diff --git a/LightSimtastic.py b/LightSimtastic.py
index 8b0d150cfb5f750ebade527ae9c0f4be6a7657de..a5a733d4e1650d58f5e966fa8e606ae562a88bbe 100644
--- a/LightSimtastic.py
+++ b/LightSimtastic.py
@@ -8,14 +8,16 @@ import matplotlib.pyplot as plt
import scipy
import scipy.stats as stats
import scipy.signal as signal
-from tqdm import tqdm, notebook
+from tqdm.auto import tqdm
from itertools import repeat, chain, product, zip_longest
from operator import add
from types import SimpleNamespace
+from AdditionalPDFs import borel
import json
import re
import codecs
import random
+from copy import deepcopy
np.set_printoptions(suppress=True)
@@ -328,18 +330,15 @@ class SiPMSimulation:
nlight_sum = np.sum(nlight)
nDC_sum = np.sum(nDC)
nPG_sum = nlight_sum + nDC_sum
-
+
npXT = np.array(ns.dist_fpXT.rvs(size=nPG_sum))
ndXT = np.array(ns.dist_fdXT.rvs(size=nPG_sum))
- tAp = np.array(ns.dist_ftAp.rvs(size=nPG_sum))
- nAp = np.array(stats.binom.rvs(
- n=np.ones(nPG_sum).astype(int),
- p=(ns.pAp*(1 - np.exp(-tAp/ns.taur))),
- size=nPG_sum
- )
- )
-# nAp = np.random.binomial(n=1, p=(ns.pAp*(1 - np.exp(-tAp/ns.taur))), size=nPG_sum)
+ npXT_sum = np.sum(npXT)
+
+ nPG_pXT_sum = nPG_sum + npXT_sum
+
+
pg_l_d = np.ones(nlight_sum)
pg_l_t = np.asarray(ns.dist_ftlight.rvs(size=nlight_sum))
@@ -358,12 +357,16 @@ class SiPMSimulation:
pXT_n = np.split(npXT, nPG_cumsum)[:-1]
dXT_n = np.split(ndXT, nPG_cumsum)[:-1]
- Ap_n = np.split(nAp, nPG_cumsum)[:-1]
- Ap_nt = np.split(tAp, nPG_cumsum)[:-1]
pg_d = [np.concatenate(elem) for elem in zip(pg_l_d, pg_dc_d)]
pg_t = [np.concatenate(elem) for elem in zip(pg_l_t, pg_dc_t)]
+
+ pg_pXT_d = [np.concatenate(elem) for elem in zip(pg_d, pXT_n)]
+ pg_pXT_t = [np.concatenate(elem) for elem in zip(pg_t, pg_t)]
+
+
+
pbar.update(1)
pbar.set_description("{0}".format(self.pb_ag_text[2]))
@@ -440,38 +443,86 @@ class SiPMSimulation:
pbar.update(1)
pbar.set_description("{0}".format(self.pb_ag_text[4]))
- Ap_d = [
- np.array(
- list(chain.from_iterable(
- [
- [1-np.exp(-elem_Ap_t/ns.tslow)]*elem_Ap_n
- if(elem_Ap_n > 0 and elem_pg_d >0)
- else []
- for elem_pg_d, elem_pg_t, elem_Ap_n, elem_Ap_t in zip(list_pg_d, list_pg_t, list_Ap_n, list_Ap_t)
- ]
- )
- )
+ Ap_d = []
+ Ap_t = []
+
+ for list_pg_pXT_d, list_pg_pXT_t in zip(pg_pXT_d, pg_pXT_t):
+ _Apd_list = []
+ _Apt_list = []
+
+ for d, t in zip(list_pg_pXT_d, list_pg_pXT_t):
- )
- for list_pg_d, list_pg_t, list_Ap_n, list_Ap_t, in zip(pg_d, pg_t, Ap_n, Ap_nt)
- ]
+ if(d>0):
+
+ _tAp = ns.dist_ftAp.rvs(size=int(d))
+
+ _nAp = stats.binom.rvs(
+ n=np.ones(int(d)).astype(int),
+ p=(ns.pAp*(1 - np.exp(-_tAp/ns.taur))),
+ size=int(d)
+ )
+
+ cond_sel = (_nAp>0)
+
+ if(sum(cond_sel)>0):
