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Merge pull request #236 from simontorres/modify_debug_mode
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Modify debug mode
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Simon Torres committed Oct 4, 2018
2 parents f6ccc9a + 89f72ca commit e5b41b2
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Showing 5 changed files with 142 additions and 43 deletions.
108 changes: 87 additions & 21 deletions goodman_pipeline/core/core.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -756,14 +756,23 @@ def extract_fractional_pixel(ccd, target_trace, target_stddev, extraction_width,

if apnum1 is None:
# TODO (simon): add secondary targets

apnum1 = '{:d} {:d} {:.2f} {:.2f}'.format(1,
1,
low_limit,
high_limit)

ccd.header.set('APNUM1',
value=apnum1,
comment="Aperture in first column")

ccd.header.set('GSP_EXTR',
value="{:.2f} {:.2f}".format(low_limit, high_limit))

log.info("Extraction aperture in first column: {:s}".format(
ccd.header['GSP_EXTR']))


column_sum = fractional_sum(data=ccd.data,
index=i,
low_limit=low_limit,
Expand Down Expand Up @@ -1189,10 +1198,16 @@ def identify_targets(ccd, nfind=3, plots=False):
order = int(round(float(slit_size) / (0.15 * serial_binning)))

if plots:
plt.title(ccd.header['GSP_FNAM'])
plt.imshow(ccd.data, clim=(30, 250))
plt.xlabel('Dispersion Axis (x)')
plt.ylabel('Spatial Axis (y)')
z1 = np.mean(ccd.data) - 0.5 * np.std(ccd.data)
z2 = np.median(ccd.data) + np.std(ccd.data)
fig, ax = plt.subplots()
fig.canvas.set_window_title(ccd.header['GSP_FNAM'])

ax.set_title(ccd.header['GSP_FNAM'])
ax.imshow(ccd.data, clim=(z1, z2))
ax.set_xlabel('Dispersion Axis (x)')
ax.set_ylabel('Spatial Axis (y)')
plt.tight_layout()
plt.show()

median_profile = np.median(ccd.data, axis=1)
Expand All @@ -1219,14 +1234,31 @@ def identify_targets(ccd, nfind=3, plots=False):
fitted_background = linear_fitter(linear_model, new_x_axis, new_profile)

if plots:
plt.title('Background Fitting Model Defined')
plt.plot(median_profile, color='k')
plt.plot(linear_model(range(ccd.data.shape[0])), color='r')
fig, ax = plt.subplots()
fig.canvas.set_window_title(ccd.header['GSP_FNAM'])

ax.set_title('Background Fitting Model Defined')
ax.plot(median_profile, color='k', label='Median profile')
ax.plot(linear_model(range(ccd.data.shape[0])),
color='r',
label='Background Linear Model')
ax.set_xlabel("Spatial Axis (Pixels)")
ax.set_ylabel("Median Intensity")
ax.legend(loc='best')
plt.tight_layout()
plt.show()

plt.title('Background Fitted Model')
plt.plot(median_profile, color='k')
plt.plot(fitted_background(range(ccd.data.shape[0])), color='r')
fig, ax = plt.subplots()
fig.canvas.set_window_title(ccd.header['GSP_FNAM'])
ax.set_title('Background Fitted Model')
ax.plot(median_profile, color='k', label='Median profile')
ax.plot(fitted_background(range(ccd.data.shape[0])),
color='r',
label='Fitted Background Linear Model')
ax.set_xlabel("Spatial Axis (Pixels)")
ax.set_ylabel("Median Intensity")
ax.legend(loc='best')
plt.tight_layout()
plt.show()

# Removing Background
Expand Down Expand Up @@ -1264,20 +1296,26 @@ def identify_targets(ccd, nfind=3, plots=False):
if plots:
plt.ioff()
plt.close()

fig, ax = plt.subplots()
fig.canvas.set_window_title(ccd.header['GSP_FNAM'])
# if plt.isinteractive():
# plt.ioff()
plt.title('Median Along Dispersion Axis (spatial)')
plt.plot(background_subtracted, label='Background Subtracted Data')
plt.plot(new_x_axis,
ax.set_title('Median Along Dispersion Axis (spatial)')
ax.plot(background_subtracted, label='Background Subtracted Data')
ax.plot(new_x_axis,
clipped_final_profile,
color='r',
label='Sigma Clip Data')
label='Sigma Clipped Data')

plt.axhline(background_level, color='m', label='Min-Max Difference')
ax.axhline(background_level, color='m', label='Min-Max Difference')
# plt.plot(final_profile, color='r')
# plt.plot(median_profile)
# plt.plot(background_array)
ax.set_xlabel("Spatial Axis (Pixels)")
ax.set_ylabel("Median Intensity")
plt.legend(loc='best')
plt.tight_layout()
if plt.isinteractive():
plt.draw()
plt.pause(5)
Expand All @@ -1287,6 +1325,9 @@ def identify_targets(ccd, nfind=3, plots=False):
# Identify targets
# Now that the profile is flat it should be easier to identify targets.

