def main(source_data='.jp2',
time_range=TimeRange('2011/10/01 09:45:00', '2011/10/01 10:15:59'),
algorithm='hough', feed_directory='~/Data/eitwave/jp2/20111001_jp2/',
use_pickle=None,diff_type='running',data_savedir=None):
'''
This is the main executable for the Automated EUV Wave Analysis and Reduction (AWARE)
code. The procedure is as follows:
- Query the HEK to find whether any flares were observed in SDO/AIA 211A during the input time range
- If yes, then read in (or download) a sequence of solar images corresponding to the time range
- Transform these images from Helioprojective Coordinates to Heliographic Coordinates,
with the origin centered at the flare origin
- Create a sequence of difference maps from these transformed maps
- Use a threshold method to create a binary map from the the difference maps.
- Apply the Hough Transform to the binary map to search for strong lines in the image
- Use the results of the Hough Transform to detect whether an EUV wave is present
- Fit an appropriate function (e.g. Gaussian) to the detected wavefront as a function of longitude
- Record fit results and return data products
Parameters
----------
source_data : string
description of the type of data being input. Allowed is '.jp2', 'fits', or 'test'.
will look for helioviewer JP2 files, FITS files, or load the test data respectively
time_range : a TimeRange object
time range within which to search for EUV waves
feed_directory : string
A directory containing data files to be analysed. If set, AWARE will assume data files are
already download and will search in this directory instead. Assumes that all files in the
directory with the appropriate extension (e.g. .jp2, .fits) are relevant to the flare detection.
use_pickle : string
BUGGED - currently not supported, always set to None
diff_type : string
The type of image differencing to use. Allowed values are 'running' or 'base'. Default is 'running'
Will perform either running differencing or base differencing on the image sequence.
data_savedir : string
directory in which to save downloaded jp2 files from Helioviewer. If None, then AWARE will construct a directory
based on the start time of the query.
Returns
-------
Outputs a pickle file containing the following data products (in order):
1) a list of maps modelling the detected wavefront, transformed back to original HPC coordinates
'''
if feed_directory != None:
feed_directory = os.path.expanduser(feed_directory)
#Check which type of data is being analysed, and establish the directory to store downloaded files,
#if appropriate
if source_data == 'test':
maps = test_wave2d()
elif source_data == '.jp2' and data_savedir == None:
data_savedir = '~/aware_data/jp2/' + time_range.start().strftime('%Y%m%d_%H%M')
elif source_data == '.fits' and data_savedir == None:
data_savedir = '~/aware_data/fits/' + time_range.start().strftime('%Y%m%d_%H%M')
if not os.path.exists(os.path.expanduser(data_savedir)):
os.makedirs(os.path.expanduser(data_savedir))
# Query the HEK to see whether there were any flares during the time range specified
# Concentrate on the AIA 211A channel as it has clearest observations of global waves
client = hek.HEKClient()
hek_result = client.query(hek.attrs.Time(time_range.t1, time_range.t2),
hek.attrs.EventType('FL'),hek.attrs.OBS.ChannelID == '211')
if hek_result is None:
# if no flares found, no analysis possible. Return
print 'No flares found in HEK database during specified time range.'
print 'No analysis possible. Returning.'
return None
# Otherwise, we have found at least one flare
print('Number of flares found = ' + str(len(hek_result)))
#assume the first result of the HEK query has the correct information
for flare in hek_result[0:1]:
if feed_directory is None:
print('Acquiring data for flare')
filelist = aware_utils.acquire_data(data_savedir, source_data,
flare)
else:
# Assumes that the necessary files are already present
filelist = aware_utils.listdir_fullpath(feed_directory,
filetype = source_data)
#filter to only grab the data files with the source_data extn in the directory
#this looks like duplication of listdir_fullpath
files_tmp = []
for f in filelist:
if f.endswith(source_data):
files_tmp.append(f)
files = files_tmp
# reduce the number of files to those that happen after the flare has
# started
files = []
for f in files_tmp:
fhv = f.split(os.sep)[-1]
if aware_utils.hv_filename2datetime(fhv) > \
parse_time(flare['event_starttime']):
files.append(f)
print('Number of files :' + str(len(files)))
if len(files) == 0:
print('No files found. Returning.')
