TRR tests in main module

.github/workflows/ci.yml

@@ -144,10 +144,7 @@ jobs:
         run: |
           pytest -n auto --dist loadscope --cov=nectarchain --cov-report=xml --ignore=src/nectarchain/user_scripts
 
-      - name: Upload coverage reports to Codecov with GitHub Action
-        uses: codecov/codecov-action@v5
-        env:
-          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
+      - uses: codecov/codecov-action@v5
 
 
   docs:

environment.yml

@@ -17,11 +17,13 @@ dependencies:
   - sphinx
   - sphinx-automodapi
   - pydata-sphinx-theme
+  - lmfit # needed into TRR
+  - h5py # needed into TRR (should be removed to use I/O methods of containers)
   - pyqt # [linux]
   - pip:
     - zeo
     - zodb
     - mechanize
     - browser-cookie3
+    - pyqt6
     - pyqtgraph
-    - pyqt6 # [osx and arm64]

pyproject.toml

@@ -27,6 +27,8 @@ dependencies = [
     "scipy==1.11.4",
     "zodb",
     "zeo",
+    "lmfit",
+    "h5py",
     "pyqt6",
     "pyqtgraph",
 ]

src/nectarchain/makers/component/tests/test_flatfield_spe_component.py

@@ -71,7 +71,6 @@ def test_call(self, instance, event):
         instance(event)
         assert len(instance.chargesComponent.trigger_list) == 1
 
-    # @pytest.mark.skip(reason="test multiproc make GitHub worker be killed")
     def test_finish_multiproc(self):
         SPEalgorithm.window_length.default_value = 2
         # We need to re-instance objects because otherwise, en exception is raised :
@@ -188,7 +187,6 @@ def test_init(self, instance):
         assert isinstance(instance.chargesComponent, ChargesComponent)
         assert instance._chargesContainers is None
 
-    # @pytest.mark.skip(reason="test multiproc make GitHub worker be killed")
     def test_finish_multiproc(self):
         # We need to re-instance objects because otherwise, en exception is raised :
         # ReferenceError('weakly-referenced object no longer exists')

src/nectarchain/makers/core.py

@@ -339,7 +339,7 @@ def setup(self, *args, **kwargs):
 
     def _setup_eventsource(self, *args, **kwargs):
         self._load_eventsource(*args, **kwargs)
-        self.__npixels = self._event_source.nectarcam_service.num_pixels
+        self.__npixels = self._event_source.camera_config.num_pixels
         self.__pixels_id = self._event_source.nectarcam_service.pixel_ids
 
     def _setup_components(self, *args, **kwargs):

src/nectarchain/trr_test_suite/__init__.py

@@ -0,0 +1,5 @@
+from .gui import TestRunner
+
+__all__ = [
+    "TestRunner",
+]

src/nectarchain/user_scripts/dmousadi/TRR_scripts/src/deadtime.py → src/nectarchain/trr_test_suite/deadtime.py

@@ -9,23 +9,33 @@
 import numpy as np
 from astropy import units as u
 from iminuit import Minuit
-from tools_components import DeadtimeTestTool
-from utils import ExponentialFitter, deadtime_labels, source_ids_deadtime
+
+from nectarchain.trr_test_suite.tools_components import DeadtimeTestTool
+from nectarchain.trr_test_suite.utils import (
+    ExponentialFitter,
+    deadtime_labels,
+    source_ids_deadtime,
+)
 
 
 def get_args():
-    """
-    Parses command-line arguments for the deadtime test script.
+    """Parses command-line arguments for the deadtime test script.
 
