#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
CERN@school - Estimate the particle rate
See the README.md file and the GitHub wiki for more information.
http://cernatschool.web.cern.ch
"""
#...for the operating system tools.
import os
#...for parsing the arguments.
import argparse
#...for the logging.
import logging as lg
#...for file manipulation.
from shutil import rmtree
# Import the JSON library.
import json
#...for the histograms.
from plotting.poisson import RateHistogram
if __name__ == "__main__":
print("*")
print("*=================================*")
print("* CERN@school - estimate the rate *")
print("*=================================*")
# Get the datafile path from the command line.
parser = argparse.ArgumentParser()
parser.add_argument("inputPath", help="Path to the input dataset.")
parser.add_argument("outputPath", help="The path for the output files.")
parser.add_argument("-v", "--verbose", help="Increase output verbosity", action="store_true")
args = parser.parse_args()
## The path to the data file.
datapath = args.inputPath
#
# Check if the input directory exists. If it doesn't, quit.
if not os.path.isdir(datapath):
raise IOError("* ERROR: '%s' input directory does not exist!" % (datapath))
## The output path.
outputpath = args.outputPath
#
# Check if the output directory exists. If it doesn't, quit.
if not os.path.isdir(outputpath):
raise IOError("* ERROR: '%s' output directory does not exist!" % (outputpath))
# Set the logging level.
if args.verbose:
level=lg.DEBUG
else:
level=lg.INFO
# Configure the logging.
lg.basicConfig(filename=outputpath + '/log_estimate-rate.log', filemode='w', level=level)
print("*")
print("* Input path : '%s'" % (datapath))
print("* Output path : '%s'" % (outputpath))
print("*")
# Set up the directories
#------------------------
# Create the subdirectories.
## The path to the frame plots.
fppath = os.path.join(outputpath, "frameplots")
#
if os.path.isdir(fppath):
rmtree(fppath)
lg.info(" * Removing directory '%s'..." % (fppath))
os.mkdir(fppath)
lg.info(" * Creating directory '%s'..." % (fppath))
lg.info("")
## The filename of the frame properties JSON.
frame_properties_json_filename = os.path.join(datapath, "frames.json")
#
# Check if it exists. If it doesn't, quit.
if not os.path.exists(frame_properties_json_filename):
raise IOError("* ERROR: '%s' output directory does not exist!" \
% (frame_properties_json_filename))
## The frame properties JSON file.
ff = open(frame_properties_json_filename, "r")
#
fd = json.load(ff)
ff.close()
# The frames
#------------
## The number of clusters per frame.
ncs = []
lg.info(" * Looping over the frames ")
lg.info(" *-------------------------")
# Loop over the frames.
for f in fd:
## The number of clusters in the frame.
n_k = f["n_kluster"]
# Add to the frame property dictionaries.
ncs.append(n_k)
lg.info(" *--> Found % 3d clusters." % (n_k))
## The number of clusters plot.
nclplot = RateHistogram(100, "ncs", fppath, True)
ncl_Lambda, ncl_Lambda_err, ncl_chi2, ncl_n_free = nclplot.fill(ncs)
# Make the plot display page.
fp = ""
fp += "\n"
fp += "\n"
fp += " \n"
fp += " \n"
fp += " \n"
fp += " \n"
fp += "
Cluster Sorting: Frame Properties
\n"
fp += "
Dataset summary
\n"
fp += "
\n"
fp += "
\n"
fp += "
Dataset path = '%s'
\n" % (datapath)
fp += "
Number of frames = %d
\n" % (len(fd))
fp += "
\n"
fp += " \n"
fp += "
Frame properties
\n"
fp += "
\n"
fp += "
Fig. 1: Clusters per frame.
\n"
fp += "
\n"
fp += "
\n"
fp += "
Poisson distribution: Λ = (% 7.3f ± % 7.3f)
\n" % (ncl_Lambda, ncl_Lambda_err)
fp += "
Fit to Poisson: χ2 = % 7.3f, Nf = %d.
\n" % (ncl_chi2, ncl_n_free)
fp += " \n"
fp += ""
## The web page filename.
framepage_filename = os.path.join(fppath, "index.html")
#
# Write out the frame property index page.
with open(framepage_filename, "w") as framepage:
framepage.write(fp)
# Now you can view the "index.html" files to see the results!
print("*")
print("* Plotting complete.")
print("* View your results by opening '%s' in a browser, e.g." % (framepage_filename))
print("* $ firefox %s &" % (framepage_filename))
![](\"ncs.png\")