--- a
+++ b/experiments/expression/codex/codex_alignment.py
@@ -0,0 +1,83 @@
+import pandas as pd
+from os.path import join as pjoin
+import numpy as np
+import matplotlib.pyplot as plt
+
+DATA_DIR = "../../../data/codex"
+data = pd.read_csv(pjoin(DATA_DIR, "codex_mrl_expression.csv"))  # , nrows=200)
+
+marker_names = data.columns.values[1:-8]
+sample_names = data.sample_Xtile_Ytile.str.split("_").str[0].values
+sample_names_unique = np.unique(sample_names)
+
+sample1_idx = np.where(sample_names == "BALBc-3")[0]
+sample2_idx = np.where(sample_names == "BALBc-2")[0]
+
+data_sample1 = data.iloc[sample1_idx, :]
+data_sample2 = data.iloc[sample2_idx, :]
+
+xtilespan = 1344
+ytilespan = 1008
+
+
+def tile_spatial_coordinates(data_df):
+
+    if "xcoord" in data_df.columns or "ycoord" in data_df.columns:
+        raise Exception("DataFrame already contains scaled coordinates.")
+
+    tile_nums_split = data_df.sample_Xtile_Ytile.str.split("_")
+    x_tile_nums = tile_nums_split.str[1].str[1:].values.astype(float)
+    y_tile_nums = tile_nums_split.str[2].str[1:].values.astype(float)
+    xcoords = (x_tile_nums - 1) * xtilespan + data_df["X.X"].values
+    ycoords = (y_tile_nums - 1) * ytilespan + data_df["Y.Y"].values
+    data_df["xcoord"] = xcoords
+    data_df["ycoord"] = ycoords
+
+
+tile_spatial_coordinates(data_sample1)
+tile_spatial_coordinates(data_sample2)
+
+# plt.scatter(data_sample1.xcoord, data_sample1.ycoord)
+# plt.show()
+# import ipdb; ipdb.set_trace()
+
+normalized_data1 = data_sample1[marker_names].values.copy()
+keep_idx = np.where((np.abs(normalized_data1) >= 10_000).sum(1) == 0)[0]
+data_sample1 = data_sample1.iloc[keep_idx]
+normalized_data2 = data_sample2[marker_names].values.copy()
+keep_idx = np.where((np.abs(normalized_data2) >= 10_000).sum(1) == 0)[0]
+data_sample2 = data_sample2.iloc[keep_idx]
+
+# import ipdb
+
+# ipdb.set_trace()
+for marker in marker_names:
+    plt.figure(figsize=(10, 5))
+
+    plt.subplot(121)
+    plt.title("Slice 1")
+    curr_data = data_sample1[marker].values
+    curr_data = (curr_data - curr_data.mean()) / curr_data.std()
+    plt.scatter(
+        data_sample1["xcoord"],
+        data_sample1["ycoord"],
+        c=data_sample1[marker],
+        s=1,
+        marker="s",
+    )
+
+    plt.subplot(122)
+    plt.title("Slice 2")
+    curr_data = data_sample2[marker].values
+    curr_data = (curr_data - curr_data.mean()) / curr_data.std()
+    plt.scatter(
+        data_sample2["xcoord"],
+        data_sample2["ycoord"],
+        c=data_sample2[marker],
+        s=1,
+        marker="s",
+    )
+    plt.show()
+import ipdb
+
+ipdb.set_trace()