cloudside: download, assess, and visualize weather data

The problem this is solving

cloudside is a library suited to parsing weather data in the METAR format. METAR is kind of a mess and not very human-readable. Hopefully this makes things a bit easier. What appears to be an official spec on the format can be found here.

Basically I wanted a library that could do something like this:

import cloudside
data = cloudside.asos.get_data('KPDX', '2012-12-01', '2015-05-01', '[email protected]')
fig, rose_data = cloudside.viz.rose(data, magcol="wind_speed", dircol="wind_direction")

And so cloudside does that. After installation, you can also directly use it from the command line

$ cloudside get-asos KPDX 2018-01-01 2018-09-30 [email protected]

You can also fetch data from Portland's Hydra Network of rain gauges:

import cloudside
data = cloudside.hydra.get_data('Beaumont')

or from the command line

$ cloudside get-hydra Beaumont

Bigger Example

import pandas
import cloudside

def summarizer(g):
    return pandas.Series({
        'start': g['datetime'].min(),
        'end': g['datetime'].max(),
        'duration_hours': (g['datetime'].max() - g['datetime'].min()).total_seconds() / 3600,
        precip_col: g[precip_col].sum()
    })


def storm_totaller(df):
    return (
        df.query('storm > 0')
          .reset_index()
          .groupby(by=['storm'])
          .apply(summarizer)
          .assign(antecedent_hours=lambda df: (df['start'] - df['end'].shift()).dt.total_seconds() / 3600)
          .assign(ends_on_weekday=lambda df: df['end'].dt.weekday < 5)
    )

data = cloudside.asos.get_data('KPDX', '2012-12-01', '2015-05-01', '[email protected]')
storms = cloudside.storms.parse_record(data, intereventHours=6, outputfreqMinutes=5, precipcol='precip_inches')
storm_stats = storm_totaller(storms)
with pandas.ExcelWriter('output.xlsx') as xl:
    data.to_excel(xl, sheet_name='Weather Data')
    storm_stats.to_excel(xl, sheet_name='Storm Stats')

Basic History

Tom Pollard originally wrote python-metar to parse weather hourly reports as they were posted to the web. Building on top of his original work, cloudside tries to provide an easy way to download and visualize data from ASOS.

You can download cloudside from the repoository from Github.

Dependencies

• Python 3.6 or greater
• recent versions of pandas, matplotlib
• python-metar to actually parse the metar codes
• Jupyter for running notebook-based examples (optional)
• pytest for testing (optional)
• sphinx to build the documentation (optional)

If you're using environments managed through conda (recommended), this will get you started:

conda create --name=cloudside python=3.6 notebook pytest pandas matplotlib requests coverage

Followed by:

conda activate cloudside
conda install metar --channel=conda-forge

Installation

• Activate your conda environment;
• Install via pip and git; and

conda activate cloudside
pip install git+https://github.com/Geosyntec/cloudside.git

Testing

Tests are run via pytest. Run them all with:

source activate cloudside # (omit "source" on Windows)
python -c "import cloudside; cloudside.test()"

Documentation

We have HTML docs built with sphinx.

Development status

This is sort of a weekend hack, but I keep adding stuff to it. So, uh, caveat emptor, I guess.