return None
# Define the transform parameters
params = aware_utils.params(flare)
# read in files and accumulate them
if use_pickle != None:
# load in a pickle file of the data
pfile = open(feed_directory + use_pickle, 'rb')
a = pickle.load(pfile)
maps = a[0]
new_maps = a[1]
diffs = a[2]
pfile.close()
else:
maps = aware_utils.accumulate(files[6:30], accum=1, nsuper=4,
verbose=True)
#temporary fix for exposure control and S/N changes
long_maps = []
for m in maps:
if m.exposure_time > 2.0:
long_maps.append(m)
maps=long_maps
# Unravel the maps
new_maps = aware_utils.map_unravel(maps, params, verbose=True)
#return new_maps
#sometimes unravelling maps leads to slight variations in the unraveled
#image dimensions. check dimensions of maps and resample to dimensions
#of first image in sequence if need be.
#new_maps[0].peek()
new_maps = aware_utils.check_dims(new_maps)
# calculate the differences
if diff_type == 'base':
diffs=aware_utils.map_basediff(new_maps)
else:
diffs = aware_utils.map_diff(new_maps)
#generate persistence maps - currently bugged, so skip this step
#persistence_maps = eitwaveutils.map_persistence(diffs)
persistence_maps=[]
#determine the threshold to apply to the difference maps.
#diffs > diff_thresh will be 1, otherwise 0.
threshold_maps = aware_utils.map_threshold(new_maps, factor=0.2)
#return threshold_maps
# transform difference maps into binary maps
binary_maps = aware_utils.map_binary(diffs, threshold_maps)
if algorithm == 'hough':
# detection based on the hough transform
detection = aware_utils.hough_detect(binary_maps, vote_thresh=10)
elif algorithm == 'prob_hough':
# detection based on the probabilistic hough transform. Takes the
# keywords of the probabilistic hough transform - see the documentation
# of skimage.transform.probabilistic_hough (scikit-image.org)
detection = aware_utils.prob_hough_detect(binary_maps, threshold=10)
# Remove areas that are too small or that don't have enough detections
detection = aware_utils.cleanup(detection,
size_thresh=50,
inv_thresh=8)
detection_maps = copy.deepcopy(binary_maps)
for i in range(0,len(detection)):
detection_maps[i].data = detection[i]
#If there is anything left in 'detection', fit a function to the original
#diffmaps in the region defined by 'detection'. Currently fits a
#Gaussian in the y-direction for each x
#use 'detection' to guess starting fit parameters.
#get just the positive elements of the difference map. Perform fitting on
#these positive diffmaps.
