frozen=True) +class ProgressEvent: + """One unit of work finishing during `generate` or `render`.""" + + stage: Literal["generate", "render"] + """Which pipeline stage this event is from.""" + + completed: int + """Number of units finished so far this stage, including this one.""" + + total: int + """Total number of units this stage will process.""" + + detail: str + """Human-readable identifier for the unit that just finished: a run directory's path for + `generate`, a tag's display string for `render`.""" + + +ProgressCallback = Callable[[ProgressEvent], None] +"""A callback invoked once per completed unit of work. Left to raise: a callback that fails is +almost certainly a bug in the caller's own integration, not something to silently swallow.""" diff --git a/src/trendify/server.py b/src/trendify/server.py deleted file mode 100644 index b114dab..0000000 --- a/src/trendify/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/store/__init__.py b/src/trendify/store/__init__.py new file mode 100644 index 0000000..cdf33fc --- /dev/null +++ b/src/trendify/store/__init__.py @@ -0,0 +1,28 @@ +from trendify.store import db +from trendify.store import record_store +from trendify.store import tags + +from trendify.store.db import ( + SCHEMA_VERSION, + connect, +) +from trendify.store.record_store import ( + RecordStore, +) +from trendify.store.tags import ( + decode_tag, + encode_tag, + tag_to_path_parts, +) + +__all__ = [ + "SCHEMA_VERSION", + "RecordStore", + "connect", + "db", + "decode_tag", + "encode_tag", + "record_store", + "tag_to_path_parts", + "tags", +] diff --git a/src/trendify/store/db.py b/src/trendify/store/db.py new file mode 100644 index 0000000..f0aa317 --- /dev/null +++ b/src/trendify/store/db.py @@ -0,0 +1,119 @@ +""" +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 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 = """ +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 records ( + id INTEGER PRIMARY KEY, + run_id INTEGER NOT NULL REFERENCES runs(id) ON DELETE CASCADE, + record_type TEXT NOT NULL, + payload TEXT NOT NULL, + created_at TEXT NOT NULL +); +CREATE INDEX IF NOT EXISTS idx_records_run ON records(run_id); + +CREATE TABLE IF NOT EXISTS record_tags ( + record_id INTEGER NOT NULL REFERENCES records(id) ON DELETE CASCADE, + tag_key TEXT NOT NULL, + record_type TEXT NOT NULL, + PRIMARY KEY (record_id, tag_key) +); +CREATE INDEX IF NOT EXISTS idx_record_tags_lookup ON record_tags(tag_key, record_type); + +CREATE TABLE IF NOT EXISTS table_entries ( + id INTEGER PRIMARY KEY, + record_id INTEGER NOT NULL REFERENCES records(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); +CREATE INDEX IF NOT EXISTS idx_table_entries_record ON table_entries(record_id); +""" + + +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), 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) + 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, 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/record_store.py b/src/trendify/store/record_store.py new file mode 100644 index 0000000..913ed0c --- /dev/null +++ b/src/trendify/store/record_store.py @@ -0,0 +1,422 @@ +""" +`RecordStore`: the SQLite-backed store for tagged `Record`s. + +Pydantic objects are what flow in and out everywhere; storage and querying happen entirely +underneath that interface. Every record's full JSON payload is stored opaquely, so new +`Record` 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 record with N +tags costs one payload row plus N small index rows rather than N duplicated payloads. `TableEntry` +is 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. `Format2D` is the +other exception: it's a singleton per tag, so `write_run` deletes any existing `Format2D` row(s) +sharing a tag (from any run) before inserting a new one, rather than accumulating duplicates. +""" + +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.helpers import R, Tag +from trendify.base.record import Record +from trendify.formats.format2d import Format2D +from trendify.formats.table import TableEntry +from trendify.store import db +from trendify.store.tags import decode_tag, encode_tag + +__all__ = ["RecordStore"] + +logger = logging.getLogger(__name__) + + +def _leaf_type_names(object_type: type[Record]) -> list[str]: + """ + Expands a (possibly non-leaf) `Record` type into the list of registered leaf + `record_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 `record_type IN (...)` clause instead of deserializing every tag-matched + row just to run an `isinstance` check in Python. + """ + return [ + name for name, cls in Record.registry().items() if issubclass(cls, object_type) + ] + + +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), tuple((isinstance(x, int), x) for x in as_tuple)) + + +@dataclass +class RecordStore: + """ + SQLite-backed store for `Record`s, grouped by run and queryable by tag/type. + + Usage: + with RecordStore.open(db_path) as store: + store.write_run(run_path, records) + traces = store.get_records_of_type(Trace2D, tag="my_tag") + """ + + _conn: sqlite3.Connection + + @classmethod + def open(cls, db_path: Path, *, readonly: bool = False) -> RecordStore: + return cls(db.connect(Path(db_path), readonly=readonly)) + + def __enter__(self) -> RecordStore: + return self + + def __exit__(self, *exc_info) -> None: + self.close() + + def close(self) -> None: + logger.debug("Closing RecordStore connection") + self._conn.close() + + def write_run(self, run_path: Path, records: Iterable[Record]) -> int: + """ + Replaces all records 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. + + Args: + run_path (Path): the raw-data run directory these records were generated from + records (Iterable[Record]): records returned by the user's `RecordGenerator` + + Returns: + (int): number of records written + + """ + records = list(records) + run_path_str = str(Path(run_path).resolve()) + now = datetime.datetime.now(datetime.UTC).isoformat() + logger.debug(f"Writing {len(records)} record(s) for run {run_path_str}") + + 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 (record_tags, table_entries) requires PRAGMA foreign_keys=ON, + # set in db.connect(). + conn.execute("DELETE FROM records WHERE run_id = ?", (run_id,)) + + # `records.id` is a plain `INTEGER PRIMARY KEY` (rowid alias, no AUTOINCREMENT), + # so SQLite's own rule for an auto-assigned rowid is just "one more than the + # current max" -- reading that once and assigning a contiguous block of ids + # ourselves is equivalent to what N individual inserts would each get via + # `lastrowid`, and it's what lets everything below be batched into a handful of + # `executemany` calls instead of one `execute` round trip per record (this is + # where the bulk of write_run's time was going). + next_id = conn.execute( + "SELECT COALESCE(MAX(id), 0) + 1 FROM records" + ).fetchone()[0] + + pending_record_rows: list[tuple] = [] + pending_tag_rows: list[tuple] = [] + table_entry_rows: list[tuple] = [] + + for record_id, record in zip( + range(next_id, next_id + len(records)), records + ): + tag_keys = [encode_tag(t) for t in record.tags] + + if isinstance(record, Format2D): + # Format2D is a singleton per tag: delete any existing Format2D row(s) + # sharing any of these tags (from any run, not just this one) before + # inserting the new one, so re-emitting the same tag-level styling from + # every run (unavoidable given `RecordGenerator` is called once per run + # with no cross-run coordination) never accumulates duplicate rows. This + # stays row-by-row (unlike the batching below) because a later Format2D + # record's dedup delete must see an earlier same-tag Format2D record + # *from this same call* as already inserted, which only holds if that + # earlier row's insert has actually happened by the time this check runs. + for tk in tag_keys: + conn.execute( + "DELETE FROM records WHERE record_type = 'Format2D' AND id IN " + "(SELECT record_id FROM record_tags WHERE tag_key = ?)", + (tk,), + ) + conn.execute( + "INSERT INTO records(id, run_id, record_type, payload, created_at) " + "VALUES (?, ?, ?, ?, ?)", + ( + record_id, + run_id, + record.record_type, + record.model_dump_json(), + now, + ), + ) + conn.executemany( + "INSERT INTO record_tags(record_id, tag_key, record_type) " + "VALUES (?, ?, ?)", + [(record_id, tk, record.record_type) for tk in tag_keys], + ) + else: + pending_record_rows.append( + ( + record_id, + run_id, + record.record_type, + record.model_dump_json(), + now, + ) + ) + pending_tag_rows.extend( + (record_id, tk, record.record_type) for tk in tag_keys + ) + + if isinstance(record, TableEntry): + value_num = value_text = value_bool = None + if isinstance(record.value, bool): + value_bool = int(record.value) + elif isinstance(record.value, (int, float)): + value_num = float(record.value) + else: + value_text = record.value + + table_entry_rows.extend( + ( + record_id, + tk, + str(record.row), + str(record.col), + value_num, + value_text, + value_bool, + record.unit, + ) + for tk in tag_keys + ) + + if pending_record_rows: + conn.executemany( + "INSERT INTO records(id, run_id, record_type, payload, created_at) " + "VALUES (?, ?, ?, ?, ?)", + pending_record_rows, + ) + if pending_tag_rows: + conn.executemany( + "INSERT INTO record_tags(record_id, tag_key, record_type) " + "VALUES (?, ?, ?)", + pending_tag_rows, + ) + if table_entry_rows: + conn.executemany( + "INSERT INTO table_entries(" + "record_id, tag_key, row_key, col_key, " + "value_num, value_text, value_bool, unit) " + "VALUES (?, ?, ?, ?, ?, ?, ?, ?)", + table_entry_rows, + ) + + logger.debug( + f"Wrote {len(records)} record(s) for run {run_path_str} (run_id={run_id})" + ) + return len(records) + + def get_tags(self, object_type: type[Record] | None = None) -> set[Tag]: + """ + Args: + object_type (type[Record] | None): restrict to tags used by records of this + type (or any registered subclass of it); `None` matches every record type. + + Returns: + (set[Tag]): the set of tags in use + + """ + if object_type is None: + rows = self._conn.execute("SELECT DISTINCT tag_key FROM record_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 record_tags " + f"WHERE record_type IN ({placeholders})", + names, + ) + return {decode_tag(r["tag_key"]) for r in rows} + + def get_tag_byte_sizes(self) -> dict[Tag, int]: + """ + Total payload size in bytes for each tag, summed across every record carrying that + tag. A record with N tags counts its full payload size toward all N (matches how + `write_run` stores one payload row referenced by N `record_tags` rows, not divided + N ways). Backs the viewer's background-hydration prioritization (`viewer.tag_tree`), + which starts prefetching the largest tag at the level the user is browsing. + + Returns: + (dict[Tag, int]): total payload bytes per tag + + """ + rows = self._conn.execute( + "SELECT rt.tag_key AS tag_key, SUM(LENGTH(r.payload)) AS size " + "FROM record_tags rt JOIN records r ON r.id = rt.record_id " + "GROUP BY rt.tag_key" + ).fetchall() + return {decode_tag(r["tag_key"]): r["size"] for r in rows} + + def tag_tree(self, object_type: type[Record] | None = None) -> list[Tag]: + """ + Returns: + (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) + + def get_records( + self, + tag: Tag | None = None, + object_type: type[R] | None = None, + ) -> Iterator[R]: + """ + Streams matching records, deserialized to their concrete pydantic type. Filtering by + `tag` and/or `object_type` happens in SQL (indexed tag lookup, `record_type IN (...)