Recent Flare List (2021-)

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Revision as of 13:48, 15 June 2021 by Dgary (talk | contribs)
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List of EOVSA Flares with Spectrogram Data

Date Time (UT) GOES Class Spectrogram STIX Coverage
2021-01-19 17:50 C1.0 No
2021-02-18 18:04 A8.0
EOVSA 20210218 A8flare.png
data
Yes
2021-04-17 16:46 B9.0
EOVSA 20210417 B9flare.png
data
Yes
2021-04-19 23:36 M1.0
EOVSA 20210419 M1flare.png
data
No
2021-05-05 22:30 B5.0
EOVSA 20210505 B1flare.png
data
Yes
2021-05-07 19:00 M4.0
EOVSA 20210507 M4flare.png
data
Yes
2021-05-08 18:30 C9.0
EOVSA 20210508 C9flare.png
data
Yes
2021-05-09 13:55 C4.0
EOVSA 20210509 C4flare.png
data
Yes
2021-05-17 19:05 B5.0
EOVSA 20210517 B5flare.png
data
Yes
2021-05-21 19:25 C5.0
EOVSA 20210521 C5flare.png
data
Yes
2021-05-22 16:10 C1.0
EOVSA 20210522 C1flare.png
data
Yes
2021-05-22 17:10 M1.0
EOVSA20210522 M1flare.png
data
Yes
2021-05-22 21:30 M1.4
EOVSA 20210522 M1.4flare.png
data
Yes
2021-05-22 23:11 C7.0
EOVSA 20210522 C7flare.png
data
Yes
2021-05-23 17:00 C2.0
EOVSA 20210523 C2flare.png
data
Yes
2021-05-27 22:00 C1.0 data No
2021-05-27 23:10 C7.0
EOVSA 20210527 C7flare.png
data
No
2021-05-28 22:30 C9.0
EOVSA 20210528 C9flare.png
data
No

Code to read spectrogram file

from __future__ import print_function
def rd_datfile(file):
    ''' Read EOVSA binary spectrogram file and return a dictionary with times 
        in Julian Date, frequencies in GHz, and cross-power data in sfu.
        
        Return Keys:
          'time'     Numpy array of nt times in JD format
          'fghz'     Numpy array of nf frequencies in GHz
          'data'     Numpy array of size [nf, nt] containing cross-power data
          
        Returns empty dictionary ({}) if file size is not compatible with inferred dimensions
    '''
    import struct
    import numpy as np
    def dims(file):
        # Determine time and frequency dimensions (assumes the file has fewer than 10000 times)
        f = open(file,'rb')
        tmp = f.read(83608)  # max 10000 times and 451 frequencies
        f.close()
        nbytes = len(tmp)
        tdat = struct.unpack(str(int(nbytes/8))+'d',tmp[:nbytes])
        nt = np.where(np.array(tdat) < 2400000.)[0]
        nf = np.where(np.array(tdat) < 1.1)[0] - nt[0]
        return nt[0], nf[0]
    nt, nf = dims(file)
    f = open(file,'rb')
    tmp = f.read(nt*8)
    times = struct.unpack(str(nt)+'d',tmp)
    tmp = f.read(nf*8)
    fghz = struct.unpack(str(nf)+'d',tmp)
    tmp = f.read()
    f.close()
    if len(tmp) != nf*nt*4:
        print('File size is incorrect for nt=',nt,'and nf=',nf)
        return {}
    data = np.array(struct.unpack(str(nt*nf)+'f',tmp)).reshape(nf,nt)
    return {'time':times, 'fghz':fghz, 'data':data}