     Returns:
         argparse.ArgumentParser: The parsed command-line arguments.
     """
     parser = argparse.ArgumentParser(
         description="Deadtime tests B-TEL-1260 & B-TEL-1270. \n"
-        + "According to the nectarchain component interface, you have to set a NECTARCAMDATA environment variable in the folder where you have the data from your runs or where you want them to be downloaded.\n"
-        + "You have to give a list of runs (run numbers with spaces inbetween), a corresponding source list and an output directory to save the final plot.\n"
-        + "If the data is not in NECTARCAMDATA, the files will be downloaded through DIRAC.\n For the purposes of testing this script, default data is from the runs used for this test in the TRR document.\n"
-        + "You can optionally specify the number of events to be processed (default 1000).\n"
+        + "According to the nectarchain component interface, you have to set a\
+            NECTARCAMDATA environment variable in the folder where you have the data\
+                from your runs or where you want them to be downloaded.\n"
+        + "You have to give a list of runs (run numbers with spaces inbetween), a \
+            corresponding source list and an output directory to save the final plot.\n"
+        + "If the data is not in NECTARCAMDATA, the files will be downloaded through \
+            DIRAC.\n For the purposes of testing this script, default data is from the\
+                runs used for this test in the TRR document.\n"
+        + "You can optionally specify the number of events to be processed \
+            (default 1000).\n"
     )
     parser.add_argument(
         "-r",
@@ -42,7 +52,8 @@
         type=int,
         choices=[0, 1, 2],
         nargs="+",
-        help="List of corresponding source for each run: 0 for random generator, 1 for nsb source, 2 for laser",
+        help="List of corresponding source for each run: 0 for random generator,\
+            1 for nsb source, 2 for laser",
         required=False,
         default=source_ids_deadtime,
     )
@@ -70,15 +81,21 @@
 
 
 def main():
-    """
-    Runs the deadtime test script, which performs deadtime tests B-TEL-1260 and B-TEL-1270.
+    """Runs the deadtime test script, which performs deadtime tests B-TEL-1260 and
+    B-TEL-1270.
 
-    The script takes command-line arguments to specify the list of runs, corresponding sources, number of events to process, and output directory. It then processes the data for each run, performs an exponential fit to the deadtime distribution, and generates two plots:
+    The script takes command-line arguments to specify the list of runs, corresponding\
+        sources, number of events to process, and output directory. It then processes\
+            the data for each run, performs an exponential fit to the deadtime\
+                distribution, and generates two plots:
 
-    1. A plot of deadtime percentage vs. collected trigger rate, with the CTA requirement indicated.
-    2. A plot of the rate from the fit vs. the collected trigger rate, with the relative difference shown in the bottom panel.
+    1. A plot of deadtime percentage vs. collected trigger rate, with the CTA\
+        requirement indicated.
+    2. A plot of the rate from the fit vs. the collected trigger rate, with the\
+        relative difference shown in the bottom panel.
 
-    The script also saves the generated plots to the specified output directory, and optionally saves them to a temporary output directory for use in a GUI.
+    The script also saves the generated plots to the specified output directory, and\
+        optionally saves them to a temporary output directory for use in a GUI.
     """
 
     parser = get_args()
@@ -189,7 +206,8 @@
         m.limits["deadtime"] = (
             0.6e-6,
             1.1e-6,
-        )  # Put some tigh constrain as the fit will be in trouble when it expect 0. and measured something instead.
+        )  # Put some tigh constrain as the fit will be in trouble when it expect 0. and
+        # measured something instead.
 
         m.print_level = 2
 
@@ -202,16 +220,19 @@
         # print(fitted_params_err)
 
         print(
-            f"Dead-Time is {1.e6*fitted_params[1]:.3f} +- {1.e6*fitted_params_err[1]:.3f} µs"
+            f"Dead-Time is {1.e6*fitted_params[1]:.3f} +- "
+            f"{1.e6*fitted_params_err[1]:.3f} µs"
         )
         print(
-            f"Rate is {1./fitted_params[2]:.2f} +- {fitted_params_err[2]/(fitted_params[2]**2):.2f} Hz"
+            f"Rate is {1./fitted_params[2]:.2f} +-"
+            f"{fitted_params_err[2]/(fitted_params[2]**2):.2f} Hz"
         )
         print(f"Expected run duration is {fitted_params[0]*fitted_params[2]:.2f} s")
 
         fitted_rate.append(1.0 / fitted_params[2])
 
