/usr/lib/python2.7/dist-packages/PyTango/tango_numpy.py is in python-pytango 8.1.8-1.
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# This file is part of PyTango (http://www.tinyurl.com/PyTango)
#
# Copyright 2006-2012 CELLS / ALBA Synchrotron, Bellaterra, Spain
# Copyright 2013-2014 European Synchrotron Radiation Facility, Grenoble, France
#
# Distributed under the terms of the GNU Lesser General Public License,
# either version 3 of the License, or (at your option) any later version.
# See LICENSE.txt for more info.
# ------------------------------------------------------------------------------
"""
This is an internal PyTango module.
"""
__all__ = [ "NumpyType", "numpy_type", "numpy_spectrum", "numpy_image" ]
__docformat__ = "restructuredtext"
from ._PyTango import Except
from ._PyTango import constants
from .attribute_proxy import AttributeProxy
import collections
def _numpy_invalid(*args, **kwds):
Except.throw_exception(
"PyTango_InvalidConversion",
"There's no registered conversor to numpy.",
"NumpyType.tango_to_numpy"
)
def _define_numpy():
if not constants.NUMPY_SUPPORT:
return None, _numpy_invalid, _numpy_invalid, _numpy_invalid
try:
import numpy
import operator
ArgType = _PyTango.CmdArgType
AttributeInfo = _PyTango.AttributeInfo
Attribute = _PyTango.Attribute
class NumpyType(object):
DevShort = numpy.int16
DevLong = numpy.int32
DevDouble = numpy.float64
DevFloat = numpy.float32
DevBoolean = numpy.bool8
DevUShort = numpy.uint16
DevULong = numpy.uint32
DevUChar = numpy.ubyte
DevLong64 = numpy.int64
DevULong64 = numpy.uint64
mapping = {
ArgType.DevShort: DevShort,
ArgType.DevLong: DevLong,
ArgType.DevDouble: DevDouble,
ArgType.DevFloat: DevFloat,
ArgType.DevBoolean: DevBoolean,
ArgType.DevUShort: DevUShort,
ArgType.DevULong: DevULong,
ArgType.DevUChar: DevUChar,
ArgType.DevLong64: DevLong64,
ArgType.DevULong: DevULong64,
}
@staticmethod
def tango_to_numpy(param):
if isinstance(param, ArgType):
tg_type = param
if isinstance(param, AttributeInfo): # or AttributeInfoEx
tg_type = param.data_type
elif isinstance(param, Attribute):
tg_type = param.get_data_type()
elif isinstance(param, AttributeProxy):
tg_type = param.get_config().data_type
else:
tg_type = param
try:
return NumpyType.mapping[tg_type]
except Exception:
_numpy_invalid()
@staticmethod
def spectrum(tg_type, dim_x):
"""
numpy_spectrum(self, tg_type, dim_x, dim_y) -> numpy.array
numpy_spectrum(self, tg_type, sequence) -> numpy.array
Get a square numpy array to be used with PyTango.
One version gets dim_x and creates an object with
this size. The other version expects any sequence to
convert.
Parameters:
- tg_type : (ArgType): The tango type. For convenience, it
can also extract this information from an
Attribute, AttributeInfo or AttributeProxy
object.
- dim_x : (int)
- sequence:
"""
np_type = NumpyType.tango_to_numpy(tg_type)
if isinstance(dim_x, collections.Sequence):
return numpy.array(dim_x, dtype=np_type)
else:
return numpy.ndarray(shape=(dim_x,), dtype=np_type)
@staticmethod
def image(tg_type, dim_x, dim_y=None):
"""
numpy_image(self, tg_type, dim_x, dim_y) -> numpy.array
numpy_image(self, tg_type, sequence) -> numpy.array
Get a square numpy array to be used with PyTango.
One version gets dim_x and dim_y and creates an object with
this size. The other version expects a square sequence of
sequences to convert.
Parameters:
- tg_type : (ArgType): The tango type. For convenience, it
can also extract this information from an
Attribute, AttributeInfo or AttributeProxy
object.
- dim_x : (int)
- dim_y : (int)
- sequence:
"""
np_type = NumpyType.tango_to_numpy(tg_type)
if dim_y is None:
return numpy.array(dim_x, dtype=np_type)
else:
return numpy.ndarray(shape=(dim_y,dim_x,), dtype=np_type)
return NumpyType, NumpyType.spectrum, \
NumpyType.image, NumpyType.tango_to_numpy
except Exception:
return None, _numpy_invalid, _numpy_invalid, _numpy_invalid
NumpyType, numpy_spectrum, numpy_image, numpy_type = _define_numpy()
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