from rpy2.robjects.robject import RObjectMixin, RObject
import rpy2.rinterface as rinterface
#import rpy2.robjects.conversion as conversion
from . import conversion
import rpy2.rlike.container as rlc
import sys, copy, os, itertools, math
import jinja2
import time
from datetime import datetime
from time import struct_time, mktime, tzname
from operator import attrgetter
from rpy2.rinterface import (Sexp, SexpVector, ListSexpVector, StrSexpVector,
IntSexpVector, BoolSexpVector, ComplexSexpVector,
FloatSexpVector, R_NilValue, NA_Real, NA_Integer,
NA_Character, NA_Logical, NULL, MissingArg)
if sys.version_info[0] == 2:
py3str = unicode
py3bytes = str
range = xrange
else:
long = int
py3str = str
py3bytes = bytes
globalenv_ri = rinterface.globalenv
baseenv_ri = rinterface.baseenv
utils_ri = rinterface.baseenv['as.environment'](rinterface.StrSexpVector(("package:utils", )))
class ExtractDelegator(object):
""" Delegate the R 'extraction' ("[") and 'replacement' ("[<-")
of items in a vector
or vector-like object. This can help making syntactic
niceties possible."""
_extractfunction = rinterface.baseenv['[']
_replacefunction = rinterface.baseenv['[<-']
def __init__(self, parent):
self._parent = parent
def __call__(self, *args, **kwargs):
""" Subset the "R-way.", using R's "[" function.
In a nutshell, R indexing differs from Python indexing on:
- indexing can be done with integers or strings (that are 'names')
- an index equal to TRUE will mean everything selected
(because of the recycling rule)
- integer indexing starts at one
- negative integer indexing means exclusion of the given integers
- an index is itself a vector of elements to select
"""
conv_args = list(None for x in range(len(args)))
for i, x in enumerate(args):
if x is MissingArg:
conv_args[i] = x
else:
conv_args[i] = conversion.py2ri(x)
kwargs = copy.copy(kwargs)
for k, v in kwargs.values():
kwargs[k] = conversion.py2ri(v)
fun = self._extractfunction
conv_args.insert(0, self._parent)
res = fun(*conv_args, **kwargs)
res = conversion.py2ro(res)
return res
def __getitem__(self, item):
fun = self._extractfunction
args = rlc.TaggedList(item)
for i, (k, v) in enumerate(args.items()):
if v is MissingArg:
continue
args[i] = conversion.py2ro(v)
args.insert(0, self._parent)
res = fun.rcall(args.items(),
globalenv_ri)
res = conversion.py2ro(res)
return res
def __setitem__(self, item, value):
""" Assign a given value to a given index position in the vector.
The index position can either be:
- an int: x[1] = y
- a tuple of ints: x[1, 2, 3] = y
- an item-able object (such as a dict): x[{'i': 1}] = y
"""
fun = self._replacefunction
if type(item) is tuple:
args = list([None, ] * (len(item)+2))
for i, v in enumerate(item):
if v is MissingArg:
continue
args[i+1] = conversion.py2ro(v)
args[-1] = conversion.py2ro(value)
args[0] = self._parent
res = fun(*args)
elif (type(item) is dict) or (type(item) is rlc.TaggedList):
args = rlc.TaggedList.from_items(item)
for i, (k, v) in enumerate(args.items()):
args[i] = conversion.py2ro(v)
args.append(conversion.py2ro(value), tag = None)
args.insert(0, self._parent, tag = None)
res = fun.rcall(tuple(args.items()),
globalenv_ri)
else:
args = [self._parent,
conversion.py2ro(item),
conversion.py2ro(value)]
res = fun(*args)
#FIXME: check refcount and copying
self._parent.__sexp__ = res.__sexp__
class DoubleExtractDelegator(ExtractDelegator):
""" Delegate the R 'extraction' ("[[") and "replacement" ("[[<-")
of items in a vector
or vector-like object. This can help making syntactic
niceties possible."""
_extractfunction = rinterface.baseenv['[[']
_replacefunction = rinterface.baseenv['[[<-']
class VectorOperationsDelegator(object):
"""
Delegate operations such as __getitem__, __add__, etc...
to the corresponding R function.
This permits a convenient coexistence between
operators on Python sequence object with their R conterparts.
