--- a
+++ b/singlecellmultiomics/countTableProcessing/downsampleDataFrame.py
@@ -0,0 +1,46 @@
+import pandas as pd
+import numpy as np
+import glob
+import math
+import re
+import sys
+import multiprocessing
+
+
+def downsampleRow(args):
+    row, targetSum = args
+    currentCount = row.sum()
+    downsampledRow = row.copy()
+    while currentCount > targetSum and currentCount != 0:
+        possible = downsampledRow[(downsampledRow > 0)]
+        desiredTossCount = int(currentCount - targetSum)
+        probabilities = [p / currentCount for p in possible]
+        for indexToLower in np.random.choice(
+                possible.index, max(0, desiredTossCount),
+                replace=True, p=probabilities):
+            if downsampledRow[indexToLower] > 0:
+                downsampledRow[indexToLower] -= 1
+
+        currentCount = downsampledRow.sum()
+    return downsampledRow
+
+# downsample_to = sample to this amount of counts per column
+# min_feature_abundance = remove all rows which have less than these counts
+
+
+def downsampleDataFrame(df, downsample_to, min_feature_abundance=50):
+    pool = multiprocessing.Pool(8)
+    try:
+        df = df.loc[:, df.sum() > downsample_to]
+        df = df.loc[df.sum(1) > min_feature_abundance, :]
+        subset = df.transpose()
+        dfDownsampled = subset.copy()
+        for idx, drow in enumerate(
+            pool.map(
+                downsampleRow, [
+                (row, downsample_to) for i, row in subset.iterrows()])):
+            dfDownsampled.iloc[idx, :] = drow
+    except Exception as e:
+        print(e)
+    pool.close()
+    return dfDownsampled.transpose()