diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 0000000..7753192 --- /dev/null +++ b/AGENTS.md @@ -0,0 +1,75 @@ +# AGENTS.md — plot-cli + +## Project Overview +`plot-cli` is a simple command-line plotting tool for terminal-based data visualization. +It uses DuckDB for data loading/transformation and tplot for rendering. Supports CSV, JSON, +NDJSON, and Parquet input with SQL-style filtering and time-series bucketing. + +## Package Environment +This project uses **uv** for all Python tooling. Do not use `pip`, `poetry`, or `conda`. + +- **Install dependencies:** `uv sync` +- **Run the CLI:** `uv run plot` or `uv run plot-cli` +- **Run tests:** `uv run pytest` +- **Run a specific test marker:** `uv run pytest -m unit` or `uv run pytest -m integration` +- **Add a dependency:** `uv add ` +- **Add a dev dependency:** `uv add --group dev ` +- **Build:** `uv build` + +The lockfile is `uv.lock`. Always commit it alongside `pyproject.toml` changes. + +## Project Layout +``` +pyproject.toml # PEP 621 metadata, dependencies, pytest config (hatchling backend) +uv.lock # Locked dependency versions +src/plot_cli/ # Package source (src-layout) + __init__.py # Entry-point, PlotApp (CmdKit Application), CLI interface + config.py # Configuration via CmdKit (TOML), logging, error handling + query.py # QueryBuilder — DuckDB queries, format detection, filtering, bucketing + plot.py # Figure/TimeSeriesFigure wrappers around tplot, tick generation +tests/ + test_app.py # Unit tests — version output + test_query.py # Unit tests — QueryBuilder, format detection, filtering, aggregation + test_cli.py # Integration tests — CLI output modes, filtering, aggregation options +``` + +## Architecture +- **CmdKit pattern:** The CLI follows the CmdKit `Application` pattern. `PlotApp` in + `__init__.py` defines the full CLI interface via `Interface` with argument groups. + See [cmdkit docs](https://cmdkit.readthedocs.io). +- **Data pipeline:** `PlotApp.run()` → `_load_data()` (QueryBuilder) → output or render. +- **QueryBuilder** (`query.py`): Builds and executes DuckDB SQL queries. Handles format + detection, column selection, WHERE/datetime filtering, time bucketing, PIVOT for `--by`, + and datetime scale conversion. +- **Figure** (`plot.py`): Thin wrapper over `tplot.Figure`. `TimeSeriesFigure` extends it + with smart datetime axis tick generation via `_CustomTplotFigure`. + +## Coding Conventions +- **License headers:** Every `.py` file starts with SPDX copyright/license comments. +- **Type annotations:** Use `from __future__ import annotations`. Prefer `Optional`, `List`, + `Tuple` from `typing` (existing convention in this codebase). +- **Imports:** Group as: type annotations, standard libs, external libs, internal libs. + Include a `# Public interface` section with `__all__`. +- **Logging:** Use `cmdkit.logging.Logger` in application code, `logging.getLogger(__name__)` + in library modules. +- **Self annotations:** Methods use explicit `self: ClassName` type annotations. + +## Testing +- **Framework:** pytest with custom markers defined in `pyproject.toml`. +- **Markers:** `@mark.unit` for fast interface tests, `@mark.integration` for CLI/system tests. +- **Run all tests:** `uv run pytest` +- **Run by marker:** `uv run pytest -m unit` +- **Test data:** Use `tmp_path` fixture for temporary files; create CSV/JSON/Parquet inline. +- **CLI tests:** Invoke via `PlotApp.main([...])` and capture output with `capsys`. + +## Git Workflow +- Work on the `wip` branch with `WIP: ` prefixed commit messages. +- Push to `wip` freely; collapse into logical commits before PR against `main`. +- Include `Co-Authored-By: Oz ` on AI-assisted commits. + +## Key Dependencies +- `cmdkit[toml]` — CLI framework (Application, Interface, Configuration, Logger) +- `duckdb` — In-process SQL for data loading and transformation +- `pandas` — DataFrame interchange between DuckDB and plotting +- `tplot` — Terminal-based plotting (line, scatter, bar, histogram) +- `pyarrow` — Parquet read/write support diff --git a/README.md b/README.md index cd36851..a08c575 100644 --- a/README.md +++ b/README.md @@ -11,10 +11,10 @@ Simple Command-line Plotting Tool A simple command-line plotting tool. -This project is merely a wrapper around [tplot](https://pypi.org/project/tplot/) and -[pandas](https://pypi.org/project/pandas/) for the CSV input and data manipulation (e.g., resampling). -The project should be considered to be in _beta_ and subject to change. It works extremely well for -well-behaved input, but can be rough around the edges when data is not clean. +This project uses [DuckDB](https://duckdb.org/) for data loading and transformation, +and [tplot](https://pypi.org/project/tplot/) for terminal-based rendering. It supports +multiple input formats (CSV, JSON, NDJSON, Parquet), SQL-style filtering, time-series +bucketing with aggregation, and scatter plots with customizable markers. Install @@ -24,9 +24,137 @@ This project should not be confused for an older, abandoned project already on t package index by the same name. Install directly from GitHub: ```shell -pipx install git+https://github.com/glentner/plot-cli@v0.3.1 +uv tool install git+https://github.com/glentner/plot-cli ``` + +Usage +----- + +``` +Usage: + plot [-h] [-v] [FILE] [-x NAME] [-y NAME] [--line | --scatter | --hist] ... + Simple command-line plotting tool. +``` + +### Input Formats + +By default, format is auto-detected from file extension. Use `--format` to override: + +```shell +# CSV (default for stdin) +cat data.csv | plot -x timestamp -y value -T + +# Parquet +plot data.parquet -x time -y metric + +# JSON array +plot data.json --format json -x x -y y + +# Newline-delimited JSON +plot logs.ndjson --format ndjson -x timestamp -y count +``` + +### Filtering + +Filter data using SQL WHERE clauses or datetime bounds: + +```shell +# Custom WHERE clause +plot data.csv --where "value > 100 AND status = 'active'" + +# Datetime filtering (requires -x to specify timestamp column) +plot timeseries.csv -x timestamp -y value --after "2024-01-01" --before "2024-02-01" +``` + +### Time-Series Bucketing + +Aggregate time-series data into buckets using `-B/--bucket` with an aggregation method: + +```shell +# 15-minute buckets with mean aggregation +plot metrics.csv -x timestamp -y value -T -B 15min --mean + +# Hourly buckets with sum +plot events.csv -x time -y count -T -B 1h --sum + +# Supported aggregation methods: --mean, --sum, --count, --min, --max, --first, --last +``` + +Bucket interval formats: `15min`, `1h`, `30m`, `1d`, `60s`, or full syntax like `15 minutes`. + +### Output Modes + +Instead of plotting, output processed data as JSON or CSV: + +```shell +# Output as JSON (useful for piping to jq) +plot data.csv --json --where "value > 50" + +# Output as CSV +plot data.parquet --csv -x timestamp -y value -B 1h --mean +``` + +### Relative Time Scaling + +Convert datetime axis to relative offset from start or end: + +```shell +# Hours from start +plot timeseries.csv -x timestamp -y value -T -S +hours + +# Minutes from end (negative offset) +plot timeseries.csv -x timestamp -y value -T -S -minutes +``` + + +Options Reference +----------------- + +| Option | Description | +|--------|-------------| +| `-x, --xdata NAME` | Column for x-axis | +| `-y, --ydata NAME...` | Column(s) for y-axis | +| `--format NAME` | Input format: `csv`, `json`, `ndjson`, `parquet` | +| `--line` | Line plot (default) | +| `--scatter` | Scatter plot | +| `--marker CHAR` | Marker character for scatter plots (default: `•`) | +| `--hist` | Histogram | +| `--where EXPR` | SQL WHERE clause for filtering | +| `--after TIME` | Filter rows after timestamp | +| `--before TIME` | Filter rows before timestamp | +| `-T, --timeseries` | Treat x-axis as datetime | +| `-S, --scale SCALE` | Relative offset (e.g., `+hours`, `-days`) | +| `-B, --bucket INTERVAL` | Time bucket interval (e.g., `15min`, `1h`) | +| `-A, --agg-method NAME` | Aggregation: `mean`, `sum`, `count`, `min`, `max`, `first`, `last` | +| `--mean`, `--sum`, etc. | Aggregation method aliases | +| `-b, --bins NUM` | Histogram bins (default: 10) | +| `-d, --density` | Show histogram as percentage | +| `-t, --title NAME` | Plot title | +| `-s, --size W,H` | Plot size in characters | +| `-c, --color SEQ` | Comma-separated colors | +| `-l, --legend POS` | Legend position | +| `-X, --xlabel NAME` | X-axis label | +| `-Y, --ylabel NAME` | Y-axis label | +| `--json` | Output data as JSON | +| `--csv` | Output data as CSV | +| `-h, --help` | Show help | +| `-v, --version` | Show version | + + +### Scatter Plots + +Render data as a scatter plot with an optional custom marker: + +```shell +# Basic scatter plot +plot data.csv -x x -y y --scatter + +# Scatter plot with custom marker +plot data.csv -x x -y y --scatter --marker x +``` + + Example ------- diff --git a/ROADMAP.md b/ROADMAP.md new file mode 100644 index 0000000..9a95348 --- /dev/null +++ b/ROADMAP.md @@ -0,0 +1,131 @@ +--- +title: "plot-cli v0.5.0 — Scatter Support & Quality Audit" +version_from: "0.4.1" +version_to: "0.5.0" +branch: wip +commit_prefix: "WIP: " +co_author: "Co-Authored-By: Oz " +project_root: "." +package_dir: "src/plot_cli" +test_command: "uv run pytest" +smoke_test_scatter: "uv run plot /tmp/data.csv --scatter -x x -y y" +smoke_test_line: "uv run plot /tmp/data.csv --line -x x -y y" +--- + +# plot-cli v0.5.0 — Scatter Support & Quality Audit + +## Summary + +Wire the existing `Figure.scatter()` method into the CLI as `--scatter`, add a +`--marker` option, fix quality issues (pandas deprecation warning, stale README), +expand test coverage for plot types, and bump to v0.5.0. + +## Key Context + +- `Figure.scatter()` already exists in `src/plot_cli/plot.py` (wraps `tplot.Figure.scatter`). + It accepts `x`, `y`, `marker`, `color`, `label`. Added in commit `612d028`. +- The CLI plot-type system uses a mutually exclusive argparse group: `--plot-type`, + `--line`, `--hist`. We add `--scatter` following this same pattern. +- `_render_plot()` in `__init__.py` branches on `self.plot_type` (`'line'` or `'hist'`). + We add a `'scatter'` branch (NaN filtering same as line, plus `self.marker`). +- sacct-plot (`../../purduercac/sacct-plot/`) imports `plot_cli.plot.TimeSeriesFigure` + as a library. It currently calls `.line()`. Once v0.5.0 is released it can call + `.scatter()` the same way. +- Test suite: 48 tests pass, but 9 emit `Pandas4Warning` about deprecated `'epoch'` + date format in `_output_json()`. + +--- + +## Phase 1 — Scatter CLI Integration + +Files: `src/plot_cli/__init__.py` + +- [x] Add `'scatter'` to `--plot-type` choices in `plot_type_interface` +- [x] Add `--scatter` as `store_const` alias (same pattern as `--line`/`--hist`) +- [x] Add `--marker` argument (string, default `'•'`) for scatter marker character +- [x] Update `APP_USAGE` to include `--scatter` in the usage line +- [x] Update `APP_HELP` to document `--scatter` and `--marker` options +- [x] Add `'scatter'` branch in `_render_plot()`: filter NaN (same as line), call `self.figure.scatter()` with `self.marker` +- [x] Commit: `WIP: wire scatter and marker into CLI` + +## Phase 2 — Quality Fixes + +### Pandas deprecation warning + +File: `src/plot_cli/__init__.py` + +- [x] In `_output_json()`, change `to_json(orient='records', indent=2)` to `to_json(orient='records', indent=2, date_format='iso')` +- [x] Commit: `WIP: