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# SPDX-FileCopyrightText: All Contributors to the PyTango project
# SPDX-License-Identifier: LGPL-3.0-or-later
"""
This is an internal PyTango module.
"""
__all__ = ("NumpyType", "numpy_type", "numpy_spectrum", "numpy_image")
__docformat__ = "restructuredtext"
import collections.abc
import numpy
from tango._tango import Except, Attribute, AttributeInfo
from tango._tango import CmdArgType as ArgType
from tango.attribute_proxy import AttributeProxy
def _numpy_invalid(*args, **kwds):
Except.throw_exception(
"PyTango_InvalidConversion",
"There's no registered conversor to numpy.",
"NumpyType.tango_to_numpy",
)
class NumpyType:
DevShort = numpy.int16
DevLong = numpy.int32
DevDouble = numpy.float64
DevFloat = numpy.float32
DevBoolean = numpy.bool_
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 tango.
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.abc.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 tango.
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,
)
numpy_spectrum = NumpyType.spectrum
numpy_image = NumpyType.image
numpy_type = NumpyType.tango_to_numpy
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