` + for the type hierarchy) before anything is deserialized, so this only ever parses JSON + for rows that actually match. + + Args: + tag (Tag | None): tag to filter by; `None` matches every tag + object_type (type[R] | None): type (or base type) to filter by; `None` matches + every type + + Yields: + (R): matching records, in insertion order + + """ + logger.debug(f"Querying records ({tag = }, {object_type = })") + select = "SELECT p.record_type, p.payload FROM records p" + joins = [] + where = [] + params: list[object] = [] + + if tag is not None: + joins.append("JOIN record_tags pt ON pt.record_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.record_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 cast(R, Record.deserialize(row["record_type"], row["payload"])) + + def get_records_of_type( + self, object_type: type[R], tag: Tag | None = None + ) -> list[R]: + """ + Same as `get_records`, but eager (a `list`), for callers that don't need streaming. + """ + return list(self.get_records(tag=tag, object_type=object_type)) + + def has_records( + self, tag: Tag | None = None, object_type: type[Record] | None = None + ) -> bool: + """ + Cheap existence check for `get_records`'s same `(tag, object_type)` filter: stops at + the first matching row instead of deserializing every one, for callers (like the + viewer's tag tree, `viewer.tag_tree._record_kinds`) that only need to know whether + *any* record matches, not what it is. + """ + return ( + next(self.get_records(tag=tag, object_type=object_type), None) is not None + ) + + def get_table_entries(self, tag: Tag) -> pl.DataFrame: + """ + Fetches `TableEntry` rows for `tag` directly from the `table_entries` table, skipping + full `Record` 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 + 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): a "melted" table with columns `row`, `col`, `value`, `unit`, one + row per `TableEntry` record matching `tag`. + + """ + 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() + logger.debug(f"Fetched {len(rows)} table_entries row(s) for {tag = }") + + 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, + }, + ) + + def has_table_entries(self, tag: Tag) -> bool: + """ + Cheap existence check for `get_table_entries`'s same `tag` filter, without building a + `pl.DataFrame` (or resolving each row's value union) just to check whether it's empty. + """ + row = self._conn.execute( + "SELECT 1 FROM table_entries WHERE tag_key = ? LIMIT 1", (encode_tag(tag),) + ).fetchone() + return row is not None diff --git a/src/trendify/store/tags.py b/src/trendify/store/tags.py new file mode 100644 index 0000000..ead8b76 --- /dev/null +++ b/src/trendify/store/tags.py @@ -0,0 +1,65 @@ +""" +Canonical encoding between the pydantic-facing `Tag` type and the `tag_key` string +stored/indexed in the `record_tags` and `table_entries` SQL tables. + +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", "tag_to_path_parts"] + + +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 + + +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/streamlit.py b/src/trendify/streamlit.py deleted file mode 100644 index 30d7f34..0000000 --- a/src/trendify/streamlit.py +++ /dev/null @@ -1,303 +0,0 @@ -import importlib.resources -from importlib.metadata import version -from pathlib import Path -import time -from typing import List, Sequence, Tuple - -import pandas as pd -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_{str('_').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_{str('_').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/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/api/styling/grid.py b/src/trendify/styling/grid.py similarity index 64% rename from src/trendify/api/styling/grid.py rename to src/trendify/styling/grid.py index 7386569..dec6c87 100644 --- a/src/trendify/api/styling/grid.py +++ b/src/trendify/styling/grid.py @@ -1,37 +1,34 @@ +""" +`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 typing import Iterable, Optional, Tuple, Union -import logging +from enum import StrEnum -import numpy as np from pydantic import ConfigDict -from trendify.api.base.helpers import HashableBase -from trendify.api.base.pen import Pen - - -logger = logging.getLogger(__name__) +from trendify.base.helpers import HashableBase +from trendify.base.pen import Pen __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): """ 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 + """Whether to display this grid axis.""" + pen: Pen = Pen( color="gray", alpha=1.0, @@ -39,6 +36,7 @@ class GridAxis(HashableBase): linestyle="-", label=None, ) + """Style and label information for drawing to matplotlib axes.""" model_config = ConfigDict(extra="forbid") @@ -46,17 +44,19 @@ class GridAxis(HashableBase): 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) + """Configuration for major grid lines.""" + minor: GridAxis = GridAxis(show=False) + """Configuration for minor grid lines.""" + enable_minor_ticks: bool = False + """Whether to enable minor ticks on the axes.""" + zorder: float = -1 + """Prioritization of the grid lines relative to plotted data.""" model_config = ConfigDict(extra="forbid") @@ -123,26 +123,3 @@ def from_theme(cls, name: GridTheme) -> Grid: 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/api/styling/legend.py b/src/trendify/styling/legend.py similarity index 72% rename from src/trendify/api/styling/legend.py rename to src/trendify/styling/legend.py index f1b60e4..b7c2812 100644 --- a/src/trendify/api/styling/legend.py +++ b/src/trendify/styling/legend.py @@ -1,81 +1,92 @@ +""" +`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 -from trendify.api.base.helpers import HashableBase +from trendify.base.helpers import HashableBase -__all__ = ["LegendLocation", "Legend"] +__all__ = ["Legend", "LegendLocation"] class LegendLocation(StrEnum): + """ + Matplotlib-recognized legend anchor locations. + """ + BEST = "best" + """Matplotlib chooses the location that overlaps plotted data the least.""" + UPPER_RIGHT = "upper right" + """Upper right corner of the axes.""" + UPPER_LEFT = "upper left" + """Upper left corner of the axes.""" + LOWER_LEFT = "lower left" + """Lower left corner of the axes.""" + LOWER_RIGHT = "lower right" + """Lower right corner of the axes.""" + RIGHT = "right" + """Right edge of the axes, vertically centered.""" + CENTER_LEFT = "center left" + """Left edge of the axes, vertically centered.""" + CENTER_RIGHT = "center right" + """Right edge of the axes, vertically centered.""" + LOWER_CENTER = "lower center" + """Bottom edge of the axes, horizontally centered.""" + UPPER_CENTER = "upper center" + """Top edge of the axes, horizontally centered.""" + CENTER = "center" + """Center of the axes.""" 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 + """Whether the legend should be displayed.""" + title: str | None = None + """Title displayed above the legend entries.""" + framealpha: float = 1 + """Opacity of the legend background. 1 is fully opaque, 0 is fully transparent.""" + loc: LegendLocation = LegendLocation.BEST + """Anchor point for the legend (e.g., upper right, lower left). See `LegendLocation` + enum for options.""" + ncol: int = 1 + """Number of columns to arrange legend entries into.""" + fancybox: bool = True + """Whether to draw a rounded (True) or square (False) legend frame.""" + edgecolor: str = "black" - zorder: int = 10 + """Color of the legend frame border.""" + + zorder: int = 1000 + """Prioritization of the legend relative to plotted data.""" + bbox_to_anchor: tuple[float, float] | None = None + """Offset position of the legend in figure or axes coordinates. If `None`, the legend + is placed inside the axes using `loc`.""" def to_kwargs(self): return { @@ -93,6 +104,7 @@ 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/styling/marker.py similarity index 81% rename from src/trendify/api/styling/marker.py rename to src/trendify/styling/marker.py index 9bdf763..33fd24b 100644 --- a/src/trendify/api/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 @@ -5,8 +10,8 @@ from matplotlib.colors import to_rgba from pydantic import ConfigDict -from trendify.api.base.helpers import HashableBase -from trendify.api.base.pen import Pen +from trendify.base.helpers import HashableBase +from trendify.base.pen import Pen __all__ = ["Marker"] @@ -16,22 +21,25 @@ 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" + color: tuple[float, float, float] | tuple[float, float, float, float] | str = "k" + """Color of line""" + size: float = 5 + """Line width""" + alpha: float = 1 + """Opacity from 0 to 1 (inclusive)""" + zorder: float = 0 + """Prioritization""" + label: str | None = None + """Legend label""" + symbol: str = "." + """Matplotlib symbol string""" @classmethod def from_pen( @@ -40,9 +48,15 @@ def from_pen( symbol: str = ".", ): """ - Converts Pen to marker with the option to specify a symbol + Converts Pen to marker with the option to specify a symbol. + + `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). """ - return cls(symbol=symbol, **pen.model_dump().pop("linestyle")) + data = pen.model_dump() + data.pop("linestyle", None) + return cls(symbol=symbol, **data) model_config = ConfigDict(extra="forbid") @@ -50,15 +64,15 @@ 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, + "color": self.color, "s": self.size, "alpha": self.alpha, "zorder": self.zorder, "label": self.label, - "marker": self.symbol, } @property @@ -88,6 +102,7 @@ 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 +132,7 @@ 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/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/viewer/__init__.py b/src/trendify/viewer/__init__.py new file mode 100644 index 0000000..eb7f2c1 --- /dev/null +++ b/src/trendify/viewer/__init__.py @@ -0,0 +1,41 @@ +from trendify.viewer import app as viewer_app +from trendify.viewer import plot_config, routes, tag_tree +from trendify.viewer.app import ( + create_app, + create_app_from_env, +) +from trendify.viewer.plot_config import ( + HoverMode, + InterpMode, + LineMode, + PlotConfig, + camel_case_dict, +) +from trendify.viewer.routes import ( + api, + pages, + router, +) +from trendify.viewer.tag_tree import ( + TagNode, + build_tag_tree, +) + +__all__ = [ + "HoverMode", + "InterpMode", + "LineMode", + "PlotConfig", + "TagNode", + "api", + "build_tag_tree", + "camel_case_dict", + "create_app", + "create_app_from_env", + "pages", + "plot_config", + "router", + "routes", + "tag_tree", + "viewer_app", +] diff --git a/src/trendify/viewer/app.py b/src/trendify/viewer/app.py new file mode 100644 index 0000000..365a47b --- /dev/null +++ b/src/trendify/viewer/app.py @@ -0,0 +1,119 @@ +""" +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.record_store import RecordStore +from trendify.viewer.hydration import HydrationRunner +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 `RecordStore` 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`. + + Those handlers also have no internal `await` points, so once one starts running it has the + event loop to itself until it returns -- fine for an ordinary click, but the viewer's + background-hydration feature (`routes.api`) deliberately fires additional, potentially + expensive requests for tags the user hasn't clicked yet, and those must not be able to + stall a real click behind them. `app.state.hydration_runner` (a `HydrationRunner`) gives + hydration-tagged requests their own worker thread and their own read-only store connection, + so they run genuinely concurrently with whatever the main thread is doing. + + 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 = RecordStore.open(db_path, readonly=True) + app.state.hydration_runner = HydrationRunner(db_path) + try: + yield + finally: + app.state.store.close() + app.state.hydration_runner.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/hydration.py b/src/trendify/viewer/hydration.py new file mode 100644 index 0000000..c882f0d --- /dev/null +++ b/src/trendify/viewer/hydration.py @@ -0,0 +1,69 @@ +""" +Runs background-hydration requests (see `routes.api`) on a dedicated worker thread with its own +read-only `RecordStore` connection, so a slow/expensive prefetch request can never block the +main event-loop thread that every real user click also depends on (this app's route handlers are +synchronous with no `await` points -- see `viewer.app.create_app`'s docstring for why the *main* +store must stay pinned to that one thread, which otherwise means one slow request blocks +everything else until it returns). A second, independent reader is safe here because the store +is opened read-only and this app's `.db` file is already in WAL mode, which supports multiple +concurrent readers with no coordination needed. +""" + +from __future__ import annotations + +import asyncio +import logging +from collections.abc import Callable +from concurrent.futures import ThreadPoolExecutor +from pathlib import Path +from typing import TypeVar + +from trendify.store.record_store import RecordStore + +__all__ = ["HydrationRunner"] + +logger = logging.getLogger(__name__) + +T = TypeVar("T") + + +class HydrationRunner: + """ + A single dedicated worker thread (`max_workers=1`, so it's always the *same* OS thread + across every call this app makes over its lifetime) plus a `RecordStore` connection opened + lazily the first time it's used -- and therefore only ever touched from that one thread, + satisfying sqlite3's thread-affinity requirement without needing `check_same_thread=False` + or any locking. + """ + + def __init__(self, db_path: Path) -> None: + self._db_path = db_path + self._executor = ThreadPoolExecutor( + max_workers=1, thread_name_prefix="trendify-hydrate" + ) + self._store: RecordStore | None = ( + None # only read/written on the executor's thread + ) + + def _get_store(self) -> RecordStore: + if self._store is None: + self._store = RecordStore.open(self._db_path, readonly=True) + return self._store + + async def run(self, fn: Callable[[RecordStore], T]) -> T: + """ + Runs `fn` against this runner's own store on its dedicated thread, off the calling + event loop, and awaits the result without blocking that loop in the meantime. + """ + loop = asyncio.get_running_loop() + return await loop.run_in_executor(self._executor, lambda: fn(self._get_store())) + + def close(self) -> None: + def _close() -> None: + if self._store is not None: + self._store.close() + + # Submitted (not called directly) so the store is also *closed* from the same thread + # that opened it, then wait for that to finish before tearing down the executor itself. + self._executor.submit(_close).result() + self._executor.shutdown(wait=True) diff --git a/src/trendify/viewer/plot_config.py b/src/trendify/viewer/plot_config.py new file mode 100644 index 0000000..8d336f3 --- /dev/null +++ b/src/trendify/viewer/plot_config.py @@ -0,0 +1,102 @@ +""" +Pydantic models for the dashboard's plot viewer `PlotConfig`. + +These are