Metadata-Version: 2.4
Name: climate_indices
Version: 2.2.0
Summary: Reference implementations of various climate indices typically used for drought monitoring
Project-URL: Homepage, https://github.com/monocongo/climate_indices
Project-URL: Bug Tracker, https://github.com/monocongo/climate_indices/issues
Author-email: James Adams <monocongo@gmail.com>
License-File: LICENSE
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering :: Atmospheric Science
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: <3.14,>=3.10
Requires-Dist: cftime>=1.6.4.post1
Requires-Dist: dask>=2025.7.0
Requires-Dist: h5netcdf>=1.6.3
Requires-Dist: scipy>=1.15.3
Requires-Dist: xarray>=2025.6.1
Provides-Extra: dev
Requires-Dist: cartopy>=0.24.1; extra == 'dev'
Requires-Dist: coverage>=7.10.1; extra == 'dev'
Requires-Dist: jupyter>=1.1.1; extra == 'dev'
Requires-Dist: matplotlib>=3.10.3; extra == 'dev'
Requires-Dist: pytest-cov>=6.2.1; extra == 'dev'
Requires-Dist: pytest>=8.4.1; extra == 'dev'
Requires-Dist: ruff>=0.12.7; extra == 'dev'
Requires-Dist: sphinx-autodoc-typehints>=3.0.1; extra == 'dev'
Requires-Dist: toml>=0.10.2; extra == 'dev'
Provides-Extra: docs
Requires-Dist: sphinx-rtd-theme>=1.0; extra == 'docs'
Requires-Dist: sphinx>=8.1.3; extra == 'docs'
Description-Content-Type: text/markdown

![Banner Image](assets/Global_Monthly_SPI.jpg)

# climate_indices

[//]: # ([![Coverage Status]&#40;https://coveralls.io/repos/github/monocongo/climate_indices/badge.svg?branch=master&#41;]&#40;https://coveralls.io/github/monocongo/climate_indices?branch=master&#41;)
[//]: # ([![Codacy Status]&#40;https://api.codacy.com/project/badge/Grade/48563cbc37504fc6aa72100370e71f58&#41;]&#40;https://www.codacy.com/app/monocongo/climate_indices?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=monocongo/climate_indices&amp;utm_campaign=Badge_Grade&#41;)
[![Actions Status](https://github.com/monocongo/climate_indices/workflows/tests/badge.svg)](https://github.com/monocongo/climate_indices/actions)
[![License](https://img.shields.io/badge/License-BSD%203--Clause-green.svg)](https://opensource.org/licenses/BSD-3-Clause)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/climate-indices)

#### Python library of indices useful for climate monitoring

This project contains Python implementations of various climate index algorithms which provide
a geographical and temporal picture of the severity and duration of precipitation and temperature
anomalies useful for climate monitoring and research.

The following indices are provided:

- [SPI](https://climatedataguide.ucar.edu/climate-data/standardized-precipitation-index-spi),
  Standardized Precipitation Index, utilizing both gamma and Pearson Type III distributions
- [SPEI](https://www.researchgate.net/publication/252361460_The_Standardized_Precipitation-Evapotranspiration_Index_SPEI_a_multiscalar_drought_index),
  Standardized Precipitation Evapotranspiration Index, utilizing both gamma and Pearson Type III distributions
- [PET](https://www.ncdc.noaa.gov/monitoring-references/dyk/potential-evapotranspiration), Potential Evapotranspiration, utilizing either [Thornthwaite](http://dx.doi.org/10.2307/21073)
  or [Hargreaves](http://dx.doi.org/10.13031/2013.26773) equations
- [PNP](http://www.droughtmanagement.info/percent-of-normal-precipitation/),
  Percentage of Normal Precipitation
- [PCI](https://www.tandfonline.com/doi/abs/10.1111/J.0033-0124.1980.00300.X), Precipitation Concentration Index

This Python implementation of the above climate index algorithms is being developed
with the following goals in mind:

- to provide an open source software package to compute a suite of
  climate indices commonly used for climate monitoring, with well
  documented code that is faithful to the relevant literature and
  which produces scientifically verifiable results
- to provide a central, open location for participation and collaboration
  for researchers, developers, and users of climate indices
- to facilitate standardization and consensus on best-of-breed
  climate index algorithms and corresponding compliant implementations in Python
- to provide transparency into the operational code used for climate
  monitoring activities at NCEI/NOAA, and consequent reproducibility
  of published datasets computed from this package
- to incorporate modern software engineering principles and scientific programming
  best practices


This is a developmental/forked version of code that was originally developed by NIDIS/NCEI/NOAA. 
See [drought.gov](https://www.drought.gov/drought/python-climate-indices).

- [__Documentation__](https://climate-indices.readthedocs.io/en/latest/)
- [__License__](LICENSE)
- [__Disclaimer__](DISCLAIMER)

## Migration Guide for v2.2.0

**Breaking Change: Exception-Based Error Handling**

Version 2.2.0 introduces a significant architectural improvement in error handling. The library now uses exception-based error handling instead of returning `None` tuples for error conditions.

### What Changed

**Before (v2.1.x and earlier):**
```python
# Old behavior - functions returned None tuples on failure
result = some_internal_function(data)
if result == (None, None, None, None):
    # Handle error case
    pass
```

**After (v2.2.0+):**
```python
# New behavior - functions raise specific exceptions
try:
    result = some_internal_function(data)
except climate_indices.compute.InsufficientDataError as e:
    # Handle insufficient data case
    print(f"Not enough data: {e.non_zero_count} values found, {e.required_count} required")
except climate_indices.compute.PearsonFittingError as e:
    # Handle fitting failure case
    print(f"Fitting failed: {e}")
```

### New Exception Hierarchy

- `DistributionFittingError` (base class)
  - `InsufficientDataError` - raised when there are too few non-zero values for statistical fitting
  - `PearsonFittingError` - raised when L-moments calculation fails for Pearson Type III distribution

### Impact on Users

- **Direct API users**: No changes needed - the public SPI/SPEI functions handle exceptions internally
- **Library integrators**: If you were checking for `None` return values from internal functions, update to use try/catch blocks
- **Benefits**: More informative error messages, better debugging, and automatic fallback from Pearson to Gamma distribution when appropriate

### Code Quality Improvements

Version 2.2.0 also addresses floating point comparison issues (`python:S1244`) throughout the codebase:

**Floating Point Comparisons:**
```python
# ❌ OLD: Direct equality checks (unreliable)
if values == 0.0:
    handle_zero_case()

# ✅ NEW: Safe comparison using numpy.isclose()
if np.isclose(values, 0.0, atol=1e-8):
    handle_zero_case()
```

**Benefits:**
- Eliminates floating point precision issues in statistical parameter validation
- Improves test reliability and numerical robustness
- Follows scientific computing best practices for floating point arithmetic
- See `docs/floating_point_best_practices.md` for comprehensive guidelines

#### Citation
You can cite `climate_indices` in your projects and research papers via the BibTeX 
entry below.
```
@misc {climate_indices,
    author = "James Adams",
    title  = "climate_indices, an open source Python library providing reference implementations of commonly used climate indices",
    url    = "https://github.com/monocongo/climate_indices",
    month  = "may",
    year   = "2017--"
}
```