"""
def __init__(self, parent):
""" The parent in expected to inherit from Vector. """
self._parent = parent
def __add__(self, x):
res = globalenv_ri.get("+")(self._parent, conversion.py2ri(x))
return conversion.ri2ro(res)
def __sub__(self, x):
res = globalenv_ri.get("-")(self._parent, conversion.py2ri(x))
return conversion.ri2ro(res)
def __matmul__(self, x):
res = globalenv_ri.get("%*%")(self._parent, conversion.py2ri(x))
return conversion.ri2ro(res)
def __mul__(self, x):
res = globalenv_ri.get("*")(self._parent, conversion.py2ri(x))
return conversion.ri2ro(res)
def __pow__(self, x):
res = globalenv_ri.get("^")(self._parent, conversion.py2ri(x))
return conversion.ri2ro(res)
if sys.version_info[0] == 2:
def __div__(self, x):
res = globalenv_ri.get("/")(self._parent, conversion.py2ri(x))
return conversion.ri2ro(res)
else:
def __floordiv__(self, x):
res = globalenv_ri.get("%/%")(self._parent, conversion.py2ri(x))
return conversion.ri2ro(res)
def __truediv__(self, x):
res = globalenv_ri.get("/")(self._parent, conversion.py2ri(x))
return conversion.ri2ro(res)
def __divmod__(self, x):
res = globalenv_ri.get("%%")(self._parent, conversion.py2ri(x))
return conversion.ri2ro(res)
def __or__(self, x):
res = globalenv_ri.get("|")(self._parent, conversion.py2ri(x))
return conversion.ri2ro(res)
def __and__(self, x):
res = globalenv_ri.get("&")(self._parent, conversion.py2ri(x))
return conversion.ri2ro(res)
# Comparisons
def __lt__(self, x):
res = globalenv_ri.get("<")(self._parent, conversion.py2ri(x))
return conversion.ri2ro(res)
def __le__(self, x):
res = globalenv_ri.get("<=")(self._parent, conversion.py2ri(x))
return conversion.ri2ro(res)
def __eq__(self, x):
res = globalenv_ri.get("==")(self._parent, conversion.py2ri(x))
return conversion.ri2ro(res)
def __ne__(self, x):
res = globalenv_ri.get("!=")(self._parent, conversion.py2ri(x))
return conversion.ri2ro(res)
def __gt__(self, x):
res = globalenv_ri.get(">")(self._parent, conversion.py2ri(x))
return conversion.ri2ro(res)
def __ge__(self, x):
res = globalenv_ri.get(">=")(self._parent, conversion.py2ri(x))
return conversion.ri2ro(res)
#
def __neg__(self):
res = globalenv_ri.get("-")(self._parent)
return res
def __contains__(self, what):
res = globalenv_ri.get("%in%")(self._parent, what)
return res
[docs]class Vector(RObjectMixin, SexpVector):
""" Vector(seq) -> Vector.
The parameter 'seq' can be an instance inheriting from
rinterface.SexpVector, or an arbitrary Python object.
In the later case, a conversion will be attempted using
conversion.py2ri().
R vector-like object. Items can be accessed with:
- the method "__getitem__" ("[" operator)
- the delegators rx or rx2
"""
_sample = rinterface.baseenv['sample']
_html_template = jinja2.Template(
"""
<span>{{ classname }} with {{ nelements }} elements.</span>
<table>
<tbody>
<tr>
{% for elt in elements %}
<td>
{{ elt }}
</td>
{% endfor %}
</tr>
</tbody>
</table>
""")
def __init__(self, o):
if not isinstance(o, SexpVector):
o = conversion.py2ri(o)
super(Vector, self).__init__(o)
self.ro = VectorOperationsDelegator(self)
self.rx = ExtractDelegator(self)
self.rx2 = DoubleExtractDelegator(self)
def __add__(self, x):
res = baseenv_ri.get("c")(self, conversion.py2ri(x))
res = conversion.ri2ro(res)
return res
def __getitem__(self, i):
res = super(Vector, self).__getitem__(i)
if isinstance(res, Sexp):
res = conversion.ri2ro(res)
return res
def __setitem__(self, i, value):
value = conversion.py2ri(value)
res = super(Vector, self).__setitem__(i, value)
def __getslice__(self, i, j):
res = super(Vector, self).__getslice__(i, j)
if isinstance(res, Sexp):
res = conversion.ri2ro(res)
return res
def _names_get(self):
res = baseenv_ri.get('names')(self)
res = conversion.ri2ro(res)
return res
def _names_set(self, value):
res = globalenv_ri.get("names<-")(self, conversion.py2ro(value))
self.__sexp__ = res.__sexp__
names = property(_names_get, _names_set,
"Names for the items in the vector.")
[docs] def items(self):
""" iterator on names and values """
#FIXME: should be a view ?
if self.names.rsame(R_NilValue):
it_names = itertools.cycle((None, ))
else:
it_names = iter(self.names)
it_self = iter(self)
for v, k in zip(it_self, it_names):
yield (k, v)
[docs] def sample(self, n, replace = False, probabilities = None):
""" Draw a sample of size n from the vector.
If 'replace' is True, the sampling is done with replacement.
