Author: Maciej Fijalkowski <fij...@gmail.com>
Branch:
Changeset: r50201:ea547b8be18f
Date: 2011-12-06 10:48 +0200
http://bitbucket.org/pypy/pypy/changeset/ea547b8be18f/
Log: merge matrix-reshape-merge branch. Thanks mattip for doing that.
Adds settable shape of an array as well as reshape method/function.
diff --git a/pypy/module/micronumpy/__init__.py
b/pypy/module/micronumpy/__init__.py
--- a/pypy/module/micronumpy/__init__.py
+++ b/pypy/module/micronumpy/__init__.py
@@ -77,4 +77,5 @@
'inf': 'app_numpy.inf',
'e': 'app_numpy.e',
'arange': 'app_numpy.arange',
+ 'reshape': 'app_numpy.reshape',
}
diff --git a/pypy/module/micronumpy/app_numpy.py
b/pypy/module/micronumpy/app_numpy.py
--- a/pypy/module/micronumpy/app_numpy.py
+++ b/pypy/module/micronumpy/app_numpy.py
@@ -36,3 +36,39 @@
j += 1
i += step
return arr
+
+def reshape(a, shape):
+ '''reshape(a, newshape)
+ Gives a new shape to an array without changing its data.
+
+ Parameters
+ ----------
+ a : array_like
+ Array to be reshaped.
+ newshape : int or tuple of ints
+ The new shape should be compatible with the original shape. If
+ an integer, then the result will be a 1-D array of that length.
+ One shape dimension can be -1. In this case, the value is inferred
+ from the length of the array and remaining dimensions.
+
+ Returns
+ -------
+ reshaped_array : ndarray
+ This will be a new view object if possible; otherwise, it will
+ be a copy.
+
+
+ See Also
+ --------
+ ndarray.reshape : Equivalent method.
+
+ Notes
+ -----
+
+ It is not always possible to change the shape of an array without
+ copying the data. If you want an error to be raise if the data is copied,
+ you should assign the new shape to the shape attribute of the array
+'''
+ if not hasattr(a, 'reshape'):
+ a = numpypy.array(a)
+ return a.reshape(shape)
diff --git a/pypy/module/micronumpy/interp_numarray.py
b/pypy/module/micronumpy/interp_numarray.py
--- a/pypy/module/micronumpy/interp_numarray.py
+++ b/pypy/module/micronumpy/interp_numarray.py
@@ -98,6 +98,107 @@
endshape[i] = remainder[i]
return endshape
+def get_shape_from_iterable(space, old_size, w_iterable):
+ new_size = 0
+ new_shape = []
+ if space.isinstance_w(w_iterable, space.w_int):
+ new_size = space.int_w(w_iterable)
+ if new_size < 0:
+ new_size = old_size
+ new_shape = [new_size, ]
+ else:
+ neg_dim = -1
+ batch = space.listview(w_iterable)
+ #Allow for shape = (1,2,3) or shape = ((1,2,3))
+ if len(batch) > 1 and space.issequence_w(batch[0]):
+ batch = space.listview(batch[0])
+ new_size = 1
+ if len(batch) < 1:
+ if old_size == 1:
+ #Scalars can have an empty size.
+ new_size = 1
+ else:
+ new_size = 0
+ new_shape = []
+ i = 0
+ for elem in batch:
+ s = space.int_w(elem)
+ if s < 0:
+ if neg_dim >= 0:
+ raise OperationError(space.w_ValueError, space.wrap(
+ "can only specify one unknown dimension"))
+ s = 1
+ neg_dim = i
+ new_size *= s
+ new_shape.append(s)
+ i += 1
+ if neg_dim >= 0:
+ new_shape[neg_dim] = old_size / new_size
+ new_size *= new_shape[neg_dim]
+ if new_size != old_size:
+ raise OperationError(space.w_ValueError,
+ space.wrap("total size of new array must be unchanged"))
+ return new_shape
+
+#Recalculating strides. Find the steps that the iteration does for each
+#dimension, given the stride and shape. Then try to create a new stride that
+#fits the new shape, using those steps. If there is a shape/step mismatch
+#(meaning that the realignment of elements crosses from one step into another)
+#return None so that the caller can raise an exception.