the single source of truth for both server-side validation (the `/api/plot` query +params in `trendify.viewer.routes.api`) 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 dashboard plot.""" + + model_config = camel_case_dict + + line_mode: LineMode = LineMode.LINES_AND_MARKERS + """Whether traces render as lines, markers, or both.""" + + interp: InterpMode = InterpMode.LINEAR + """Interpolation method drawn between data points.""" + + hover: HoverMode = HoverMode.CLOSEST + """Tooltip behavior on hover.""" + + show_spike: bool = True + """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 x-domain buckets (equal coverage across the x range regardless of variable point spacing). `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..7fe830b --- /dev/null +++ b/src/trendify/viewer/routes/__init__.py @@ -0,0 +1,11 @@ +from trendify.viewer.routes import api +from trendify.viewer.routes import pages + +from trendify.viewer.routes.api import ( + router, +) +from trendify.viewer.routes.pages import ( + router, +) + +__all__ = ["api", "pages", "router"] diff --git a/src/trendify/viewer/routes/api.py b/src/trendify/viewer/routes/api.py new file mode 100644 index 0000000..9a36857 --- /dev/null +++ b/src/trendify/viewer/routes/api.py @@ -0,0 +1,339 @@ +""" +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), a liveness/db-change ping, and the table/plot data endpoints. + +Every handler reads from the process-lifetime, read-only `RecordStore` 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 `viewer` 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. + +`/table` and `/plot` requests tagged `X-Trendify-Hydrate` (the frontend's background-prefetch +scheduler, not a real click -- see `_is_hydration_request`) instead run against +`request.app.state.hydration_runner`'s own dedicated thread/connection, so a slow prefetch can +never block a real click on this app's single-threaded event loop (see `viewer.app.create_app`'s +docstring and `viewer.hydration.HydrationRunner`). +""" + +from __future__ import annotations + +import base64 +import logging +from collections.abc import Awaitable, Callable +from pathlib import Path +from typing import Any, Literal, cast + +import numpy as np +from fastapi import APIRouter, Query, Request +from pydantic import BaseModel + +from trendify.formats.format2d import Format2D +from trendify.generator.table_builder import TableBuilder +from trendify.plotting.axline import AxLine +from trendify.plotting.figure import PlotlyFigure +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.record_store import RecordStore +from trendify.store.tags import decode_tag +from trendify.viewer.plot_config import HoverMode, InterpMode, LineMode +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): + """Response body for the `/table` endpoint.""" + + available: bool + """Whether any `TableEntry` records exist for the requested tag/view.""" + + columns: list[str] + """Column names for the returned rows, in display order.""" + + rows: list[dict[str, Any]] + """Table rows, each a mapping from column name to cell value.""" + + +class PlotResponse(BaseModel): + """Response body for the `/plot` endpoint.""" + + available: bool + """Whether any plottable records exist for the requested tag.""" + + data: list[dict[str, Any]] + """Plotly trace definitions, one per series.""" + + layout: dict[str, Any] + """Plotly figure layout (axes, legend, grid) built from the tag's `Format2D`.""" + + +def _get_store(request: Request) -> RecordStore: + return request.app.state.store + + +def _is_hydration_request(request: Request) -> bool: + """ + `X-Trendify-Hydrate` is set only by the frontend's background-prefetch scheduler + (`prefetch.ts`), never by a real user click. Hydration-tagged requests are routed to + `app.state.hydration_runner`'s dedicated worker thread/connection instead of the main one + (see `HydrationRunner`'s docstring for why), so a slow background request can never block a + real click on this app's single-threaded event loop. + """ + return bool(request.headers.get("x-trendify-hydrate")) + + +async def _cached[T]( + request: Request, cache_key: tuple, build: Callable[[], Awaitable[T]] +) -> T: + """ + Process-lifetime response cache: `.db` files are static for the life of a `viewer` process + (no write path exists in this feature), so a handler's expensive work only ever needs to + run once per distinct cache key -- whichever request (hydration or a real click) happens to + compute it first, the other reuses the result. + """ + cache: dict[tuple, object] = request.app.state.response_cache + if cache_key not in cache: + cache[cache_key] = await 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, this keeps it on the event loop's + # thread, matching the RecordStore connection's thread affinity. + is_hydration = _is_hydration_request(request) + if is_hydration: + logger.debug("Hydrating tag tree in the background") + + def build(store: RecordStore) -> list[TagNode]: + return build_tag_tree(store) + + async def resolve() -> list[TagNode]: + if is_hydration: + return await request.app.state.hydration_runner.run(build) + return build(_get_store(request)) + + return await _cached(request, ("tags",), resolve) + + +@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 `RecordStore` 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. + """ + decoded_tag = decode_tag(tag) + is_hydration = _is_hydration_request(request) + if is_hydration: + logger.debug( + f"Hydrating tag {decoded_tag!r} in the background (table, view={view})" + ) + + def build(store: RecordStore) -> 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() + ) + + async def resolve() -> TableResponse: + if is_hydration: + return await request.app.state.hydration_runner.run(build) + return build(_get_store(request)) + + return await _cached(request, ("table", view, tag), resolve) + + +def _plain_list(value: Any) -> list[Any]: + """ + `Figure.to_plotly_json()` encodes large numeric arrays as a compact + `{"dtype": ..., "bdata": }` typed-array form (Plotly.js's own array spec) instead of + a plain JSON list, whenever a trace's `x`/`y` came from a numpy array (as `Trace2D`'s `VecN` + fields do). Downsampling needs a plain, indexable list, so this decodes that form back; + already-plain lists (small/non-numpy traces) pass through unchanged. + """ + if isinstance(value, dict) and "bdata" in value and "dtype" in value: + raw_bytes = base64.b64decode(value["bdata"]) + return np.frombuffer(raw_bytes, dtype=value["dtype"]).tolist() + return list(value) if value is not None else [] + + +def _downsample_xy( + x: list[float], y: list[float], max_points: int +) -> tuple[list[float], list[float]]: + """ + Downsamples one trace's `(x, y)` to at most `max_points` points, bucketing `x`'s range into + `max_points` uniform intervals and keeping the first point seen in each bucket -- gives equal + coverage across the x range regardless of variable point spacing, rather than just keeping + every Nth point (which would over-represent densely-sampled regions). + """ + n = len(x) + if n <= max_points: + return x, y + + x_min, x_max = x[0], x[-1] + if x_max == x_min: + stride = max(1, n // max_points) + indices = range(0, n, stride) + return [x[i] for i in indices], [y[i] for i in indices] + + span = x_max - x_min + seen: set[int] = set() + indices = [] + for i, xv in enumerate(x): + bucket = min(int((xv - x_min) / span * max_points), max_points - 1) + if bucket not in seen: + seen.add(bucket) + indices.append(i) + return [x[i] for i in indices], [y[i] for i in indices] + + +@router.get("/plot", response_model=PlotResponse) +async def get_plot( + tag: str, + request: Request, + line_mode: LineMode = LineMode.LINES_AND_MARKERS, + interp: InterpMode = InterpMode.LINEAR, + hover: HoverMode = HoverMode.CLOSEST, + show_spike: bool = False, + max_points: int | None = Query(None, gt=0), +) -> PlotResponse: + """ + Plotly figure JSON (`{available, data, layout}`) for the plot viewer, built the same way as + the static Plotly/matplotlib renderers (`generator.render._render_tag_assets`): every + `PlottableData2D` record sharing `tag` is added to one shared `PlotlyFigure`, then that + tag's `Format2D` (if any) is applied. + + `line_mode`/`interp`/`hover`/`show_spike`/`max_points` are the dashboard's view-only + `PlotConfig` settings (`viewer.plot_config`), applied as a post-process pass over the + already-built figure JSON rather than baked into `PlotlyFigure`/the record classes + themselves -- they're display overrides the *viewer* controls, independent of whatever + style a record's own `Pen`/`Marker` authored. `AxLine` renders as layout shapes + (`add_hline`/`add_vline`), not `data` traces, so it's naturally unaffected by the + trace-level overrides below. + """ + decoded_tag = decode_tag(tag) + is_hydration = _is_hydration_request(request) + if is_hydration: + logger.debug(f"Hydrating tag {decoded_tag!r} in the background (plot)") + + def build(store: RecordStore) -> PlotResponse: + format2d_records = store.get_records_of_type(Format2D, tag=decoded_tag) + format2d = format2d_records[0] if format2d_records else None + + points = store.get_records_of_type(Point2D, tag=decoded_tag) + traces = store.get_records_of_type(Trace2D, tag=decoded_tag) + scatters = store.get_records_of_type(Scatter2D, tag=decoded_tag) + axlines = store.get_records_of_type(AxLine, tag=decoded_tag) + histogram_entries = store.get_records_of_type(HistogramEntry, tag=decoded_tag) + + figure = PlotlyFigure.new(decoded_tag) + for record in (*points, *traces, *scatters, *axlines, *histogram_entries): + figure.add_record(record) + + if not figure.fig.data: + return PlotResponse(available=False, data=[], layout={}) + + if format2d is not None: + figure.apply_format(format2d) + + raw = figure.fig.to_plotly_json() + traces_json = cast(list[dict[str, Any]], raw["data"]) + layout_json = cast(dict[str, Any], raw["layout"]) + + for trace in traces_json: + if trace.get("type") != "scatter": + continue + trace["mode"] = line_mode.value + trace.setdefault("line", {})["shape"] = interp.value + trace["x"] = _plain_list(trace.get("x")) + trace["y"] = _plain_list(trace.get("y")) + if max_points is not None and len(trace["x"]) > max_points: + trace["x"], trace["y"] = _downsample_xy( + trace["x"], trace["y"], max_points + ) + + layout_json["hovermode"] = False if hover == HoverMode.NONE else hover.value + if show_spike: + for axis_key in ("xaxis", "yaxis"): + axis = layout_json.setdefault(axis_key, {}) + axis["showspikes"] = True + axis["spikemode"] = "across" + axis["spikedash"] = "dot" + # Plotly's spike default (a heavy black/white line) reads harshly against either + # theme -- rose-500 (this app's one accent color, see DASHBOARD.md's "Color + # Scheme") stays legible and consistent in both, and a thinner line is less + # visually loud than the default. + axis["spikecolor"] = "#f43f5e" + axis["spikethickness"] = 1 + + return PlotResponse(available=True, data=traces_json, layout=layout_json) + + async def resolve() -> PlotResponse: + if is_hydration: + return await request.app.state.hydration_runner.run(build) + return build(_get_store(request)) + + return await _cached( + request, + ("plot", tag, line_mode, interp, hover, show_spike, max_points), + resolve, + ) diff --git a/src/trendify/viewer/routes/pages.py b/src/trendify/viewer/routes/pages.py new file mode 100644 index 0000000..6ca751b --- /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, which is +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 + # RecordStore'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..cb0e152 --- /dev/null +++ b/src/trendify/viewer/tag_tree.py @@ -0,0 +1,148 @@ +""" +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.record_store import RecordStore +from trendify.store.tags import tag_to_path_parts + +__all__ = ["TagNode", "build_tag_tree"] + +logger = logging.getLogger(__name__) + + +class TagNode(BaseModel): + """One node of the sidebar's nested tag hierarchy.""" + + key: Tag + """The tag (or tag-path segment) this node represents.""" + + label: str + """Display label shown in the sidebar.""" + + children: list[TagNode] + """Nested tag nodes one level below this one.""" + + has_records: bool + """Whether records are tagged with this exact node's `key`, as opposed to only a + descendant's.""" + + record_kinds: list[Literal["plot", "table"]] + """Kinds of records tagged with this exact node's `key`.""" + + size_bytes: int + """Total payload bytes of records tagged with this exact node's `key` (0 for a node with + no records of its own). Used by the viewer's background hydration to pick the largest + sibling tag at whatever level the user is currently browsing.""" + + 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 `record_kinds` and every descendant's, so a folder can be + filtered by record type without needing to walk the tree again client-side (a folder + should stay visible under a "table" filter if any record anywhere inside it is a + table, even if the folder itself has no records of its own). + """ + kinds = set(self.record_kinds) + for child in self.children: + kinds.update(child.subtree_kinds()) + return sorted(kinds) + + def record_count(self) -> int: + """ + Recursive count of self-and-descendant nodes with `has_records`, shown as a + subtle badge next to folder labels in the sidebar. + """ + count = 1 if self.has_records else 0 + for child in self.children: + count += child.record_count() + return count + + +class _TrieNode: + def __init__(self) -> None: + self.children: dict[str, _TrieNode] = {} + self.tag: Tag | None = None + + +def _record_kinds(store: RecordStore, tag: Tag) -> list[Literal["plot", "table"]]: + kinds: list[Literal["plot", "table"]] = [] + if store.has_records(tag=tag, object_type=PlottableData2D): + kinds.append("plot") + if store.has_table_entries(tag): + 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.record_kinds: + return 1 + if "table" in node.record_kinds: + return 2 + return 3 + + +def build_tag_tree(store: RecordStore) -> 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_records` is + tracked separately from `children` being non-empty, since 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 + + sizes = store.get_tag_byte_sizes() + + 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_records=tag is not None, + record_kinds=_record_kinds(store, tag) if tag is not None else [], + size_bytes=sizes.get(tag, 0) if tag is not None else 0, + ) + ) + # 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..f4e4f1b --- /dev/null +++ b/src/trendify/viewer/templates/base.html @@ -0,0 +1,60 @@ + + + + + + + {% 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..ef97410 --- /dev/null +++ b/src/trendify/viewer/templates/index.html @@ -0,0 +1,131 @@ +{% extends "base.html" %} + +{% block content %} +
