metloom 0.2.0

Location Oriented Observed Meteorology (LOOM)

metloom

Location Oriented Observed Meteorology

metloom is a python library created with the goal of consistent, simple sampling of meteorology and snow related point measurments from a variety of datasources across the Western US. metloom is developed by M3 Works as a tool for validating computational hydrology model results. Contributions welcome!

Warning - This software is provided as is (see the license), so use at your own risk. This is an opensource package with the goal of making data wrangling easier. We make no guarantees about the quality or accuracy of the data and any interpretation of the meaning of the data is up to you.

• Free software: BSD license

Features

• Sampling of daily, hourly, and snow course data

• Searching for stations from a datasource within a shapefile

• Current data sources:

  • CDEC

  • SNOTEL

  • MESOWEST

Requirements

python >= 3.7

Install

python3 -m pip install metloom

Local install for dev

The recommendation is to use virtualenv, but other local python environment isolation tools will work (pipenv, conda) .. code-block:: bash

python3 -m pip install –upgrade pip python3 -m pip install -r requirements_dev python3 -m pip install .

Testing

pytest

If contributing to the codebase, code coverage should not decrease from the contributions. Make sure to check code coverage before opening a pull request.

pytest --cov=metloom

Documentation

readthedocs coming soon

https://metloom.readthedocs.io.

Usage

See usage documentation https://metloom.readthedocs.io/en/latest/usage.html

NOTES: PointData methods that get point data return a GeoDataFrame indexed on both datetime and station code. To reset the index simply run df.reset_index(inplace=True)

Usage Examples

Use metloom to find data for a station

from datetime import datetime
from metloom.pointdata import SnotelPointData

snotel_point = SnotelPointData("713:CO:SNTL", "MyStation")
df = snotel_point.get_daily_data(
    datetime(2020, 1, 2), datetime(2020, 1, 20),
    [snotel_point.ALLOWED_VARIABLES.SWE]
)
print(df)

Use metloom to find snow courses within a geometry

from metloom.pointdata import CDECPointData
import geopandas as gpd

fp = <path to shape file>
obj = gpd.read_file(fp)

vrs = [
    CdecStationVariables.SWE,
    CdecStationVariables.SNOWDEPTH
]
points = CDECPointData.points_from_geometry(obj, vrs, snow_courses=True)
df = points.to_dataframe()
print(df)

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

History

0.1.1 (2021-10-05)

• This is the first release!

• Create the package

• Add CDEC functionality

• Add SNOTEL functionality

• Add CLI to find stations from shapefile

0.1.2 (2021-10-15)

• Remove dependency on climata to fix suds-jurko issue when building with newest setuptools

• Write a custom Snotel client using zeep

0.1.3 (2021-10-25)

• Filter out _unused columns resulting from joining multiple sensor result dataframes in CDEC and SNOTEL point data

• Change CDEC timezone to avoid impossible date logic when datetimes returned from the sensors don’t exist in US/Pacific timezone

• Account for nodata returns when validating dataframes

• Include datasource in dataframes

0.1.4 (2021-11-08)

• Add more network options to Snotel API usage

• Fix dataframe joining bug

0.1.5 (2021-12-23)

• Adjust filtering to snow course logic in CDEC client to handle perviously unhandled scenario

• Only include measurementDate column with snow courses

0.2.0 (2021-12-29)

• Added mesowest

• Added in a token json arg to the get_*_data functions

• Pinned docutils for an update that happened

• Added in a resample_df function for the highway stations where the returned data is 5min for air temp.