cdasws 1.7.31

NASA's Coordinated Data Analysis System Web Service Client Library

Synopsis

This library provides a simple python interface to the data and services of NASA's Coordinated Data Analysis System (CDAS). This library implements the client side of the CDAS RESTful web services and can return data in the SpacePy data model with all the original ISTP/SPDF metadata. Frequently asked questions concerning this library are at FAQ. For more general details about the CDAS web services, see https://cdaweb.sci.gsfc.nasa.gov/WebServices/REST/.

Code Example

This package contains example code calling most of the available web services. To run the included example, do the following

python -m cdasws

---

The following code demonstrates how to access magnetic field measurements from the ACE mission dataset.

from cdasws import CdasWs
import matplotlib.pyplot as plt

cdas = CdasWs()
data = cdas.get_data('AC_H1_MFI', ['Magnitude', 'BGSEc'],
                     '2009-06-01T00:00:00Z', '2009-06-01T00:10:00Z')[1]
print(data)
{'Epoch': VarCopy([datetime.datetime(2009, 6, 1, 0, 0),
     datetime.datetime(2009, 6, 1, 0, 4),
     datetime.datetime(2009, 6, 1, 0, 8)], dtype=object), 'Magnitude': VarCopy([3.495, 3.474, 3.477], dtype=float32), 'BGSEc': VarCopy([[-0.106,  2.521, -2.391],
     [-0.412,  2.402, -2.449],
     [-0.094,  2.309, -2.587]], dtype=float32), 'cartesian': VarCopy(['x_component', 'y_component', 'z_component'], dtype='<U11'), 'metavar0': VarCopy(['Bx GSE', 'By GSE', 'Bz GSE'], dtype='<U6')}

print(data['Magnitude'].attrs)

{'FIELDNAM': 'B-field magnitude', 'VALIDMIN': 0.0, 'VALIDMAX': 500.0, 'SCALEMIN': 0.0, 'SCALEMAX': 10.0, 'UNITS': 'nT', 'FORMAT': 'F8.3', 'VAR_TYPE': 'data', 'DICT_KEY': 'magnetic_field>magnitude', 'FILLVAL': -1e+31, 'DEPEND_0': 'Epoch', 'CATDESC': 'B-field magnitude', 'LABLAXIS': '<|B|>', 'DISPLAY_TYPE': 'time_series', 'DIM_SIZES': 0}

plt.plot(data['Epoch'], data['Magnitude'])
plt.xlabel(data['Epoch'].attrs['LABLAXIS'])
plt.ylabel(data['Magnitude'].attrs['LABLAXIS'] + ' ' +
           data['Magnitude'].attrs['UNITS'])
plt.show()

---

To have uniformly spaced (with respect to time) values computed (with optional spike removal), add the binData keyword paramter like this

status, data = cdas.get_data('AC_H1_MFI', ['Magnitude', 'BGSEc'],
                             '2009-06-01T00:00:00Z', '2009-06-01T00:10:00Z',
                             binData={
                                 'interval': 60.0,
                                 'interpolateMissingValues': True,
                                 'sigmaMultiplier': 4
                             })
print(data)

