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from __future__ import division
import numpy as np
import sys
import matplotlib.pyplot as plt
import seaborn.apionly as sns

from composition.analysis.load_sim import load_sim
import composition.analysis.plotting_functions as plotting
from composition.analysis.effective_area import get_effective_area

%matplotlib inline


    

    




/home/jbourbeau/.local/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')

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sns.set_palette('Paired', 10)
sns.set_color_codes()


    

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# Import ShowerLLH sim reconstructions and cuts to be made
df, cut_dict = load_sim(return_cut_dict=True)
standard_cut_keys = ['reco_exists', 'reco_zenith', 'min_hits', 'IceTopMaxSignalInEdge',
                     'IceTopMaxSignal', 'IceTopNeighbourMaxSignal', 'StationDensity',
                     'reco_containment']
selection_mask = np.array([True] * len(df))
for key in standard_cut_keys:
    selection_mask *= cut_dict[key]


    

    




/home/jbourbeau/composition/analysis/load_sim.py:53: RuntimeWarning: divide by zero encountered in log10
  cut_dict['min_energy'] = (np.log10(df['reco_energy']) > 6.2)
/home/jbourbeau/composition/analysis/load_sim.py:57: RuntimeWarning: divide by zero encountered in log10
  df['reco_log_energy'] = np.nan_to_num(np.log10(df['reco_energy']))
/home/jbourbeau/composition/analysis/load_sim.py:59: RuntimeWarning: invalid value encountered in log10
  np.log10(df['InIce_charge_SRTCoincPulses']))

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eff_area, eff_area_error, energy_midpoints = get_effective_area(df, selection_mask)


    

    




Calculating effective area...

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fig, ax = plt.subplots()
ax.errorbar(energy_midpoints, eff_area, yerr=eff_area_error, marker='.')
ax.grid()
ax.set_xscale('log')
ax.set_ylabel('$\mathrm{Effective \ Area} \ [\mathrm{m^2}]$')
ax.set_xlabel('$\mathrm{E_{MC}}/\mathrm{GeV}$')
ax.axvline(10**6.2, marker='None', linestyle='-.')
plt.show()


    

In [11]:

    

standard_cut_keys = ['reco_exists', 'reco_zenith', 'min_hits', 'IceTopMaxSignalInEdge', 'IceTopMaxSignal', 'IceTopNeighbourMaxSignal',
                     'StationDensity', 'reco_containment', 'min_energy']
labels = ['Reco success', 'Reco zenith', 'NChannel/NStation', 'IceTopMaxSignalInEdge', 'IceTopMaxSignal', 'IceTopNeighbourMaxSignal',
          'StationDensity', 'Reco containment', '$\log_{10}(E/GeV) > 6.2$']
# Import ShowerLLH sim reconstructions and cuts to be made
df, cut_dict = load_sim(return_cut_dict=True)
selection_mask = np.array([True]*len(df))
fig, ax = plt.subplots()
eff_area, eff_area_error, energy_midpoints = get_effective_area(df, selection_mask)
ax.errorbar(energy_midpoints, eff_area, yerr=eff_area_error, marker='.', label='Coincident')
for cut_key, label in zip(standard_cut_keys, labels):
    selection_mask = selection_mask & cut_dict[cut_key]
    eff_area, eff_area_error, energy_midpoints = get_effective_area(df, selection_mask)
    ax.errorbar(energy_midpoints, eff_area, yerr=eff_area_error, marker='.', label='+ {}'.format(label))
# ax.legend()
plt.legend(bbox_to_anchor=(1.05, 1), loc=2,
            borderaxespad=0.)
ax.grid()
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_ylabel('$\mathrm{Effective \ Area} \ [\mathrm{m^2}]$')
ax.set_xlabel('$\mathrm{E_{MC}}/\mathrm{GeV}$')
ax.axvline(10**6.2, marker='None', linestyle='-.')
ax.set_ylim([0,1e6])
plt.show()


    

    




Calculating effective area...
Calculating effective area...
Calculating effective area...
Calculating effective area...
Calculating effective area...
Calculating effective area...
Calculating effective area...
Calculating effective area...
Calculating effective area...
Calculating effective area...






    

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