-        #     plt.savefig(figurepath + 'deadtime_exponential_fit_nsb_run{}_newfit_cutoff.png'.format(run))
+        #     plt.savefig(figurepath + 'deadtime_exponential_fit_nsb_run{}_newfit
+        # _cutoff.png'.format(run))
 
         y = data_content
         y_fit = fitter.expected_distribution(fitted_params)
@@ -236,13 +257,13 @@
 
         parameter_R2_new_list.append(r2)
 
-    deadtime_from_fit = parameter_tau_new_list
-    deadtime_from_fit_err = parameter_tau_err_new_list
+    # deadtime_from_fit = parameter_tau_new_list
+    # deadtime_from_fit_err = parameter_tau_err_new_list
     lambda_from_fit = parameter_lambda_new_list
     lambda_from_fit_err = parameter_lambda_err_new_list
-    A2_from_fit = parameter_A2_new_list
-    A2_from_fit_err = parameter_A2_err_new_list
-    R2_from_fit = parameter_R2_new_list
+    # A2_from_fit = parameter_A2_new_list
+    # A2_from_fit_err = parameter_A2_err_new_list
+    # R2_from_fit = parameter_R2_new_list
 
     #######################################
     # PLOT
@@ -260,9 +281,9 @@
     ids = np.array(ids)
     runlist = np.array(runlist)
 
-    ratio_list = []
-    collected_rate = []
-    err = []
+    # ratio_list = []
+    # collected_rate = []
+    # err = []
 
     for source in range(0, 3):
         # runl = np.where(ids==source)[0]
@@ -399,15 +420,17 @@
         ax1.errorbar(
             collected_trigger_rate[runl] / 1000,
             rate[runl],
-            # xerr=((df_mean_nsb[df_mean_nsb['Run']==run]['Collected_trigger_rate[Hz]_err']))/1000,
+            # xerr=((df_mean_nsb[df_mean_nsb['Run']==run]['Collected_trigger
+            # _rate[Hz]_err']))/1000,
             yerr=rate_err[runl],
             alpha=0.9,
             ls=" ",
             marker="o",
             color=labels[source]["color"],
             label=labels[source]["source"],
         )
-    #                  label = 'Run {} ({} V)'.format(run, df_mean_rg[df_mean_rg['Run']==run]['Voltage[V]'].values[0]))
+    #                  label = 'Run {} ({} V)'.format(run, df_mean_rg[df_mean_rg['Run']=
+    # =run]['Voltage[V]'].values[0]))
 
     ax1.legend(frameon=False, prop={"size": 10}, loc="upper left", ncol=1)
 
@@ -424,7 +447,7 @@
     main()
 
 
-##################################PREVIOUS###############################
+# ##################################PREVIOUS###############################
 # collected_rate = []
 
 
@@ -445,8 +468,10 @@
 
 # for i, run in enumerate(runlist):
 #     deadtime_run, deadtime_bin_run, deadtime_err_run, deadtime_bin_length_run, \
-#     total_delta_t_for_busy_time, parameter_A_new, parameter_R_new, parameter_A_err_new, parameter_R_err_new, \
-#     first_bin_length, tot_nr_events_histo = deadtime_and_expo_fit(time_tot[i],deadtime_us[i], run)
+#     total_delta_t_for_busy_time, parameter_A_new, parameter_R_new, parameter_A_err_
+# new, parameter_R_err_new, \
+#     first_bin_length, tot_nr_events_histo = deadtime_and_expo_fit(time_tot[i],deadt
+# ime_us[i], run)
 #     total_delta_t_for_busy_time_list.append(total_delta_t_for_busy_time)
 #     parameter_A_new_list.append(parameter_A_new)
 #     parameter_R_new_list.append(parameter_R_new)
@@ -468,7 +493,8 @@
 # rate_err = (np.array(parameter_R_err_new_list) * 1 / u.us).to(u.kHz).to_value()
 # A_from_fit = (parameter_A_new_list)
 # A_from_fit_err = (parameter_A_err_new_list)
-# ucts_busy_rate = (np.array(busy_counter[:,-1]) / (np.array(time_tot) * u.s).to(u.s)).to(
+# ucts_busy_rate = (np.array(busy_counter[:,-1]) / (np.array(time_tot) * u.s).to(u.s))
+# .to(
 #     u.kHz).value
 # nr_events_from_histo = (tot_nr_events_histo)
 # first_bin_delta_t = first_bin_length
@@ -478,7 +504,7 @@
 # deadtime_average_err_nsb = np.sqrt(1 / (np.sum(1 / deadtime_bin_length ** 2)))
 
