cshimmin

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)

cshimmin

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))

cshimmin

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

cshimmin

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:]

cshimmin

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')

cshimmin

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

cshimmin

cshimmin

cshimmin

#!/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]

cshimmin

#!/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):