Anaconda Downloads:

PIP Downloads:

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 xmACIS2Py through Anaconda

conda update xmacis2py

Update via pip

This is for users who initially installed xmACIS2Py through pip

pip install --upgrade xmacis2py

Documentation and Jupyter Lab Examples

xmACIS2Py 2.0 Series Documentation and Jupyter Lab Tutorials

Jupyter Lab Tutorials

1. Data Access & Analysis
2. Graphical Summaries
3. Multi-Station Data Retrieval
4. Retrieving Station Meta-Data
5. Retrieving 30-Year Climatological Normals and Departures From Normal
6. Calculating Daily Normals and Performing Analysis of ENSO Analog Years at KJFK
7. Creating an ENSO Analog Composite for Temperatures Across the Pacific Northwest (Plotting xmACIS2 Data on a Map)
8. Creating an ENSO Analog Composite for Precipitation Across California and Nevada (Plotting xmACIS2 Data on a Map)

Documentation

Data Access

1. Get Single Station Data
2. Get Multi Station Data
3. Get Single Station Climate Normals
4. Get Multi Station Climate Normals
5. Get Single Station Departures From Normal
6. Get Multi Station Departures From Normal
7. Get Single Station Meta-Data
8. Get Multi Station Meta-Data

Analysis Tools

1. Period Mean
2. Period Median
3. Period Mode
4. Period Percentile
5. Period Standard Deviation
6. Period Variance
7. Period Skewness
8. Period Kurtosis
9. Period Maximum
10. Period Minimum
11. Period Sum
12. Period Rankings
13. Running Sum
14. Running Mean
15. Detrend Data
16. Number of Missing Days
17. Number of Days At Or Below Value
18. Number of Days At Or Above Value
19. Number of Days Below Value
20. Number of Days Above Value
21. Number of Days At Value
22. Calculate Daily Normals
23. Filter Analog Years
24. Calculate Weighted Mean For Analog Years
25. Calculate Weighted Percentile For Analog Years
26. Calculate Daily Departures From Normal

Graphical Summaries

1. Compreheisive Temperature Summary
2. Maximum Temperature Summary
3. Minimum Temperature Summary
4. Average Temperature Summary
5. Average Temperature Departure Summary
6. Heating Degree Day Summary
7. Cooling Degree Day Summary
8. Growing Degree Day Summary
9. Precipitation Summary

xmACIS2Py Imports

"""
xmACIS2Py is a Python package that performs various statistical operations on ACIS2 Climate Data.

xmACIS2Py also is a graphics library of various different xmACIS2 stat graphics.

xmACIS2Py is powered by the xmACIS2 client in the WxData Python Library

For more information on the xmACIS2 Client in the WxData Library, visit: https://pypi.org/project/wxdata/

(C) Eric J. Drewitz 2025-2026
"""


# This is the module to create xmACIS2 temperature graphics
import xmacis2py.graphics.temperature as temperature

# This is the module to create xmACIS2 precipitation graphics
import xmacis2py.graphics.precipitation as precipitation

# ACIS2 Data Access
# - Single ACIS Station Data
# - Multi ACIS Station Data
# - Single ACIS Station 30-Year Climate Normals
# - Multi ACIS Station 30-Year Climate Normals
# - Single ACIS Station Departures from Normal
# - Multi ACIS Station Departures from Normal.
from xmacis2py.data_access.station_data import(
    get_single_station_acis_data,
    get_multi_station_acis_data,
    get_single_station_climate_normals,
    get_multi_station_climate_normals,
    get_single_station_departures,
    get_multi_station_departures
)

# ACIS2 Station Meta-Data
# - Single Station
# - Multi Station
from xmacis2py.data_access.station_meta import (
    single_station_meta,
    multi_station_meta
)


"""
Module
-------

xmacis2py.analysis_tools.analysis

Functions:
- number_of_days_at_value
- number_of_days_above_value
- number_of_days_below_value
- number_of_days_at_or_below_value
- number_of_days_at_or_above_value
- number_of_missing_days
- period_mean
- period_median
- period_standard_deviation
- period_mode
- period_variance
- period_skewness
- period_kurtosis
- period_maximum
- period_minimum
- period_sum
- period_rankings
- running_sum
- running_mean
- calculate_daily_normals
- filter_analog_years
- analog_weighted_mean
- analog_weighted_percentile
- calculate_daily_departures
"""
import xmacis2py.analysis_tools.analysis as analysis

Documentation For Legacy Users

xmACIS2Py 1.0 Series (Depreciated/Legacy) Documentation and Jupyter Lab Tutorials

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. (2026). edrewitz/WxData: WxData 1.6 Released (WxData1.6). Zenodo. https://doi.org/10.5281/zenodo.19644125

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

7. requests: K. Reitz, "Requests: HTTP for Humans". Available: https://requests.readthedocs.io/.