Changed np.NaN -> np.nan for numpy 2.0 compatibility

pytplot/AncillaryPlots/spec_slicer.py

@@ -152,7 +152,7 @@ def update(t, name):
                     data_at_slice = tplot_utilities.reduce_spec_dataset(tplot_dataset=y_values_avgd)
                     data_values_at_slice = data_at_slice.values
                     if y_axis_log:
-                        data_values_at_slice[data_values_at_slice <= 0] = np.NaN
+                        data_values_at_slice[data_values_at_slice <= 0] = np.nan
 
                     try:
                         plot_data.setData(bins, data_values_at_slice)
@@ -167,7 +167,7 @@ def update(t, name):
 
                         data_values_at_slice = data_at_slice.values
                         if y_axis_log:
-                            data_values_at_slice[data_values_at_slice<=0] = np.NaN
+                            data_values_at_slice[data_values_at_slice<=0] = np.nan
                         #Create a Mask for Nan Values
                         locations_where_nan = np.argwhere(np.isnan(data_values_at_slice))
                         if len(locations_where_nan) > 0:

pytplot/QtPlotter/TVarFigureSpec.py

@@ -201,7 +201,7 @@ def _visdata(self):
 
         # The the log of the z values if we are using a logarithmic x axis
         if self._getzaxistype() == 'log':
-            data[data <= 0] = np.NaN
+            data[data <= 0] = np.nan
             data = np.log10(data)
             zmin = np.log10(self.zmin)
             zmax = np.log10(self.zmax)
@@ -268,8 +268,8 @@ def _format_spec_data_as_image(self, x_pixel_length=1000, y_pixel_height=100):
 
             # Try cutting the data off that is outside the bounds
             try:
-                temp_data[yp < np.nanmin(y_values_at_xi)] = np.NaN
-                temp_data[yp > np.nanmax(y_values_at_xi)] = np.NaN
+                temp_data[yp < np.nanmin(y_values_at_xi)] = np.nan
+                temp_data[yp > np.nanmax(y_values_at_xi)] = np.nan
             except RuntimeWarning:
                 # If the entire bin is NaN the above stuff fails, so just continue on
                 pass

pytplot/tplot_math/clean_spikes.py

@@ -100,14 +100,14 @@ def clean_spikes(names, nsmooth=10, thresh=0.3, sub_avg=False,
             for i in range(dim[0]):
                 # compare smoothed out values to original values
                 if abs(d0[i] - ds[i]) > thresh * abs(ds[i]):
-                    dn[i] = np.NaN  # for spikes, set to NaN
+                    dn[i] = np.nan  # for spikes, set to NaN
         else:
             # More than one dim data.
             for j in range(dim[1]):
                 for i in range(dim[0]):
                     # compare smoothed out values to original values
                     if abs(d0[i, j] - ds[i, j]) > thresh * abs(ds[i, j]):
-                        dn[i, j] = np.NaN  # for spikes, set to NaN
+                        dn[i, j] = np.nan  # for spikes, set to NaN
 
         # pytplot.data_quants[new] = d
         pytplot.replace_data(new, dn)

pytplot/tplot_math/tests/tests.py

@@ -140,7 +140,7 @@ def test_tsmooth(self):
         r = [1.0, 1.3333333333333333, 2.0, 3.0, 2.6666666666666665,
              3.0, 2.6666666666666665, 3.0, 2.0, 1.3333333333333333, 1.0]
         self.assertTrue(x == r)
-        b = [1.0, 1.0, 2.0, 3.0, np.NaN, np.NaN, np.NaN, np.NaN, 2.0, 1.0, 1.0]
+        b = [1.0, 1.0, 2.0, 3.0, np.nan, np.nan, np.nan, np.nan, 2.0, 1.0, 1.0]
         y = smooth(b, width=3)
         ry = [1.0, 1.3333333333333333, 2.0, 1.6666666666666665, 1.0,
               np.nan, np.nan, 0.6666666666666666, 1.0, 1.3333333333333333, 1.0]

pytplot/tplot_math/tsmooth.py

@@ -49,13 +49,13 @@ def smooth(data, width=10, preserve_nans=None):
 
     for i, d in enumerate(data):
         if (i >= (width-1)/2) and (i <= N-(width+1)/2):
-            if (preserve_nans is not None) and data[i] is np.NaN:
+            if (preserve_nans is not None) and data[i] is np.nan:
                 continue
             tsum = 0
             count = 0
             for j in range(int(width)):
                 idx = math.ceil(i+j-width/2)
-                if data[idx] is not np.NaN:
+                if data[idx] is not np.nan:
                     tsum += data[idx]
                     count += 1
             if count > 0:  # otherwise, all NaN