diff --git a/Analyser/FitAnalyser.py b/Analyser/FitAnalyser.py
index a5c5539..55c8a22 100644
--- a/Analyser/FitAnalyser.py
+++ b/Analyser/FitAnalyser.py
@@ -428,6 +428,8 @@ class DensityProfileBEC2dModel(lmfit.Model):
         # reshaping the image to 2D in the form [[a_00, a_01, .., a_0Y], [a_10,.., a_1Y], .., [a_X0, .., a_XY]], with a_xy
         x_width = len(np.unique(x))
         y_width = len(np.unique(y))
+        x_1d = np.linspace(x[0], x[-1], x_width)
+        y_1d = np.linspace(y[0], y[-1], y_width)
 
         data = np.reshape(data, (y_width, x_width))
         data = data.T
@@ -440,13 +442,15 @@ class DensityProfileBEC2dModel(lmfit.Model):
         # binarizing image to guess BEC width and calculate center
         thresh = self.calc_thresh(data,thresh_val=0.5)
         # calculating center of cloud by statistical distribution of binarized image
-        center = self.calc_cen(thresh)
+        center_pix = self.calc_cen_pix(thresh)
+        center = self.center_pix_conv(center_pix, x_1d, y_1d)
         # guessing BEC width, or better of width of center blob if no BEC is present
-        BEC_width_guess = self.guess_BEC_width(thresh, center)
+        BEC_width_guess = self.guess_BEC_width(thresh, center_pix)
 
         # plot binarized image and center position for debugging
         if self.is_debug:
-            plt.pcolormesh(thresh.T, cmap='jet')
+            X, Y = np.meshgrid(x_1d,y_1d)
+            plt.pcolormesh(X,Y, thresh.T, cmap='jet')
             plt.plot(center[0], center[1], marker='x', markersize=25, color='green')
             plt.gca().set_aspect('equal')
             plt.title(f'Binarized image for guessing BEC width + center position (BEC_width: x={BEC_width_guess[0]:.0f}, y={BEC_width_guess[1]:.0f} pix)')
@@ -459,17 +463,19 @@ class DensityProfileBEC2dModel(lmfit.Model):
             if self.is_debug:
                 print(f'x smaller y, 1d fit along x')
             s_width_ind = 0
+            x_fit = x_1d
             # slice of the image along the short BEC axis with width of BEC width is taken
-            X_guess = np.sum(data[:, round(center[1] - BEC_width_guess[1]/2) : round(center[1] + BEC_width_guess[1]/2)], 1) / len(data[0,round(center[1] - BEC_width_guess[1]/2) : round(center[1] + BEC_width_guess[1]/2)])
+            X_guess = np.sum(data[:, round(center_pix[1] - BEC_width_guess[1]/2) : round(center_pix[1] + BEC_width_guess[1]/2)], 1) / len(data[0,round(center_pix[1] - BEC_width_guess[1]/2) : round(center_pix[1] + BEC_width_guess[1]/2)])
         else:
             if self.is_debug:
                 print(f'y smaller x, 1d fit along y')
             s_width_ind = 1
-            X_guess = np.sum(data[round(center[0] - BEC_width_guess[0]/2) : round(center[0] + BEC_width_guess[0]/2), :], 0) / len(data[0,round(center[0] - BEC_width_guess[0]/2) : round(center[0] + BEC_width_guess[0]/2)])
+            x_fit = y_1d
+            X_guess = np.sum(data[round(center_pix[0] - BEC_width_guess[0]/2) : round(center_pix[0] + BEC_width_guess[0]/2), :], 0) / len(data[0,round(center_pix[0] - BEC_width_guess[0]/2) : round(center_pix[0] + BEC_width_guess[0]/2)])
 
 
         # Creating 1d fit init params + Performing fit 
-        x = np.linspace(0, len(X_guess), len(X_guess))
+        
 
         max_val = np.max(X_guess)
 
@@ -487,7 +493,7 @@ class DensityProfileBEC2dModel(lmfit.Model):
         params_1d.add('sigma_th', 3*BEC_width_guess[0], min=0, expr=f'0.632*sigma_bec + 0.518*deltax')
 
