1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
|
```{eval-rst}
.. currentmodule:: tango
```
(pytango-data-types)=
# 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 the numpy type is used for the input arguments.
Also, it is recommended 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)**
```{eval-rst}
+-------------------------+---------------------------------------------------------------------------+
| Tango data type | Python data type |
+=========================+===========================================================================+
| DEV_VOID | No data |
+-------------------------+---------------------------------------------------------------------------+
| DEV_BOOLEAN | :py:obj:`bool` |
+-------------------------+---------------------------------------------------------------------------+
| DEV_SHORT | :py:obj:`int` |
+-------------------------+---------------------------------------------------------------------------+
| DEV_LONG | :py:obj:`int` |
+-------------------------+---------------------------------------------------------------------------+
| DEV_LONG64 | :py:obj:`int` |
+-------------------------+---------------------------------------------------------------------------+
| DEV_FLOAT | :py:obj:`float` |
+-------------------------+---------------------------------------------------------------------------+
| DEV_DOUBLE | :py:obj:`float` |
+-------------------------+---------------------------------------------------------------------------+
| DEV_USHORT | :py:obj:`int` |
+-------------------------+---------------------------------------------------------------------------+
| DEV_ULONG | :py:obj:`int` |
+-------------------------+---------------------------------------------------------------------------+
| DEV_ULONG64 | :py:obj:`int` |
+-------------------------+---------------------------------------------------------------------------+
| DEV_STRING | :py:obj:`str` (decoded with *latin-1*, aka *ISO-8859-1*) |
+-------------------------+---------------------------------------------------------------------------+
| | sequence of two elements: |
| DEV_ENCODED | |
| | 0. :py:obj:`str` (decoded with *latin-1*, aka *ISO-8859-1*) |
| (*New in PyTango 8.0*) | 1. :py:obj:`bytes` (for any value of *extract_as*, except String. |
| | In this case it is :py:obj:`str` (decoded with default python |
| | encoding *utf-8*)) |
+-------------------------+---------------------------------------------------------------------------+
| | * :py:obj:`int` (for value) |
| | * :py:class:`list` <:py:obj:`str`> (for enum_labels) |
| DEV_ENUM | |
| | Note: |
| (*New in PyTango 9.0*) | Direct attribute access via DeviceProxy will return |
| | :py:obj:`enum.IntEnum`. |
+-------------------------+---------------------------------------------------------------------------+
```
**For array types (SPECTRUM/IMAGE)**
```{eval-rst}
+-------------------------+-----------------+-------------------------------------------------------------------------+
| Tango data type | ExtractAs | Python data type |
+=========================+=================+=========================================================================+
| DEVVAR_CHARARRAY | Numpy | :py:class:`numpy.ndarray` (dtype= :py:obj:`numpy.uint8`) |
| +-----------------+-------------------------------------------------------------------------+
| | Bytes | :py:obj:`bytes` |
| +-----------------+-------------------------------------------------------------------------+
| | ByteArray | :py:obj:`bytearray` |
| +-----------------+-------------------------------------------------------------------------+
| | String | :py:obj:`str` |
| | | |
| | | (decoded with *latin-1*, aka *ISO-8859-1*) |
| +-----------------+-------------------------------------------------------------------------+
| | List | :py:class:`list` <:py:obj:`int`> |
| +-----------------+-------------------------------------------------------------------------+
| | Tuple | :py:class:`tuple` <:py:obj:`int`> |
+-------------------------+-----------------+-------------------------------------------------------------------------+
| DEVVAR_SHORTARRAY | Numpy | :py:class:`numpy.ndarray` (dtype= :py:obj:`numpy.uint16`) |
| +-----------------+-------------------------------------------------------------------------+
| (DEV_SHORT + SPECTRUM) | Bytes | :py:obj:`bytes` |
| +-----------------+-------------------------------------------------------------------------+
| (DEV_SHORT + IMAGE) | ByteArray | :py:obj:`bytearray` |
| +-----------------+-------------------------------------------------------------------------+
| | String | :py:obj:`str` |
| | | |
| | | (decoded with *latin-1*, aka *ISO-8859-1*) |
| +-----------------+-------------------------------------------------------------------------+
| | List | :py:class:`list` <:py:obj:`int`> |
| +-----------------+-------------------------------------------------------------------------+
| | Tuple | :py:class:`tuple` <:py:obj:`int`> |
+-------------------------+-----------------+-------------------------------------------------------------------------+
| DEVVAR_LONGARRAY | Numpy | :py:class:`numpy.ndarray` (dtype= :py:obj:`numpy.uint32`) |
| +-----------------+-------------------------------------------------------------------------+
| (DEV_LONG + SPECTRUM) | Bytes | :py:obj:`bytes` |
| +-----------------+-------------------------------------------------------------------------+