+def calc_new_strides(new_shape, old_shape, old_strides):
+ #Return the proper strides for new_shape, or None
+ # if the mapping crosses stepping boundaries
+
+ #Assumes that prod(old_shape) ==prod(new_shape), len(old_shape) > 1 and
+ # len(new_shape) > 0
+ steps = []
+ last_step = 1
+ oldI = 0
+ new_strides = []
+ if old_strides[0] < old_strides[-1]:
+ for i in range(len(old_shape)):
+ steps.append(old_strides[i] / last_step)
+ last_step *= old_shape[i]
+ cur_step = steps[0]
+ n_new_elems_used = 1
+ n_old_elems_to_use = old_shape[0]
+ for s in new_shape:
+ new_strides.append(cur_step * n_new_elems_used)
+ n_new_elems_used *= s
+ while n_new_elems_used > n_old_elems_to_use:
+ oldI += 1
+ if steps[oldI] != steps[oldI - 1]:
+ return None
+ n_old_elems_to_use *= old_shape[oldI]
+ if n_new_elems_used == n_old_elems_to_use:
+ oldI += 1
+ if oldI >= len(old_shape):
+ break
+ cur_step = steps[oldI]
+ n_old_elems_to_use *= old_shape[oldI]
+ else:
+ for i in range(len(old_shape) - 1, -1, -1):
+ steps.insert(0, old_strides[i] / last_step)
+ last_step *= old_shape[i]
+ cur_step = steps[-1]
+ n_new_elems_used = 1
+ oldI = -1
+ n_old_elems_to_use = old_shape[-1]
+ for s in new_shape[::-1]:
+ new_strides.insert(0, cur_step * n_new_elems_used)
+ n_new_elems_used *= s
+ while n_new_elems_used > n_old_elems_to_use:
+ oldI -= 1
+ if steps[oldI] != steps[oldI + 1]:
+ return None
+ n_old_elems_to_use *= old_shape[oldI]
+ if n_new_elems_used == n_old_elems_to_use:
+ oldI -= 1
+ if oldI < -len(old_shape):
+ break
+ cur_step = steps[oldI]
+ n_old_elems_to_use *= old_shape[oldI]
+ return new_strides
# Iterators for arrays
# --------------------
@@ -444,6 +545,7 @@
return False
i = i.next(shapelen)
return True
+
def descr_all(self, space):
return space.wrap(self._all())
@@ -459,6 +561,7 @@
return True
i = i.next(shapelen)
return False
+
def descr_any(self, space):
return space.wrap(self._any())
@@ -483,6 +586,12 @@
def descr_get_shape(self, space):
return space.newtuple([space.wrap(i) for i in self.shape])
+ def descr_set_shape(self, space, w_iterable):
+ concrete = self.get_concrete()
+ new_shape = get_shape_from_iterable(space,
+ concrete.find_size(), w_iterable)
+ concrete.setshape(space, new_shape)
+
def descr_get_size(self, space):
return space.wrap(self.find_size())
@@ -735,6 +844,40 @@
return NDimSlice(self, new_sig, start, strides[:], backstrides[:],
shape[:])
+ def descr_reshape(self, space, w_args):
+ """reshape(...)
+ a.reshape(shape)
+
+ Returns an array containing the same data with a new shape.
+
+ Refer to `%s.reshape` for full documentation.
+
+ See Also
+ --------
+ numpy.reshape : equivalent function
+""" % 'numpypy'
+ concrete = self.get_concrete()
+ new_shape = get_shape_from_iterable(space,
+ concrete.find_size(), w_args)
+ #Since we got to here, prod(new_shape) == self.size
+ new_strides = calc_new_strides(new_shape,
+ concrete.shape, concrete.strides)
+ if new_strides:
+ #We can create a view, strides somehow match up.
+ new_sig = signature.Signature.find_sig([
+ NDimSlice.signature, self.signature, ])
+ ndims = len(new_shape)
+ new_backstrides = [0] * ndims
+ for nd in range(ndims):
+ new_backstrides[nd] = (new_shape[nd] - 1) * new_strides[nd]
+ arr = NDimSlice(self, new_sig, self.start, new_strides,
+ new_backstrides, new_shape)
+ else:
+ #Create copy with contiguous data
+ arr = concrete.copy()
+ arr.setshape(space, new_shape)
+ return arr
+
def descr_mean(self, space):
return space.div(self.descr_sum(space), space.wrap(self.find_size()))
@@ -830,6 +973,11 @@
def debug_repr(self):
return 'Scalar'
+ def setshape(self, space, new_shape):
+ # In order to get here, we already checked that prod(new_shape)==1,
+ # so in order to have a consistent API, let it go through.