The optional argument 'probabilities' can indicate sampling probabilities. """
assert isinstance(n, int)
assert isinstance(replace, bool)
if probabilities is not None:
probabilities = FloatVector(probabilities)
res = self._sample(self, IntVector((n,)),
replace = BoolVector((replace, )),
prob = probabilities)
res = conversion.ri2ro(res)
return res
def repr_format_elt(self, elt, max_width = 12):
max_width = int(max_width)
if elt is NA_Real or elt is NA_Integer or elt is NA_Character or elt is NA_Logical:
res = repr(elt)
elif isinstance(elt, long) or isinstance(elt, int):
res = '%8i' %elt
elif isinstance(elt, float):
res = '%8f' %elt
else:
if isinstance(elt, py3str):
elt = elt.__repr__()
else:
elt = type(elt).__name__
if len(elt) < max_width:
res = elt
else:
res = "%s..." % (str(elt[ : (max_width - 3)]))
return res
def _iter_formatted(self, max_items=9):
format_elt = self.repr_format_elt
l = len(self)
half_items = max_items // 2
if l == 0:
return
elif l < max_items:
for elt in self:
yield format_elt(elt, max_width = math.floor(52 / l))
else:
for elt in self[ : half_items]:
yield format_elt(elt)
yield '...'
for elt in self[-half_items : ]:
yield format_elt(elt)
def __repr_content__(self):
return ''.join(('[', ', '.join(self._iter_formatted()), ']'))
def __repr__(self):
return super(Vector, self).__repr__() + os.linesep + \
self.__repr_content__()
def _repr_html_(self):
d = {'elements': self._iter_formatted(),
'classname': type(self).__name__,
'nelements': len(self)}
html = self._html_template.render(d)
return html
[docs]class StrVector(Vector, StrSexpVector):
""" Vector of string elements
StrVector(seq) -> StrVector.
The parameter 'seq' can be an instance inheriting from
rinterface.SexpVector, or an arbitrary Python sequence.
In the later case, all elements in the sequence should be either
strings, or have a str() representation.
"""
_factorconstructor = rinterface.baseenv['factor']
@property
def NAvalue(self):
return rinterface.NA_Character
def __init__(self, obj):
obj = StrSexpVector(obj)
super(StrVector, self).__init__(obj)
[docs] def factor(self):
"""
factor() -> FactorVector
Construct a factor vector from a vector of strings.
"""
res = self._factorconstructor(self)
return conversion.ri2ro(res)
[docs]class IntVector(Vector, IntSexpVector):
""" Vector of integer elements
IntVector(seq) -> IntVector.
The parameter 'seq' can be an instance inheriting from
rinterface.SexpVector, or an arbitrary Python sequence.
In the later case, all elements in the sequence should be either
integers, or have an int() representation.
"""
_tabulate = rinterface.baseenv['tabulate']
@property
def NAvalue(self):
return rinterface.NA_Integer
def __init__(self, obj):
obj = IntSexpVector(obj)
super(IntVector, self).__init__(obj)
def repr_format_elt(self, elt, max_width = 8):
return '{:,}'.format(elt)
[docs] def tabulate(self, nbins = None):
""" Like the R function tabulate,
count the number of times integer values are found """
if nbins is None:
nbins = max(1, max(self))
res = self._tabulate(self)
return conversion.ri2ro(res)
[docs]class BoolVector(Vector, BoolSexpVector):
""" Vector of boolean (logical) elements
BoolVector(seq) -> BoolVector.
The parameter 'seq' can be an instance inheriting from
rinterface.SexpVector, or an arbitrary Python sequence.
In the later case, all elements in the sequence should be either
booleans, or have a bool() representation.
"""
@property
def NAvalue(self):
return rinterface.NA_Logical
def __init__(self, obj):
obj = BoolSexpVector(obj)
super(BoolVector, self).__init__(obj)
class ComplexVector(Vector, ComplexSexpVector):
""" Vector of complex elements
ComplexVector(seq) -> ComplexVector
The parameter 'seq' can be an instance inheriting from
rinterface.SexpVector, or an arbitrary Python sequence.
In the later case, all elements in the sequence should be either
complex, or have a complex() representation.
"""
@property
def NAvalue(self):
return rinterface.NA_Complex
def __init__(self, obj):
obj = ComplexSexpVector(obj)
super(ComplexVector, self).__init__(obj)
[docs]class FloatVector(Vector, FloatSexpVector):
""" Vector of float (double) elements
FloatVector(seq) -> FloatVector.
The parameter 'seq' can be an instance inheriting from
rinterface.SexpVector, or an arbitrary Python sequence.
In the later case, all elements in the sequence should be either
float, or have a float() representation.
