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@bamford
Created February 4, 2016 00:51
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Create compact arrangements of grouped subplots
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{
"cells": [
{
"metadata": {},
"cell_type": "markdown",
"source": "A general method for compactly arranging groups of subplots on a page"
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "%matplotlib inline\nfrom matplotlib import pyplot as plt\nimport matplotlib.gridspec as gridspec\nfrom matplotlib.ticker import MaxNLocator\nimport itertools",
"execution_count": 1,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "# define the structure of the subplots\n# instead of strings, the elements would normally be the data to be plotted\ndata = [['a1', 'a2', 'a3'],\n ['a1', 'a2'],\n ['a1', 'a2'],\n ['a1', 'a2'],\n ['a1', 'a2', 'a3', 'a4'],\n ['a1', 'a2'],\n ['a1', 'a2', 'a3'],\n ['a1', 'a2', 'a3'],\n ['a1', 'a2', 'a3', 'a4', 'a5'],\n ['a1', 'a2'],\n ['a1', 'a2', 'a3', 'a4', 'a5'],]\n\nquestions = ['q{}'.format(i) for i in range(len(data))]",
"execution_count": 2,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "# determine an optimal arrangement for the groups\ndef optimise_order(data, col_per_plot=2, col_per_gap=1, max_perms=1000):\n nq = len(data)\n nas = np.array([len(q) for q in data])\n max_on_row = max(nas) * col_per_plot + col_per_gap\n nrow_best = nq\n orders = itertools.permutations(range(nq))\n for i_perm, order in enumerate(orders):\n if i_perm >= max_perms:\n break\n nrow = 0\n ncol = 0\n count_on_row = 0\n positions = np.zeros((nq, 2), np.int)\n order = np.array(order)\n for iq in order:\n na = nas[iq]\n count_on_row += na * col_per_plot\n if count_on_row > max_on_row:\n nrow += 1\n ncol = 0\n count_on_row = na * col_per_plot\n count_on_row += col_per_gap\n positions[iq] = (nrow, ncol)\n ncol = count_on_row\n if nrow < nrow_best:\n nrow_best = nrow\n order_best = order\n positions_best = positions\n return positions_best, order_best, nrow_best + 1, max_on_row",
"execution_count": 3,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "# get the optimal arrangement for the specific set of subplot groups\ncol_per_plot = 2\ncol_per_gap = 1\npositions, order, nrow, ncol = optimise_order(data, col_per_plot, col_per_gap)",
"execution_count": 4,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "# create plot\nfig = plt.figure(figsize=(ncol, nrow*2))\ngs = gridspec.GridSpec(nrow, ncol, wspace=0)\nmaster_ax = None\nfor iq, (r, c), answers in zip(range(len(data)), positions, data):\n for ia, a in enumerate(answers):\n ax = fig.add_subplot(gs[r, c : c + col_per_plot],\n sharex=master_ax, sharey=master_ax)\n if master_ax is None:\n master_ax = ax\n ax.set_xlabel(a)\n ax.xaxis.set_major_locator(MaxNLocator(5, prune='both'))\n ax.hist(np.random.random(100), histtype='step', normed=True,\n range=(0, 1), bins=10)\n if ia > 0:\n plt.setp(ax.get_yticklabels(), visible=False)\n else:\n ax.set_ylabel('{}\\nfraction'.format(questions[iq]))\n c += col_per_plot\nfig.tight_layout()\n",
"execution_count": 5,
"outputs": [
{
"output_type": "display_data",
"data": {
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ZmbOAt0bE0cBiYBHwtl7FK0nd5BgpSaqTWiUSmfmfwKQxypwLnNudiMY2NDQ0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OAAAg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\nIaqDBsmHn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"text/plain": "<matplotlib.figure.Figure at 0x7fd47d6e5da0>"
},
"metadata": {}
}
]
}
],
"metadata": {
"language_info": {
"mimetype": "text/x-python",
"version": "3.5.1",
"name": "python",
"pygments_lexer": "ipython3",
"nbconvert_exporter": "python",
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3",
"language": "python"
},
"hide_input": false,
"gist": {
"id": "",
"data": {
"description": "Create compact arrangements of grouped subplots",
"public": true
}
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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