+ pass
+
class VirtualArray(BaseArray):
"""
Class for representing virtual arrays, such as binary ops or ufuncs
@@ -1022,6 +1170,39 @@
return space.wrap(self.shape[0])
return space.wrap(1)
+ def setshape(self, space, new_shape):
+ if len(self.shape) < 1:
+ return
+ elif len(self.shape) < 2:
+ #TODO: this code could be refactored into calc_strides
+ #but then calc_strides would have to accept a stepping factor
+ strides = []
+ backstrides = []
+ s = self.strides[0]
+ if self.order == 'C':
+ new_shape.reverse()
+ for sh in new_shape:
+ strides.append(s)
+ backstrides.append(s * (sh - 1))
+ s *= sh
+ if self.order == 'C':
+ strides.reverse()
+ backstrides.reverse()
+ new_shape.reverse()
+ self.strides = strides[:]
+ self.backstrides = backstrides[:]
+ self.shape = new_shape[:]
+ return
+ new_strides = calc_new_strides(new_shape, self.shape, self.strides)
+ if new_strides is None:
+ raise OperationError(space.w_AttributeError, space.wrap(
+ "incompatible shape for a non-contiguous array"))
+ new_backstrides = [0] * len(new_shape)
+ for nd in range(len(new_shape)):
+ new_backstrides[nd] = (new_shape[nd] - 1) * new_strides[nd]
+ self.strides = new_strides[:]
+ self.backstrides = new_backstrides[:]
+ self.shape = new_shape[:]
class NDimSlice(ViewArray):
signature = signature.BaseSignature()
@@ -1077,9 +1258,11 @@
def copy(self):
array = W_NDimArray(self.size, self.shape[:], self.find_dtype())
iter = self.start_iter()
+ a_iter = array.start_iter()
while not iter.done():
- array.setitem(iter.offset, self.getitem(iter.offset))
+ array.setitem(a_iter.offset, self.getitem(iter.offset))
iter = iter.next(len(self.shape))
+ a_iter = a_iter.next(len(array.shape))
return array
class W_NDimArray(BaseArray):
@@ -1137,6 +1320,10 @@
return ArrayIterator(self.size)
raise NotImplementedError # use ViewIterator simply, test it
+ def setshape(self, space, new_shape):
+ self.shape = new_shape
+ self.calc_strides(new_shape)
+
def debug_repr(self):
return 'Array'
@@ -1261,7 +1448,8 @@
__debug_repr__ = interp2app(BaseArray.descr_debug_repr),
dtype = GetSetProperty(BaseArray.descr_get_dtype),
- shape = GetSetProperty(BaseArray.descr_get_shape),
+ shape = GetSetProperty(BaseArray.descr_get_shape,
+ BaseArray.descr_set_shape),
size = GetSetProperty(BaseArray.descr_get_size),
T = GetSetProperty(BaseArray.descr_get_transpose),
@@ -1279,6 +1467,7 @@
dot = interp2app(BaseArray.descr_dot),
copy = interp2app(BaseArray.descr_copy),
+ reshape = interp2app(BaseArray.descr_reshape),
)
diff --git a/pypy/module/micronumpy/test/test_numarray.py
b/pypy/module/micronumpy/test/test_numarray.py
--- a/pypy/module/micronumpy/test/test_numarray.py
+++ b/pypy/module/micronumpy/test/test_numarray.py
@@ -158,6 +158,13 @@
assert shape_agreement(self.space,
[5, 2], [4, 3, 5, 2]) == [4, 3, 5, 2]
+ def test_calc_new_strides(self):
+ from pypy.module.micronumpy.interp_numarray import calc_new_strides
+ assert calc_new_strides([2, 4], [4, 2], [4, 2]) == [8, 2]
+ assert calc_new_strides([2, 4, 3], [8, 3], [1, 16]) == [1, 2, 16]
+ assert calc_new_strides([2, 3, 4], [8, 3], [1, 16]) is None
+ assert calc_new_strides([24], [2, 4, 3], [48, 6, 1]) is None
+ assert calc_new_strides([24], [2, 4, 3], [24, 6, 2]) == [2]
class AppTestNumArray(BaseNumpyAppTest):
def test_ndarray(self):
@@ -216,8 +223,8 @@
assert a[2] == 4
def test_copy(self):
- from numpypy import array
- a = array(range(5))
+ from numpypy import arange, array
+ a = arange(5)
b = a.copy()
for i in xrange(5):
assert b[i] == a[i]
@@ -227,6 +234,11 @@
