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Space Physics made EASY¶

Speasy is a free and open-source Python package that makes it easy to find and load space physics data from a variety of data sources, whether it is online and public such as CDAWEB and AMDA, or any described archive, local or remote. This task, where any science project starts, would seem easy a priori but, considering the very diverse array of missions and instrument nowaday available, proves to be one of the major bottleneck, especially for students and newcomers. Speasy solves this problem by providing a single, easy-to-use interface to over 70 space missions and 65,000 products.

Main features¶

Simple and intuitive API (spz.get_data(...) to get them all)
Speasy variables are like Pandas DataFrame with seemless conversion to/from it.
Local cache to avoid redundant downloads
Uses the SciQLOP ultra fast community cache server
Full support of AMDA API
Can retrieve time-series from AMDA, CDAWeb, CSA, SSCWeb
Support data access from any local or remote archives described by YAML file.

Help us improve Speasy!¶

We want Speasy to be the best possible tool for space physics research. You can help us by:

Answering our user survey here.
Reporting bugs or requesting features here.
Creating or participating in discussions here.

Your feedback is essential to making Speasy a better tool for everyone.

Quickstart¶

Installing Speasy with pip (more details here):

$ python -m pip install speasy
# or
$ python -m pip install --user speasy

This simple code example shows how easy it is to get data using Speasy. The code imports the Speasy package and defines a variable named ace_mag. This variable stores the data for the ACE IMF product, for the time period from June 2, 2016 to June 5, 2016. The code then uses the Speasy plot() function to plot the data.

In [1]:

import speasy as spz
ace_mag = spz.get_data('amda/imf', "2016-6-2", "2016-6-5")
ace_mag.plot();

Where amda is the webservice and imf is the product id you will get with this request.

Using the dynamic inventory will produce the same result as the previous example, but it has the advantage of being easier to manipulate, since you can discover available data from your favorite Python environment completion tool, such as IPython or notebooks.

In [2]:

import speasy as spz
amda_tree = spz.inventories.data_tree.amda
ace_mag = spz.get_data(amda_tree.Parameters.ACE.MFI.ace_imf_all.imf, "2016-6-2", "2016-6-5")
ace_mag.plot();

This code example shows how to use Speasy to plot multiple time series of space physics data from the MMS1 spacecraft on a single figure, with a shared x-axis. The code imports the Speasy package and the Matplotlib plotting library. It then creates a figure with six subplots, arranged in a single column. Next, it defines a list of products and axes to plot. Finally, it iterates over the list of products and axes, plotting each product on the corresponding axis. The code uses the Speasy get_data() function to load the data for each product, and the replace_fillval_by_nan() function to replace any fill values with NaNs.

In [4]:

import speasy as spz
import matplotlib.pyplot as plt

fig = plt.figure(figsize=(8, 16), layout="constrained")
gs = fig.add_gridspec(6, hspace=0, wspace=0)
axes = gs.subplots(sharex=True)

plots = [
    (spz.inventories.tree.cda.MMS.MMS1.FGM.MMS1_FGM_SRVY_L2.mms1_fgm_b_gse_srvy_l2_clean, axes[0]),
    (spz.inventories.tree.cda.MMS.MMS1.SCM.MMS1_SCM_SRVY_L2_SCSRVY.mms1_scm_acb_gse_scsrvy_srvy_l2 , axes[1]),
    (spz.inventories.tree.cda.MMS.MMS1.DES.MMS1_FPI_FAST_L2_DES_MOMS.mms1_des_bulkv_gse_fast, axes[2]),
    (spz.inventories.tree.cda.MMS.MMS1.DES.MMS1_FPI_FAST_L2_DES_MOMS.mms1_des_temppara_fast, axes[3]),
    (spz.inventories.tree.cda.MMS.MMS1.DES.MMS1_FPI_FAST_L2_DES_MOMS.mms1_des_tempperp_fast, axes[3]),
    (spz.inventories.tree.cda.MMS.MMS1.DES.MMS1_FPI_FAST_L2_DES_MOMS.mms1_des_energyspectr_omni_fast, axes[4]),
    (spz.inventories.tree.cda.MMS.MMS1.DIS.MMS1_FPI_FAST_L2_DIS_MOMS.mms1_dis_energyspectr_omni_fast, axes[5])
]

def plot_product(product, ax):
    values = spz.get_data(product, "2019-01-02T15", "2019-01-02T22")
    values.replace_fillval_by_nan().plot(ax=ax)

for p in plots:
    plot_product(p[0], p[1])

plt.show()

More complex requests like this one are supported:

In [5]:

import speasy as spz
products = [
    spz.inventories.tree.amda.Parameters.Wind.SWE.wnd_swe_kp.wnd_swe_vth,
    spz.inventories.tree.amda.Parameters.Wind.SWE.wnd_swe_kp.wnd_swe_pdyn,
    spz.inventories.tree.amda.Parameters.Wind.SWE.wnd_swe_kp.wnd_swe_n,
    spz.inventories.tree.cda.Wind.WIND.MFI.WI_H2_MFI.BGSE,
    spz.inventories.tree.ssc.Trajectories.wind,
]
intervals = [["2010-01-02", "2010-01-02T10"], ["2009-08-02", "2009-08-02T10"]]
data = spz.get_data(products, intervals)
data

Out[5]:

[[<speasy.products.variable.SpeasyVariable at 0x7fddf2168d00>,
  <speasy.products.variable.SpeasyVariable at 0x7fddf2570ac0>],
 [<speasy.products.variable.SpeasyVariable at 0x7fddf220bc00>,
  <speasy.products.variable.SpeasyVariable at 0x7fddf2158780>],
 [<speasy.products.variable.SpeasyVariable at 0x7fddf2176c00>,
  <speasy.products.variable.SpeasyVariable at 0x7fddf2177500>],
 [<speasy.products.variable.SpeasyVariable at 0x7fddf2185c80>,
  <speasy.products.variable.SpeasyVariable at 0x7fddf2187cc0>],
 [<speasy.products.variable.SpeasyVariable at 0x7fddf2187940>,
  <speasy.products.variable.SpeasyVariable at 0x7fddf21905c0>]]

Documentation and examples¶

Check out Speasy documentation and examples.

Caveats¶

Speasy is not a plotting package. basic plotting capabilities are here for illustration purposes and making quick-and-dirty plots. It is not meant to produce publication ready figures, prefer using Matplotlib directly for example.

Credits¶

The development of Speasy is supported by the CDPP.

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.