Refactor

examples/demo_visualization.py

@@ -49,7 +49,7 @@ def binary_polygon(
  """
 
  # Creates a Polygon abject out of the polygon vertices in pzs
- poly = Polygon(pzs, True)
+ poly = Polygon(pzs, closed=True)
  # Checks which points are enclosed by polygon.
  ind = np.nonzero(poly.contains_points(xys))[0]
  phi = np.ones((nrow, ncol)) * outside # SD - create matrix of 'outside'
@@ -693,7 +693,7 @@ def plot_results(
 
  if Y is not None:
  # WHP - h test + training
- fig_dir = jp(base_dir, root)
+ fig_dir = jp(base_dir, root, "uq")
  ff = jp(fig_dir, "whpa.png") # figure name
  Y = check_array(Y, allow_nd=True)
  try:
@@ -717,8 +717,6 @@ def plot_results(
  labels = ["Training", "Test"]
  legend = _proxy_annotate(annotation=[], loc=2, fz=14)
  _proxy_legend(legend1=legend, colors=colors, labels=labels, fig_file=ff)
- if show:
- plt.show()
  plt.close()
 
  # WHPs
@@ -777,8 +775,6 @@ def plot_results(
  labels=labels,
  fig_file=ff,
  )
- if show:
- plt.show()
  plt.close()
 
 

examples/whpa_demo.py

@@ -65,20 +65,14 @@ def init_bel():
  sub_dir = jp(os.getcwd(), "results")
 
  # Folders
- obj_dir = jp(sub_dir, "obj") # Location to save the BEL model
  fig_data_dir = jp(sub_dir, "data") # Location to save the raw data figures
- fig_pca_dir = jp(sub_dir, "pca") # Location to save the PCA figures
- fig_cca_dir = jp(sub_dir, "regression_model") # Location to save the CCA figures
  fig_pred_dir = jp(sub_dir, "uq") # Location to save the prediction figures
 
  # Creates directories
  [
  utils.dirmaker(f, erase=True)
  for f in [
- obj_dir,
  fig_data_dir,
- fig_pca_dir,
- fig_cca_dir,
  fig_pred_dir,
  ]
  ]

requirements.txt

@@ -1,12 +1,11 @@
 scikit-learn
 matplotlib
 scipy
-numpy
 joblib
-pandas
+pandas<2
 pytest
 scikit-image
 seaborn
-tensorflow
+tensorflow==2.14
 loguru
-tensorflow_probability
+tensorflow_probability==0.22