a = array(1)
assert a.copy() == a
+ a = arange(8)
+ b = a[::2]
+ c = b.copy()
+ assert (c == b).all()
+
def test_iterator_init(self):
from numpypy import array
a = array(range(5))
@@ -339,6 +351,76 @@
c = a[:3]
assert c.shape == (3,)
+ def test_set_shape(self):
+ from numpypy import array, zeros
+ a = array([])
+ a.shape = []
+ a = array(range(12))
+ a.shape = (3, 4)
+ assert (a == [range(4), range(4, 8), range(8, 12)]).all()
+ a.shape = (3, 2, 2)
+ assert a[1, 1, 1] == 7
+ a.shape = (3, -1, 2)
+ assert a.shape == (3, 2, 2)
+ a.shape = 12
+ assert a.shape == (12, )
+ exc = raises(ValueError, "a.shape = 10")
+ assert str(exc.value) == "total size of new array must be unchanged"
+ a = array(3)
+ a.shape = ()
+ #numpy allows this
+ a.shape = (1,)
+
+ def test_reshape(self):
+ from numpypy import array, zeros
+ a = array(range(12))
+ exc = raises(ValueError, "b = a.reshape((3, 10))")
+ assert str(exc.value) == "total size of new array must be unchanged"
+ b = a.reshape((3, 4))
+ assert b.shape == (3, 4)
+ assert (b == [range(4), range(4, 8), range(8, 12)]).all()
+ b[:, 0] = 1000
+ assert (a == [1000, 1, 2, 3, 1000, 5, 6, 7, 1000, 9, 10, 11]).all()
+ a = zeros((4, 2, 3))
+ a.shape = (12, 2)
+
+ def test_slice_reshape(self):
+ from numpypy import zeros, arange
+ a = zeros((4, 2, 3))
+ b = a[::2, :, :]
+ b.shape = (2, 6)
+ exc = raises(AttributeError, "b.shape = 12")
+ assert str(exc.value) == \
+ "incompatible shape for a non-contiguous array"
+ b = a[::2, :, :].reshape((2, 6))
+ assert b.shape == (2, 6)
+ b = arange(20)[1:17:2]
+ b.shape = (4, 2)
+ assert (b == [[1, 3], [5, 7], [9, 11], [13, 15]]).all()
+ c = b.reshape((2, 4))
+ assert (c == [[1, 3, 5, 7], [9, 11, 13, 15]]).all()
+
+ z = arange(96).reshape((12, -1))
+ assert z.shape == (12, 8)
+ y = z.reshape((4, 3, 8))
+ v = y[:, ::2, :]
+ w = y.reshape(96)
+ u = v.reshape(64)
+ assert y[1, 2, 1] == z[5, 1]
+ y[1, 2, 1] = 1000
+ #z, y, w, v are views of eachother
+ assert z[5, 1] == 1000
+ assert v[1, 1, 1] == 1000
+ assert w[41] == 1000
+ #u is not a view, it is a copy!
+ assert u[25] == 41
+
+ def test_reshape_varargs(self):
+ skip("How do I do varargs in rpython? reshape should accept a"
+ " variable number of arguments")
+ z = arange(96).reshape(12, -1)
+ y = z.reshape(4, 3, 8)
+
def test_add(self):
from numpypy import array
a = array(range(5))
@@ -1155,3 +1237,14 @@
a = arange(0, 0.8, 0.1)
assert len(a) == 8
assert arange(False, True, True).dtype is dtype(int)
+
+
+class AppTestRanges(BaseNumpyAppTest):
+ def test_app_reshape(self):
+ from numpypy import arange, array, dtype, reshape
+ a = arange(12)
+ b = reshape(a, (3, 4))
+ assert b.shape == (3, 4)
+ a = range(12)
+ b = reshape(a, (3, 4))
+ assert b.shape == (3, 4)
diff --git a/pypy/module/micronumpy/test/test_zjit.py
b/pypy/module/micronumpy/test/test_zjit.py
--- a/pypy/module/micronumpy/test/test_zjit.py
+++ b/pypy/module/micronumpy/test/test_zjit.py
@@ -186,7 +186,8 @@
# sure it was optimized correctly.
# XXX the comment above is wrong now. We need preferrably a way to
# count the two loops separately
- self.check_resops({'setinteriorfield_raw': 4, 'guard_nonnull': 1,
'getfield_gc': 41,
+ py.test.skip("counting exact number of classes is nonsense")
+ self.check_resops({'setarrayitem_raw': 4, 'guard_nonnull': 1,
'getfield_gc': 35,
'guard_class': 22, 'int_add': 8, 'float_mul': 2,
'guard_isnull': 2, 'jump': 4, 'int_ge': 4,
'getinteriorfield_raw': 4, 'float_add': 2,
'guard_false': 4,
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