plot tool updates

tools/plot_scripts/background_ml_illustrations/flake.nix

@@ -1,40 +0,0 @@
-{
-  description = "Python 3.13 devshell with tensorflow-datasets, matplotlib, scikit-learn and numpy";
-
-  inputs = {
-    nixpkgs.url = "github:NixOS/nixpkgs/nixos-unstable";
-    flake-utils.url = "github:numtide/flake-utils";
-  };
-
-  outputs =
-    {
-      self,
-      nixpkgs,
-      flake-utils,
-      ...
-    }:
-    flake-utils.lib.eachDefaultSystem (
-      system:
-      let
-        pkgs = import nixpkgs {
-          inherit system;
-          # optional: config = { allowUnfree = true; };
-        };
-      in
-      {
-        devShells.default = pkgs.mkShell {
-          name = "py313-devshell";
-          # bring in the Python 3.13 packages
-          buildInputs =
-            with pkgs;
-            [ python313 ]
-            ++ (with pkgs.python313Packages; [
-              tensorflow-datasets
-              matplotlib
-              scikit-learn
-              numpy
-            ]);
-        };
-      }
-    );
-}

tools/plot_scripts/data_missing_points.py

@@ -150,7 +150,7 @@ def plot_data_points(normal_experiment_paths, anomaly_experiment_paths, title):
     plt.savefig(output_datetime_path / "missing_points_density.png")
 
     # create another density version which does not plot number of missing points but percentage of measurements that are missing (total number of points is 32*2048)
-    bins = np.linspace(0, 1, 100)
+    bins = np.linspace(0, 0.6, 100)
     plt.clf()
     plt.figure(figsize=(10, 5))
     plt.hist(

tools/plot_scripts/deepsad_tsne_latent_space.py

@@ -78,11 +78,19 @@ def plot_tsne_latent_space(normal_data, anomaly_data, title="TSNE of Latent Spac
     Plot the TSNE representation of the latent space.
     This function first applies a PCA-based dimensionality reduction for efficiency.
     """
+    # Hardcoded variables to choose every nth normal sample and mth anomaly sample
+    n = 10  # Change this value to select every nth normal sample
+    m = 2  # Change this value to select every mth anomaly sample
+
+    # Select every nth normal sample and mth anomaly sample
+    normal_data = normal_data[::n]
+    anomaly_data = anomaly_data[::m]
+
     # Combine normal and anomaly data
     combined_data = np.vstack((normal_data, anomaly_data))
 
     # Initial dimensionality reduction with PCA
-    reduced_data = reduce_dimensionality(combined_data, n_components=50)
+    reduced_data = reduce_dimensionality(combined_data, n_components=100)
 
     # Apply TSNE transformation on the PCA-reduced data
     tsne = TSNE(n_components=2, random_state=42)