posdiffs = copy.deepcopy(diffs)
for i in range(0, len(diffs)):
temp = diffs[i].data < 0
posdiffs[i].data[temp] = 0
#fit a function to the difference maps in the cases where there has been a
#detection
fitparams, wavefront = aware_utils.fit_wavefront(posdiffs, detection)
#transform the detected model wavefront back into heliocentric coordinates so it can be overlayed
wavefront_hc = aware_utils.map_reravel(wavefront,params,verbose=True)
#strip out the velocity information from the wavefront fitting
velocity = aware_utils.wavefront_velocity(fitparams)
#strip out the position and width information from the wavefront fitting
pos_width = aware_utils.wavefront_position_and_width(fitparams)
#now save products we have created in a pickle file for future reference
#Will save output in ~/aware_results
extn=time_range.start().strftime('%Y%m%d_%H%M')
save_path=os.path.expanduser('~/aware_results/')
save_file='aware_results_' + extn + '.pickle'
if not os.path.exists(save_path):
os.makedirs(save_path)
output=open(save_path + save_file,'wb')
print 'Saving result products to: '+ save_path + save_file
pickle.dump(wavefront_hc,output)
output.close()
#visualize the model wavefront
visualize(wavefront_hc)
return maps, new_maps, diffs, threshold_maps, binary_maps, detection_maps, wavefront, velocity, pos_width, persistence_maps, wavefront_hc
def main(source_data='.jp2',
time_range=TimeRange('2011/10/01 09:45:00', '2011/10/01 10:15:59'),
algorithm='hough',
feed_directory='~/Data/eitwave/jp2/20111001_jp2/',
use_pickle=None,
diff_type='running',
data_savedir=None):
'''
This is the main executable for the Automated EUV Wave Analysis and Reduction (AWARE)
code. The procedure is as follows:
- Query the HEK to find whether any flares were observed in SDO/AIA 211A during the input time range
- If yes, then read in (or download) a sequence of solar images corresponding to the time range
- Transform these images from Helioprojective Coordinates to Heliographic Coordinates,
with the origin centered at the flare origin
- Create a sequence of difference maps from these transformed maps
- Use a threshold method to create a binary map from the the difference maps.
- Apply the Hough Transform to the binary map to search for strong lines in the image
- Use the results of the Hough Transform to detect whether an EUV wave is present
- Fit an appropriate function (e.g. Gaussian) to the detected wavefront as a function of longitude
- Record fit results and return data products
Parameters
----------
source_data : string
description of the type of data being input. Allowed is '.jp2', 'fits', or 'test'.
will look for helioviewer JP2 files, FITS files, or load the test data respectively
time_range : a TimeRange object
time range within which to search for EUV waves
feed_directory : string
A directory containing data files to be analysed. If set, AWARE will assume data files are
already download and will search in this directory instead. Assumes that all files in the
directory with the appropriate extension (e.g. .jp2, .fits) are relevant to the flare detection.
use_pickle : string
BUGGED - currently not supported, always set to None
diff_type : string
The type of image differencing to use. Allowed values are 'running' or 'base'. Default is 'running'
Will perform either running differencing or base differencing on the image sequence.
data_savedir : string
directory in which to save downloaded jp2 files from Helioviewer. If None, then AWARE will construct a directory
based on the start time of the query.
Returns
-------
Outputs a pickle file containing the following data products (in order):
1) a list of maps modelling the detected wavefront, transformed back to original HPC coordinates
'''
if feed_directory != None:
feed_directory = os.path.expanduser(feed_directory)
#Check which type of data is being analysed, and establish the directory to store downloaded files,
#if appropriate
if source_data == 'test':
maps = test_wave2d()
elif source_data == '.jp2' and data_savedir == None:
data_savedir = '~/aware_data/jp2/' + time_range.start().strftime(
'%Y%m%d_%H%M')
elif source_data == '.fits' and data_savedir == None:
data_savedir = '~/aware_data/fits/' + time_range.start().strftime(
'%Y%m%d_%H%M')
if not os.path.exists(os.path.expanduser(data_savedir)):
os.makedirs(os.path.expanduser(data_savedir))
# Query the HEK to see whether there were any flares during the time range specified
# Concentrate on the AIA 211A channel as it has clearest observations of global waves
client = hek.HEKClient()
hek_result = client.query(hek.attrs.Time(time_range.t1, time_range.t2),
hek.attrs.EventType('FL'),
hek.attrs.OBS.ChannelID == '211')
if hek_result is None:
# if no flares found, no analysis possible. Return
print 'No flares found in HEK database during specified time range.'
print 'No analysis possible. Returning.'
