updated basic example to use local dataset

examples/basic_example.py

@@ -1,44 +1,28 @@
-# coding: utf-8
-
-# In[1]:
-
+import pathlib
 
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
-import root_pandas
+import matplotlib
 
 import templatefitter as tf
-
-
-# In[3]:
-import matplotlib
 matplotlib.use('Agg')
 
-ceph = "/ceph/welsch/moritz_data/"
-
-signal = pd.read_pickle(ceph+"PI0_SIGNAL.pkl")
-remaining = pd.read_pickle(ceph+"PI0_remaining.pkl")
-xulnu = pd.read_pickle(ceph+"PI0_xulnu.pkl")
-data = pd.read_pickle(ceph+"DATA.pkl")
+path = pathlib.Path(__file__).resolve().parent.parent / "datasets/b_to_pizlnu"
+signal = pd.read_csv(path/"signal.csv")
+remaining = pd.read_csv(path/"remaining.csv")
+xulnu = pd.read_csv(path/"xulnu.csv")
+data = pd.read_csv(path/"data.csv")
 
 data.head()
 
 
 # ## Prepare Templates
-
-# In[4]:
-
-
 num_bins = 25
 limits = (-1, 3)
 
 
 # ### $e$ channel
-
-# In[5]:
-
-
 e_sig = signal.query("SignalChannel==1.")
 e_remaining = remaining.query("SignalChannel==1.")
 e_xulnu = xulnu.query("SignalChannel==1.")
@@ -58,20 +42,12 @@
 e_tremaining = tf.templates.Template1d("Remaining", "missingMass", e_hremaining, color="indianred")
 e_txulnu = tf.templates.Template1d("Xulnu", "missingMass", e_hxulnu, color="khaki")
 
-
-# In[6]:
-
-
 for template in [e_tsig,e_tremaining, e_txulnu]:
     fig, axis = plt.subplots(1,1)
     template.plot_on(axis)
 
 
 # ### $\mu$ channel
-
-# In[7]:
-
-
 mu_sig = signal.query("SignalChannel==2.")
 mu_remaining = remaining.query("SignalChannel==2.")
 mu_xulnu = xulnu.query("SignalChannel==2.")
@@ -91,20 +67,13 @@
 mu_tremaining = tf.templates.Template1d("Remaining", "missingMass", mu_hremaining, color="indianred")
 mu_txulnu = tf.templates.Template1d("Xulnu", "missingMass", mu_hxulnu, color="khaki")
 
-
-# In[8]:
-
-
 for template in [mu_tsig, mu_tremaining, mu_txulnu]:
     fig, axis = plt.subplots(1,1)
     template.plot_on(axis)
 
 
 # ## MultiChannelTemplate
 
-# In[9]:
-
-
 mct = tf.templates.MultiChannelTemplate()
 
 mct.define_channel("e", num_bins, limits)
@@ -125,47 +94,24 @@
 mct.add_data(e=tf.histograms.Hist1d(num_bins, limits, data=e_data.missingMass))
 mct.add_data(mu=tf.histograms.Hist1d(num_bins, limits, data=mu_data.missingMass))
 
-e_tsig.yield_param
-
-
-# In[10]:
-
+print(e_tsig.yield_param)
 
 for channel in mct.channels.values():
     fig, axis = plt.subplots(1,1, figsize=(8,8))
     channel.plot_stacked_on(axis)
 
-
-# In[11]:
-
-
 fitter = tf.TemplateFitter(mct, "iminuit")
 
 
-# In[12]:
-
-
 fitter.do_fit(update_templates=True)
 
-
-# In[18]:
-
-
 print(fitter.get_significance("signal"))
 
-
-# In[14]:
-
-
 for channel in mct.channels.values():
     fig, axis = plt.subplots(1,1, figsize=(8,8))
     channel.plot_stacked_on(axis)
     axis.legend()
 
-
-# In[15]:
-
-
 sig_yield, nll_profile, hesse = fitter.profile("signal_yield", num_cpu=25)
 fig, ax = plt.subplots(1,1, figsize=(8,8), dpi=200)
 ax.plot(sig_yield, nll_profile, label="profile")