# The data shows a normal distribution, so we're going to use a # logistic mapping function. # This time, load the entire dataset into memory. word_vecs = h5f['word_vecs'][:] # Calculate the mean using a sample of the vectors. center = np.mean(word_vecs[0:sample_size].flatten()) # Verify that the mean is roughly zero. assert(abs(center - 0.0) < 0.01) # Learn the parameters for the logistic mapping function. # The mapping function will saturate at 8 standard deviations, and will convert # the values to 8-bit integers. tf = Transforms.Transforms(np.uint8) standard_deviations = 8 tf.learn_logistic(word_vecs, num_std=standard_deviations) # Apply logistic mapping to the dataset vectors. word_vecs_int = tf.apply_logistic(word_vecs) # Load the query vectors into memory and map the values. query_vecs = h5f['query_vecs'][:] # Apply logistic mapping to the query vectors. query_vecs_int = tf.apply_logistic(query_vecs) # Take a small subset of the dataset and plot a histogram of the values.
if my_args.output != None:
plotpath = my_args.output + "/"
# DATA_PATH = "./data/pythia/"
# filename = "reco_simu_Zdd.csv"
#
# BCG_PATH = './data/'
# Filename_background = "reco_0.csv"
h = Hits.Hits(filename)
ev = Hits.Event(h) #, 11)
# h.drawAllEvents()
# Combine events (background, or several events)
# ev.combineEvents([Hits.Event(h_background)])
# ev.combineEvents([Hits.Event(h, 12), Hits.Event(h, 11)])
ev.drawEvent3D(plotName=plotpath + "3D_Zdd.pdf")
# ev.drawEventXY()#plotName=plotpath+"3tracks_XY.pdf")
# ev.drawEventXZ()
# ev.drawEventYZ()
d = ev.data
tr = Transforms.Transforms(ev)
H, xedges, yedges = tr.HoughTransform_phi(numpoints=200,
binx=200,
biny=50,
plotName=plotpath +
"HT_Zdd_maxima.pdf")
tr.plotConformalTransform(plotpath + "CT_Zdd.pdf")
