├── CONTRIBUTING
├── LICENSE
├── README.md
├── notebooks
    ├── clusters.ipynb
    ├── network.ipynb
    └── polar_plot.ipynb
└── requirements.txt


/CONTRIBUTING:
--------------------------------------------------------------------------------
1 | Contributing
2 | ============
3 | 
4 | To contribute with a snippet, please create a notebook with as
5 | many comments as you can. If the notebook has a dependency that is
6 | not listed in the requirements.txt file, please update it.
7 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
  1 |                     GNU GENERAL PUBLIC LICENSE
  2 |                        Version 2, June 1991
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341 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | python_plotting_snippets
 2 | ========================
 3 | 
 4 | A series of code snippets to make it easier to create nice and useful plots with python.
 5 | 
 6 | The plots are mostly based on the [seaborn library](http://stanford.edu/~mwaskom/software/seaborn/),
 7 | but also the good 'ol [matplotlib](http://matplotlib.org/).
 8 | Each example comes in the form of a [Jupyter](https://jupyter.org/) notebook.
 9 | 
10 | Contents
11 | --------
12 | 
13 | * [Highlight clusters in a heatmap](notebooks/clusters.ipynb)
14 | * [Multiple heatmaps as polar plots](notebooks/polar_plot.ipynb)
15 | * [Graphical visualization of network properties](notebooks/network.ipynb)
16 | 


--------------------------------------------------------------------------------
/notebooks/network.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {
  7 |     "collapsed": true
  8 |    },
  9 |    "outputs": [],
 10 |    "source": [
 11 |     "# Plotting imports\n",
 12 |     "%matplotlib inline\n",
 13 |     "\n",
 14 |     "import matplotlib.pyplot as plt\n",
 15 |     "import seaborn as sns\n",
 16 |     "\n",
 17 |     "sns.set_style('white')"
 18 |    ]
 19 |   },
 20 |   {
 21 |    "cell_type": "code",
 22 |    "execution_count": 2,
 23 |    "metadata": {
 24 |     "collapsed": true
 25 |    },
 26 |    "outputs": [],
 27 |    "source": [
 28 |     "# Other imports\n",
 29 |     "import networkx as nx\n",
 30 |     "import random"
 31 |    ]
 32 |   },
 33 |   {
 34 |    "cell_type": "code",
 35 |    "execution_count": 3,
 36 |    "metadata": {
 37 |     "collapsed": true
 38 |    },
 39 |    "outputs": [],
 40 |    "source": [
 41 |     "# Generate a series of random graphs\n",
 42 |     "gs = [nx.random_graphs.powerlaw_cluster_graph(n=random.randint(10, 20),\n",
 43 |     "                                             m=random.randint(1, 3),\n",
 44 |     "                                             p=random.random()*0.05)\n",
 45 |     "     for x in range(7)]"
 46 |    ]
 47 |   },
 48 |   {
 49 |    "cell_type": "code",
 50 |    "execution_count": 4,
 51 |    "metadata": {
 52 |     "collapsed": true
 53 |    },
 54 |    "outputs": [],
 55 |    "source": [
 56 |     "# Concatenate then in a single graph\n",
 57 |     "# (there might be a more efficient way)\n",
 58 |     "g = gs[0]\n",
 59 |     "for g1 in gs[1:]:\n",
 60 |     "    i = max(g.nodes()) + 1\n",
 61 |     "    g.add_edges_from([(x+i, y+i) for (x, y) in g1.edges()])"
 62 |    ]
 63 |   },
 64 |   {
 65 |    "cell_type": "code",
 66 |    "execution_count": 5,
 67 |    "metadata": {
 68 |     "collapsed": true
 69 |    },
 70 |    "outputs": [],
 71 |    "source": [
 72 |     "# Calculate nodes and edge properties\n",
 73 |     "# to have something to plot\n",
 74 |     "betw_cent = nx.betweenness.betweenness_centrality(g).values()\n",
