John Bradshaw john-bradshaw

## numpy_utils_intersect.py
import numpy as np

def intersect_indcs_two_arrays(x_array, y_array):
    """intersect_indcs_two_arrays(x, y) -> xm, ym
    x, y: 1-d arrays of unique values
    xm, ym: indices into x and y giving sorted intersection
    code taken from http://stackoverflow.com/questions/33529057/indices-that-intersect-and-sort-two-numpy-arrays?lq=1
    """
    assert np.all(np.logical_not(map(are_there_duplicates, [x_array, y_array]))), "Intersect assumes arrays are unique."
    # basic idea taken from numpy.lib.arraysetops.intersect1d

## diff_privacy_functions_hall_demo.py
"""
Code for section 4.1 of:
Hall, R., Rinaldo, A. and Wasserman, L., 2013. Differential privacy for
functions and functional data. Journal of Machine Learning Research, 14(Feb), pp.703-727.


Coded up in a bit of a rush so apologies if there are bugs.
"""


## gaussian_morphing.py
"""
Code for Figure 1a of
Saul, L.K. and Jordan, M.I., 1997. A variational principle for model-based morphing.
In Advances in Neural Information Processing Systems (pp. 267-273).
"""

import numpy as np
from scipy.integrate import odeint
from scipy.optimize import fsolve
from scipy import stats

## mixture_of_gaussians_morphing.py
"""
Code for Figure 1c of
Saul, L.K. and Jordan, M.I., 1997. A variational principle for model-based morphing.
In Advances in Neural Information Processing Systems (pp. 267-273).

Note not exactly the same setup and do not know settings of ell and variance to use
Also not sure how to deal with the change points

Written in a bit of a rush and not thoroughly tested so use with caution
"""

## bayesian_optimisation_simple_demo.py

import enum

import numpy as np
from scipy import stats
import fire
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
from matplotlib.widgets import Button

## tf_control_dep_demo.py
import numpy as np
import tensorflow as tf

def main1():
    x = tf.get_variable("x", initializer=np.float64(0))

    x_id = tf.identity(x)
    x_rd = x.read_value()
    x_p0 = x + 0
    with tf.control_dependencies([x_id, x_rd, x_p0]):

## tf_control_dep_demo2.py


import tensorflow as tf

def main1():

    sess = tf.Session()
    x = tf.Variable(1.)
    sess.run(tf.initialize_variables([x]))

## split_reagents.py
"""
Module for identifying for RDKit functions working with reaction SMILES strings.
"""


import collections
import itertools
import functools
import typing

## different_smiles_same_fp.ipynb

      
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              April 28, 2022 00:26
            
              
                Notebook showing how different SMILES can sometimes have the same fingerprint
              
          
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## flax_mem_leak.py
"""
Simple demonstration of memory leak.
 XLA_PYTHON_CLIENT_PREALLOCATE=false CUDA_VISIBLE_DEVICES=0 python  flax_mem_leak.py
 Breaks on last iter through loop.

# Tested on version:

    Name: flax
    Version: 0.4.1
    Summary: Flax: A neural network library for JAX designed for flexibility
	import numpy as np

	def intersect_indcs_two_arrays(x_array, y_array):
	"""intersect_indcs_two_arrays(x, y) -> xm, ym
	x, y: 1-d arrays of unique values
	xm, ym: indices into x and y giving sorted intersection
	code taken from http://stackoverflow.com/questions/33529057/indices-that-intersect-and-sort-two-numpy-arrays?lq=1
	"""
	assert np.all(np.logical_not(map(are_there_duplicates, [x_array, y_array]))), "Intersect assumes arrays are unique."
	# basic idea taken from numpy.lib.arraysetops.intersect1d
	"""
	Code for section 4.1 of:
	Hall, R., Rinaldo, A. and Wasserman, L., 2013. Differential privacy for
	functions and functional data. Journal of Machine Learning Research, 14(Feb), pp.703-727.


	Coded up in a bit of a rush so apologies if there are bugs.
	"""
	"""
	Code for Figure 1a of
	Saul, L.K. and Jordan, M.I., 1997. A variational principle for model-based morphing.
	In Advances in Neural Information Processing Systems (pp. 267-273).
	"""

	import numpy as np
	from scipy.integrate import odeint
	from scipy.optimize import fsolve
	from scipy import stats
	"""
	Code for Figure 1c of
	Saul, L.K. and Jordan, M.I., 1997. A variational principle for model-based morphing.
	In Advances in Neural Information Processing Systems (pp. 267-273).

	Note not exactly the same setup and do not know settings of ell and variance to use
	Also not sure how to deal with the change points

	Written in a bit of a rush and not thoroughly tested so use with caution
	"""

	import enum

	import numpy as np
	from scipy import stats
	import fire
	import matplotlib
	matplotlib.use('TkAgg')
	import matplotlib.pyplot as plt
	from matplotlib.widgets import Button
	import numpy as np
	import tensorflow as tf

	def main1():
	x = tf.get_variable("x", initializer=np.float64(0))

	x_id = tf.identity(x)
	x_rd = x.read_value()
	x_p0 = x + 0
	with tf.control_dependencies([x_id, x_rd, x_p0]):


	import tensorflow as tf

	def main1():

	sess = tf.Session()
	x = tf.Variable(1.)
	sess.run(tf.initialize_variables([x]))
	"""
	Module for identifying for RDKit functions working with reaction SMILES strings.
	"""


	import collections
	import itertools
	import functools
	import typing
	"""
	Simple demonstration of memory leak.
	XLA_PYTHON_CLIENT_PREALLOCATE=false CUDA_VISIBLE_DEVICES=0 python flax_mem_leak.py
	Breaks on last iter through loop.

	# Tested on version:

	Name: flax
	Version: 0.4.1
	Summary: Flax: A neural network library for JAX designed for flexibility