def test_slice(self):
print('TestTrainData: skim')
a = self.createSimpleArray('int32', 600)
b = self.createSimpleArray('float32', 600)
d = self.createSimpleArray('float32', 600)
a_slice = a.getSlice(2, 3)
b_slice = b.getSlice(2, 3)
d_slice = d.getSlice(2, 3)
td = TrainData()
td._store([a, b], [d], [])
td_slice = td.getSlice(2, 3)
fl = td_slice.transferFeatureListToNumpy(False)
tl = td_slice.transferTruthListToNumpy(False)
a_tdslice = SimpleArray(fl[0], fl[1])
b_tdslice = SimpleArray(fl[2], fl[3])
d_tdslice = SimpleArray(tl[0], tl[1])
self.assertEqual(a_slice, a_tdslice)
self.assertEqual(b_slice, b_tdslice)
self.assertEqual(d_slice, d_tdslice)
#test skim
td.skim(2)
fl = td.transferFeatureListToNumpy(False)
tl = td.transferTruthListToNumpy(False)
a_tdslice = SimpleArray(fl[0], fl[1])
b_tdslice = SimpleArray(fl[2], fl[3])
d_tdslice = SimpleArray(tl[0], tl[1])
self.assertEqual(a_slice, a_tdslice)
self.assertEqual(b_slice, b_tdslice)
self.assertEqual(d_slice, d_tdslice)
def sub_test_store(self, readWrite):
td = TrainData()
x,y,w = self.createSimpleArray('int32'), self.createSimpleArray('float32'), self.createSimpleArray('int32')
x_orig=x.copy()
x2,y2,_ = self.createSimpleArray('float32'), self.createSimpleArray('float32'), self.createSimpleArray('int32')
x2_orig=x2.copy()
y_orig=y.copy()
td._store([x,x2], [y,y2], [w])
if readWrite:
td.writeToFile("testfile.tdjctd")
td = TrainData()
td.readFromFile("testfile.tdjctd")
os.system('rm -f testfile.tdjctd')
shapes = td.getNumpyFeatureShapes()
self.assertEqual([[3, 5, 6], [1], [3, 5, 6], [1]], shapes,"shapes")
self.assertEqual(2, td.nFeatureArrays())
self.assertEqual(2, td.nTruthArrays())
self.assertEqual(1, td.nWeightArrays())
f = td.transferFeatureListToNumpy(False)
t = td.transferTruthListToNumpy(False)
w = td.transferWeightListToNumpy(False)
xnew = SimpleArray(f[0],np.array(f[1],dtype='int64'))
self.assertEqual(x_orig, xnew)
xnew = SimpleArray(f[2],np.array(f[3],dtype='int64'))
self.assertEqual(x2_orig, xnew)
ynew = SimpleArray(t[0],np.array(t[1],dtype='int64'))
self.assertEqual(y_orig, ynew)
def test_TrainDataRead(self):
print('TestCompatibility TrainData')
td = TrainData()
td.readFromFile('trainData_previous.djctd')
self.assertEqual(td.nFeatureArrays(), 1)
arr = np.load("np_arr.npy")
rs = np.load("np_rs.npy")
b = SimpleArray(arr, rs)
a = td.transferFeatureListToNumpy(False)
a, rs = a[0], a[1]
a = SimpleArray(a, np.array(rs, dtype='int64'))
self.assertEqual(a, b)
import matplotlib.patches as patches
import math
from numba import jit
from inference import collect_condensates, make_inference_dict
parser = ArgumentParser('make plots')
parser.add_argument('inputFile')
args = parser.parse_args()
#use traindata as data storage
td = TrainData()
td.readFromFile(args.inputFile)
td.x = td.transferFeatureListToNumpy()
data = make_inference_dict(td.x[0], td.x[1], td.x[2])
betaselection = collect_condensates(data, 0.1, 0.8) #0.2/2.0
print('betaselection', betaselection.shape)
def makeRectangle(size, pos, edgecolor='y'):
return patches.Rectangle([pos[0] - size[0] / 2., pos[1] - size[1] / 2.],
size[0],
size[1],
linewidth=1,
edgecolor=edgecolor,
facecolor='none')
#!/usr/bin/env python
import numpy as np
from argparse import ArgumentParser
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
parser = ArgumentParser('make plots')
parser.add_argument('inputFile')
args = parser.parse_args()
from DeepJetCore.TrainData import TrainData
from tools import make_particle_resolution_plots
#just read the stored data
td = TrainData()
td.readFromFile(args.inputFile)
indata = td.transferFeatureListToNumpy()
pred, feat, truth = indata[0], indata[1], indata[2]
del td
print('pred', pred.shape)
print('feat', feat.shape)
print('truth', truth.shape)
make_particle_resolution_plots(feat, pred, truth, outfile="reso.pdf")
from plotting_tools import plotevent
import numpy as np
parser = ArgumentParser('Make some plots')
parser.add_argument('inputFile')
args = parser.parse_args()
infile = str(args.inputFile)
from DeepJetCore.TrainData import TrainData
import matplotlib.pyplot as plt
td=TrainData()
td.readFromFile(infile)
feat = td.transferFeatureListToNumpy()[0]
truth = td.transferTruthListToNumpy()[0]
nevents = min(len(feat),10)
for e in range(nevents):
print('true energy', truth[e])
print('reco sum ', np.sum(feat[e,:,:,:,0]))
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.set_xlabel("x [idx]")
ax.set_zlabel("y [idx]")
ax.set_ylabel("z [idx]")
ax.grid(False)