diff --git a/python_scripts/colormap.py b/python_scripts/colormap.py
new file mode 100644
index 0000000..37ef2da
--- /dev/null
+++ b/python_scripts/colormap.py
@@ -0,0 +1,26 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import ast
+import seaborn as sns
+
+sns.set_theme()
+
+def plot():
+    with open("data/color_map.txt") as f:
+        lines = f.readlines();
+        size = int(lines[0])
+        for i, line in enumerate(lines[1:]):
+            fig, ax = plt.subplots()
+            arr = line.strip().split("\t")
+            arr = np.asarray(list(map(lambda x: ((a := complex(*ast.literal_eval(x)))*a.conjugate()).real, arr)))
+
+            arr = arr.reshape(size,size)
+
+            ax.imshow(arr.T, interpolation="nearest")
+            plt.savefig(f"latex/images/color_map_{i}.pdf")
+            plt.close(fig)
+
+
+if __name__ == "__main__":
+    plot()
+    
diff --git a/python_scripts/detector.py b/python_scripts/detector.py
new file mode 100644
index 0000000..6795735
--- /dev/null
+++ b/python_scripts/detector.py
@@ -0,0 +1,41 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import ast
+import seaborn as sns
+
+sns.set_theme()
+
+def plot():
+    files = [
+        "data/screen/single_slit.txt",
+        "data/screen/double_slit.txt",
+        "data/screen/triple_slit.txt",
+    ]
+    outputs = [
+        "latex/images/single_slit_detector.pdf",
+        "latex/images/double_slit_detector.pdf",
+        "latex/images/triple_slit_detector.pdf",
+    ]
+    for file, output in zip(files, outputs):
+        with open(file) as f:
+            lines = f.readlines();
+            size = int(lines[0])
+            column = int(.8 * size)
+            fig, ax = plt.subplots()
+            arr = lines[1].strip().split("\t")
+            arr = np.asarray(list(map(lambda x: complex(*ast.literal_eval(x)), arr)))
+
+            arr = arr.reshape(size,size)
+            x = np.linspace(0,1, size)
+            slice = arr[column, :]
+            norm = 1. / np.sqrt(sum([(i*i.conjugate()).real for i in slice]))
+            slice *= norm
+            slice = np.asarray([i * i.conjugate() for i in slice])
+
+            ax.plot(x, slice)
+            plt.savefig(output)
+            plt.close(fig)
+
+
+if __name__ == "__main__":
+    plot()
diff --git a/python_scripts/normalization.py b/python_scripts/normalization.py
new file mode 100644
index 0000000..8a63f28
--- /dev/null
+++ b/python_scripts/normalization.py
@@ -0,0 +1,25 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib
+from matplotlib.animation import FuncAnimation
+import ast
+
+def plot():
+    with open("data/probability_deviation.txt") as f:
+        lines = f.readlines();
+        x = []
+        arr_narrow = []
+        arr_wide = []
+        for line in lines:
+            tmp = line.strip().split(",")
+            x.append(float(tmp[0]))
+            arr_narrow.append(float(tmp[1]))
+            arr_wide.append(float(tmp[2]))
+
+        plt.plot(x,arr_narrow)
+        plt.plot(x,arr_wide)
+        plt.savefig("latex/images/probability_deviation.pdf")
+
+if __name__ == "__main__":
+    plot()
+