# apply median filter
final_profile = signal.medfilt(final_profile, kernel_size=1)

filtered_profile = np.where(np.abs(
final_profile > final_profile.min() + 0.03 * final_profile.max()),
final_profile,
Expand All @@ -1302,6 +1343,8 @@ def identify_targets(ccd, nfind=3, plots=False):
# convert the list to array
filtered_profile = np.array(none_to_zero_prof)

order *=2

# find the peaks
peaks = signal.argrelmax(filtered_profile, axis=0, order=order)[0]

Expand Down Expand Up @@ -1329,10 +1372,18 @@ def identify_targets(ccd, nfind=3, plots=False):

if plots:
plt.ioff()
plt.plot(final_profile)
plt.axhline(_upper_limit, color='g')

fig, ax = plt.subplots()
fig.canvas.set_window_title(ccd.header['GSP_FNAM'])

ax.plot(final_profile, label='Background subtracted profile')
ax.axhline(_upper_limit, color='g', label='Upper limit')
for peak in selected_peaks:
plt.axvline(peak, color='r')
ax.axvline(peak, color='r', label='Peak location')
ax.set_xlabel("Spatial Axis (Pixels)")
ax.set_ylabel("Background subtracted median intensity")
ax.legend(loc='best')
plt.tight_layout()
plt.show()

# build the model to return
Expand Down Expand Up @@ -1384,9 +1435,16 @@ def identify_targets(ccd, nfind=3, plots=False):
log.error("Discarding target with stddev: {:.3f}".format(
fitted_gaussian.stddev.value))
if plots:
plt.plot(median_profile, color='b')
fig, ax = plt.subplots()
fig.canvas.set_window_title(ccd.header['GSP_FNAM'])

ax.plot(median_profile, color='b', label='Median Profile')
for profile in profile_model:
plt.plot(profile(range(len(median_profile))), color='r')
ax.plot(profile(range(len(median_profile))), color='r', label="Fitted profile(s)")
ax.set_xlabel("Spatial Axis (Pixels)")
ax.set_ylabel("Median Intensity")
ax.legend(loc='best')
plt.tight_layout()
plt.show()

# plt.imshow(ccd.data, clim=(50, 200), cmap='gray')
Expand Down Expand Up @@ -1646,8 +1704,10 @@ def read_fits(full_path, technique='Unknown'):
GSP_SLIT: Slit trim section, obtained from the slit illuminated area.
GSP_BIAS: Master bias image used. Default `none`.
GSP_FLAT: Master flat image used. Default `none`.
GSP_SCTR: Science target file
GSP_NORM: Flat normalization method.
GSP_COSM: Cosmic ray rejection method.
GSP_EXTR: Extraction window at first column
GSP_WRMS: Wavelength solution RMS Error.
GSP_WPOI: Number of points used to calculate the wavelength solution
Error.
Expand Down Expand Up @@ -1734,6 +1794,11 @@ def read_fits(full_path, technique='Unknown'):
value='none',
comment='Cosmic ray rejection method')

if 'GSP_EXTR' not in all_keys:
ccd.header.set('GSP_EXTR',
value='none',
comment='Extraction window at first column')

if 'GSP_WRMS' not in all_keys:
ccd.header.set('GSP_WRMS',
value='none',
Expand Down Expand Up @@ -1791,7 +1856,7 @@ def save_extracted(ccd, destination, prefix='e', target_number=1):
log.info("Saving uncalibrated(w) extracted spectrum to file: "
"{:s}".format(new_file_name))
full_path = os.path.join(destination, new_file_name)
write_fits(ccd=ccd, full_path=full_path, parent_file=file_name)
ccd = write_fits(ccd=ccd, full_path=full_path, parent_file=file_name)
return ccd


Expand Down Expand Up @@ -2126,6 +2191,7 @@ def write_fits(ccd,
ccd.header.set('GSP_FNAM', value=os.path.basename(full_path))

# write to file
log.info("Saving FITS file to {:s}".format(os.path.basename(full_path)))
ccd.write(full_path, overwrite=overwrite)
assert os.path.isfile(full_path)
return ccd
Expand Down
21 changes: 15 additions & 6 deletions goodman_pipeline/spectroscopy/redspec.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -40,7 +40,7 @@
import matplotlib.pyplot as plt
import warnings

SHOW_PLOTS = False
SHOW_PLOTS = True

warnings.filterwarnings('ignore')

Expand Down Expand Up @@ -125,6 +125,11 @@ def get_args(arguments=None):
dest='debug_mode',
help="Debugging Mode")

parser.add_argument('--debug-plot',
action='store_true',
dest='debug_with_plots',
help="Debugging show debugging plots")

parser.add_argument('--max-targets',
action='store',
dest='max_n_targets',
Expand Down Expand Up @@ -359,7 +364,7 @@ def _run(self, data_container, extraction_type):
"identification.")
target_list = identify_targets(ccd=ccd,
nfind=3,
plots=SHOW_PLOTS)
plots=self.args.debug_with_plots)

# trace
if len(target_list) > 0:
Expand Down Expand Up @@ -389,15 +394,19 @@ def _run(self, data_container, extraction_type):
target_trace=single_trace,
spatial_profile=single_profile,
extraction_name=extraction_type)
save_extracted(ccd=extracted,
destination=self.args.destination,
target_number=target_number)
saved_ccd = save_extracted(ccd=extracted,
destination=self.args.destination,
target_number=target_number)
# print(spec_file)

# lamp extraction
all_lamps = []
if comp_ccd_list:
for comp_lamp in comp_ccd_list:
comp_lamp.header.set('GSP_SCTR',
value=saved_ccd.header['GSP_FNAM'],
comment='Science target file the lamp was extracted for.')

extracted_lamp = extraction(
ccd=comp_lamp,
target_trace=single_trace,
Expand All @@ -411,7 +420,7 @@ def _run(self, data_container, extraction_type):
extracted_target_and_lamps.append([extracted,
all_lamps])

if self.args.debug_mode:
if self.args.debug_with_plots:
# print(plt.get_backend())
plt.close('all')
fig, ax = plt.subplots(1, 1)
Expand Down
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