Fix deprecated numpy aliases of builtin types

A fresh install of kCSD installs the latest numpy (1.25+), which breaks kCSD because
[`np.complex` was deprecated in Numpy 1.20](https://numpy.org/doc/stable/release/1.20.0-notes.html#using-the-aliases-of-builtin-types-like-np-int-is-deprecated)

Fixes #141

Author: guptadivyansh

figures/kCSD_properties/different_error_maps.py

@@ -26,8 +26,8 @@ def grid(x, y, z):
     x = x.flatten()
     y = y.flatten()
     z = z.flatten()
-    xi, yi = np.mgrid[min(x):max(x):np.complex(0, 100),
-                      min(y):max(y):np.complex(0, 100)]
+    xi, yi = np.mgrid[min(x):max(x):complex(0, 100),
+                      min(y):max(y):complex(0, 100)]
     zi = griddata((x, y), z, (xi, yi), method='linear')
     return xi, yi, zi
 

figures/kCSD_properties/kCSD_with_reliability_map_2D.py

@@ -171,8 +171,8 @@ def grid(x, y, z, resX=100, resY=100):
     x = x.flatten()
     y = y.flatten()
     z = z.flatten()
-    xi, yi = np.mgrid[min(x):max(x):np.complex(0, resX),
-                      min(y):max(y):np.complex(0, resY)]
+    xi, yi = np.mgrid[min(x):max(x):complex(0, resX),
+                      min(y):max(y):complex(0, resY)]
     zi = griddata((x, y), z, (xi, yi), method='linear')
     return xi, yi, zi
 

figures/kCSD_properties/tutorial3.py

@@ -27,8 +27,8 @@ def grid(x, y, z):
     x = x.flatten()
     y = y.flatten()
     z = z.flatten()
-    xi, yi = np.mgrid[min(x):max(x):np.complex(0, 100),
-                      min(y):max(y):np.complex(0, 100)]
+    xi, yi = np.mgrid[min(x):max(x):complex(0, 100),
+                      min(y):max(y):complex(0, 100)]
     zi = griddata((x, y), z, (xi, yi), method='linear')
     return xi, yi, zi
 

figures/kCSD_properties/tutorial_basic.py

@@ -28,8 +28,8 @@ def grid(x, y, z):
     x = x.flatten()
     y = y.flatten()
     z = z.flatten()
-    xi, yi = np.mgrid[min(x):max(x):np.complex(0, 100),
-                      min(y):max(y):np.complex(0, 100)]
+    xi, yi = np.mgrid[min(x):max(x):complex(0, 100),
+                      min(y):max(y):complex(0, 100)]
     zi = griddata((x, y), z, (xi, yi), method='linear')
     return xi, yi, zi
 