 75 |     "edge_betw_cent = nx.edge_betweenness_centrality(g).values()"
 76 |    ]
 77 |   },
 78 |   {
 79 |    "cell_type": "code",
 80 |    "execution_count": 6,
 81 |    "metadata": {
 82 |     "collapsed": true
 83 |    },
 84 |    "outputs": [],
 85 |    "source": [
 86 |     "# Graph layout\n",
 87 |     "graph_pos = nx.layout.fruchterman_reingold_layout(g)"
 88 |    ]
 89 |   },
 90 |   {
 91 |    "cell_type": "code",
 92 |    "execution_count": 7,
 93 |    "metadata": {
 94 |     "collapsed": false
 95 |    },
 96 |    "outputs": [
 97 |     {
 98 |      "data": {
 99 |       "image/png": 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tm8YQiIiIiAKBiIiIKBCIiIgICgQiIiKCAoGIiIigQCAiIiIoEIiIiAgKBCIiIoICgYiI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\n",
100 |       "text/plain": [
101 |        "<matplotlib.figure.Figure at 0x7fabc7cb23d0>"
102 |       ]
103 |      },
104 |      "metadata": {},
105 |      "output_type": "display_data"
106 |     }
107 |    ],
108 |    "source": [
109 |     "plt.figure(figsize=(9, 9))\n",
110 |     "\n",
111 |     "# Draw nodes\n",
112 |     "nx.draw_networkx_nodes(g, graph_pos,\n",
113 |     "                       # Node size depends on node degree\n",
114 |     "                       node_size=[x*10 for x in nx.degree(g).values()],\n",
115 |     "                       # Node color depends on node centrality\n",
116 |     "                       node_color=betw_cent,\n",
117 |     "                       cmap=plt.get_cmap('Blues'),\n",
118 |     "                       vmax=max(betw_cent),\n",
119 |     "                       vmin=0)\n",
120 |     "# Draw edges\n",
121 |     "nx.draw_networkx_edges(g, graph_pos,\n",
122 |     "                       # Width depends on edge centrality\n",
123 |     "                       width=[x*250 for x in edge_betw_cent],\n",
124 |     "                       color='k')\n",
125 |     "sns.despine(bottom=True, left=True)\n",
126 |     "plt.xticks([])\n",
127 |     "plt.yticks([])\n",
128 |     "pass"
129 |    ]
130 |   }
131 |  ],
132 |  "metadata": {
133 |   "kernelspec": {
134 |    "display_name": "Python 2",
135 |    "language": "python",
136 |    "name": "python2"
137 |   },
138 |   "language_info": {
139 |    "codemirror_mode": {
140 |     "name": "ipython",
141 |     "version": 2
142 |    },
143 |    "file_extension": ".py",
144 |    "mimetype": "text/x-python",
145 |    "name": "python",
146 |    "nbconvert_exporter": "python",
147 |    "pygments_lexer": "ipython2",
148 |    "version": "2.7.9"
149 |   }
150 |  },
151 |  "nbformat": 4,
152 |  "nbformat_minor": 0
153 | }
154 | 


--------------------------------------------------------------------------------
/notebooks/polar_plot.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {
  7 |     "collapsed": true
  8 |    },
  9 |    "outputs": [],
 10 |    "source": [
 11 |     "# Plotting imports\n",
 12 |     "%matplotlib inline\n",
 13 |     "\n",
 14 |     "import matplotlib.pyplot as plt\n",
 15 |     "import matplotlib.colors as colors"
 16 |    ]
 17 |   },
 18 |   {
 19 |    "cell_type": "code",
 20 |    "execution_count": 2,
 21 |    "metadata": {
 22 |     "collapsed": true
 23 |    },
 24 |    "outputs": [],
 25 |    "source": [
 26 |     "# Other imports\n",
 27 |     "import numpy as np"
 28 |    ]
 29 |   },
 30 |   {
 31 |    "cell_type": "code",
 32 |    "execution_count": 3,
 33 |    "metadata": {
 34 |     "collapsed": true
 35 |    },
 36 |    "outputs": [],
 37 |    "source": [
 38 |     "# Sample names\n",
