Organization, documentation: Added additional functions from colab no…

…tebook, collected all visualization functions into visualization module, added additional descriptions and documentation.

icedyno/metrics/metrics.py

@@ -1,34 +1,6 @@
-import matplotlib.pyplot as plt
 import numba
 import numpy as np
 
-## Visualization for edge metrics ##
-
-
-def plot_ice_edges_with_metrics(observed_edges, model_edges):
-    """
-    Visualize observed and model ice edges on a grid, including the calculation of metrics.
-
-    Parameters:
-    - observed_edges: numpy array of observed ice edge points.
-    - model_edges: numpy array of model ice edge points.
-    """
-    # Plotting
-    plt.figure(figsize=(8, 8))
-    plt.plot(observed_edges[:, 0], observed_edges[:, 1], "bo-", label="Example Edge 1")
-    plt.plot(model_edges[:, 0], model_edges[:, 1], "ro-", label="Example Edge 2")
-
-    # Enhance plot
-    plt.title("Ice Edge Comparison with Metrics")
-    plt.xlabel("X Coordinate")
-    plt.ylabel("Y Coordinate")
-    plt.legend()
-    plt.grid(True)
-    plt.axis("equal")
-
-    plt.show()
-
-
 ## Metrics ##
 
 

icedyno/metrics/process_edges.py

@@ -3,6 +3,7 @@
 
 def process_binary_edge_image_into_coordinates(activated: np.array) -> np.array:
     """
+    Takes a numpy array of edge detected pixels (not SIE).
     Cannot take in a tensorflow tensor, input array must be between 0 and 1.
     Input array is the result of running edge-detection on Ice/No Ice array.
 
@@ -14,7 +15,7 @@ def process_binary_edge_image_into_coordinates(activated: np.array) -> np.array:
         activated: Numpy array with >= 0.5 indicating sea-ice edge.
 
     Returns:
-        numpy array of integer indices in input array corresponding to edge. Of shape (N Edges, 2)
+        numpy array of integer indices in input array corresponding to edge pixels. Of shape (N Edges, 2)
 
     """
     edges = np.where(np.round(activated) >= 1)

icedyno/visualization/sie.py

@@ -5,7 +5,12 @@
 import icedyno.preprocess.geolocation
 
 
-def plot_sie_window(sie: np.array, window: int, marker: tuple = None):
+def plot_sie_window(sie: np.array, marker: tuple[int, int] = None) -> None:
+    """
+    Parameters:
+    - SIE is 2D numpy array
+    - marker is a tuple of x, y polar sterographic coordinates
+    """
     colors = ["#E500E5", "#0066FF", "#01FF00", "#FFC100", "#E50000"]
     cmap = ListedColormap(colors, name="custom_colormap", N=len(colors))
 
@@ -37,3 +42,112 @@ def plot_sie_window(sie: np.array, window: int, marker: tuple = None):
         )
 
     plt.show()
+
+
+def plot_binary_model_predictions_with_errors(
+    X: np.array, y_true: np.array, y_pred: np.array, year: int, day: int
+) -> None:
+    """
+    Parameters:
+     - X: np.array of shape (window_size, window_size, n_forecasts)
+     - y_true: np.array of 0,1 (window_size, window_size, 1)
+     - y_pred: model output (window_size, window_size, 1)
+     - year: Integer year like 2023, for titling plot
+     - day: Integer day like 18, for titling plot
+    """
+    X_cmap = ListedColormap(["#0000FF", "#00FFFF", "#008B8B"])
+    binary_cmap = ListedColormap(["#008B8B", "#00FFFF"])
+
+    # Calculate the incorrect predictions (difference between the predicted and true labels)
+    incorrect_predictions = np.not_equal(np.round(y_pred), y_true).astype(int)
+    num_incorrect = np.sum(incorrect_predictions)
+    percent_incorrect = num_incorrect / (X.shape[1] ** 2)
+
+    current_SIE = X[:, :, -1].copy()
+    current_SIE[current_SIE == 2] = 0
+    change_from_curr_to_next = np.not_equal(current_SIE, y_true[:, :, 0]).astype(int)
+
+    # Plotting the first example of the batch
+    fig, axes = plt.subplots(1, 5, figsize=(25, 10))
+
+    # Plot the last channel of input which is the most recent SIE
+    im1 = axes[0].imshow(X[:, :, -1], cmap=X_cmap)
+    axes[0].set_title("Most Recent SIE Input")
+    axes[0].axis("off")
+
+    # Plot the true label for next day's SIE
+    im2 = axes[1].imshow(y_true[:, :, 0], cmap=binary_cmap)
+    axes[1].set_title("True Next Day's SIE")
+    axes[1].axis("off")
+
+    # Plot the predicted next day's SIE
+    im3 = axes[2].imshow(y_pred[:, :, 0], cmap=binary_cmap)
+    axes[2].set_title("Predicted Next Day's SIE")
+    axes[2].axis("off")
+
+    # Plot the incorrect predictions
+    axes[3].imshow(incorrect_predictions[:, :, 0], cmap="hot")
+    axes[3].set_title("Incorrect Predictions")
+    axes[3].axis("off")
+
+    # Plot the SIE change
+    axes[4].imshow(change_from_curr_to_next, cmap="hot")
+    axes[4].set_title("Change from Current SIE to Next Day")
+    axes[4].axis("off")
+
+    # Add a color bar for the SIE plots
+    cbar = fig.colorbar(im1, ax=axes[0], fraction=0.046, pad=0.04)
+    cbar.set_ticks([0, 1, 2])
+    cbar.set_ticklabels(["Open Water", "Sea Ice", "Land"])
+
+    # Add a color bar for the binary SIE plots
+    cbar = fig.colorbar(im2, ax=axes[1], fraction=0.046, pad=0.04)
+    cbar.set_ticks([0, 1])
+    cbar.set_ticklabels(["Not Sea Ice", "Sea Ice"])
+
+    # Add a color bar for the binary SIE plots
+    cbar = fig.colorbar(im3, ax=axes[2], fraction=0.046, pad=0.04)
+    cbar.set_ticks([0, 1])
+    cbar.set_ticklabels(["Not Sea Ice", "Sea Ice"])
+
+    fig.suptitle(
+        f"Model Predictions for {year} {day}'s Next Day Forecast\nIncorrectly Classified Pixels: {num_incorrect} pixels, {percent_incorrect}%",
+        fontsize=14,
+    )
+
+    plt.tight_layout()
+    plt.show()
+
+
+## Visualization for edge metrics ##
+
+
+def plot_ice_edges_with_metrics(
+    observed_edges: np.array,
+    model_edges: np.array,
+    observed_edges_label: str = "Observed Edges",
+    model_edges_label: str = "Predicted Edges",
+) -> None:
+    """
+    Visualize observed and model ice edges on a grid, including the calculation of metrics.
+    An example of the expected edge arrays is output of process_binary_edge_image_into_coordinates.
+
+    Parameters:
+    - observed_edges: numpy array of observed ice edge points.
+    - model_edges: numpy array of model ice edge points.
+    """
+    # Plotting
+    plt.figure(figsize=(8, 8))
+    plt.plot(
+        observed_edges[:, 0], observed_edges[:, 1], "bo-", label=observed_edges_label
+    )
+    plt.plot(model_edges[:, 0], model_edges[:, 1], "ro-", label=model_edges_label)
+
+    plt.title("Ice Edge Comparison with Metrics")
+    plt.xlabel("X Coordinate")
+    plt.ylabel("Y Coordinate")
+    plt.legend()
+    plt.grid(True)
+    plt.axis("equal")
+
+    plt.show()