print(len(dvpi0p16.index))
bin_size = [100,100]
xvals2 = df['Mpi0']
xvals = dvpi0p16['Mpi0']
x_name = "Gamma-Gamma Invariant Mass (GeV)"
output_dir = "figures/"
#ranges = "none"
ranges = [0,0.3,100]
make_histos.plot_1dhist(xvals,[x_name,],ranges=ranges,second_x=None,annotation=None,
saveplot=False,pics_dir=output_dir,plot_title="Reconstructed Pion Mass, NF Sampled",
density=False,proton_line=0.135,first_color="blue",xlabel_1="NF Data")
sys.exit()
x_data = df["Mpi0"]
y_data = df["pmass"]
var_names = ["E Px","E Py"]
saveplots = False
output_dir = "figures/"
title = "Pi Mass vs EPx"
filename = title
units = ["GeV","Gev"]
ranges = [[0,.3,100],[.9,.96,100]]
#interactive(True)
make_histos.plot_2dhist(x_data,y_data,var_names,ranges,colorbar=False,
#x_name = "Gamma-Gamma Invariant Mass (GeV)"
x_name = "Reconstructed Pion Mass (GeV)"
#ranges = "none"
#ranges = [12,28,100]
ranges = [.02, .3, 100]
print("PLOTTTING")
make_histos.plot_1dhist(
xvals3, [
x_name,
],
ranges=ranges,
second_x=xvals2,
annotation="yes",
third_x=xvals,
xlabel_3="QT-3-Feature Model",
saveplot=False,
pics_dir=output_dir,
plot_title=
"Reconstructed Pion Mass, (4 Feature) and (3 Feature with QT) NF Models vs. Standard Simulations",
density=True,
proton_line=0.135,
first_color="red",
xlabel_1="Physics Recon. Data",
xlabel_2="4-Feature Model")
sys.exit()
df_4 = pd.read_pickle("4_feature_pion.pkl")
ic(df_4)
#df = df_4.head(50000)
print(df)
print(df2)
#sys.exit()
bin_size = [100, 100]
xvals = df["emass2"]
x_name = "px E"
output_dir = "./"
#ranges = "none"
ranges = [-0.2, 0.2, 100]
make_histos.plot_1dhist(xvals, [
x_name,
],
ranges=ranges,
second_x=None,
saveplot=False,
pics_dir=output_dir,
plot_title="Squared Electron Mass, NF Model",
density=False)
x_data = df[1]
y_data = df[2]
var_names = ["E Px", "E Py"]
saveplots = False
output_dir = "."
title = "Electron X vs. Y Mom., NF Model"
filename = title
units = ["GeV", "Gev"]
ranges = [[-1.5, 1.5, 100], [-1.5, 1.5, 100]]
filename="gen-recon_{}",
units=["GeV", "GeV"])
# plt.scatter(df["recon_{}".format(name)],df["recon_{}".format(name)]-df["nf_{}".format(name)])
# plt.scatter(df["recon_{}".format(name)],df["nf_{}".format(name)])
#plt.title("Recon vs. NF, {}".format(name))
#plt.xlabel("Recon")
#plt.ylabel("NF")
#plt.show()
ic(df_ps.mean())
df_ps.to_pickle("4_feature_pion_QT_INV.pkl")
make_histos.plot_1dhist(df_ps['recon_Mpi0'], [
'nf pion mass',
],
ranges=[0.02, 0.4, 150],
second_x=df_ps['nf_Mpi0'],
first_color='red')
# # make_histos.plot_1dhist(xvals_1,[x_name,],ranges="none",second_x=xvals_2,
# # saveplot=True,pics_dir=output_dir,plot_title="{}, NF 4-Feature Model".format(x_name),density=True,
# # annotation=emd_nflow,first_color="red",xlabel_1="Microphysics Data",xlabel_2="NF Model Sample")
# dvpi0p = df_nflow_data
# #dvpi0p = df_test_data
# e=4
# dvpi0p.loc[:,'pmass'] = np.sqrt(dvpi0p[e]**2-dvpi0p[e+1]**2-dvpi0p[e+2]**2-dvpi0p[e+3]**2)/0.938
# dvpi0p.loc[:, "Gpx"] = dvpi0p.loc[:, 9]
# dvpi0p.loc[:, "Gpy"] = dvpi0p.loc[:, 10]
dfs = [df0,df1]
df = pd.concat(dfs)
#print(df)
df.hist()
zX = df.to_numpy()
bin_size = [100,100]
xvals = df[1]
x_name = "px E"
output_dir = "./"
make_histos.plot_1dhist(xvals,[x_name,],ranges="none",second_x="none",
saveplot=False,pics_dir=output_dir,plot_title=x_name)
# pairs = [(0,1),(1,2),(0,4),(8,12),(5,6),(9,10)]
# for a,b in pairs:
# fig, ax = plt.subplots(figsize =(10, 7))
# plt.hist2d(zX[:,a], zX[:,b],bins =bin_size,norm=mpl.colors.LogNorm())# cmap = plt.cm.nipy_spectral)
# #plt.xlim([-2,2])
# #plt.ylim([-2,2])
# #plt.colorbar()
# plotname = "finalplotname2.jpeg"
# plt.show()
if run_2:
print(df2)
#sys.exit()
bin_size = [100, 100]
xvals = df["pmass"]
x_name = "NF Model Proton Mass"
output_dir = "./"
#ranges = "none"
ranges = [.925, .951, 100]
make_histos.plot_1dhist(xvals, [
x_name,
],
ranges=ranges,
second_x=None,
saveplot=False,
pics_dir=output_dir,
plot_title="Reconstructed Proton Mass, NF Model",
density=False,
proton_line=True,
first_color="green",
xlabel_1="NF Data")
sys.exit()
x_data = df[1]
y_data = df[2]
var_names = ["E Px", "E Py"]
saveplots = True
output_dir = "./"
title = "Electron $P_X$ vs. $P_Y$, NF 4-Feat. Model"
filename = title
