NSB tunning : pre-generated NSB only waveforms and a new class

lstchain/data/lstchain_lhfit_config.json

@@ -279,8 +279,9 @@
   },
   "waveform_nsb_tuning":{
     "nsb_tuning": false,
-    "nsb_tuning_ratio": 0.52,
-    "spe_location": null
+    "nsb_tuning_ratio": 0.5,
+    "spe_location": null,
+    "pre_computed_multiplicity": 10
   },
   "lh_fit_config": {
     "sigma_s": [

lstchain/data/lstchain_standard_config.json

@@ -274,8 +274,9 @@
   },
   "waveform_nsb_tuning":{
     "nsb_tuning": false,
-    "nsb_tuning_ratio": 0.52,
-    "spe_location": null
+    "nsb_tuning_ratio": 0.5,
+    "spe_location": null,
+    "pre_computed_multiplicity": 10
   },
   "write_interleaved_events":{
     "DataWriter": {

lstchain/image/tests/test_modifier.py

@@ -77,17 +77,18 @@ def test_calculate_required_additional_nsb(mc_gamma_testfile, observed_dl1_files
 
 def test_tune_nsb_on_waveform():
     import astropy.units as u
+    from ctapipe_io_lst import LSTEventSource
     from scipy.interpolate import interp1d
     from lstchain.io.io import get_resource_path
-    from lstchain.image.modifier import tune_nsb_on_waveform
+    from lstchain.image.modifier import WaveformNsbTunner
     from lstchain.data.normalised_pulse_template import NormalizedPulseTemplate
 
     waveform = np.array(
-        [[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],
-         [0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]]
+        [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+         [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]
     )
-    added_nsb_fraction, original_nsb = 1.0, 0.1 * u.GHz
-    dt = 1 * u.ns
+    added_nsb_fraction, original_nsb = 1.0, {1: 0.1 * u.GHz}
+    subarray = LSTEventSource.create_subarray(1)
     amplitude_HG = np.zeros(40)
     amplitude_LG = np.zeros(40)
     amplitude_HG[19] = 0.25
@@ -96,17 +97,32 @@ def test_tune_nsb_on_waveform():
     amplitude_LG[19] = 0.4
     amplitude_LG[20] = 1.0
     amplitude_LG[21] = 0.4
-    time = np.linspace(-10,30,40)
-    pulse_templates = NormalizedPulseTemplate(amplitude_HG, amplitude_LG, time, amplitude_HG_err=None,
-                                              amplitude_LG_err=None)
-    gain = np.array(['HG', 'LG'])
+    time = np.linspace(-10, 30, 40)
+    pulse_templates = {1: NormalizedPulseTemplate(amplitude_HG, amplitude_LG, time, amplitude_HG_err=None,
+                                                  amplitude_LG_err=None)}
+    gain = np.array([1, 0])
     spe = np.loadtxt(get_resource_path("data/SinglePhE_ResponseInPhE_expo2Gaus.dat")).T
     spe_integral = np.cumsum(spe[1])
     charge_spe_cumulative_pdf = interp1d(spe_integral, spe[0], kind='cubic',
                                          bounds_error=False, fill_value=0.,
                                          assume_sorted=True)
-    tune_nsb_on_waveform(waveform, added_nsb_fraction, original_nsb,
-                         dt, pulse_templates, gain, charge_spe_cumulative_pdf)
-    #  assert may be randomly wrong in very unusual cases
+    nsb_tunner = WaveformNsbTunner(added_nsb_fraction,
+                                   original_nsb,
+                                   pulse_templates,
+                                   charge_spe_cumulative_pdf,
+                                   pre_computed_multiplicity=0)
+    nsb_tunner.tune_nsb_on_waveform(waveform, 1, gain, subarray)
+    assert np.any(waveform != 0)
+    assert np.isclose(np.mean(waveform), 0.0, atol=0.2)
+    waveform = np.array(
+        [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+         [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]
+    )
+    nsb_tunner = WaveformNsbTunner(added_nsb_fraction,
+                                   original_nsb,
+                                   pulse_templates,
+                                   charge_spe_cumulative_pdf,
+                                   pre_computed_multiplicity=10)
+    nsb_tunner.tune_nsb_on_waveform(waveform, 1, gain, subarray)
     assert np.any(waveform != 0)
     assert np.isclose(np.mean(waveform), 0.0, atol=0.2)

lstchain/io/io.py

@@ -1279,7 +1279,7 @@ def extract_simulation_nsb(filename):
         for _, line in f.history:
             line = line.decode('utf-8').strip().split(' ')
             if next_nsb and line[0] == 'NIGHTSKY_BACKGROUND':
-                nsb[tel_id] = float(line[1].strip('all:'))
+                nsb[tel_id] = float(line[1].strip('all:')) * u.GHz
                 tel_id = tel_id+1
             if line[0] == 'STORE_PHOTOELECTRONS':
                 next_nsb = True
@@ -1288,7 +1288,7 @@ def extract_simulation_nsb(filename):
     log.warning('Original MC night sky background extracted from the config history in the simtel file.\n'
                 'This is done for existing LST MC such as the one created using: '
                 'https://github.com/cta-observatory/lst-sim-config/tree/sim-tel_LSTProd2_MAGICST0316'
-                '\nExtracted values are: ' + str(np.asarray(nsb)) + 'GHz. Check that it corresponds to expectations.')
+                '\nExtracted values are: ' + str(np.asarray(nsb)) + '. Check that it corresponds to expectations.')
     return nsb
 
 

lstchain/io/tests/test_io.py

@@ -154,9 +154,10 @@ def test_trigger_type_in_dl1_params(simulated_dl1_file):
 
 def test_extract_simulation_nsb(mc_gamma_testfile):
     from lstchain.io.io import extract_simulation_nsb
+    import astropy.units as u
 
     nsb = extract_simulation_nsb(mc_gamma_testfile)
-    assert np.isclose(nsb[1], 0.246, rtol=0.1)
+    assert np.isclose(nsb[1].to_value(u.GHz), 0.246, rtol=0.1)
 
 
 def test_remove_duplicated_events():