Fitting with polynomials, to z-x plane reversed - not sure if this is smart....

Fitting with polynomials, to z-x plane reversed - not sure if this is smart. and added simmple plots

analysis.py

@@ -39,12 +39,12 @@ class Analysis:
         #self.Bounds(self.xBounds['High'], self.xBounds['Low'], call=0)
         #self.Bounds(self.yBounds['High'], self.yBounds['Low'], call=1)
 
-        self.xPoints = pd.DataFrame({'Mean' : (bounds[:,0,0] + bounds[:,0,1])/2.,
-                                    'Error' : (bounds[:,0,0] - bounds[:,0,1])/2.})
-        self.yPoints = pd.DataFrame({'Mean': (bounds[:,1,0] + bounds[:,1,1])/2.,
-                                    'Error': (bounds[:,1,0] - bounds[:,1,1])/2.})
-        self.zPoints = pd.DataFrame({'Mean': (bounds[:,2,0] + bounds[:,2,1])/2.,
-                                    'Error': (bounds[:,2,0] - bounds[:,2,1])/2.})
+        self.xPoints = pd.DataFrame({'Mean' : (bounds[1:,0,0] + bounds[1:,0,1])/2.,
+                                    'Error' : (bounds[1:,0,0] - bounds[1:,0,1])/2.})
+        self.yPoints = pd.DataFrame({'Mean': (bounds[1:,1,0] + bounds[1:,1,1])/2.,
+                                    'Error': (bounds[1:,1,0] - bounds[1:,1,1])/2.})
+        self.zPoints = pd.DataFrame({'Mean': (bounds[1:,2,0] + bounds[1:,2,1])/2.,
+                                    'Error': (bounds[1:,2,0] - bounds[1:,2,1])/2.})
         
         print(self.xPoints)
         print(self.yPoints)
@@ -82,22 +82,31 @@ class Analysis:
         return y
 
 
+    def xPol(self, B, x):
+        a = B[1]/B[0]
+        b = - (B[0]*B[0])/(2*B[1]**2)
+        c = - (B[3]*B[0]**2)/(6*B[1]**3)
+        return a*x + b*x**2 + c*x**3#B[0] + B[1]*x + B[2]*(x**2) + B[3]*(x**3)
+
     def pol(self, B, x):
         return B[0] + B[1]*x + B[2]*(x**2) + B[3]*(x**3)
 
     def xFit(self):
         xModel  = odr.Model(self.pol)#self.xEOM)
-        xData   = odr.RealData(self.xPoints['Mean'],
-                               self.zPoints['Mean'],
-                               sx = self.xPoints['Error'],
-                               sy = self.zPoints['Error'])
+        xData   = odr.RealData(self.zPoints['Mean'],
+                               self.xPoints['Mean'],
+                               sx = self.zPoints['Error'],
+                               sy = self.xPoints['Error'])
         xODR = odr.ODR(xData, xModel, beta0=[1, 100, 100, 100])
         xOUT = xODR.run()
         xOUT.pprint()
-        print(xOUT.beta)
-        plt.plot(self.pol(xOUT.beta, self.xPoints['Mean']), self.xPoints['Mean'])
-        plt.plot(self.zPoints['Mean']+5, self.xPoints['Mean'])
-        plt.show()
+        print('x ', xOUT.res_var)
+        plt.plot(self.zPoints['Mean'],self.pol(xOUT.beta, self.zPoints['Mean']))
+        plt.plot(self.zPoints['Mean'], self.xPoints['Mean'], 'o')
+        plt.xlabel('z')
+        plt.ylabel('x')
+        plt.savefig('x-z.pdf', format='pdf')
+        plt.gcf().clear()
         """
         xPopt, xPcov = curve_fit(self.xEOM,
                                  self.xPoints['Mean'],
@@ -127,17 +136,21 @@ class Analysis:
         """
         #ODR
         
-        yModel  = odr.Model(self.yEOM)
+        yModel  = odr.Model(self.pol)
         yData   = odr.RealData(self.zPoints['Mean'],
                                self.yPoints['Mean'],
                                sx = self.zPoints['Error'],
                                sy = self.yPoints['Error'])
-        yODR = odr.ODR(yData, yModel, beta0=[1e3, 100, 100])
+        yODR = odr.ODR(yData, yModel, beta0=[1e3, 100, 100,100])
         yOUT = yODR.run()
         yOUT.pprint()
-        plt.plot(self.zPoints['Mean'], self.yEOM(yOUT.beta, self.zPoints['Mean']))
-        plt.plot(self.zPoints['Mean'], self.yPoints['Mean'])
-        plt.show()
+        print('y :', yOUT.res_var)
+        plt.plot(self.zPoints['Mean'], self.pol(yOUT.beta, self.zPoints['Mean']))
+        plt.plot(self.zPoints['Mean'], self.yPoints['Mean'], 'o')
+        plt.xlabel('z')
+        plt.ylabel('y')
+        plt.savefig('y-z.pdf', format='pdf')
+        plt.gcf().clear()
         """
         self.results['R'][0] = yPopt[0]
         self.results['R'][1] = yPerr[0]

test.py

@@ -3,8 +3,8 @@ from Detector import Detector
 
 class Particle:
     def __init__(self):
-        self.vx = 1
-        self.vy = 1
+        self.vx = 10
+        self.vy = 10
         self.vz = 300
         self.m = 0.522
         self.q = 1
@@ -15,5 +15,6 @@ DETRes = DET.detect(Particle())
 
 ANA = Analysis()
 ANA.fill(DETRes)
+ANA.xFit()
 ANA.yFit()
 

testAnalysis.py

@@ -15,8 +15,8 @@ class testAnalysis(unittest.TestCase):
         #self.assertEqual(A.results['Mean'][0], 5.5)
         #self.assertEqual(A.results['Error'][0], 0.5)
 
-        x = np.arange(0, 6)
-        z = A.xEOM([1,20,10,100], x)
+        x = np.arange(0, 10, 2)
+        z = A.xEOM([1,10,10,300], x)
         #plt.plot(z,x)
         #plt.show()
         
@@ -43,8 +43,8 @@ class testAnalysis(unittest.TestCase):
         #self.assertEqual(A.results['Error'][0], 0.5)
         
 
-        z = np.arange(0, 6)
-        y = A.yEOM([1000,100,100], z)
+        z = np.arange(90,150,10)
+        y = A.yEOM([1000,10,10,300], z)
         #plt.plot(z,y)
         #plt.show()