xmacis2py 2.2.1
A Python library that downloads data from the Applied Climate Information System (ACIS) Database, performs various types of analyses on the data and makes various types of graphical summaries of the data.
Requires Python: >=3.10
Keywords: meteorology, atmospheric sciences

![image](https://github.com/user-attachments/assets/fb5ecdf9-bd51-4243-be7d-92af0952bfd8) ![image](https://github.com/user-attachments/assets/da1b43c0-2b6a-4a5c-9eb4-f08b30cab42b)

[![Conda Version](https://img.shields.io/conda/vn/conda-forge/xmacis2py.svg)](https://anaconda.org/conda-forge/xmacis2py)
![PyPI - Version](https://img.shields.io/pypi/v/xmacis2py)
<a href="https://anaconda.org/conda-forge/xmacis2py"> <img src="https://anaconda.org/conda-forge/xmacis2py/badges/latest_release_date.svg" /> </a>
<a href="https://anaconda.org/conda-forge/xmacis2py"> <img src="https://anaconda.org/conda-forge/xmacis2py/badges/platforms.svg" /> </a>
<a href="https://anaconda.org/conda-forge/xmacis2py"> <img src="https://anaconda.org/conda-forge/xmacis2py/badges/license.svg" /> </a>
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.18227996.svg)](https://doi.org/10.5281/zenodo.18227996)

[![Conda Recipe](https://img.shields.io/badge/recipe-xmacis2py-green.svg)](https://anaconda.org/conda-forge/xmacis2py)


Anaconda Downloads:

[![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/xmacis2py.svg)](https://anaconda.org/conda-forge/xmacis2py)

PIP Downloads:

![PyPI - Downloads](https://img.shields.io/pypi/dm/xmacis2py)

# xmACIS2Py

***(C) Eric J. Drewitz 2025-2026***

***ANNOUNCEMENT: xmACIS2Py < 2.0 is now depreciated and replaced with xmACIS2Py >= 2.0***

**How To Install**

Copy and paste either command into your terminal or anaconda prompt:

*Install via Anaconda*

`conda install xmacis2py`

*Install via pip*

`pip install xmacis2py`

**How To Update To The Latest Version**

Copy and paste either command into your terminal or anaconda prompt:

*Update via Anaconda*

***This is for users who initially installed WxData through Anaconda***

`conda update xmacis2py`

*Update via pip*

***This is for users who initially installed WxData through pip***

`pip install --upgrade xmacis2py`

### Documentation and Jupyter Lab Examples

**xmACIS2Py 2.0 Series Documentation and Jupyter Lab Tutorials**

**Jupyter Lab Tutorials**

1) [xmACIS2Py Data Access & Analysis](https://github.com/edrewitz/xmACIS2Py-Jupyter-Lab-Tutorials/blob/main/Tutorials/xmacis_analysis.ipynb)
2) [xmACIS2Py Graphical Summaries](https://github.com/edrewitz/xmACIS2Py-Jupyter-Lab-Tutorials/blob/main/Tutorials/xmacis_graphics.ipynb)

**Documentation**

***Data Access***

1) [Get Data](https://github.com/edrewitz/xmACIS2Py/blob/main/Documentation/xmACIS2.0/data_access.md#xmacis2py-data-access)

***Analysis Tools***

1) [Period Mean](https://github.com/edrewitz/xmACIS2Py/blob/main/Documentation/xmACIS2.0/analysis_tools.md#period_mean)
2) [Period Median](https://github.com/edrewitz/xmACIS2Py/blob/main/Documentation/xmACIS2.0/analysis_tools.md#period_median)
3) [Period Mode](https://github.com/edrewitz/xmACIS2Py/blob/main/Documentation/xmACIS2.0/analysis_tools.md#period_mode)
4) [Period Percentile](https://github.com/edrewitz/xmACIS2Py/blob/main/Documentation/xmACIS2.0/analysis_tools.md#period_percentile)
5) [Period Standard Deviation](https://github.com/edrewitz/xmACIS2Py/blob/main/Documentation/xmACIS2.0/analysis_tools.md#period_standard_deviation)
6) [Period Variance](https://github.com/edrewitz/xmACIS2Py/blob/main/Documentation/xmACIS2.0/analysis_tools.md#period_variance)
7) [Period Skewness](https://github.com/edrewitz/xmACIS2Py/blob/main/Documentation/xmACIS2.0/analysis_tools.md#period_skewness)
8) [Period Kurtosis](https://github.com/edrewitz/xmACIS2Py/blob/main/Documentation/xmACIS2.0/analysis_tools.md#period_kurtosis)
9) [Period Maximum](https://github.com/edrewitz/xmACIS2Py/blob/main/Documentation/xmACIS2.0/analysis_tools.md#period_maximum)
10) [Period Minimum](https://github.com/edrewitz/xmACIS2Py/blob/main/Documentation/xmACIS2.0/analysis_tools.md#period_minimum)
11) [Period Sum](https://github.com/edrewitz/xmACIS2Py/blob/main/Documentation/xmACIS2.0/analysis_tools.md#period_sum)
12) [Period Rankings](https://github.com/edrewitz/xmACIS2Py/blob/main/Documentation/xmACIS2.0/analysis_tools.md#period_rankings)
13) [Running Sum](https://github.com/edrewitz/xmACIS2Py/blob/main/Documentation/xmACIS2.0/analysis_tools.md#running_sum)
14) [Running Mean](https://github.com/edrewitz/xmACIS2Py/blob/main/Documentation/xmACIS2.0/analysis_tools.md#running_mean)
15) [Detrend Data](https://github.com/edrewitz/xmACIS2Py/blob/main/Documentation/xmACIS2.0/analysis_tools.md#detrend_data)
16) [Number of Missing Days](https://github.com/edrewitz/xmACIS2Py/blob/main/Documentation/xmACIS2.0/analysis_tools.md#number_of_missing_days)
17) [Number of Days At Or Below Value](https://github.com/edrewitz/xmACIS2Py/blob/main/Documentation/xmACIS2.0/analysis_tools.md#number_of_days_at_or_below_value)
18) [Number of Days At Or Above Value](https://github.com/edrewitz/xmACIS2Py/blob/main/Documentation/xmACIS2.0/analysis_tools.md#number_of_days_at_or_above_value)
19) [Number of Days Below Value](https://github.com/edrewitz/xmACIS2Py/blob/main/Documentation/xmACIS2.0/analysis_tools.md#number_of_days_below_value)
20) [Number of Days Above Value](https://github.com/edrewitz/xmACIS2Py/blob/main/Documentation/xmACIS2.0/analysis_tools.md#number_of_days_above_value)
21) [Number of Days At Value](https://github.com/edrewitz/xmACIS2Py/blob/main/Documentation/xmACIS2.0/analysis_tools.md#number_of_days_at_value)

