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add benchmark for sampling calorimeter

Merged add benchmark for sampling calorimeter
sampling_cal into master
Merged Chao Peng requested to merge sampling_cal into master
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benchmarks/sampling_ecal/scripts/draw_cluster.py 0 → 100644
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'''
A script to visualize the cluster
It reads the output from the Juggler component ImagingClusterReco, which is supposed to be clusters of hits after
digitization, reconstruction, and clustering
Author: Chao Peng (ANL)
Date: 04/30/2021
'''
import os
import numpy as np
import pandas as pd
import argparse
import matplotlib
from matplotlib import cm
from matplotlib import pyplot as plt
from matplotlib.ticker import MultipleLocator
from matplotlib.collections import PatchCollection
from matplotlib.patches import Rectangle
from mpl_toolkits.axes_grid1 import make_axes_locatable
from utils import *
import sys
# draw cluster in a 3d axis, expect a numpy array of (nhits, 4) shape with each row contains (x, y, z, E)
# note z and x axes are switched
def draw_hits3d(axis, data, cmap, units=('mm', 'mm', 'mm', 'MeV'), fontsize=24, **kwargs):
# normalize energy to get colors
x, y, z, edep = np.transpose(data)
cvals = edep - min(edep) / (max(edep) - min(edep))
cvals[np.isnan(cvals)] = 1.0
colors = cmap(cvals)
# hits
axis.scatter(z, y, x, c=colors, marker='o', **kwargs)
axis.tick_params(labelsize=fontsize)
axis.set_zlabel('x ({})'.format(units[2]), fontsize=fontsize + 2, labelpad=fontsize)
axis.set_ylabel('y ({})'.format(units[1]), fontsize=fontsize + 2, labelpad=fontsize)
axis.set_xlabel('z ({})'.format(units[0]), fontsize=fontsize + 2, labelpad=fontsize)
cb = plt.colorbar(cm.ScalarMappable(norm=matplotlib.colors.Normalize(vmin=min(edep), vmax=max(edep)), cmap=cmap),
ax=axis, shrink=0.85)
cb.ax.tick_params(labelsize=fontsize)
cb.ax.get_yaxis().labelpad = fontsize
cb.ax.set_ylabel('Energy Deposit ({})'.format(units[3]), rotation=90, fontsize=fontsize + 4)
return axis
# draw a cylinder in 3d axes
# note z and x axes are switched
def draw_cylinder3d(axis, r, z, order=['x', 'y', 'z'], rsteps=500, zsteps=500, **kwargs):
x = np.linspace(-r, r, rsteps)
z = np.linspace(-z, z, zsteps)
Xc, Zc = np.meshgrid(x, z)
Yc = np.sqrt(r**2 - Xc**2)
axis.plot_surface(Zc, Yc, Xc, alpha=0.1, **kwargs)
axis.plot_surface(Zc, -Yc, Xc, alpha=0.1, **kwargs)
return axis
# fit the track of cluster and draw the fit
def draw_track_fit(axis, dfh, length=200, stop_layer=8, scat_kw=dict(), line_kw=dict()):
dfh = dfh[dfh['layer'] <= stop_layer]
data = dfh.groupby('layer')[['z', 'y','x']].mean().values
# data = dfh[['z', 'y', 'x']].values
# ax.scatter(*data.T, **scat_kw)
datamean = data.mean(axis=0)
uu, dd, vv = np.linalg.svd(data - datamean)
linepts = vv[0] * np.mgrid[-length:length:2j][:, np.newaxis]
linepts += datamean
axis.plot3D(*linepts.T, 'k:')
return axis
# color map
def draw_heatmap(axis, x, y, weights, bins=1000, cmin=0., cmap=plt.get_cmap('rainbow'), pc_kw=dict()):
w, xedg, yedg = np.histogram2d(x, y, weights=weights, bins=bins)
xsz = np.mean(np.diff(xedg))
ysz = np.mean(np.diff(yedg))
wmin, wmax = w.min(), w.max()
recs, clrs = [], []
for i in np.arange(len(xedg) - 1):
for j in np.arange(len(yedg) - 1):
if w[i][j] > cmin:
recs.append(Rectangle((xedg[i], yedg[j]), xsz, ysz))
clrs.append(cmap((w[i][j] - wmin) / (wmax - wmin)))
axis.add_collection(PatchCollection(recs, facecolor=clrs, **pc_kw))
axis.set_xlim(xedg[0], xedg[-1])
axis.set_ylim(yedg[0], yedg[-1])
return axis, cm.ScalarMappable(norm=matplotlib.colors.Normalize(vmin=wmin, vmax=wmax), cmap=cmap)
# execute this script
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Visualize the cluster from analysis')
parser.add_argument('file', type=str, help='path to root file')
parser.add_argument('-e', type=int, default=0, dest='iev', help='event number to plot')
parser.add_argument('-c', type=int, default=0, dest='icl', help='cluster number to plot')
parser.add_argument('-s', type=int, default=8, dest='stop', help='stop layer for track fit')
parser.add_argument('-o', type=str, default='./plots', dest='outdir', help='output directory')
parser.add_argument('--compact', type=str, default='', dest='compact', help='compact file')
parser.add_argument('-m', '--macros', type=str, default='rootlogon.C', dest='macros',
help='root macros to load (accept multiple paths separated by \",\")')
parser.add_argument('-b', '--branch-name', type=str, default='EcalBarrelClustersLayers', dest='branch',
help='branch name in the root file (outputLayerCollection from ImagingClusterReco)')
parser.add_argument('--topo-size', type=float, default=2.0, dest='topo_size',
help='bin size for projection plot (mrad)')
parser.add_argument('--topo-range', type=float, default=50.0, dest='topo_range',
help='half range for projection plot (mrad)')
args = parser.parse_args()
# we can read these values from xml file
desc = compact_constants(args.compact, ['cb_ECal_RMin', 'cb_ECal_ReadoutLayerThickness',
'cb_ECal_ReadoutLayerNumber', 'cb_ECal_Length'])
if not len(desc):
# or define Ecal shapes
rmin, thickness, length = 890, 20*(10. + 1.65), 860*2+500
else:
# convert cm to mm
rmin = desc[0]*10.
thickness = desc[1]*desc[2]*10.
length = desc[3]*10.
