final corrections, everything works now

Detector.py

@@ -73,7 +73,7 @@ class Layer:
 
 class Detector:
     
-    def __init__ (self, LayerArray = [100,110,120,130,140], Magnet=0.522):
+    def __init__ (self, LayerArray = [100,110,120,130,140], Magnet=0.511):
         """Initialize the Layer and magnetic fiel of the detector"""
 
         self.BField = Magnet 	#Microesla

Particles_software.py

@@ -14,17 +14,23 @@ class Particle:
         
         """If we want two different masses (electron, proton), I have here 
         created them with equal probability"""
-        self.e = np.random.uniform(1, 0)
-        if self.e > 0.5:
+        self.e = np.random.uniform(0,1)
+        if self.e >= 0.5:
             self.m = 0.511 #MeV
+            self.q = -1
         else:
-            self.m = 938.272 #Mev
+            self.m = 0.511 #Mev
+            self.q = 1
         
         
         """Velocity: random velocity between X and Y"""
         self.v = np.random.uniform(0.2, 0.6) #Natural units
         #Alter the range of velocities
         #Perhaps make vx, vy, vz
+        self.vx = np.random.uniform(0, 80)
+        self.vy = np.random.uniform(0, 80)
+        self.vz = np.random.uniform(200, 500)
+
         
         """Charge, 1,-1"""
         self.k = np.random.uniform(1, 0)

RunSimulation.py

@@ -3,7 +3,7 @@ Script to run the full detector simulation.
 """
 
 #TODO: Import all classes
-from Particles_software import Particles
+from Particles_software import Particle
 from Detector import Layer, Detector
 from analysis import Analysis
 

analysis.py

 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
-from scipy.optimize import curve_fit
+from mpl_toolkits.mplot3d import Axes3D
 from scipy import odr
 
 
@@ -38,7 +38,8 @@ class Analysis:
         self.results = pd.DataFrame({'w'    : np.zeros(2),
                                     'vx'    : np.zeros(2),
                                     'vy'    : np.zeros(2),
-                                    'vz'    : np.zeros(2)})
+                                    'vz'    : np.zeros(2),
+									'm'		: np.zeros(2)})
 
         #Determine mean and std of input
 
@@ -100,24 +101,22 @@ class Analysis:
         #Extract correct results from fitting and fill to results container
         self.results['vx'] = np.array([xResults.beta[0], xResults.sd_beta[0]])
 
-        #w = (np.abs(yResults.beta[0]) + np.abs(zResults.beta[0]))/2.
-        #wStd = np.sqrt(yResults.sd_beta[0]**2 + zResults.sd_beta[0]**2)
         w = wSgn*np.abs(zResults.beta[0])
         wStd = zResults.sd_beta[0]
         self.results['w'] = np.array([w , wStd])
 
-        #vy = ySgn*(np.abs(yResults.beta[1]) + np.abs(zResults.beta[1]))/2.
-        #vyStd = np.sqrt(yResults.sd_beta[1]**2 + zResults.sd_beta[1]**2)
         vy = vySgn*np.abs(zResults.beta[1])
         vyStd = zResults.sd_beta[1]
         self.results['vy'] = np.array([vy,vyStd])
 
-        #vz = zSgn*(np.abs(yResults.beta[2]) + np.abs(zResults.beta[2]))/2.
-        #vzStd = np.sqrt(yResults.sd_beta[2]**2 + zResults.sd_beta[2]**2)
         vz = np.abs(zResults.beta[2])
         vzStd = zResults.sd_beta[2]
         self.results['vz'] = np.array([vz,vzStd])
 
+        m = 0.511/np.abs(w)
+        mStd = m*wStd/np.abs(w)
+        self.results['m'] = np.array([m, mStd])
+
 
     """
     Equations of motion:
@@ -175,15 +174,6 @@ class Analysis:
         print('The x fit was performend with chi2 = ', xOUT.res_var)
         #print(xOUT.sd_beta)
         return xOUT
-        """
-        print('x ', xOUT.res_var)
-        plt.plot(self.tPoints['Mean'],self.xEOM(xOUT.beta, self.tPoints['Mean']))
-        plt.plot(self.tPoints['Mean'], self.xPoints['Mean'], 'o')
-        plt.xlabel('t')
-        plt.ylabel('x')
-        plt.savefig('x.pdf', format='pdf')
-        plt.gcf().clear()
-        """
     
     def yFit(self):
         yModel  = odr.Model(self.yEOM)
@@ -196,15 +186,6 @@ class Analysis:
         print('The y fit was performend with chi2 = ', yOUT.res_var)
         #yOUT.pprint()
         return yOUT
-        """
-        print('y :', yOUT.res_var)
-        plt.plot(self.tPoints['Mean'], self.yEOM(yOUT.beta, self.tPoints['Mean']))
-        plt.plot(self.tPoints['Mean'], self.yPoints['Mean'], 'o')
-        plt.xlabel('t')
-        plt.ylabel('y')
-        plt.savefig('y.pdf', format='pdf')
-        plt.gcf().clear()
-        """
 
     def zFit(self):
         zModel  = odr.Model(self.zEOM)
@@ -218,14 +199,7 @@ class Analysis:
         #zOUT.pprint()
         #print('z :', zOUT.res_var)
         return zOUT
-        """
-        plt.plot(self.tPoints['Mean'], self.zEOM(zOUT.beta, self.tPoints['Mean']))
-        plt.plot(self.tPoints['Mean'], self.zPoints['Mean'], 'o')
-        plt.xlabel('t')
-        plt.ylabel('z')
-        plt.savefig('z.pdf', format='pdf')
-        plt.gcf().clear()
-        """
+    
     def output(self):
         """
         A function to print results to terminal.
@@ -251,7 +225,7 @@ class Analysis:
         x_eom = np.array(self.xEOM([self.results["vx"][0]], times))
         y_eom = np.array(self.yEOM([self.results["w"][0], self.results["vy"][0], self.results["vz"][0]], times), dtype=pd.Series)
         z_eom = np.array(self.zEOM([self.results["w"][0], self.results["vy"][0], self.results["vz"][0]], times), dtype=pd.Series)
-        spaces = np.zeros(7)
+        spaces = np.zeros(5)
 
         fig = plt.figure()
 
@@ -295,4 +269,4 @@ class Analysis:
         plt.subplots_adjust(left=0, right=0.99)
         fig.tight_layout()
 
-        plt.savefig('3D.png', format='png')
\ No newline at end of file
+        plt.savefig('3D.png', format='png')

test.py

@@ -11,7 +11,7 @@ class Particle():
         self.vx = 30
         self.vy = 20
         self.vz = 300
-        self.m = 0.522
+        self.m = 0.511
         self.q = -1
 
 class Test(unittest.TestCase):