Recent Flare List (2021-)

List of EOVSA Flares with Spectrogram Data

April 2021

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

May 2021

Date	Time (UT)	GOES Class	Spectrogram	STIX Coverage
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

July 2021

Date	Time (UT)	GOES Class	Spectrogram	STIX Coverage
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

August 2021

Date	Time (UT)	GOES Class	Spectrogram	STIX Coverage
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	18:12	C3.0	plot data	Yes
2021-08-26	23:20	C3.9	plot data	Yes
2021-08-27	21:00	C7.3	plot data	Yes
2021-08-29	17:21	C2.9	plot data	Yes (occulted?)
2021-08-30	16:12	C2.0	plot data	Yes (occulted)
2021-08-30	18:23	B7.0	plot data	Yes (occulted)
2021-08-30	20:47	B8.0	plot data	Yes (occulted)
2021-08-31	14:48	B2.5	plot data	No
2021-08-31	19:08	B7.1	plot data	No

September 2021

Date	Time (UT)	GOES Class	Spectrogram	STIX Coverage
2021-09-08	17:24	C8.3	plot data	Yes
2021-09-20	all day	low B	plot data plot data plot data	Yes
2021-09-21	20:24	B5.1	plot data	Yes
2021-09-22	20:12	C1.1	plot data	Yes
2021-09-23	15:24	M1.9	plot data	Yes
2021-09-23	20:40	C1.0	plot data	Yes
2021-09-24	16:45	none?	plot data	Yes
2021-09-24	21:16	B2.0	plot data	Yes

October 2021

Date	Time (UT)	GOES Class	Spectrogram	STIX Coverage
2021-10-11	19:21	B2	plot data	Yes
2021-10-12	15:57	B6	plot data	Yes
2021-10-28	15:26	X1	plot data	Yes

November 2021

Date	Time (UT)	GOES Class	Spectrogram	STIX Coverage
2021-11-01	20:43	C2.1	plot data	Yes
2021-11-01	21:11	C4.0	plot data	Yes
2021-11-01	23:39	C4.5	plot data	Yes
2021-11-03	21:14	C5.2	plot data	Yes

December 2021

Date	Time (UT)	GOES Class	Spectrogram	STIX Coverage	Comment
2021-12-12	21:13	C1.2	plot data	Yes
2021-12-14	20:33	C1.6	plot data	Yes
2021-12-31	19:42	C8.1	plot data	Yes	Surprisingly weak in microwaves considering it is a C8 in soft X-rays.

January 2022

Date	Time (UT)	GOES Class	Spectrogram	STIX Coverage
2022-01-18	17:38	M1.2	plot data	Yes
2022-01-28	19:31	C2.8	plot data	Yes
2022-01-28	20:10-20:50	B2-B6	plot data	Yes
2022-01-29	23:00	M1	plot data	Yes

February 2022

Date	Time (UT)	GOES Class	Spectrogram	STIX Coverage
2022-02-03	20:42	C1.3	plot data	Yes
2022-02-05	17:00	C2.1	plot data	Yes
2022-02-08	21:40	C5.1	plot data	Yes

March 2022

Date	Time (UT)	GOES Class	Spectrogram	STIX Coverage	Comment
2022-03-30	17:30	X1.4	plot data	Yes	Interesting oscillations

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[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 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[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