updated the gui PostProcessing with the arrays for openpiv

since some version of openpiv the local median is using 2D arrays,
so the replace outliers and and smoothing
the smoothing seems to be too long, probably one needs Cython or manage to compile it for any platform.

.gitignore

@@ -3,3 +3,9 @@
 dist/*
 openpivgui.egg-info/*
 .vscode/*
+build/*
+docs/_build/*
+docs/_build/doctrees/*
+docs/_build/html/*
+docs/_build/html/_static/*
+dist/*

openpivgui/PostProcessing.py

@@ -55,8 +55,8 @@ def sig2noise(self):
         result_fnames = []
         for i, f in enumerate(self.p['fnames']):
             data = np.loadtxt(f)
-            u, v, mask = piv_vld.sig2noise_val(
-                data[:, 2], data[:, 3], data[:, 5],
+            mask = piv_vld.sig2noise_val(
+                data[:, 5],
                 threshold=self.p['sig2noise_threshold'])
 
             save_fname = create_save_vec_fname(
@@ -65,7 +65,8 @@ def sig2noise(self):
 
             save(data[:, 0],
                  data[:, 1],
-                 u, v,
+                 data[:, 2],
+                 data[:, 3],
                  data[:, 4] + mask,
                  sig2noise=data[:, 5],
                  filename=save_fname,
@@ -84,15 +85,18 @@ def global_std(self):
         result_fnames = []
         for i, f in enumerate(self.p['fnames']):
             data = np.loadtxt(f)
-            u, v, mask = piv_vld.global_std(
+
+            mask = piv_vld.global_std(
                 data[:, 2], data[:, 3],
                 std_threshold=self.p['global_std_threshold'])
+
             save_fname = create_save_vec_fname(
                 path=f,
                 postfix='_std_thrhld')
             save(data[:, 0],
                  data[:, 1],
-                 u, v,
+                 data[:, 2], 
+                 data[:, 3],
                  data[:, 4] + mask,
                  data[:, 5],
                  save_fname,
@@ -110,16 +114,19 @@ def global_val(self):
         result_fnames = []
         for i, f in enumerate(self.p['fnames']):
             data = np.loadtxt(f)
-            u, v, mask = piv_vld.global_val(
+            mask = piv_vld.global_val(
                 data[:, 2], data[:, 3],
                 u_thresholds=(self.p['MinU'], self.p['MaxU']),
                 v_thresholds=(self.p['MinV'], self.p['MaxV']))
+
             save_fname = create_save_vec_fname(
                 path=f,
                 postfix='_glob_thrhld')
+
             save(data[:, 0],
                  data[:, 1],
-                 u, v,
+                 data[:, 2],
+                 data[:, 3],
                  data[:, 4] + mask,
                  data[:, 5],
                  save_fname,
@@ -138,18 +145,25 @@ def local_median(self):
         result_fnames = []
         for i, f in enumerate(self.p['fnames']):
             data = np.loadtxt(f)
-            u, v, mask = piv_vld.local_median_val(
-                data[:, 2], data[:, 3],
+
+            u = data[:, 2].reshape(len(set(data[:,0])), len(set(data[:,1])))
+            v = data[:, 3].reshape(len(set(data[:,0])), len(set(data[:,1])))
+
+            mask = piv_vld.local_median_val(
+                u, v,
                 u_threshold=self.p['local_median_threshold'],
                 v_threshold=self.p['local_median_threshold'],
                 size=self.p['local_median_size'])
+
             save_fname = create_save_vec_fname(
                 path=f,
                 postfix='_med_thrhld')
+
             save(data[:, 0],
                  data[:, 1],
-                 u, v,
-                 data[:, 4] + mask,
+                 data[:, 2], 
+                 data[:, 3],
+                 data[:, 4] + mask.flatten(),
                  data[:, 5],
                  save_fname,
                  delimiter=self.delimiter)
@@ -161,8 +175,16 @@ def repl_outliers(self):
         result_fnames = []
         for i, f in enumerate(self.p['fnames']):
             data = np.loadtxt(f)
+
+            shapes = (len(set(data[:,0])), len(set(data[:,1])))
+
+            u = data[:, 2].reshape(shapes)
+            v = data[:, 3].reshape(shapes)
+            flags = data[:, 4].reshape(shapes).astype(bool)            
+
             u, v = piv_flt.replace_outliers(
-                np.array([data[:, 2]]), np.array([data[:, 3]]),
+                u, v,
+                flags,  # <- flags for outliers, mask for the static image mask
                 method=self.p['repl_method'],
                 max_iter=self.p['repl_iter'],
                 kernel_size=self.p['repl_kernel'])
@@ -171,7 +193,8 @@ def repl_outliers(self):
                 postfix='_repl')
             save(data[:, 0],
                  data[:, 1],
-                 u, v,
+                 u.flatten(), 
+                 v.flatten(), # <- holes filled version
                  data[:, 4],
                  data[:, 5],
                  save_fname,
@@ -184,6 +207,11 @@ def smoothn_r(self):
         result_fnames = []
         for i, f in enumerate(self.p['fnames']):
             data = np.loadtxt(f)
+            shapes = (len(set(data[:,0])), len(set(data[:,1])))
+
+            u = data[:, 2].reshape(shapes)
+            v = data[:, 3].reshape(shapes)
+
             u, _, _, _ = piv_smt.smoothn(
                 data[:, 2], s=self.p['smoothn_val'], isrobust=self.p['robust'])
             v, _, _, _ = piv_smt.smoothn(