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fit_pion_scaling_unc.py
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import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import scipy as sp
from scipy.optimize import curve_fit
import matplotlib as mpl
import os
from JetEtaBins import JetEtaBins, PtBins
from helpers import read_data
# from correction_fitter_helpers import save_correction_txt_file, init_vals_2014, init_two_gaus, fit_corrections
from plotters.pltStyle import pltStyle
import mplhep as hep
pltStyle(style='hep')
plt.rcParams['figure.dpi'] = 110
# plt.rcParams['image.cmap'] = 'viridis'
from data_tools import read_or_recreate_data, read_or_recreate_data_txt
from collections.abc import Iterable
out_txt_path = 'out_txt'
def read_data4plot(tag, closure=1, path=out_txt_path):
'''Read the Mean, MeanStd, Median, MedianStd and RecoPt values of the data with tag `tag`.
If closure==1, there is no clusure, otherwise it has to be of the same shape as the data read
'''
# file_path = f'../out_txt/fit_results_L5_{tag}.json'
# with open(file_path, 'r') as json_file:
# json_data = json.load(json_file)
data = read_or_recreate_data(tag, out_txt_path)['data']
if not isinstance(closure, Iterable):
closure_tmp = np.array([closure])
else:
closure_tmp = np.array(closure).copy()
closure_tmp[closure_tmp==0] = np.nan
close = ["Median", "Mean"]
for flav in data:
for mean_name in close:
data[flav][mean_name] = data[flav][mean_name]/closure_tmp #[2:]
for typeii in ["MedianStd", "MeanStd", "MeanRecoPt"]:
data[flav][typeii] = np.array(data[flav][typeii])
return data
flavors = ['b', 'c', 's', 'd', 'u', 'ud', 'q']
# flavors = ['b', 'bbar', 'c', 'cbar', 's', 'sbar', 'ud', 'udbar', 'q', 'qbar', 'unmatched', 'all']
plotvspt = True
eta_binning = "HCalPart" ### HCalPart, JERC, CoarseCalo, CaloTowers, Summer20Flavor, onebin;
eta_binning_str = '_'+eta_binning if eta_binning != "HCalPart" else ''
etabins = JetEtaBins(eta_binning, absolute=True)
ptbins = PtBins("MC_truth")
outdir = 'fig/pion_scaling'
os.makedirs(outdir, exist_ok=True)
tag1 = '_L5_not_scaled_pion'+'_split_antiflav'+eta_binning_str
tag2 = '_L5_scaled_pion'+'_split_antiflav'+eta_binning_str
# tag3 = '_L5_scaled_times2_pion'+'_split_antiflav'+eta_binning_str
tagx5 = '_L5_scaled_times5_pion'+'_split_antiflav'+eta_binning_str
tagx10 = '_L5_scaled_times10_pion'+'_split_antiflav'+eta_binning_str
for flav in flavors:
data1 = read_data4plot(tag1)
data2 = read_data4plot(tag2)
# data3 = read_data4plot(tag3)
datax5 = read_data4plot(tagx5)
datax10 = read_data4plot(tagx10)
data_div = data1[flav]['Median'].copy()
data_div[data_div==0] = 1
data_div_qbar = data1[flav+'bar']['Median'].copy()
data_div_qbar[data_div_qbar==0] = 1
mean_q = data2[flav]['Median']/data_div
mean_qbar = data2[flav+'bar']['Median']/data_div_qbar
mean_q_x5 = datax5[flav]['Median']/data_div
mean_qbar_x5 = datax5[flav+'bar']['Median']/data_div_qbar
mean_q_x10 = datax10[flav]['Median']/data_div
mean_qbar_x10 = datax10[flav+'bar']['Median']/data_div_qbar
# mean_q = data_div/data2[flav]['Median']
# mean_qbar = data_div_qbar/data2[flav+'bar']['Median']
uncertainty_q = np.sqrt((data2[flav]['MedianStd']/data_div)**2 + (data2[flav]['Median']*data1[flav]['MeanStd']/data_div**2)**2)
