update match_transport for numpy2

Author: mmcky

.gitignore

@@ -1,2 +1,4 @@
 _build/
-.DS_Store
+.DS_Store
+.ipynb_checkpoints/
+.virtual_documents/

lectures/match_transport.md

@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.16.4
+    jupytext_version: 1.17.2
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
@@ -13,13 +13,11 @@ kernelspec:
 
 # Composite Sorting
 
-+++
-
 ## Overview 
 
 Optimal transport theory studies how a marginal probabilty measure  can  be related to another marginal probability measure in an ideal way.
 
-  * here ideal means to minimize some  cost criterion. 
+* here ideal means to minimize some  cost criterion. 
 
 The output of such a theory is a **coupling** of the two probability measures, i.e., a joint probabilty
 measure having those two  marginal probability measures.  
@@ -575,9 +573,9 @@ def plot_H_z(self, figsize=(15, 8), range_x_axis=None, scatter=True):
     plt.axhline(0, color='black', linewidth=1)
     
     # determine the step points for horizontal lines
-    step = np.concatenate(([self.support_z.min() - .05 * self.support_z.ptp()],
+    step = np.concatenate(([self.support_z.min() - .05 * np.ptp(self.support_z)],
                             self.support_z,
-                           [self.support_z.max() + .05 * self.support_z.ptp()]))
+                           [self.support_z.max() + .05 * np.ptp(self.support_z)]))
     height = np.concatenate(([0], H_z, [0]))
     
     # plot the horizontal lines of the step function
@@ -699,9 +697,9 @@ def plot_layers(self, figsize=(15, 8)):
     plt.figure(figsize=figsize)
 
     # Plot H(z)
-    step = np.concatenate(([self.support_z.min() - .05 * self.support_z.ptp()],
+    step = np.concatenate(([self.support_z.min() - .05 * np.ptp(self.support_z)],
                            self.support_z,
-                           [self.support_z.max() + .05 * self.support_z.ptp()]))
+                           [self.support_z.max() + .05 * np.ptp(self.support_z)]))
     height = np.concatenate((H_z, [0]))
     plt.step(step, height, where='post', color='black', label='CDF', zorder=1)
     
@@ -984,7 +982,7 @@ def plot_layer_matching(self, layer, matching_layer):
     ax.spines['top'].set_color('none') 
     ax.spines['right'].set_color('none')  
     ax.yaxis.set_ticks([])
-    ax.set_ylim(bottom= -self.support_z.ptp() / 100)
+    ax.set_ylim(bottom= -np.ptp(self.support_z) / 100)
 
     plt.show()
 
@@ -1319,29 +1317,29 @@ def plot_matching(self, matching_off_diag, title, figsize=(15, 15),
     ax.spines['top'].set_color('none')
     ax.spines['right'].set_color('none')
     ax.yaxis.set_ticks([])
-    ax.set_ylim(- self.X_types.ptp() / 10, 
-                (max_height / 2) + self.X_types.ptp()*.01)  
+    ax.set_ylim(- np.ptp(self.X_types) / 10, 
+                (max_height / 2) + np.ptp(self.X_types)*.01)  
 
     # Plot H_z on the main axis if enabled
     if plot_H_z:
         H_z = np.cumsum(self.q_z)  
 
         step = np.concatenate(([self.support_z.min() 
-                                - .02 * self.support_z.ptp()],
+                                - .02 * np.ptp(self.support_z)],
                                 self.support_z,
                                 [self.support_z.max() 
-                                + .02 * self.support_z.ptp()]))
+                                + .02 * np.ptp(self.support_z)]))
 
-        H_z = H_z/H_z.ptp() * self.support_z.ptp() /2
+        H_z = H_z/np.ptp(H_z) * np.ptp(self.support_z) /2
         height = np.concatenate(([0], H_z, [0]))
 
         # Plot the compressed H_z on the same main x-axis
         ax.step(step, height, color='green', lw=2, 
                                 label='$H_z$', where='post')
         
         # Set the y-limit to keep H_z and maximum circle size in the plot
-        ax.set_ylim(np.min(H_z) - H_z.ptp() *.01,
-                    np.maximum(np.max(H_z), max_height / 2) + H_z.ptp() *.01) 
+        ax.set_ylim(np.min(H_z) - np.ptp(H_z) *.01,
+                    np.maximum(np.max(H_z), max_height / 2) + np.ptp(H_z) *.01) 
 
         # Add label and legend for H_z
         ax.legend(loc="upper right")
@@ -1614,7 +1612,7 @@ example_2.plot_matching(matching_NAM, title = 'NAM',
 
 +++ {"user_expressions": []}
 
-### Example 3 
+### Example 3
 
 +++ {"user_expressions": []}
 
@@ -1907,7 +1905,7 @@ def plot_hierarchies(self, subpairs, scatter=True, range_x_axis=None):
 
     if range_x_axis is not None:
         ax.set_xlim(range_x_axis)
-        ax.set_ylim(- self.X_types.ptp() / 10, 
+        ax.set_ylim(- np.ptp(self.X_types) / 10, 
                     (range_x_axis[1] - range_x_axis[0]) / 2 )
 
     # Title and layout settings for the main plot
@@ -2230,12 +2228,11 @@ We plot the wage standard deviation for the sorted occupations.
 ---
 mystnb:
   figure:
-    caption: "Average wage for each Standard Occupational Classification (SOC) code.
-    The codes are sorted by average wage on the horizontal axis. In red,
-    a polynomial of degree 5 is fitted to the data. The size of the marker is 
-    proportional to the number of individuals in the occupation."
+    caption: Average wage for each Standard Occupational Classification (SOC) code.
+      The codes are sorted by average wage on the horizontal axis. In red, a polynomial
+      of degree 5 is fitted to the data. The size of the marker is proportional to
+      the number of individuals in the occupation.
 ---
-
 # Scatter plot wage dispersion for each occupation
 plt.figure(figsize=(10, 6))
 
@@ -2273,11 +2270,10 @@ We also plot the average wages for each occupation (SOC code). Again, occupation
 ---
 mystnb:
   figure:
-    caption: "Average wage for each Standard Occupational Classification (SOC) code.
-    The codes are sorted by average wage on the horizontal axis. In red,
-    a polynomial of degree 5 is fitted to the data."
+    caption: Average wage for each Standard Occupational Classification (SOC) code.
+      The codes are sorted by average wage on the horizontal axis. In red, a polynomial
+      of degree 5 is fitted to the data.
 ---
-
 # Scatter plot average wage for each occupation
 plt.figure(figsize=(10, 6))