项目作者: st-bender

项目描述 :
Space weather indices for python
高级语言: Python
项目地址: git://github.com/st-bender/pyspaceweather.git
创建时间: 2020-04-05T20:58:12Z
项目社区:https://github.com/st-bender/pyspaceweather

开源协议:Other

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PySpaceWeather

Python interface for space weather indices

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This python module interfaces the space weather data available at
https://celestrak.com/SpaceData,
https://kp.gfz-potsdam.de/en/data,
and https://omniweb.gsfc.nasa.gov/ow.html.
It includes the geomagnetic Ap and Kp indices, both the 3h values and
the daily sum/averages.
The data also include the solar f10.7 cm radio fluxes,
the observed values as well as the 1 AU adjusted values,
daily values and the 81-day running means.
See Data sources below.

:warning: This package is in beta stage, that is, it works for the most part
and the interface should not change (much) in future versions.

Documentation is available at https://pyspaceweather.readthedocs.io.

Install

Requirements

  • numpy - required
  • pandas - required
  • requests - required for updating the data files
  • pytest, pytest-mock - optional, for testing

spaceweather

A pip package called spaceweather is available from the
main package repository, and can be installed with:

  1. $ pip install spaceweather

The latest development version can be installed
with pip directly from github
(see https://pip.pypa.io/en/stable/reference/pip_install/#vcs-support
and https://pip.pypa.io/en/stable/reference/pip_install/#git):

  1. $ pip install [-e] git+https://github.com/st-bender/pyspaceweather.git

The other option is to use a local clone:

  1. $ git clone https://github.com/st-bender/pyspaceweather.git
  2. $ cd pyspaceweather

and then using pip (optionally using -e, see
https://pip.pypa.io/en/stable/reference/pip_install/#install-editable):

  1. $ pip install [-e] .

or using setup.py:

  1. $ python setup.py install

Optionally, test the correct function of the module with

  1. $ py.test [-v]

or even including the doctests
in this document:

  1. $ py.test [-v] --doctest-glob='*.md'

Usage

The python module itself is named spaceweather and is imported as usual
by calling

  1. >>> import spaceweather

Celestrak

The module provides two functions to access the data from
Celestrak,
sw_daily() for the daily data
as available from the website, and ap_kp_3h() for the 3h Ap and Kp values.
Both functions return pandas.DataFrames.
When the data available in the packaged version are too old for the use case,
they can be updated by passing update=True to both functions, or by calling
update_data() explicitly.

  1. >>> import spaceweather as sw
  2. >>> df_d = sw.sw_daily()
  3. >>> df_d.loc["2000-01-01"].Apavg
  4. 30.0
  5. >>> df_3h = sw.ap_kp_3h()
  6. >>> df_3h.loc["2000-01-01 01:30:00"]
  7. Ap 56.0
  8. Kp 5.3
  9. Name: 2000-01-01 01:30:00, dtype: float64
  10. >>> # All 3h values for one day
  11. >>> df_3h.loc["2000-01-01"]
  12. Ap Kp
  13. 2000-01-01 01:30:00 56 5.3
  14. 2000-01-01 04:30:00 39 4.7
  15. 2000-01-01 07:30:00 27 4.0
  16. 2000-01-01 10:30:00 18 3.3
  17. 2000-01-01 13:30:00 32 4.3
  18. 2000-01-01 16:30:00 15 3.0
  19. 2000-01-01 19:30:00 32 4.3
  20. 2000-01-01 22:30:00 22 3.7

GFZ

The “GFZ” module supports the ascii and WDC files as offered by the
GFZ German Research Centre for Geosciences
on their data page.
In contrast to the official python client, this module reads the data
from the (downloaded) files and does not access the web service API.
The interface is mostly the same as for the “Celestrak” data:

  1. >>> import spaceweather as sw
  2. >>> df_d = sw.gfz_daily()
  3. >>> df_d.loc["2000-01-01"].Apavg
  4. 30.0
  5. >>> df_3h = sw.gfz_3h()
  6. >>> df_3h.loc["2000-01-01 01:30:00"]
  7. Ap 56.000
  8. Kp 5.333
  9. Name: 2000-01-01 01:30:00, dtype: float64

Currently, the data are not included in the package, downloads can be triggered
by passing update=True to sw.gfz_daily() or by running sw.update_gfz().
The lower-level interface functions are called read_gfz(<filename>)
for the ascii .txt files, and read_gfz_wdc(<filename>) for the WDC format.
They can also be used directly for reading already downloaded data files
outside of the package’s data directory.

