def bin_corrtag(corrtag_list, xtype='XCORR', ytype='YCORR', times=None):
"""Bin corrtag event lists into a 2D image.
Filtering of unwanted events will be done before binning. SDQFLAGS with
the addition of PHA out of bounds (512), pixel outside active area (128),
and bad times (2048) will be discarded.
Parameters
----------
corrtag_list : list
list of datasets to combine into an image
xtype : str, optional
X-coordinate type
ytype : str, optional
Y-coordinate type
Returns
-------
image : np.ndarray
2D image of the corrtag
"""
if not isinstance(corrtag_list, list):
corrtag_list = [corrtag_list]
final_image = np.zeros((1024, 16384)).astype(np.float32)
for filename in corrtag_list:
image = np.zeros((1024, 16384)).astype(np.float32)
hdu = fits.open(filename)
events = hdu['events'].data
#-- No COS observation has data below ~923
data_index = np.where((hdu[1].data['time'] >= times[0]) &
(hdu[1].data['time'] <= times[1]))[0]
if not len(data_index):
return image
else:
events = events[data_index]
# Call for this is x_values, y_values, image to bin to, offset in x
# ccos.binevents(x, y, array, x_offset, dq, sdqflags, epsilon)
ccos.binevents(events[xtype].astype(np.float32),
events[ytype].astype(np.float32),
image,
0,
events['dq'],
0)
final_image += image
return final_image
def bin_corrtag(corrtag_list, xtype='XCORR', ytype='YCORR', times=None):
"""Bin corrtag event lists into a 2D image.
Filtering of unwanted events will be done before binning. SDQFLAGS with
the addition of PHA out of bounds (512), pixel outside active area (128),
and bad times (2048) will be discarded.
Parameters
----------
corrtag_list : list
list of datasets to combine into an image
xtype : str, optional
X-coordinate type
ytype : str, optional
Y-coordinate type
Returns
-------
image : np.ndarray
2D image of the corrtag
"""
if not isinstance(corrtag_list, list):
corrtag_list = [corrtag_list]
final_image = np.zeros((1024, 16384)).astype(np.float32)
for filename in corrtag_list:
image = np.zeros((1024, 16384)).astype(np.float32)
hdu = fits.open(filename)
events = hdu['events'].data
#-- No COS observation has data below ~923
data_index = np.where((hdu[1].data['time'] >= times[0])
& (hdu[1].data['time'] <= times[1]))[0]
if not len(data_index):
return image
else:
events = events[data_index]
# Call for this is x_values, y_values, image to bin to, offset in x
# ccos.binevents(x, y, array, x_offset, dq, sdqflags, epsilon)
ccos.binevents(events[xtype].astype(np.float32),
events[ytype].astype(np.float32), image, 0,
events['dq'], 0)
final_image += image
return final_image
def locate_stims(fits_file, start=0, increment=None):
print(fits_file)
DAYS_PER_SECOND = 1. / 60. / 60. / 24.
file_path, file_name = os.path.split(fits_file)
with fits.open(fits_file) as hdu:
exptime = hdu[1].header['exptime']
expstart = hdu[1].header['expstart']
segment = hdu[0].header['segment']
stim_info = {'rootname': hdu[0].header['rootname']}
try:
hdu[1].data
except:
yield stim_info
if not len(hdu[1].data):
yield stim_info
# If increment is not supplied, use the rates supplied by the detector
if not increment:
if exptime < 10:
increment = .03
elif exptime < 100:
increment = 1
else:
increment = 30
increment *= 4
stop = start + increment
# Iterate from start to stop, excluding final bin if smaller than increment
for sub_start in np.arange(start, exptime-increment, increment):
events = hdu['events'].data
#-- No COS observation has data below ~923
data_index = np.where((hdu[1].data['time'] >= sub_start) &
(hdu[1].data['time'] <= sub_start+increment))[0]
events = events[data_index]
# Call for this is x_values, y_values, image to bin to, offset in x
# ccos.binevents(x, y, array, x_offset, dq, sdqflags, epsilon)
im = np.zeros((1024, 16384)).astype(np.float32)
ccos.binevents(events['RAWX'].astype(np.float32),
events['RAWY'].astype(np.float32),
im,
0,
events['dq'],
0)
ABS_TIME = expstart + sub_start * DAYS_PER_SECOND
found_ul_x, found_ul_y = find_stims(im, segment, 'ul', brf_file)
found_lr_x, found_lr_y = find_stims(im, segment, 'lr', brf_file)
stim_info['time'] = round(sub_start, 5)
stim_info['abs_time'] = round(ABS_TIME, 5)
stim_info['stim1_x'] = round(found_ul_x, 3)
stim_info['stim1_y'] = round(found_ul_y, 3)
stim_info['stim2_x'] = round(found_lr_x, 3)
stim_info['stim2_y'] = round(found_lr_y, 3)
stim_info['counts'] = round(im.sum(), 7)
yield stim_info
def locate_stims(fits_file, start=0, increment=None, brf_file=None):
### change this to pull brf file from the header if not specified
if not brf_file:
brf_file = os.path.join(os.environ['lref'], 's7g1700el_brf.fits')
DAYS_PER_SECOND = 1. / 60. / 60. / 24.
file_path, file_name = os.path.split(fits_file)
with fits.open(fits_file) as hdu:
exptime = hdu[1].header['exptime']
expstart = hdu[1].header['expstart']
segment = hdu[0].header['segment']
stim_info = {'rootname': hdu[0].header['rootname'], 'segment': segment}
try:
hdu[1].data
except:
yield stim_info
raise StopIteration
if not len(hdu[1].data):
yield stim_info
raise StopIteration
# If increment is not supplied, use the rates supplied by the detector
if not increment:
if exptime < 10:
increment = .03
elif exptime < 100:
increment = 1
else:
increment = 30
increment *= 4
stop = start + increment
# Iterate from start to stop, excluding final bin if smaller than increment
start_times = np.arange(start, exptime - increment, increment)
if not len(start_times):
yield stim_info
for sub_start in start_times:
events = hdu['events'].data
#-- No COS observation has data below ~923
data_index = np.where((hdu[1].data['time'] >= sub_start) & (
hdu[1].data['time'] <= sub_start + increment))[0]
events = events[data_index]
# Call for this is x_values, y_values, image to bin to, offset in x
# ccos.binevents(x, y, array, x_offset, dq, sdqflags, epsilon)
im = np.zeros((1024, 16384)).astype(np.float32)
ccos.binevents(events['RAWX'].astype(np.float32),
events['RAWY'].astype(np.float32), im, 0,
events['dq'], 0)
ABS_TIME = expstart + sub_start * DAYS_PER_SECOND
found_ul_x, found_ul_y = find_stims(im, segment, 'ul', brf_file)
found_lr_x, found_lr_y = find_stims(im, segment, 'lr', brf_file)
stim_info['time'] = round(sub_start, 5)
stim_info['abs_time'] = round(ABS_TIME, 5)
stim_info['stim1_x'] = round(found_ul_x, 3)
stim_info['stim1_y'] = round(found_ul_y, 3)
stim_info['stim2_x'] = round(found_lr_x, 3)
stim_info['stim2_y'] = round(found_lr_y, 3)
stim_info['counts'] = round(im.sum(), 7)
yield stim_info