 
-        res_1d = fitmodel_1d.fit(X_guess, x=x, params=params_1d)
+        res_1d = fitmodel_1d.fit(X_guess, x=x_fit, params=params_1d)
         bval_1d = res_1d.best_values
 
         if self.is_debug:
@@ -501,17 +507,16 @@ class DensityProfileBEC2dModel(lmfit.Model):
             print('1d fitted values')
             self.print_bval(res_1d)
 
-            x = np.linspace(0, len(X_guess), len(X_guess))
-            plt.plot(x, X_guess, label='1d int. data')
-            plt.plot(x, density_1d(x,**bval_1d), label='bimodal fit')
-            plt.plot(x, thermal(x,x0=bval_1d['x0_th'], amp=bval_1d['amp_th'], sigma=bval_1d['sigma_th']), label='thermal part')
+            plt.plot(x_fit, X_guess, label='1d int. data')
+            plt.plot(x_fit, density_1d(x_fit,**bval_1d), label='bimodal fit')
+            plt.plot(x_fit, thermal(x_fit,x0=bval_1d['x0_th'], amp=bval_1d['amp_th'], sigma=bval_1d['sigma_th']), label='thermal part')
             plt.legend()
             if s_width_ind==0:
                 plt.title('1d fit of data along x-axis')
-                plt.xlabel('x_axis (pix)')
+                plt.xlabel('x_')
             else:
                 plt.title('1d fit of data along y-axis')
-                plt.xlabel('y_axis (pix)')
+                plt.xlabel('y_axis')
             plt.show()
 
         # scaling amplitudes of 1d fit with the maximum value of blurred 2d data 
@@ -600,6 +605,7 @@ class DensityProfileBEC2dModel(lmfit.Model):
 
         bval = res.best_values
         # Do described post_check if enabled
+
         if res.params['amp_bec'].vary and res.params['amp_th'].vary and bval['amp_bec']>0.5*bval['amp_th'] and self.post_check:
             
             # creating image by cutting out region around BEC and counting number of atoms
@@ -648,8 +654,8 @@ class DensityProfileBEC2dModel(lmfit.Model):
 
         return thresh
 
-    def calc_cen(self, thresh1):
-        """Calculating the center of a blob (atom cloud) in a binarized image by first calculating the probability distribution along both axes and afterwards the expectation value
+    def calc_cen_pix(self, thresh1):
+        """Calculating the center (in pixel) of a blob (atom cloud) in a binarized image by first calculating the probability distribution along both axes and afterwards the expectation value
 
         :param thresh1: Binary 2D image in the form [[a_00, a_01, .., a_0Y], [a_10,.., a_1Y], .., [a_X0, .., a_XY]], with a_xy, x_dim=X, y_dim=Y
         :type thresh1: 2D numpy array
@@ -669,13 +675,30 @@ class DensityProfileBEC2dModel(lmfit.Model):
         cen[0] = np.sum(dx * np.arange(X))
         cen[1] = np.sum(dy * np.arange(Y))
         return cen
+    
+    def center_pix_conv(self, center_pix, x, y):
+        """Converts center in pixel to center in values of x and y 
+
+        :param center_pix: pixel values of center
+        :type center_pix: numpy array (shape=(1,2))
+        :param x: x-axis
+        :type x: 1d numpy array
+        :param y: y-axis
+        :type y: 1d numpy array
+        :return: center coordinates in form [x_center, y_center] with respect to the axes
+        :rtype: numpy array (shap=(1,2))
+        """
+        center = np.empty(2)
+        center[0] = x[round(center_pix[0])]
+        center[1] = y[round(center_pix[1])]
+        return center
 
     def guess_BEC_width(self, thresh, center):
         """ returns width of blob in binarized image along both axis through the center 
 
         :param thresh: Binary 2D image in the form [[a_00, a_01, .., a_0Y], [a_10,.., a_1Y], .., [a_X0, .., a_XY]], with a_xy, x_dim=X, y_dim=Y
         :type thresh: 2d numpy array
-        :param center: center of blob in image in form [x_center, y_center]
+        :param center: center of blob in image in form [x_center, y_center] in pixel
         :type center: 1d numpy array (shape=(1,2))
         :return: width of blob in image as [x_width, y_width]
         :rtype: 1d numpy array (shape=(1,2))