Metadata-Version: 2.4
Name: pyqicharts
Version: 2.0.0
Summary: Quality Improvement and Statistical Process Control charts for Python
Author: Assegai Studios Ltd
License-Expression: MIT
Project-URL: Homepage, https://github.com/AssegaiStudios/Quality-improvement-charts
Project-URL: Repository, https://github.com/AssegaiStudios/Quality-improvement-charts
Project-URL: Issues, https://github.com/AssegaiStudios/Quality-improvement-charts/issues
Keywords: quality improvement,statistical process control,spc,run charts,control charts,healthcare analytics,power bi,excel
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Healthcare Industry
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.22
Requires-Dist: pandas>=1.5
Requires-Dist: matplotlib>=3.5
Provides-Extra: reporting
Requires-Dist: openpyxl>=3.1; extra == "reporting"
Requires-Dist: python-pptx>=0.6.23; extra == "reporting"
Provides-Extra: excel
Requires-Dist: xlwings>=0.30; extra == "excel"
Requires-Dist: openpyxl>=3.1; extra == "excel"
Requires-Dist: python-pptx>=0.6.23; extra == "excel"
Provides-Extra: interactive
Requires-Dist: plotly>=5; extra == "interactive"
Requires-Dist: altair>=5; extra == "interactive"
Requires-Dist: ipywidgets>=8; extra == "interactive"
Provides-Extra: dev
Requires-Dist: pytest>=7; extra == "dev"
Requires-Dist: pytest-cov>=5; extra == "dev"
Requires-Dist: build>=1.0; extra == "dev"
Requires-Dist: twine>=5; extra == "dev"
Requires-Dist: check-manifest>=0.50; extra == "dev"
Requires-Dist: openpyxl>=3.1; extra == "dev"
Requires-Dist: python-pptx>=0.6.23; extra == "dev"
Requires-Dist: xlwings>=0.30; extra == "dev"
Requires-Dist: plotly>=5; extra == "dev"
Requires-Dist: altair>=5; extra == "dev"
Requires-Dist: ipywidgets>=8; extra == "dev"
Dynamic: license-file

﻿# pyqicharts

**2.0.0 stable production release**

pyqicharts is a Python package for Quality Improvement and Statistical Process Control charting. It is designed for analysts who need practical run charts, control charts, validation examples, sample data, reporting exports and education material.

The package is an independent open-source project by Assegai Studios Ltd. It is influenced by widely used quality-improvement and SPC methods, but it is not an official publication or endorsed implementation of any public body, vendor or external software project.

## Install

```bash
python -m pip install pyqicharts
```

For reporting exports:

```bash
python -m pip install pyqicharts[reporting]
```

For interactive notebook outputs:

```bash
python -m pip install pyqicharts[interactive]
```

For development and test work:

```bash
python -m pip install -e .[dev]
python -m pytest --cov=pyqicharts
```

## Quick Start

```python
from pyqicharts import qic, sample_healthcare_qi_data

sample = sample_healthcare_qi_data()
chart = qic(sample, x="month", y="wait_time", chart="i")
chart.save_png("waiting_times.png")
```

## Sample Data

```python
from pyqicharts import sample_healthcare_qi_data, sample_subgroup_measurements, get_sample_data_path

healthcare = sample_healthcare_qi_data()
subgroups = sample_subgroup_measurements()
path = get_sample_data_path("sample_healthcare_qi_data.csv")
```

## Main Public APIs

- `qic(...)` creates a matplotlib chart and returns a `QicResult`.
- `qic_table(...)` returns the calculation table as a pandas DataFrame.
- `pareto_chart(...)` and `pareto_table(...)` support Pareto workflows.
- `export_png(...)`, `export_excel(...)`, `export_powerpoint(...)` and `create_report_bundle(...)` support reporting.
- `create_report_pack_from_config(...)`, `load_chart_config(...)` and `run_chart_batch(...)` support batch report workflows.
- `powerbi_table(...)`, `spc_summary_table(...)`, `signal_table(...)` and related helpers support dashboard imports.
- `validation_manifest(...)`, `validation_summary(...)` and `compare_to_expected(...)` support reviewed validation workflows.
- `qic_plotly(...)`, `qic_altair(...)` and `qic_widget(...)` provide optional interactive outputs.

## Command Line

```bash
pyqicharts chart data.csv --x week --y value --chart i --output chart.png
pyqicharts report config.yml --output report
pyqicharts validate data.csv --config chart.yml
```

## Validation Scope

The v2 stable production release distinguishes three kinds of evidence:

1. Internal regression tests: automated tests that guard package behaviour.
2. Reviewed examples: deterministic fixtures and expected-output tables reviewed for methodology consistency.
3. External certification: independently certified reference outputs. These are not bundled in this stable production release.

See `VALIDATION_REPORT.md`, `PARITY_REPORT.md` and `docs/reports/statistical_review.md` for details.

## Governance

For high-stakes use, validate local workflows with local subject-matter experts, maintain a record of data definitions, review chart choice, and document any operational decisions made from chart outputs.