+ _tAp = _tAp[cond_sel]
+ _nAp = _nAp[cond_sel]
+
+
+ _Apd = (1-np.exp(-_tAp/ns.tslow))*_nAp
+ _Apdt = t + _tAp
+ else:
+ _Apd = []
+ _Apdt = []
+
+ else:
+ _Apd = []
+ _Apdt = []
+
+ _Apd_list.extend(list(_Apd))
+ _Apt_list.extend(list(_Apdt))
+
+ Ap_d.append(_Apd_list)
+ Ap_t.append(_Apt_list)
+
+ Ap_d = np.array(Ap_d)
+ Ap_t = np.array(Ap_t)
+
- Ap_t = [
- np.array(
- list(chain.from_iterable(
- [
- [elem_pg_t + elem_Ap_t]*elem_Ap_n
- if(elem_Ap_n > 0 and elem_pg_d >0)
- else []
- for elem_pg_d, elem_pg_t, elem_Ap_n, elem_Ap_t in zip(list_pg_d, list_pg_t, list_Ap_n, list_Ap_t)
- ]
- )
- )
- )
+
+# Ap_d = [
+# np.array(
+# list(chain.from_iterable(
+# [
+# [1-np.exp(-elem_Ap_t/ns.tslow)]*elem_Ap_n
+# if(elem_Ap_n > 0 and elem_pg_d >0)
+# else []
+# for elem_pg_d, elem_pg_t, elem_Ap_n, elem_Ap_t in zip(list_pg_d, list_pg_t, list_Ap_n, list_Ap_t)
+# ]
+# )
+# )
+
+# )
+# for list_pg_d, list_pg_t, list_Ap_n, list_Ap_t, in zip(pg_pXT_d, pg_pXT_t, Ap_n, Ap_t)
+# ]
+
+
+# Ap_t = [
+# np.array(
+# list(chain.from_iterable(
+# [
+# [elem_pg_t + elem_Ap_t]*elem_Ap_n
+# if(elem_Ap_n > 0 and elem_pg_d >0)
+# else []
+# for elem_pg_d, elem_pg_t, elem_Ap_n, elem_Ap_t in zip(list_pg_d, list_pg_t, list_Ap_n, list_Ap_t)
+# ]
+# )
+# )
+# )
- for list_pg_d, list_pg_t, list_Ap_n, list_Ap_t, in zip(pg_d, pg_t, Ap_n, Ap_nt)
- ]
+# for list_pg_d, list_pg_t, list_Ap_n, list_Ap_t, in zip(pg_pXT_d, pg_pXT_t, Ap_n, Ap_t)
+# ]
pbar.update(1)
pbar.set_description("{0}".format(self.pb_ag_text[5]))
@@ -533,7 +584,6 @@ class SiPMSimulation:
Q = np.array(ns.dist_Q.rvs(ns.n_sim))
-
n_ag_d = np.asarray([len(list_ag_d) for list_ag_d in ns.ag_d])
Sig1 = ns.dist_Sig1.rvs(np.sum(n_ag_d))
n_ag_d_cumsum = np.cumsum(n_ag_d)[:-1]
@@ -670,7 +720,7 @@ class SiPMSimulation:
elif(ns.Gen_npXT == "Binomial"):
ns.dist_fpXT = scipy.stats.binom(n=1, p = ns.ppXT)
elif(ns.Gen_npXT == "Borel"):
- ns.dist_fpXT = self.borel_gen(name="borel_npXT")(mu=ns.ppXT)
+ ns.dist_fpXT = borel(mu=ns.ppXT)
else:
raise Exception("The Prompt Cross-talk Discharge distribution mode that has been selected is invalid. Please choose from the following options: {0}".format(
", ".join(self.VariableDictionary["Gen_npXT"]["options"])))
@@ -680,7 +730,7 @@ class SiPMSimulation:
elif(ns.Gen_ndXT== "Binomial"):
ns.dist_fdXT = scipy.stats.binom(n=1, p = ns.pdXT)
elif(ns.Gen_ndXT == "Borel"):
- ns.dist_fdXT = self.borel_gen(name="borel_ndXT")(mu=ns.pdXT)
+ ns.dist_fdXT = borel(mu=ns.pdXT)
else:
raise Exception("The Delayed Cross-talk Discharge mode that has been selected is invalid. Please choose from the following options: {0}".format(
", ".join(self.VariableDictionary["Gen_ndXT"]["options"])))