"""
@property
def NAvalue(self):
return rinterface.NA_Real
def __init__(self, obj):
obj = FloatSexpVector(obj)
super(FloatVector, self).__init__(obj)
[docs]class FactorVector(IntVector):
""" Vector of 'factors'
FactorVector(obj,
levels = rinterface.MissingArg,
labels = rinterface.MissingArg,
exclude = rinterface.MissingArg,
ordered = rinterface.MissingArg) -> FactorVector
obj: StrVector or StrSexpVector
levels: StrVector or StrSexpVector
labels: StrVector or StrSexpVector (of same length as levels)
exclude: StrVector or StrSexpVector
ordered: boolean
"""
_factor = baseenv_ri['factor']
_levels = baseenv_ri['levels']
_levels_set = baseenv_ri['levels<-']
_nlevels = baseenv_ri['nlevels']
_isordered = baseenv_ri['is.ordered']
@property
def NAvalue(self):
return rinterface.NA_Integer
def __init__(self, obj,
levels = rinterface.MissingArg,
labels = rinterface.MissingArg,
exclude = rinterface.MissingArg,
ordered = rinterface.MissingArg):
if not isinstance(obj, Sexp):
obj = StrSexpVector(obj)
if ('factor' in obj.rclass) and \
all(p is rinterface.MissingArg for p in (labels,
exclude,
ordered)):
res = obj
else:
res = self._factor(obj,
levels = levels,
labels = labels,
exclude = exclude,
ordered = ordered)
self.__sexp__ = res.__sexp__
self.ro = VectorOperationsDelegator(self)
self.rx = ExtractDelegator(self)
self.rx2 = DoubleExtractDelegator(self)
def repr_format_elt(self, elt, max_width = 8):
max_width = int(max_width)
levels = self._levels(self)
if elt is NA_Integer:
res = repr(elt)
else:
res = levels[elt-1]
if len(res) >= max_width:
res = "%s..." % (res[ : (max_width - 3)])
return res
def __levels_get(self):
res = self._levels(self)
return conversion.ri2ro(res)
def __levels_set(self, value):
res = self._levels_set(self, conversion.py2ro(value))
self.__sexp__ = res.__sexp__
levels = property(__levels_get, __levels_set)
def __nlevels_get(self):
res = self._nlevels(self)
return res[0]
nlevels = property(__nlevels_get, None, None, "number of levels ")
def __isordered_get(self):
res = self._isordered(self)
return res[0]
isordered = property(__isordered_get, None, None,
"are the levels in the factor ordered ?")
[docs] def iter_labels(self):
""" Iterate the over the labels, that is iterate over
the items returning associated label for each item """
levels = self.levels
for x in self:
yield levels[x-1]
[docs]class ListVector(Vector, ListSexpVector):
""" R list (vector of arbitray elements)
ListVector(itemable) -> ListVector.
The parameter 'itemable' can be:
- an object with a method `items()`, such for example a dict,
a rpy2.rlike.container.TaggedList,
an rpy2.rinterface.SexpVector of type VECSXP.
- an iterable of (name, value) tuples
"""
_vector = rinterface.baseenv['vector']
_html_template = jinja2.Template(
"""
<span>{{ classname }} with {{ nelements }} elements.</span>
<table>
<tbody>
{% for name, elt in names_elements %}
<tr>
<th>
{{ name }}
</th>
<td>
{{ elt }}
</td>
</tr>
{% endfor %}
</tbody>
</table>
""")
def __init__(self, tlist):
if isinstance(tlist, rinterface.SexpVector):
if tlist.typeof != rinterface.VECSXP:
raise ValueError("tlist should have "
"tlist.typeof == rinterface.VECSXP")
super(ListVector, self).__init__(tlist)
elif hasattr(tlist, 'items') and callable(tlist.items):
kv = [(k, conversion.py2ri(v)) for k,v in tlist.items()]
kv = tuple(kv)
df = baseenv_ri.get("list").rcall(kv, globalenv_ri)
super(ListVector, self).__init__(df)
elif hasattr(tlist, "__iter__"):
if not callable(tlist.__iter__):
raise ValueError("tlist should have a /method/ __iter__ (not an attribute)")
kv = [(str(k), conversion.py2ri(v)) for k,v in tlist]
kv = tuple(kv)
df = baseenv_ri.get("list").rcall(kv, globalenv_ri)
super(ListVector, self).__init__(df)
else:
raise ValueError("tlist can be either "+
"an iter-able " +
" or an instance of rpy2.rinterface.SexpVector" +
" of type VECSXP, or a Python dict.")
def _iter_repr(self, max_items=9):
if len(self) <= max_items:
for elt in self:
yield elt
else:
half_items = max_items // 2
for i in range(0, half_items):
yield self[i]
yield '...'