figures/kCSD_properties/tutorial_basic_3d.py

@@ -19,9 +19,9 @@ def generate_csd_3D(csd_profile, csd_seed,
     """
     Gives CSD profile at the requested spatial location, at 'res' resolution
     """
-    csd_at = np.mgrid[start_x:end_x:np.complex(0,res_x), 
-                      start_y:end_y:np.complex(0,res_y), 
-                      start_z:end_z:np.complex(0,res_z)]
+    csd_at = np.mgrid[start_x:end_x:complex(0,res_x), 
+                      start_y:end_y:complex(0,res_y), 
+                      start_z:end_z:complex(0,res_z)]
     f = csd_profile(csd_at, seed=csd_seed) 
     f = f / np.max(np.abs(f))
     return csd_at, f
@@ -42,9 +42,9 @@ def generate_electrodes(xlims=[0.1,0.9], ylims=[0.1,0.9], zlims=[0.1,0.9], res=5
     """
     Places electrodes in a square grid
     """
-    ele_x, ele_y, ele_z = np.mgrid[xlims[0]:xlims[1]:np.complex(0,res), 
-                                   ylims[0]:ylims[1]:np.complex(0,res), 
-                                   zlims[0]:zlims[1]:np.complex(0,res)]
+    ele_x, ele_y, ele_z = np.mgrid[xlims[0]:xlims[1]:complex(0,res), 
+                                   ylims[0]:ylims[1]:complex(0,res), 
+                                   zlims[0]:zlims[1]:complex(0,res)]
     ele_x = ele_x.flatten()
     ele_y = ele_y.flatten()
     ele_z = ele_z.flatten()
@@ -70,7 +70,7 @@ def make_plots(fig_title,
     gs = gridspec.GridSpec(z_steps+1, 4, height_ratios=height_ratios)
     t_max = np.max(np.abs(true_csd))
     levels = np.linspace(-1*t_max, t_max, 16)
-    ind_interest = np.mgrid[0:t_csd_z.shape[2]:np.complex(0,z_steps+2)]
+    ind_interest = np.mgrid[0:t_csd_z.shape[2]:complex(0,z_steps+2)]
     ind_interest = np.array(ind_interest, dtype=int)[1:-1]
     for ii, idx in enumerate(ind_interest):
         ax = plt.subplot(gs[ii, 0])
@@ -130,7 +130,7 @@ def make_plots(fig_title,
     # #KCSD - pre_cv
     t_max = np.max(np.abs(pre_cv[:,:,:,0]))
     levels_kcsd = np.linspace(-1*t_max, t_max, 16)
-    ind_interest = np.mgrid[0:k_csd_z.shape[2]:np.complex(0,z_steps+2)]
+    ind_interest = np.mgrid[0:k_csd_z.shape[2]:complex(0,z_steps+2)]
     ind_interest = np.array(ind_interest, dtype=int)[1:-1]
     for ii, idx in enumerate(ind_interest):
         ax = plt.subplot(gs[ii, 2])
@@ -158,7 +158,7 @@ def make_plots(fig_title,
     # kCSD post CV
     t_max = np.max(np.abs(est_csd[:,:,:,0]))
     levels_kcsd = np.linspace(-1*t_max, t_max, 16)
-    ind_interest = np.mgrid[0:k_csd_z.shape[2]:np.complex(0,z_steps+2)]
+    ind_interest = np.mgrid[0:k_csd_z.shape[2]:complex(0,z_steps+2)]
     ind_interest = np.array(ind_interest, dtype=int)[1:-1]
     for ii, idx in enumerate(ind_interest):
         ax = plt.subplot(gs[ii, 3])

figures/kCSD_properties/tutorial_broken_electrodes.py

@@ -52,8 +52,8 @@ def grid(x, y, z):
     x = x.flatten()
     y = y.flatten()
     z = z.flatten()
-    xi, yi = np.mgrid[min(x):max(x):np.complex(0, 100),
-                      min(y):max(y):np.complex(0, 100)]
+    xi, yi = np.mgrid[min(x):max(x):complex(0, 100),
+                      min(y):max(y):complex(0, 100)]
     zi = griddata((x, y), z, (xi, yi), method='linear')
     return xi, yi, zi
 

figures/kCSD_properties/tutorial_broken_electrodes_diff_err.py

@@ -50,8 +50,8 @@ def grid(x, y, z):
     x = x.flatten()
     y = y.flatten()
     z = z.flatten()
-    xi, yi = np.mgrid[min(x):max(x):np.complex(0, 100),
-                      min(y):max(y):np.complex(0, 100)]
+    xi, yi = np.mgrid[min(x):max(x):complex(0, 100),
+                      min(y):max(y):complex(0, 100)]
     zi = griddata((x, y), z, (xi, yi), method='linear')
     return xi, yi, zi
 

figures/kCSD_properties/tutorial_noisy_electrodes.py

@@ -28,8 +28,8 @@ def grid(x, y, z):
     x = x.flatten()
     y = y.flatten()
     z = z.flatten()
-    xi, yi = np.mgrid[min(x):max(x):np.complex(0, 100),
-                      min(y):max(y):np.complex(0, 100)]
+    xi, yi = np.mgrid[min(x):max(x):complex(0, 100),
+                      min(y):max(y):complex(0, 100)]
     zi = griddata((x, y), z, (xi, yi), method='linear')
     return xi, yi, zi
 

