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first commit of rmagic
Jonathan Taylor -
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@@ -0,0 +1,259 b''
1 # -*- coding: utf-8 -*-
2 """
3 R related magics.
4
5 Author:
6 * Jonathan Taylor
7
8 """
9
10 import sys
11 import tempfile
12 from glob import glob
13 from shutil import rmtree
14 from getopt import getopt
15
16 # numpy and rpy2 imports
17
18 import rpy2.rinterface as ri
19 import numpy as np
20 import rpy2.robjects as ro
21 from rpy2.robjects.numpy2ri import numpy2ri
22 ro.conversion.py2ri = numpy2ri
23
24 # IPython imports
25
26 from IPython.core.displaypub import publish_display_data
27 from IPython.core.magic import Magics, magics_class, cell_magic, line_magic
28 from IPython.testing.skipdoctest import skip_doctest
29 from IPython.core.magic_arguments import (
30 argument, magic_arguments, parse_argstring
31 )
32
33 @magics_class
34 class RMagics(Magics):
35
36 def __init__(self, shell, Rconverter=np.asarray,
37 pyconverter=np.asarray):
38 super(RMagics, self).__init__(shell)
39 ri.set_writeconsole(self.write_console)
40
41 # the embedded R process from rpy2
42 self.r = ro.R()
43 self.output = []
44 self.Rconverter = Rconverter
45 self.pyconverter = pyconverter
46
47 def eval(self, line):
48 try:
49 ri.baseenv['eval'](ri.parse(line))
50 except ri.RRuntimeError as msg:
51 self.output.append('ERROR parsing "%s": %s\n' % (line, msg))
52 pass
53
54 def write_console(self, output):
55 '''
56 A hook to capture R's stdout.
57 '''
58 self.output.append(output)
59
60 def flush(self):
61 value = ''.join(self.output)
62 self.output = []
63 return value
64
65 @line_magic
66 def Rpush(self, line):
67 '''
68 A line-level magic for R that pushes
69 variables from python to rpy2.
70
71 Parameters
72 ----------
73
74 line: input
75
76 A white space separated string of
77 names of objects in the python name space to be
78 assigned to objects of the same name in the
79 R name space.
80
81 '''
82
83 inputs = line.split(' ')
84 for input in inputs:
85 self.r.assign(input, self.pyconverter(self.shell.user_ns[input]))
86
87 @line_magic
88 def Rpull(self, line):
89 '''
90 A line-level magic for R that pushes
91 variables from python to rpy2.
92
93 Parameters
94 ----------
95
96 line: output
97
98 A white space separated string of
99 names of objects in the R name space to be
100 assigned to objects of the same name in the
101 python name space.
102
103 Notes
104 -----
105
106 Beware that R names can have '.' so this is not fool proof.
107 To avoid this, don't name your R objects with '.'s...
108
109 '''
110
111 outputs = line.split(' ')
112 for output in outputs:
113 self.shell.push({output:self.Rconverter(self.r(output))})
114
115
116 @line_magic
117 def Rinline(self, line):
118 '''
119 A line-level magic for R that executes
120 some code in R and stores the resulting
121 expression in the ipython shell.
122
123 Parameters
124 ----------
125
126 line: str
127
128 A string of Rcode to be evaluted
129 with return value passed to self.Rconverter.
130
131 '''
132
133 result = ri.baseenv['eval'](ri.parse(line))
134 return self.Rconverter(result)
135
136
137 @magic_arguments()
138 @argument(
139 '-i', '--input', action='append',
140 help='Names of input variable from shell.user_ns to be assigned to R variables of the same names after calling self.pyconverter. Multiple names can be passed separated only by commas with no whitespace.'
141 )
142 @argument(
143 '-o', '--output', action='append',
144 help='Names of variables to be pushed from rpy2 to shell.user_ns after executing cell body and applying self.Rconverter. Multiple names can be passed separated only by commas with no whitespace.'
145 )
146 @argument(
147 '-w', '--width', type=int,
148 help='Width of png plotting device sent as an argument to *png* in R.'
149 )
150 @argument(
151 '-h', '--height', type=int,
152 help='Height of png plotting device sent as an argument to *png* in R.'
153 )
154
155 @argument(
156 '-u', '--units', type=int,
157 help='Units of png plotting device sent as an argument to *png* in R. One of ["px", "in", "cm", "mm"].'
158 )
159 @argument(
160 '-p', '--pointsize', type=int,
161 help='Pointsize of png plotting device sent as an argument to *png* in R.'
162 )
163 @argument(
164 '-b', '--bg',
165 help='Background of png plotting device sent as an argument to *png* in R.'
