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May 23, 2016 23:23
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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# Comparing probability scale and plotting positions\n", | |
"## Looking at normal vs Weibull scales + Cunnane vs Weibull plotting positions" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"%matplotlib inline" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"import numpy\n", | |
"from matplotlib import pyplot\n", | |
"from scipy import stats\n", | |
"import seaborn\n", | |
"\n", | |
"clear_bkgd = {'axes.facecolor':'none', 'figure.facecolor':'none'}\n", | |
"seaborn.set(style='ticks', context='talk', color_codes=True, rc=clear_bkgd)\n", | |
"\n", | |
"import probscale\n", | |
"\n", | |
"\n", | |
"def format_axes(ax1, ax2):\n", | |
" \"\"\" Sets axes labels and grids \"\"\"\n", | |
" for ax in (ax1, ax2):\n", | |
" ax.set_ylim(bottom=1, top=99)\n", | |
" ax.set_xlabel('Values of Data')\n", | |
" seaborn.despine(ax=ax)\n", | |
" ax.yaxis.grid(True)\n", | |
" \n", | |
" if ax.is_first_col():\n", | |
" ax.legend(loc='upper left', numpoints=1, frameon=False)\n", | |
" ax.set_ylabel('Weibull Probability Scale')\n", | |
" else:\n", | |
" ax.set_ylabel('Normal Probability Scale')" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## Generate some fake data and compute the plotting positions" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"numpy.random.seed(0) # reproducible\n", | |
"data = numpy.random.normal(loc=5, scale=1.25, size=37)\n", | |
"\n", | |
"# simple weibull distribution\n", | |
"weibull = stats.weibull_min(2)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## Plot the data on each combination of plotting positions and scales" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 7, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
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wp12rhWrghiNptRDVhKWLbpRjgQHtLh8I7CNM1j7b9CeDkjMaG1uapwVTA881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0eP4v1GAauB0dEtp9iK6rA8AFxKmGB70JUFcLnqsIjEQ0+KLT1BiH/JB0vGvnBJQd+tu/a+9oH5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\nRU5ErkJggg==\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x29c275629e8>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"w_opts = {'markeredgecolor': 'b', 'label': 'Weibull Plot. Pos.'}\n", | |
"n_opts = {'markeredgecolor': 'g', 'label': 'Cunnane Plot. Pos.'}\n", | |
"\n", | |
"common_opts = {\n", | |
" 'markerfacecolor': 'none',\n", | |
" 'marker': 'o',\n", | |
" 'markeredgewidth': 1.25,\n", | |
" 'linestyle': 'none'\n", | |
"}\n", | |
"\n", | |
"fig, (ax1, ax2) = pyplot.subplots(figsize=(8, 8), ncols=2, sharex=True, sharey=False)\n", | |
"\n", | |
"for dist, ax in zip([None, weibull], [ax1, ax2]):\n", | |
" for opts, postype in zip([w_opts, n_opts], ['weibull', 'cunnane']):\n", | |
" probscale.probplot(data, ax=ax, dist=dist, probax='y', \n", | |
" scatter_kws={**opts, **common_opts}, \n", | |
" pp_kws={'postype': postype})\n", | |
"\n", | |
"format_axes(ax1, ax2)\n", | |
"fig.tight_layout()" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## Print samples of the different plotting position values" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 5, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"weibull: [ 2.63 5.26 7.89 10.53 13.16 15.79 18.42 21.05 23.68 26.32]\n", | |
"cunnane: [ 1.61 4.3 6.99 9.68 12.37 15.05 17.74 20.43 23.12 25.81]\n" | |
] | |
} | |
], | |
"source": [ | |
"# weibull plotting positions and sorted data\n", | |
"w_probs, _ = probscale.plot_pos(data, postype='weibull')\n", | |
"\n", | |
"# normal plotting positions, returned \"data\" is identical to above\n", | |
"n_probs, _ = probscale.plot_pos(data, postype='cunnane')\n", | |
"\n", | |
"# convert to percentages\n", | |
"w_probs *= 100\n", | |
"n_probs *= 100\n", | |
"\n", | |
"print('weibull: ', numpy.round(w_probs[:10],2))\n", | |
"print('cunnane: ', numpy.round(n_probs[:10],2))" | |
] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "Python 3", | |
"language": "python", | |
"name": "python3" | |
}, | |
"language_info": { | |
"codemirror_mode": { | |
"name": "ipython", | |
"version": 3 | |
}, | |
"file_extension": ".py", | |
"mimetype": "text/x-python", | |
"name": "python", | |
"nbconvert_exporter": "python", | |
"pygments_lexer": "ipython3", | |
"version": "3.5.1" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 0 | |
} |
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