--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/projective_transformation_points.py	Sat Feb 09 14:45:19 2019 -0500
@@ -0,0 +1,35 @@
+from skimage.transform import ProjectiveTransform
+import numpy as np
+import pandas as pd
+import argparse
+
+
+def warp_coords_batch(coord_map, coords, dtype=np.float64, batch_size=1000000):
+    tf_coords = coords.astype(np.float32)
+
+    for i in range(0, (tf_coords.shape[0]//batch_size+1)):
+        tf_coords[batch_size*i:batch_size*(i+1)] = coord_map(tf_coords[batch_size*i:batch_size*(i+1)])
+
+    return np.unique(np.round(tf_coords).astype(coords.dtype),axis=0)
+
+
+def transform(coords, warp_matrix, out):
+    indices = np.array(pd.read_csv(coords, delimiter="\t"))
+    a_matrix = np.array(pd.read_csv(warp_matrix, delimiter="\t", header=None))
+    
+    trans = ProjectiveTransform(matrix=a_matrix)
+    warped_coords = warp_coords_batch(trans, indices)
+
+    df = pd.DataFrame()
+    df['x'] = warped_coords[:,0]
+    df['y'] = warped_coords[:,1]
+    df.to_csv(out, index = False, sep="\t")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Transform coordinates")
+    parser.add_argument("coords", help="Paste path to .csv with coordinates to transform (tab separated)")
+    parser.add_argument("warp_matrix", help="Paste path to .csv that should be used for transformation (, separated)")
+    parser.add_argument("out", help="Paste path to file in which transformed coords should be saved (tab separated)")
+    args = parser.parse_args()
+    transform(args.coords, args.warp_matrix, args.out)