+ {% include "partials/nav.html" %} + +
+ {% include "partials/sidebar.html" %} + +
+ +
+ + +
+
+ + {% include "partials/footer.html" %} + +
+ +
+
+{% endblock %} diff --git a/gallery/trendify/products/analysis/reserving_index.lock b/src/trendify/viewer/templates/partials/__init__.py similarity index 100% rename from gallery/trendify/products/analysis/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..f8c2a1d --- /dev/null +++ b/src/trendify/viewer/templates/partials/nav.html @@ -0,0 +1,179 @@ + diff --git a/src/trendify/viewer/templates/partials/plot_view.html b/src/trendify/viewer/templates/partials/plot_view.html new file mode 100644 index 0000000..13592f9 --- /dev/null +++ b/src/trendify/viewer/templates/partials/plot_view.html @@ -0,0 +1,221 @@ +
+
+
+ + +
+ +
+ +
+ + + + + + + + + + + + Settings +
+ +
+ + + +
+ +
+ + +
+ +
+ + +
+ + + +
+ + + +
+ +
+ +
+ + + +
+
+
+

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+

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+
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+ + + + +
+
+
diff --git a/src/trendify/viewer/templates/partials/sidebar.html b/src/trendify/viewer/templates/partials/sidebar.html new file mode 100644 index 0000000..70c1280 --- /dev/null +++ b/src/trendify/viewer/templates/partials/sidebar.html @@ -0,0 +1,163 @@ +{% macro render_node(node, path=[]) %} +{% set full_path = path + [node.label] %} +
+
+ {% if node.children %} + + + + + + {% else %} + + {% endif %} + + {% if node.has_records %} + + {% if "plot" in node.record_kinds %} + + + + + {% endif %} + {% if "table" in node.record_kinds %} + + + + + {% endif %} + {{ node.label }} + + {% else %} + {{ node.label }} + {% endif %} + + {% if node.children %} + {{ node.record_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..9771090 --- /dev/null +++ b/src/trendify/viewer/templates/partials/table_view.html @@ -0,0 +1,43 @@ +
+
+ + + +
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+

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diff --git a/gallery/trendify/products/clusters/reserving_index.lock b/src/trendify/viewer/templates/static/__init__.py similarity index 100% rename from gallery/trendify/products/clusters/reserving_index.lock rename to src/trendify/viewer/templates/static/__init__.py diff --git a/gallery/trendify/products/comparison/reserving_index.lock b/src/trendify/viewer/templates/static/assets/__init__.py similarity index 100% rename from gallery/trendify/products/comparison/reserving_index.lock rename to src/trendify/viewer/templates/static/assets/__init__.py diff --git a/src/trendify/viewer/templates/static/assets/main-logo-white-bg.svg b/src/trendify/viewer/templates/static/assets/main-logo-white-bg.svg new file mode 100644 index 0000000..ea66caf --- /dev/null +++ b/src/trendify/viewer/templates/static/assets/main-logo-white-bg.svg @@ -0,0 +1,75 @@ + + + + diff --git a/src/trendify/viewer/templates/static/assets/main-logo.svg b/src/trendify/viewer/templates/static/assets/main-logo.svg new file mode 100644 index 0000000..d0a26a4 --- /dev/null +++ b/src/trendify/viewer/templates/static/assets/main-logo.svg @@ -0,0 +1,66 @@ + + + + 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..d95b171 --- /dev/null +++ b/src/trendify/viewer/templates/static/css/app.css @@ -0,0 +1,75 @@ +/* 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 */ +} + +/* Plotly's own on-page notifications (e.g. axis-drag warnings) are relayed through this app's + toast system instead (see lib/plotly-toast-bridge.ts) -- suppress Plotly's native ones. */ +.plotly-notifier { + display: none !important; +} 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..308db61 --- /dev/null +++ b/src/trendify/viewer/templates/static/js/main.js @@ -0,0 +1,2 @@ +"use strict";(()=>{var x="trendify:theme";function 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b/src/trendify/viewer/templates/static/ts/build.mjs new file mode 100644 index 0000000..4a98846 --- /dev/null +++ b/src/trendify/viewer/templates/static/ts/build.mjs @@ -0,0 +1,23 @@ +import * as esbuild from "esbuild"; + +const watch = process.argv.includes("--watch"); + +const options = { + entryPoints: ["src/main.ts"], + bundle: true, + outfile: "../js/main.js", + format: "iife", + target: "es2020", + platform: "browser", + sourcemap: true, + minify: !watch, + logLevel: "info", +}; + +if (watch) { + const ctx = await esbuild.context(options); + await ctx.watch(); + console.log("watching for changes..."); +} else { + await esbuild.build(options); +} diff --git a/src/trendify/viewer/templates/static/ts/package-lock.json b/src/trendify/viewer/templates/static/ts/package-lock.json new file mode 100644 index 0000000..6313d16 --- /dev/null +++ b/src/trendify/viewer/templates/static/ts/package-lock.json @@ -0,0 +1,545 @@ +{ + "name": "ts", + "lockfileVersion": 3, + "requires": true, + 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"@types/jquery": "^3.5.30", + "@types/plotly.js": "^3.0.10", + "datatables.net": "^2.3.8", + "esbuild": "^0.24.0", + "typescript": "^5.6.0" + } +} diff --git a/src/trendify/viewer/templates/static/ts/src/lib/api.ts b/src/trendify/viewer/templates/static/ts/src/lib/api.ts new file mode 100644 index 0000000..f16f8db --- /dev/null +++ b/src/trendify/viewer/templates/static/ts/src/lib/api.ts @@ -0,0 +1,140 @@ +/** + * Typed fetch wrappers for the dashboard's JSON API, each backed by an in-memory cache so + * revisiting a previously-viewed tag doesn't re-fetch from the server. The server-side cache + * (see routes/api.py) means even a cache miss here is cheap; this cache exists purely to avoid + * redundant network round-trips within one page session. + */ + +import type { PlotConfig } from "./plot-config.generated"; + +export type Tag = string | number | (string | number)[]; + +export interface FetchOptions { + signal?: AbortSignal; + priority?: RequestPriority; + headers?: HeadersInit; +} + +const cache = new Map(); +// In-flight requests keyed by URL: a second caller for a URL already being fetched (e.g. the +// click flow catching up to a request background hydration already started, see prefetch.ts) +// awaits the same promise instead of firing a duplicate request. +const pending = new Map>(); + +async function cachedGet(url: string, options?: FetchOptions): Promise { + const cached = cache.get(url); + if (cached !== undefined) { + return cached as T; + } + + const inFlight = pending.get(url); + if (inFlight !== undefined) { + try { + return await (inFlight as Promise); + } catch (err) { + // Whoever actually started this request may have cancelled it for their own reasons + // that have nothing to do with *this* caller (most commonly: background hydration's + // request for this exact tag got aborted because the user just clicked it, which is + // also what landed us here attached to hydration's now-dead request instead of firing + // our own). If our own signal wasn't the one that aborted, the work we actually wanted + // is simply gone now -- retry as a fresh, independent request rather than silently + // propagating someone else's cancellation (which would otherwise leave the UI showing + // nothing until the user clicks again). + if (isAbortError(err) && options?.signal?.aborted !== true) { + return cachedGet(url, options); + } + throw err; + } + } + + const request = (async () => { + const res = await fetch(url, { + signal: options?.signal, + priority: options?.priority, + headers: options?.headers, + }); + if (!res.ok) { + throw new Error(`Request failed: ${url} (${res.status})`); + } + const data = (await res.json()) as T; + cache.set(url, data); + return data; + })(); + + pending.set(url, request); + try { + return await request; + } finally { + pending.delete(url); + } +} + +export type TableView = "melted" | "pivot" | "stats"; + +export interface TableResponse { + available: boolean; + columns: string[]; + rows: Record[]; +} + +export function getTable( + tag: Tag, + view: TableView, + options?: FetchOptions, +): Promise { + const url = `/api/table?tag=${encodeURIComponent(JSON.stringify(tag))}&view=${view}`; + return cachedGet(url, options); +} + +export interface PlotResponse { + available: boolean; + data: Record[]; + layout: Record; +} + +export function getPlot( + tag: Tag, + config: PlotConfig, + options?: FetchOptions, +): Promise { + // `PlotConfig`'s fields are camelCase (pydantic's `to_camel` alias, for the generated TS + // interface); `/api/plot`'s query params are plain snake_case FastAPI params -- this is the + // one place that mapping happens. + const params = new URLSearchParams({ + tag: JSON.stringify(tag), + line_mode: config.lineMode ?? "lines+markers", + interp: config.interp ?? "linear", + hover: config.hover ?? "closest", + show_spike: String(config.showSpike ?? false), + }); + if (config.maxPoints != null) { + params.set("max_points", String(config.maxPoints)); + } + return cachedGet(`/api/plot?${params.toString()}`, options); +} + +export interface TagNode { + key: Tag; + label: string; + children: TagNode[]; + has_records: boolean; + record_kinds: ("plot" | "table")[]; + size_bytes: number; +} + +export function getTags(options?: FetchOptions): Promise { + return cachedGet("/api/tags", options); +} + +/** `err instanceof DOMException && err.name === "AbortError"`, but also covers non-DOM-shaped + * rejects some environments/polyfills use for an aborted `fetch`. */ +export function isAbortError(err: unknown): boolean { + return err instanceof Error && err.name === "AbortError"; +} + +// The db is static for the life of a `viewer` process except for exactly this case (someone +// regenerates it while the server is still up); `connection.ts` detects that via `/api/ping`'s +// mtime and dispatches this event, so this client-side cache doesn't keep serving stale data. +window.addEventListener("trendify:db-changed", () => cache.clear()); + +export { cachedGet }; diff --git a/src/trendify/viewer/templates/static/ts/src/lib/clipboard.ts b/src/trendify/viewer/templates/static/ts/src/lib/clipboard.ts new file mode 100644 index 0000000..3a8c73c --- /dev/null +++ b/src/trendify/viewer/templates/static/ts/src/lib/clipboard.ts @@ -0,0 +1,31 @@ +/** + * Copies `text` to the clipboard, falling back to the legacy `execCommand` approach when the + * async Clipboard API is unavailable -- `navigator.clipboard` only exists in secure contexts + * (HTTPS or localhost), so it's missing entirely when the dashboard is reached over plain HTTP + * on a LAN address (e.g. the "Mobile" URL `trendify viewer --host 0.0.0.0` prints). + */ +export async function copyToClipboard(text: string): Promise { + if (navigator.clipboard?.writeText) { + try { + await navigator.clipboard.writeText(text); + return true; + } catch { + // Fall through to the legacy fallback below. + } + } + + try { + const textarea = document.createElement("textarea"); + textarea.value = text; + textarea.style.position = "fixed"; + textarea.style.opacity = "0"; + document.body.appendChild(textarea); + textarea.focus(); + textarea.select(); + const ok = document.execCommand("copy"); + document.body.removeChild(textarea); + return ok; + } catch { + return false; + } +} diff --git a/src/trendify/viewer/templates/static/ts/src/lib/connection.ts b/src/trendify/viewer/templates/static/ts/src/lib/connection.ts new file mode 100644 index 0000000..f07a1f7 --- /dev/null +++ b/src/trendify/viewer/templates/static/ts/src/lib/connection.ts @@ -0,0 +1,56 @@ +export interface PingResponse { + ok: boolean; + db_updated_at: number | null; +} + +export interface ConnectionMonitorOptions { + pingUrl: string; + intervalMs: number; + timeoutMs: number; + onConnectionChange: (connected: boolean) => void; + onDbChanged: () => void; +} + +/** Polls `pingUrl`, reporting connect/disconnect transitions and db-file-changed events. */ +export function startConnectionMonitor({ + pingUrl, + intervalMs, + timeoutMs, + onConnectionChange, + onDbChanged, +}: ConnectionMonitorOptions): () => void { + let connected = true; + let lastDbUpdatedAt: number | null = null; + + async function checkOnce() { + const controller = new AbortController(); + const timer = setTimeout(() => controller.abort(), timeoutMs); + try { + const response = await fetch(pingUrl, { signal: controller.signal, cache: "no-store" }); + if (!response.ok) throw new Error(`ping failed: ${response.status}`); + const body = (await response.json()) as PingResponse; + + if (!connected) { + connected = true; + onConnectionChange(true); + } + if (body.db_updated_at !