├── MANIFEST.in
├── setup.cfg
├── graph_extract
    ├── __init__.py
    └── graph_extract.py
├── requirements.txt
├── examples
    ├── graph_1.png
    └── graph-extract_example.ipynb
├── README.md
├── setup.py
├── .gitignore
└── LICENSE


/MANIFEST.in:
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1 | README.md
2 | 


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/setup.cfg:
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1 | [metadata]
2 | description-file = README.md
3 | 


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/graph_extract/__init__.py:
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1 | from .graph_extract import Graph
2 | 


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/requirements.txt:
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1 | numpy
2 | pandas
3 | pillow
4 | matplotlib
5 | 


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/examples/graph_1.png:
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https://raw.githubusercontent.com/lewisacidic/graph-extract/HEAD/examples/graph_1.png


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/README.md:
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1 | graph-extract
2 | =============
3 | 
4 | A small library for extracting data from graphs.
5 | 
6 | See examples directory for how to use.
7 | 


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/setup.py:
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 1 | from setuptools import setup, find_packages
 2 | 
 3 | NAME = 'graph-extract'
 4 | 
 5 | CLASSIFIERS = [
 6 | "Development Status :: 3 - Alpha",
 7 | "Intended Audience :: Science/Research",
 8 | "License :: OSI Approved :: MIT License"
 9 | ]
10 | 
11 | VERSION = '0.0.2'
12 | 
13 | DESCRIPTION = 'Library for extracting values from graphs'
14 | 
15 | with open('README.md') as f:
16 |     LONG_DESCRIPTION = f.read()
17 | 
18 | AUTHOR = 'Rich Lewis'
19 | AUTHOR_EMAIL = 'opensource@richlew.is'
20 | URL = 'https://github.com/lewisacidic/graph-extract'
21 | LICENSE = 'MIT License'
22 | INSTALL_REQUIRES = ['numpy', 'pillow']
23 | 
24 | if __name__ == '__main__':
25 |     setup(
26 |           name=NAME,
27 |           classifiers=CLASSIFIERS,
28 |           version=VERSION,
29 |           description=DESCRIPTION,
30 |           long_description=LONG_DESCRIPTION,
31 |           author=AUTHOR,
32 |           author_email=AUTHOR_EMAIL,
33 |           url=URL,
34 |           license=LICENSE,
35 |           install_requires=INSTALL_REQUIRES,
36 |           packages=find_packages()
37 |     )
38 | 


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/.gitignore:
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 1 | # Stop OS X from dirtying the repo
 2 | .DS_Store
 3 | 
 4 | # Byte-compiled / optimized / DLL files
 5 | __pycache__/
 6 | *.py[cod]
 7 | *$py.class
 8 | 
 9 | # C extensions
10 | *.so
11 | 
12 | # Distribution / packaging
13 | .Python
14 | env/
15 | build/
16 | develop-eggs/
17 | dist/
18 | downloads/
19 | eggs/
20 | .eggs/
21 | lib/
22 | lib64/
23 | parts/
24 | sdist/
25 | var/
26 | *.egg-info/
27 | .installed.cfg
28 | *.egg
29 | 
30 | # PyInstaller
31 | #  Usually these files are written by a python script from a template
32 | #  before PyInstaller builds the exe, so as to inject date/other infos into it.
33 | *.manifest
34 | *.spec
35 | 
36 | # Installer logs
37 | pip-log.txt
38 | pip-delete-this-directory.txt
39 | 
40 | # Unit test / coverage reports
41 | htmlcov/
42 | .tox/
43 | .coverage
44 | .coverage.*
45 | .cache
46 | nosetests.xml
47 | coverage.xml
48 | *,cover
49 | .hypothesis/
50 | 
51 | # Translations
52 | *.mo
53 | *.pot
54 | 
55 | # Django stuff:
56 | *.log
57 | 
58 | # Sphinx documentation
59 | docs/_build/
60 | 
61 | # PyBuilder
62 | target/
63 | 
64 | #Ipython Notebook
65 | .ipynb_checkpoints
66 | 


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/LICENSE:
--------------------------------------------------------------------------------
 1 | The MIT License (MIT)
 2 | 
 3 | Copyright (c) 2016 Rich P. I. Lewis
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


