if pixel_parameter[PIXEL_PARAMETER.c.high_prob_bin] is None:
pixel_histogram = connection.execute(select([PIXEL_HISTOGRAM]).where(
and_(PIXEL_HISTOGRAM.c.pxresult_id == row[PIXEL_RESULT.c.pxresult_id],
PIXEL_HISTOGRAM.c.pxparameter_id == pixel_parameter[PIXEL_PARAMETER.c.pxparameter_id],
PIXEL_HISTOGRAM.c.hist_value ==
select([func.max(PIXEL_HISTOGRAM.c.hist_value)]).
where(and_(PIXEL_HISTOGRAM.c.pxresult_id == row[PIXEL_RESULT.c.pxresult_id],
PIXEL_HISTOGRAM.c.pxparameter_id == pixel_parameter[PIXEL_PARAMETER.c.pxparameter_id]))))).first()
if pixel_histogram is not None:
array_highest_prob_bin_v[row__y, row__x, index] = pixel_histogram[PIXEL_HISTOGRAM.c.x_axis]
# Update the database
connection.execute(PIXEL_PARAMETER.update().
where(PIXEL_PARAMETER.c.pxparameter_id == pixel_parameter[PIXEL_PARAMETER.c.pxparameter_id]).
values(high_prob_bin = pixel_histogram[PIXEL_HISTOGRAM.c.x_axis]))
else:
array_highest_prob_bin_v[row__y, row__x, index] = pixel_parameter[PIXEL_PARAMETER.c.high_prob_bin]
# Commit any changes
transaction.commit()
name_count = 0
utc_now = datetime.utcnow().strftime('%Y-%m-%dT%H:%m:%S')
for name in IMAGE_NAMES:
hdu = pyfits.PrimaryHDU(array_best_fit[:,:,name_count])
hdu_list = pyfits.HDUList([hdu])
# Write the header
hdu_list[0].header.update('MAGPHYST', name, 'MAGPHYS Parameter')
PIXEL_HISTOGRAM.c.pxresult_id == row[
PIXEL_RESULT.c.pxresult_id],
PIXEL_HISTOGRAM.c.pxparameter_id ==
pixel_parameter[
PIXEL_PARAMETER.c.pxparameter_id])
)))).first()
if pixel_histogram is not None:
array_highest_prob_bin_v[row__y, row__x,
index] = pixel_histogram[
PIXEL_HISTOGRAM.c.x_axis]
# Update the database
connection.execute(PIXEL_PARAMETER.update().where(
PIXEL_PARAMETER.c.pxparameter_id ==
pixel_parameter[
PIXEL_PARAMETER.c.pxparameter_id]).values(
high_prob_bin=pixel_histogram[
PIXEL_HISTOGRAM.c.x_axis]))
else:
array_highest_prob_bin_v[
row__y, row__x, index] = pixel_parameter[
PIXEL_PARAMETER.c.high_prob_bin]
# Commit any changes
transaction.commit()
name_count = 0
utc_now = datetime.utcnow().strftime('%Y-%m-%dT%H:%m:%S')
for name in IMAGE_NAMES:
hdu = pyfits.PrimaryHDU(array_best_fit[:, :, name_count])