fix pandas date_format deprecation warning` + +### README updates + +File: `README.md` + +- [x] Change install command to `uv tool install git+https://github.com/glentner/plot-cli` (remove version pin) +- [x] Add `--scatter` to the usage synopsis line +- [x] Add scatter and marker rows to the Options Reference table +- [x] Add a brief scatter usage example section +- [x] Mention scatter support in the project description paragraph +- [x] Commit: `WIP: update README for scatter, marker, and install command` + +## Phase 3 — Test Coverage + +Files: `tests/test_app.py`, `tests/test_cli.py` + +- [x] `test_app.py`: add unit test that `--scatter` is accepted (e.g., `PlotApp.main([csv, '--scatter', '--json'])` does not error) +- [x] `test_cli.py`: add `TestPlotTypeOptions` class with integration tests: + - `test_scatter_json_output` — verifies `--scatter` + `--json` produces valid JSON + - `test_line_json_output` — verifies `--line` + `--json` produces valid JSON (parity) + - `test_scatter_csv_output` — verifies `--scatter` + `--csv` produces valid CSV +- [x] Commit: `WIP: add scatter and plot-type test coverage` + +## Phase 4 — Version Bump & Validation + +- [x] Bump `pyproject.toml` version from `0.4.1` to `0.5.0` +- [x] Run `uv run pytest` — all tests pass, zero warnings +- [x] Smoke test: `uv run plot /tmp/data.csv --scatter -x x -y y` renders scatter +- [x] Smoke test: `uv run plot /tmp/data.csv --scatter --marker x -x x -y y` renders with `x` marker +- [x] Smoke test: `uv run plot /tmp/data.csv --line -x x -y y` still works +- [x] Final commit: `WIP: bump version to 0.5.0` + +--- + +## Continuation Prompt + +```text +Read ROADMAP.md and AGENTS.md in this project. These documents contain the +full implementation plan and project conventions for plot-cli v0.5.0. + +Execute the following procedure: + +1. Read the YAML frontmatter in ROADMAP.md. Extract: + - branch (work must happen on this branch) + - commit_prefix (prepend to all commit messages) + - co_author (append to all commit messages on its own line) + - test_command (run after making changes) + - smoke_test_scatter and smoke_test_line (run during Phase 4 only) + +2. Scan the phase checklists (Phase 1 through Phase 4) and identify the + first phase that contains any unchecked item (- [ ]). This is the + current phase. Phases with all items checked (- [x]) are already done + — do not redo them. + +3. Execute ONLY the current phase: + a. Implement all unchecked items in that phase. + b. Run the test_command (`uv run pytest`) and confirm all tests pass. + If Phase 4, also run the smoke test commands. + c. If tests fail, fix the issue before proceeding. + +4. After the phase is fully implemented and tests pass: + a. Update ROADMAP.md: change each completed item from `- [ ]` to `- [x]`. + b. Stage all changes and commit on the wip branch using the commit + message specified in that phase's final checklist item (the one that + starts with "Commit:"). Use the commit_prefix and co_author line. + c. If the phase has multiple commit items, make separate commits for + each logical group as indicated. + +5. STOP. Report what was completed in this phase and wait for further + instructions. Do NOT proceed to the next phase automatically. + +If all phases are already checked off, report that the roadmap is complete. +``` diff --git a/poetry.lock b/poetry.lock deleted file mode 100644 index c9afc74..0000000 --- a/poetry.lock +++ /dev/null @@ -1,676 +0,0 @@ -# This file is automatically @generated by Poetry 2.1.1 and should not be changed by hand. - -[[package]] -name = "asttokens" -version = "3.0.0" -description = "Annotate AST trees with source code positions" -optional = false -python-versions = ">=3.8" -groups = ["dev"] -files = [ - {file = "asttokens-3.0.0-py3-none-any.whl", hash = "sha256:e3078351a059199dd5138cb1c706e6430c05eff2ff136af5eb4790f9d28932e2"}, - {file = "asttokens-3.0.0.tar.gz", hash = "sha256:0dcd8baa8d62b0c1d118b399b2ddba3c4aff271d0d7a9e0d4c1681c79035bbc7"}, -] - -[package.extras] -astroid = ["astroid (>=2,<4)"] -test = ["astroid (>=2,<4)", "pytest", "pytest-cov", "pytest-xdist"] - -[[package]] -name = "cmdkit" -version = "2.7.7" -description = "A command-line utility toolkit for Python." -optional = false -python-versions = "<4.0,>=3.9" -groups = ["main"] -files = [ - 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Beta", "Programming Language :: Python :: 3.10", @@ -19,24 +23,29 @@ classifiers = [ "Programming Language :: Python :: 3.13", "License :: OSI Approved :: MIT License", ] +requires-python = ">=3.10" +dependencies = [ + "cmdkit[toml]>=2.7.7", + "pandas>=2.2.2", + "tplot>=0.3.4", + "duckdb>=1.0.0", + "pyarrow>=15.0.0", +] + +[dependency-groups] +dev = [ + "ipython>=8.26.0", + "pytest>=8.3.2", + "hatchling>=1.27.0", +] -[tool.poetry.scripts] +[project.scripts] plot-cli = "plot_cli:main" plot = "plot_cli:main" -[tool.poetry.dependencies] -python = ">=3.10,<4.0" -cmdkit = {extras = ["toml"], version = "^2.7.7"} -pandas = "^2.2.2" -tplot = "^0.3.4" - -[tool.poetry.group.dev.dependencies] -ipython = "^8.26.0" -pytest = "^8.3.2" - [build-system] -requires = ["poetry-core>=1.0.0"] -build-backend = "poetry.core.masonry.api" +requires = ["hatchling"] +build-backend = "hatchling.build" [tool.pytest.ini_options] addopts = ["--rootdir", "tests", "--strict-markers", ] diff --git a/src/plot_cli/__init__.py b/src/plot_cli/__init__.py index 8cfd980..edaf2a3 100644 --- a/src/plot_cli/__init__.py +++ b/src/plot_cli/__init__.py @@ -4,11 +4,11 @@ """Package initialization and entry-point.""" -# type annotations +# Type annotations from __future__ import annotations -from typing import Tuple, Dict, List, Type, Optional, Any +from typing import Tuple, List, Optional, Any -# standard libs +# Standard libs import os import sys from importlib.metadata import version as get_version @@ -16,31 +16,32 @@ from functools import partial, cached_property from shutil import get_terminal_size -# external libs +# External libs from cmdkit.app import Application, exit_status from cmdkit.cli import Interface from cmdkit.config import ConfigurationError from cmdkit.logging import Logger +from pandas import DataFrame -# internal libs +# Internal libs from plot_cli.config import write_traceback -from plot_cli.provider import PlotInterface, TPlot, TPlotLine, TPlotHist -from plot_cli.data import DataSet +from plot_cli.query import QueryBuilder +from plot_cli.plot import Figure, TimeSeriesFigure, generate_time_ticks -# public interface +# Public interface __all__ = ['main', 'PlotApp', '__version__', ] -# metadata +# Metadata __version__ = get_version(__name__) -# application logger +# Application logger log = Logger.with_name(__name__) APP_NAME = os.path.basename(sys.argv[0]) APP_USAGE = f"""\ Usage: - {APP_NAME} [-h] [-v] [FILE] [-x NAME] [-y NAME] [--line | --hist] ... + {APP_NAME} [-h] [-v] [FILE] [-x NAME] [-y NAME] [--line | --scatter | --hist] ... Simple command-line plotting tool.\ """ @@ -53,17 +54,25 @@ Options: -x, --xdata NAME Field to use for x-axis. -y, --ydata NAME... Field(s) for y-axis. - --backend NAME Method for plotting data (default: tplot). - --plot-type NAME Type of plot_cli to create (default: line). + --by NAME Group by column to plot multiple series. + --format NAME Input format: csv, json, ndjson, parquet (default: auto). + --plot-type NAME Type of plot to create (default: line). --line Alias for --plot-type=line + --scatter Alias for --plot-type=scatter --hist Alias for --plot-type=hist + --marker CHAR Marker character for scatter plots (default: '•'). + +Filtering: + --where EXPR SQL WHERE clause for filtering. + --after TIME Filter rows after timestamp (timeseries only). + --before TIME Filter rows before timestamp (timeseries only). Formatting: --xlabel NAME Content for x-axis label. --ylabel NAME Content for y-axis label. - -t, --title NAME Content for plot_cli title. + -t, --title NAME Content for plot title. -c, --color SEQ Comma-separated color names (e.g., 'blue,red,green'). - -s, --size SHAPE Width and height in pixels (default: 100,20). + -s, --size SHAPE Width and height in characters (default: terminal size). -l, --legend POS Legend position (default: 'bottomright'). Histogram: @@ -71,32 +80,30 @@ -d, --density Display frequency as percentage. Timeseries: - -T, --timeseries [NAME] Fields to parse as date and/or time. - -S, --scale SCALE Re-scale datetime axis (e.g., +/- hours). - -F, --resample FREQ Re-sample on some frequency (e.g., '1min'). - -A, --agg-method NAME Aggregation method (required for --resample). + -T, --timeseries Treat x-axis as datetime. + -S, --scale SCALE Re-scale datetime axis (e.g., +hours, -days). + -B, --bucket INTERVAL Time bucket interval (e.g., '1min', '15min', '1h'). + -F, --resample FREQ [DEPRECATED] Use -B/--bucket instead. + -A, --agg-method NAME Aggregation method for bucketing. --mean Alias for --agg-method=mean. --sum Alias for --agg-method=sum. --count Alias for --agg-method=count. --max Alias for --agg-method=max. --min Alias for --agg-method=min. + --first Alias for --agg-method=first. + --last Alias for --agg-method=last. + +Output: + --json Output processed data as JSON (instead of plotting). + --csv Output processed data as CSV (instead of plotting). -h, --help Show this message and exit. -v, --version Show the version and exit.\ """ -# Mapping of provider and plot_cli type interfaces -plot_interface: Dict[str, Dict[str, Type[PlotInterface]]] = { - 'tplot': { - 'line': TPlotLine, - 'hist': TPlotHist, - }, -} - - # Cycle through colors -default_colors = ['blue', 'yellow', 'green', 'red', 'magenta', 'cyan', ] +DEFAULT_COLORS = ['blue', 'yellow', 'green', 'red', 'magenta', 'cyan', ] def color_list(spec: str, sep: str = ',') -> List[str]: @@ -104,9 +111,9 @@ def color_list(spec: str, sep: str = ',') -> List[str]: return spec.strip().split(sep) -def split_size(spec: str, sep: str = ',') -> Tuple[float, float]: - """Split size value (e.g., '6,10') into floats.""" - width, height = map(float, spec.strip().split(sep)) +def split_size(spec: str, sep: str = ',') -> Tuple[int, int]: + """Split size value (e.g., '100,20') into integers.""" + width, height = map(int, spec.strip().split(sep)) return width, height @@ -119,51 +126,74 @@ class PlotApp(Application): source: str = '-' interface.add_argument('source', nargs='?', default=source) - backend: str = 'tplot' - backend_interface = interface.add_mutually_exclusive_group() - backend_interface.add_argument('--backend', default=backend, choices=list(plot_interface)) - backend_interface.add_argument('--terminal', '--tplot', action='store_const', const=backend, dest='backend') + input_format: Optional[str] = None + interface.add_argument('--format', default=None, dest='input_format', + choices=['csv', 'json', 'ndjson', 'parquet']) plot_type: str = 'line' plot_type_interface = interface.add_mutually_exclusive_group() - plot_type_interface.add_argument('--plot-type', default=plot_type, choices=['line', ]) + plot_type_interface.add_argument('--plot-type', default=plot_type, choices=['line', 'scatter', 'hist']) plot_type_interface.add_argument('--line', action='store_const', const='line', dest='plot_type') + plot_type_interface.add_argument('--scatter', action='store_const', const='scatter', dest='plot_type') plot_type_interface.add_argument('--hist', action='store_const', const='hist', dest='plot_type') + marker: str = '•' + interface.add_argument('--marker', default=marker) + xdata: Optional[str] = None interface.add_argument('-x', '--xdata', default=None) - ydata: Optional[List[str]] = [] + ydata: List[str] = [] interface.add_argument('-y', '--ydata', nargs='*', default=[]) + group_by: Optional[str] = None + interface.add_argument('--by', default=None, dest='group_by', + help='Group by column to plot multiple series.') + + # Filtering options + where_clause: Optional[str] = None + interface.add_argument('--where', default=None, dest='where_clause') + + after_datetime: Optional[str] = None + interface.add_argument('--after', default=None, dest='after_datetime') + + before_datetime: Optional[str] = None + interface.add_argument('--before', default=None, dest='before_datetime') + + # Timeseries options timeseries: bool = False interface.add_argument('-T', '--timeseries', action='store_true') timeseries_scale: Optional[str] = None interface.add_argument('-S', '--scale', default=None, dest='timeseries_scale') - resample_freq: str = None + bucket_interval: Optional[str] = None + interface.add_argument('-B', '--bucket', default=None, dest='bucket_interval') + + resample_freq: Optional[str] = None interface.add_argument('-F', '--resample', default=None, dest='resample_freq') - agg_method: str = None + agg_method: Optional[str] = None agg_interface = interface.add_mutually_exclusive_group() - agg_interface.add_argument('-A', '--agg-method', default=None, choices=['mean', 'sum', 'count', 'max', 'min']) + agg_interface.add_argument('-A', '--agg-method', default=None, + choices=['mean', 'sum', 'count', 'max', 'min', 'first', 'last']) agg_interface.add_argument('--mean', action='store_const', const='mean', dest='agg_method') agg_interface.add_argument('--sum', action='store_const', const='sum', dest='agg_method') agg_interface.add_argument('--count', action='store_const', const='count', dest='agg_method') agg_interface.add_argument('--min', action='store_const', const='min', dest='agg_method') agg_interface.add_argument('--max', action='store_const', const='max', dest='agg_method') + agg_interface.add_argument('--first', action='store_const', const='first', dest='agg_method') + agg_interface.add_argument('--last', action='store_const', const='last', dest='agg_method') + # Histogram options hist_bins: int = 10 interface.add_argument('-b', '--bins', type=int, default=hist_bins, dest='hist_bins') hist_density: bool = False interface.add_argument('-d', '--density', action='store_true', dest='hist_density') - drop_missing: bool = False - interface.add_argument('--drop-missing', action='store_true') - - colors: List[str] = default_colors + # Formatting options + colors: List[str] = DEFAULT_COLORS interface.add_argument('-c', '--color', type=color_list, default=colors, dest='colors') title: Optional[str] = None @@ -175,13 +205,20 @@ class PlotApp(Application): ylabel: Optional[str] = None interface.add_argument('-Y', '--ylabel', default=None) - size: Optional[Tuple[float, float]] = None + size: Optional[Tuple[int, int]] = None interface.add_argument('-s', '--size', default=None, type=split_size) legend: str = 'bottomright' interface.add_argument('-l', '--legend', default=legend, choices=['bottomleft', 'bottomright', 'topleft', 'topright']) + # Output mode options + output_json: bool = False + interface.add_argument('--json', action='store_true', dest='output_json') + + output_csv: bool = False + interface.add_argument('--csv', action='store_true', dest='output_csv') + log_critical = log.critical log_exception = log.exception @@ -190,39 +227,197 @@ class PlotApp(Application): Exception: partial(write_traceback, logger=log) } - dataset: DataSet | None = None + # Data loaded via QueryBuilder + _dataframe: DataFrame | None = None + _x_column: str | None = None def run(self: PlotApp) -> None: """Run the program.""" - self.load_dataset() - self.add_all(self.ydata or self.dataset.columns) - self.draw() + self._handle_deprecated_options() + self._load_data() + if self.output_json: + self._output_json() + elif self.output_csv: + self._output_csv() + else: + self._render_plot() + + def _handle_deprecated_options(self: PlotApp) -> None: + """Handle deprecated CLI options with warnings.""" + if self.resample_freq: + log.warning('-F/--resample is deprecated, use -B/--bucket instead') + if not self.bucket_interval: + self.bucket_interval = self.resample_freq + + def _load_data(self: PlotApp) -> None: + """Load and transform data using DuckDB QueryBuilder.""" + log.info(f'Loading data from {self.source}') + query = QueryBuilder( + source=self.source, + format=self.input_format, + x_column=self.xdata, + y_columns=self.ydata if self.ydata else None, + group_by=self.group_by, + where_clause=self.where_clause, + after_datetime=self.after_datetime, + before_datetime=self.before_datetime, + bucket_interval=self.bucket_interval, + agg_method=self.agg_method, + timeseries=self.timeseries, + scale=self.timeseries_scale, + ) + self._dataframe = query.execute() + self._x_column = self._dataframe.columns[0] + log.debug(f'Loaded {len(self._dataframe)} rows') + + def _output_json(self: PlotApp) -> None: + """Output data as JSON.""" + # TODO: Implement JSON output + print(self._dataframe.to_json(orient='records', indent=2, date_format='iso')) + + def _output_csv(self: PlotApp) -> None: + """Output data as CSV.""" + # TODO: Implement CSV output + print(self._dataframe.to_csv(index=False)) + + def _render_plot(self: PlotApp) -> None: + """Render the plot to terminal.""" + import math + y_columns = list(self._dataframe.columns[1:]) + + # Get x values - convert to epoch if using TimeSeriesFigure + if self._use_timeseries_figure: + x_values = [v.timestamp() for v in self._dataframe[self._x_column]] + else: + x_values = self._dataframe[self._x_column].tolist() + + for column, color in zip(y_columns, cycle(self.colors)): + y_values = self._dataframe[column].tolist() + + if self.plot_type == 'line': + # Filter out NaN values - tplot can't handle them + valid_pairs = [ + (x, y) for x, y in zip(x_values, y_values) + if not (isinstance(y, float) and math.isnan(y)) + ] + if valid_pairs: + x_valid, y_valid = zip(*valid_pairs) + self.figure.line( + x=list(x_valid), + y=list(y_valid), + color=color, + label=column, + ) + elif self.plot_type == 'scatter': + # Filter out NaN values - tplot can't handle them + valid_pairs = [ + (x, y) for x, y in zip(x_values, y_values) + if not (isinstance(y, float) and math.isnan(y)) + ] + if valid_pairs: + x_valid, y_valid = zip(*valid_pairs) + self.figure.scatter( + x=list(x_valid), + y=list(y_valid), + marker=self.marker, + color=color, + label=column, + ) + else: + # For histograms, filter NaN values + valid_data = [y for y in y_values if not (isinstance(y, float) and math.isnan(y))] + if valid_data: + self.figure.hist( + data=valid_data, + bins=self.hist_bins, + density=self.hist_density, + color=color, + label=column, + ) + self.figure.draw() - def draw(self: PlotApp) -> None: - """Issue final render command to the finished plot_cli.""" - self.plotter.draw() + @cached_property + def figure(self: PlotApp) -> Figure: + """Create and return the Figure instance.""" + # Use TimeSeriesFigure for datetime x-axis without scale conversion + if self._use_timeseries_figure: + x_values = self._dataframe[self._x_column].tolist() + # Convert timestamps to epoch for tick generation + min_epoch = x_values[0].timestamp() + max_epoch = x_values[-1].timestamp() + ticks = generate_time_ticks(min_epoch, max_epoch) + + # Build formatter based on tick labels + tick_map = dict(zip(ticks.tick_epochs, ticks.tick_labels)) + + def formatter(epoch: float) -> str: + # Find closest tick label or format generically + if epoch in tick_map: + return tick_map[epoch] + from datetime import datetime + # Use UTC to match pandas timestamp behavior + dt = datetime.utcfromtimestamp(epoch) + if ticks.granularity.name == 'HOURS': + return f"{dt.hour:02d}:{dt.minute:02d}" + elif ticks.granularity.name == 'MINUTES': + return f"{dt.hour:02d}:{dt.minute:02d}" + elif ticks.granularity.name == 'DAYS': + return f"{dt.month:02d}-{dt.day:02d}" + return str(epoch) + + secondary_label = None + if ticks.secondary_labels: + secondary_label = ticks.secondary_labels[0][1] + + return TimeSeriesFigure( + title=self.plot_title, + xlabel=self.plot_xlabel, + ylabel=self.plot_ylabel, + size=self.plot_size, + legend=self.legend, + x_tick_formatter=formatter, + x_tick_values=ticks.tick_epochs, + secondary_xlabel=secondary_label, + ) + + return Figure( + title=self.plot_title, + xlabel=self.plot_xlabel, + ylabel=self.plot_ylabel, + size=self.plot_size, + legend=self.legend, + ) + + @property + def _use_timeseries_figure(self: PlotApp) -> bool: + """Whether to use TimeSeriesFigure for smart datetime axis labels.""" + # Use TimeSeriesFigure when: + # - timeseries flag is set + # - scale is NOT set (scale converts to numeric, so no datetime formatting needed) + # - x-axis data is datetime type + if not self.timeseries or self.timeseries_scale: + return False + # Check if x-column is datetime + x_dtype = str(self._dataframe[self._x_column].dtype) + return 'datetime' in x_dtype @cached_property - def plotter(self: PlotApp) -> PlotInterface: - """Prepared plotting interface.""" - plot_type = plot_interface.get(self.backend).get(self.plot_type) - plotter = plot_type(title=self.title or os.path.basename(self.source), - xlabel=self.plot_xlabel, ylabel=self.plot_ylabel, - size=self.plot_size, legend=self.legend) - plotter.setup() - return plotter + def plot_title(self: PlotApp) -> str: + """Plot title.""" + if self.title: + return self.title + return os.path.basename(self.source) if self.source != '-' else 'stdin' @cached_property def plot_xlabel(self: PlotApp) -> str: """X-axis label.""" if self.xlabel: return self.xlabel - if self.timeseries and self.timeseries_scale: - return f'{self.dataset.index.name} ({self.timeseries_scale})' - if self.plot_type == 'hist': - return '' - else: - return f'{self.dataset.index.name}' + if self._x_column: + if self.timeseries and self.timeseries_scale: + return f'{self._x_column} ({self.timeseries_scale})' + return self._x_column + return '' @cached_property def plot_ylabel(self: PlotApp) -> str: @@ -231,51 +426,15 @@ def plot_ylabel(self: PlotApp) -> str: return self.ylabel if self.plot_type == 'hist' and self.hist_density: return 'percent' - else: - return 'value' + return 'value' @cached_property - def plot_size(self: PlotApp) -> Optional[Tuple[float, float]]: + def plot_size(self: PlotApp) -> Tuple[int, int]: """Plot size in width, height.""" - if not self.size and self.backend != 'tplot': - return None if self.size: return self.size - else: - width, height = get_terminal_size() - return width - 10, height - 8 - - def add_all(self: PlotApp, columns: List[str]) -> None: - """Add each `column` to the plot_cli.""" - for column, color in zip(columns, cycle(self.colors)): - self.plotter.add(self.dataset, **self.prepare_plot_options(column=column, color=color, label=column)) - - def prepare_plot_options(self: PlotApp, **options: Any) -> Dict[str, Any]: - """Prepare plot_cli-type specify parameters, forward `options`.""" - if self.plot_type == 'line': - return options - if self.plot_type == 'hist': - return {'bins': self.hist_bins, 'density': self.hist_density, **options} - - def load_dataset(self: PlotApp) -> None: - """Load the dataset.""" - if self.source == '-': - log.info('Reading from ') - self.dataset = DataSet.from_io(sys.stdin) - else: - log.info(f'Reading from {self.source}') - self.dataset = DataSet.from_local(self.source) - xcol = self.xdata or self.dataset.columns[0] - if self.timeseries: - ts_column = self.timeseries if self.timeseries != '-' else self.dataset.columns[0] - log.info(f'Parsing timeseries ({ts_column})') - self.dataset.set_type(xcol, 'datetime64[ns]') - scale_for_index = self.timeseries_scale if not self.resample_freq else None - self.dataset.set_index(xcol, timeseries_scale=scale_for_index) - if self.resample_freq: - self.dataset.resample(self.resample_freq, agg=self.agg_method, scale=self.timeseries_scale) - if self.drop_missing: - self.dataset.drop_missing() + width, height = get_terminal_size() + return width - 10, height - 8 def main() -> int: diff --git a/src/plot_cli/data.py b/src/plot_cli/data.py deleted file mode 100644 index 0c105d6..0000000 --- a/src/plot_cli/data.py +++ /dev/null @@ -1,123 +0,0 @@ -# SPDX-FileCopyrightText: 2023 Geoffrey Lentner -# SPDX-License-Identifier: Apache-2.0 - -"""Data loading and transformations.""" - - -# type annotations -from __future__ import annotations -from typing import List, IO, Union, Type - -# standard libs -import re -import logging -from io import StringIO - -# external libs -from pandas import DataFrame, Series, Index, read_csv, to_datetime - -# public interface -__all__ = ['DataSet', ] - -# module level logger -log = logging.getLogger(__name__) - - -DAY_SCALE = 86400 -HOUR_SCALE = 3600 -MINUTE_SCALE = 60 -SECOND_SCALE = 1 - -OFFSET_PATTERN = re.compile(r'([+-]?)