# 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:

0. `str`
1. `bytes` (for any value of extract_as)

sequence of two elements:

0. `str` (decoded with latin-1, aka ISO-8859-1)
1. `bytes` (for any value of extract_as, except String. In this case it is `str` (decoded with default python encoding utf-8))

DEV_ENUM (New in PyTango 9.0)

- `int` (for value)
- `list` <`str`> (for enum_labels)

Note: direct attribute access via DeviceProxy will return enumerated
  type `enum.IntEnum`. This type uses the package enum34.

- `int` (for value)
- `list` <`str`> (for enum_labels)

Note: direct attribute access via DeviceProxy will return enumerated
  type `enum.IntEnum`. Python < 3.4, uses the package enum34. Python >= 3.4, uses standard package enum.

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` | `str` (decoded with latin-1, aka ISO-8859-1)
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` | `str` (decoded with latin-1, aka ISO-8859-1)
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` | `str` (decoded with latin-1, aka ISO-8859-1)
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` | `str` (decoded with latin-1, aka ISO-8859-1)
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` | `str` (decoded with latin-1, aka ISO-8859-1)
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` | `str` (decoded with latin-1, aka ISO-8859-1)
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` | `str` (decoded with latin-1, aka ISO-8859-1)
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` | `str` (decoded with latin-1, aka ISO-8859-1)
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` | `str` (decoded with latin-1, aka ISO-8859-1)
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:

0. `numpy.ndarray` (dtype= `numpy.int32`) or sequence<`int`>
1. sequence<`str`>

sequence of two elements:

0. `numpy.ndarray` (dtype= `numpy.int32`) or sequence<`int`>
1. sequence<`str`> (decoded with latin-1, aka ISO-8859-1)

DEV_DOUBLESTRINGARRAY

sequence of two elements:

0. `numpy.ndarray` (dtype= `numpy.float64`) or sequence<`int`>
1. sequence<`str`>

sequence of two elements:

0. `numpy.ndarray` (dtype= `numpy.float64`) or sequence<`int`>
1. 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`.

1. for properties the sequence is always a `list`. Example:

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

2. 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',) },
     )
    )

## DevEnum pythonic usage

When using regular tango DeviceProxy and AttributeProxy DevEnum is treated just like in cpp tango (see enumerated attributes for more info). However, since PyTango >= 9.2.5 there is a more pythonic way of using DevEnum data types if you use the high level API, both in server and client side.

In server side you can use python `enum.IntEnum` class to deal with DevEnum attributes:

    import time
    from enum import IntEnum
    from tango import AttrWriteType
    from tango.server import Device, attribute, command

    class Noon(IntEnum):
        AM = 0  # DevEnum's must start at 0
        PM = 1  # and increment by 1

    class DisplayType(IntEnum):
        ANALOG = 0  # DevEnum's must start at 0
        DIGITAL = 1  # and increment by 1

    class Clock(Device):

        display_type = DisplayType(0)

        @attribute(dtype=float)
        def time(self):
            return time.time()

        gmtime = attribute(dtype=(int,), max_dim_x=9)

        def read_gmtime(self):
            return time.gmtime()

        @attribute(dtype=Noon)
        def noon(self):
            time_struct = time.gmtime(time.time())
            return Noon.AM if time_struct.tm_hour < 12 else Noon.PM

        display = attribute(dtype=DisplayType, access=AttrWriteType.READ_WRITE)

        def read_display(self):
            return self.display_type

        def write_display(self, display_type):
            self.display_type = display_type

        @command(dtype_in=float, dtype_out=str)
        def ctime(self, seconds):
            """
            Convert a time in seconds since the Epoch to a string in local time.
            This is equivalent to asctime(localtime(seconds)). When the time tuple
            is not present, current time as returned by localtime() is used.
            """
            return time.ctime(seconds)

        @command(dtype_in=(int,), dtype_out=float)
        def mktime(self, tupl):
            return time.mktime(tupl)

    if __name__ == "__main__":
        Clock.run_server()

On the client side you can also use a pythonic approach for using DevEnum attributes:

    import sys
    import PyTango

    if len(sys.argv) != 2:
        print("must provide one and only one clock device name")
        sys.exit(1)

    clock = PyTango.DeviceProxy(sys.argv[1])
    t = clock.time
    gmt = clock.gmtime
    noon = clock.noon
    display = clock.display
    print(t)
    print(gmt)
    print(noon, noon.name, noon.value)
    if noon == noon.AM:
        print('Good morning!')
    print(clock.ctime(t))
    print(clock.mktime(gmt))
    print(display, display.name, display.value)
    clock.display = display.ANALOG
    clock.display = 'DIGITAL'  # you can use a valid string to set the value
    print(clock.display, clock.display.name, clock.display.value)
    display_type = type(display)  # or even create your own IntEnum type
    analog = display_type(0)
    clock.display = analog
    print(clock.display, clock.display.name, clock.display.value)
    clock.display = clock.display.DIGITAL
    print(clock.display, clock.display.name, clock.display.value)