for i in range(-half_items, 0):
yield self[i]
def __repr__(self):
res = []
for i, elt in enumerate(self._iter_repr()):
if isinstance(elt, ListVector):
res.append(super(ListVector, self).__repr__())
elif elt == '...':
res.append(elt)
else:
try:
name = self.names[i]
except TypeError as te:
name = '<no name>'
res.append(" %s: %s%s %s" %(name,
type(elt),
os.linesep,
elt.__repr__()))
res = super(ListVector, self).__repr__() + os.linesep + \
os.linesep.join(res)
return res
def _repr_html_(self, max_items=7):
elements = list()
for e in self._iter_repr(max_items=max_items):
if hasattr(e, '_repr_html_'):
elements.append(e._repr_html_())
else:
elements.append(e)
names = list()
if len(self) <= max_items:
names.extend(self.names)
else:
half_items = max_items // 2
for i in range(0, half_items):
try:
name = self.names[i]
except TypeError:
name = '[no name]'
names.append(name)
names.append('...')
for i in range(-half_items, 0):
try:
name = self.names[i]
except TypeError:
name = '[no name]'
names.append(name)
d = {'names_elements': zip(names, elements),
'nelements': len(self),
'classname': type(self).__name__}
html = self._html_template.render(d)
return html
[docs] @staticmethod
def from_length(length):
""" Create a list of given length """
res = ListVector._vector(StrSexpVector(("list", )), length)
res = conversion.ri2ro(res)
return res
class DateVector(FloatVector):
""" Vector of dates """
pass
class POSIXt(object):
""" POSIX time vector. This is an abstract class. """
pass
[docs]class POSIXlt(POSIXt, Vector):
""" Representation of dates with a 9-component structure
(similar to Python's time.struct_time).
POSIXlt(seq) -> POSIXlt.
The constructor accepts either an R vector
or a sequence (an object with the Python
sequence interface) of time.struct_time objects.
"""
def __init__(self, seq):
"""
"""
if isinstance(seq, Sexp):
super(self, Vector)(seq)
else:
for elt in seq:
if not isinstance(elt, struct_time):
raise ValueError('All elements must inherit from time.struct_time')
as_posixlt = baseenv_ri['as.POSIXlt']
origin = StrSexpVector([time.strftime("%Y-%m-%d",
time.gmtime(0)),])
rvec = FloatSexpVector([mktime(x) for x in seq])
sexp = as_posixlt(rvec, origin = origin)
self.__sexp__ = sexp.__sexp__
def __getitem__(self, i):
# "[[" operator returns the components of a time object
# (and yes, this is confusing)
tmp = self.rx2(i-1)
return struct_time(*tuple(tmp))
[docs]class POSIXct(POSIXt, FloatVector):
""" Representation of dates as seconds since Epoch.
This form is preferred to POSIXlt for inclusion in a DataFrame.
POSIXlt(seq) -> POSIXlt.
The constructor accepts either an R vector floats
or a sequence (an object with the Python
sequence interface) of time.struct_time objects.
"""
_as_posixct = baseenv_ri['as.POSIXct']
_ISOdatetime = baseenv_ri['ISOdatetime']
def __init__(self, seq):
""" Create a POSIXct from either an R vector or a sequence
of Python dates.
"""
if isinstance(seq, Sexp):
super(FloatVector, self).__init__(seq)
elif isinstance(seq[0], struct_time):
sexp = POSIXct.sexp_from_struct_time(seq)
self.__sexp__ = sexp.__sexp__
elif isinstance(seq[0], datetime):
sexp = POSIXct.sexp_from_datetime(seq)
self.__sexp__ = sexp.__sexp__
else:
raise ValueError('All elements must inherit from time.struct_time or datetime.datetime.')
@staticmethod
def _sexp_from_seq(seq, tz_info_getter, isodatetime_columns):
""" return a POSIXct vector from a sequence of time.struct_time
elements. """
tz_count = 0
tz_info = None
for elt in seq:
tmp = tz_info_getter(elt)
if tz_info is None:
tz_info = tmp
tz_count = 1
elif tz_info == tmp:
tz_count += 1
else:
# different time zones
#FIXME: create a list of time zones with tz_count times
# tz_info, add the current tz_info and append further.
raise ValueError("Sequences of dates with different time zones not yet allowed.")
if tz_info is None:
tz_info = tzname[0]