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/graph_extract/graph_extract.py:
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  1 | """
  2 | graph_extract.graph_extract
  3 | ===========================
  4 | 
  5 | Main source file of graph_extract, defining the Graph object.
  6 | """
  7 | 
  8 | import os
  9 | 
 10 | from PIL import Image
 11 | import pandas as pd
 12 | import numpy as np
 13 | from matplotlib import pyplot as plt
 14 | 
 15 | 
 16 | class Graph(object):
 17 | 
 18 |     """
 19 | 
 20 |     Class modelling a graph, providing methods for producing a series
 21 |     from plotted data.
 22 | 
 23 |     Attributes:
 24 |         name (str): The name of the graph
 25 |         img_data (np.ndarray): The image data
 26 |         data (pd.Series): The plotted values
 27 | 
 28 |     """
 29 | 
 30 |     def __init__(self, path, xlim=(0, 1), ylim=(0, 1), *args, **kwargs):
 31 | 
 32 |         """Create a graph object from image at path.  Optionally pass the x
 33 |         and y limits.
 34 | 
 35 |         Args:
 36 |             path (str): The path to the image of the graph.
 37 |             xlim (tuple[float]): The range of the x axis.
 38 |             ylim (tuple[float]): The range of the y axis.
 39 | 
 40 |         """
 41 |         self.name = os.path.splitext(os.path.basename(path))[0]
 42 |         self.img_data = self._read_image(path, *args, **kwargs)
 43 |         self.xlim = xlim
 44 |         self.ylim = ylim
 45 |         self._data = None
 46 | 
 47 |     @property
 48 |     def data(self):
 49 | 
 50 |         """ Return a series of the data contained. """
 51 | 
 52 |         if self._data is not None:
 53 |             return self._data
 54 |         per_pixel = np.argmax(self.img_data, axis=0) / self.img_data.shape[0]
 55 |         y_scaled = per_pixel * (self.ylim[1] - self.ylim[0]) + self.ylim[0]
 56 |         x_ax = np.linspace(self.xlim[0], self.xlim[1], self.img_data.shape[1])
 57 | 
 58 |         self._data = pd.Series(y_scaled, x_ax, name=self.name)
 59 |         return self._data
 60 | 
 61 |     def show_img(self, *args, **kwargs):
 62 | 
 63 |         """ Show the image. """
 64 | 
 65 |         extent = (self.xlim[0], self.xlim[1], self.ylim[1], self.ylim[0])
 66 | 
 67 |         return plt.imshow(self.img_data, *args, extent=extent, **kwargs)
 68 | 
 69 |     def show_data(self, *args, **kwargs):
 70 | 
 71 |         """ Show a plot of the extracted data. """
 72 | 
 73 |         return self.data.plot(*args, **kwargs)
 74 | 
 75 |     def show_fit(self):
 76 | 
 77 |         """Shows the extracted data plotted over the original image. """
 78 | 
 79 |         self.show_img()
 80 |         self.show_data(color='r', style='--')
 81 | 
 82 |     @staticmethod
 83 |     def _read_image(img_path, channels=4, average='simple'):
 84 | 
 85 |         """ Read an image into a numpy array.
 86 | 
 87 |         Args:
 88 |             img_path (str): The path to the image file.
 89 |             channels (int): The number of colour channels (RGB=3, RGBA=4).
 90 |             average (str): The technique to use to extract the important color.
 91 |                            Defaults to an average of all the colors."""
 92 | 
 93 |         img = Image.open(img_path)
 94 |         arr = 255 - np.array(img.getdata())\
 95 |                       .reshape(img.size[1], img.size[0], channels)
 96 |         if average == 'simple':
 97 |             arr = arr.mean(axis=2)  # average over colors
 98 |         else:
 99 |             raise NotImplementedError('{} not implemented'.format(average))
100 |         return arr
101 | 