# read data
load_root_macros(args.macros)
df = get_hits_data(args.file, args.iev, branch=args.branch)
dfmcp = get_mcp_data(args.file, args.iev, 'mcparticles2')
vec = dfmcp.loc[dfmcp['status'] == 24578, ['px', 'py', 'pz']].iloc[0].values
vec = vec/np.linalg.norm(vec)
df = df[df['cluster'] == args.icl]
if not len(df):
print("Error: do not find any hits for cluster {:d} in event {:d}".format(args.icl, args.iev))
exit(-1)
# particle line from (0, 0, 0) to the inner Ecal surface
length = rmin/np.sqrt(vec[0]**2 + vec[1]**2)
pline = np.transpose(vec*np.mgrid[0:length:2j][:, np.newaxis])
os.makedirs(args.outdir, exist_ok=True)
# cluster plot
cmap = truncate_colormap(plt.get_cmap('jet'), 0.1, 0.9)
fig = plt.figure(figsize=(20, 16), dpi=160)
ax = fig.add_subplot(111, projection='3d')
# draw particle line
ax.plot(*pline[[2, 1]], '--', zs=pline[0], color='green')
# draw hits
draw_hits3d(ax, df[['x', 'y', 'z', 'edep']].values, cmap, s=5.0)
draw_cylinder3d(ax, rmin, length, rstride=10, cstride=10, color='royalblue')
draw_cylinder3d(ax, rmin + thickness, length, rstride=10, cstride=10, color='forestgreen')
ax.set_zlim(-(rmin + thickness), rmin + thickness)
ax.set_ylim(-(rmin + thickness), rmin + thickness)
ax.set_xlim(-length, length)
fig.tight_layout()
fig.savefig(os.path.join(args.outdir, 'e{}_cluster.png'.format(args.iev)))
# zoomed-in cluster plot
fig = plt.figure(figsize=(20, 16), dpi=160)
ax = fig.add_subplot(111, projection='3d')
# draw particle line
ax.plot(*pline[[2, 1]], '--', zs=pline[0], color='green')
# draw hits
draw_hits3d(ax, df[['x', 'y', 'z', 'edep']].values, cmap, s=20.0)
draw_track_fit(ax, df, stop_layer=args.stop,
scat_kw=dict(color='k', s=50.0), line_kw=dict(linestyle=':', color='k', lw=3))
# view range
center = (length + thickness/2.)*vec
ranges = np.vstack([center - thickness/4., center + thickness/4.]).T
ax.set_zlim(*ranges[0])
ax.set_ylim(*ranges[1])
ax.set_xlim(*ranges[2])
fig.tight_layout()
fig.savefig(os.path.join(args.outdir, 'e{}_cluster_zoom.png'.format(args.iev)))
# projection plot
# convert to polar coordinates (mrad), and stack all r values
df['r'] = np.sqrt(df['x'].values**2 + df['y'].values**2 + df['z'].values**2)
df['phi'] = np.arctan2(df['y'].values, df['x'].values)*1000.
df['theta'] = np.arccos(df['z'].values/df['r'].values)*1000.
# convert to mrad
vecp = np.asarray([np.arccos(vec[2]), np.arctan2(vec[1], vec[0])])*1000.
phi_rg = np.asarray([vecp[1] - args.topo_range, vecp[1] + args.topo_range])
th_rg = np.asarray([vecp[0] - args.topo_range, vecp[0] + args.topo_range])
fig, axs = plt.subplots(1, 2, figsize=(17, 16), dpi=160, gridspec_kw={'wspace':0., 'width_ratios': [16, 1]})
ax, sm = draw_heatmap(axs[0], df['theta'].values, df['phi'].values, weights=df['edep'].values,
bins=(np.arange(*th_rg, step=args.topo_size), np.arange(*phi_rg, step=args.topo_size)),
cmap=cmap, cmin=0., pc_kw=dict(alpha=0.8, edgecolor='k'))
ax.set_ylabel(r'$\phi$ (mrad)', fontsize=28)
ax.set_xlabel(r'$\theta$ (mrad)', fontsize=28)
ax.tick_params(labelsize=24)
ax.xaxis.set_minor_locator(MultipleLocator(5))
ax.yaxis.set_minor_locator(MultipleLocator(5))
ax.grid(linestyle=':', which='both')
ax.set_axisbelow(True)
cb = plt.colorbar(sm, cax=axs[1], shrink=0.85)
cb.ax.tick_params(labelsize=24)
cb.ax.get_yaxis().labelpad = 24
cb.ax.set_ylabel('Energy Deposit (MeV)', rotation=90, fontsize=28)
fig.savefig(os.path.join(args.outdir, 'e{}_topo.png'.format(args.iev)))