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@@ -46,8 +46,13 b' import numpy as np' | |||||
46 |
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46 | |||
47 | import rpy2.rinterface as ri |
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47 | import rpy2.rinterface as ri | |
48 | import rpy2.robjects as ro |
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48 | import rpy2.robjects as ro | |
49 | from rpy2.robjects.numpy2ri import numpy2ri |
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49 | try: | |
50 | ro.conversion.py2ri = numpy2ri |
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50 | from rpy2.robjects import pandas2ri | |
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51 | pandas2ri.activate() | |||
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52 | except ImportError: | |||
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53 | pandas2ri = None | |||
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54 | from rpy2.robjects import numpy2ri | |||
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55 | numpy2ri.activate() | |||
51 |
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56 | |||
52 | # IPython imports |
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57 | # IPython imports | |
53 |
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58 | |||
@@ -58,6 +63,7 b' from IPython.testing.skipdoctest import skip_doctest' | |||||
58 | from IPython.core.magic_arguments import ( |
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63 | from IPython.core.magic_arguments import ( | |
59 | argument, magic_arguments, parse_argstring |
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64 | argument, magic_arguments, parse_argstring | |
60 | ) |
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65 | ) | |
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66 | from IPython.external.simplegeneric import generic | |||
61 | from IPython.utils.py3compat import str_to_unicode, unicode_to_str, PY3 |
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67 | from IPython.utils.py3compat import str_to_unicode, unicode_to_str, PY3 | |
62 |
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68 | |||
63 | class RInterpreterError(ri.RRuntimeError): |
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69 | class RInterpreterError(ri.RRuntimeError): | |
@@ -114,16 +120,32 b' def Rconverter(Robj, dataframe=False):' | |||||
114 | Robj = np.rec.fromarrays(Robj, names = names) |
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120 | Robj = np.rec.fromarrays(Robj, names = names) | |
115 | return np.asarray(Robj) |
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121 | return np.asarray(Robj) | |
116 |
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122 | |||
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123 | @generic | |||
117 | def pyconverter(pyobj): |
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124 | def pyconverter(pyobj): | |
118 |
"""Convert Python objects to R objects. |
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125 | """Convert Python objects to R objects. Add types using the decorator: | |
119 | if 'pandas' in sys.modules: |
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126 | ||
120 | # We only do this if pandas is already loaded |
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127 | @pyconverter.when_type | |
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128 | """ | |||
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129 | return pyobj | |||
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130 | ||||
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131 | # The default conversion for lists seems to make them a nested list. That has | |||
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132 | # some advantages, but is rarely convenient, so for interactive use, we convert | |||
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133 | # lists to a numpy array, which becomes an R vector. | |||
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134 | @pyconverter.when_type(list) | |||
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135 | def pyconverter_list(pyobj): | |||
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136 | return np.asarray(pyobj) | |||
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137 | ||||
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138 | if pandas2ri is None: | |||
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139 | # pandas2ri was new in rpy2 2.3.3, so for now we'll fallback to pandas' | |||
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140 | # conversion function. | |||
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141 | try: | |||
121 | from pandas import DataFrame |
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142 | from pandas import DataFrame | |
122 | if isinstance(pyobj, DataFrame): |
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123 |
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143 | from pandas.rpy.common import convert_to_r_dataframe | |
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144 | @pyconverter.when_type(DataFrame) | |||
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145 | def pyconverter_dataframe(pyobj): | |||
124 | return convert_to_r_dataframe(pyobj, strings_as_factors=True) |
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146 | return convert_to_r_dataframe(pyobj, strings_as_factors=True) | |
125 |
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147 | except ImportError: | ||
126 | return np.asarray(pyobj) |
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148 | pass | |
127 |
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149 | |||
128 | @magics_class |
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150 | @magics_class | |
129 | class RMagics(Magics): |
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151 | class RMagics(Magics): |
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