 39 |     "# The third sample is gonna be a subtraction\n",
 40 |     "a = ['Sample 1',\n",
 41 |     "     'Sample 2',\n",
 42 |     "     'Sample 2 - Sample 1']"
 43 |    ]
 44 |   },
 45 |   {
 46 |    "cell_type": "code",
 47 |    "execution_count": 4,
 48 |    "metadata": {
 49 |     "collapsed": false
 50 |    },
 51 |    "outputs": [],
 52 |    "source": [
 53 |     "# Generate random normal data\n",
 54 |     "# then subtract sample 1 from sample 2\n",
 55 |     "b = [abs(np.random.normal(size=96)),\n",
 56 |     "     abs(np.random.normal(size=96))]\n",
 57 |     "b = np.array(b)\n",
 58 |     "c = [(y - x) for (x, y) in zip(b[0], b[1])]\n",
 59 |     "# Keep track of the min, max value\n",
 60 |     "# useful later when the data will be plotted\n",
 61 |     "vmin = min(b.flatten())\n",
 62 |     "vmax = max(b.flatten())\n",
 63 |     "vmin_diff = -max(abs(np.array(c)))\n",
 64 |     "vmax_diff= -vmin_diff"
 65 |    ]
 66 |   },
 67 |   {
 68 |    "cell_type": "code",
 69 |    "execution_count": 5,
 70 |    "metadata": {
 71 |     "collapsed": true
 72 |    },
 73 |    "outputs": [],
 74 |    "source": [
 75 |     "# Plot parameters\n",
 76 |     "# Starting point\n",
 77 |     "i = 0.05\n",
 78 |     "# Track height\n",
 79 |     "i_space = 0.05\n",
 80 |     "# How further up should the labels be\n",
 81 |     "# with respect to the track\n",
 82 |     "text_incr = 0.015\n",
 83 |     "# Track distance\n",
 84 |     "i_incr = 0.07\n",
 85 |     "# Text size\n",
 86 |     "t_size = 10"
 87 |    ]
 88 |   },
 89 |   {
 90 |    "cell_type": "code",
 91 |    "execution_count": 6,
 92 |    "metadata": {
 93 |     "collapsed": false
 94 |    },
 95 |    "outputs": [
 96 |     {
 97 |      "data": {
 98 |       "image/png": 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KzuprxtHt66N9T8cdTU3QvXvWswiC4EWmFh5+uLLvKW9i5GH9jg98lrNVTArio6prgd+I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\n",
 99 |       "text/plain": [
100 |        "<matplotlib.figure.Figure at 0x7f5b502b4d50>"
101 |       ]
102 |      },
103 |      "metadata": {},
104 |      "output_type": "display_data"
105 |     }
106 |    ],
107 |    "source": [
108 |     "fig = plt.figure(figsize=(12, 12), dpi=300)\n",
109 |     "# Polar plot!\n",
110 |     "ax = fig.add_subplot(111, polar = True)\n",
111 |     "# Start from the top\n",
112 |     "ax.set_theta_offset(np.pi/2)\n",
113 |     "# Go clockwise\n",
114 |     "ax.set_theta_direction(-1)\n",
115 |     "\n",
116 |     "# Plot fake tracks to report the sample names\n",
117 |     "for s_id in a:\n",
118 |     "    radius = np.linspace(i, i+i_space, 10)\n",
119 |     "    theta = np.linspace(0, 2*np.pi, 628)\n",
120 |     "    R,T  = np.meshgrid(radius,theta)\n",
121 |     "\n",
122 |     "    ax.pcolor(T, R,\n",
123 |     "              np.array([[1 for y in range(10)]\n",
124 |     "                         for x in range(628)]),\n",
125 |     "              cmap=plt.cm.Greys,\n",
126 |     "              vmin=0, vmax=4)\n",
127 |     "\n",
128 |     "    ax.text(0,\n",
129 |     "            i+text_incr,\n",
130 |     "            s_id,\n",
131 |     "            size=t_size,\n",
132 |     "            weight='black',\n",
133 |     "            alpha=0.77,\n",
134 |     "            ha='center')\n",
135 |     "\n",
136 |     "    i += i_incr\n",
137 |     "\n",
138 |     "# Plot the two tracks with the original samples\n",
139 |     "for data in b:\n",
140 |     "    radius = np.linspace(i, i+i_space, 10)\n",
141 |     "    theta = np.linspace(0, 2*np.pi, data.shape[0])\n",
142 |     "    R,T  = np.meshgrid(radius,theta)\n",
