treat muons like Monte Carlo

1 files changed, 5 insertions, 1 deletions

diff --git a/utils/chi2 b/utils/chi2
index b393954..bf750cd 100755
--- a/utils/chi2
+++ b/utils/chi2
@@ -168,12 +168,15 @@ FIT_PARS = [
 
 def make_nll(data, muons, mc, bins):
     data_hists = get_data_hists(data,bins)
-    muon_hists = get_data_hists(muons,bins)
 
     ke_dict = {}
     for id in (20,22,2020,2022,2222):
         ke_dict[id] = mc[mc.id == id].ke.values
 
+    ke_dict_muon = {}
+    for id in (20,22,2020,2022,2222):
+        ke_dict_muon[id] = muons[muons.id == id].ke.values
+
     def nll(x, grad=None):
         if any(x[i] < 0 for i in range(len(x))):
             return np.inf
@@ -211,6 +214,7 @@ def make_nll(data, muons, mc, bins):
         # Also, it's critical that I first adjust the data energy by whatever
         # amount I find with the stopping muons and Michel distributions.
         mc_hists = get_mc_hists_fast(ke_dict,x,bins,apply_norm=True)
+        muon_hists = get_mc_hists_fast(ke_dict_muon,x,bins,apply_norm=False)
 
         # Calculate the negative log of the likelihood of observing the data
         # given the fit parameters