# We could use R's as.POSIXct instead of ISOdatetime
# since as.POSIXct is used by it anyway, but the overall
# interface for dates and conversion between formats
# is not exactly straightforward. Someone with more
# time should look into this.
d = isodatetime_columns(seq)
sexp = POSIXct._ISOdatetime(*d, tz = StrSexpVector((tz_info, )))
return sexp
@staticmethod
def sexp_from_struct_time(seq):
def f(seq):
return [IntVector([x.tm_year for x in seq]),
IntVector([x.tm_mon for x in seq]),
IntVector([x.tm_mday for x in seq]),
IntVector([x.tm_hour for x in seq]),
IntVector([x.tm_min for x in seq]),
IntVector([x.tm_sec for x in seq])]
return POSIXct._sexp_from_seq(seq, lambda elt: time.tzname[0], f)
[docs] @staticmethod
def sexp_from_datetime(seq):
""" return a POSIXct vector from a sequence of
datetime.datetime elements. """
def f(seq):
return [IntVector([x.year for x in seq]),
IntVector([x.month for x in seq]),
IntVector([x.day for x in seq]),
IntVector([x.hour for x in seq]),
IntVector([x.minute for x in seq]),
IntVector([x.second for x in seq])]
return POSIXct._sexp_from_seq(seq, attrgetter('tzinfo'), f)
[docs]class Array(Vector):
""" An R array """
_dimnames_get = baseenv_ri['dimnames']
_dimnames_set = baseenv_ri['dimnames<-']
_dim_get = baseenv_ri['dim']
_dim_set = baseenv_ri['dim<-']
_isarray = baseenv_ri['is.array']
def __init__(self, obj):
super(Array, self).__init__(obj)
#import pdb; pdb.set_trace()
if not self._isarray(self)[0]:
raise(TypeError("The object must be representing an R array"))
def __dim_get(self):
res = self._dim_get(self)
res = conversion.ri2ro(res)
return res
def __dim_set(self, value):
value = conversion.py2ro(value)
res = self._dim_set(self, value)
#FIXME: not properly done
raise(Exception("Not yet implemented"))
dim = property(__dim_get, __dim_set,
"Get or set the dimension of the array.")
def __dimnames_get(self):
""" Return a list of name vectors
(like the R function 'dimnames' does)."""
res = self._dimnames_get(self)
res = conversion.ri2ro(res)
return res
def __dimnames_set(self, value):
""" Set list of name vectors
(like the R function 'dimnames' does)."""
value = conversion.ri2ro(value)
res = self._dimnames_set(self, value)
self.__sexp__ = res.__sexp__
names = property(__dimnames_get, __dimnames_set, None,
"names associated with the dimension.")
dimnames = names
[docs]class Matrix(Array):
""" An R matrix """
_transpose = baseenv_ri['t']
_rownames = baseenv_ri['rownames']
_colnames = baseenv_ri['colnames']
_dot = baseenv_ri['%*%']
_crossprod = baseenv_ri['crossprod']
_tcrossprod = baseenv_ri['tcrossprod']
_svd = baseenv_ri['svd']
_eigen = baseenv_ri['eigen']
def __nrow_get(self):
""" Number of rows.
:rtype: integer """
return self.dim[0]
nrow = property(__nrow_get, None, None, "Number of rows")
def __ncol_get(self):
""" Number of columns.
:rtype: integer """
return self.dim[1]
ncol = property(__ncol_get, None, None, "Number of columns")
def __rownames_get(self):
""" Row names
:rtype: SexpVector
"""
res = self._rownames(self)
return conversion.ri2ro(res)
def __rownames_set(self, rn):
if isinstance(rn, StrSexpVector):
if len(rn) != self.nrow:
raise ValueError('Invalid length.')
if self.dimnames is NULL:
dn = ListVector.from_length(2)
dn[0] = rn
self.do_slot_assign('dimnames', dn)
else:
dn = self.dimnames
dn[0] = rn
else:
raise ValueError('The rownames attribute can only be an R string vector.')
rownames = property(__rownames_get, __rownames_set, None, "Row names")
def __colnames_get(self):
""" Column names
:rtype: SexpVector
"""
res = self._colnames(self)
return conversion.ri2ro(res)
def __colnames_set(self, cn):
if isinstance(cn, StrSexpVector):
if len(cn) != self.ncol:
raise ValueError('Invalid length.')
if self.dimnames is NULL:
dn = ListVector.from_length(2)
dn[1] = cn
self.do_slot_assign('dimnames', dn)
else:
dn = self.dimnames
dn[1] = cn
else:
raise ValueError('The colnames attribute can only be an R string vector.')
colnames = property(__colnames_get, __colnames_set, None, "Column names")
[docs] def transpose(self):
""" transpose the matrix """
res = self._transpose(self)
return conversion.ri2ro(res)
[docs] def crossprod(self, m):
""" crossproduct X'.Y"""
res = self._crossprod(self, conversion.ri2ro(m))
return conversion.ri2ro(res)
[docs] def tcrossprod(self, m):
""" crossproduct X.Y'"""
res = self._tcrossprod(self, m)
return conversion.ri2ro(res)
[docs] def svd(self, nu = None, nv = None, linpack = False):
""" SVD decomposition.
If nu is None, it is given the default value min(tuple(self.dim)).
If nv is None, it is given the default value min(tuple(self.dim)).
"""
if nu is None:
nu = min(tuple(self.dim))
if nv is None:
nv = min(tuple(self.dim))
res = self._svd(self, nu = nu, nv = nv, LINPACK = False)
return conversion.ri2ro(res)
[docs] def dot(self, m):
""" Matrix multiplication """
res = self._dot(self, m)
return conversion.ri2ro(res)
[docs] def eigen(self):
""" Eigen values """
res = self._eigen(self)
return conversion.ri2ro(res)
[docs]class DataFrame(ListVector):
""" R 'data.frame'.
