Arase

A module for downloading and reading Arase spacecraft data.

Installation

Install from PyPI:

pip3 install Arase --user

or

python3 -m pip install Arase --user

Set the ARASE_PATH variable by placing the following at the end of ~/.bashrc:

export ARASE_PATH=/path/to/arase/data

Downloading Data

Most instrument data can be downloaded using the DownloadData function contained in the instruments submodule, e.g.:

Arase.XXX.DownloadData(L,prod,Date=Date,Overwrite=Overwrite)

where XXX can be replaced with the instrument names: HEP, LEPe, LEPi, MEPe, MEPi, MGF or XEP. L is an integer and prod is a string which correspond to the level and data product provided by the instrument, repsectively (see the table in "Current Progress"). Date determines the range of dates to download data for. The Date keyword can be a single date, a list of specific dates to download, or a 2 element list defining the start and end dates (by default Date = [20170101,20200101]). Overwrite will force the routine to overwrite existing data.

This method will work for PWE data:

Arase.PWE.DownloadData(subcomp,L,prod,Date=Date,Overwrite=Overwrite)

where subcomp is the sub-component of the instrument (see table below).

To download the position data:

Arase.Pos.DownloadData(prod,Date=Date,Overwrite=Overwrite)

where prod is either 'l3' or 'def'. The 'def' option is needed for position-related functions elsewhere in the Arase module.

Position and tracing

1. Download position data:

Arase.Pos.DownloadData('def')

2. Convert to a binary format (this allows for quicker reading):

Arase.Pos.ConvertPos()

3. Save field traces

Arase.Pos.SaveFieldTraces(Model=Model,StartDate=StartDate,EndDate=EndDate)

where Model is either 'T89', 'T96', 'T01' or 'TS05' ('T96' by default). StartDate and EndDate are the start and end dates to perform traces for, both are integers of the format yyymmdd.

4. To read the position data :

pos = Arase.Pos.GetPos()

To read the traces:

tr = Arase.Pos.ReadFieldTraces(Date)

Reading Data

MGF data

data = Arase.MGF.ReadMGF(Date)

This returns a numpy.recarray object which contains the time-series data. The Date argument may be a single date, a list of dates or a 2 element list of dates defining the start and end date to load.

Particle Omni-directional Spectra

data = Arase.LEPe.ReadOmni(Date)

For other instruments, replace LEPe with one of the following: LEPi, MEPe, MEPi, HEP or XEP. data is a dictionary which will contain dates, times, energy bins and instances of the Arase.Tools.PSpecCls object. The PSpecCls object contains all of the spectral information stored within it, and is usually identified by the dictionary key containing 'Flux'. The PSpecCls object has an in-built method for plotting the spectrograms, e.g.:

data['eFlux'].Plot()

will plot the electron flux spectrogram from the LEPe data loaded above. To list the keys of a dictionary, use list(data.keys())

Combined Particle Spectra

Two functions are available which will load the data for multiple instruments at the same time.

For electrons:

E = Arase.Electrons.ReadOmni(Date)

and for ions:

H,He,O = Arase.Ions.ReadOmni(Date)

where E, H, He and O are all instances of SpecCls.

Single Spectra

The SpecCls object has the ability to return single spectra, e.g.:

import Arase
import matplotlib.pyplot as plt

#read in the electrons - this should work with and SpecCls object
spec = Arase.Electrons.ReadOmni(Date)

#for the energy bins and particle flux data
e,dJdE,_ = spec.GetSpectrum(Date,ut)

#for velocity and phase space density
v,f,_ = spec.GetSpectrum(Date,ut,xparam='V',yparam='PSD')

#or to plot
plt.figure(figsize=(8,4))
ax0 = spec.PlotSpectrum(Date,ut,xparam='E',yparam='Flux',Split=True,fig=plt,maps=[2,1,0,0])
ax1 = spec.PlotSpectrum(Date,ut,xparam='V',yparam='PSD',Split=True,fig=plt,maps=[2,1,1,0])
plt.tight_layout()

#for more information, read the docstrings:
spec.GetSpectrum?
spec.PlotSpectrum?

3D Particle Spectra

These data are not currently placed into an object like PSpecCls, for instruments which provide 3D spectra, there is a function Read3D which will simply read the CDF file for a given date, and list all of the data and corresponding metadata into two dictionaries, e.g.:

data,meta = Arase.LEPe.Read3D(Date)

Pitch Angle Distributions

For particle instruments with 3D flux data, there is a method to convert these to pitch angle distributions (PADs). The PADs are calculated using the MGF data and the elevation/azimuth angles of the instruments in GSE coordinates where provided in the level 2 3dflux data products. It was possible to compare this mehod to the angles provided by the level 3 3dflux product from the MEPe instrument and almost all pitch angles were within about 1-2 degrees. WARNING: these data should be used with caution - they may not be correct.

To store the PADs:

import Arase
Arase.LEPe.SavePADs(Date,na=18,Overwrite=False,DownloadMissingData=True,DeleteNewData=True,Verbose=True)

The above code will bin up the 3D LEPe fluxes from a single date into na pitch angle bins (always in the range 0 to 180 degrees). The Overwrite keyword will force the overwriting of previously created PAD files. DownloadMissingData will download any missing 3dflux data and MGF data. DeleteNewData will delete the newly downloaded 3dflux data after creating the PAD data because some of the 3dflux files are > 500 MB.

To read PADs:

pad = Arase.LEPe.ReadPAD(Date,SpecType,ReturnSpecObject=True)

This will read the PAD spectra from a single date for a given SpecType (e.g. 'eFlux' or 'H+Flux', depending on the instrument). The returned object will either be a dict containing just the data if ReturnSpecObject=False, or a Arase.Tools.PSpecPADCls object if ReturnSpecObject=True. The PSpecPADCls object allows the plotting of spectrograms, 1D spectra and 2D spectra.

For a spectrogram of a specific pitch angle bin:

pad = Arase.MEPe.ReadPAD(20180101,'eFlux')
pad.PlotSpectrogram(Bin=5)

Or a 1D spectrum

pad.PlotSpectrum1D(12.0,Bin=5,xparam='V',yparam='PSD')

Or a 2D spectrum:

pad.PlotSpectrum2D(12.0,xparam='V',zparam='PSD')

Current progress

• ✔ - Works
• ✖ - Not working yet. In the case of 3D data, a SpecCls3D object needs to be written. For MGF and level 3 hfa data, it's a simple case of plotting a line.
• ✝ - Probably works, but have no access to the data to be able to test it.
• ✱ - Currently 3D spectra can only be read into dictionaries as a SpecCls3D object is needed.