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ragged_plotting_tools.py
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ragged_plotting_tools.py
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import matplotlib
matplotlib.rcParams.update({'figure.max_open_warning': 0})
import warnings
warnings.filterwarnings("ignore", module="matplotlib")
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
from plotting_tools import base_plotter, plotter_3d
from index_dicts import create_index_dict
# from numba import jit
import math
import index_dicts
from matplotlib.patches import Patch
import networkx as nx
import tensorflow as tf
'''
Everything here assumes non flattened format:
B x V x F
'''
# tools before making the ccoords plot working on all events
# @jit(nopython=True)
def c_collectoverthresholds(betas,
ccoords,
sorting,
betasel,
beta_threshold, in_distance_threshold,
n_ccoords):
distance_threshold = in_distance_threshold ** 2
for e in range(len(betasel)):
selected = []
for si in range(len(sorting[e])):
i = sorting[e][si]
use = True
for s in selected:
distance = 0
for cci in range(n_ccoords):
distance += (s[cci] - ccoords[e][i][cci]) ** 2
if distance < distance_threshold:
use = False
break
if not use:
betasel[e][i] = False
continue
else:
selected.append(ccoords[e][i])
return betasel
def collectoverthresholds(data,
beta_threshold, distance_threshold):
betas = np.reshape(data['predBeta'], [data['predBeta'].shape[0], -1])
ccoords = np.reshape(data['predCCoords'], [data['predCCoords'].shape[0], -1, data['predCCoords'].shape[2]])
sorting = np.argsort(-betas, axis=1)
betasel = betas > beta_threshold
bsel = c_collectoverthresholds(betas,
ccoords,
sorting,
betasel,
beta_threshold, distance_threshold,
data['predCCoords'].shape[2]
)
return np.reshape(bsel, [data['predBeta'].shape[0], data['predBeta'].shape[1], data['predBeta'].shape[2]])
# alredy selected for one event here!
def selectEvent(rs, feat, truth, event):
rs = np.array(rs, dtype='int')
rs = rs[:rs[-1]]
feat = feat[rs[event]:rs[event + 1], ...]
return feat, truth[rs[event]:rs[event + 1], ...]
def createRandomizedColors(basemap, seed=0):
cmap = plt.get_cmap(basemap)
vals = np.linspace(0, 1, 256)
np.random.seed(seed)
np.random.shuffle(vals)
return plt.cm.colors.ListedColormap(cmap(vals))
def make_cluster_coordinates_plot(plt, ax,
truthHitAssignementIdx, # [ V ] or [ V x 1 ]
predBeta, # [ V ] or [ V x 1 ]
predCCoords, # [ V x 2 ]
identified_coords=None,
beta_threshold=0.2, distance_threshold=0.8,
cmap=None,
noalpha=False,
direct_color=False,
beta_plot_threshold=1e-2,
data_dump=None #dump in pandas dataframe
):
# data = create_index_dict(truth,pred,usetf=False)
if len(truthHitAssignementIdx.shape) > 1:
truthHitAssignementIdx = np.array(truthHitAssignementIdx[:, 0])
if len(predBeta.shape) > 1:
predBeta = np.array(predBeta[:, 0])
if np.max(predBeta) > 1.:
raise ValueError("make_cluster_coordinates_plot: at least one beta value is above 1. Check your model!")
if predCCoords.shape[1] == 2:
ax.set_aspect(aspect=1.)