return None
# Otherwise, we have found at least one flare
print('Number of flares found = ' + str(len(hek_result)))
#assume the first result of the HEK query has the correct information
for flare in hek_result[0:1]:
if feed_directory is None:
print('Acquiring data for flare')
filelist = aware_utils.acquire_data(data_savedir, source_data,
flare)
else:
# Assumes that the necessary files are already present
filelist = aware_utils.listdir_fullpath(feed_directory,
filetype=source_data)
#filter to only grab the data files with the source_data extn in the directory
#this looks like duplication of listdir_fullpath
files_tmp = []
for f in filelist:
if f.endswith(source_data):
files_tmp.append(f)
files = files_tmp
# reduce the number of files to those that happen after the flare has
# started
files = []
if source_data == '.jp2':
for f in files_tmp:
fhv = f.split(os.sep)[-1]
if aware_utils.hv_filename2datetime(fhv) > \
parse_time(flare['event_starttime']):
files.append(f)
print('Number of files :' + str(len(files)))
if len(files) == 0:
print('No files found. Returning.')
return None
else:
files = files_tmp
# Define the transform parameters
params = aware_utils.params(flare)
# read in files and accumulate them
if use_pickle != None:
# load in a pickle file of the data
pfile = open(feed_directory + use_pickle, 'rb')
a = pickle.load(pfile)
maps = a[0]
new_maps = a[1]
diffs = a[2]
pfile.close()
else:
maps = aware_utils.accumulate(files[6:30],
accum=1,
nsuper=4,
verbose=True)
#temporary fix for exposure control and S/N changes
long_maps = []
for m in maps:
if m.exposure_time > 2.0:
long_maps.append(m)
maps = long_maps
# Unravel the maps
new_maps = aware_utils.map_unravel(maps, params, verbose=True)
#return new_maps
#sometimes unravelling maps leads to slight variations in the unraveled
#image dimensions. check dimensions of maps and resample to dimensions
#of first image in sequence if need be.
#new_maps[0].peek()
new_maps = aware_utils.check_dims(new_maps)
# calculate the differences
if diff_type == 'base':
diffs = aware_utils.map_basediff(new_maps)
else:
diffs = aware_utils.map_diff(new_maps)
#generate persistence maps - currently bugged, so skip this step
#persistence_maps = eitwaveutils.map_persistence(diffs)
persistence_maps = []
#determine the threshold to apply to the difference maps.
#diffs > diff_thresh will be 1, otherwise 0.
threshold_maps = aware_utils.map_threshold(new_maps, factor=0.2)
#return threshold_maps
# transform difference maps into binary maps
binary_maps = aware_utils.map_binary(diffs, threshold_maps)
if algorithm == 'hough':
# detection based on the hough transform
detection = aware_utils.hough_detect(binary_maps, vote_thresh=10)
elif algorithm == 'prob_hough':
# detection based on the probabilistic hough transform. Takes the
# keywords of the probabilistic hough transform - see the documentation
# of skimage.transform.probabilistic_hough (scikit-image.org)
detection = aware_utils.prob_hough_detect(binary_maps,
threshold=10)
# Remove areas that are too small or that don't have enough detections
detection = aware_utils.cleanup(detection,
size_thresh=50,
inv_thresh=8)
detection_maps = copy.deepcopy(binary_maps)
for i in range(0, len(detection)):
detection_maps[i].data = detection[i]
#If there is anything left in 'detection', fit a function to the original
#diffmaps in the region defined by 'detection'. Currently fits a
#Gaussian in the y-direction for each x
#use 'detection' to guess starting fit parameters.
#get just the positive elements of the difference map. Perform fitting on
#these positive diffmaps.
