From fdd977217315dcd816dc0978e5ff21f6395f1d12 Mon Sep 17 00:00:00 2001
From: unknown <yair@tami.org.il>
Date: Thu, 17 Oct 2024 18:19:47 +0300
Subject: [PATCH] mnist stuff

---
 .gitignore        |  1 +
 gen_stimulat.py   | 40 ++++++++++++++++++++++++++++++++++++++++
 list_all_nines.py | 15 +++++++++++++++
 3 files changed, 56 insertions(+)
 create mode 100644 .gitignore
 create mode 100644 gen_stimulat.py
 create mode 100644 list_all_nines.py

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..f7275bb
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+venv/
diff --git a/gen_stimulat.py b/gen_stimulat.py
new file mode 100644
index 0000000..e68cca0
--- /dev/null
+++ b/gen_stimulat.py
@@ -0,0 +1,40 @@
+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()
+
+# Find the first '9' in the dataset
+index_of_nine = np.where(y_train == 9)[0][0]
+
+# Get the 28x28 pixel image of the number 9
+image_of_nine = x_train[index_of_nine]
+
+# 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
+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
+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.show()
+
+# Optionally, you could output this to an external device or system that applies electrical stimulation.
diff --git a/list_all_nines.py b/list_all_nines.py
new file mode 100644
index 0000000..8704626
--- /dev/null
+++ b/list_all_nines.py
@@ -0,0 +1,15 @@
+# Load the MNIST dataset
+from tensorflow.keras.datasets import mnist
+import numpy as np
+
+
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
+
+# Find all indices of the digit '9' in the training data
+indices_of_nines = np.where(y_train == 9)[0]
+
+# Count how many '9's are there in the training set
+num_nines = len(indices_of_nines)
+
+# Display the count
+num_nines