Merge pull request #1062 from cta-observatory/calibration_bug_fixes

Calibration bug fixes
cta-observatory · Dec 14, 2022 · 7dc71a6 · 7dc71a6
2 parents 3a04678 + d9cdf8c
commit 7dc71a6
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Showing 3 changed files with 15 additions and 9 deletions.
diff --git a/lstchain/calib/camera/pedestals.py b/lstchain/calib/camera/pedestals.py
@@ -306,7 +306,7 @@ def calculate_pedestal_results(self, trace_integral, masked_pixels_of_sample):
         )
 
         # mask pixels with NaN mean, due to missing statistics
-        pixels_without_stat = np.where(np.isnan(pixel_mean)==True)[0]
+        pixels_without_stat = np.where(np.isnan(pixel_mean)==True)
         charge_median_outliers[pixels_without_stat] = True
         charge_std_outliers[pixels_without_stat] = True
 

diff --git a/lstchain/scripts/onsite/onsite_create_calibration_files_with_batch.py b/lstchain/scripts/onsite/onsite_create_calibration_files_with_batch.py
@@ -171,6 +171,7 @@ def main():
                     "onsite_create_calibration_file",
                     f"-r {run}",
                     f"-v {prod_id}",
+                    f"--r0-dir {r0_dir}",
                     f"--sub_run={sub_run}",
                     f"-b {base_dir}",
                     f"-s {stat_events}",

diff --git a/lstchain/visualization/plot_calib.py b/lstchain/visualization/plot_calib.py
@@ -83,13 +83,15 @@ def plot_all(ped_data, ff_data, calib_data, run=0, plot_file=None):
             pad = 420
             image = ff_data.charge_median
             mask = ff_data.charge_median_outliers
+
             for chan in np.arange(2):
                 pad += 1
                 plt.subplot(pad)
                 plt.tight_layout()
+                select = np.logical_not(mask[chan])
                 disp = CameraDisplay(camera)
-                mymin = np.median(image[chan]) - 2 * np.std(image[chan])
-                mymax = np.median(image[chan]) + 2 * np.std(image[chan])
+                mymin = np.median(image[chan][select]) - 2 * np.std(image[chan][select])
+                mymax = np.median(image[chan][select]) + 2 * np.std(image[chan][select])
                 disp.set_limits_minmax(mymin, mymax)
                 disp.highlight_pixels(mask[chan], linewidth=2)
                 disp.image = image[chan]
@@ -104,9 +106,10 @@ def plot_all(ped_data, ff_data, calib_data, run=0, plot_file=None):
                 pad += 1
                 plt.subplot(pad)
                 plt.tight_layout()
+                select = np.logical_not(mask[chan])
                 disp = CameraDisplay(camera)
-                mymin = np.median(image[chan]) - 2 * np.std(image[chan])
-                mymax = np.median(image[chan]) + 2 * np.std(image[chan])
+                mymin = np.median(image[chan][select]) - 2 * np.std(image[chan][select])
+                mymax = np.median(image[chan][select]) + 2 * np.std(image[chan][select])
                 disp.set_limits_minmax(mymin, mymax)
                 disp.highlight_pixels(mask[chan], linewidth=2)
                 disp.image = image[chan]
@@ -121,9 +124,10 @@ def plot_all(ped_data, ff_data, calib_data, run=0, plot_file=None):
                 pad += 1
                 plt.subplot(pad)
                 plt.tight_layout()
+                select = np.logical_not(mask[chan])
                 disp = CameraDisplay(camera)
-                mymin = np.median(image[chan]) - 2 * np.std(image[chan])
-                mymax = np.median(image[chan]) + 2 * np.std(image[chan])
+                mymin = np.median(image[chan][select]) - 2 * np.std(image[chan][select])
+                mymax = np.median(image[chan][select]) + 2 * np.std(image[chan][select])
                 disp.set_limits_minmax(mymin, mymax)
                 disp.highlight_pixels(mask[chan], linewidth=2)
                 disp.image = image[chan]
@@ -138,9 +142,10 @@ def plot_all(ped_data, ff_data, calib_data, run=0, plot_file=None):
                 pad += 1
                 plt.subplot(pad)
                 plt.tight_layout()
+                select = np.logical_not(mask[chan])
                 disp = CameraDisplay(camera)
-                mymin = np.median(image[chan]) - 2 * np.std(image[chan])
-                mymax = np.median(image[chan]) + 2 * np.std(image[chan])
+                mymin = np.median(image[chan][select]) - 2 * np.std(image[chan][select])
+                mymax = np.median(image[chan][select]) + 2 * np.std(image[chan][select])
                 disp.set_limits_minmax(mymin, mymax)
                 disp.highlight_pixels(mask[chan], linewidth=2)
                 disp.image = image[chan]