166 )
167 @cell_magic
168 def R(self, line, cell):
169 '''
170 A cell-level magic for R.
171 The contents of the cell are evaluated using rpy2 with stdout
172 and graphics being captured and returned in the output of the cell.
173
174 Parameters
175 ----------
176
177 line: {input, output, width, height, units, pointsize, bg}
178
179 Optional long arguments recognized by the cell magic.
180 The first two relate to passing python objects
181 back and forth between rpy2 and python:
182 '--input' should be a comma separated list of
183 names of objects in the namespace of the shell;
184 '--output' are names of object in the R namespace
185 that are returned as arrays.
186
187 All other line are passed as arguments to
188 the png plotting device in R.
189
190 cell: str
191
192 String of R code to be executed by rpy2.
193 Anything written to stdout of R is captured,
194 as are plots (in the form of .png files) that
195 are published via ipython's frontend
196 publishing system.
197
198 '''
199 # need to get the ipython instance for assigning
200
201 args = parse_argstring(self.R, line)
202
203 if args.input:
204 for input in ','.join(args.input).split(','):
205 self.r.assign(input, self.pyconverter(self.shell.user_ns[input]))
206
207 png_argdict = dict([(n, getattr(args, n)) for n in ['units', 'height', 'width', 'bg', 'pointsize']])
208 png_args = ','.join(['%s=%s' % (o,v) for o, v in png_argdict.items() if v is not None])
209 # execute the R code in a temporary directory
210
211 tmpd = tempfile.mkdtemp()
212 self.r('png("%s/Rplots%%03d.png",%s)' % (tmpd, png_args))
213 lines = cell.split('\n')
214 [self.eval(line) for line in lines]
215 self.r('dev.off()')
216
217 # read out all the saved .png files
218
219 images = [file(imgfile).read() for imgfile in glob("%s/Rplots*png" % tmpd)]
220
221 # now publish the images
222 # mimicking IPython/zmq/pylab/backend_inline.py
223 fmt = 'png'
224 mimetypes = { 'png' : 'image/png', 'svg' : 'image/svg+xml' }
225 mime = mimetypes[fmt]
226
227 # publish the printed R objects, if any
228 publish_display_data('Rmagic.cell_magic', {'text/plain':self.flush()})
229
230 # flush text streams before sending figures, helps a little with output
231 for image in images:
232 # synchronization in the console (though it's a bandaid, not a real sln)
233 sys.stdout.flush(); sys.stderr.flush()
234 publish_display_data(
235 'Rmagic.cell_magic',
236 {mime : image}
237 )
238 value = {}
239
240 # try to turn every output into a numpy array
241 # this means that output are assumed to be castable
242 # as numpy arrays
243
244 if args.output:
245 for output in ','.join(args.output).split(','):
246 # with self.shell, we assign the values to variables in the shell
247 self.shell.push({output:self.Rconverter(self.r(output))})
248
249 # kill the temporary directory
250 rmtree(tmpd)
251
252
253 _loaded = False
254 def load_ipython_extension(ip):
255 """Load the extension in IPython."""
256 global _loaded
257 if not _loaded:
258 ip.register_magics(RMagics)
259 _loaded = True
@@ -0,0 +1,323 b''
1 {
2 "metadata": {
3 "name": "rmagic_extension"
4 },
5 "nbformat": 3,
6 "worksheets": [
7 {
8 "cells": [
9 {
10 "cell_type": "heading",
11 "level": 1,
12 "source": [
13 "Rmagic Functions Extension"
14 ]
15 },
16 {
17 "cell_type": "heading",
18 "level": 2,
19 "source": [
20 "Line magics"
21 ]
22 },
23 {
24 "cell_type": "markdown",
25 "source": [
26 "IPython has an `rmagic` extension that contains a some magic functions for working with R via rpy2. This extension can be loaded using the `%load_ext` magic as follows:"
27 ]
28 },
29 {
30 "cell_type": "code",
31 "collapsed": true,
32 "input": [
33 "%load_ext rmagic",
34 " "
35 ],
36 "language": "python",
37 "outputs": [],
38 "prompt_number": 25
39 },
40 {
41 "cell_type": "markdown",
42 "source": [
43 "A typical use case one imagines is having some numpy arrays, wanting to compute some statistics of interest on these",
44 " arrays and return the result back to python. Let's suppose we just want to fit a simple linear model to a scatterplot."
