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# FAQ

Answers to general Tango questions can be found at http://www.tango-controls.org/tutorials

Please also check http://www.tango-controls.org/howtos for a list of Tango howtos

## Where are the usual bjam files?

Starting from PyTango 7.0.0 the prefered way to build PyTango is using the standard python distutils package. This means that:

- you do NOT have to install the additional bjam package
- you do NOT have to change 3 configuration files
- you do NOT need to have 2Gb of RAM to compile PyTango.

Please check the compilation chapter for details on how to build PyTango.

## I got a libbost_python error when I try to import PyTango module

doing:

      >>> import PyTango
      ImportError: libboost_python-gcc43-mt-1_38.so.1.38.0: cannot open shared object file: No such file or directory

You must check that you have the correct boost python installed on your computer. To see which boost python file PyTango needs type:

    $ ldd /usr/lib/python2.5/site-packages/PyTango/_PyTango.so
    linux-vdso.so.1 =>  (0x00007fff48bfe000)
    libtango.so.7 => /home/homer/local/lib/libtango.so.7 (0x00007f393fabb000)
    liblog4tango.so.4 => /home/homer/local/lib/liblog4tango.so.4 (0x00007f393f8a0000)
    **libboost_python-gcc43-mt-1_38.so.1.38.0 => not found**
    libpthread.so.0 => /lib/libpthread.so.0 (0x00007f393f65e000)
    librt.so.1 => /lib/librt.so.1 (0x00007f393f455000)
    libdl.so.2 => /lib/libdl.so.2 (0x00007f393f251000)
    libomniORB4.so.1 => /usr/local/lib/libomniORB4.so.1 (0x00007f393ee99000)
    libomniDynamic4.so.1 => /usr/local/lib/libomniDynamic4.so.1 (0x00007f393e997000)
    libomnithread.so.3 => /usr/local/lib/libomnithread.so.3 (0x00007f393e790000)
    libCOS4.so.1 => /usr/local/lib/libCOS4.so.1 (0x00007f393e359000)
    libgcc_s.so.1 => /lib/libgcc_s.so.1 (0x00007f393e140000)
    libc.so.6 => /lib/libc.so.6 (0x00007f393ddce000)
    libstdc++.so.6 => /usr/lib/libstdc++.so.6 (0x00007f393dac1000)
    libm.so.6 => /lib/libm.so.6 (0x00007f393d83b000)
    /lib64/ld-linux-x86-64.so.2 (0x00007f3940a4c000)

## My python code uses PyTango 3.0.4 API. How do I change to 7.0.0 API?

To ease migration effort, PyTango 7 provides an alternative module called PyTango3.

Changing your python import from:

    import PyTango

to:

    import PyTango3 as PyTango

should allow you to execute your old PyTango code using the new PyTango 7 library.

Please note that you should as soon as possible migrate the code to Tango 7 since the PyTango team cannot assure the maintainability of the PyTango3 module.

Please find below a basic set of rules to migrate from PyTango 3.0.x to 7:

### General rule of thumb for data types

The first important thing to be aware of when migrating from PyTango <= 3.0.4 to PyTango >= 7 is that the data type mapping from tango to python and vice versa is not always the same. The following table summarizes the differences:

Tango data type | PyTango 7 type | PyTango <= 3.0.4 type
--- | --- | ---
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) or int (on a 64 bits computer) | long (on a 32 bits computer) or int (on a 64 bits computer)
DEV_FLOAT | float | float
DEV_DOUBLE | float | float
DEV_USHORT | int | int
DEV_ULONG | int | int
DEV_ULONG64 | long (on a 32 bits computer) or int (on a 64 bits computer) | long (on a 32 bits computer) or int (on a 64 bits computer)
DEV_STRING | str | str
DEVVAR_CHARARRAY | sequence<int> | list<int>
DEVVAR_SHORTARRAY | sequence<int> | list<int>
DEVVAR_LONGARRAY | sequence<int> | list<int>
DEVVAR_LONG64ARRAY | sequence<long> (on a 32 bits computer) or sequence<int> (on a 64 bits computer) | list<long> (on a 32 bits computer) or list<int> (on a 64 bits computer)
DEVVAR_FLOATARRAY | sequence<float> | list<float>
DEVVAR_DOUBLEARRAY | sequence<float> | list<float>
DEVVAR_USHORTARRAY | sequence<int> | list<int>
DEVVAR_ULONGARRAY | sequence<int> | list<int>
DEVVAR_ULONG64ARRAY | sequence<long> (on a 32 bits computer) or sequence<int> (on a 64 bits computer) | list<long> (on a 32 bits computer) or list<int> (on a 64 bits computer)
DEVVAR_STRINGARRAY | sequence<str> | list<str>