***Graphical Summaries***

1) [Compreheisive Temperature Summary](https://github.com/edrewitz/xmACIS2Py/blob/main/Documentation/xmACIS2.0/compreheisive_summary.md#comprehensive-temperature-summary)
2) [Maximum Temperature Summary](https://github.com/edrewitz/xmACIS2Py/blob/main/Documentation/xmACIS2.0/maximum_temperature_summary.md#maximum-temperature-summary)
3) [Minimum Temperature Summary](https://github.com/edrewitz/xmACIS2Py/blob/main/Documentation/xmACIS2.0/minimum_temperature_summary.md#minimum-temperature-summary)
4) [Average Temperature Summary](https://github.com/edrewitz/xmACIS2Py/blob/main/Documentation/xmACIS2.0/average_temperature_summary.md#average-temperature-summary)
5) [Average Temperature Departure Summary](https://github.com/edrewitz/xmACIS2Py/blob/main/Documentation/xmACIS2.0/average_temperature_departure_summary.md#average-temperature-departure-summary)
6) [Heating Degree Day Summary](https://github.com/edrewitz/xmACIS2Py/blob/main/Documentation/xmACIS2.0/heating_degree_day_summary.md#heating-degree-day-summary)
7) [Cooling Degree Day Summary](https://github.com/edrewitz/xmACIS2Py/blob/main/Documentation/xmACIS2.0/cooling_degree_day_summary.md#cooling-degree-day-summary)
8) [Growing Degree Day Summary](https://github.com/edrewitz/xmACIS2Py/blob/main/Documentation/xmACIS2.0/growing_degree_day_summary.md#growing-degree-day-summary)
9) [Precipitation Summary](https://github.com/edrewitz/xmACIS2Py/blob/main/Documentation/xmACIS2.0/precipitation_summary.md#precipitation-summary)


**Documentation For Legacy Users**

*[xmACIS2Py 1.0 Series (Depreciated/Legacy) Documentation and Jupyter Lab Tutorials](https://github.com/edrewitz/xmACIS2Py/blob/main/Documentation/xmACIS1.0/user_docs.md)*


#### References


1) **xmACIS2**: https://www.rcc-acis.org/docs_webservices.html 

2) **MetPy**: May, R. M., Goebbert, K. H., Thielen, J. E., Leeman, J. R., Camron, M. D., Bruick, Z., Bruning, E. C., Manser, R. P., Arms, S. C., and Marsh, P. T., 2022: MetPy: A Meteorological Python Library for Data Analysis and Visualization. Bull. Amer. Meteor. Soc., 103, E2273-E2284, https://doi.org/10.1175/BAMS-D-21-0125.1.

3) **NumPy**: Harris, C.R., Millman, K.J., van der Walt, S.J. et al. Array programming with NumPy. Nature 585, 357–362 (2020). DOI: 10.1038/s41586-020-2649-2. (Publisher link).

4) **Pandas**: Pandas: McKinney, W., & others. (2010). Data structures for statistical computing in python. In Proceedings of the 9th Python in Science Conference (Vol. 445, pp. 51–56).

5) **WxData**: Eric J. Drewitz. (2025). edrewitz/WxData: WxData 1.1.4 Released (WxData1.1.4). Zenodo. https://doi.org/10.5281/zenodo.17862030

6) **scipy**: Virtanen, P., Gommers, R., Oliphant, T.E. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods 17, 261–272 (2020). https://doi.org/10.1038/s41592-019-0686-2