Proactive fixes of some things that will break in numpy 2.0.0

Author: bbfrederick

capcalc/fit.py

@@ -1555,7 +1555,7 @@ def mlregress(x, y, intercept=True):
         xc = x
         beta = np.ones(p)
 
-    solution = np.linalg.lstsq(np.mat(xc).T, np.mat(y).T, rcond=-1)
+    solution = np.linalg.lstsq(np.asmatrix(xc).T, np.asmatrix(y).T, rcond=-1)
 
     # Computation of the coefficient of determination.
     Rx = np.atleast_2d(np.corrcoef(x, rowvar=1))
@@ -1737,7 +1737,7 @@ def peakdetect(y_axis, x_axis=None, lookahead=200, delta=0.0):
 
     # maxima and minima candidates are temporarily stored in
     # mx and mn respectively
-    mn, mx = np.Inf, -np.Inf
+    mn, mx = np.inf, -np.inf
 
     # Only detect peak if there is 'lookahead' amount of points after it
     for index, (x, y) in enumerate(zip(x_axis[:-lookahead], y_axis[:-lookahead])):
@@ -1749,15 +1749,15 @@ def peakdetect(y_axis, x_axis=None, lookahead=200, delta=0.0):
             mnpos = x
 
         ####look for max####
-        if y < mx - delta and mx != np.Inf:
+        if y < mx - delta and mx != np.inf:
             # Maxima peak candidate found
             # look ahead in signal to ensure that this is a peak and not jitter
             if y_axis[index : index + lookahead].max() < mx:
                 max_peaks.append([mxpos, mx])
                 dump.append(True)
                 # set algorithm to only find minima now
-                mx = np.Inf
-                mn = np.Inf
+                mx = np.inf
+                mn = np.inf
                 if index + lookahead >= length:
                     # end is within lookahead no more peaks can be found
                     break
@@ -1767,15 +1767,15 @@ def peakdetect(y_axis, x_axis=None, lookahead=200, delta=0.0):
                 #    mxpos = x_axis[np.where(y_axis[index:index+lookahead]==mx)]
 
         ####look for min####
-        if y > mn + delta and mn != -np.Inf:
+        if y > mn + delta and mn != -np.inf:
             # Minima peak candidate found
             # look ahead in signal to ensure that this is a peak and not jitter
             if y_axis[index : index + lookahead].min() > mn:
                 min_peaks.append([mnpos, mn])
                 dump.append(False)
                 # set algorithm to only find maxima now
-                mn = -np.Inf
-                mx = -np.Inf
+                mn = -np.inf
+                mx = -np.inf
                 if index + lookahead >= length:
                     # end is within lookahead no more peaks can be found
                     break