== null) { + if (lastDbUpdatedAt !== null && body.db_updated_at !== lastDbUpdatedAt) { + onDbChanged(); + } + lastDbUpdatedAt = body.db_updated_at; + } + } catch { + if (connected) { + connected = false; + onConnectionChange(false); + } + } finally { + clearTimeout(timer); + } + } + + const timer = setInterval(checkOnce, intervalMs); + checkOnce(); + return () => clearInterval(timer); +} diff --git a/src/trendify/viewer/templates/static/ts/src/lib/content-tabs.ts b/src/trendify/viewer/templates/static/ts/src/lib/content-tabs.ts new file mode 100644 index 0000000..23f2cff --- /dev/null +++ b/src/trendify/viewer/templates/static/ts/src/lib/content-tabs.ts @@ -0,0 +1,27 @@ +import type { Tag } from "./api"; + +// Same ancestor-scope-merging convention as `table-view.ts`/`sidebar-node.ts`: `selectedTag`/ +// `selectedRecordKinds` live on the `appShell` scope this component is nested inside. +interface ContentTabsContext { + contentTab: "plot" | "table"; + selectedTag: Tag | null; + selectedRecordKinds: string[]; + $watch(property: string, callback: (value: unknown) => void): void; +} + +/** + * Alpine data factory for the main view's outer Plot/Table tab switcher. Defaults to "plot" + * (Plot is the first tab) whenever a newly-selected tag has plot records, falling back to + * "table" otherwise -- a tag with only one kind never shows the tab bar at all (see + * `index.html`'s `x-show` guard), so this only matters for tags with both. + */ +export function contentTabs() { + return { + contentTab: "plot" as "plot" | "table", + init(this: ContentTabsContext) { + this.$watch("selectedTag", () => { + this.contentTab = this.selectedRecordKinds.includes("plot") ? "plot" : "table"; + }); + }, + }; +} diff --git a/src/trendify/viewer/templates/static/ts/src/lib/fullscreen.ts b/src/trendify/viewer/templates/static/ts/src/lib/fullscreen.ts new file mode 100644 index 0000000..b0b4816 --- /dev/null +++ b/src/trendify/viewer/templates/static/ts/src/lib/fullscreen.ts @@ -0,0 +1,33 @@ +/** + * Fullscreen toggle for the whole page. Escape-to-exit is native browser behavior (no JS + * needed for that part). A `fullscreenchange` listener notifies interested code (Plotly/ + * DataTables resize hooks, wired in later milestones) via a plain window event, since neither + * library reliably auto-observes a fullscreen transition on its own. + */ + +export interface FullscreenComponent { + isFullscreen: boolean; + init(): void; + toggle(): void; +} + +export function fullscreenToggle(): FullscreenComponent { + return { + isFullscreen: false, + + init() { + document.addEventListener("fullscreenchange", () => { + this.isFullscreen = document.fullscreenElement !== null; + window.dispatchEvent(new Event("trendify:layout-changed")); + }); + }, + + toggle() { + if (document.fullscreenElement) { + document.exitFullscreen(); + } else { + document.documentElement.requestFullscreen(); + } + }, + }; +} diff --git a/src/trendify/viewer/templates/static/ts/src/lib/local-storage.ts b/src/trendify/viewer/templates/static/ts/src/lib/local-storage.ts new file mode 100644 index 0000000..58756b0 --- /dev/null +++ b/src/trendify/viewer/templates/static/ts/src/lib/local-storage.ts @@ -0,0 +1,14 @@ +/** JSON get/set over localStorage, tolerant of missing keys and malformed/stale JSON. */ +export function loadJSON(key: string): T | null { + const raw = localStorage.getItem(key); + if (raw === null) return null; + try { + return JSON.parse(raw) as T; + } catch { + return null; + } +} + +export function saveJSON(key: string, value: unknown): void { + localStorage.setItem(key, JSON.stringify(value)); +} diff --git a/src/trendify/viewer/templates/static/ts/src/lib/metadata-filter.ts b/src/trendify/viewer/templates/static/ts/src/lib/metadata-filter.ts new file mode 100644 index 0000000..9091bc8 --- /dev/null +++ b/src/trendify/viewer/templates/static/ts/src/lib/metadata-filter.ts @@ -0,0 +1,98 @@ +/** + * Client-side metadata filter for the plot viewer: lets a user hide traces whose + * `Record.metadata` doesn't match a small set of conditions. Entirely client-side -- metadata + * is already attached to each trace's Plotly `meta` field by `PlotlyFigure.add_record` (see + * `figure.py`), so filtering never touches the server or its response cache, it just changes + * which of an already-fetched response's traces get handed to `Plotly.react`. + * + * Syntax: comma-separated `keyvalue` conditions, ANDed together, e.g. + * `run=5, quality!=bad, label~prod`. Recognized operators (checked so `!=`/`>=`/`<=` are never + * misread as `=`/`>`/`<`): `!=` `>=` `<=` `=` `>` `<` `~` (substring, case-insensitive). + * `=`/`!=` are case-sensitive exact match; `>`/`<`/`>=`/`<=` compare numerically (both sides + * must parse as numbers, else the condition is false). A trace missing the key entirely fails + * every operator -- there's nothing to compare. + */ + +export interface MetadataCondition { + key: string; + op: "!=" | ">=" | "<=" | "=" | ">" | "<" | "~"; + value: string; +} + +// Order matters for correct parsing of a shared starting character (">"/">=", "<"/"<="): the +// longer operator must be listed first so it wins a tie at the same string index. +const OPERATORS: MetadataCondition["op"][] = ["!=", ">=", "<=", "=", ">", "<", "~"]; + +/** + * Parses a filter expression into conditions, silently skipping any comma-separated segment + * that doesn't contain a recognized operator (or has an empty key) -- so an expression the + * user is still mid-typing doesn't collapse into an always-false filter that hides everything. + */ +export function parseMetadataFilter(expr: string): MetadataCondition[] { + const conditions: MetadataCondition[] = []; + + for (const segment of expr.split(",")) { + const trimmed = segment.trim(); + if (!trimmed) continue; + + let best: { op: MetadataCondition["op"]; index: number } | null = null; + for (const op of OPERATORS) { + const index = trimmed.indexOf(op); + // `index > 0` (not `>= 0`) doubles as the empty-key check: an operator at position 0 + // has nothing before it to be a key. + if (index > 0 && (best === null || index < best.index)) { + best = { op, index }; + } + } + if (best === null) continue; + + const key = trimmed.slice(0, best.index).trim(); + const value = trimmed.slice(best.index + best.op.length).trim(); + if (!key) continue; + conditions.push({ key, op: best.op, value }); + } + + return conditions; +} + +function matchesCondition( + metadata: Record, + condition: MetadataCondition, +): boolean { + const actual = metadata[condition.key]; + if (actual === undefined) return false; + + switch (condition.op) { + case "=": + return actual === condition.value; + case "!=": + return actual !== condition.value; + case "~": + return actual.toLowerCase().includes(condition.value.toLowerCase()); + case ">": + case "<": + case ">=": + case "<=": { + const a = parseFloat(actual); + const b = parseFloat(condition.value); + if (Number.isNaN(a) || Number.isNaN(b)) return false; + if (condition.op === ">") return a > b; + if (condition.op === "<") return a < b; + if (condition.op === ">=") return a >= b; + return a <= b; + } + } +} + +/** + * Whether `meta` (a trace's Plotly `meta` field -- possibly `undefined` for a trace whose + * record had no metadata) satisfies every one of `conditions` (AND). An empty `conditions` + * list always matches: that's the "no filter" state. + */ +export function matchesMetadataFilter(meta: unknown, conditions: MetadataCondition[]): boolean { + if (conditions.length === 0) return true; + if (typeof meta !== "object" || meta === null) return false; + return conditions.every((condition) => + matchesCondition(meta as Record, condition), + ); +} diff --git a/src/trendify/viewer/templates/static/ts/src/lib/plot-config.generated.ts b/src/trendify/viewer/templates/static/ts/src/lib/plot-config.generated.ts new file mode 100644 index 0000000..8952feb --- /dev/null +++ b/src/trendify/viewer/templates/static/ts/src/lib/plot-config.generated.ts @@ -0,0 +1,105 @@ +// ============================================================ +// 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"; + +/** a single filter in a filter stack — type-discriminated, open-ended properties. */ +export type FilterEntry = { type: string; enabled?: boolean; [key: string]: unknown }; + +export type HoverMode = "x unified" | "y unified" | "closest" | "x" | "y" | "none"; +export const HOVER_MODE_VALUES: HoverMode[] = ["x unified", "y unified", "closest", "x", "y", "none"]; + +export type InterpMode = "linear" | "spline" | "hv" | "vh" | "hvh" | "vhv"; +export const INTERP_MODE_VALUES: InterpMode[] = ["linear", "spline", "hv", "vh", "hvh", "vhv"]; + +export type LineMode = "lines" | "markers" | "lines+markers"; +export const LINE_MODE_VALUES: LineMode[] = ["lines", "markers", "lines+markers"]; + +/** Complete serialisable state of a dashboard plot. */ +export interface PlotConfig { + lineMode?: LineMode; + interp?: InterpMode; + hover?: HoverMode; + showSpike?: boolean; + maxPoints?: number | null; +} + +/** Full JSON Schema for PlotConfig - used for additive client-side validation. */ +export const PLOT_CONFIG_SCHEMA: JsonSchemaNode = { + "$defs": { + "HoverMode": { + "description": "Tooltip behavior when hovering over the plot.", + "enum": [ + "x unified", + "y unified", + "closest", + "x", + "y", + "none" + ], + "title": "HoverMode", + "type": "string" + }, + "InterpMode": { + "description": "Line interpolation method between data points.", + "enum": [ + "linear", + "spline", + "hv", + "vh", + "hvh", + "vhv" + ], + "title": "InterpMode", + "type": "string" + }, + "LineMode": { + "description": "Controls whether traces are drawn as lines, markers, or both.", + "enum": [ + "lines", + "markers", + "lines+markers" + ], + "title": "LineMode", + "type": "string" + } + }, + "description": "Complete serialisable state of a dashboard plot.", + "properties": { + "lineMode": { + "$ref": "#/$defs/LineMode", + "default": "lines+markers" + }, + "interp": { + "$ref": "#/$defs/InterpMode", + "default": "linear" + }, + "hover": { + "$ref": "#/$defs/HoverMode", + "default": "closest" + }, + "showSpike": { + "default": false, + "title": "Showspike", + "type": "boolean" + }, + "maxPoints": { + "anyOf": [ + { + "exclusiveMinimum": 0, + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Maxpoints" + } + }, + "title": "PlotConfig", + "type": "object" +}; diff --git a/src/trendify/viewer/templates/static/ts/src/lib/plot-view.ts b/src/trendify/viewer/templates/static/ts/src/lib/plot-view.ts new file mode 100644 index 0000000..c0e798a --- /dev/null +++ b/src/trendify/viewer/templates/static/ts/src/lib/plot-view.ts @@ -0,0 +1,370 @@ +import { getPlot, isAbortError, type PlotResponse, type Tag } from "./api"; +import type { PlotConfig } from "./plot-config.generated"; +import { loadJSON, saveJSON } from "./local-storage"; +import { matchesMetadataFilter, parseMetadataFilter } from "./metadata-filter"; +import type { Data, Layout, PlotlyHTMLElement } from "plotly.js"; + +// Alpine injects `$watch`/`$refs`/`$nextTick` at runtime and merges the ancestor `appShell` +// scope's `selectedTag` into this component's data proxy; TS has no way to infer either from +// this plain factory's return value, so each method that needs them declares `this` explicitly +// (same convention as `table-view.ts`). +interface ZoomRange { + xaxis?: [number, number]; + yaxis?: [number, number]; +} + +interface PlotViewContext { + config: PlotConfig; + unavailable: boolean; + filteredOut: boolean; + metadataFilter: string; + lastResponse: PlotResponse | null; + loading: boolean; + requestId: number; + currentAbortController: AbortController | null; + historyStack: string[]; + historyIndex: number; + isUndoing: boolean; + zoomRange: ZoomRange | null; + selectedTag: Tag | null; + $watch(property: string, callback: (value: unknown) => void): void; + $refs: Record; + $nextTick(callback: () => void): void; + render(): Promise; + applyMetadataFilter(): void; + undo(): void; + redo(): void; +} + +const MAX_HISTORY = 50; + +const DEFAULT_CONFIG: PlotConfig = { + lineMode: "lines+markers", + interp: "linear", + hover: "closest", + showSpike: false, + maxPoints: null, +}; + +/** Per-tag, so each plot remembers its own view settings (mirrors `table-view.ts`'s per-tag key). */ +function configStorageKey(tag: Tag): string { + return `trendify:plot-config:${JSON.stringify(tag)}`; +} + +/** Exported for reuse by prefetch.ts, which needs to warm the exact cache key (including any + * per-tag saved settings) that this view will actually request when the tag is clicked. */ +export function loadConfigForTag(tag: Tag | null): PlotConfig { + const saved = tag !