143 |     "\n",
144 |     "    ax.pcolor(T, R,\n",
145 |     "              np.array([[x for y in range(10)] for x in data]),\n",
146 |     "                  cmap=plt.cm.Greens,\n",
147 |     "                  vmin=0, vmax=vmax)\n",
148 |     "\n",
149 |     "    i += i_incr\n",
150 |     "\n",
151 |     "# Plot the subtraction track\n",
152 |     "# using a different colormap\n",
153 |     "radius = np.linspace(i, i+i_space, 10)\n",
154 |     "theta = np.linspace(0, 2*np.pi, len(c))\n",
155 |     "R,T  = np.meshgrid(radius,theta)\n",
156 |     "\n",
157 |     "ax.pcolor(T, R,\n",
158 |     "          np.array([[x for y in range(10)] for x in c]),\n",
159 |     "              cmap=plt.cm.bwr_r,\n",
160 |     "              vmin=vmin_diff, vmax=vmax_diff)\n",
161 |     "\n",
162 |     "i += i_incr\n",
163 |     "\n",
164 |     "#Turn off polar labels\n",
165 |     "ax.axes.get_xaxis().set_visible(False)\n",
166 |     "ax.axes.get_yaxis().set_visible(False)\n",
167 |     "\n",
168 |     "# Plot terminates here\n",
169 |     "ax.set_rmax(i)\n",
170 |     "\n",
171 |     "# First colorbar\n",
172 |     "cNorm  = colors.Normalize(vmin=vmin_diff, vmax=vmax_diff)\n",
173 |     "scalarMap = plt.cm.ScalarMappable(norm=cNorm, cmap=plt.cm.bwr_r)\n",
174 |     "scalarMap.set_array(np.array(range(int(vmin_diff), int(vmax_diff), 100)))\n",
175 |     "cax = fig.add_axes([0.04, 0.2, 0.03, 0.6])\n",
176 |     "plt.colorbar(scalarMap, cax=cax)\n",
177 |     "\n",
178 |     "# Subtraction colorbar\n",
179 |     "cNorm  = colors.Normalize(vmin=0, vmax=vmax)\n",
180 |     "scalarMap = plt.cm.ScalarMappable(norm=cNorm, cmap=plt.cm.Greens)\n",
181 |     "scalarMap.set_array(np.array(range(0, int(vmax), 100)))\n",
182 |     "cax = fig.add_axes([0.93, 0.2, 0.03, 0.6])\n",
183 |     "plt.colorbar(scalarMap, cax=cax)\n",
184 |     "pass"
185 |    ]
186 |   }
187 |  ],
188 |  "metadata": {
189 |   "kernelspec": {
190 |    "display_name": "Python 2",
191 |    "language": "python",
192 |    "name": "python2"
193 |   },
194 |   "language_info": {
195 |    "codemirror_mode": {
196 |     "name": "ipython",
197 |     "version": 2
198 |    },
199 |    "file_extension": ".py",
200 |    "mimetype": "text/x-python",
201 |    "name": "python",
202 |    "nbconvert_exporter": "python",
203 |    "pygments_lexer": "ipython2",
204 |    "version": "2.7.9"
205 |   }
206 |  },
207 |  "nbformat": 4,
208 |  "nbformat_minor": 0
209 | }
210 | 


--------------------------------------------------------------------------------
/requirements.txt:
--------------------------------------------------------------------------------
 1 | Jinja2==2.8
 2 | MarkupSafe==0.23
 3 | Pygments==2.0.2
 4 | argparse==1.2.1
 5 | backports.ssl-match-hostname==3.4.0.2
 6 | certifi==2015.4.28
 7 | decorator==4.0.2
 8 | fastcluster==1.1.20
 9 | functools32==3.2.3.post2
10 | ipykernel==4.0.3
11 | ipython==4.0.0
12 | ipython-genutils==0.1.0
13 | jsonschema==2.5.1
14 | jupyter-client==4.0.0
15 | jupyter-core==4.0.4
16 | matplotlib==1.4.3
17 | mistune==0.7.1
18 | mock==1.3.0
19 | nbconvert==4.0.0
20 | nbformat==4.0.0
21 | networkx==1.10
22 | nose==1.3.7
23 | notebook>=5.7.2
24 | numpy==1.9.2
25 | pandas==0.16.2
26 | path.py==8.1
27 | pbr==1.6.0
28 | pexpect==3.3
29 | pickleshare==0.5
30 | pyparsing==2.0.3
31 | python-dateutil==2.4.2
32 | pytz==2015.4
33 | pyzmq==14.7.0
34 | scipy==0.16.0
35 | seaborn==0.6.0
36 | simplegeneric==0.8.1
37 | six==1.9.0
38 | tornado==4.2.1
39 | traitlets==4.0.0
40 | wsgiref==0.1.2
41 | 


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