  1. >>> import spaceweather as sw
  2. >>> df_d = sw.read_gfz("./tests/Kp_ap_Ap_SN_F107_since_2024.txt")
  3. >>> df_d.loc["2024-01-01"].Apavg
  4. 10.0

OMNI

The OMNI 1-hour yearly data
are accessible via omnie_hourly(<year>) or read_omnie(<file>).
Both functions should work with the OMNI2 standard and extended text files.
If the data are not already available locally, they can be cached by passing
cache=True to that function or by calling cache_omnie(<year>) explicitly.
As for the Celestrak data, pandas.DataFrames are returned.

  1. >>> import spaceweather as sw
  2. >>> df_h = sw.omnie_hourly(2000) # doctest: +SKIP
  3. >>> # or with automatic caching (downloading)
  4. >>> df_h = sw.omnie_hourly(2000, cache=True) # doctest: +SKIP

If the data are already available locally, you can point the parser
to that location:

  1. >>> import spaceweather as sw
  2. >>> df_h = sw.omnie_hourly(2000, local_path="/path/to/omni/data/") # doctest: +SKIP

Another option is to provide a filename directly to read_omnie():

  1. >>> import spaceweather as sw
  2. >>> df = sw.read_omnie("/path/to/omni/data/file.dat") # doctest: +SKIP

Reference

Basic class and method documentation is accessible via pydoc:

  1. $ pydoc spaceweather
  2. $ pydoc spaceweather.celestrak
  3. $ pydoc spaceweather.gfz
  4. $ pydoc spaceweather.omni

License

This python interface is free software: you can redistribute it or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, version 2 (GPLv2), see local copy
or online version.

Data sources

Celestrak

The “celestrak” data can be found at https://celestrak.com/SpaceData
and is included with kind permission from Dr. T.S. Kelso at
celestrak,
for details see the included COPYING.data file.

The data sources and file format are described at
http://celestrak.com/SpaceData/SpaceWx-format.php
(see file_format.txt for a local copy of the format description).

GFZ

The “GFZ” data are provided as tabulated ascii files
(format description,
local copy)
and in WDC format
(local copy)
by the
GFZ German Research Centre for Geosciences
on their official data webpage.
The data have the doi: 10.5880/Kp.0001,
and they are provided under the “Creative Commons attribution license”
CC-by 4.0
(local copy COPYING.CCby4.0).
See also COPYING.gfz for details.

OMNI

This package includes part of the hourly-resolved OMNI data,
accessible through https://spdf.gsfc.nasa.gov/pub/data/omni/low_res_omni,
and it enables easy downloading of it.
The file format is described at
https://spdf.gsfc.nasa.gov/pub/data/omni/low_res_omni/omni2.text
(local copy omni_format.txt)
and the “extended” format at
https://spdf.gsfc.nasa.gov/pub/data/omni/low_res_omni/extended/aareadme_extended
(local copy omnie_format.txt).

If you use the OMNI data in your work, please read COPYING.omni
carefully and cite the following publication:

King, Joseph H. and Natalia E. Papitashvili,
Solar wind spatial scales in and comparisons of hourly Wind and ACE plasma and magnetic field data,
J. Geophys. Res., 110, A02104, 2005.

Please acknowledge the OMNI sources, using the following DOIs for the OMNI datasets:

Papitashvili, Natalia E. and King, Joseph H. (2022), “OMNI 1-min Data” [Data set],
NASA Space Physics Data Facility, https://doi.org/10.48322/45bb-8792

Papitashvili, Natalia E. and King, Joseph H. (2022), “OMNI 5-min Data” [Data set],
NASA Space Physics Data Facility, https://doi.org/10.48322/gbpg-5r77

Papitashvili, Natalia E. and King, Joseph H. (2022), “OMNI Hourly Data” [Data Set],
NASA Space Physics Data Facility, https://doi.org/10.48322/1shr-ht18

Papitashvili, Natalia E. and King, Joseph H. (2022), “OMNI Daily Data” [Data set],
NASA Space Physics Data Facility, https://doi.org/10.48322/5fmx-hv56

Papitashvili, Natalia E. and King, Joseph H. (2022), “OMNI 27-Day Data” [Data set],
NASA Space Physics Data Facility, https://doi.org/10.48322/nmh3-jf75

The OMNI data are also available from CDAWeb, and thus available via various other methods
https://cdaweb.gsfc.nasa.gov/alternative_access_methods.html
In particular, you might find our Python web service library useful
https://pypi.org/project/cdasws
Or through the HAPI streaming protocol https://github.com/hapi-server/client-python