Update examples

dots.py

@@ -15,8 +15,8 @@
  dots = np.zeros([1000, 1000])
  for i in range(10):
  for j in range(10):
- adjx = np.random.choice(np.arange(-25, 25, 1, np.int), 1)[0]
- adjy = np.random.choice(np.arange(-25, 25, 1, np.int), 1)[0]
+ adjx = np.random.choice(np.arange(-25, 25, 1, int), 1)[0]
+ adjy = np.random.choice(np.arange(-25, 25, 1, int), 1)[0]
  dots[(100*i)+50+adjx, (100*j)+50+adjy] = 1
 
  dt = spim.distance_transform_edt(dots)
@@ -88,7 +88,7 @@ def interpolated_sq_disp(rw):
  plt.figure()
  plt.imshow(dt)
  # thresh = np.linspace(0.525, 0.55, 3)
- thresh = [0.525]
+ thresh = [0.6]
  rws = []
  ims = []
  for i in range(len(thresh)):

frequency.py

@@ -15,7 +15,7 @@
 plt.close('all')
 def main():
  im = ps.generators.blobs(shape=[1000, 1000], blobiness=3, porosity=0.5)
- im = ps.filters.fill_blind_pores(im).astype(np.int)
+ im = ps.filters.fill_blind_pores(im).astype(int)
  dt = spim.distance_transform_edt(im)
  grey = dt.copy()/dt.max()
  grey = np.pad(grey, 1, mode='constant', constant_values=0)

grey.py

@@ -11,7 +11,7 @@
 import matplotlib.pyplot as plt
 import scipy.ndimage as spim
 if __name__ == '__main__':
- im = ps.generators.blobs(shape=[200, 200], porosity=0.5).astype(np.int)
+ im = ps.generators.blobs(shape=[200, 200], porosity=0.5).astype(int)
  plt.figure()
  plt.imshow(im)
  dt = spim.distance_transform_edt(im)