(d|day|days|h|hour|hours|m|min|mins|minute|minutes|s|sec|secs|second|seconds)') -DATETIME_SCALE = { - 'd': DAY_SCALE, 'day': DAY_SCALE, 'days': DAY_SCALE, - 'h': HOUR_SCALE, 'hour': HOUR_SCALE, 'hours': HOUR_SCALE, - 'm': MINUTE_SCALE, 'min': MINUTE_SCALE, 'mins': MINUTE_SCALE, 'minute': MINUTE_SCALE, 'minutes': MINUTE_SCALE, - 's': SECOND_SCALE, 'sec': SECOND_SCALE, 'secs': SECOND_SCALE, 'second': SECOND_SCALE, 'seconds': SECOND_SCALE, -} - - -def apply_datetime_offset(values: Index, offset: str) -> Index: - """Apply offset to Unix epoch `values`.""" - if match := OFFSET_PATTERN.match(offset): - sign, scale_name = match.groups() - scale = DATETIME_SCALE[scale_name] - if sign in ('', '+'): - return (values - values[0]) / scale - else: - return (values - values[-1]) / scale - else: - raise ValueError(f'Unsupported offset \'{offset}\'') - - -class DataSet: - """Relational dataset with rows and columns.""" - - frame: DataFrame - - def __init__(self: DataSet, source: Union[DataFrame, DataSet]) -> None: - """Direct initialization with existing `pandas.DataFrame`.""" - if isinstance(source, DataSet): - self.frame = source.frame - else: - self.frame = DataFrame(source) - - @classmethod - def from_text(cls: Type[DataSet], block: str, **options) -> DataSet: - """Build by parsing raw text `block`.""" - return cls.from_io(StringIO(block), **options) - - @classmethod - def from_io(cls: Type[DataSet], stream: IO, **options) -> DataSet: - """Parse input data from existing I/O `stream`.""" - return cls(source=read_csv(filepath_or_buffer=stream, **options)) # noqa: pandas doesn't understand type? - - @classmethod - def from_local(cls: Type[DataSet], filepath: str, encoding: str = 'utf-8', **options) -> DataSet: - """Parse local file from `filepath`.""" - with open(filepath, mode='r', encoding=encoding) as stream: - return cls.from_io(stream, **options) - - def __getitem__(self: DataSet, key: str) -> Series: - """Select a column from the dataset.""" - series = self.frame[key] - if series.dtype in ('object', ): - raise RuntimeError(f'Unsupported dtype \'{series.dtype}\'') - else: - return series - - def set_type(self: DataSet, name: str, dtype: str) -> None: - """Apply a new `dtype` to column `name`.""" - if dtype.startswith('datetime'): - self.frame[name] = to_datetime(self.frame[name]) - else: - self.frame[name] = self.frame[name].astype(dtype) - - def set_index(self: DataSet, name: str = None, timeseries_scale: str = None) -> None: - """Set the index for the x-axis of the plot.""" - self.frame = self.frame.set_index(name) - if self.index.dtype == 'datetime64[ns]' and timeseries_scale: - self.frame.index = self.frame.index.astype('int64') / 10 ** 9 - self.frame.index = apply_datetime_offset(self.frame.index, offset=timeseries_scale) - - def resample(self: DataSet, freq: str, agg: str = 'mean', scale: str = None) -> None: - """Resample a time-series DataSet.""" - self.frame = self.frame.resample(freq).agg(agg) - if scale: - if self.index.dtype == 'datetime64[ns]': - self.frame.index = self.frame.index.astype('int64') / 10**9 - self.frame.index = apply_datetime_offset(self.frame.index, offset=scale) - - @property - def index(self: DataSet) -> Index: - """The index for columns in the dataset (used for x-axis of plot).""" - return self.frame.index - - @property - def columns(self: DataSet) -> List[str]: - """List of columns names.""" - return list(self.frame.columns) - - def drop_missing(self: DataSet) -> None: - """Drop missing values.""" - self.frame.dropna(inplace=True) diff --git a/src/plot_cli/plot.py b/src/plot_cli/plot.py new file mode 100644 index 0000000..56a36c6 --- /dev/null +++ b/src/plot_cli/plot.py @@ -0,0 +1,477 @@ +# SPDX-FileCopyrightText: 2023 Geoffrey Lentner +# SPDX-License-Identifier: Apache-2.0 + +"""Plotting interface using tplot.""" + + +# Type annotations +from __future__ import annotations +from typing import Tuple, Optional, List, Callable + +# Standard libs +import math +import logging +from enum import Enum, auto +from datetime import datetime +from dataclasses import dataclass + +# External libs +import tplot +import tplot.utils as tplot_utils +from numpy import histogram + +# Public interface +__all__ = [ + 'Figure', + 'TimeSeriesFigure', + 'TimeGranularity', + 'TimeTickResult', + 'generate_time_ticks', + 'detect_time_granularity', +] + +# Module level logger +log = logging.getLogger(__name__) + + +class Figure: + """ + Wrapper around tplot.Figure for terminal plotting. + + Provides a simplified interface for line plots, histograms, and bar charts. + """ + + title: Optional[str] + xlabel: Optional[str] + ylabel: Optional[str] + size: Optional[Tuple[int, int]] + legend: Optional[str] + _figure: tplot.Figure + + def __init__( + self: Figure, + title: Optional[str] = None, + xlabel: Optional[str] = None, + ylabel: Optional[str] = None, + size: Optional[Tuple[int, int]] = None, + legend: str = 'bottomright', + ) -> None: + """Initialize figure with formatting options.""" + self.title = title + self.xlabel = xlabel + self.ylabel = ylabel + self.size = size + self.legend = legend + self._setup() + + def _setup(self: Figure) -> None: + """Create underlying tplot figure.""" + width = height = None + if self.size: + width, height = self.size + self._figure = tplot.Figure( + title=self.title, + xlabel=self.xlabel, + ylabel=self.ylabel, + width=width, + height=height, + legendloc=self.legend, + ) + + def line( + self: Figure, + x: List[float], + y: List[float], + color: str = 'blue', + label: Optional[str] = None, + ) -> None: + """Add a line plot to the figure.""" + self._figure.line(x=x, y=y, color=color, label=label) + + def scatter( + self: Figure, + x: List[float], + y: List[float], + marker: str = '•', + color: str = 'blue', + label: Optional[str] = None, + ) -> None: + """Add a scatter plot to the figure.""" + self._figure.scatter(x=x, y=y, marker=marker, color=color, label=label) + + def bar( + self: Figure, + x: List[float], + y: List[float], + color: str = 'blue', + label: Optional[str] = None, + ) -> None: + """Add a bar chart to the figure.""" + self._figure.bar(x=x, y=y, color=color, label=label) + + def hist( + self: Figure, + data: List[float], + bins: int = 10, + density: bool = False, + color: str = 'blue', + label: Optional[str] = None, + ) -> None: + """Add a histogram to the figure.""" + hist_vals, bin_edges = histogram(data, bins=bins, density=density) + x = 0.5 * (bin_edges[:-1] + bin_edges[1:]) + self._figure.bar(x=x, y=hist_vals, color=color, label=label) + + def draw(self: Figure) -> None: + """Render the plot to terminal.""" + print() # Leave a space + self._figure.show() + + +class TimeGranularity(Enum): + """Detected granularity of time range for axis labeling.""" + + SECONDS = auto() # Range < 2 minutes + MINUTES = auto() # Range < 2 hours + HOURS = auto() # Range < 2 days + DAYS = auto() # Range < 2 months + MONTHS = auto() # Range < 2 years + YEARS = auto() # Range >= 2 years + + +@dataclass +class TimeTickResult: + """Result of time tick generation.""" + + tick_epochs: List[float] # Epoch seconds for tick positions + tick_labels: List[str] # Primary labels for each tick + secondary_labels: List[Tuple[float, str]] # (position, label) for year/date markers + granularity: TimeGranularity + xlabel_suffix: str # e.g., "(hours)" or "(HH:MM)" + + +def detect_time_granularity(min_epoch: float, max_epoch: float) -> TimeGranularity: + """Detect appropriate granularity based on time range.""" + range_seconds = max_epoch - min_epoch + + if range_seconds < 120: # < 2 minutes + return TimeGranularity.SECONDS + elif range_seconds < 7200: # < 2 hours + return TimeGranularity.MINUTES + elif range_seconds < 172800: # < 2 days + return TimeGranularity.HOURS + elif range_seconds < 5184000: # < 60 days (~2 months) + return TimeGranularity.DAYS + elif range_seconds < 63072000: # < 2 years + return TimeGranularity.MONTHS + else: + return TimeGranularity.YEARS + + +def _nice_step(raw_step: float, nice_values: List[float]) -> float: + """Round step to a 'nice' value.""" + if raw_step <= 0: + return nice_values[0] + for nv in nice_values: + if nv >= raw_step: + return nv + return nice_values[-1] + + +def _epoch_to_datetime(epoch: float, utc: bool = True) -> datetime: + """Convert epoch seconds to datetime, optionally using UTC.""" + if utc: + return datetime.utcfromtimestamp(epoch) + return datetime.fromtimestamp(epoch) + + +def _datetime_to_epoch(dt: datetime, utc: bool = True) -> float: + """Convert datetime to epoch seconds.""" + # Note: For UTC datetimes without tzinfo, we need to calculate manually + if utc: + from calendar import timegm + return float(timegm(dt.timetuple())) + return dt.timestamp() + + +def generate_time_ticks( + min_epoch: float, + max_epoch: float, + max_ticks: int = 10, + utc: bool = True, +) -> TimeTickResult: + """ + Generate smart time-series ticks based on the data range. + + Args: + min_epoch: Minimum epoch timestamp (seconds) + max_epoch: Maximum epoch timestamp (seconds) + max_ticks: Maximum number of ticks to generate + utc: If True, treat epochs as UTC (default for pandas timestamps) + + Returns TimeTickResult with tick positions, labels, and formatting info. + """ + granularity = detect_time_granularity(min_epoch, max_epoch) + + # Convert to datetime using consistent UTC handling + min_dt = _epoch_to_datetime(min_epoch, utc) + max_dt = _epoch_to_datetime(max_epoch, utc) + + tick_epochs: List[float] = [] + tick_labels: List[str] = [] + secondary_labels: List[Tuple[float, str]] = [] + xlabel_suffix = "" + + if granularity == TimeGranularity.SECONDS: + # Ticks every N seconds, labels as :SS or MM:SS + step = _nice_step((max_epoch - min_epoch) / max_ticks, [1, 2, 5, 10, 15, 30]) + start = math.ceil(min_epoch / step) * step + t = start + while t <= max_epoch: + tick_epochs.append(t) + dt = _epoch_to_datetime(t, utc) + tick_labels.append(f":{dt.second:02d}") + t += step + xlabel_suffix = "(MM:SS)" + + elif granularity == TimeGranularity.MINUTES: + # Ticks every N minutes, labels as HH:MM + step = _nice_step((max_epoch - min_epoch) / max_ticks / 60, [1, 2, 5, 10, 15, 30]) * 60 + start = math.ceil(min_epoch / step) * step + t = start + while t <= max_epoch: + tick_epochs.append(t) + dt = _epoch_to_datetime(t, utc) + tick_labels.append(f"{dt.hour:02d}:{dt.minute:02d}") + t += step + xlabel_suffix = "(HH:MM)" + + elif granularity == TimeGranularity.HOURS: + # Ticks every N hours, labels as HH:00 + step = _nice_step((max_epoch - min_epoch) / max_ticks / 3600, [1, 2, 3, 4, 6, 12]) * 3600 + # Align to hour boundaries + start_dt = datetime(min_dt.year, min_dt.month, min_dt.day, min_dt.hour) + start = _datetime_to_epoch(start_dt, utc) + if start < min_epoch: + start += step + t = start + prev_date = None + while t <= max_epoch + step * 0.1: + tick_epochs.append(t) + dt = _epoch_to_datetime(t, utc) + tick_labels.append(f"{dt.hour:02d}:00") + # Add date markers when date changes + curr_date = dt.date() + if prev_date is not None and curr_date != prev_date: + secondary_labels.append((t, dt.strftime("%Y-%m-%d"))) + prev_date = curr_date + t += step + xlabel_suffix = "(HH:MM)" + # Add start date as secondary label if not already there + if not secondary_labels: + secondary_labels.append((min_epoch, min_dt.strftime("%Y-%m-%d"))) + + elif granularity == TimeGranularity.DAYS: + # Ticks every N days, labels as MM-DD + step = _nice_step((max_epoch - min_epoch) / max_ticks / 86400, [1, 2, 7, 14]) * 86400 + # Align to day boundaries + start_dt = datetime(min_dt.year, min_dt.month, min_dt.day) + start = _datetime_to_epoch(start_dt, utc) + if start < min_epoch: + start += step + t = start + prev_month = None + while t <= max_epoch + step * 0.1: + tick_epochs.append(t) + dt = _epoch_to_datetime(t, utc) + tick_labels.append(f"{dt.month:02d}-{dt.day:02d}") + # Add year markers when year changes + if prev_month is not None and dt.month != prev_month and dt.month == 1: + secondary_labels.append((t, str(dt.year))) + prev_month = dt.month + t += step + xlabel_suffix = "(MM-DD)" + if not secondary_labels: + secondary_labels.append((min_epoch, str(min_dt.year))) + + elif granularity == TimeGranularity.MONTHS: + # Ticks every N months, labels as month name + month_names = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', + 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'] + range_months = (max_dt.year - min_dt.year) * 12 + (max_dt.month - min_dt.month) + step_months = int(_nice_step(range_months / max_ticks, [1, 2, 3, 6])) + + # Start at first of month + curr = datetime(min_dt.year, min_dt.month, 1) + prev_year = None + curr_epoch = _datetime_to_epoch(curr, utc) + while curr_epoch <= max_epoch: + if curr_epoch >= min_epoch: + tick_epochs.append(curr_epoch) + tick_labels.append(month_names[curr.month - 1]) + # Add year markers + if prev_year is not None and curr.year != prev_year: + secondary_labels.append((curr_epoch, str(curr.year))) + prev_year = curr.year + # Advance by step_months + new_month = curr.month + step_months + new_year = curr.year + (new_month - 1) // 12 + new_month = ((new_month - 1) % 12) + 1 + curr = datetime(new_year, new_month, 1) + curr_epoch = _datetime_to_epoch(curr, utc) + xlabel_suffix = "" + if not secondary_labels and tick_epochs: + secondary_labels.append((tick_epochs[0], str(min_dt.year))) + + elif granularity == TimeGranularity.YEARS: + # Ticks every N years, labels as year + range_years = max_dt.year - min_dt.year + step_years = int(_nice_step(range_years / max_ticks, [1, 2, 5, 10, 20, 50, 100])) + step_years = max(1, step_years) + + # Start at round year + start_year = (min_dt.year // step_years) * step_years + if start_year < min_dt.year: + start_year += step_years + + year = start_year + while year <= max_dt.year: + dt = datetime(year, 1, 1) + tick_epochs.append(_datetime_to_epoch(dt, utc)) + tick_labels.append(str(year)) + year += step_years + xlabel_suffix = "" + + return TimeTickResult( + tick_epochs=tick_epochs, + tick_labels=tick_labels, + secondary_labels=secondary_labels, + granularity=granularity, + xlabel_suffix=xlabel_suffix, + ) + + +class _CustomTplotFigure(tplot.Figure): + """ + Extended tplot.Figure with custom x-axis tick formatting. + + Overrides internal methods to support custom tick labels and + secondary label rows for hierarchical time display. + """ + + def __init__( + self, + *args, + x_tick_formatter: Optional[Callable[[float], str]] = None, + x_tick_values: Optional[List[float]] = None, + secondary_xlabel: Optional[str] = None, + **kwargs, + ): + super().__init__(*args, **kwargs) + self._x_tick_formatter = x_tick_formatter + self._custom_xtick_values = x_tick_values + self._secondary_xlabel = secondary_xlabel + + def _xax_height(self) -> int: + """Account for secondary label row.""" + base = 2 + bool(self._xlabel) + if self._secondary_xlabel: + base += 1 + return base + + def _fmt_x(self, value) -> str: + """Format x-axis tick value.""" + if self._x_tick_formatter: + return self._x_tick_formatter(value) + # Smart default formatting - prefer integers + if isinstance(value, (int, float)) and value == int(value): + return str(int(value)) + if isinstance(value, float): + return f"{value:.3g}" + return str(value) + + def _draw_x_axis(self) -> None: + """Override to support custom x-axis formatting and secondary label row.""" + # Use custom tick values if provided, filtered to data range + if self._custom_xtick_values: + xmin, xmax = self._xtick_values[0], self._xtick_values[-1] + tick_values = [v for v in self._custom_xtick_values if xmin <= v <= xmax] + if not tick_values: + tick_values = self._xtick_values + else: + tick_values = self._xtick_values + + tick_positions = [round(v) for v in self._xscale.transform(tick_values)] + labels = [self._fmt_x(v) for v in tick_values] + + # Draw axis line + axis_start = round(self._xscale.transform(self._xtick_values[0])) + axis_end = round(self._xscale.transform(self._xtick_values[-1])) + axis_row = -self._xax_height() + self._canvas[axis_row, axis_start:axis_end] = "─" + + # Draw ticks + for tick_pos in tick_positions: + self._canvas[axis_row, tick_pos] = "┬" + + # Draw primary labels + anchors = tplot_utils._optimize_xticklabel_anchors( + tick_positions=tick_positions, labels=labels, width=self.width + ) + for (start, end), label in zip(anchors, labels): + label = label[: end - start] + self._canvas[axis_row + 1, start:end] = list(label) + + # Draw secondary label row (e.g., date below hours) + if self._secondary_xlabel: + self._center_draw(self._secondary_xlabel, self._canvas[axis_row + 2, axis_start:axis_end]) + + # Draw axis label + if self._xlabel: + xlabel = self._xlabel[: axis_end - axis_start] + self._center_draw(xlabel, self._canvas[-1, axis_start:axis_end]) + + +class TimeSeriesFigure(Figure): + """ + Extended Figure with smart time-series tick handling. + + Provides custom x-axis tick label formatting based on data range + granularity. Supports secondary label row for hierarchical time + display (e.g., hours with date markers below). + """ + + _x_tick_formatter: Optional[Callable[[float], str]] + _custom_xtick_values: Optional[List[float]] + _secondary_xlabel: Optional[str] + + def __init__( + self: TimeSeriesFigure, + *args, + x_tick_formatter: Optional[Callable[[float], str]] = None, + x_tick_values: Optional[List[float]] = None, + secondary_xlabel: Optional[str] = None, + **kwargs, + ) -> None: + """Initialize with optional custom tick formatting.""" + self._x_tick_formatter = x_tick_formatter + self._custom_xtick_values = x_tick_values + self._secondary_xlabel = secondary_xlabel + super().__init__(*args, **kwargs) + + def _setup(self: TimeSeriesFigure) -> None: + """Create custom tplot figure with extended tick support.""" + width = height = None + if self.size: + width, height = self.size + self._figure = _CustomTplotFigure( + title=self.title, + xlabel=self.xlabel, + ylabel=self.ylabel, + width=width, + height=height, + legendloc=self.legend, + x_tick_formatter=self._x_tick_formatter, + x_tick_values=self._custom_xtick_values, + secondary_xlabel=self._secondary_xlabel, + ) diff --git a/src/plot_cli/provider.py b/src/plot_cli/provider.py deleted file mode 100644 index 5ded21b..0000000 --- a/src/plot_cli/provider.py +++ /dev/null @@ -1,92 +0,0 @@ -# SPDX-FileCopyrightText: 2023 Geoffrey Lentner -# SPDX-License-Identifier: Apache-2.0 - -"""Plotting interface implementations.""" - - -# type annotations -from __future__ import annotations -from typing import Tuple, Optional - -# standard libs -from abc import ABC, abstractmethod - -# external libs -import tplot -from numpy import histogram - -# internal libs -from plot_cli.data import DataSet - -# public interface -__all__ = ['PlotInterface', 'TPlot', 'TPlotLine', 'TPlotHist', ] - - -class PlotInterface(ABC): - """Abstract plotting interface for all implementations.""" - - title: Optional[str] - xlabel: Optional[str] - ylabel: Optional[str] - size: Optional[Tuple[float, float]] - legend: Optional[str] - - def __init__(self: PlotInterface, - title: str = None, xlabel: str = None, ylabel: str = None, - size: Tuple[float, float] = None, legend: str = None) -> None: - self.title = title - self.xlabel = xlabel - self.ylabel = ylabel - self.size = size - self.legend = legend - - @abstractmethod - def setup(self: PlotInterface) -> None: - """Initialize figure object.""" - - @abstractmethod - def add(self: PlotInterface, data: DataSet, column: str, **options) -> None: - """Add data to the plot.""" - - @abstractmethod - def draw(self: PlotInterface) -> None: - """Render the plot.""" - - -class TPlot(PlotInterface, ABC): - """Terminal based plotting backend using `tplot`.""" - - figure: tplot.Figure - - def setup(self: TPlotLine) -> None: - """Create tplot figure.""" - width = height = None - if self.size: - width, height = map(int, self.size) - self.figure = tplot.Figure(title=self.title, xlabel=self.xlabel, ylabel=self.ylabel, - width=width, height=height, legendloc=self.legend) - - def draw(self: TPlot) -> None: - """Render the plot.""" - print() # leave a space!! - self.figure.show() - - -class TPlotLine(TPlot): - """Line plotting with `tplot`.""" - - def add(self: TPlotLine, data: DataSet, column: str, **options) -> None: - """Add data to plot.""" - self.figure.line(x=data.index, y=data[column], **options) - - -class TPlotHist(TPlot): - """Histogram plotting with `tplot`.""" - - def add(self: TPlotLine, data: DataSet, column: str, **options) -> None: - """Add data to plot_cli.""" - hist, bin_edges = histogram(data[column], - bins=options.pop('bins', 10), - density=options.pop('density', None)) - x = 0.5 * (bin_edges[:-1] + bin_edges[1:]) - self.figure.bar(x=x, y=hist, **options) diff --git a/src/plot_cli/query.py b/src/plot_cli/query.py new file mode 100644 index 0000000..60b0c3b --- /dev/null +++ b/src/plot_cli/query.py @@ -0,0 +1,442 @@ +# SPDX-FileCopyrightText: 2023 Geoffrey Lentner +# SPDX-License-Identifier: Apache-2.0 + +"""DuckDB query building and data loading.""" + + +# Type annotations +from __future__ import annotations +from typing import List, Optional, Dict, Any + +# Standard libs +import re +import sys +import logging +from pathlib import Path + +# External libs +import duckdb +from pandas import DataFrame + +# Public interface +__all__ = ['QueryBuilder', 'apply_datetime_scale', ] + +# Module level logger +log = logging.getLogger(__name__) + + +# Scale factors for datetime offset conversion +DAY_SCALE = 86400 +HOUR_SCALE = 3600 +MINUTE_SCALE = 60 +SECOND_SCALE = 1 + +OFFSET_PATTERN = re.compile( + r'([+-]?)