== null ? loadJSON>(configStorageKey(tag)) : null; + return { ...DEFAULT_CONFIG, ...(saved ?? {}) }; +} + +/** Per-tag, same as `configStorageKey` -- each plot remembers its own zoom/pan range. */ +function zoomStorageKey(tag: Tag): string { + return `trendify:plot-zoom:${JSON.stringify(tag)}`; +} + +function loadZoomForTag(tag: Tag | null): ZoomRange | null { + return tag !== null ? loadJSON(zoomStorageKey(tag)) : null; +} + +// Deliberately *not* per-tag (unlike configStorageKey/zoomStorageKey above): a metadata filter +// is something a user sets up once and expects to keep seeing applied while browsing between +// tags, not a per-plot setting that resets on every tag switch. +const METADATA_FILTER_STORAGE_KEY = "trendify:metadata-filter"; + +// Plotly paints its own colors rather than picking them up from Tailwind's `dark:` CSS +// variants, so theme changes are applied via `Plotly.relayout` with this app's existing +// slate palette (see DASHBOARD.md's "Color Scheme" section) instead of a named Plotly template. +const LIGHT_THEME_LAYOUT = { + paper_bgcolor: "rgba(0,0,0,0)", + plot_bgcolor: "rgba(0,0,0,0)", + "font.color": "#475569", + "xaxis.gridcolor": "#cbd5e1", + "xaxis.linecolor": "#94a3b8", + "xaxis.zerolinecolor": "#cbd5e1", + "yaxis.gridcolor": "#cbd5e1", + "yaxis.linecolor": "#94a3b8", + "yaxis.zerolinecolor": "#cbd5e1", + // Unified hover mode ("x unified"/"y unified") draws one shared tooltip box styled from + // this global `layout.hoverlabel`, ignoring each trace's own per-point `hoverlabel` (which + // only applies to the normal, per-trace hover box) -- left unset, Plotly defaults it to a + // plain white box regardless of theme. Matches `.card`'s colors (app.css). + "hoverlabel.bgcolor": "#e2e8f0", + "hoverlabel.bordercolor": "#94a3b8", + "hoverlabel.font.color": "#1e293b", +}; +const DARK_THEME_LAYOUT = { + paper_bgcolor: "rgba(0,0,0,0)", + plot_bgcolor: "rgba(0,0,0,0)", + "font.color": "#94a3b8", + "xaxis.gridcolor": "#334155", + "xaxis.linecolor": "#475569", + "xaxis.zerolinecolor": "#334155", + "yaxis.gridcolor": "#334155", + "yaxis.linecolor": "#475569", + "yaxis.zerolinecolor": "#334155", + "hoverlabel.bgcolor": "#1e293b", + "hoverlabel.bordercolor": "#475569", + "hoverlabel.font.color": "#e2e8f0", +}; + +function currentThemeLayout() { + return document.documentElement.classList.contains("dark") + ? DARK_THEME_LAYOUT + : LIGHT_THEME_LAYOUT; +} + +/** + * Alpine data factory for the Plotly plot viewer. Mounted once (inside an `x-if` in + * `index.html`, same as `table-view.ts`) and reused across every plot-tagged selection. + * + * Settings (`PlotConfig`) are applied entirely server-side (`/api/plot`'s query params) -- + * this component never constructs a Plotly trace itself, it only hands the server's `data`/ + * `layout` JSON to `Plotly.react`. Every settings change is therefore a full re-fetch, kept + * simple by a single deep `$watch("config", ...)` that handles history-tracking, per-tag + * persistence, and re-rendering uniformly, so individual settings-panel controls only need to + * mutate `config.*` directly. + */ +export function plotView() { + return { + config: { ...DEFAULT_CONFIG } as PlotConfig, + unavailable: false, + filteredOut: false, + metadataFilter: "", + lastResponse: null as PlotResponse | null, + loading: false, + requestId: 0, + currentAbortController: null as AbortController | null, + historyStack: [] as string[], + historyIndex: -1, + isUndoing: false, + zoomRange: null as ZoomRange | null, + _teardown: null as (() => void) | null, + _zoomListenerAttached: false, + + init(this: PlotViewContext & { _teardown: (() => void) | null }) { + this.config = loadConfigForTag(this.selectedTag); + this.zoomRange = loadZoomForTag(this.selectedTag); + this.metadataFilter = loadJSON(METADATA_FILTER_STORAGE_KEY) ?? ""; + this.historyStack = [JSON.stringify(this.config)]; + this.historyIndex = 0; + + this.$watch("selectedTag", () => { + const nextConfig = loadConfigForTag(this.selectedTag); + this.historyStack = [JSON.stringify(nextConfig)]; + this.historyIndex = 0; + this.config = nextConfig; + this.zoomRange = loadZoomForTag(this.selectedTag); + }); + + this.$watch("metadataFilter", () => { + saveJSON(METADATA_FILTER_STORAGE_KEY, this.metadataFilter); + this.applyMetadataFilter(); + }); + + this.$watch("config", () => { + const snapshot = JSON.stringify(this.config); + if (!this.isUndoing && this.historyStack[this.historyIndex] !== snapshot) { + this.historyStack = this.historyStack.slice(0, this.historyIndex + 1); + this.historyStack.push(snapshot); + if (this.historyStack.length > MAX_HISTORY) this.historyStack.shift(); + this.historyIndex = this.historyStack.length - 1; + } + if (this.selectedTag !== null) { + saveJSON(configStorageKey(this.selectedTag), this.config); + } + this.render(); + }); + + const handleKeydown = (event: KeyboardEvent) => { + const target = event.target as HTMLElement | null; + const isTextInput = + target !== null && + (["INPUT", "TEXTAREA", "SELECT"].includes(target.tagName) || + target.isContentEditable); + if (isTextInput) return; + + const cmdOrCtrl = event.metaKey || event.ctrlKey; + if (!cmdOrCtrl) return; + const key = event.key.toLowerCase(); + if (key === "z" && event.shiftKey) { + event.preventDefault(); + this.redo(); + } else if (key === "z") { + event.preventDefault(); + this.undo(); + } else if (key === "y") { + event.preventDefault(); + this.redo(); + } + }; + + const handleResize = () => { + const el = this.$refs.plot; + if (el && el.offsetParent !== null) Plotly.Plots.resize(el); + }; + + const handleThemeChange = () => { + const el = this.$refs.plot; + if (el && el.offsetParent !== null) Plotly.relayout(el, currentThemeLayout()); + }; + + document.addEventListener("keydown", handleKeydown); + window.addEventListener("resize", handleResize); + window.addEventListener("trendify:layout-changed", handleResize); + window.addEventListener("trendify:theme-changed", handleThemeChange); + + // Alpine calls a mounted component's `destroy()` automatically when its `x-data` element + // is removed (e.g. this ancestor `x-if` unmounting when the tag has no 'plot' kind), so + // these listeners are torn down there rather than leaking across remounts. + this._teardown = () => { + document.removeEventListener("keydown", handleKeydown); + window.removeEventListener("resize", handleResize); + window.removeEventListener("trendify:layout-changed", handleResize); + window.removeEventListener("trendify:theme-changed", handleThemeChange); + }; + + this.render(); + }, + + destroy(this: { _teardown: (() => void) | null }) { + this._teardown?.(); + }, + + async render(this: PlotViewContext) { + const tag = this.selectedTag; + if (tag === null) return; + + // Guards against out-of-order async resolution, same as `table-view.ts`'s `render()`. + const requestId = ++this.requestId; + // Cancelling the previous tag's still-in-flight request (rather than just letting the + // requestId guard above ignore its result) frees up its connection/bandwidth immediately + // instead of leaving it to finish in the background -- this is what makes switching tags + // feel instant rather than queued behind whatever was already loading. + this.currentAbortController?.abort(); + const controller = new AbortController(); + this.currentAbortController = controller; + this.loading = true; + try { + const response = await getPlot(tag, this.config, { + signal: controller.signal, + priority: "high", + }); + if (requestId !== this.requestId) return; + + this.lastResponse = response; + this.unavailable = !response.available; + if (!response.available) { + this.filteredOut = false; + Plotly.purge(this.$refs.plot); + return; + } + this.applyMetadataFilter(); + } catch (err) { + // Expected whenever a newer render() aborts this one via currentAbortController above -- + // the newer request already owns `loading`/`unavailable`, so there's nothing to do here. + if (!isAbortError(err)) throw err; + } finally { + // A stale in-flight request finishing after a newer one started must not clear the + // spinner out from under the request that's still actually loading. + if (requestId === this.requestId) this.loading = false; + } + }, + + /** + * Re-draws the currently-fetched response (`lastResponse`), applying `metadataFilter` + * without a re-fetch: metadata is already sitting in each trace's `meta` field (see + * `figure.py`'s `PlotlyFigure.add_record`), so a filter change is purely a presentation + * concern. Called after every successful `render()`, and again whenever `metadataFilter` + * itself changes. + */ + applyMetadataFilter(this: PlotViewContext & { _zoomListenerAttached: boolean }) { + const response = this.lastResponse; + if (response === null || !response.available) return; + + const conditions = parseMetadataFilter(this.metadataFilter); + const allTraces = response.data as Record[]; + // Shallow-copied, not the cached response's own trace objects: showlegend gets rewritten + // below based on *this* filter pass, and mutating the shared getPlot() cache in place + // would leak one pass's bookkeeping into the next (e.g. after the filter is cleared). + const survivors = allTraces + .filter((trace) => matchesMetadataFilter(trace.meta, conditions)) + .map((trace) => ({ ...trace })); + this.filteredOut = survivors.length === 0 && allTraces.length > 0; + + // The server pre-computes showlegend so exactly one trace per legendgroup shows a + // legend entry; if filtering happens to remove that specific trace, re-derive it over + // the survivors so a legend entry doesn't silently vanish while its groupmates are + // still drawn. + const seenGroups = new Set(); + for (const trace of survivors) { + const group = trace.legendgroup; + if (group !== undefined && group !== null) { + trace.showlegend = !seenGroups.has(group); + seenGroups.add(group); + } + } + + const el = this.$refs.plot; + // Copied for the same reason as the traces above (zoomRange gets baked in below). + const layout: Partial = { ...(response.layout as Partial) }; + // Every re-render replaces the server's fresh (autoranged) layout wholesale, so a + // previously-captured zoom/pan has to be re-applied on top of it each time -- otherwise + // changing a setting (or switching tags) would silently reset the user's zoom. + if (this.zoomRange?.xaxis) { + layout.xaxis = { ...layout.xaxis, autorange: false, range: this.zoomRange.xaxis }; + } + if (this.zoomRange?.yaxis) { + layout.yaxis = { ...layout.yaxis, autorange: false, range: this.zoomRange.yaxis }; + } + + Plotly.react(el, survivors as Data[], layout, { + responsive: true, + displaylogo: false, + }); + Plotly.relayout(el, currentThemeLayout()); + + if (!this._zoomListenerAttached) { + this._zoomListenerAttached = true; + (el as unknown as PlotlyHTMLElement).on("plotly_relayout", (eventData) => { + const raw = eventData as unknown as Record; + if (raw["xaxis.autorange"] || raw["yaxis.autorange"]) { + this.zoomRange = null; + } else { + const next: ZoomRange = { ...this.zoomRange }; + const x0 = raw["xaxis.range[0]"]; + const x1 = raw["xaxis.range[1]"]; + const y0 = raw["yaxis.range[0]"]; + const y1 = raw["yaxis.range[1]"]; + if (typeof x0 === "number" && typeof x1 === "number") next.xaxis = [x0, x1]; + if (typeof y0 === "number" && typeof y1 === "number") next.yaxis = [y0, y1]; + this.zoomRange = next; + } + if (this.selectedTag !== null) { + saveJSON(zoomStorageKey(this.selectedTag), this.zoomRange); + } + }); + } + }, + + undo(this: PlotViewContext) { + if (this.historyIndex <= 0) return; + this.isUndoing = true; + this.historyIndex--; + this.config = JSON.parse(this.historyStack[this.historyIndex]) as PlotConfig; + this.$nextTick(() => { + this.isUndoing = false; + }); + }, + + redo(this: PlotViewContext) { + if (this.historyIndex >= this.historyStack.length - 1) return; + this.isUndoing = true; + this.historyIndex++; + this.config = JSON.parse(this.historyStack[this.historyIndex]) as PlotConfig; + this.$nextTick(() => { + this.isUndoing = false; + }); + }, + }; +} diff --git a/src/trendify/viewer/templates/static/ts/src/lib/plotly-toast-bridge.ts b/src/trendify/viewer/templates/static/ts/src/lib/plotly-toast-bridge.ts new file mode 100644 index 0000000..2f3f32e --- /dev/null +++ b/src/trendify/viewer/templates/static/ts/src/lib/plotly-toast-bridge.ts @@ -0,0 +1,31 @@ +/** + * Plotly.js shows its own dismissible on-page notifications (e.g. axis-drag warnings, "too many + * points" WebGL limits) by appending `.notifier-note` elements into a body-level + * `.plotly-notifier` container it creates on demand. This app has its own toast system + * (`toast.ts`), so Plotly's native ones are hidden via CSS (`app.css`'s `.plotly-notifier` rule) + * and relayed through ours here instead. + * + * Watching the DOM (rather than monkey-patching `Plotly.Plots.notifier`) is what actually catches + * every Plotly-internal call site: some of them call its logging helper directly rather than + * through that one public re-export, so a DOM observer is the only interception point that's + * guaranteed not to miss one. + */ +export function installPlotlyToastBridge(): void { + const observer = new MutationObserver((mutations) => { + for (const mutation of mutations) { + mutation.addedNodes.forEach((node) => { + if (!