Data types¶

This chapter describes the mapping of data types between Python and Tango.

Tango has more data types than Python which is more dynamic. The input and output values of the commands are translated according to the array below. Note that if PyTango is compiled with numpy support the numpy type will be the used for the input arguments. Also, it is recomended to use numpy arrays of the appropiate type for output arguments as well, as they tend to be much more efficient.

For scalar types (SCALAR)

Tango data type	Python 2.x type	Python 3.x type (New in PyTango 8.0)
DEV_VOID	No data	No data
DEV_BOOLEAN	`bool`	`bool`
DEV_SHORT	`int`	`int`
DEV_LONG	`int`	`int`
DEV_LONG64	`long` (on a 32 bits computer) `int` (on a 64 bits computer)	`int`
DEV_FLOAT	`float`	`float`
DEV_DOUBLE	`float`	`float`
DEV_USHORT	`int`	`int`
DEV_ULONG	`int`	`int`
DEV_ULONG64	`long` (on a 32 bits computer) `int` (on a 64 bits computer)	`int`
DEV_STRING	`str`	`str` (decoded with latin-1, aka ISO-8859-1)
DEV_ENCODED (New in PyTango 8.0)	sequence of two elements: `str` `bytes` (for any value of extract_as)	sequence of two elements: `str` (decoded with latin-1, aka ISO-8859-1) `bytes` (for any value of extract_as, except String. In this case it is `str` (decoded with default python encoding utf-8))

For array types (SPECTRUM/IMAGE)