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/examples/graph-extract_example.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Graph extract example"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "markdown",
 12 |    "metadata": {},
 13 |    "source": [
 14 |     "Import:"
 15 |    ]
 16 |   },
 17 |   {
 18 |    "cell_type": "code",
 19 |    "execution_count": 1,
 20 |    "metadata": {
 21 |     "collapsed": false
 22 |    },
 23 |    "outputs": [],
 24 |    "source": [
 25 |     "%matplotlib inline\n",
 26 |     "\n",
 27 |     "from graph_extract import Graph"
 28 |    ]
 29 |   },
 30 |   {
 31 |    "cell_type": "markdown",
 32 |    "metadata": {},
 33 |    "source": [
 34 |     "Define properties of your graph:"
 35 |    ]
 36 |   },
 37 |   {
 38 |    "cell_type": "code",
 39 |    "execution_count": 2,
 40 |    "metadata": {
 41 |     "collapsed": true
 42 |    },
 43 |    "outputs": [],
 44 |    "source": [
 45 |     "path = 'graph_1.png'\n",
 46 |     "x = (1500, 900)\n",
 47 |     "y = (75, -100)"
 48 |    ]
 49 |   },
 50 |   {
 51 |    "cell_type": "markdown",
 52 |    "metadata": {},
 53 |    "source": [
 54 |     "Make the graph object:"
 55 |    ]
 56 |   },
 57 |   {
 58 |    "cell_type": "code",
 59 |    "execution_count": 3,
 60 |    "metadata": {
 61 |     "collapsed": false
 62 |    },
 63 |    "outputs": [],
 64 |    "source": [
 65 |     "g = Graph('graph_1.png', xlim=x, ylim=y)"
 66 |    ]
 67 |   },
 68 |   {
 69 |    "cell_type": "markdown",
 70 |    "metadata": {},
 71 |    "source": [
 72 |     "The image name is used as the name:"
 73 |    ]
 74 |   },
 75 |   {
 76 |    "cell_type": "code",
 77 |    "execution_count": 4,
 78 |    "metadata": {
 79 |     "collapsed": false
 80 |    },
 81 |    "outputs": [
 82 |     {
 83 |      "data": {
 84 |       "text/plain": [
 85 |        "'graph_1'"
 86 |       ]
 87 |      },
 88 |      "execution_count": 4,
 89 |      "metadata": {},
 90 |      "output_type": "execute_result"
 91 |     }
 92 |    ],
 93 |    "source": [
 94 |     "g.name"
 95 |    ]
 96 |   },
 97 |   {
 98 |    "cell_type": "markdown",
 99 |    "metadata": {},
100 |    "source": [
101 |     "Can visualize the image:"
102 |    ]
103 |   },
104 |   {
105 |    "cell_type": "code",
106 |    "execution_count": 5,
107 |    "metadata": {
108 |     "collapsed": false
109 |    },
110 |    "outputs": [
111 |     {
112 |      "data": {
113 |       "text/plain": [
114 |        "<matplotlib.image.AxesImage at 0x117e2f588>"
115 |       ]
116 |      },
117 |      "execution_count": 5,
118 |      "metadata": {},
119 |      "output_type": "execute_result"
120 |     },
121 |     {
122 |      "data": {
123 |       "image/png": 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LOTk5mrvbt2+nhISEUqe1Ps9MmzaNjh07VqFuun540zP9yvv7xz/+QZs3by526C87O5tG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\nWBgwDMMwYGHAMAzDgIUBwzAMA+D/A1YG6bIERJvAAAAAAElFTkSuQmCC\n",
124 |       "text/plain": [
125 |        "<matplotlib.figure.Figure at 0x1010d4a58>"
126 |       ]
127 |      },
128 |      "metadata": {},
129 |      "output_type": "display_data"
130 |     }
131 |    ],
132 |    "source": [
133 |     "g.show_img()"
134 |    ]
135 |   },
136 |   {
137 |    "cell_type": "markdown",
138 |    "metadata": {},
139 |    "source": [
140 |     "The fit is obtained as a series from the `data` property of the `Graph` object:"
141 |    ]
142 |   },
143 |   {
144 |    "cell_type": "code",
145 |    "execution_count": 6,
146 |    "metadata": {
147 |     "collapsed": false
148 |    },
149 |    "outputs": [
150 |     {
151 |      "data": {
