adding script to write ROOT trees for parametrized learning

scripts/write_parametrized.py

+#!/usr/bin/env python
+"""
+Write new TTrees with signal parameters as branches. For the
+backgrounds the parameters are generated following the total
+distribution for all signals. The discrete values for the whole ntuple
+of signal parameters are counted, such that correlations between
+signal parameters are taken into account.
+"""
+
+import argparse, re, os
+
+import ROOT
+
+from root_numpy import list_trees
+from root_pandas import read_root
+import numpy as np
+
+if __name__ == "__main__":
+
+    input_filename = "/project/etp4/nhartmann/trees/allTrees_m1.8_NoSys.root"
+    output_filename = "/project/etp4/nhartmann/trees/allTrees_m1.8_NoSys_parametrized.root"
+
+    param_names = ["mg", "mc", "mn"]
+
+    param_match = "GG_oneStep_(.*?)_(.*?)_(.*?)_NoSys"
+
+    output_signal_treename = "GG_oneStep_NoSys"
+
+    bkg_trees = [
+        "diboson_Sherpa221_NoSys",
+        "singletop_NoSys",
+        "ttbar_NoSys",
+        "ttv_NoSys",
+        "wjets_Sherpa221_NoSys",
+        "zjets_Sherpa221_NoSys",
+    ]
+
+    # read in the number of events for each combination of parameters
+    f = ROOT.TFile.Open(input_filename)
+    count_dict = {}
+    for key in f.GetListOfKeys():
+        tree_name = key.GetName()
+        match = re.match(param_match, tree_name)
+        if match is not None:
+            tree = f.Get(tree_name)
+            params = tuple([float(i) for i in match.groups()])
+            if not params in count_dict:
+                count_dict[params] = 0
+            count_dict[params] += tree.GetEntries()
+    f.Close()
+
+    # calculate cumulative sum of counts to sample signal parameters for background from
+    numbers = np.array(count_dict.keys(), dtype=np.float)
+    counts = np.array(count_dict.values(), dtype=np.float)
+    probs = counts/counts.sum()
+    prob_bins = np.cumsum(probs)
+
+    # read and write the rest in chunks
+    if os.path.exists(output_filename):
+        os.remove(output_filename)
+    for tree_name in list_trees(input_filename):
+        match_signal = re.match(param_match, tree_name)
+        if match_signal is not None or tree_name in bkg_trees:
+            print("Writing {}".format(tree_name))
+            nwritten = 0
+            for df in read_root(input_filename, tree_name, chunksize=100000):
+                print("Writing event {}".format(nwritten))
+                if match_signal is None:
+                    rnd = np.random.random(len(df))
+                    rnd_idx = np.digitize(rnd, prob_bins)
+                    param_values = numbers[rnd_idx]
+                    for param_idx, param_name in enumerate(param_names):
+                        df[param_name] = param_values[:,param_idx]
+                else:
+                    for param_name, param_value in zip(param_names, match_signal.groups()):
+                        df[param_name] = float(param_value)
+                if match_signal is None:
+                    out_tree_name = tree_name
+                else:
+                    out_tree_name = output_signal_treename
+                df.to_root(output_filename, mode="a", key=out_tree_name)
+                nwritten += len(df)