Chase cshimmin

cshimmin / jet-thrust.py

Last active April 5, 2023 17:49

	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 / amps.py

Created August 5, 2021 21:06

	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 / masked_mse.py

Created February 6, 2020 19:54

pseudo-keras code for masked MSE loss

	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 / ecf.py

Last active July 15, 2019 17:09

calculate ECFs on jet constituents using einsum

	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 / loss_hack.py

Last active June 25, 2019 13:10

	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 / rotation2d.py

Created June 13, 2019 21:17

keras layer to rotate a (tensor ending with) 2d vector

	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 / cells.ipynb

Created March 28, 2018 01:23

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cshimmin / cells.ipynb

Created March 28, 2018 01:16

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cshimmin / example.py

Last active October 23, 2016 23:31

	#!/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 / hough.py

Created June 23, 2016 15:30

sketch of hough transform based track finding/fitting

	#!/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):