# print(truthHitAssignementIdx)
if cmap is None:
rgbcolor = plt.get_cmap('prism')((truthHitAssignementIdx + 1.) / (np.max(truthHitAssignementIdx) + 1.))[:, :-1]
else:
rgbcolor = cmap((truthHitAssignementIdx + 1.) / (np.max(truthHitAssignementIdx) + 1.))[:, :-1]
rgbcolor[truthHitAssignementIdx < 0] = [0.92, 0.92, 0.92]
# print(rgbcolor)
# print(rgbcolor.shape)
betasel = predBeta > beta_plot_threshold
alphas = predBeta**2
alphas = np.clip(alphas, a_min=1e-2, a_max=1. - 1e-2)
alphas = np.expand_dims(alphas, axis=1)
if noalpha:
alphas = np.ones_like(alphas)
rgba_cols = np.concatenate([rgbcolor, alphas], axis=-1)
rgb_cols = np.concatenate([rgbcolor, np.zeros_like(alphas + 1.)], axis=-1)
if direct_color:
rgba_cols = truthHitAssignementIdx
if np.max(rgba_cols) >= 1.:
rgba_cols /= np.max(rgba_cols) + 1e-3
sorting = np.reshape(np.argsort(alphas, axis=0), [-1])
sorted_betasel=betasel[sorting]
if predCCoords.shape[1] == 2:
ax.scatter(predCCoords[:, 0][sorting][sorted_betasel],
predCCoords[:, 1][sorting][sorted_betasel],
s=.1 * matplotlib.rcParams['lines.markersize'] ** 2,
c=rgba_cols[sorting][sorted_betasel])
elif predCCoords.shape[1] == 3:
ax.scatter(predCCoords[:, 0][sorting][sorted_betasel],
predCCoords[:, 1][sorting][sorted_betasel],
predCCoords[:, 2][sorting][sorted_betasel],
s=.1 * matplotlib.rcParams['lines.markersize'] ** 2,
c=rgba_cols[sorting][sorted_betasel])
if beta_threshold < 0. or beta_threshold > 1 or distance_threshold < 0:
return
data = {'predBeta': np.expand_dims(np.expand_dims(predBeta, axis=-1), axis=0),
'predCCoords': np.expand_dims(predCCoords, axis=0)}
if identified_coords is None:
# run the inference part
identified = collectoverthresholds(data, beta_threshold, distance_threshold)[0, :, 0] # V
if predCCoords.shape[1] == 2:
ax.scatter(predCCoords[:, 0][identified],
predCCoords[:, 1][identified],
s=2. * matplotlib.rcParams['lines.markersize'] ** 2,
c='#000000', # rgba_cols[identified],
marker='+')
elif predCCoords.shape[1] == 3:
ax.scatter(predCCoords[:, 0][identified],
predCCoords[:, 1][identified],
predCCoords[:, 2][identified],
s=2. * matplotlib.rcParams['lines.markersize'] ** 2,
c='#000000', # rgba_cols[identified],
marker='+')
return identified
else:
if predCCoords.shape[1] == 2:
ax.scatter(identified_coords[:, 0],
identified_coords[:, 1],
s=2. * matplotlib.rcParams['lines.markersize'] ** 2,
c='#000000', # rgba_cols[identified],
marker='+')
elif predCCoords.shape[1] == 3:
ax.scatter(identified_coords[:, 0],
identified_coords[:, 1],
identified_coords[:, 3],
s=2. * matplotlib.rcParams['lines.markersize'] ** 2,
c='#000000', # rgba_cols[identified],
marker='+')
def make_original_truth_shower_plot(plt, ax,
truthHitAssignementIdx,
recHitEnergy,
recHitX,
recHitY,
recHitZ,
cmap=None,
rgbcolor=None,
alpha=0.5,
predBeta=None):
if len(truthHitAssignementIdx.shape) > 1:
truthHitAssignementIdx = np.array(truthHitAssignementIdx[:, 0])
if len(recHitEnergy.shape) > 1:
recHitEnergy = np.array(recHitEnergy[:, 0])
if len(recHitX.shape) > 1:
recHitX = np.array(recHitX[:, 0])
if len(recHitY.shape) > 1:
recHitY = np.array(recHitY[:, 0])
if len(recHitZ.shape) > 1:
recHitZ = np.array(recHitZ[:, 0])
pl = plotter_3d(output_file="/tmp/plot", colorscheme=None) # will be ignored
if rgbcolor is None:
if cmap is None:
rgbcolor = plt.get_cmap('prism')((truthHitAssignementIdx + 1.) / (np.max(truthHitAssignementIdx) + 1.))[:,
:-1]
else:
rgbcolor = cmap((truthHitAssignementIdx + 1.) / (np.max(truthHitAssignementIdx) + 1.))[:, :-1]
rgbcolor[truthHitAssignementIdx < 0] = [0.92, 0.92, 0.92]
if predBeta is not None:
alpha = None # use beta instead
if len(predBeta.shape) > 1:
predBeta = np.array(predBeta[:, 0])
alphas = predBeta
alphas = np.clip(alphas, a_min=5e-1, a_max=1. - 1e-2)
alphas = np.arctanh(alphas) / np.arctanh(1. - 1e-2)
# alphas *= alphas
alphas[alphas < 0.05] = 0.05
alphas = np.expand_dims(alphas, axis=1)
rgbcolor = np.concatenate([rgbcolor, alphas], axis=-1)
if np.max(rgbcolor) >= 1.:
rgbcolor /= np.max(rgbcolor)
pl.set_data(x=recHitX, y=recHitY, z=recHitZ, e=recHitEnergy, c=rgbcolor)
pl.marker_scale = 2.