uncertainty_qbar = np.sqrt((data2[flav+'bar']['MedianStd']/data_div_qbar)**2 + (data2[flav+'bar']['Median']*data1[flav+'bar']['MeanStd']/data_div_qbar**2)**2)
uncertainty_q_x10 = np.sqrt((datax10[flav]['MeanStd']/data_div)**2 + (datax10[flav]['Median']*data1[flav]['MeanStd']/data_div**2)**2)
uncertainty_qbar_x10 = np.sqrt((datax10[flav+'bar']['MeanStd']/data_div_qbar)**2 + (datax10[flav+'bar']['Median']*data1[flav+'bar']['MeanStd']/data_div_qbar**2)**2)
uncertainty_q_x5 = np.sqrt((datax5[flav]['MeanStd']/data_div)**2 + (datax5[flav]['Median']*data1[flav]['MeanStd']/data_div**2)**2)
uncertainty_qbar_x5 = np.sqrt((datax5[flav+'bar']['MeanStd']/data_div_qbar)**2 + (datax5[flav+'bar']['Median']*data1[flav+'bar']['MeanStd']/data_div_qbar**2)**2)
uncertainty_q = uncertainty_q/100
uncertainty_qbar = uncertainty_qbar/100
uncertainty_q_x10 = uncertainty_q_x10/100
uncertainty_qbar_x10 = uncertainty_qbar_x10/100
uncertainty_q_x5 = uncertainty_q_x5/100
uncertainty_qbar_x5 = uncertainty_qbar_x5/100
difference = mean_q - mean_qbar
uncertainty = np.sqrt(uncertainty_q**2 + uncertainty_qbar**2)
difference_x10 = (mean_q_x10 - mean_qbar_x10)
uncertainty_x10 = np.sqrt(uncertainty_q_x10**2 + uncertainty_qbar_x10**2)
difference_x5 = (mean_q_x5 - mean_qbar_x5)
uncertainty_x5 = np.sqrt(uncertainty_q_x5**2 + uncertainty_qbar_x5**2)
for binidx in etabins.get_bin_idx([0, 1.305, 2.5, 4]):
eta_string = etabins.idx2str(binidx)
fig, ax = plt.subplots()
# breakpoint()
# print('flav: ', flav)
# print(np.transpose([data1[flav]['MeanRecoPt'][:,binidx], mean_q[:, binidx], mean_qbar[:, binidx]]))
# print('mean_q: ', mean_q[:, binidx])
# print('mean_qbar: ', mean_qbar[:, binidx])
ax.errorbar(data1[flav]['MeanRecoPt'][:,binidx], difference[:, binidx], yerr=uncertainty[:, binidx], fmt='o', label='x1')
ax.errorbar(data1[flav]['MeanRecoPt'][:,binidx], difference_x10[:, binidx], yerr=uncertainty_x10[:, binidx], fmt='o', label='x10')
ax.errorbar(data1[flav]['MeanRecoPt'][:,binidx], difference_x5[:, binidx], yerr=uncertainty_x5[:, binidx], fmt='o', label='x5')
ax.set_xlabel('Reco Pt')
ax.set_ylabel('$R(scaled, q)/R(central, q)$'+ f'\n'+ '$ - R(scaled, \overline{q})/R(central, \overline{q})$')
ax.set_xscale('log')
good_xlims = ax.get_xlim()
ax.hlines(0,1, 10000, linestyles='--',color="black",
linewidth=1,)
ax.set_xticks([20, 50, 100, 500, 1000, 5000])
ax.set_xlim(good_xlims)
ax.get_xaxis().set_major_formatter(mpl.ticker.ScalarFormatter())
hep.label.exp_text(text=f'{etabins.idx2plot_str(binidx)}\n{flav} jets', loc=2, ax=ax)
figname = f'{outdir}/difference_{flav}_pion_{eta_string}'
hep.cms.label("Private work", loc=0, data=False, ax=ax, rlabel='')
ax.legend(loc="upper right")
[left_lim, right_lim] = ax.get_ylim()
lim_pad = (right_lim - left_lim)/5
ax.set_ylim(left_lim, right_lim+lim_pad)
# inclrease the figure left margin so that the y-axis label is not cut off
plt.subplots_adjust(left=0.23)
plt.savefig(figname+'.png')
plt.savefig(figname+'.pdf')
print(f'Saved {figname}.png /.pdf')
plt.close()
# ax.set_title(f'{flav} quark')