Plot the histograms for general investigation and visual inspection
Functionality for selecting a single run and plotting all lumisections belonging to that run.
Functionality for plotting the moments of the distributions as a function of LS number. 

### imports

# external modules
import sys
import os
import numpy as np
import importlib

# local modules
sys.path.append('../utils')
import dataframe_utils as dfu
import plot_utils as pu
import hist_utils as hu
importlib.reload(dfu)
importlib.reload(pu)
importlib.reload(hu)
sys.path.append('../src')
import DataLoader
importlib.reload(DataLoader)

<module 'DataLoader' from '/eos/home-l/llambrec/SWAN_projects/ML4DQM-DC/tutorials/../src/DataLoader.py'>

### read the data
# note: this cell assumes you have a csv file stored at the specified location,
#       containing only histograms of the specified type;
#       see the tutorial read_and_write_data for examples on how to create such files!

histname = 'chargeInner_PXLayer_2'
filename = 'DF2017_'+histname+'.csv'
datadir = '../data'

dloader = DataLoader.DataLoader()
df = dloader.get_dataframe_from_file( os.path.join(datadir, filename) )
print('raw input data shape: {}'.format( dfu.get_hist_values(df)[0].shape ))

# select a single run
runs = dfu.get_runs(df)
print('number of runs: '+str(len(runs)))
#print(runs) # uncomment this to see a printed list of available runs
runnbs = [305351] # you can also add multiple runs to the list to plot them all together
df = dfu.select_runs(df,runnbs)

# select DCS-bit on data
#df = dfu.select_dcson(df)

INFO in DataLoader.get_dataframe_from_file: loading dataframe from file ../data/DF2017_chargeInner_PXLayer_2.csv...
INFO in DataLoader.get_dataframe_from_file: sorting the dataframe...
INFO in DataLoader.get_dataframe_from_file: loaded a dataframe with 225954 rows and 16 columns.
raw input data shape: (225954, 102)
number of runs: 599

# make a plot of the histograms
xmin = df.at[0,'Xmin']
xmax = df.at[0,'Xmax']
nbins = df.at[0,'Xbins']
(values,_,ls) = dfu.get_hist_values(df)
# (note: get_hist_values returns the raw histograms as stored in the dataframe;
# check out utils/hist_utils.py/preparedatafromdf for more advanced data loading, e.g. normalizing)
print('shape of histogram array: '+str(values.shape))
# just plot all the histograms:
pu.plot_hists_multi(values,xlims=(xmin,xmax))
# plot the histograms with a color according to their lumisection number:
pu.plot_hists_multi(values,colorlist=ls,xlims=(xmin,xmax))
# same as before but normalizing each histogram:
pu.plot_hists_multi(hu.normalizehists(values),colorlist=ls,xlims=(xmin,xmax))

shape of histogram array: (874, 102)





(<Figure size 432x288 with 2 Axes>, <AxesSubplot:>)

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# select a single lumisection and plot it on top of all the other lumisections
lsnumber = 869
pu.plot_anomalous(values,ls,highlight=lsnumber,hrange=10)

(<Figure size 432x288 with 2 Axes>, <AxesSubplot:>)

png

# make a plot of the moments of the (normalized) histograms
# use xmin = 0 and xmax = 1 as a kind of normalization
hists = values[:,1:-1]
bins = np.linspace(0,1,num=hists.shape[1],endpoint=True)
moments = hu.histmoments(bins,hists,orders=[1,2,3])
_ = pu.plot_moments(moments,ls,(0,1))

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