(d|day|days|h|hour|hours|m|min|mins|minute|minutes|s|sec|secs|second|seconds)' +) +DATETIME_SCALE: Dict[str, int] = { + 'd': DAY_SCALE, 'day': DAY_SCALE, 'days': DAY_SCALE, + 'h': HOUR_SCALE, 'hour': HOUR_SCALE, 'hours': HOUR_SCALE, + 'm': MINUTE_SCALE, 'min': MINUTE_SCALE, 'mins': MINUTE_SCALE, + 'minute': MINUTE_SCALE, 'minutes': MINUTE_SCALE, + 's': SECOND_SCALE, 'sec': SECOND_SCALE, 'secs': SECOND_SCALE, + 'second': SECOND_SCALE, 'seconds': SECOND_SCALE, +} + + +def apply_datetime_scale(df: DataFrame, column: str, scale: str) -> DataFrame: + """ + Apply datetime scale offset to convert timestamps to relative values. + + Args: + df: DataFrame with datetime column + column: Name of the datetime column + scale: Offset specification (e.g., '+hours', '-days', 'minutes') + + Returns: + DataFrame with column converted to numeric offset values. + """ + if match := OFFSET_PATTERN.match(scale): + sign, scale_name = match.groups() + divisor = DATETIME_SCALE[scale_name] + + # Convert datetime to epoch seconds + # DuckDB returns datetime64[us] (microseconds), pandas uses int64 representation + df = df.copy() + dtype_str = str(df[column].dtype) + if 'datetime64[us]' in dtype_str: + # Microsecond precision (DuckDB default) + epoch_values = df[column].astype('int64') / 10**6 + elif 'datetime64[ns]' in dtype_str: + # Nanosecond precision (pandas default) + epoch_values = df[column].astype('int64') / 10**9 + elif 'datetime64[ms]' in dtype_str: + # Millisecond precision + epoch_values = df[column].astype('int64') / 10**3 + else: + # Try to convert via timestamp + epoch_values = df[column].apply(lambda x: x.timestamp() if hasattr(x, 'timestamp') else float(x)) + + # Apply offset from start or end + if sign in ('', '+'): + df[column] = (epoch_values - epoch_values.iloc[0]) / divisor + else: + df[column] = (epoch_values - epoch_values.iloc[-1]) / divisor + + return df + else: + raise ValueError(f"Unsupported scale offset: '{scale}'") + + +# Bucket interval patterns for parsing shorthand like '15min', '1h', '1d' +BUCKET_PATTERN = re.compile(r'^(\d+)\s*(s|sec|secs|second|seconds|m|min|mins|minute|minutes|h|hour|hours|d|day|days)$') +BUCKET_UNITS: Dict[str, str] = { + 's': 'seconds', 'sec': 'seconds', 'secs': 'seconds', 'second': 'seconds', 'seconds': 'seconds', + 'm': 'minutes', 'min': 'minutes', 'mins': 'minutes', 'minute': 'minutes', 'minutes': 'minutes', + 'h': 'hours', 'hour': 'hours', 'hours': 'hours', + 'd': 'days', 'day': 'days', 'days': 'days', +} + + +def parse_bucket_interval(interval: str) -> str: + """ + Parse bucket interval shorthand into DuckDB INTERVAL syntax. + + Args: + interval: Shorthand like '15min', '1h', '1d' or full syntax like '15 minutes' + + Returns: + DuckDB-compatible interval string (e.g., '15 minutes') + """ + # Already in full format? + if ' ' in interval: + return interval + + if match := BUCKET_PATTERN.match(interval.lower()): + value, unit = match.groups() + return f"{value} {BUCKET_UNITS[unit]}" + + # Return as-is and let DuckDB handle it + return interval + + +class QueryBuilder: + """ + Build and execute DuckDB queries for data loading and transformation. + + Supports CSV, Parquet, JSON, and NDJSON formats with automatic detection + based on file extension. Provides SQL-based filtering and time-series + bucketing via DuckDB's time_bucket() function. + """ + + source: str + format: Optional[str] + x_column: Optional[str] + y_columns: List[str] + group_by: Optional[str] + where_clause: Optional[str] + after_datetime: Optional[str] + before_datetime: Optional[str] + bucket_interval: Optional[str] + agg_method: Optional[str] + timeseries: bool + scale: Optional[str] + _cached_columns: Optional[List[str]] + + def __init__( + self: QueryBuilder, + source: str, + format: Optional[str] = None, + x_column: Optional[str] = None, + y_columns: Optional[List[str]] = None, + group_by: Optional[str] = None, + where_clause: Optional[str] = None, + after_datetime: Optional[str] = None, + before_datetime: Optional[str] = None, + bucket_interval: Optional[str] = None, + agg_method: Optional[str] = None, + timeseries: bool = False, + scale: Optional[str] = None, + ) -> None: + """Initialize query builder with data source and options.""" + self.source = source + self.format = format + self.x_column = x_column + self.y_columns = y_columns or [] + self.group_by = group_by + self.where_clause = where_clause + self.after_datetime = after_datetime + self.before_datetime = before_datetime + self.bucket_interval = bucket_interval + self.agg_method = agg_method + self.timeseries = timeseries + self.scale = scale + self._conn: Optional[duckdb.DuckDBPyConnection] = None + self._cached_columns: Optional[List[str]] = None + self._stdin_loaded: bool = False + + @property + def conn(self: QueryBuilder) -> duckdb.DuckDBPyConnection: + """Lazy-initialize DuckDB connection.""" + if self._conn is None: + self._conn = duckdb.connect() + return self._conn + + def detect_format(self: QueryBuilder) -> str: + """Detect file format from extension or explicit format.""" + if self.format: + return self.format + if self.source == '-': + return 'csv' # Default stdin to CSV + path = Path(self.source) + ext = path.suffix.lower() + format_map = { + '.csv': 'csv', + '.parquet': 'parquet', + '.pq': 'parquet', + '.json': 'json', + '.ndjson': 'ndjson', + '.jsonl': 'ndjson', + } + return format_map.get(ext, 'csv') + + def _read_function(self: QueryBuilder) -> str: + """Return DuckDB read function for detected format.""" + fmt = self.detect_format() + read_funcs = { + 'csv': 'read_csv_auto', + 'parquet': 'read_parquet', + 'json': 'read_json_auto', + 'ndjson': 'read_json_auto', + } + return read_funcs.get(fmt, 'read_csv_auto') + + def _build_source_expr(self: QueryBuilder, stdin_data: Optional[str] = None) -> str: + """Build the FROM clause source expression.""" + if self.source == '-': + # For stdin, we'll register the data as a temp table + return '__stdin_data' + return f"'{self.source}'" + + def _load_stdin(self: QueryBuilder) -> None: + """Load stdin data into a temporary table.""" + if self.source != '-' or self._stdin_loaded: + return + log.info('Reading from ') + fmt = self.detect_format() + if fmt == 'csv': + self.conn.execute("CREATE TEMP TABLE __stdin_data AS SELECT * FROM read_csv_auto('/dev/stdin')") + elif fmt in ('json', 'ndjson'): + self.conn.execute("CREATE TEMP TABLE __stdin_data AS SELECT * FROM read_json_auto('/dev/stdin')") + else: + raise ValueError(f"Unsupported stdin format: {fmt}") + self._stdin_loaded = True + + def get_columns(self: QueryBuilder) -> List[str]: + """Get available column names from the data source.""" + if self._cached_columns is not None: + return self._cached_columns + + if self.source == '-': + self._load_stdin() + result = self.conn.execute("SELECT * FROM __stdin_data LIMIT 0") + else: + read_func = self._read_function() + source_expr = self._build_source_expr() + result = self.conn.execute(f"SELECT * FROM {read_func}({source_expr}) LIMIT 0") + + self._cached_columns = [desc[0] for desc in result.description] + return self._cached_columns + + def _build_where_clause(self: QueryBuilder, x_col: str) -> str: + """Build WHERE clause from filter options.""" + where_parts = [] + if self.where_clause: + where_parts.append(f"({self.where_clause})") + if self.after_datetime: + where_parts.append(f'"{x_col}" > \'{self.after_datetime}\'') + if self.before_datetime: + where_parts.append(f'"{x_col}" < \'{self.before_datetime}\'') + + if where_parts: + return 'WHERE ' + ' AND '.join(where_parts) + return '' + + def _build_from_clause(self: QueryBuilder) -> str: + """Build FROM clause.""" + if self.source == '-': + return '__stdin_data' + read_func = self._read_function() + source_expr = self._build_source_expr() + return f"{read_func}({source_expr})" + + def build_query(self: QueryBuilder) -> str: + """ + Build the SQL query based on configured options. + + Returns the SQL string to execute. + """ + # Determine columns to select + columns = self.get_columns() + x_col = self.x_column or columns[0] + + if self.y_columns: + y_cols = self.y_columns + else: + # All columns except x and group_by + exclude = {x_col} + if self.group_by: + exclude.add(self.group_by) + y_cols = [c for c in columns if c not in exclude] + + from_clause = self._build_from_clause() + where_clause = self._build_where_clause(x_col) + + # Use PIVOT query when group_by is specified + if self.group_by: + return self._build_pivot_query(x_col, y_cols, from_clause, where_clause) + + # Standard query (no grouping) + return self._build_standard_query(x_col, y_cols, from_clause, where_clause) + + def _build_standard_query( + self: QueryBuilder, + x_col: str, + y_cols: List[str], + from_clause: str, + where_clause: str, + ) -> str: + """Build standard SELECT query without PIVOT.""" + if self.bucket_interval and self.agg_method: + # Time bucketing with aggregation + interval_str = parse_bucket_interval(self.bucket_interval) + x_select = f"time_bucket(INTERVAL '{interval_str}', \"{x_col}\") AS \"{x_col}\"" + + # Special case: --count without y-columns counts rows per bucket + if self.agg_method.lower() == 'count' and not y_cols: + y_selects = ['COUNT(*) AS "count"'] + else: + y_selects = [f"{self.agg_method.upper()}(\"{y}\") AS \"{y}\"" for y in y_cols] + + select_clause = ', '.join([x_select] + y_selects) + group_clause = "GROUP BY 1 ORDER BY 1" + else: + # Simple select - quote column names for safety + select_clause = ', '.join([f'"{x_col}"'] + [f'"{y}"' for y in y_cols]) + group_clause = f'ORDER BY "{x_col}"' + + query = f"SELECT {select_clause} FROM {from_clause} {where_clause} {group_clause}" + log.debug(f'Query: {query}') + return query + + def _build_pivot_query( + self: QueryBuilder, + x_col: str, + y_cols: List[str], + from_clause: str, + where_clause: str, + ) -> str: + """ + Build PIVOT query for --by grouping. + + Transforms long-format data into wide format where each unique value + in the group_by column becomes a separate y-column. + """ + # Determine aggregation method (default to SUM for PIVOT) + agg = (self.agg_method or 'sum').upper() + + # Special case: --count without y-columns counts rows per group + use_count_star = (agg == 'COUNT' and not y_cols) + y_col = 'count' if use_count_star else (y_cols[0] if y_cols else 'value') + + if self.bucket_interval: + # PIVOT with time bucketing + interval_str = parse_bucket_interval(self.bucket_interval) + if use_count_star: + agg_expr = 'COUNT(*) AS "count"' + else: + agg_expr = f'{agg}("{y_col}") AS "{y_col}"' + + inner_select = f""" + SELECT + time_bucket(INTERVAL '{interval_str}', "{x_col}") AS "{x_col}", + "{self.group_by}", + {agg_expr} + FROM {from_clause} + {where_clause} + GROUP BY 1, 2 + """ + else: + # PIVOT without bucketing + if use_count_star: + # Need to aggregate even without bucket for count + inner_select = f""" + SELECT "{x_col}", "{self.group_by}", COUNT(*) AS "count" + FROM {from_clause} + {where_clause} + GROUP BY 1, 2 + """ + else: + inner_select = f""" + SELECT "{x_col}", "{self.group_by}", "{y_col}" + FROM {from_clause} + {where_clause} + """ + + query = f""" + PIVOT ({inner_select}) + ON "{self.group_by}" + USING SUM("{y_col}") + ORDER BY "{x_col}" + """ + log.debug(f'Query: {query}') + return query + + def execute(self: QueryBuilder) -> DataFrame: + """Execute the query and return results as pandas DataFrame.""" + if self.source == '-': + self._load_stdin() + query = self.build_query() + log.debug(f'Executing: {query}') + result = self.conn.execute(query) + df = result.fetchdf() + + # Apply scale offset if specified + if self.scale: + x_col = self.x_column or df.columns[0] + df = apply_datetime_scale(df, x_col, self.scale) + + return df + + @classmethod + def from_file( + cls, + filepath: str, + x_column: Optional[str] = None, + y_columns: Optional[List[str]] = None, + **kwargs, + ) -> QueryBuilder: + """Create QueryBuilder from a file path.""" + return cls( + source=filepath, + x_column=x_column, + y_columns=y_columns, + **kwargs, + ) + + @classmethod + def from_stdin( + cls, + format: str = 'csv', + x_column: Optional[str] = None, + y_columns: Optional[List[str]] = None, + **kwargs, + ) -> QueryBuilder: + """Create QueryBuilder for stdin input.""" + return cls( + source='-', + format=format, + x_column=x_column, + y_columns=y_columns, + **kwargs, + ) diff --git a/tests/test_app.py b/tests/test_app.py index da37d99..e5aa0ed 100644 --- a/tests/test_app.py +++ b/tests/test_app.py @@ -19,3 +19,14 @@ def test_version(capsys: CaptureFixture, opt: str) -> None: captured = capsys.readouterr() assert captured.out.strip() == __version__ assert captured.err.strip() == '' + + +@mark.unit +def test_scatter_accepted(capsys: CaptureFixture, tmp_path) -> None: + """Verify --scatter flag is accepted and produces output.""" + csv_path = tmp_path / "data.csv" + csv_path.write_text("x,y\n1,10\n2,20\n3,30\n") + PlotApp.main([str(csv_path), '--scatter', '--json']) + captured = capsys.readouterr() + assert captured.out.strip() != '' + assert captured.err.strip() == '' diff --git a/tests/test_cli.py b/tests/test_cli.py new file mode 100644 index 0000000..a56235b --- /dev/null +++ b/tests/test_cli.py @@ -0,0 +1,286 @@ +# SPDX-FileCopyrightText: 2023 Geoffrey Lentner +# SPDX-License-Identifier: Apache-2.0 + +"""Integration tests for CLI output modes and options.""" + + +# Standard libs +import json + +# External libs +from pytest import fixture, mark, CaptureFixture + +# Internal libs +from plot_cli import PlotApp + + +@fixture +def sample_csv_file(tmp_path): + """Create a temporary CSV file with sample data.""" + csv_path = tmp_path / "sample.csv" + csv_path.write_text( + "timestamp,value,count\n" + "2024-01-01 00:00:00,10.5,100\n" + "2024-01-01 01:00:00,20.3,150\n" + "2024-01-01 02:00:00,15.8,120\n" + ) + return str(csv_path) + + +# ========================================================= +# Output mode tests +# ========================================================= + + +@mark.integration +class TestOutputModes: + """Tests for --json and --csv output modes.""" + + def test_json_output(self, capsys: CaptureFixture, sample_csv_file): + """Output data as JSON.""" + PlotApp.main([sample_csv_file, "--json"]) + captured = capsys.readouterr() + data = json.loads(captured.out) + assert len(data) == 3 + assert data[0]["value"] == 10.5 + assert data[0]["count"] == 100 + + def test_csv_output(self, capsys: CaptureFixture, sample_csv_file): + """Output data as CSV.""" + PlotApp.main([sample_csv_file, "--csv"]) + captured = capsys.readouterr() + lines = captured.out.strip().split("\n") + assert len(lines) == 4 # header + 3 rows + assert "timestamp,value,count" in lines[0] + + def test_json_with_where_filter(self, capsys: CaptureFixture, sample_csv_file): + """JSON output with WHERE filter applied.""" + PlotApp.main([sample_csv_file, "--json", "--where", "value > 15"]) + captured = capsys.readouterr() + data = json.loads(captured.out) + assert len(data) == 2 # 20.3 and 15.8 + for row in data: + assert row["value"] > 15 + + def test_csv_with_column_selection(self, capsys: CaptureFixture, sample_csv_file): + """CSV output with specific column selection.""" + PlotApp.main([sample_csv_file, "--csv", "-x", "timestamp", "-y", "value"]) + captured = capsys.readouterr() + lines = captured.out.strip().split("\n") + assert "timestamp,value" in lines[0] + assert "count" not in lines[0] + + +# ========================================================= +# Filtering option tests +# ========================================================= + + +@mark.integration +class TestFilteringOptions: + """Tests for --where, --after, --before options.""" + + def test_where_option(self, capsys: CaptureFixture, sample_csv_file): + """Filter with --where option.""" + PlotApp.main([sample_csv_file, "--json", "--where", "count >= 120"]) + captured = capsys.readouterr() + data = json.loads(captured.out) + assert len(data) == 2 # 150 and 120 + + def test_after_option(self, capsys: CaptureFixture, sample_csv_file): + """Filter with --after option.""" + PlotApp.main([ + sample_csv_file, "--json", + "-x", "timestamp", + "--after", "2024-01-01 00:30:00" + ]) + captured = capsys.readouterr() + data = json.loads(captured.out) + assert len(data) == 2 # 01:00 and 02:00 + + def test_before_option(self, capsys: CaptureFixture, sample_csv_file): + """Filter with --before option.""" + PlotApp.main([ + sample_csv_file, "--json", + "-x", "timestamp", + "--before", "2024-01-01 01:30:00" + ]) + captured = capsys.readouterr() + data = json.loads(captured.out) + assert len(data) == 2 # 00:00 and 01:00 + + +# ========================================================= +# Aggregation option tests +# ========================================================= + + +@mark.integration +class TestAggregationOptions: + """Tests for -B/--bucket and aggregation method options.""" + + @fixture + def minute_data_csv(self, tmp_path): + """CSV with per-minute data for aggregation tests.""" + csv_path = tmp_path / "minute_data.csv" + rows = ["timestamp,value"] + from datetime import datetime, timedelta + base = datetime(2024, 1, 1, 0, 0, 0) + for i in range(30): + ts = base + timedelta(minutes=i) + rows.append(f"{ts.strftime('%Y-%m-%d %H:%M:%S')},{i}") + csv_path.write_text("\n".join(rows)) + return str(csv_path) + + def test_bucket_with_mean(self, capsys: CaptureFixture, minute_data_csv): + """Bucket with --mean aggregation.""" + PlotApp.main([ + minute_data_csv, "--json", + "-x", "timestamp", "-y", "value", + "-B", "15min", "--mean" + ]) + captured = capsys.readouterr() + data = json.loads(captured.out) + assert len(data) == 2 # 30 minutes / 15 = 2 buckets + + def test_bucket_with_sum(self, capsys: CaptureFixture, minute_data_csv): + """Bucket with --sum aggregation.""" + PlotApp.main([ + minute_data_csv, "--json", + "-x", "timestamp", "-y", "value", + "-B", "30min", "--sum" + ]) + captured = capsys.readouterr() + data = json.loads(captured.out) + assert len(data) == 1 + + def test_bucket_shorthand_formats(self, capsys: CaptureFixture, minute_data_csv): + """Bucket interval shorthand formats.""" + # Test '15m' shorthand + PlotApp.main([ + minute_data_csv, "--json", + "-x", "timestamp", "-y", "value", + "-B", "15m", "--count" + ]) + captured = capsys.readouterr() + data = json.loads(captured.out) + assert len(data) == 2 + # Each bucket should have 15 rows + assert all(row["value"] == 15 for row in data) + + +# ========================================================= +# Deprecated option tests +# ========================================================= + + +@mark.integration +class TestDeprecatedOptions: + """Tests for deprecated -F/--resample option.""" + + @fixture + def minute_data_csv(self, tmp_path): + """CSV with per-minute data.""" + csv_path = tmp_path / "minute_data.csv" + rows = ["timestamp,value"] + from datetime import datetime, timedelta + base = datetime(2024, 1, 1, 0, 0, 0) + for i in range(30): + ts = base + timedelta(minutes=i) + rows.append(f"{ts.strftime('%Y-%m-%d %H:%M:%S')},{i}") + csv_path.write_text("\n".join(rows)) + return str(csv_path) + + def test_resample_deprecation_works(self, capsys: CaptureFixture, caplog, minute_data_csv): + """Deprecated -F/--resample still works but issues warning.""" + import logging + with caplog.at_level(logging.WARNING): + PlotApp.main([ + minute_data_csv, "--json", + "-x", "timestamp", "-y", "value", + "-F", "15min", "--mean" + ]) + captured = capsys.readouterr() + # Should still produce output + data = json.loads(captured.out) + assert len(data) == 2 + # Warning logged via cmdkit logger + assert any("deprecated" in record.message.lower() for record in caplog.records) + + +# ========================================================= +# Format option tests +# ========================================================= + + +@mark.integration +class TestFormatOption: + """Tests for --format option.""" + + @fixture + def ndjson_file(self, tmp_path): + """Create NDJSON file.""" + path = tmp_path / "data.ndjson" + lines = [ + '{"x": 1, "y": 10}', + '{"x": 2, "y": 20}', + '{"x": 3, "y": 30}', + ] + path.write_text("\n".join(lines)) + return str(path) + + def test_ndjson_format(self, capsys: CaptureFixture, ndjson_file): + """Read NDJSON format.""" + PlotApp.main([ndjson_file, "--json"]) + captured = capsys.readouterr() + data = json.loads(captured.out) + assert len(data) == 3 + assert data[0]["x"] == 1 + + +# ========================================================= +# Plot type option tests +# ========================================================= + + +@mark.integration +class TestPlotTypeOptions: + """Tests for plot type options (--scatter, --line) with output modes.""" + + @fixture + def numeric_csv(self, tmp_path): + """Create a temporary CSV with numeric x/y data.""" + csv_path = tmp_path / "numeric.csv" + csv_path.write_text( + "x,y\n" + "1,10\n" + "2,20\n" + "3,30\n" + ) + return str(csv_path) + + def test_scatter_json_output(self, capsys: CaptureFixture, numeric_csv): + """Verify --scatter + --json produces valid JSON.""" + PlotApp.main([numeric_csv, "--scatter", "--json", "-x", "x", "-y", "y"]) + captured = capsys.readouterr() + data = json.loads(captured.out) + assert len(data) == 3 + assert data[0]["x"] == 1 + assert data[0]["y"] == 10 + + def test_line_json_output(self, capsys: CaptureFixture, numeric_csv): + """Verify --line + --json produces valid JSON (parity with scatter).""" + PlotApp.main([numeric_csv, "--line", "--json", "-x", "x", "-y", "y"]) + captured = capsys.readouterr() + data = json.loads(captured.out) + assert len(data) == 3 + assert data[0]["x"] == 1 + assert data[0]["y"] == 10 + + def test_scatter_csv_output(self, capsys: CaptureFixture, numeric_csv): + """Verify --scatter + --csv produces valid CSV.""" + PlotApp.main([numeric_csv, "--scatter", "--csv", "-x", "x", "-y", "y"]) + captured = capsys.readouterr() + lines = captured.out.strip().split("\n") + assert len(lines) == 4 # header + 3 rows + assert "x,y" in lines[0] diff --git a/tests/test_query.py b/tests/test_query.py new file mode 100644 index 0000000..8e4a0bc --- /dev/null +++ b/tests/test_query.py @@ -0,0 +1,391 @@ +# SPDX-FileCopyrightText: 2023 Geoffrey Lentner +# SPDX-License-Identifier: Apache-2.0 + +"""Unit tests for QueryBuilder and data processing.""" + + +# Standard libs +import os +import json +import tempfile +from datetime import datetime, timedelta + +# External libs +from pytest import fixture, mark, raises +import pandas as pd + +# Internal libs +from plot_cli.query import ( + QueryBuilder, + apply_datetime_scale, + parse_bucket_interval, +) + + +@fixture +def sample_csv_file(tmp_path): + """Create a temporary CSV file with sample data.""" + csv_path = tmp_path / "sample.csv" + csv_path.write_text( + "timestamp,value,count\n" + "2024-01-01 00:00:00,10.5,100\n" + "2024-01-01 01:00:00,20.3,150\n" + "2024-01-01 02:00:00,15.8,120\n" + "2024-01-01 03:00:00,25.1,200\n" + "2024-01-01 04:00:00,18.7,180\n" + ) + return str(csv_path) + + +@fixture +def sample_json_file(tmp_path): + """Create a temporary JSON file with sample data.""" + json_path = tmp_path / "sample.json" + data = [ + {"timestamp": "2024-01-01 00:00:00", "value": 10.5, "count": 100}, + {"timestamp": "2024-01-01 01:00:00", "value": 20.3, "count": 150}, + {"timestamp": "2024-01-01 02:00:00", "value": 15.8, "count": 120}, + ] + json_path.write_text(json.dumps(data)) + return str(json_path) + + +@fixture +def sample_ndjson_file(tmp_path): + """Create a temporary NDJSON file with sample data.""" + ndjson_path = tmp_path / "sample.ndjson" + lines = [ + '{"timestamp": "2024-01-01 00:00:00", "value": 10.5, "count": 100}', + '{"timestamp": "2024-01-01 01:00:00", "value": 20.3, "count": 150}', + '{"timestamp": "2024-01-01 02:00:00", "value": 15.8, "count": 120}', + ] + ndjson_path.write_text("\n".join(lines)) + return str(ndjson_path) + + +@fixture +def sample_parquet_file(tmp_path): + """Create a temporary Parquet file with sample data.""" + parquet_path = tmp_path / "sample.parquet" + df = pd.DataFrame({ + "timestamp": pd.to_datetime([ + "2024-01-01 00:00:00", + "2024-01-01 01:00:00", + "2024-01-01 02:00:00", + ]), + "value": [10.5, 20.3, 15.8], + "count": [100, 150, 120], + }) + df.to_parquet(parquet_path) + return str(parquet_path) + + +# ========================================================= +# Format detection tests +# ========================================================= + + +@mark.unit +class TestFormatDetection: + """Tests for automatic format detection.""" + + def test_csv_extension(self, sample_csv_file): + """CSV format detected from .csv extension.""" + qb = QueryBuilder(source=sample_csv_file) + assert qb.detect_format() == "csv" + + def test_json_extension(self, sample_json_file): + """JSON format detected from .json extension.""" + qb = QueryBuilder(source=sample_json_file) + assert qb.detect_format() == "json" + + def test_ndjson_extension(self, sample_ndjson_file): + """NDJSON format detected from .ndjson extension.""" + qb = QueryBuilder(source=sample_ndjson_file) + assert qb.detect_format() == "ndjson" + + def test_explicit_format_override(self, sample_csv_file): + """Explicit format overrides extension detection.""" + qb = QueryBuilder(source=sample_csv_file, format="json") + assert qb.detect_format() == "json" + + def test_stdin_defaults_to_csv(self): + """Stdin defaults to CSV format.""" + qb = QueryBuilder(source="-") + assert qb.detect_format() == "csv" + + +# ========================================================= +# QueryBuilder execution tests +# ========================================================= + + +@mark.unit +class TestQueryBuilderExecution: + """Tests for QueryBuilder query execution.""" + + def test_load_csv(self, sample_csv_file): + """Load data from CSV file.""" + qb = QueryBuilder(source=sample_csv_file) + df = qb.execute() + assert len(df) == 5 + assert list(df.columns) == ["timestamp", "value", "count"] + + def test_load_json(self, sample_json_file): + """Load data from JSON file.""" + qb = QueryBuilder(source=sample_json_file) + df = qb.execute() + assert len(df) == 3 + assert "timestamp" in df.columns + assert "value" in df.columns + + def test_load_ndjson(self, sample_ndjson_file): + """Load data from NDJSON file.""" + qb = QueryBuilder(source=sample_ndjson_file) + df = qb.execute() + assert len(df) == 3 + + def test_load_parquet(self, sample_parquet_file): + """Load data from Parquet file.""" + qb = QueryBuilder(source=sample_parquet_file) + df = qb.execute() + assert len(df) == 3 + + def test_select_columns(self, sample_csv_file): + """Select specific x and y columns.""" + qb = QueryBuilder( + source=sample_csv_file, + x_column="timestamp", + y_columns=["value"], + ) + df = qb.execute() + assert list(df.columns) == ["timestamp", "value"] + + def test_get_columns(self, sample_csv_file): + """Get available column names.""" + qb = QueryBuilder(source=sample_csv_file) + columns = qb.get_columns() + assert columns == ["timestamp", "value", "count"] + + +# ========================================================= +# Filtering tests +# ========================================================= + + +@mark.unit +class TestFiltering: + """Tests for SQL WHERE clause filtering.""" + + def test_where_clause(self, sample_csv_file): + """Filter with custom WHERE clause.""" + qb = QueryBuilder( + source=sample_csv_file, + where_clause="value > 20", + ) + df = qb.execute() + assert len(df) == 2 # 20.3 and 25.1 + assert all(df["value"] > 20) + + def test_after_datetime(self, sample_csv_file): + """Filter rows after timestamp.""" + qb = QueryBuilder( + source=sample_csv_file, + x_column="timestamp", + after_datetime="2024-01-01 02:00:00", + ) + df = qb.execute() + assert len(df) == 2 # 03:00 and 04:00 + + def test_before_datetime(self, sample_csv_file): + """Filter rows before timestamp.""" + qb = QueryBuilder( + source=sample_csv_file, + x_column="timestamp", + before_datetime="2024-01-01 02:00:00", + ) + df = qb.execute() + assert len(df) == 2 # 00:00 and 01:00 + + def test_combined_after_before(self, sample_csv_file): + """Filter with both after and before.""" + qb = QueryBuilder( + source=sample_csv_file, + x_column="timestamp", + after_datetime="2024-01-01 00:30:00", + before_datetime="2024-01-01 02:30:00", + ) + df = qb.execute() + assert len(df) == 2 # 01:00 and 02:00 + + +# ========================================================= +# Bucket interval parsing tests +# ========================================================= + + +@mark.unit +class TestBucketIntervalParsing: + """Tests for bucket interval parsing.""" + + @mark.parametrize("shorthand,expected", [ + ("15min", "15 minutes"), + ("1h", "1 hours"), + ("30m", "30 minutes"), + ("1d", "1 days"), + ("60s", "60 seconds"), + ("5sec", "5 seconds"), + ("2hour", "2 hours"), + ("3day", "3 days"), + ]) + def test_shorthand_parsing(self, shorthand, expected): + """Parse shorthand bucket intervals.""" + assert parse_bucket_interval(shorthand) == expected + + def test_full_syntax_passthrough(self): + """Full syntax passes through unchanged.""" + assert parse_bucket_interval("15 minutes") == "15 minutes" + + +# ========================================================= +# Aggregation tests +# ========================================================= + + +@mark.unit +class TestAggregation: + """Tests for time bucketing and aggregation.""" + + @fixture + def minute_data_csv(self, tmp_path): + """CSV with per-minute data for aggregation tests.""" + csv_path = tmp_path / "minute_data.csv" + rows = ["timestamp,value"] + base = datetime(2024, 1, 1, 0, 0, 0) + for i in range(60): + ts = base + timedelta(minutes=i) + rows.append(f"{ts.strftime('%Y-%m-%d %H:%M:%S')},{i * 1.5}") + csv_path.write_text("\n".join(rows)) + return str(csv_path) + + def test_bucket_with_mean(self, minute_data_csv): + """Bucket by 15 minutes with mean aggregation.""" + qb = QueryBuilder( + source=minute_data_csv, + x_column="timestamp", + y_columns=["value"], + bucket_interval="15min", + agg_method="mean", + timeseries=True, + ) + df = qb.execute() + assert len(df) == 4 # 60 minutes / 15 = 4 buckets + + def test_bucket_with_sum(self, minute_data_csv): + """Bucket by 30 minutes with sum aggregation.""" + qb = QueryBuilder( + source=minute_data_csv, + x_column="timestamp", + y_columns=["value"], + bucket_interval="30m", + agg_method="sum", + timeseries=True, + ) + df = qb.execute() + assert len(df) == 2 # 60 minutes / 30 = 2 buckets + + def test_bucket_with_count(self, minute_data_csv): + """Bucket by 15 minutes with count aggregation.""" + qb = QueryBuilder( + source=minute_data_csv, + x_column="timestamp", + y_columns=["value"], + bucket_interval="15min", + agg_method="count", + timeseries=True, + ) + df = qb.execute() + assert len(df) == 4 + assert all(df["value"] == 15) # 15 minutes per bucket + + def test_bucket_with_max(self, minute_data_csv): + """Bucket with max aggregation.""" + qb = QueryBuilder( + source=minute_data_csv, + x_column="timestamp", + y_columns=["value"], + bucket_interval="1h", + agg_method="max", + timeseries=True, + ) + df = qb.execute() + assert len(df) == 1 + assert df["value"].iloc[0] == 59 * 1.5 # max value + + def test_bucket_with_min(self, minute_data_csv): + """Bucket with min aggregation.""" + qb = QueryBuilder( + source=minute_data_csv, + x_column="timestamp", + y_columns=["value"], + bucket_interval="1h", + agg_method="min", + timeseries=True, + ) + df = qb.execute() + assert len(df) == 1 + assert df["value"].iloc[0] == 0.0 # min value + + +# ========================================================= +# Datetime scale tests +# ========================================================= + + +@mark.unit +class TestDatetimeScale: + """Tests for datetime scale offset conversion.""" + + @fixture + def datetime_df(self): + """DataFrame with datetime column.""" + return pd.DataFrame({ + "timestamp": pd.to_datetime([ + "2024-01-01 00:00:00", + "2024-01-01 01:00:00", + "2024-01-01 02:00:00", + "2024-01-01 03:00:00", + ]), + "value": [10, 20, 30, 40], + }) + + def test_scale_positive_hours(self, datetime_df): + """Scale to positive hours offset.""" + result = apply_datetime_scale(datetime_df, "timestamp", "+hours") + assert result["timestamp"].iloc[0] == 0.0 + assert result["timestamp"].iloc[1] == 1.0 + assert result["timestamp"].iloc[-1] == 3.0 + + def test_scale_negative_hours(self, datetime_df): + """Scale to negative hours offset (from end).""" + result = apply_datetime_scale(datetime_df, "timestamp", "-hours") + assert result["timestamp"].iloc[-1] == 0.0 + assert result["timestamp"].iloc[0] == -3.0 + + def test_scale_minutes(self, datetime_df): + """Scale to minutes offset.""" + result = apply_datetime_scale(datetime_df, "timestamp", "+minutes") + assert result["timestamp"].iloc[0] == 0.0 + assert result["timestamp"].iloc[1] == 60.0 + + def test_scale_days(self, datetime_df): + """Scale to days offset.""" + result = apply_datetime_scale(datetime_df, "timestamp", "+days") + assert result["timestamp"].iloc[0] == 0.0 + # 3 hours = 3/24 = 0.125 days + assert abs(result["timestamp"].iloc[-1] - 0.125) < 0.001 + + def test_invalid_scale(self, datetime_df): + """Invalid scale raises ValueError.""" + with raises(ValueError, match="Unsupported scale offset"): + apply_datetime_scale(datetime_df, "timestamp", "invalid") diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..8c3c1c9 --- /dev/null +++ b/uv.lock @@ -0,0 +1,892 @@ +version = 1 +revision = 3 +requires-python = ">=3.10" +resolution-markers = [ + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.11' and python_full_version < '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.11' and python_full_version < '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.11' and python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform 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