(node instanceof HTMLElement) || !node.classList.contains("notifier-note")) { + return; + } + const message = node.querySelector("p")?.textContent?.trim(); + node.remove(); + if (message) { + window.dispatchEvent( + new CustomEvent("trendify:toast", { detail: { message, kind: "info" } }), + ); + } + }); + } + }); + observer.observe(document.body, { childList: true, subtree: true }); +} diff --git a/src/trendify/viewer/templates/static/ts/src/lib/prefetch.ts b/src/trendify/viewer/templates/static/ts/src/lib/prefetch.ts new file mode 100644 index 0000000..cd4c2c8 --- /dev/null +++ b/src/trendify/viewer/templates/static/ts/src/lib/prefetch.ts @@ -0,0 +1,142 @@ +import { getPlot, getTable, getTags, type FetchOptions, type Tag, type TagNode } from "./api"; +import { loadConfigForTag } from "./plot-view"; +import { resolveTableView } from "./table-view"; +import { tagToParts } from "./sidebar-node"; + +const LOG_PREFIX = "[trendify:prefetch]"; + +let activeController: AbortController | null = null; + +/** `X-Trendify-Hydrate` marks a request as background hydration (as opposed to a real user + * click), for two reasons: the server logs it, and -- more importantly -- routes it to a + * dedicated worker thread/connection instead of the main one, so it can never block a real + * click on this app's single-threaded event loop (see routes/api.py, viewer/hydration.py). This + * applies to *every* fetch this module makes, including the tag-tree lookup below, not just the + * per-tag plot/table calls -- any of them can be expensive enough to matter. */ +function hydrationOptions(signal: AbortSignal): FetchOptions { + return { signal, priority: "low", headers: { "X-Trendify-Hydrate": "1" } }; +} + +/** Walks `tree` down through `parentParts` (matching each level's node by `label`, same as the + * server-rendered sidebar's trie), returning the children list at that depth -- the root list + * itself if `parentParts` is empty. */ +function findLevel(tree: TagNode[], parentParts: string[]): TagNode[] { + let level = tree; + for (const part of parentParts) { + const node = level.find((n) => n.label === part); + if (!node) return []; + level = node.children; + } + return level; +} + +/** This node's own payload bytes plus every descendant's. Used only to order traversal + * (biggest branch hydrates first), never reported directly. */ +function subtreeBytes(node: TagNode): number { + return node.children.reduce((sum, child) => sum + subtreeBytes(child), node.size_bytes); +} + +/** + * Flattens `level` (and everything nested under it) into hydration order: a depth-first + * pre-order walk -- fully descending into a branch before moving on to the next sibling + * ("wide") -- with siblings at each branching point visited biggest-subtree-first. + */ +function collectHydrationOrder(level: TagNode[]): TagNode[] { + const ordered = [...level].sort((a, b) => subtreeBytes(b) - subtreeBytes(a)); + const queue: TagNode[] = []; + for (const node of ordered) { + if (node.has_records) queue.push(node); + if (node.children.length > 0) queue.push(...collectHydrationOrder(node.children)); + } + return queue; +} + +/** + * Builds the full hydration queue by radiating outward from `selectedTag`'s position in the + * tree: first every *other* branch under its immediate parent folder, walked down to their + * leaves -- then, once that ring is exhausted, every other branch under the grandparent, and + * so on out to the tree root. At each ring, exactly one child (the branch leading back toward + * `selectedTag`, already covered by the previous, deeper ring -- or `selectedTag` itself, at + * the innermost ring) is skipped; everything else at that level is fully hydrated depth-first. + * The net effect: the entire tree (minus `selectedTag` itself, already owned by the real click + * flow) eventually gets cached, nearest branches first. + */ +function buildRadiatingQueue(tree: TagNode[], selectedTag: Tag | null): TagNode[] { + const fullPath = tagToParts(selectedTag); + let ancestorParts = fullPath.slice(0, -1); + let excludeLabel: string | null = fullPath.length > 0 ? fullPath[fullPath.length - 1] : null; + const queue: TagNode[] = []; + + for (;;) { + const level = findLevel(tree, ancestorParts); + const otherBranches = level.filter((node) => node.label !== excludeLabel); + queue.push(...collectHydrationOrder(otherBranches)); + if (ancestorParts.length === 0) break; + excludeLabel = ancestorParts[ancestorParts.length - 1]; + ancestorParts = ancestorParts.slice(0, -1); + } + return queue; +} + +function tagLabel(tag: Tag): string { + return Array.isArray(tag) ? tag.join("/") : String(tag); +} + +/** Warms the cache for one tag's plot and/or table data (whichever kinds it has), concurrently. */ +async function hydrateOne(target: TagNode, signal: AbortSignal): Promise { + const options = hydrationOptions(signal); + const tasks: Promise[] = []; + if (target.record_kinds.includes("plot")) { + tasks.push(getPlot(target.key, loadConfigForTag(target.key), options)); + } + if (target.record_kinds.includes("table")) { + tasks.push(getTable(target.key, resolveTableView(target.key), options)); + } + // Best-effort: a prefetch failing or being aborted is expected and silently ignored, + // Promise.allSettled never rejects. + await Promise.allSettled(tasks); +} + +/** + * Continuously hydrates the client-side response cache (`api.ts`'s `cachedGet`) for the whole + * tag tree, radiating outward from wherever the user is currently browsing (see + * `buildRadiatingQueue`): nearby branches first, each walked all the way down to its leaves + * before moving on to the next, until every tag is cached or a new selection restarts the walk + * from wherever the user is now. Already-cached tags resolve instantly (no network call) when + * the walk reaches them again after a restart, so repeatedly re-selecting tags is cheap. + * + * Always cancels whatever hydration was previously in flight first -- even if the new + * selection lands on a tag mid-walk -- so a real click never has to compete with a + * low-priority background request for connection slots/bandwidth. + */ +export function schedulePrefetch(selectedTag: Tag | null): void { + activeController?.abort(); + const controller = new AbortController(); + activeController = controller; + const { signal } = controller; + + void (async () => { + let tree: TagNode[]; + try { + tree = await getTags(hydrationOptions(signal)); + } catch { + return; + } + if (signal.aborted) return; + + const queue = buildRadiatingQueue(tree, selectedTag); + if (queue.length === 0) return; + + console.info(`${LOG_PREFIX} hydrating ${queue.length} tag(s), radiating out from current selection`); + for (const target of queue) { + if (signal.aborted) return; + console.info( + `${LOG_PREFIX} hydrating "${tagLabel(target.key)}" (${target.size_bytes} bytes, ${target.record_kinds.join("+")})`, + ); + await hydrateOne(target, signal); + } + if (!signal.aborted) { + console.info(`${LOG_PREFIX} finished hydrating entire tree`); + } + })(); +} diff --git a/src/trendify/viewer/templates/static/ts/src/lib/resize.ts b/src/trendify/viewer/templates/static/ts/src/lib/resize.ts new file mode 100644 index 0000000..b426b9c --- /dev/null +++ b/src/trendify/viewer/templates/static/ts/src/lib/resize.ts @@ -0,0 +1,60 @@ +export interface HorizontalResizeOptions { + storageKey: string; + elementId: string; + defaultWidth: number; + minWidth: number; + maxWidth: number; +} + +/** Alpine data factory for a draggable-width panel, persisted to localStorage by `storageKey`. */ +export function horizontalResize({ + storageKey, + elementId, + defaultWidth, + minWidth, + maxWidth, +}: HorizontalResizeOptions) { + const stored = Number(localStorage.getItem(storageKey)); + const initialWidth = Number.isFinite(stored) && stored > 0 ? stored : defaultWidth; + + return { + width: initialWidth, + dragging: false, + startDrag(event: MouseEvent) { + event.preventDefault(); + this.dragging = true; + const startX = event.clientX; + const startWidth = this.width; + + const onMove = (moveEvent: MouseEvent) => { + const next = startWidth + (moveEvent.clientX - startX); + this.width = Math.min(maxWidth, Math.max(minWidth, next)); + }; + const onUp = () => { + this.dragging = false; + localStorage.setItem(storageKey, String(this.width)); + window.removeEventListener("mousemove", onMove); + window.removeEventListener("mouseup", onUp); + // Content that reflows into the newly-sized main area (e.g. the plot viewer) only + // needs to resize once the drag settles, not on every intermediate mousemove. + window.dispatchEvent(new Event("trendify:layout-changed")); + }; + window.addEventListener("mousemove", onMove); + window.addEventListener("mouseup", onUp); + }, + fitToContent() { + const el = document.getElementById(elementId); + if (!el) return; + // Momentarily release the fixed width so the browser lays out every child at its natural + // (untruncated) size, then read that back via scrollWidth -- happens within one + // synchronous task, so nothing is ever painted mid-measurement. + const restoreWidth = el.style.width; + el.style.width = "max-content"; + const natural = el.scrollWidth; + el.style.width = restoreWidth; + + this.width = Math.min(maxWidth, Math.max(minWidth, natural)); + localStorage.setItem(storageKey, String(this.width)); + }, + }; +} diff --git a/src/trendify/viewer/templates/static/ts/src/lib/schema-validate.ts b/src/trendify/viewer/templates/static/ts/src/lib/schema-validate.ts new file mode 100644 index 0000000..f17003d --- /dev/null +++ b/src/trendify/viewer/templates/static/ts/src/lib/schema-validate.ts @@ -0,0 +1,6 @@ +/** + * Minimal type for the embedded JSON Schema `plot-config.generated.ts` exports alongside + * `PlotConfig`. No runtime validator is implemented here -- trendify has no raw-JSON config + * editor to validate against, unlike the reference project this schema shape was borrowed from. + */ +export type JsonSchemaNode = Record; diff --git a/src/trendify/viewer/templates/static/ts/src/lib/sidebar-filter.ts b/src/trendify/viewer/templates/static/ts/src/lib/sidebar-filter.ts new file mode 100644 index 0000000..df2ff2f --- /dev/null +++ b/src/trendify/viewer/templates/static/ts/src/lib/sidebar-filter.ts @@ -0,0 +1,30 @@ +import { loadJSON, saveJSON } from "./local-storage"; + +const KIND_FILTER_STORAGE_KEY = "trendify:sidebar-kind-filter"; + +export interface KindFilter { + plot: boolean; + table: boolean; +} + +const DEFAULT_KIND_FILTER: KindFilter = { plot: true, table: true }; + +interface SidebarFilterContext { + kindFilter: KindFilter; +} + +/** Alpine data factory for the sidebar's search box and Plot/Table quick filter toggles. */ +export function sidebarFilter() { + return { + search: "", + kindFilter: { ...DEFAULT_KIND_FILTER } as KindFilter, + init(this: SidebarFilterContext) { + const saved = loadJSON>(KIND_FILTER_STORAGE_KEY); + if (saved) this.kindFilter = { ...DEFAULT_KIND_FILTER, ...saved }; + }, + toggleKind(this: SidebarFilterContext, kind: keyof KindFilter) { + this.kindFilter[kind] = !this.kindFilter[kind]; + saveJSON(KIND_FILTER_STORAGE_KEY, this.kindFilter); + }, + }; +} diff --git a/src/trendify/viewer/templates/static/ts/src/lib/sidebar-node.ts b/src/trendify/viewer/templates/static/ts/src/lib/sidebar-node.ts new file mode 100644 index 0000000..2a17435 --- /dev/null +++ b/src/trendify/viewer/templates/static/ts/src/lib/sidebar-node.ts @@ -0,0 +1,49 @@ +import type { Tag } from "./api"; +import { loadJSON, saveJSON } from "./local-storage"; + +function folderOpenStorageKey(path: string[]): string { + return `trendify:folder-open:${JSON.stringify(path)}`; +} + +export function tagToParts(tag: Tag | null): string[] { + if (tag === null) return []; + return Array.isArray(tag) ? tag.map(String) : [String(tag)]; +} + +interface SidebarNodeContext { + open: boolean; + autoExpanded: boolean; + selectedTag: Tag | null; +} + +/** + * Alpine data factory for a sidebar folder row. `path` is this node's full label path from + * the tree root (e.g. `["nested_plots", "group_a"]`), used both as a stable localStorage key + * (a bare label isn't unique across the tree -- two different folders can share a name at + * different levels) and to detect whether this folder is an ancestor of `selectedTag` (the + * ancestor `appShell` scope's currently-viewed tag, restored from the URL on page load), so + * it auto-expands to reveal it rather than leaving it hidden in a collapsed folder. + */ +export function sidebarNode(path: string[]) { + const storageKey = folderOpenStorageKey(path); + + return { + open: false, + autoExpanded: false, + init(this: SidebarNodeContext) { + const saved = loadJSON(storageKey); + if (saved !