 
-# #######################################################################################
+# ######################################################################################
 
 
 # #B-TEL-1260
@@ -502,7 +528,8 @@
 
 #                 ratio = rate[i]/freq
 #                 ratio_list.append(np.array(ratio)*100)
-#                 ratio_err = np.sqrt((rate_err[i]/freq)**2 + (freq_err*rate[i]/(freq**2)))
+#                 ratio_err = np.sqrt((rate_err[i]/freq)**2 + (freq_err*rate[i]/
+# (freq**2)))
 #                 err.append(ratio_err*100)
 
 
@@ -512,7 +539,8 @@
 #         X_sorted = [x for y, x in sorted(zip(Y, X))]
 #         err_sorted = [err for y,err in sorted(zip(Y,err))]
 
-#         plt.errorbar(sorted(Y), X_sorted, yerr = err_sorted, alpha=0.6,  ls='-', marker='o',color=labels[source]['color'], label = labels[source]['source'])
+#         plt.errorbar(sorted(Y), X_sorted, yerr = err_sorted, alpha=0.6,  ls='-',
+# marker='o',color=labels[source]['color'], label = labels[source]['source'])
 
 # plt.xlabel('Collected Trigger Rate [kHz]')
 # plt.ylabel(r'Deadtime [%]')
@@ -564,7 +592,8 @@
 # ax1.plot(x, x, color='gray', ls='--', alpha=0.5)
 
 # ax2.plot(x, np.zeros(len(x)), color='gray', ls='--', alpha=0.5)
-# ax2.fill_between(x, np.ones(len(x))*(-10), np.ones(len(x))*(10), color='gray',  alpha=0.1)
+# ax2.fill_between(x, np.ones(len(x))*(-10), np.ones(len(x))*(10), color='gray',
+# alpha=0.1)
 
 # ax2.set_xlabel('Collected Trigger Rate [kHz]')
 # ax1.set_ylabel(r'Rate from fit [kHz]')
@@ -581,11 +610,14 @@
 #     #print(collected_triger_rate[runl])
 #     ax1.errorbar(collected_triger_rate[runl]/1000,
 #                 rate[runl],
-#                 #xerr=((df_mean_nsb[df_mean_nsb['Run']==run]['Collected_trigger_rate[Hz]_err']))/1000,
+#                 #xerr=((df_mean_nsb[df_mean_nsb['Run']==run]
+# ['Collected_trigger_rate[Hz]_err']))/1000,
 #                 yerr=rate_err[runl],
 #                 alpha=0.9,
-#                 ls=' ', marker='o', color=labels[source]['color'], label = labels[source]['source'])
-# #                  label = 'Run {} ({} V)'.format(run, df_mean_rg[df_mean_rg['Run']==run]['Voltage[V]'].values[0]))
+#                 ls=' ', marker='o', color=labels[source]['color'],
+# label = labels[source]['source'])
+# #                  label = 'Run {} ({} V)'.format(run, df_mean_rg[df_mean_rg['Run']==
+# run]['Voltage[V]'].values[0]))
 
 # ax1.legend(frameon=False,  prop={'size':10},
 #            loc="upper left", ncol=1)