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DoNotShowCatalogStarsInMaskedRegionsOfAstrometryReport #242

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DoNotShowCatalogStarsInMaskedRegionsOfAstrometryReport #242

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2 changes: 1 addition & 1 deletion RMS/Routines/MaskImage.py
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Original file line number Diff line number Diff line change
Expand Up @@ -72,7 +72,7 @@ def checkMask(self, x_res, y_res):

def getMaskFile(dir_path, config, file_list=None):
"""
From a directory, fine the mask file, load it and return it
From a directory, find the mask file, load it and return it
"""
if file_list is None:
file_list = os.listdir(dir_path)
Expand Down
33 changes: 21 additions & 12 deletions Utils/CalibrationReport.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,6 +22,8 @@
from RMS.Routines import Image
from RMS.Routines.AddCelestialGrid import addEquatorialGrid

from RMS.Routines import MaskImage

# Import Cython functions
import pyximport
pyximport.install(setup_args={'include_dirs':[np.get_include()]})
Expand Down Expand Up @@ -60,22 +62,22 @@ def generateCalibrationReport(config, night_dir_path, match_radius=2.0, platepar



### Load recalibrated platepars, if they exist ###
### Load re-calibrated platepars, if they exist ###

# Find recalibrated platepars file per FF file
# Find re-calibrated platepars file per FF file
platepars_recalibrated_file = None
for file_name in os.listdir(night_dir_path):
if file_name == config.platepars_recalibrated_name:
platepars_recalibrated_file = file_name
break


# Load all recalibrated platepars if the file is available
# Load all re-calibrated platepars if the file is available
recalibrated_platepars = None
if platepars_recalibrated_file:
with open(os.path.join(night_dir_path, platepars_recalibrated_file)) as f:
recalibrated_platepars = json.load(f)
print('Loaded recalibrated platepars JSON file for the calibration report...')
print('Loaded re-calibrated platepars JSON file for the calibration report...')

### ###

Expand Down Expand Up @@ -140,7 +142,7 @@ def generateCalibrationReport(config, night_dir_path, match_radius=2.0, platepar
dt = getMiddleTimeFF(ff_name, config.fps, ret_milliseconds=True)
jd = date2JD(*dt)

# Add the time and the stars to the dict
# Add the time and the stars to the dictionary
star_dict[jd] = star_data
ff_dict[jd] = ff_name

Expand All @@ -152,7 +154,7 @@ def generateCalibrationReport(config, night_dir_path, match_radius=2.0, platepar
return None


# If the recalibrated platepars file exists, take the one with the most stars
# If the re-calibrated platepars file exists, take the one with the most stars
max_jd = 0
using_recalib_platepars = False
if recalibrated_platepars is not None:
Expand All @@ -167,7 +169,7 @@ def generateCalibrationReport(config, night_dir_path, match_radius=2.0, platepar
if (jd not in star_dict) or (jd not in ff_dict):
continue

# Make sure that the chosen file has been successfuly recalibrated
# Make sure that the chosen file has been successfully re-calibrated
if "auto_recalibrated" in recalibrated_platepars[ff_name_temp]:
if not recalibrated_platepars[ff_name_temp]["auto_recalibrated"]:
continue
Expand All @@ -178,12 +180,12 @@ def generateCalibrationReport(config, night_dir_path, match_radius=2.0, platepar
max_stars = len(star_dict[jd])


# Set a flag to indicate if using recalibrated platepars has failed
# Set a flag to indicate if using re-calibrated platepars has failed
if max_jd == 0:
using_recalib_platepars = False
else:

print('Using recalibrated platepars, file:', ff_dict[max_jd])
print('Using re-calibrated platepars, file:', ff_dict[max_jd])
using_recalib_platepars = True

# Select the platepar where the FF file has the most stars
Expand Down Expand Up @@ -224,7 +226,7 @@ def generateCalibrationReport(config, night_dir_path, match_radius=2.0, platepar



# If no recalibrated platepars where found, find the image with the largest number of matched stars
# If no re-calibrated platepars where found, find the image with the largest number of matched stars
if (not using_recalib_platepars) or (max_jd == 0):

max_jd = 0
Expand Down Expand Up @@ -299,7 +301,7 @@ def generateCalibrationReport(config, night_dir_path, match_radius=2.0, platepar

### Plot match residuals ###

# Compute preducted positions of matched image stars from the catalog
# Compute predicted positions of matched image stars from the catalog
x_predicted, y_predicted = raDecToXYPP(matched_catalog_stars[:, 0], \
matched_catalog_stars[:, 1], max_jd, platepar)

Expand Down Expand Up @@ -376,6 +378,13 @@ def generateCalibrationReport(config, night_dir_path, match_radius=2.0, platepar
temp_arr = temp_arr[temp_arr[:, 1] <= ff.avepixel.shape[0]]
x_catalog, y_catalog, mag_catalog = temp_arr.T

# Filter out catalog stars in the masked regions of the image
mask = MaskImage.getMaskFile(night_dir_path, config)
temp_arr = np.c_[x_catalog, y_catalog, mag_catalog]
temp_int_arr = temp_arr.astype(int)
temp_arr = temp_arr[mask.img[temp_int_arr[:, 1], temp_int_arr[:, 0]] > 0]
x_catalog, y_catalog, mag_catalog = temp_arr.T

# Plot catalog stars on the image
cat_stars_handle = plt.scatter(x_catalog, y_catalog, c='none', marker='D', lw=1.0, alpha=0.4, \
s=((4.0 + (faintest_mag - mag_catalog))/3.0)**(2*2.512), edgecolor='r', label='Catalog stars')
Expand Down Expand Up @@ -469,7 +478,7 @@ def generateCalibrationReport(config, night_dir_path, match_radius=2.0, platepar
if config.use_flat:
platepar.vignetting_coeff = 0.0

# Extact intensities and mangitudes
# Extract intensities and magnitudes
star_intensities = image_stars[:, 2]
catalog_ra, catalog_dec, catalog_mags = matched_catalog_stars.T

Expand Down