DEVVAR_LONGSTRINGARRAY

A sequence with two elements: 1. sequence<int> 2. sequence<str>

A list with two elements:
  1. list<int>
  2. list<str>

DEVVAR_DOUBLESTRINGARRAY

A sequence with two elements: 1. sequence<float> 2. sequence<str>

A list with two elements:
  1. list<float>
  2. list<str>

Note that starting from PyTango 7 you cannot assume anything about the concrete sequence implementation for the tango array types in PyTango. This means that the following code (valid in PyTango <= 3.0.4):

    import PyTango
    dp = PyTango.DeviceProxy("my/device/experiment")
    da = dp.read_attribute("array_attr")
    if isinstance(da.value, list):
        print "array_attr is NOT a scalar attribute"

must be replaced with:

    import operator, types
    import PyTango
    dp = PyTango.DeviceProxy("my/device/experiment")
    da = dp.read_attribute("array_attr")
    if operator.isSequence(da.value) and not type(da.value) in types.StringTypes:
        print "array_attr is NOT a scalar attribute"

Note that the above example is intended for demonstration purposes only. For reference, the proper code would be:

    import PyTango
    dp = PyTango.DeviceProxy("my/device/experiment")
    da = dp.read_attribute("array_attr")
    if not da.data_format is PyTango.AttrDataFormat.SCALAR:
        print "array_attr is NOT a scalar attribute"

### Server

1. replace PyTango.PyUtil with `Util`

2. replace PyTango.PyDeviceClass with `DeviceClass`

3. state and status overwrite

     in PyTango <= 3.0.4, in order to overwrite the default state and status in a device server, you had to reimplement State() and Status() methods respectively.

     in PyTango 7 the methods have been renamed to dev_state() and dev_status() in order to match the C++ API.

### General

1. AttributeValue does NOT exist anymore.
     - the result of a read_attribute call on a `DeviceProxy` / `Group` is now a `DeviceAttribute` object
     - write_attribute does not accept AttributeValue anymore

     (See `DeviceProxy` API documentation for more details)

2. command_inout for commands with parameter type DevVar****StringArray don’t accept items in second sequence not being strings:

     For example, a tango command ‘DevVoid Go(DevVarDoubleArray)’ in tango 3.0.4 could be executed by calling:

         dev_proxy.command_inout( 'Go', [[1.0, 2.0], [1, 2, 3]] )

     and the second list would internally be converted to [‘1’, ‘2’, ‘3’]. Starting from PyTango 7 this is not allowed anymore. So the above code must be changed to:

         dev_proxy.command_inout( 'Go', [[1.0, 2.0], ['1', '2', '3']] )

3. `EventType` enumeration constants changed to match C++ enumeration
     - CHANGE -> CHANGE_EVENT
     - QUALITY -> QUALITY_EVENT
     - PERIODIC -> PERIODIC_EVENT
     - ARCHIVE -> ARCHIVE_EVENT
     - USER -> USER_EVENT
     - ATTR_CONF_EVENT remains
4. Exception handling

     in 3.0.4 `DevFailed` was a tuple of dictionaries. Now `DevFailed` is a tuple of `DevError`. This means that code:

         try:
             tango_fail()
         except PyTango.DevFailed as e:
             print e.args[0]['reason']

     needs to be replaced with:

         try:
             tango_fail()
         except PyTango.DevFailed as e:
             print e.args[0].reason

### Optional

The following is a list of API improvements. Some where added for performance reasons, others to allow for a more pythonic interface, others still to reflect more adequately the C++ interface. They are not mandatory since the original interface will still be available.

