======== metloom

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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 is developed by M3 Works <https://m3works.io>_ 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

.. code-block:: python

# Find your data with ease
# !pip install folium mapclassify matplotlib
from metloom.pointdata import SnotelPointData, CDECPointData, USGSPointData
import geopandas as gpd
import pandas as pd

# Shapefile for the US states
shp = gpd.read_file('https://eric.clst.org/assets/wiki/uploads/Stuff/gz_2010_us_040_00_500k.json').to_crs("EPSG:4326")
# Filter to states of interest
west_states = ["Washington", "Oregon", "California", "Idaho", "Nevada", "Utah", "Wyoming", "Montana", "Colorado" ]  # , "Arizona", "New Mexico"]
shp = shp.loc[shp["NAME"].isin(west_states)].dissolve()

# Collect all points with SWE from CDEC and NRCS
dfs = []
for src in  [CDECPointData, SnotelPointData]:
    dfs.append(src.points_from_geometry(shp, [src.ALLOWED_VARIABLES.SWE]).to_dataframe())
# Combine dataframes
gdf = pd.concat(dfs)
# plot the shapefile
m = shp.explore(
    tooltip=False, color="grey", highlight=False, style_kwds={"opacity": 0.2}, popup=["NAME"]
)
# plot the points on top of the shapefile
gdf.explore(m=m, tooltip=["name", "id", "datasource"], color="red", marker_kwds={"radius":4})

.. image:: docs/images/map_of_swe.png :alt: Resulting plot of SWE trace at Banner summit

Features

.. code-block:: python

# !pip install plotly
from metloom.pointdata import SnotelPointData
import plotly.express as px
import pandas as pd

# Initialize your point
pt = SnotelPointData("312:ID:SNTL", "Banner Summit")
swe_variable = pt.ALLOWED_VARIABLES.SWE
# Get the data
df = pt.get_daily_data(
    pd.to_datetime("2024-10-01"), pd.to_datetime("2025-03-11"), [swe_variable]
).reset_index()
# Create a time series plot using Plotly Express
px.line(df, x="datetime", y=swe_variable.name, title=f"{pt.name} SWE")

.. image:: docs/images/banner_swe.png :alt: Resulting plot of SWE trace at Banner summit

• Sampling of daily, hourly, and snow course data
• Searching for stations from a datasource within a shapefile
• Current data sources:
  • CDEC <https://cdec.water.ca.gov/>_
  • SNOTEL <https://www.nrcs.usda.gov/wps/portal/wcc/home/dataAccessHelp/webService/webServiceReference/>_
  • MESOWEST <https://developers.synopticdata.com/mesonet/>_
  • USGS <https://waterservices.usgs.gov/rest/>_
  • NWS FORECAST <https://api.weather.gov>_
  • GEOSPHERE AUSTRIA <https://data.hub.geosphere.at/dataset/>_
  • UCSB CUES <https://snow.ucsb.edu/#>_
  • MET NORWAY <https://frost.met.no/index.html>_
  • SNOWEX MET STATIONS <https://nsidc.org/data/snex_met/versions/1>_
  • CENTER FOR SNOW AND AVALANCHE STUDIES (CSAS) <https://snowstudies.org/csas-facilities/>_

Requirements

python >= 3.7

Install

.. code-block:: bash

python3 -m pip install metloom

• Common install issues:
  • Macbook M1 and M2 chips: some python packages run into issues with the new M chips

    • error : from lxml import etree in utils.py ((mach-o file, but is an incompatible architecture (have 'x86_64', need 'arm64) The solution is the following

      .. code-block:: bash

        pip uninstall lxml
        pip install --no-binary lxml lxml
      

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

.. code-block:: bash

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.

.. code-block:: bash

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)

Simple usage examples are provided in this readme and in the docs. See our examples <https://github.com/M3Works/metloom/tree/main/docs/gallery>_ for code walkthroughs and more complicated use cases.

Usage Examples

Use metloom to find data for a station

.. code-block:: python

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

.. code-block:: python

from metloom.pointdata import CDECPointData
from metloom.variables import CdecStationVariables

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)

Tutorials

In the Examples folder, there are multiple Jupyter notbook based tutorials. You can edit and run these notebooks by running Jupyter Lab from the command line

.. code-block:: bash

pip install jupyterlab
jupyter lab

This will open a Jupyter Lab session in your default browser.

Credits

This package was created with Cookiecutter_ and the audreyr/cookiecutter-pypackage_ project template.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter .. _audreyr/cookiecutter-pypackage: https://github.com/audreyr/cookiecutter-pypackage