Merge branch 'main' of https://forge.bexus.org/Ava/FPRAK

testimpulse.py

@@ -0,0 +1,120 @@
+import numpy as np
+from matplotlib import pyplot as plt
+import scipy
+
+plt.rcParams["figure.figsize"] = (15,8)
+plt.rcParams['xtick.labelsize'] = 15
+plt.rcParams['ytick.labelsize'] = 15
+plt.rcParams['axes.labelsize'] = 20
+plt.rcParams['axes.titlesize']=25
+plt.rcParams['legend.fontsize']=20
+plt.rcParams['savefig.bbox']='tight'
+
+def gauss(x, *p):
+    A, mu, sigma = p
+    return A*np.exp(-(x-mu)**2/(2.*sigma**2))
+
+
+def fwhm(a):
+    p,cov=scipy.optimize.curve_fit(gauss,xdata=np.arange(len(a)),ydata=a,p0=[150,3880,20])
+    #plt.plot(gauss(np.arange(len(a)),*p))
+    return abs(2*np.sqrt(2*np.log(2))*p[2])
+
+'''
+def fwhm(a):
+    h=max(a)/2
+    u=False
+    k=0
+    for i in a:
+        if not u:
+            if i<=h:
+                k+=1
+                continue
+            else:
+                u=k
+                k+=1
+        else:
+            if i>=h:
+                k+=1
+                continue
+            else:
+                o=k
+                break  
+    return o-u
+'''
+c0=np.loadtxt('data/c0.TKA')
+c20=np.loadtxt('data/c20.TKA')
+c95=np.loadtxt('data/c95.TKA')
+c245=np.loadtxt('data/c245.TKA')
+c320=np.loadtxt('data/c320.TKA')
+c1020=np.loadtxt('data/c1020.TKA')
+c1770=np.loadtxt('data/c1770.TKA')
+
+cv0=np.loadtxt('data/cv0.TKA')
+cv1=np.loadtxt('data/cv1.TKA')
+cv2=np.loadtxt('data/cv2.TKA')
+cv3=np.loadtxt('data/cv3.TKA')
+cv4=np.loadtxt('data/cv4.TKA')
+cv5=np.loadtxt('data/cv5.TKA')
+cv10=np.loadtxt('data/cv10.TKA')
+cv40=np.loadtxt('data/cv40.TKA')
+
+halbC=[]
+for c in [c0,c20,c95,c245,c320,c1020,c1770]:
+    halbC.append(fwhm(c))
+    #plt.plot(c,'b')
+#plt.show()
+
+halbV=[]
+for v in [cv0,cv1,cv2,cv3,cv4,cv5,cv10,cv40]:
+    halbV.append(fwhm(v))
+    #plt.plot(v,'r')
+#plt.show()
+
+halbC=np.array(halbC)
+halbV=np.array(halbV)
+fig,axs=plt.subplots()
+axs.plot(halbC,[0,20,95,245,320,1020,1770],'b',label='Detektor mit verschiedenen Spannungen')
+axs.plot(halbC,[0,20,95,245,320,1020,1770],'r',label='Kapazitive Lasten')
+axs.legend(loc=1)
+axs2=axs.twinx()
+axs2.plot(halbV,[0,1,2,3,4,5,10,40],'b')
+axs2.set_ylabel('In Sperrichtung angelegte Spannung [V]')
+axs.set_ylabel('Kapazitive Last [pF]')
+axs.set_xlabel('Halbwertsbreite [bins]')
+axs.axvline(halbV[-1],c='black',linestyle='--')
+axs.axhline(277.35,xmax=0.313,c='black',linestyle='--')
+plt.show()
+
+
+lin10=np.loadtxt('data/linear10.TKA')
+lin15=np.loadtxt('data/linear15.TKA')
+lin20=np.loadtxt('data/linear20.TKA')
+lin25=np.loadtxt('data/linear25.TKA')
+lin30=np.loadtxt('data/linear30.TKA')
+lin35=np.loadtxt('data/linear35.TKA')
+lin40=np.loadtxt('data/linear40.TKA')
+lin45=np.loadtxt('data/linear45.TKA')
+lin50=np.loadtxt('data/linear50.TKA')
+lin55=np.loadtxt('data/linear55.TKA')
+linmax=[]
+for l in [lin10,lin15,lin20,lin25,lin30,lin35,lin40,lin45,lin50,lin55]:
+    plt.plot(l)
+    linmax.append(np.argmax(l))
+
+plt.show()
+
+
+
+def lin(x,a,b):
+    return a*x+b
+
+linmax=np.array(linmax)
+A=np.array([1,1.5,2,2.5,3,3.5,4,4.5,5,5.5])
+p,cov=scipy.optimize.curve_fit(lin,A,linmax)
+
+plt.scatter(A,linmax,c='r')
+plt.plot(A,lin(A,p[0],p[1]),label=r'Fit pixel(E)=678$\frac{1}{MeV}\cdot$E[MeV]+163',c='b')
+plt.legend()
+plt.show()
+print(p,cov)