45 ]
46 },
47 {
48 "cell_type": "code",
49 "collapsed": false,
50 "input": [
51 "import numpy as np",
52 "import pylab",
53 "X = np.array([0,1,2,3,4])",
54 "Y = np.array([3,5,4,6,7])",
55 "pylab.scatter(X, Y)",
56 ""
57 ],
58 "language": "python",
59 "outputs": [
60 {
61 "output_type": "pyout",
62 "prompt_number": 26,
63 "text": [
64 "<matplotlib.collections.PathCollection at 0x10ba53e10>"
65 ]
66 },
67 {
68 "output_type": "display_data",
69 "png": 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70 }
71 ],
72 "prompt_number": 26
73 },
74 {
75 "cell_type": "markdown",
76 "source": [
77 "We can accomplish this by first pushing variables to R, fitting a model and returning the results."
78 ]
79 },
80 {
81 "cell_type": "code",
82 "collapsed": false,
83 "input": [
84 "%Rpush X Y",
85 "%Rinline lm(Y~X)$coef"
86 ],
87 "language": "python",
88 "outputs": [
89 {
90 "output_type": "pyout",
91 "prompt_number": 27,
92 "text": [
93 "array([ 3.2, 0.9])"
94 ]
95 }
96 ],
97 "prompt_number": 27
98 },
99 {
100 "cell_type": "markdown",
101 "source": [
102 "We can check that this is correct fairly easily:"
103 ]
104 },
105 {
106 "cell_type": "code",
107 "collapsed": false,
108 "input": [
109 "Xr = X - X.mean(); Yr = Y - Y.mean()",
110 "slope = (Xr*Yr).sum() / (Xr**2).sum()",
111 "intercept = Y.mean() - X.mean() * slope",
112 "(intercept, slope)"
113 ],
114 "language": "python",
115 "outputs": [
116 {
117 "output_type": "pyout",
118 "prompt_number": 28,
119 "text": [
120 "(3.2000000000000002, 0.90000000000000002)"
121 ]
122 }
123 ],
124 "prompt_number": 28
125 },
126 {
127 "cell_type": "markdown",
128 "source": [
129 "There is one more line magic, %Rpull which assumes that some R code has been executed and there are variables",
130 "in the rpy2 namespace that one would like to export to the python namespace. See below for an example."
131 ]
132 },
133 {
134 "cell_type": "heading",
135 "level": 2,
136 "source": [
137 "Cell level magic"
138 ]
139 },
140 {
141 "cell_type": "markdown",
142 "source": [
143 "For the cell level magic, inputs can be passed via the -i or --inputs argument in the line. These variables are copied ",
144 "from the shell namespace to R's namespace. It would be nice not to have to copy these -- rnumpy ( http://bitbucket.org/njs/rnumpy/wiki/API ) has done some work to limit or at least make transparent the number of copies of an array. This seems like a natural thing to try to build on. Arrays can be output from R via the -o or --outputs argument in the line. All other arguments are sent to R's png function, which is the graphics device used to create the plots.",
145 "",
146 "We can redo the above calculations in one ipython cell. We might also want to add some output such as a summary",
147 " from R or perhaps the standard plotting diagnostics of the lm."
148 ]
149 },
150 {
151 "cell_type": "code",
152 "collapsed": false,
153 "input": [
154 "%%R -i X,Y -o XYcoef",
155 "XYlm = lm(Y~X)",
156 "XYcoef = coef(XYlm)",
157 "print(summary(XYlm))",
158 "par(mfrow=c(2,2))",
159 "plot(XYlm)"
160 ],
161 "language": "python",
162 "outputs": [
163 {
164 "output_type": "display_data",
165 "text": [
166 "",
167 "Call:",
168 "lm(formula = Y ~ X)",
169 "",
170 "Residuals:",
171 " Min 1Q Median 3Q Max ",
172 "-1.96237 -0.61663 -0.00429 0.65777 2.32413 ",
173 "",
174 "Coefficients:",
175 " Estimate Std. Error t value Pr(>|t|) ",
176 "(Intercept) 0.128188 0.105971 1.210 0.22962 ",
177 "X1 0.004199 0.094165 0.045 0.96453 ",
178 "X2 -0.073072 0.113738 -0.642 0.52222 ",
179 "X3 0.023679 0.111375 0.213 0.83212 ",
180 "X4 -0.027820 0.119539 -0.233 0.81651 ",
181 "X5 0.027729 0.107063 0.259 0.79624 ",
182 "X6 -0.228168 0.106485 -2.143 0.03487 * ",
183 "X7 0.121276 0.108748 1.115 0.26777 ",
184 "X8 0.109123 0.117501 0.929 0.35556 ",
185 "X9 -0.304313 0.112143 -2.714 0.00799 **",
186 "X10 -0.205212 0.111994 -1.832 0.07024 . ",
187 "---",
188 "Signif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1 ",
189 "",
190 "Residual standard error: 1.033 on 89 degrees of freedom",
191 "Multiple R-squared: 0.1596,\tAdjusted R-squared: 0.06519 ",
192 "F-statistic: 1.69 on 10 and 89 DF, p-value: 0.09529 ",
193 "",
194 ""
195 ]
196 },
197 {
198 "output_type": "display_data",
199 "png": 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l95CQEBISEjh27BhKSkpfTYPR0iQScAkZPXo0pqamzJw5E4CePXuyefNmUlJSkEqluLi4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\n"
200 }
201 ],
202 "prompt_number": 69
203 },
204 {
205 "cell_type": "markdown",
206 "source": [
207 "Often, we will want to do more than a simple linear regression model. There may be several lines of R code that we want to ",
208 "use before returning to python. This is the cell-level magic.",
209 "",
210 "In this example, we will generate some random data, fit a LASSO model using the lars package and plot the results, returning the array of coefficients. This example assumes that the *lars* package is installed in R."