"""
_dataframe_name = rinterface.StrSexpVector(('data.frame',))
_read_csv = utils_ri['read.csv']
_write_table = utils_ri['write.table']
_cbind = rinterface.baseenv['cbind.data.frame']
_rbind = rinterface.baseenv['rbind.data.frame']
_is_list = rinterface.baseenv['is.list']
_html_template = jinja2.Template(
"""
<span>R/rpy2 DataFrame ({{ nrows }} x {{ ncolumns }})</span>
<table>
<thead>
<tr>
{% for name in column_names %}
<th>{{ name }}</th>
{% endfor %}
</tr>
</thead>
<tbody>
{% for row_i in rows %}
<tr>
{% for col_i in columns %}
<td>
{{ elements[col_i][row_i] }}
</td>
{% endfor %}
</tr>
{% endfor %}
</tbody>
</table>
""")
def __init__(self, obj, stringsasfactor=False):
""" Create a new data frame.
:param obj: object inheriting from rpy2.rinterface.SexpVector,
or inheriting from TaggedList
or a mapping name -> value
:param stringsasfactors: Boolean indicating whether vectors
of strings should be turned to vectors. Note
that factors will not be turned to string vectors.
"""
if isinstance(obj, rinterface.SexpVector):
if obj.typeof != rinterface.VECSXP:
raise ValueError("obj should of typeof VECSXP"+\
" (and we get %s)" % rinterface.str_typeint(obj.typeof))
if self._is_list(obj)[0] or \
globalenv_ri.get('inherits')(obj, self._dataframe_name)[0]:
#FIXME: is it really a good idea to pass R lists
# to the constructor ?
super(DataFrame, self).__init__(obj)
return
else:
raise ValueError(
"When passing R objects to build a DataFrame," +\
" the R object must be a list or inherit from" +\
" the R class 'data.frame'")
elif isinstance(obj, rlc.TaggedList):
kv = [(k, conversion.py2ri(v)) for k,v in obj.items()]
else:
try:
kv = [(str(k), conversion.py2ri(obj[k])) for k in obj]
except TypeError:
raise ValueError("obj can be either "+
"an instance of an iter-able class" +
"(such a Python dict, rpy2.rlike.container OrdDict" +
" or an instance of rpy2.rinterface.SexpVector" +
" of type VECSXP")
# Check if there is a conflicting column name
if 'stringsAsFactors' in (k for k,v in kv):
warnings.warn('The column name "stringsAsFactors" is '
'conflicting with named parameter '
'in underlying R function "data.frame()".')
else:
kv.append(('stringsAsFactors', stringsasfactor))
# Call R's data frame constructor
kv = tuple(kv)
df = baseenv_ri.get("data.frame").rcall(kv, globalenv_ri)
super(DataFrame, self).__init__(df)
def _repr_html_(self, max_items=7):
names = list()
if len(self) <= max_items:
names.extend(self.names)
else:
half_items = max_items // 2
for i in range(0, half_items):
try:
name = self.names[i]
except TypeError:
name = '[no name]'
names.append(name)
names.append('...')
for i in range(-half_items, 0):
try:
name = self.names[i]
except TypeError:
name = '[no name]'
names.append(name)
elements = list()
for e in self._iter_repr(max_items=max_items):
if hasattr(e, '_repr_html_'):
elements.append(tuple(e._iter_formatted()))
else:
elements.append(['...',] * len(elements[-1]))
d = {'column_names': names,
'rows': tuple(range(len(elements))),
'columns': tuple(range(len(names))),
'nrows': self.nrow,
'ncolumns': self.ncol,
'elements': elements}
html = self._html_template.render(d)
return html
def _get_nrow(self):
""" Number of rows.
:rtype: integer """
return baseenv_ri["nrow"](self)[0]
nrow = property(_get_nrow, None, None)
def _get_ncol(self):
""" Number of columns.
:rtype: integer """
return baseenv_ri["ncol"](self)[0]
ncol = property(_get_ncol, None, None)
def _get_rownames(self):
res = baseenv_ri["rownames"](self)
return conversion.ri2ro(res)
def _set_rownames(self, rownames):
res = baseenv_ri["rownames<-"](self, conversion.py2ri(rownames))
self.__sexp__ = res.__sexp__
rownames = property(_get_rownames, _set_rownames, None,
"Row names")
def _get_colnames(self):
res = baseenv_ri["colnames"](self)
return conversion.ri2ro(res)
def _set_colnames(self, colnames):
res = baseenv_ri["colnames<-"](self, conversion.py2ri(colnames))
self.__sexp__ = res.__sexp__
colnames = property(_get_colnames, _set_colnames, None)
def __getitem__(self, i):