| (DEV_LONG + IMAGE) | ByteArray | :py:obj:`bytearray` |
| +-----------------+-------------------------------------------------------------------------+
| | String | :py:obj:`str` |
| | | |
| | | (decoded with *latin-1*, aka *ISO-8859-1*) |
| +-----------------+-------------------------------------------------------------------------+
| | List | :py:class:`list` <:py:obj:`int`> |
| +-----------------+-------------------------------------------------------------------------+
| | Tuple | :py:class:`tuple` <:py:obj:`int`> |
+-------------------------+-----------------+-------------------------------------------------------------------------+
| DEVVAR_LONG64ARRAY | Numpy | :py:class:`numpy.ndarray` (dtype= :py:obj:`numpy.uint64`) |
| +-----------------+-------------------------------------------------------------------------+
| (DEV_LONG64 + SPECTRUM) | Bytes | :py:obj:`bytes` |
| +-----------------+-------------------------------------------------------------------------+
| (DEV_LONG64 + IMAGE) | ByteArray | :py:obj:`bytearray` |
| +-----------------+-------------------------------------------------------------------------+
| | String | :py:obj:`str` |
| | | |
| | | (decoded with *latin-1*, aka *ISO-8859-1*) |
| +-----------------+-------------------------------------------------------------------------+
| | List | :py:class:`list` <:py:obj:`int`> |
| +-----------------+-------------------------------------------------------------------------+
| | Tuple | :py:class:`tuple` <:py:obj:`int`> |
+-------------------------+-----------------+-------------------------------------------------------------------------+
| DEVVAR_FLOATARRAY | Numpy | :py:class:`numpy.ndarray` (dtype= :py:obj:`numpy.float32`) |
| +-----------------+-------------------------------------------------------------------------+
| (DEV_FLOAT + SPECTRUM) | Bytes | :py:obj:`bytes` |
| +-----------------+-------------------------------------------------------------------------+
| (DEV_FLOAT + IMAGE) | ByteArray | :py:obj:`bytearray` |
| +-----------------+-------------------------------------------------------------------------+
| | String | :py:obj:`str` |
| | | |
| | | (decoded with *latin-1*, aka *ISO-8859-1*) |
| +-----------------+-------------------------------------------------------------------------+
| | List | :py:class:`list` <:py:obj:`float`> |
| +-----------------+-------------------------------------------------------------------------+
| | Tuple | :py:class:`tuple` <:py:obj:`float`> |
+-------------------------+-----------------+-------------------------------------------------------------------------+
| DEVVAR_DOUBLEARRAY | Numpy | :py:class:`numpy.ndarray` (dtype= :py:obj:`numpy.float64`) |
| +-----------------+-------------------------------------------------------------------------+
| (DEV_DOUBLE + SPECTRUM) | Bytes | :py:obj:`bytes` |
| +-----------------+-------------------------------------------------------------------------+
| (DEV_DOUBLE + IMAGE) | ByteArray | :py:obj:`bytearray` |
| +-----------------+-------------------------------------------------------------------------+
| | String | :py:obj:`str` |
| | | |
| | | (decoded with *latin-1*, aka *ISO-8859-1*) |
| +-----------------+-------------------------------------------------------------------------+
| | List | :py:class:`list` <:py:obj:`float`> |
| +-----------------+-------------------------------------------------------------------------+
| | Tuple | :py:class:`tuple` <:py:obj:`float`> |
+-------------------------+-----------------+-------------------------------------------------------------------------+
| DEVVAR_USHORTARRAY | Numpy | :py:class:`numpy.ndarray` (dtype= :py:obj:`numpy.uint16`) |
| +-----------------+-------------------------------------------------------------------------+
| (DEV_USHORT + SPECTRUM) | Bytes | :py:obj:`bytes` |
| +-----------------+-------------------------------------------------------------------------+
| (DEV_USHORT + IMAGE) | ByteArray | :py:obj:`bytearray` |
| +-----------------+-------------------------------------------------------------------------+
| | String | :py:obj:`str` |
| | | |
| | | (decoded with *latin-1*, aka *ISO-8859-1*) |
| +-----------------+-------------------------------------------------------------------------+
| | List | :py:class:`list` <:py:obj:`int`> |
| +-----------------+-------------------------------------------------------------------------+
| | Tuple | :py:class:`tuple` <:py:obj:`int`> |
+-------------------------+-----------------+-------------------------------------------------------------------------+
| DEVVAR_ULONGARRAY | Numpy | :py:class:`numpy.ndarray` (dtype= :py:obj:`numpy.uint32`) |
| +-----------------+-------------------------------------------------------------------------+
| (DEV_ULONG + SPECTRUM) | Bytes | :py:obj:`bytes` |
| +-----------------+-------------------------------------------------------------------------+
| (DEV_ULONG + IMAGE) | ByteArray | :py:obj:`bytearray` |
| +-----------------+-------------------------------------------------------------------------+
| | String | :py:obj:`str` |
| | | |
| | | (decoded with *latin-1*, aka *ISO-8859-1*) |
| +-----------------+-------------------------------------------------------------------------+
| | List | :py:class:`list` <:py:obj:`int`> |
| +-----------------+-------------------------------------------------------------------------+