figures/npx/Neuropixels_for_NEURON.ipynb

@@ -136,8 +136,8 @@
     "                          letter='C', filt=False)\n",
     "    \n",
     "    ax4 = plt.subplot(244, aspect='equal')\n",
-    "    xx, yy = np.mgrid[xmin:xmax:np.complex(0, true_csd.shape[0]),\n",
-    "                      ymin:ymax:np.complex(0, true_csd.shape[1])]\n",
+    "    xx, yy = np.mgrid[xmin:xmax:complex(0, true_csd.shape[0]),\n",
+    "                      ymin:ymax:complex(0, true_csd.shape[1])]\n",
     "    tcf.plot_csd_smooth(ax4, xmin, xmax, ymin, ymax, true_csd[:, :],\n",
     "                    xx, yy, letter='D')\n",
     "\n",

figures/npx/traub_data_kcsd_column_figure.py

@@ -264,8 +264,8 @@ def plot_all_currents(ax, xmin, xmax, ymin, ymax, all_x, all_y, all_z, all_val,
 def plot_csd_slice(ax, xmin, xmax, ymin, ymax, all_x, all_y, all_z, all_val,
                    letter='', title=''):
     nx, ny = int(800/50), int(2500/50)
-    vx, vy = np.mgrid[-400:400:np.complex(0, nx+1),
-                      -2000:500:np.complex(0, ny+1)]
+    vx, vy = np.mgrid[-400:400:complex(0, nx+1),
+                      -2000:500:complex(0, ny+1)]
     voxels_csd = np.zeros((nx, ny))
 
     counter = 0
@@ -308,7 +308,7 @@ def plot_dense_potentials(ax, h, pop_names, time_pts, time_pt_interest,
     z = 0
     tot_ele = nx * ny
     zz = np.ones((tot_ele, 1)) * z
-    xx, yy = np.mgrid[-450:450:np.complex(0, nx), -2000:500:np.complex(0, ny)]
+    xx, yy = np.mgrid[-450:450:complex(0, nx), -2000:500:complex(0, ny)]
     xx = xx.reshape(tot_ele, 1)
     yy = yy.reshape(tot_ele, 1)
     elec_pos = np.hstack((xx, yy, zz))
@@ -352,7 +352,7 @@ def prepare_electrodes():
     dist_from_center_axis = 1.5*22.5+20
     od, do = 0, -(10+22.5)*ny
     xx, yy = np.mgrid[-dist_from_center_axis:dist_from_center_axis:
-                      np.complex(0, nx), od:do:np.complex(0, ny)]
+                      complex(0, nx), od:do:complex(0, ny)]
     xx = xx.reshape(tot_ele, 1)
     yy = yy.reshape(tot_ele, 1)
     elec4 = np.hstack((xx, yy, zz))
@@ -372,14 +372,14 @@ def prepare_electrodes():
     left_border = -30
 
     od, do = 500, -(40*ny/2 - 500)
-    x1, y1 = np.mgrid[left_border:left_border+2*dist_h:np.complex(0, nx/2),
-                      od:do:np.complex(0, ny/2)]
+    x1, y1 = np.mgrid[left_border:left_border+2*dist_h:complex(0, nx/2),
+                      od:do:complex(0, ny/2)]
     x1 = x1.reshape(int(tot_ele/2), 1)
     y1 = y1.reshape(int(tot_ele/2), 1)
 
     x2, y2 = np.mgrid[left_border+dist_h:left_border +
-                      3*dist_h:np.complex(0, nx/2),
-                      od-dist_v:do-dist_v:np.complex(0, ny/2)]
+                      3*dist_h:complex(0, nx/2),
+                      od-dist_v:do-dist_v:complex(0, ny/2)]
     x2 = x2.reshape(int(tot_ele/2), 1)
     y2 = y2.reshape(int(tot_ele/2), 1)
 
@@ -434,8 +434,8 @@ def make_column_plot(h, pop_names, time_pts, time_pt_interest, elec_pos_list, na
                           letter='C', filt=False)
 
     ax4 = plt.subplot(244, aspect='equal')
-    xx, yy = np.mgrid[xmin:xmax:np.complex(0, true_csd.shape[0]),
-                      ymin:ymax:np.complex(0, true_csd.shape[1])]
+    xx, yy = np.mgrid[xmin:xmax:complex(0, true_csd.shape[0]),
+                      ymin:ymax:complex(0, true_csd.shape[1])]
     plot_csd_smooth(ax4, xmin, xmax, ymin, ymax, true_csd[:, :],
                     xx, yy, letter='D')