Recent Flare List (2021-)

List of EOVSA Flares with Spectrogram Data

Date	Time (UT)	GOES Class	Spectrogram	STIX Coverage
2021-04-17	16:46	B9.0	plot data	Yes
2021-04-19	23:36	M1.0	plot data	No
2021-05-05	22:30	B5.0	plot data	Yes
2021-05-07	19:00	M4.0	plot data	Yes
2021-05-08	18:30	C9.0	plot data	Yes
2021-05-09	13:55	C4.0	plot data	Yes
2021-05-17	19:05	B5.0	plot data	Yes
2021-05-21	19:25	C5.0	plot data	Yes
2021-05-22	16:10	C1.0	plot data	Yes
2021-05-22	17:10	M1.0	plot data	Yes
2021-05-22	21:30	M1.4	plot data	Yes
2021-05-22	23:11	C7.0	plot data	Yes
2021-05-23	17:00	C2.0	plot data	Yes
2021-05-27	22:00	C1.0	plot data	No
2021-05-27	23:10	C7.0	plot data	No
2021-05-28	22:30	C9.0	plot data	No
2021-05-28	22:30	C9.0	plot data	No
2021-07-03	14:27	X1.5	plot data	No
2021-07-06	19:15	C1.1	plot data	No
2021-07-06	20:58	C1.2	plot data	No
2021-07-06	21:49	C1.1	plot data	No
2021-07-09	17:20	C4.7	plot data	No
2021-08-20	15:55	C3.0	plot data	Yes
2021-08-20	21:45	C1.4	plot data	Yes
2021-08-21	18:41	B3.9	plot data	Yes
2021-08-21	21:57	B3.9	plot data	Yes
2021-08-21	22:29	B4.7	plot data	Yes
2021-08-26	23:20	C3.9	plot data	Yes?

Python code to read plot data 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 = np.array(struct.unpack(str(int(nbytes/8))+'d',tmp[:nbytes]))
        nt = np.where(tdat < 2400000.)[0]
        nf = np.where(np.logical_or(tdat > 1e10, abs(tdat) < 1e-10))[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}

IDL code to read plot data file

function rd_datfile,file
    ; Read EOVSA binary spectrogram file and return a structure with times 
    ; in Julian Date, frequencies in GHz, and cross-power data in sfu.
    ;    
    ; Return tags:
    ;      'time'     Array of nt times in JD format
    ;      'fghz'     Array of nf frequencies in GHz
    ;      'data'     Array of size [nf, nt] containing cross-power data
    ;
    ;    Returns empty dictionary ({}) if file size is not compatible with inferred dimensions
    openr,/get_lun,lun,file
    tmp = dblarr(10451)
    readu,lun,tmp
    free_lun,lun
    nt = (where(tmp lt 2400000.))[0]
    nf = (where(tmp gt 1e10 or abs(tmp) lt 1e-10))[0] - nt
    times = dblarr(nt)
    fghz = dblarr(nf)
    data = fltarr(nt, nf)
    openr,/get_lun,lun,file
    readu,lun,times
    readu,lun,fghz
    readu,lun,data
    free_lun,lun
    data = create_struct('time',times,'fghz',fghz,'data',transpose(data))
    return, data
    end