Andrew Port mathandy

## tool4c.py
"""
Description:
Takes in a color image and creates a two CSV files storing downsized
matrix (grayscale) and 3-tensor representations of the image.

Instructions:
Put your image in the same folder as this file (tool4c.py),
then open a terminal in that folder and enter:
python tool4c <your_image>

## distance-between-two-svg-paths-example.py
from svgpathtools import *

# create some example paths
path1 = CubicBezier(1,2+3j,3-5j,4+1j)
path2 = path1.rotated(60).translated(3)

# find minimizer
from scipy.optimize import fminbound
def dist(t):
	return path1.radialrange(path2.point(t))[0][0]

## xfinity_att_debate.rst

      
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                mathandy
                / xfinity_att_debate.rst
            
            
              Last active
              January 4, 2017 02:23
            
              
                We're getting ripped off. No matter what, we need to do something about it.
              
          
    A Fake Scientific Analysis of this Xfinity/AT&T bullshit

Abstract

We're getting ripped off. No matter what, we need to do something about it.
Introduction


## compute-many-points-quickly-using-numpy-arrays.py
"""The goal of this gist is to show how to compute many points on a path
quickly using NumPy arrays.  I.e. there's a much faster way than using, say
[some_path.point(t) for t in many_tvals].  The example below assumes the
`Path` object is composed entirely of `CubicBezier` objects, but this can
easily be generalized to paths containing `Line` and `QuadraticBezier` objects
also.
Note: The relevant matrix transformation for quadratics can be found in the
svgpathtools.bezier module."""

import numpy as np

## define.py
"""Scrape the definition of a word (or phrase) from dictionary.com

Usage:
======
python define.py onomatopoeia

Warning:
========
Sometimes (at least with phrases) you'll be unexpectedly redirected
to the definition of another only loosely related word,

## getpdfs.py
"""Downloads ArXiv papers linked to in a Google Doc converted to HTML.

Notes:
------
  * tested on OS X with Python 3
  * Requires arxiv (`pip install arxiv`)
  * Names of PDFs will be the papers' titles on ArXiv (with some
    slight formatting changes).
"""

## build-tensorflow-from-source.md

      
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                mathandy
                / build-tensorflow-from-source.md
            
            
              Created
              July 26, 2018 01:12
                — forked from Brainiarc7/build-tensorflow-from-source.md
            
              
                Build Tensorflow from source, for better performance on Ubuntu.
              
          
    Building Tensorflow from source on Ubuntu 16.04LTS for maximum performance:
TensorFlow is now distributed under an Apache v2 open source license on GitHub.
On Ubuntu 16.04LTS+:
Step 1. Install NVIDIA CUDA:
To use TensorFlow with NVIDIA GPUs, the first step is to install the CUDA Toolkit as shown:

  
## roi_pooling
"""Implementation of RoI Pooling

Credit: This is from tutorial available at
    https://medium.com/xplore-ai/992508b6592b
"""


import tensorflow as tf
from tensorflow.keras.layers import Layer

## cse30_variable_names_example.py
# probably about right
def variance(samples):
    mean = sum(samples) / len(samples)
    return sum((x - mean)**2 for x in samples) / len(samples)

# probably too short
def var(s):
    m = sum(s) / len(s)
    return sum((x - m)**2 for x in s) / len(s)

## retrain.py
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
	"""
	Description:
	Takes in a color image and creates a two CSV files storing downsized
	matrix (grayscale) and 3-tensor representations of the image.

	Instructions:
	Put your image in the same folder as this file (tool4c.py),
	then open a terminal in that folder and enter:
	python tool4c <your_image>
	from svgpathtools import *

	# create some example paths
	path1 = CubicBezier(1,2+3j,3-5j,4+1j)
	path2 = path1.rotated(60).translated(3)

	# find minimizer
	from scipy.optimize import fminbound
	def dist(t):
	return path1.radialrange(path2.point(t))[0][0]
	"""The goal of this gist is to show how to compute many points on a path
	quickly using NumPy arrays. I.e. there's a much faster way than using, say
	[some_path.point(t) for t in many_tvals]. The example below assumes the
	`Path` object is composed entirely of `CubicBezier` objects, but this can
	easily be generalized to paths containing `Line` and `QuadraticBezier` objects
	also.
	Note: The relevant matrix transformation for quadratics can be found in the
	svgpathtools.bezier module."""

	import numpy as np
	"""Scrape the definition of a word (or phrase) from dictionary.com

	Usage:
	======
	python define.py onomatopoeia

	Warning:
	========
	Sometimes (at least with phrases) you'll be unexpectedly redirected
	to the definition of another only loosely related word,
	"""Downloads ArXiv papers linked to in a Google Doc converted to HTML.

	Notes:
	------
	* tested on OS X with Python 3
	* Requires arxiv (`pip install arxiv`)
	* Names of PDFs will be the papers' titles on ArXiv (with some
	slight formatting changes).
	"""
	"""Implementation of RoI Pooling

	Credit: This is from tutorial available at
	https://medium.com/xplore-ai/992508b6592b
	"""


	import tensorflow as tf
	from tensorflow.keras.layers import Layer
	# probably about right
	def variance(samples):
	mean = sum(samples) / len(samples)
	return sum((x - mean)**2 for x in samples) / len(samples)

	# probably too short
	def var(s):
	m = sum(s) / len(s)
	return sum((x - m)**2 for x in s) / len(s)
	# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,