# Make sure this is not a List returned
# 3rd-party conversions could return objects
# that no longer inherit from rpy2's R objects.
# We need to use the low-level __getitem__
# to bypass the conversion mechanism.
# R's data.frames have no representation at the C-API level
# (they are lists)
tmp = rinterface.ListSexpVector.__getitem__(self, i)
if tmp.typeof == rinterface.VECSXP:
return DataFrame(tmp)
else:
return conversion.ri2ro(tmp)
[docs] def cbind(self, *args, **kwargs):
""" bind objects as supplementary columns """
new_args = [self, ] + [conversion.ri2ro(x) for x in args]
new_kwargs = dict([(k, conversion.ri2ro(v)) for k,v in kwargs.items()])
res = self._cbind(*new_args, **new_kwargs)
return conversion.ri2ro(res)
[docs] def rbind(self, *args, **kwargs):
""" bind objects as supplementary rows """
new_args = [conversion.ri2ro(x) for x in args]
new_kwargs = dict([(k, conversion.ri2ro(v)) for k,v in kwargs.items()])
res = self._rbind(self, *new_args, **new_kwargs)
return conversion.ri2ro(res)
[docs] def head(self, *args, **kwargs):
""" Call the R generic 'head()'. """
res = utils_ri['head'](self, *args, **kwargs)
return conversion.ri2ro(res)
[docs] @staticmethod
def from_csvfile(path, header = True, sep = ",",
quote = "\"", dec = ".",
row_names = rinterface.MissingArg,
col_names = rinterface.MissingArg,
fill = True, comment_char = "",
na_strings = [],
as_is = False):
""" Create an instance from data in a .csv file.
path : string with a path
header : boolean (heading line with column names or not)
sep : separator character
quote : quote character
row_names : column name, or column index for column names (warning: indexing starts at one in R)
fill : boolean (fill the lines when less entries than columns)
comment_char : comment character
na_strings : a list of strings which are interpreted to be NA values
as_is : boolean (keep the columns of strings as such, or turn them into factors)
"""
path = conversion.py2ro(path)
header = conversion.py2ro(header)
sep = conversion.py2ro(sep)
quote = conversion.py2ro(quote)
dec = conversion.py2ro(dec)
if row_names is not rinterface.MissingArg:
row_names = conversion.py2ro(row_names)
if col_names is not rinterface.MissingArg:
col_names = conversion.py2ro(col_names)
fill = conversion.py2ro(fill)
comment_char = conversion.py2ro(comment_char)
as_is = conversion.py2ro(as_is)
na_strings = conversion.py2ro(na_strings)
res = DataFrame._read_csv(path,
**{'header': header, 'sep': sep,
'quote': quote, 'dec': dec,
'row.names': row_names,
'col.names': col_names,
'fill': fill,
'comment.char': comment_char,
'na.strings': na_strings,
'as.is': as_is})
res = conversion.ri2ro(res)
return res
[docs] def to_csvfile(self, path, quote = True, sep = ",", eol = os.linesep, na = "NA", dec = ".",
row_names = True, col_names = True, qmethod = "escape", append = False):
""" Save the data into a .csv file.
path : string with a path
quote : quote character
sep : separator character
eol : end-of-line character(s)
na : string for missing values
dec : string for decimal separator
row_names : boolean (save row names, or not)
col_names : boolean (save column names, or not)
comment_char : method to 'escape' special characters
append : boolean (append if the file in the path is already existing, or not)
"""
path = conversion.py2ro(path)
append = conversion.py2ro(append)
sep = conversion.py2ro(sep)
eol = conversion.py2ro(eol)
na = conversion.py2ro(na)
dec = conversion.py2ro(dec)
row_names = conversion.py2ro(row_names)
col_names = conversion.py2ro(col_names)
qmethod = conversion.py2ro(qmethod)
res = self._write_table(self, **{'file': path, 'quote': quote, 'sep': sep,
'eol': eol, 'na': na, 'dec': dec,
'row.names': row_names,
'col.names': col_names, 'qmethod': qmethod, 'append': append})
return res
[docs] def iter_row(self):
""" iterator across rows """
for i in range(self.nrow):
yield self.rx(i+1, rinterface.MissingArg)
[docs] def iter_column(self):
""" iterator across columns """
for i in range(self.ncol):
yield self.rx(rinterface.MissingArg, i+1)
# end of definition for DataFrame
rtypeof2rotype = {
rinterface.INTSXP: IntVector,
rinterface.REALSXP: FloatVector,
rinterface.STRSXP: StrVector,
rinterface.CPLXSXP: ComplexVector,
rinterface.LGLSXP: BoolVector
}
__all__ = ['Vector', 'StrVector', 'IntVector', 'BoolVector', 'ComplexVector',
'FloatVector', 'FactorVector', 'Vector',
'ListVector', 'POSIXlt', 'POSIXct',
'Array', 'Matrix', 'DataFrame']