== null) { + this.open = saved; + return; + } + const selectedParts = tagToParts(this.selectedTag); + this.open = + selectedParts.length > path.length && + path.every((part, i) => part === selectedParts[i]); + }, + toggle(this: SidebarNodeContext) { + this.open = !this.open; + saveJSON(storageKey, this.open); + }, + }; +} diff --git a/src/trendify/viewer/templates/static/ts/src/lib/table-view.ts b/src/trendify/viewer/templates/static/ts/src/lib/table-view.ts new file mode 100644 index 0000000..f02aa23 --- /dev/null +++ b/src/trendify/viewer/templates/static/ts/src/lib/table-view.ts @@ -0,0 +1,288 @@ +import type { Api } from "datatables.net"; +import { getTable, isAbortError, type Tag, type TableView as TableViewKind } from "./api"; +import { loadTableViewFromUrl, saveTableViewToUrl } from "./url-state"; +import { loadJSON, saveJSON } from "./local-storage"; + +// Alpine injects `$watch`/`$refs` at runtime and merges the ancestor `appShell` scope's +// `selectedTag` into this component's data proxy; TS has no way to infer either from this +// plain factory's return value, so each method that needs them declares `this` explicitly. +interface TableViewContext { + view: TableViewKind; + unavailable: boolean; + loading: boolean; + requestId: number; + currentAbortController: AbortController | null; + selectedTag: Tag | null; + $watch(property: string, callback: (value: unknown) => void): void; + $refs: Record; + setView(view: TableViewKind): void; + render(): Promise; +} + +const MIN_COLUMN_WIDTH_PX = 60; +const PAGE_LENGTH_STORAGE_KEY = "trendify:table-page-length"; +const DEFAULT_PAGE_LENGTH = 10; + +/** Per-tag-and-view, since column names (and their meaning) differ across tags/tabs. */ +function filtersStorageKey(tag: Tag, view: TableViewKind): string { + return `trendify:table-filters:${JSON.stringify(tag)}:${view}`; +} + +/** Per-tag, so each table product remembers which tab (Melted/Pivot/Statistics) it was last on. */ +function viewStorageKey(tag: Tag): string { + return `trendify:table-view:${JSON.stringify(tag)}`; +} + +/** + * Same fallback `init()`'s `selectedTag` watcher below uses. Exported for reuse by + * prefetch.ts, which needs to warm the exact cache key (including any per-tag saved tab) that + * this view will actually request when the tag is clicked. + */ +export function resolveTableView(tag: Tag): TableViewKind { + return loadJSON(viewStorageKey(tag)) ?? "stats"; +} + +/** + * Adds a small draggable handle to the right edge of every header cell for manual column resize. + * Locates cells via the DataTables API (`column.header()`), not a raw `querySelectorAll` on the + * table element: with `scrollX` enabled, DataTables physically moves the real `` out of + * the data table and into a separate cloned header table for the scrolling header row, so the + * cells actually visible to the user no longer live inside the table element at all. + * + * Dragging updates every `` at this column's index, not just the header cell: + * with `scrollX` on, each of the real (data) table and the cloned scrolling header table has + * its own separate colgroup, and `table-layout: fixed` in each one is governed by *its own* + * `` width, not by an individual cell's inline style. Updating only one (or only the cell) + * leaves the two tables disagreeing about the column's width until the next redraw happens to + * resync them (`_fnScrollDraw` re-clones the real table's colgroup into the header's on every + * draw) -- updating both directly, live, avoids depending on that timing at all. + */ +function attachColumnResizeHandles(this: Api): void { + const dt = this; + const realTable = this.table().node() as HTMLTableElement; + this.columns().every(function () { + const th = this.header() as HTMLTableCellElement; + const colIndex = this.index(); + const headerTable = th.closest("table"); + const colgroupTables = new Set([realTable, headerTable]); + th.style.position = "relative"; + const handle = document.createElement("span"); + handle.className = "dt-col-resize-handle"; + handle.addEventListener("mousedown", (event) => { + event.preventDefault(); + event.stopPropagation(); + const startX = event.clientX; + const startWidth = th.offsetWidth; + + const onMove = (moveEvent: MouseEvent) => { + const next = Math.max( + MIN_COLUMN_WIDTH_PX, + startWidth + (moveEvent.clientX - startX), + ); + th.style.width = `${next}px`; + for (const table of colgroupTables) { + const colEl = table?.querySelectorAll("colgroup col")[ + colIndex + ] as HTMLTableColElement | undefined; + if (colEl) { + colEl.style.width = `${next}px`; + colEl.style.minWidth = `${next}px`; + } + } + }; + const onUp = () => { + window.removeEventListener("mousemove", onMove); + window.removeEventListener("mouseup", onUp); + // Beyond the two colgroups updated live above, `scrollX` also tracks an *explicit* + // pixel width on the scroll-head wrapper divs (`_fnScrollDraw`'s `outerWidth`, + // recomputed from the real table's total column width) to keep the header's own box + // sized correctly -- that value is cached from the last real DataTables draw and + // doesn't know about manual resizes happening outside its API. `draw(false)` re-runs + // `_fnScrollDraw` (registered as a draw callback) to resync it against the widths we + // just set, without `false` triggering a content-based `autoWidth` recalculation that + // would undo the manual resize. Only on drag end, not every `mousemove`, since a full + // draw is too expensive to do continuously. + dt.draw(false); + // The browser fires a `click` immediately after this mouseup, and since dragging + // moves the mouse away from `handle` before release, that click's target is the + // header cell (or an ancestor) -- not `handle` -- so a listener on `handle` itself + // can never catch it. DataTables binds sort-on-click as a delegated listener up on + // the header (`_fnBindAction`), so left alone this click still bubbles up to it and + // toggles sort. Capturing and swallowing exactly one click at the document level, + // registered right as the drag ends, catches it regardless of where it lands. + document.addEventListener( + "click", + (event) => { + event.stopPropagation(); + event.preventDefault(); + }, + { capture: true, once: true }, + ); + }; + window.addEventListener("mousemove", onMove); + window.addEventListener("mouseup", onUp); + }); + th.appendChild(handle); + }); +} + +/** + * Adds a per-column text filter input below each header cell's title, wired to DataTables' + * column search. Prefills from (and persists to) `storageKey` in localStorage, so filters + * survive a refresh -- keyed by column title (via `column.title()`), not position, since + * pivot tables have differently-named columns per tag. Not keyed by `dataSrc()`: every + * column's `data` is a same-shaped `(row) => row[col]` closure (see `render()`), so its + * stringified source is identical across columns and can't disambiguate them. + */ +function attachColumnFilters(this: Api, storageKey: string): void { + const savedFilters = loadJSON>(storageKey) ?? {}; + const api = this; + + api.columns().every(function () { + const column = this; + const columnName = column.title(); + const initialValue = savedFilters[columnName] ?? ""; + const header = $(column.header()); + + $("") + .attr("type", "text") + .attr("placeholder", "Filter") + .addClass("dt-column-filter") + .val(initialValue) + .on("click", (event) => event.stopPropagation()) + .on("keyup change clear", function () { + const value = (this as HTMLInputElement).value; + if (column.search() !== value) { + column.search(value).draw(); + } + const filters = loadJSON>(storageKey) ?? {}; + if (value) { + filters[columnName] = value; + } else { + delete filters[columnName]; + } + saveJSON(storageKey, filters); + }) + .appendTo(header); + + if (initialValue) { + column.search(initialValue); + } + }); + + api.draw(); +} + +/** + * Alpine data factory for the table viewer (Melted/Pivot/Statistics tabs over DataTables). + * Mounted once (inside an `x-if` in `index.html`) and reused across every table-tagged + * selection: `selectedTag` lives on the ancestor `appShell` scope, so switching tags while + * this stays mounted is handled by the `$watch` in `init()`, not by remounting. + */ +export function tableView() { + return { + view: "stats" as TableViewKind, + unavailable: false, + loading: false, + requestId: 0, + currentAbortController: null as AbortController | null, + setView(this: TableViewContext, view: TableViewKind) { + this.view = view; + saveTableViewToUrl(view); + if (this.selectedTag !== null) { + saveJSON(viewStorageKey(this.selectedTag), view); + } + }, + init(this: TableViewContext) { + const storedView = loadTableViewFromUrl(); + if (storedView) this.view = storedView; + + this.$watch("view", () => this.render()); + this.$watch("selectedTag", (tag) => { + this.setView(tag !== null ? resolveTableView(tag as Tag) : "stats"); + }); + this.render(); + }, + async render(this: TableViewContext) { + const tag = this.selectedTag; + if (tag === null) return; + + // Guards against out-of-order async resolution if the user switches tabs/tags again + // before this fetch resolves -- only the most recently started render() may apply. + const requestId = ++this.requestId; + // Cancelling the previous request (rather than just letting the requestId guard above + // ignore its result) frees up its connection/bandwidth immediately, same as plot-view.ts. + this.currentAbortController?.abort(); + const controller = new AbortController(); + this.currentAbortController = controller; + this.loading = true; + + let data; + try { + data = await getTable(tag, this.view, { signal: controller.signal, priority: "high" }); + } catch (err) { + // Expected whenever a newer render() aborts this one above -- the newer request + // already owns `unavailable`/`loading`, so there's nothing to do here. + if (isAbortError(err)) return; + throw err; + } finally { + // A stale in-flight request finishing after a newer one started must not clear the + // spinner out from under the request that's still actually loading, same as plot-view.ts. + if (requestId === this.requestId) this.loading = false; + } + if (requestId !== this.requestId) return; + + const container = this.$refs.tableContainer; + const existingTable = container.querySelector("table"); + if (existingTable && $.fn.DataTable.isDataTable(existingTable)) { + $(existingTable).DataTable().destroy(); + } + // Rebuild the `` element from scratch on every render rather than clearing and + // reusing the same node: with `scrollX` enabled, DataTables physically moves the real + // `` into a separately cloned header table for the scrolling header row, and + // relying on `.destroy()` to precisely unwind that wrapper before reinitializing the same + // node proved unreliable in practice (stale `.dt-container` wrappers piled up on every tab + // switch instead of being replaced -- confirmed by counting them in the DOM). Discarding + // the whole subtree and starting from a bare table sidesteps that entirely; `.destroy()` + // above still runs first so DataTables deregisters/cleans up the old instance's internal + // state rather than leaking it. + container.innerHTML = ""; + const tableEl = document.createElement("table"); + tableEl.className = "display w-full"; + tableEl.style.width = "100%"; + container.appendChild(tableEl); + + this.unavailable = !data.available; + if (!data.available) return; + + const filtersKey = filtersStorageKey(tag, this.view); + const dt = $(tableEl).DataTable({ + data: data.rows, + // `data` is a function (not the bare column name) because DataTables treats a string + // `data` as dot-notation path into the row object -- a column literally named "2.0" + // (numeric row/col keys from a pivoted table render this way) would otherwise be + // misread as nested property `row["2"]["0"]` instead of the flat key `row["2.0"]`. + columns: data.columns.map((col) => ({ + data: (row: Record) => row[col], + title: col, + })), + // `autoWidth` must stay enabled (the default) for `scrollX` to work: DataTables' own + // column-width measurement (which lets the table grow wider than its container so + // there's something to scroll) is gated entirely behind `autoWidth`, per + // `_fnCalculateColumnWidths` bailing out immediately when it's off. This function only + // runs at init, on a scrollbar-visibility change, or on window resize -- never on a + // plain pagination/sort/filter redraw -- so it doesn't fight the manual column-resize + // handles below; `table-layout: fixed` (app.css) is what protects those across redraws. + scrollX: true, + pageLength: loadJSON(PAGE_LENGTH_STORAGE_KEY) ?? DEFAULT_PAGE_LENGTH, + initComplete: function () { + attachColumnFilters.call(this.api(), filtersKey); + attachColumnResizeHandles.call(this.api()); + }, + }); + dt.on("length", (_event: unknown, _settings: unknown, len: number) => { + saveJSON(PAGE_LENGTH_STORAGE_KEY, len); + }); + }, + }; +} diff --git a/src/trendify/viewer/templates/static/ts/src/lib/theme.ts b/src/trendify/viewer/templates/static/ts/src/lib/theme.ts new file mode 100644 index 0000000..d56d58a --- /dev/null +++ b/src/trendify/viewer/templates/static/ts/src/lib/theme.ts @@ -0,0 +1,50 @@ +/** + * Light/dark/system theme selection. The actual pre-paint application of the stored theme + * lives in a blocking inline