152 |       "text/plain": [
153 |        "1500.000000   -4.631295\n",
154 |        "1499.598125   -4.316547\n",
155 |        "1499.196249   -4.316547\n",
156 |        "1498.794374   -4.316547\n",
157 |        "1498.392498   -4.316547\n",
158 |        "Name: graph_1, dtype: float64"
159 |       ]
160 |      },
161 |      "execution_count": 6,
162 |      "metadata": {},
163 |      "output_type": "execute_result"
164 |     }
165 |    ],
166 |    "source": [
167 |     "g.data.head()"
168 |    ]
169 |   },
170 |   {
171 |    "cell_type": "markdown",
172 |    "metadata": {},
173 |    "source": [
174 |     "This can be visualized:"
175 |    ]
176 |   },
177 |   {
178 |    "cell_type": "code",
179 |    "execution_count": 7,
180 |    "metadata": {
181 |     "collapsed": false
182 |    },
183 |    "outputs": [
184 |     {
185 |      "data": {
186 |       "text/plain": [
187 |        "<matplotlib.axes._subplots.AxesSubplot at 0x112e1f588>"
188 |       ]
189 |      },
190 |      "execution_count": 7,
191 |      "metadata": {},
192 |      "output_type": "execute_result"
193 |     },
194 |     {
195 |      "data": {
196 |       "image/png": 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dshJQT/mr7ElUM7FqsXUtVEp56WMvnuLZ/X34vB5uvHp9Vk9WWDgHTgxTE/DStSi3BHom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\nSUVORK5CYII=\n",
197 |       "text/plain": [
198 |        "<matplotlib.figure.Figure at 0x117e2fc18>"
199 |       ]
200 |      },
201 |      "metadata": {},
202 |      "output_type": "display_data"
203 |     }
204 |    ],
205 |    "source": [
206 |     "g.show_data()"
207 |    ]
208 |   },
209 |   {
210 |    "cell_type": "markdown",
211 |    "metadata": {},
212 |    "source": [
213 |     "This can be superimposed over the original image:"
214 |    ]
215 |   },
216 |   {
217 |    "cell_type": "code",
218 |    "execution_count": 8,
219 |    "metadata": {
220 |     "collapsed": false
221 |    },
222 |    "outputs": [
223 |     {
224 |      "data": {
225 |       "image/png": 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bm4uoqCgMHDgQkyZNAn3+OX5cvx5NevZESEgISJZRcPkyFn/xBZYtW4YuXbog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\nAABJRU5ErkJggg==\n",
226 |       "text/plain": [
227 |        "<matplotlib.figure.Figure at 0x112ef1390>"
228 |       ]
229 |      },
230 |      "metadata": {},
231 |      "output_type": "display_data"
232 |     }
233 |    ],
234 |    "source": [
235 |     "plt.figure(figsize=(10,8))\n",
236 |     "plt.axis('off')\n",
237 |     "g.show_fit()"
238 |    ]
239 |   },
240 |   {
241 |    "cell_type": "markdown",
242 |    "metadata": {},
243 |    "source": [
244 |     "We can save out the data using pandas' to_csv method:"
245 |    ]
246 |   },
247 |   {
248 |    "cell_type": "code",
249 |    "execution_count": 9,
250 |    "metadata": {
251 |     "collapsed": true
252 |    },
253 |    "outputs": [],
254 |    "source": [
255 |     "g.data.to_csv('graph_1.csv')"
256 |    ]
257 |   }
258 |  ],
259 |  "metadata": {
260 |   "kernelspec": {
261 |    "display_name": "Python 3",
262 |    "language": "python",
263 |    "name": "python3"
264 |   },
265 |   "language_info": {
266 |    "codemirror_mode": {
267 |     "name": "ipython",
268 |     "version": 3
269 |    },
270 |    "file_extension": ".py",
271 |    "mimetype": "text/x-python",
272 |    "name": "python",
273 |    "nbconvert_exporter": "python",
274 |    "pygments_lexer": "ipython3",
275 |    "version": "3.5.1"
276 |   }
277 |  },
278 |  "nbformat": 4,
279 |  "nbformat_minor": 0
280 | }
281 | 


--------------------------------------------------------------------------------