Resolve "Image creation bugs with 0 size windows"

Closes #21 (closed)

skais/ais/ais_points.py

@@ -73,35 +73,59 @@ class AISPoints:
         self.df = self.df[self.df["heading"] <= 360]
         self.df = self.df[self.df["heading"] >= 0]
 
-    def normalize(self, features, normalization_type="min-max"):
-        normalization_dict = {}
-        if normalization_type == "min-max":
-            for f in features:
-                minimum = self.df[f].min()
-                maximum = self.df[f].max()
-                diff = (maximum - minimum)
-                if diff == 0:
-                    print("Warning: diff = %d", diff)
-                    diff = 1
-                self.df[f] = (self.df[f] - minimum) / diff
-                normalization_dict[f"{f}_minimum"] = minimum
-                normalization_dict[f"{f}_maximum"] = maximum
-
-        elif normalization_type == "standardization":
-            normalisation_factors = ("standardization", {})
-            for f in features:
-                mean = self.df[f].mean()
-                std = self.df[f].std()
-                if std == 0:
-                    print("Warning: std = %d", std)
-                    std = 1
-                self.df[f] = (self.df[f] - mean) / std
-                normalization_dict[f"{f}_mean"] = mean
-                normalization_dict[f"{f}_std"] = std
+    def normalize(self, features, normalization_type="min-max", normalization_dict=None):
+        if normalization_dict is None:
+            normalization_dict = {'normalization_type': normalization_type}
+            if normalization_type == "min-max":
+                for f in features:
+                    minimum = self.df[f].min()
+                    maximum = self.df[f].max()
+                    diff = (maximum - minimum)
+                    if diff == 0:
+                        print("Warning: diff = %d", diff)
+                        diff = 1
+                    self.df[f] = (self.df[f] - minimum) / diff
+                    normalization_dict[f"{f}_minimum"] = minimum
+                    normalization_dict[f"{f}_maximum"] = maximum
+
+            elif normalization_type == "standardization":
+                for f in features:
+                    mean = self.df[f].mean()
+                    std = self.df[f].std()
+                    if std == 0:
+                        print("Warning: std = %d", std)
+                        std = 1
+                    self.df[f] = (self.df[f] - mean) / std
+                    normalization_dict[f"{f}_mean"] = mean
+                    normalization_dict[f"{f}_std"] = std
 
+            else:
+                raise ValueError(f"{normalization_type} not a valid normalization method. Must be on of [min-max, "
+                                 f"standardization]")
         else:
-            raise ValueError(f"{normalization_type} not a valid normalization method. Must be on of [min-max, "
-                             f"standardization]")
+            normalization_type = normalization_dict['normalization_type']
+            if normalization_type == "min-max":
+                for f in features:
+                    minimum = normalization_dict[f"{f}_minimum"]
+                    maximum = normalization_dict[f"{f}_maximum"]
+                    diff = (maximum - minimum)
+                    if diff == 0:
+                        print("Warning: diff = %d", diff)
+                        diff = 1
+                    self.df[f] = (self.df[f] - minimum) / diff
+
+            elif normalization_type == "standardization":
+                for f in features:
+                    mean = normalization_dict[f"{f}_mean"]
+                    std = normalization_dict[f"{f}_std"]
+                    if std == 0:
+                        print("Warning: std = %d", std)
+                        std = 1
+                    self.df[f] = (self.df[f] - mean) / std
+
+            else:
+                raise ValueError(f"{normalization_type} not a valid normalization method. Must be on of [min-max, "
+                                 f"standardization]")
         return normalization_type, normalization_dict
 
     # New features