Tango data type	ExtractAs	Data type (Python 2.x)	Data type (Python 3.x) (New in PyTango 8.0)
DEVVAR_CHARARRAY	Numpy	`numpy.ndarray` (dtype= `numpy.uint8`)	`numpy.ndarray` (dtype= `numpy.uint8`)
	Bytes	`bytes` (which is in fact equal to `str`)	`bytes`
	ByteArray	`bytearray`	`bytearray`
	String	`str`	String `str` (decoded with default python encoding utf-8!!!)
	List	`list` <`int`>	`list` <`int`>
	Tuple	`tuple` <`int`>	`tuple` <`int`>
DEVVAR_SHORTARRAY or (DEV_SHORT + SPECTRUM) or (DEV_SHORT + IMAGE)	Numpy	`numpy.ndarray` (dtype= `numpy.uint16`)	`numpy.ndarray` (dtype= `numpy.uint16`)
	Bytes	`bytes` (which is in fact equal to `str`)	`bytes`
	ByteArray	`bytearray`	`bytearray`
	String	`str`	String `str` (decoded with default python encoding utf-8!!!)
	List	`list` <`int`>	`list` <`int`>
	Tuple	`tuple` <`int`>	`tuple` <`int`>
DEVVAR_LONGARRAY or (DEV_LONG + SPECTRUM) or (DEV_LONG + IMAGE)	Numpy	`numpy.ndarray` (dtype= `numpy.uint32`)	`numpy.ndarray` (dtype= `numpy.uint32`)
	Bytes	`bytes` (which is in fact equal to `str`)	`bytes`
	ByteArray	`bytearray`	`bytearray`
	String	`str`	String `str` (decoded with default python encoding utf-8!!!)
	List	`list` <`int`>	`list` <`int`>
	Tuple	`tuple` <`int`>	`tuple` <`int`>
DEVVAR_LONG64ARRAY or (DEV_LONG64 + SPECTRUM) or (DEV_LONG64 + IMAGE)	Numpy	`numpy.ndarray` (dtype= `numpy.uint64`)	`numpy.ndarray` (dtype= `numpy.uint64`)
	Bytes	`bytes` (which is in fact equal to `str`)	`bytes`
	ByteArray	`bytearray`	`bytearray`
	String	`str`	String `str` (decoded with default python encoding utf-8!!!)
	List	`list` <int (64 bits) / long (32 bits)>	`list` <`int`>
	Tuple	`tuple` <int (64 bits) / long (32 bits)>	`tuple` <`int`>
DEVVAR_FLOATARRAY or (DEV_FLOAT + SPECTRUM) or (DEV_FLOAT + IMAGE)	Numpy	`numpy.ndarray` (dtype= `numpy.float32`)	`numpy.ndarray` (dtype= `numpy.float32`)
	Bytes	`bytes` (which is in fact equal to `str`)	`bytes`
	ByteArray	`bytearray`	`bytearray`
	String	`str`	String `str` (decoded with default python encoding utf-8!!!)
	List	`list` <`float`>	`list` <`float`>
	Tuple	`tuple` <`float`>	`tuple` <`float`>
DEVVAR_DOUBLEARRAY or (DEV_DOUBLE + SPECTRUM) or (DEV_DOUBLE + IMAGE)	Numpy	`numpy.ndarray` (dtype= `numpy.float64`)	`numpy.ndarray` (dtype= `numpy.float64`)
	Bytes	`bytes` (which is in fact equal to `str`)	`bytes`
	ByteArray	`bytearray`	`bytearray`
	String	`str`	String `str` (decoded with default python encoding utf-8!!!)
	List	`list` <`float`>	`list` <`float`>
	Tuple	`tuple` <`float`>	`tuple` <`float`>
DEVVAR_USHORTARRAY or (DEV_USHORT + SPECTRUM) or (DEV_USHORT + IMAGE)	Numpy	`numpy.ndarray` (dtype= `numpy.uint16`)	`numpy.ndarray` (dtype= `numpy.uint16`)
	Bytes	`bytes` (which is in fact equal to `str`)	`bytes`
	ByteArray	`bytearray`	`bytearray`
	String	`str`	String `str` (decoded with default python encoding utf-8!!!)
	List	`list` <`int`>	`list` <`int`>
	Tuple	`tuple` <`int`>	`tuple` <`int`>
DEVVAR_ULONGARRAY or (DEV_ULONG + SPECTRUM) or (DEV_ULONG + IMAGE)	Numpy	`numpy.ndarray` (dtype= `numpy.uint32`)	`numpy.ndarray` (dtype= `numpy.uint32`)
	Bytes	`bytes` (which is in fact equal to `str`)	`bytes`
	ByteArray	`bytearray`	`bytearray`
	String	`str`	String `str` (decoded with default python encoding utf-8!!!)
	List	`list` <`int`>	`list` <`int`>
	Tuple	`tuple` <`int`>	`tuple` <`int`>
DEVVAR_ULONG64ARRAY or (DEV_ULONG64 + SPECTRUM) or (DEV_ULONG64 + IMAGE)	Numpy	`numpy.ndarray` (dtype= `numpy.uint64`)	`numpy.ndarray` (dtype= `numpy.uint64`)
	Bytes	`bytes` (which is in fact equal to `str`)	`bytes`
	ByteArray	`bytearray`	`bytearray`
	String	`str`	String `str` (decoded with default python encoding utf-8!!!)
	List	`list` <int (64 bits) / long (32 bits)>	`list` <`int`>
	Tuple	`tuple` <int (64 bits) / long (32 bits)>	`tuple` <`int`>
DEVVAR_STRINGARRAY or (DEV_STRING + SPECTRUM) or (DEV_STRING + IMAGE)		sequence<`str`>	sequence<`str`> (decoded with latin-1, aka ISO-8859-1)
DEV_LONGSTRINGARRAY		sequence of two elements: `numpy.ndarray` (dtype= `numpy.int32`) or sequence<`int`> sequence<`str`>	sequence of two elements: `numpy.ndarray` (dtype= `numpy.int32`) or sequence<`int`> sequence<`str`> (decoded with latin-1, aka ISO-8859-1)
DEV_DOUBLESTRINGARRAY		sequence of two elements: `numpy.ndarray` (dtype= `numpy.float64`) or sequence<`int`> sequence<`str`>	sequence of two elements: `numpy.ndarray` (dtype= `numpy.float64`) or sequence<`int`> sequence<`str`> (decoded with latin-1, aka ISO-8859-1)

For SPECTRUM and IMAGES the actual sequence object used depends on the context where the tango data is used, and the availability of numpy.

for properties the sequence is always a list. Example:

>>> import tango
>>> db = tango.Database()
>>> s = db.get_property(["TangoSynchrotrons"])
>>> print type(s)
<type 'list'>

for attribute/command values
- numpy.ndarray if PyTango was compiled with numpy support (default) and numpy is installed.
- list otherwise

Pipe data types¶

Pipes require different data types. You can think of them as a structured type.

