Recent Flare List (2021-)

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	data	Yes
2021-04-17	16:46	B9.0	data	Yes
2021-04-19	23:36	M1.0	data	No
2021-05-05	22:30	B5.0	data	Yes
2021-05-07	19:00	M4.0	data	Yes
2021-05-08	18:30	C9.0	data	Yes
2021-05-09	13:55	C4.0	data	Yes
2021-05-17	19:05	B5.0	data	Yes
2021-05-21	19:25	C5.0	data	Yes
2021-05-22	16:10	C1.0	data	Yes
2021-05-22	17:10	M1.0	data	Yes
2021-05-22	21:30	M1.4	data	Yes
2021-05-22	23:11	C7.0	data	Yes
2021-05-23	17:00	C2.0	data	Yes
2021-05-27	22:00	C1.0	data	No
2021-05-27	23:10	C7.0	data	No
2021-05-28	22:30	C9.0	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}