| | Tuple | :py:class:`tuple` <:py:obj:`int`> |
+-------------------------+-----------------+-------------------------------------------------------------------------+
| DEVVAR_ULONG64ARRAY | Numpy | :py:class:`numpy.ndarray` (dtype= :py:obj:`numpy.uint64`) |
| +-----------------+-------------------------------------------------------------------------+
| (DEV_ULONG64 + SPECTRUM)| Bytes | :py:obj:`bytes` |
| +-----------------+-------------------------------------------------------------------------+
| (DEV_ULONG64 + IMAGE) | ByteArray | :py:obj:`bytearray` |
| +-----------------+-------------------------------------------------------------------------+
| | String | :py:obj:`str` |
| | | |
| | | (decoded with *latin-1*, aka *ISO-8859-1*) |
| +-----------------+-------------------------------------------------------------------------+
| | List | :py:class:`list` <:py:obj:`int`> |
| +-----------------+-------------------------------------------------------------------------+
| | Tuple | :py:class:`tuple` <:py:obj:`int`> |
+-------------------------+-----------------+-------------------------------------------------------------------------+
| DEVVAR_STRINGARRAY | | sequence<:py:obj:`str`> |
| | | |
| (DEV_STRING + SPECTRUM) | | (decoded with *latin-1*, aka *ISO-8859-1*) |
| | | |
| (DEV_STRING + IMAGE) | | |
+-------------------------+-----------------+-------------------------------------------------------------------------+
| | | sequence of two elements: |
| DEV_LONGSTRINGARRAY | | |
| | | 0. :py:class:`numpy.ndarray` (dtype= :py:obj:`numpy.int32`) or |
| | | sequence<:py:obj:`int`> |
| | | 1. sequence<:py:obj:`str`> (decoded with *latin-1*, aka *ISO-8859-1*) |
+-------------------------+-----------------+-------------------------------------------------------------------------+
| | | sequence of two elements: |
| DEV_DOUBLESTRINGARRAY | | |
| | | 0. :py:class:`numpy.ndarray` (dtype= :py:obj:`numpy.float64`) or |
| | | sequence<:py:obj:`int`> |
| | | 1. sequence<:py:obj:`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.
1. for properties the sequence is always a {py:class}`list`. Example:
```pycon
>>> import tango
>>> db = tango.Database()
>>> s = db.get_property(["TangoSynchrotrons"])
>>> print type(s)
<type 'list'>
```
2. for attribute/command values:
- For non-string data types: {py:class}`numpy.ndarray`
- For string data types:
- {py:class}`tuple` \<{py:class}`str`> for clients reading attributes
- {py:class}`list` \<{py:class}`str`> in attribute write and command handlers within devices
(pytango-pipe-data-types)=
## 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 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 ({py:class}`list`, {py:class}`tuple`, or other) of data elements
where each element is a {py:class}`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 {ref}`Data type equivalence <pytango-hlapi-datatypes>`. If dtype
key is not given, PyTango will try to find the proper tango type by
inspecting the value.
- a {py:class}`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
{py:class}`collections.OrderedDict` instead.
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:
```{code-block} python
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',) },
)
)
```
(pytango-devenum-data-types)=
## DevEnum pythonic usage
When using regular tango DeviceProxy and AttributeProxy DevEnum is treated just
like in cpp tango (see [enumerated attributes](https://tango-controls.readthedocs.io/en/latest/development/device-api/enumerated-attribute.html)
for more info). However, since PyTango >= 9.2.5 there is a more pythonic way of
using DevEnum data types if you use the {ref}`high level API <pytango-hlapi>`,
both in server and client side.
:::{note}
DevEnum is only support for device attributes, not for commands.
:::
In server side you can use python {py:obj}`enum.IntEnum` class to deal with
DevEnum attributes (here we use type hints, see {ref}`type-hint`, but we can also set
`dtype=Noon` when defining the attribute - see earlier versions of this documentation):
```{code-block} python
import time
from enum import IntEnum
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.ANALOG
@attribute
def time(self) -> float:
return time.time()
@attribute(max_dim_x=9)
def gmtime(self) -> tuple[int]:
return time.gmtime()
@attribute
def noon(self) -> Noon:
time_struct = time.gmtime(time.time())
return Noon.AM if time_struct.tm_hour < 12 else Noon.PM
@attribute
def display(self) -> DisplayType:
return self.display_type
@display.setter
def display(self, display_type: int):
# note that we receive an integer, not an enum instance,
# so we have to convert that to an instance of our enum.
self.display_type = DisplayType(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
def mktime(self, tupl: tuple[int]) -> float:
return time.mktime(tuple(tupl))
if __name__ == "__main__":
Clock.run_server()
```
On the client side you can also use a pythonic approach for using DevEnum attributes:
```{code-block} python
import sys
import tango
if len(sys.argv) != 2:
print("must provide one and only one clock device name")
sys.exit(1)
clock = tango.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)
```
Example output:
```console
$ python client.py test/clock/1
1699433430.714272
[2023 11 8 8 50 30 2 312 0]
0 AM 0
Good morning!
Wed Nov 8 09:50:30 2023
1699429830.0
0 ANALOG 0
1 DIGITAL 1
0 ANALOG 0
1 DIGITAL 1
```
|