211 ]
212 },
213 {
214 "cell_type": "code",
215 "collapsed": true,
216 "input": [
217 "X = np.random.standard_normal((100,10))",
218 "Y = np.random.standard_normal((100,1))"
219 ],
220 "language": "python",
221 "outputs": [],
222 "prompt_number": 70
223 },
224 {
225 "cell_type": "code",
226 "collapsed": false,
227 "input": [
228 "%%R -i X,Y -o larscoef",
229 "library(lars)",
230 "larsobj = lars(X, Y)",
231 "plot(larsobj)",
232 "larscoef = coef(larsobj)",
233 "larspred = predict(larsobj, s=seq(0,1,length=101), mode='fraction')",
234 "lcoef = larspred$coefficients",
235 "lfrac = larspred$fraction"
236 ],
237 "language": "python",
238 "outputs": [
239 {
240 "output_type": "display_data",
241 "text": [
242 "Warning message:",
243 "In predict.lars(larsobj, s = seq(0, 1, length = 101), mode = \"fraction\") :",
244 " Type=fit with no newx argument; type switched to coefficients",
245 ""
246 ]
247 },
248 {
249 "output_type": "display_data",
250 "png": 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EqBASCy6xKvVLTuYep12Njjxf+yUAnO2qa5xMiPsjBSyEsFm5pltPRbjYubI3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\njXt6Y4weqa87O+cnX1GzqKwsVEwM6pMNcPkyKi6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sXf6yq46Ojvj5+f3pHvuBAwe4dOkS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\nrhZgRxWYAAAAAElFTkSuQmCC\n"
251 }
252 ],
253 "prompt_number": 71
254 },
255 {
256 "cell_type": "code",
257 "collapsed": false,
258 "input": [
259 "larscoef.shape"
260 ],
261 "language": "python",
262 "outputs": [
263 {
264 "output_type": "pyout",
265 "prompt_number": 72,
266 "text": [
267 "(11, 10)"
268 ]
269 }
270 ],
271 "prompt_number": 72
272 },
273 {
274 "cell_type": "heading",
275 "level": 3,
276 "source": [
277 "Using Rpull"
278 ]
279 },
280 {
281 "cell_type": "code",
282 "collapsed": false,
283 "input": [
284 "%Rpull lcoef lfrac",
285 "f = [pylab.plot(lfrac, c) for c in lcoef.T]"
286 ],
287 "language": "python",
288 "outputs": [
289 {
290 "output_type": "display_data",
291 "png": 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OpaeHdfVcugRcvMgujYdCYarwjYex4qfzadsfhcJd6lB34LUfXsPn5z/Hlvlb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\nQsjo4Oniwqa+entb3H9Tq8W53rGKkwo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292 }
293 ],
294 "prompt_number": 73
295 },
296 {
297 "cell_type": "heading",
298 "level": 2,
299 "source": [
300 "Passing data back and forth"
301 ]
302 },
303 {
304 "cell_type": "markdown",
305 "source": [
306 "Currently, data is passed through RMagics.pyconverter when going from python to R and RMagics.Rconverter when ",
307 "going from R to python. These currently default to numpy.ndarray. Future work will involve writing better converters, most likely involving integration with http://pandas.sourceforge.net.",
308 ""
309 ]
310 },
311 {
312 "cell_type": "code",
313 "collapsed": true,
314 "input": [
315 ""
316 ],
317 "language": "python",
318 "outputs": []
319 }
320 ]
321 }
322 ]
323 } No newline at end of file
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