posdiffs = copy.deepcopy(diffs)
for i in range(0, len(diffs)):
temp = diffs[i].data < 0
posdiffs[i].data[temp] = 0
#fit a function to the difference maps in the cases where there has been a
#detection
fitparams, wavefront = aware_utils.fit_wavefront(posdiffs, detection)
#transform the detected model wavefront back into heliocentric coordinates so it can be overlayed
wavefront_hc = aware_utils.map_reravel(wavefront, params, verbose=True)
#strip out the velocity information from the wavefront fitting
velocity = aware_utils.wavefront_velocity(fitparams)
#strip out the position and width information from the wavefront fitting
pos_width = aware_utils.wavefront_position_and_width(fitparams)
#now save products we have created in a pickle file for future reference
#Will save output in ~/aware_results
extn = time_range.start().strftime('%Y%m%d_%H%M')
save_path = os.path.expanduser('~/aware_results/')
save_file = 'aware_results_' + extn + '.pickle'
if not os.path.exists(save_path):
os.makedirs(save_path)
output = open(save_path + save_file, 'wb')
print 'Saving result products to: ' + save_path + save_file
pickle.dump(wavefront_hc, output)
output.close()
#visualize the model wavefront
visualize(wavefront_hc)
return maps, new_maps, diffs, threshold_maps, binary_maps, detection_maps, wavefront, velocity, pos_width, persistence_maps, wavefront_hc
def make_euvwave_colour_map(transformed, mc, example, info, result, thresh=15.0):
# two inputs
# transformed - this is the cleaned running difference persistence map (RDPI) cube
# mc - this is the original map cube
startind = {
"previous1": 9,
"corpita_fig4": 1,
"corpita_fig6": 0,
"corpita_fig8a": 3,
"corpita_fig8e": 3,
"corpita_fig7": 3,
"simulate": 1,
}
n = startind[example]
# mask = np.zeroes_like(transformed[0])
mask = deepcopy(transformed[0])
mask.data = mask.data * 0.0
counter = 1
tims = []
for h in transformed[n:]:
ind = np.where(h.data > thresh)
mask.data[ind] = counter
if example == "simulate":
tims.append(h.meta["date_obs"][11:19])
else:
tims.append(h.meta["t_obs"][11:19])
counter = counter + 1
ind2 = np.where(mask.data == 0.0)
mask.data[ind2] = np.nan
cbar_tickvals = np.int32(np.arange(1, counter - 1, 3))
# cbar_ticklabels = (np.array(cbar_tickvals) + n).tolist()
# timescale = accum * 12
# plt.figure(7)
# now do some tricks to plot an arc on the top.
params = aware_utils.params(result[info["result"]])
arcmask = aware.unravel([mc[5]], params)
arcmask[0].data[:] = 0.0
arcmask[0].data[:, 2] = 1.0
arcmap = aware_utils.map_reravel([arcmask[0]], params)
arcmap2 = arcmap[0]
inds = np.where(arcmap2.data == 0.0)
arcmap2.data[inds] = np.nan
# now make the composite map for the plot!
compmap = sunpy.map.Map(mc[5], mask, composite=True) # arcmap2,composite=True)
# plt.clim([0,20000])
compmap.set_colors(1, "nipy_spectral")
compmap.set_colors(0, "gray_r")
compmap.set_alpha(1, 0.8)
# compmap.set_colors(2,'binary')
# compmap.set_alpha(2,0.6)
figure = plt.figure()
axes = figure.add_subplot(111)
ret = compmap.plot(axes=axes)
compmap.draw_limb()
compmap.draw_grid()
# plt.text(result[info[example]['result']]['hpc_x'],result[info[example]['result']]['hpc_y'],'x',fontsize=12,color='r')
if not example == "simulate":
plt.plot(result[info[example]["result"]]["hpc_x"], result[info[example]["result"]]["hpc_y"], "ro")
plt.clim([0, 10000])
cbar = figure.colorbar(ret[1], ticks=cbar_tickvals) # [2,4,6,8,10,12,14,16,18,20,22,24,26])
# print cbar_tickvals
# print cbar_ticklabels
# print len(tims)
tims2 = []
# need to get labels right
for u in cbar_tickvals:
tims2.append(tims[u])
cbar.ax.set_yticklabels(tims2) # [cbar_ticklabels[:])# (tims[0],tims[2],tims[4],tims[6],tims[8],tims[10]
# ,tims[12],tims[14],tims[16],tims[18],tims[20], tims[22],tims[24]))
plt.title("AWARE detection " + info["tr"].start.split(" ")[0])
plt.savefig("euvwave_contour_map_" + example + ".eps")
figure.show()
