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import numpy as np | |
import scipy.optimize | |
import scipy.optimize | |
def norm_or_zero(v): | |
# normalize vectors along the last axis, avoiding div0 for zero vectors. | |
denom = np.linalg.norm(v, axis=-1, keepdims=True) | |
return np.where(denom > 0, v / denom, 0) |
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na = np.loadtxt('../nanoamps') | |
_,bins,_ = plt.hist(na, bins=40, color='gray', alpha=0.2, density=False); | |
plt.hist(na.reshape(4,-1).T, bins=bins, histtype='step', density=False); | |
na_ = na.reshape(4,-1) | |
plt.hist(np.mean(na_, axis=0), histtype='step', bins=30); | |
_,bins,_ = plt.hist(na, bins=40, color='gray', alpha=0.2, density=False); | |
plt.hist(np.std(na_, axis=0, ddof=1), bins=bins); | |
np.argmax(np.std(na_,axis=0,ddof=1)) |
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encoder_input = layers.Input( input_shape ) | |
# ... define encoder sub-network | |
encoder = keras.models.Model(encoder_input, ...) | |
decoder_input = layers.Input( latent_shape ) | |
# ... define decoder sub-network |
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import itertools as it | |
import numpy as np | |
# calculates ECF for a batch of jet constituents. | |
# x should have a shape like [batch_axis, particle_axis, 3] | |
# the last axis should contain (pT, eta, phi) | |
def ecf_numpy(N, beta, x): | |
pt = x[:,:,0] | |
eta = x[:,:,1:2] | |
phi = x[:,:,2:] |
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def some_mode(): | |
auto_input = K.Input((whatever,)) | |
auto_z = encoder(auto_input) | |
auto_output = decoder(auto_z) | |
model = Model(input_layer, output_layer) | |
loss1_ = K.mean(K.square(auto_input - auto_output)) | |
loss2_ = K.mean(K.square(auto_z)) | |
model.hp_loss2_weight = K.variable(1.0, name='loss2_weight') |
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class RotationLayer(layers.Layer): | |
def __init__(self, theta, learning_phase_only=True, **kwargs): | |
self.theta = theta | |
self.vec_dim = dim | |
self.R = K.constant([[1,0],[0,1]])*tf.cos(theta) \ | |
+ K.constant([[0,-1],[1,0]])*tf.sin(theta) | |
self.uses_learning_phase = learning_phase_only |
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#!/usr/bin/env python | |
import ROOT as r | |
import numpy as np | |
class FatJet: | |
def __init__(self, tree, idx): | |
# make an object representing the jet at index `idx` by pulling | |
# interesting attributes out of the tree. | |
self.m = tree.fjet_m[idx] |
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#!/usr/bin/env python | |
import ROOT as r | |
import sys | |
import itertools as it | |
import numpy as np | |
import pylab as pl | |
from scipy.special import cotdg | |
from scipy.sparse import dok_matrix | |
def hough_radial(points, radius=5): |
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