{'Epoch_bin': VarCopy([datetime.datetime(2009, 6, 1, 0, 0, 30),
     datetime.datetime(2009, 6, 1, 0, 1, 30),
     datetime.datetime(2009, 6, 1, 0, 2, 30),
     datetime.datetime(2009, 6, 1, 0, 3, 30),
     datetime.datetime(2009, 6, 1, 0, 4, 30),
     datetime.datetime(2009, 6, 1, 0, 5, 30),
     datetime.datetime(2009, 6, 1, 0, 6, 30),
     datetime.datetime(2009, 6, 1, 0, 7, 30),
     datetime.datetime(2009, 6, 1, 0, 8, 30),
     datetime.datetime(2009, 6, 1, 0, 9, 30)], dtype=object), 'Epoch': VarCopy([datetime.datetime(2009, 6, 1, 0, 0),
     datetime.datetime(2009, 6, 1, 0, 4),
     datetime.datetime(2009, 6, 1, 0, 8)], dtype=object), 'Magnitude': VarCopy([3.495  , 3.48975, 3.4845 , 3.47925, 3.474  , 3.47475, 3.4755 ,
     3.47625, 3.477  , 3.477  ], dtype=float32), 'BGSEc': VarCopy([[-0.106    ,  2.521    , -2.391    ],
     [-0.1825   ,  2.49125  , -2.4055   ],
     [-0.259    ,  2.4615   , -2.42     ],
     [-0.3355   ,  2.4317498, -2.4345   ],
     [-0.412    ,  2.402    , -2.449    ],
     [-0.3325   ,  2.3787498, -2.4835   ],
     [-0.253    ,  2.3555   , -2.518    ],
     [-0.1735   ,  2.33225  , -2.5524998],
     [-0.094    ,  2.309    , -2.587    ],
     [-0.094    ,  2.309    , -2.587    ]], dtype=float32), 'MAGNITUDE_NBIN': VarCopy([1., 0., 0., 0., 1., 0., 0., 0., 1., 0.], dtype=float32), 'MAGNITUDE_BIN_DELTA_MINUS_VAR': VarCopy([-1.e+31, -1.e+31, -1.e+31, -1.e+31, -1.e+31, -1.e+31, -1.e+31,
     -1.e+31, -1.e+31, -1.e+31], dtype=float32), 'MAGNITUDE_BIN_DELTA_PLUS_VAR': VarCopy([-1.e+31, -1.e+31, -1.e+31, -1.e+31, -1.e+31, -1.e+31, -1.e+31,
     -1.e+31, -1.e+31, -1.e+31], dtype=float32), 'BGSEC_NBIN': VarCopy([[ 1.,  1.,  1.],
     [-0., -0., -0.],
     [-0., -0., -0.],
     [-0., -0., -0.],
     [ 1.,  1.,  1.],
     [-0., -0., -0.],
     [-0., -0., -0.],
     [-0., -0., -0.],
     [ 1.,  1.,  1.],
     [-0., -0., -0.]], dtype=float32), 'BGSEC_BIN_DELTA_MINUS_VAR': VarCopy([[-1.e+31, -1.e+31, -1.e+31],
     [-1.e+31, -1.e+31, -1.e+31],
     [-1.e+31, -1.e+31, -1.e+31],
     [-1.e+31, -1.e+31, -1.e+31],
     [-1.e+31, -1.e+31, -1.e+31],
     [-1.e+31, -1.e+31, -1.e+31],
     [-1.e+31, -1.e+31, -1.e+31],
     [-1.e+31, -1.e+31, -1.e+31],
     [-1.e+31, -1.e+31, -1.e+31],
     [-1.e+31, -1.e+31, -1.e+31]], dtype=float32), 'BGSEC_BIN_DELTA_PLUS_VAR': VarCopy([[-1.e+31, -1.e+31, -1.e+31],
     [-1.e+31, -1.e+31, -1.e+31],
     [-1.e+31, -1.e+31, -1.e+31],
     [-1.e+31, -1.e+31, -1.e+31],
     [-1.e+31, -1.e+31, -1.e+31],
     [-1.e+31, -1.e+31, -1.e+31],
     [-1.e+31, -1.e+31, -1.e+31],
     [-1.e+31, -1.e+31, -1.e+31],
     [-1.e+31, -1.e+31, -1.e+31],
     [-1.e+31, -1.e+31, -1.e+31]], dtype=float32), 'cartesian_bin': VarCopy(['x_component', 'y_component', 'z_component'], dtype='<U11'), 'cartesian': VarCopy(['x_component', 'y_component', 'z_component'], dtype='<U11'), 'metavar0': VarCopy(['Bx GSE', 'By GSE', 'Bz GSE'], dtype='<U6'), 'metavar1': VarCopy(['# of Bx GSE', '# of By GSE', '# of Bz GSE'], dtype='<U11'), 'metavar2': VarCopy('# of ', dtype='<U5')}

Motivation

This library hides the HTTP, JSON/XML, and CDF details of the CDAS web services. A python developer only has to deal with python objects and methods (primarily the SpacePy data model object with full ISTP/SPDF metadata).

Dependencies

Accept for common, fundamental depenencies like requests, the primary dependency is SpacePy. And SpacePy is only required if you call the get_data method that returns the data in the SpacePy data model. Refer to the SpacePy documentation for the details of SpacePy's dependencies. In particular, SpacePy's data model import capability is dependent upon CDF which is not (at the time of this writing) automatically installed with SpacePy.

Installation

As noted in the dependencies above, if you intend to call the get_data method, you must install SpacePy and the CDF library (following the procedures at the SpacePy and CDF web sites).

Then, to install this package

$ pip install -U cdasws

API Reference

Refer to cdasws package API reference

or use the standard python help mechanism.

from cdasws import CdasWs
help(CdasWs)

Tests

The tests directory contains unittest tests.

Contributors

Bernie Harris.
e-mail for support.

License

This code is licensed under the NASA Open Source Agreement (NOSA).