add MNIST dataset, now without tensorflow dep

gen_stimulat.py

@@ -1,40 +1,47 @@
+import gzip
 import numpy as np
-from tensorflow.keras.datasets import mnist
 import matplotlib.pyplot as plt
 
-# Load the MNIST dataset
-(x_train, y_train), (x_test, y_test) = mnist.load_data()
+# Function to read the images file (3D)
+def read_images(filename):
+    with gzip.open(filename, 'rb') as f:
+        # Skip the first 16 bytes (magic number, number of images, height, and width)
+        f.read(16)
+        # Read the images (remaining data)
+        return np.frombuffer(f.read(), dtype=np.uint8).reshape(-1, 28, 28)
 
-# Find the first '9' in the dataset
-index_of_nine = np.where(y_train == 9)[0][0]
+# Function to convert a pixel value to an electrical stimulation level
+def pixel_to_stimulation(pixel_value, max_stimulation=5.0):
+    return pixel_value / 255.0 * max_stimulation
 
-# Get the 28x28 pixel image of the number 9
-image_of_nine = x_train[index_of_nine]
+# Function to read the labels file (1D)
+def read_labels(filename):
+    with gzip.open(filename, 'rb') as f:
+        # Skip the first 8 bytes (magic number and label count)
+        f.read(8)
+        # Read the labels (remaining data)
+        return np.frombuffer(f.read(), dtype=np.uint8)
+
+
+# Load train images and labels
+train_images = read_images('dataset/train-images-idx3-ubyte.gz')
+train_labels = read_labels('dataset/train-labels-idx1-ubyte.gz')
+
+# Find all indices of the digit '9' in the training labels
+indices_of_nines = np.where(train_labels == 9)[0]
+
+# Get the first '9' image for demonstration
+image_of_nine = train_images[indices_of_nines[0]]
 
 # Normalize the pixel values to [0, 1] range
 normalized_image = image_of_nine / 255.0
 
-# Function to convert a pixel value to an electrical stimulation level
-def pixel_to_stimulation(pixel_value, max_stimulation=5.0):
-    """
-    Converts a normalized pixel value (0.0 - 1.0) to an electrical stimulation (e.g., voltage).
-    Assumes max_stimulation is the maximum output voltage/current, e.g., 5V.
-    """
-    return pixel_value * max_stimulation
-
-# Apply the conversion to the entire image
+# Convert the normalized image to an electrical stimulation pattern
 stimulation_pattern = np.vectorize(pixel_to_stimulation)(normalized_image)
 
-# Now `stimulation_pattern` is a 28x28 array of electrical stimulation levels (e.g., voltages)
-print("Electrical stimulation pattern for '9':")
-print(stimulation_pattern)
-
-
-# Create a plot to visualize the stimulation pattern
+# Visualize the stimulation pattern
 plt.figure(figsize=(5, 5))
 plt.imshow(stimulation_pattern, cmap='gray', interpolation='nearest')
 plt.title("Electrical Stimulation Pattern for '9'")
-plt.axis('off')  # Hide the axis for a cleaner look
+plt.axis('off')
 plt.show()
-
-# Optionally, you could output this to an external device or system that applies electrical stimulation.

list_all_nines.py

@@ -1,15 +1,23 @@
-# Load the MNIST dataset
-from tensorflow.keras.datasets import mnist
+import gzip
 import numpy as np
 
+# Function to read the labels file (1D)
+def read_labels(filename):
+    with gzip.open(filename, 'rb') as f:
+        # Skip the first 8 bytes (magic number and label count)
+        f.read(8)
+        # Read the labels (remaining data)
+        return np.frombuffer(f.read(), dtype=np.uint8)
 
-(x_train, y_train), (x_test, y_test) = mnist.load_data()
+# Load train labels
+train_labels = read_labels('dataset/train-labels-idx1-ubyte.gz')
 
-# Find all indices of the digit '9' in the training data
-indices_of_nines = np.where(y_train == 9)[0]
+# Find all indices of the digit '9' in the training labels
+indices_of_nines = np.where(train_labels == 9)[0]
 
 # Count how many '9's are there in the training set
 num_nines = len(indices_of_nines)
 
-# Display the count
-num_nines
+# Output the count and the first 10 indices
+print(f"Number of '9's in the training set: {num_nines}")
+print(f"First 10 indices of '9': {indices_of_nines[:10]}")