diff --git a/qdx/Bind.py b/qdx/Bind.py
index 0a145f2..db887ff 100644
--- a/qdx/Bind.py
+++ b/qdx/Bind.py
@@ -1,7 +1,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 
-from .utils import get_hist, GMM_slash
+from .utils import get_hist, GMM_clip
 from .model import Linear1D, FixedSlopeLine, pXLine
 from .fit import fit_line, fit_hist_gaussian
 
@@ -65,16 +65,16 @@ class Bind(object):
             else np.full(len(x), px)
         )
 
-    def slash(self):
-        """Using Gaussian Mixture Method (GMM) to decompose the data into noise and slashes"""
-        data = GMM_slash(
+    def clip(self):
+        """Using Gaussian Mixture Method (GMM) to decompose the data into noise and available data"""
+        data = GMM_clip(
             np.array(list(zip(self.x[0], self.y[0], self.px[0])), dtype=object)
         )
         self.x[0] = data[:, 0]
         self.y[0] = data[:, 1]
         self.px[0] = data[:, 2]
 
-        data = GMM_slash(
+        data = GMM_clip(
             np.array(list(zip(self.x[1], self.y[1], self.px[1])), dtype=object)
         )
         self.x[1] = data[:, 0]
@@ -276,8 +276,8 @@ class Bind(object):
     def _r_square(self, y, yp):
         mean = np.mean(y)
         SST = np.sum((y - mean) ** 2)
-        SSR = np.sum((yp - mean) ** 2)
-        return SSR / SST
+        SSE = np.sum((y - yp) ** 2)
+        return 1 - SSE / SST
 
     @property
     def RSquare1(self):
diff --git a/qdx/calibration.py b/qdx/calibration.py
index fd5b9bb..64013f9 100644
--- a/qdx/calibration.py
+++ b/qdx/calibration.py
@@ -62,7 +62,7 @@ class Calibration(object):
         for i in range(n):
             for j in range(m):
                 bind: Bind = self.binds[i][j]
-                bind.slash()
+                bind.clip()
                 bind.get_line()
                 bind.get_kb(bias_b, delta_e)
                 pbar.update(1)
diff --git a/qdx/utils.py b/qdx/utils.py
index 83bb1c7..10f7657 100644
--- a/qdx/utils.py
+++ b/qdx/utils.py
@@ -85,7 +85,7 @@ def draw_scatter(data, title, s=0.1):
     plt.close()
 
 
-def get_hist(data, step=1, maxN=50):
+def get_hist(data, step=1, maxN=50, return_edge=False):
     """Gets the boundary of histogram that the maximum count is bigger than threshold
 
     Parameters
@@ -94,6 +94,8 @@ def get_hist(data, step=1, maxN=50):
         Minimum bin width. The bin width is an integer multiple of step.
     maxN : int, optional
         Maximum count threshold
+    return_edge: bool, optional
+        If True, then the bin edges are also returned.
     """
     delta = step
     edge = np.arange(data.min(), data.max() + 1, delta)
@@ -107,11 +109,22 @@ def get_hist(data, step=1, maxN=50):
         edge = np.arange(data.min(), data.max() + 1, step)
         count, _ = np.histogram(data, bins=edge)
 
-    return count / delta, (edge[1:] + edge[:-1]) / 2
+    if return_edge:
+        return count / delta, (edge[1:] + edge[:-1]) / 2, edge
+    else:
+        return count / delta, (edge[1:] + edge[:-1]) / 2
 
 
-def GMM_slash(data):
-    """Using Gaussian Mixture Method (GMM) to decompose the data into noise and slashes"""
+def GMM_clip(data, return_all=False):
+    """Using Gaussian Mixture Method (GMM) to decompose the data into noise and available data
+    
+    Parameters
+    ----------
+    data : numpy.ndarray
+        Data to be clipped
+    return_all: bool, optional
+        If True, then all data will be returned.
+    """
     fit_data = np.array([])
 
     model = GaussianMixture(n_components=2)
@@ -122,4 +135,7 @@ def GMM_slash(data):
         idx = np.where(ny == i)[0]
         fit_data = idx if len(idx) > len(fit_data) else fit_data
 
-    return data[fit_data]
+    if return_all:
+        return ny
+    else:
+        return data[fit_data]