Python utilities for reading and plotting TOLNet data.
TOLNet data provides information on the vertical evolution of ozone at sites around the country. The recent API makes accessing the data easier, and this utility provides an interface for the API to connect TOLNet to xarray and pandas. This simplifies comparison of TOLNet to other datasets.
To Install: python -m pip install --user git+https://github.com/barronh/pytolnet.git
Documentation:
https://barronh.github.io/pytolnet
Example:
import pytolnet
api = pytolnet.TOLNetAPI()
# Find newest data from UAH
cldf = api.data_calendar('UAH')
newest_data_id = cldf.index.values[0]
# Retrieve data
ds = api.to_dataset(newest_data_id)
# Print statistics (including coordinates)
print(ds.to_dataframe().reset_index().describe())
# time altitude derived_ozone
# count 174096 174096.00000 63538.000000
# mean 2023-08-16 19:17:36.874643968 7.72500 48.525455
# min 2023-08-16 13:06:59 0.30000 0.015444
# 25% 2023-08-16 16:10:39 4.01250 40.799999
# 50% 2023-08-16 19:18:37 7.72500 47.500000
# 75% 2023-08-16 22:24:22 11.43750 55.299999
# max 2023-08-17 01:31:57 15.15000 100.000000
# std NaN 4.29549 13.209246
# Make a curtain plot
qm = ds['derived_ozone'].T.plot(figsize=(12, 4))
qm.figure.savefig('pytolnet.png')
