Sensor response low photoelectron signal function

.github/workflows/testsuite.yml

@@ -19,25 +19,24 @@ jobs:
       steps:
         - uses: actions/checkout@v4
 
-        - name: Set up
+        - name: Test suite
           run: |
             wget https://repo.anaconda.com/miniconda/Miniconda3-py312_24.9.2-0-Linux-x86_64.sh -O miniconda.sh
             bash miniconda.sh -b -p $HOME/miniconda
             source $HOME/miniconda/etc/profile.d/conda.sh
 
             echo 
-            echo ************************************
+            echo "************************************"
             echo ----------Conda Installed-----------
-            echo ************************************
+            echo "************************************"
             echo 
-            
+
             source setup.sh
-            
+
             echo 
-            echo ************************************
+            echo "************************************"
             echo ---------------test-----------------
-            echo ************************************
-            echo
-            
+            echo "************************************"
+            echo 
+
             PYTEST_ADDOPTS=--color=yes HYPOTHESIS_PROFILE=travis-ci python -m pytest -v 
-

packs/temporal_test.py

@@ -0,0 +1,2 @@
+def test_dummy():
+    pass

packs/tools/fit_functions.py

@@ -0,0 +1,59 @@
+import numpy as np
+import scipy.stats as scs
+
+
+def finger_signal(xs         : np.array,
+                  bl         : float   ,
+                  amp        : float   ,
+                  gain       : float   ,
+                  sigmabl    : float   ,
+                  sigmaq     : float   ,
+                  poismu     : float   ,
+                  maxpercent : float = 0.999
+                  ) -> np.array:
+    '''
+    A function that returns a `finger plot' distribution; a
+    poissonian distribution convoluted with gaussians. This distribution
+    characterises the output expected from a PMT/SiPM charge histogram
+    where x is the charge or PEs and y is the number of counts.
+
+    Parameters
+    ----------
+    xs             : An array of 'x' values, typically PEs or ADCs
+    bl             : The value beyond zero at which the poisson peaks occur [1]
+    amp            : The amplitude applied to the poissonian peaks
+    gain           : The gain (shift in x) applied to the poissonian peaks
+    sigmabl        : The inherent sigma of each gaussian.
+    sigmaq         : The sigma of each gaussian related to which poisson peak
+                     it's related to.
+    maxpercent     : The percentage of the poissonian distribution that is  
+
+    Returns
+    -------
+    result         : The expected y values that given (x,y) describe the
+                     finger plot distribution.
+
+    Footnotes
+    ---------
+    [1] Generally these peaks would initialise at begin at x = 0 but in
+    practice this isn't always the case
+    '''
+
+    # Collect the position and amplitudes of the finger plot peaks
+    if poismu != 0:
+        poispeaks_pos = np.arange(0, scs.poisson.ppf(maxpercent, poismu))
+    else:
+        poispeaks_pos = np.array([0])
+    realpeaks_pos = gain * poispeaks_pos + bl
+    realpeaks_amp = amp * scs.poisson.pmf(poispeaks_pos, poismu)
+
+    # start y values collection
+    result        = np.zeros_like(xs)
+
+    # generate y values (results) that describe the poissonian distribution with
+    # gaussian convolution across each peak
+    for i in range(0, len(poispeaks_pos)):
+            result += realpeaks_amp[i] * scs.norm.pdf(xs, loc=realpeaks_pos[i],
+                                                    scale=np.sqrt(sigmabl**2 + sigmaq**2 * i))
+
+    return result

packs/tools/fit_functions_test.py

@@ -0,0 +1,19 @@
+import numpy       as np
+import scipy.stats as scs
+
+from . fit_functions import finger_signal
+
+from hypothesis            import given
+from hypothesis.strategies import floats
+
+@given(floats(min_value =  -10, max_value =  10),
+       floats(min_value = -500, max_value = 500),
+       floats(min_value =    1, max_value =   2),
+       floats(min_value =    0, max_value = 200))
+def test_finger_signal_gaus_when_poisson_zero(bl, gain, sbl, sq):
+    xaux = np.linspace(-10, 10, 1000)
+    yres = finger_signal(xaux,  bl, 1, gain, sbl, sq, 0)
+    ygau =  scs.norm.pdf(xaux, loc=bl, scale=sbl)
+
+    assert np.array_equal(yres, ygau)
+