pytolnet package

Subpackages

• pytolnet.tests package

Module contents

TOLNet API

Utilities for retrieving and plotting TOLNet data.

To Install

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

Example

import pytolnet
api = pytolnet.TOLNetAPI()
cldf = api.data_calendar('UAH')
newest_data_id = cldf.index.values[0]
ds = api.to_dataset(newest_data_id)
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

class pytolnet.TOLNetAPI(token='anonymous', cache='.', root='https://tolnet.larc.nasa.gov/api')

Bases: object

data_calendar(igname=None, igid=None, product_type='4', processing_type='1,2', file_type='1', ascending=False)

Retrieve a data calendar.

Parameters:

• igname (str or None) – Instruments Group name (see instruments_group)

• igid (int or None) – Instruments Group id (see instruments_group); supersedes igname. If igname and igid are None, returns calendar from all instruments

• product_type (int or str) – Defaults to 4 (HIRES), which is the supported data to be read. Other formats (5=CALVAL; 6=CLIM) are not tested. Remaining formats (7=gridded; 8=legacy) not likely to work.

• processing_type (int or str) – Defaults to ‘1,2’ (central,inhouse). Unprocessed (3) is not yet supported.

• file_type (int or str) – Defaults to ‘1’ (HDF GEOMS). See file_types for other options.

Returns:

caldf – DataFrame of data by date

Return type:

pandas.DataFrame

Example

import pytolnet
api = pytolnet.TOLNetAPI()
cldf = api.data_calendar('UAH')
print(cldf.columns)
# 'start_data_date', 'public', 'near_real_time', 'isAccessible'

file_types()

Returns:

fldf – File types dataframe

Return type:

pandas.DataFrame

get_product_type4(id, cache=None, overwrite=False)

Acquire data from product_type=4 and return it as an xarray.Dataset Same as to_dataset(…, product_type=4)

Parameters:

• id (int) – Must come from data with product_type=4

• cache (str) – Path to keep cahed files

• overwrite (bool) – If False (default), use cached files in cache folder. If True, remake all files

Returns:

ds – Dataset for file requested

Return type:

xarray.Dataset

get_product_type5(id, cache=None, overwrite=False)

Product type 5 has the same format as 4, so this is a thin wrapper.

Same as to_dataset(…, product_type=5)

get_product_type6(id, cache=None, overwrite=False)

Product type 6 has the same format as 4, so this is a thin wrapper.

Same as to_dataset(…, product_type=5)

instruments_groups()

Returns:

igdf – Instrument groups dataframe

Return type:

pandas.DataFrame

processing_types()

Returns:

ptdf – Processing types dataframe

Return type:

pandas.DataFrame

product_types()

Returns:

prdf – Product types dataframe

Return type:

pandas.DataFrame

set_token(token=None)

Parameters:

token (str) – Token to use for access. Use ‘anonymous’ if you don’t have one. Use None if you want to be prompted

to_dataset(id, cache=None, overwrite=False, product_type=4)

Acquire data from product_type and return it as an xarray.Dataset

Parameters:

• id (int) – Must come from data with product_type=4

• cache (str) – Path to keep cahed files

• overwrite (bool) – If False (default), use cached files in cache folder. If True, remake all files

• product_type (int) – Currently supports 4, 5 and 6 (all same)

Returns:

ds – Dataset for file requested

Return type:

xarray.Dataset

Example

import pytolnet
api = pytolnet.TOLNetAPI()
ds = api.to_dataset(2115)