TOLNet Ozone Curtain Plot

This example shows how to acquire TOLNet data from UAH and make a curtain plot.

Initialize API and Find Data

# python -m pip install git+https://github.com/barronh/pytolnet.git
import pytolnet

api = pytolnet.TOLNetAPI()

# Find newest data from UAH
cldf = api.data_calendar('UAH')
# Choose newest?
data_id = cldf.index.values[0]
# Choose specific version
# data_id = 2115

Retrieve and Characterize Data

ds = api.to_dataset(data_id)

df = ds.to_dataframe().reset_index()
print(df.describe())

# Make a curtain plot
qm = ds['derived_ozone'].T.plot(figsize=(12, 4), vmin=30, vmax=100)
qm.figure.savefig(f'derived_ozone_{data_id}.png')

                                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