def arange(context, queue, *args, **kwargs):
"""Create an array filled with numbers spaced `step` apart,
starting from `start` and ending at `stop`.
For floating point arguments, the length of the result is
`ceil((stop - start)/step)`. This rule may result in the last
element of the result being greater than stop.
"""
# argument processing -----------------------------------------------------
# Yuck. Thanks, numpy developers. ;)
from pytools import Record
class Info(Record):
pass
explicit_dtype = False
inf = Info()
inf.start = None
inf.stop = None
inf.step = None
inf.dtype = None
if isinstance(args[-1], numpy.dtype):
dtype = args[-1]
args = args[:-1]
explicit_dtype = True
argc = len(args)
if argc == 0:
raise ValueError, "stop argument required"
elif argc == 1:
inf.stop = args[0]
elif argc == 2:
inf.start = args[0]
inf.stop = args[1]
elif argc == 3:
inf.start = args[0]
inf.stop = args[1]
inf.step = args[2]
else:
raise ValueError, "too many arguments"
admissible_names = ["start", "stop", "step", "dtype"]
for k, v in kwargs.iteritems():
if k in admissible_names:
if getattr(inf, k) is None:
setattr(inf, k, v)
if k == "dtype":
explicit_dtype = True
else:
raise ValueError, "may not specify '%s' by position and keyword" % k
else:
raise ValueError, "unexpected keyword argument '%s'" % k
if inf.start is None:
inf.start = 0
if inf.step is None:
inf.step = 1
if inf.dtype is None:
inf.dtype = numpy.array([inf.start, inf.stop, inf.step]).dtype
# actual functionality ----------------------------------------------------
dtype = numpy.dtype(inf.dtype)
start = dtype.type(inf.start)
step = dtype.type(inf.step)
stop = dtype.type(inf.stop)
if not explicit_dtype:
raise TypeError("arange requires dtype argument")
from math import ceil
size = int(ceil((stop-start)/step))
result = Array(context, (size,), dtype, queue=queue)
knl = elementwise.get_arange_kernel(context, dtype)
knl(queue, result._global_size, result._local_size,
result.data, start, step, size)
return result
def _arange_knl(result, start, step):
return elementwise.get_arange_kernel(result.context, result.dtype)
def _arange(result, start, step):
return elementwise.get_arange_kernel(
result.context, result.dtype)
def arange(context, queue, *args, **kwargs):
"""Create an array filled with numbers spaced `step` apart,
starting from `start` and ending at `stop`.
For floating point arguments, the length of the result is
`ceil((stop - start)/step)`. This rule may result in the last
element of the result being greater than stop.
"""
# argument processing -----------------------------------------------------
# Yuck. Thanks, numpy developers. ;)
from pytools import Record
class Info(Record):
pass
explicit_dtype = False
inf = Info()
inf.start = None
inf.stop = None
inf.step = None
inf.dtype = None
if isinstance(args[-1], numpy.dtype):
dtype = args[-1]
args = args[:-1]
explicit_dtype = True
argc = len(args)
if argc == 0:
raise ValueError, "stop argument required"
elif argc == 1:
inf.stop = args[0]
elif argc == 2:
inf.start = args[0]
inf.stop = args[1]
elif argc == 3:
inf.start = args[0]
inf.stop = args[1]
inf.step = args[2]
else:
raise ValueError, "too many arguments"
admissible_names = ["start", "stop", "step", "dtype"]
for k, v in kwargs.iteritems():
if k in admissible_names:
if getattr(inf, k) is None:
setattr(inf, k, v)
if k == "dtype":
explicit_dtype = True
else:
raise ValueError, "may not specify '%s' by position and keyword" % k
else:
raise ValueError, "unexpected keyword argument '%s'" % k
if inf.start is None:
inf.start = 0
if inf.step is None:
inf.step = 1
if inf.dtype is None:
inf.dtype = numpy.array([inf.start, inf.stop, inf.step]).dtype
# actual functionality ----------------------------------------------------
dtype = numpy.dtype(inf.dtype)
start = dtype.type(inf.start)
step = dtype.type(inf.step)
stop = dtype.type(inf.stop)
if not explicit_dtype:
raise TypeError("arange requires dtype argument")
from math import ceil
size = int(ceil((stop - start) / step))
result = Array(context, (size, ), dtype, queue=queue)
knl = elementwise.get_arange_kernel(context, dtype)
knl(queue, result._global_size, result._local_size, result.data, start,
step, size)
return result