pl.plot3d(ax=ax, alpha=alpha)
def make_eta_phi_projection_truth_plot(plt, ax,
truthHitAssignementIdx,
recHitEnergy,
recHitEta,
recHitPhi,
predEta,
predPhi,
truthEta,
truthPhi,
truthEnergy,
predBeta, # [ V ] or [ V x 1 ]
predCCoords, # [ V x 2 ]
beta_threshold=0.2, distance_threshold=0.8,
cmap=None,
identified=None,
predEnergy=None):
if len(truthHitAssignementIdx.shape) > 1:
truthHitAssignementIdx = np.array(truthHitAssignementIdx[:, 0])
if len(recHitEnergy.shape) > 1:
recHitEnergy = np.array(recHitEnergy[:, 0])
if len(recHitEta.shape) > 1:
recHitEta = np.array(recHitEta[:, 0])
if len(recHitPhi.shape) > 1:
recHitPhi = np.array(recHitPhi[:, 0])
if len(predEta.shape) > 1:
predEta = np.array(predEta[:, 0])
if len(predPhi.shape) > 1:
predPhi = np.array(predPhi[:, 0])
if len(truthEta.shape) > 1:
truthEta = np.array(truthEta[:, 0])
if len(truthPhi.shape) > 1:
truthPhi = np.array(truthPhi[:, 0])
if len(truthEnergy.shape) > 1:
truthEnergy = np.array(truthEnergy[:, 0])
if len(truthHitAssignementIdx.shape) > 1:
truthHitAssignementIdx = np.array(truthHitAssignementIdx[:, 0])
if len(predBeta.shape) > 1:
predBeta = np.array(predBeta[:, 0])
ax.set_aspect(aspect=1.)
# print(truthHitAssignementIdx)
if cmap is None:
rgbcolor = plt.get_cmap('prism')((truthHitAssignementIdx + 1.) / (np.max(truthHitAssignementIdx) + 1.))[:, :-1]
else:
rgbcolor = cmap((truthHitAssignementIdx + 1.) / (np.max(truthHitAssignementIdx) + 1.))[:, :-1]
rgbcolor[truthHitAssignementIdx < 0] = [0.92, 0.92, 0.92]
size_scaling = np.log(recHitEnergy + 1) + 0.1
size_scaling /= np.max(size_scaling)
ax.scatter(recHitPhi,
recHitEta,
s=.25 * size_scaling,
c=rgbcolor)
_, truth_idxs = np.unique(truthHitAssignementIdx, return_index=True)
truth_size_scaling = np.log(truthEnergy[truth_idxs][truthHitAssignementIdx[truth_idxs] >= 0] + 1.) + 0.1
truth_size_scaling /= np.max(truth_size_scaling)
true_sel_phi = truthPhi[truth_idxs][truthHitAssignementIdx[truth_idxs] >= 0]
true_sel_eta = truthEta[truth_idxs][truthHitAssignementIdx[truth_idxs] >= 0]
true_sel_col = rgbcolor[truth_idxs][truthHitAssignementIdx[truth_idxs] >= 0]
ax.scatter(true_sel_phi,
true_sel_eta,
s=100. * truth_size_scaling,
c=true_sel_col,
marker='x')
if beta_threshold < 0. or beta_threshold > 1 or distance_threshold < 0:
return
data = {'predBeta': np.expand_dims(np.expand_dims(predBeta, axis=-1), axis=0),
'predCCoords': np.expand_dims(predCCoords, axis=0)}
# run the inference part
if identified is None:
identified = collectoverthresholds(data, beta_threshold, distance_threshold)[0, :, 0] # V
ax.scatter(predPhi[identified],
predEta[identified],
s=2. * matplotlib.rcParams['lines.markersize'] ** 2,
c='#000000', # rgba_cols[identified],
marker='+')
if predEnergy is not None:
if len(predEnergy.shape) > 1:
predEnergy = np.array(predEnergy[:, 0])
predE = predEnergy[identified]
for i in range(len(predE)):
# predicted
ax.text(predPhi[identified][i],
predEta[identified][i],
s=str(predE[i])[:4],
verticalalignment='bottom', horizontalalignment='right',
rotation=30,
fontsize='small')
# truth
true_sel_en = truthEnergy[truth_idxs][truthHitAssignementIdx[truth_idxs] >= 0]
for i in range(len(true_sel_en)):
ax.text(true_sel_phi[i], true_sel_eta[i],
s=str(true_sel_en[i])[:4],
color=true_sel_col[i] / 1.2,
verticalalignment='top', horizontalalignment='left',
rotation=30,
fontsize='small')