A pipe transports data which is called a blob. A blob consists of name and a list of fields. Each field is called data element. Each data element consists of a name and a value. Data element names must be unique in the same blob.

The value can be of any of the SCALAR or SPECTRUM tango data types (except DevEnum).

Additionally, the value can be a blob itself.

In PyTango, a blob is represented by a sequence of two elements:

blob name (str)
data is either:
- sequence (list, tuple, or other) of data elements where each element is a dict with the following keys:
  - name (mandatory): (str) data element name
  - value (mandatory): data (compatible with any of the SCALAR or SPECTRUM data types except DevEnum). If value is to be a sub-blob then it should be sequence of [blob name, sequence of data elements] (see above)
  - dtype (optional, mandatory if a DevEncoded is required): see Data type equivalence. If dtype key is not given, PyTango will try to find the proper tango type by inspecting the value.
- a dict where key is the data element name and value is the data element value (compact version)

When using the compact dictionary version note that the order of the data elements is lost. If the order is important for you, consider using collections.OrderedDict instead (if you have python >=2.7. If not you can use ordereddict backport module available on pypi). Also, in compact mode it is not possible to enforce a specific type. As a consequence, DevEncoded is not supported in compact mode.

The description sounds more complicated that it actually is. Here are some practical examples of what you can return in a server as a read request from a pipe:

import numpy as np

# plain (one level) blob showing different tango data types
# (explicity and implicit):

PIPE0 = \
('BlobCase0',
 ({'name': 'DE1', 'value': 123,},                                # converts to DevLong64
  {'name': 'DE2', 'value': np.int32(456),},                      # converts to DevLong
  {'name': 'DE3', 'value': 789, 'dtype': 'int32'},               # converts to DevLong
  {'name': 'DE4', 'value': np.uint32(123)},                      # converts to DevULong
  {'name': 'DE5', 'value': range(5), 'dtype': ('uint16',)},      # converts to DevVarUShortArray
  {'name': 'DE6', 'value': [1.11, 2.22], 'dtype': ('float64',)}, # converts to DevVarDoubleArray
  {'name': 'DE7', 'value': numpy.zeros((100,))},                 # converts to DevVarDoubleArray
  {'name': 'DE8', 'value': True},                                # converts to DevBoolean
 )
)


# similar as above but in compact version (implicit data type conversion):

PIPE1 = \
('BlobCase1', dict(DE1=123, DE2=np.int32(456), DE3=np.int32(789),
                   DE4=np.uint32(123), DE5=np.arange(5, dtype='uint16'),
                   DE6=[1.11, 2.22], DE7=numpy.zeros((100,)),
                   DE8=True)
)

# similar as above but order matters so we use an ordered dict:

import collections

data = collections.OrderedDict()
data['DE1'] = 123
data['DE2'] = np.int32(456)
data['DE3'] = np.int32(789)
data['DE4'] = np.uint32(123)
data['DE5'] = np.arange(5, dtype='uint16')
data['DE6'] = [1.11, 2.22]
data['DE7'] = numpy.zeros((100,))
data['DE8'] = True

PIPE2 = 'BlobCase2', data

# another plain blob showing string, string array and encoded data types:

PIPE3 = \
('BlobCase3',
 ({'name': 'stringDE',  'value': 'Hello'},
  {'name': 'VectorStringDE', 'value': ('bonjour', 'le', 'monde')},
  {'name': 'DevEncodedDE', 'value': ('json', '"isn\'t it?"'), 'dtype': 'bytes'},
 )
)

# blob with sub-blob which in turn has a sub-blob

PIPE4 = \
('BlobCase4',
 ({'name': '1DE', 'value': ('Inner', ({'name': '1_1DE', 'value': 'Grenoble'},
                                      {'name': '1_2DE', 'value': ('InnerInner',
                                                                  ({'name': '1_1_1DE', 'value': np.int32(111)},
                                                                   {'name': '1_1_2DE', 'value': [3.33]}))
                                     })
  )},
  {'name': '2DE', 'value': (3,4,5,6), 'dtype': ('int32',) },
 )
)

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