2020

List of EOVSA Flares with Spectrogram Data

June

Date	Time (UT)	GOES Class	Position	Spectrogram	STIX Coverage	AIA Movie	EOVSA Images	Comment
2020-06-07	21:43	B6.4		plot data	[No]	[AIA]

October

Date	Time (UT)	GOES Class	Position	Spectrogram	STIX Coverage	AIA Movie	EOVSA Images	Comment
2020-10-18	16:33	B2.0		plot data	[No]	[AIA]
2020-10-28	22:10	C1.5		plot data	[No]	[AIA]
2020-10-29	16:01	C1.3	S17 W41	plot data	[No]	[AIA]
2020-10-29	20:16	C1.5	S17 W43	plot data	[No]	[AIA]

November

Date	Time (UT)	GOES Class	Position	Spectrogram	STIX Coverage	AIA Movie	EOVSA Images	Comment
2020-11-05	20:53	B7.0		plot data	[No]	[AIA]
2020-11-05	21:55	B9.0		plot data	[No]	[AIA]
2020-11-05	22:20	C1.0		plot data	[No]	[AIA]
2020-11-05	22:40	B7.0		plot data	[No]	[AIA]
2020-11-06	16:13	B4.6		plot data	[No]	[AIA]
2020-11-06	21:47	B2.8		plot data	[No]	[AIA]
2020-11-08	16:24	B3.4		plot data	[No]	[AIA]
2020-11-08	19:26	B4.4		plot data	[No]	[AIA]
2020-11-10	19:44	C1.3	S24 W20	plot data	[No]	[AIA]
2020-11-20	17:00	C1.9	S22 E39	plot data	[No]	[AIA]

Python code to read plotdata 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[nt[0]:] > 18, tdat[nt[0]:] < 1))[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 plotdata 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[nt[0]:*] gt 18 or tmp[nt[0]:*] lt 1))[0]
    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