#### Server side V3 to V4 upgrade

If you want your server to support the V4 interface provided by Tango 7 instead of the V3 provided by Tango 6:

- replace the inheritance of your device class from `Device_3Impl` to `Device_4Impl`

- in the init_device method replace the call:

      Device_3Impl.init_device(self)

  with:

      Device_4Impl.init_device(self)

  or better yet, if your device class only inherits from `Device_4Impl`:

      super(<your class>, self).init_device()

#### Improved server side image attribute read API

In PyTango <= 3.0.4, to set the value of an image attribute you needed it as a flat list. Consider you want to set as value the following image:

    # Image:
    #  | 1  2 |
    #  | 3  4 |

In order to tell tango the dimensions of the image you had to specify them as:

    image = [ 1, 2, 3, 4]
    dim_x = 2
    dim_y = 2
    attr.set_value(image, dim_x, dim_y)

In PyTango 8 it is still supported, but the preferred way is to use a sequence of sequences (instead of a flat sequence), so the dimensions are inherent and not needed anymore:

    image = [ [1, 2], [3, 4]]
    attr.set_value(image)

If you use a numpy array as the sequence of sequences you can get better performance:

    image = numpy.array([ [1, 2], [3, 4]], dtype=numpy.int32)
    attr.set_value(image)

Likewise, calls to:

    PyTango.set_attribute_value_date_quality(attr, value, date, quality, dim_x, dim_y)

can be replaced with:

    attr.set_value_date_quality(value, date, quality)

#### Improved server side attribute write API

Imagine the following value is written to our IMAGE attribute:

    # Image:
    #  | 1  2 |
    #  | 3  4 |

This is what you would do with PyTango <= 3.0.4:

    flatList = []
    attr.get_write_value(flatList)
    print "flatList =", flatList
    # flatList = [ 1, 2, 3, 4 ]

You can still do it with PyTango 8. However I recommend:

    image = attr.get_write_value()
    print "image =", image
    # image = numpy.array([[1, 2], [3, 4]])

If PyTango 8 is compiled without numpy support, you will get a sequence of sequences, which makes more sense than a flat list.

If PyTango 8 is compiled with numpy support it does not only makes more sense but it is also considerably faster and memory friendlier.

If PyTango is compiled with numpy support but you prefer a list of lists for some attribute, you can do:

    image = attr.get_write_value(PyTango.ExtractAs.List)
    print "image =", image
    # image = [[1, 2], [3, 4]]

Also the SCALAR attribute case is much cleaner now. Instead of:

    data = []
    attr.get_write_value(data)
    actualData = data[0]

You can just write:

    actualData = attr.get_write_value()

## Why is there a “-Wstrict-prototypes” warning when I compile PyTango?

The PyTango prefered build system (distutils) uses the same flags used to compile Python to compile PyTango. It happens that Python is compiled as a pure C library while PyTango is a C++ library. Unfortunately one of the flags used by Python is the “-Wstrict-prototypes” which makes sence in a C compilation but not in a C++ compilation. For reference here is the complete error message you may have:

> cc1plus: warning: command line option “-Wstrict-prototypes” is valid for Ada/C/ObjC but not for C++

Do not worry about this warning since the compiler is ignoring the presence of this flag in the compilation.

## Why are there so many warnings when generating the documentation?

PyTango uses boost python for the binding between C++ and Python and sphinx for document generation. When sphinx generates the PyTango API documentation it uses introspection to search for documentation in the python code. It happens that boost overrides some python introspection API for functions and methods which sphinx expects to have. Therefore you should see many warnings of type:

> (WARNING/2) error while formatting signature for PyTango.Device_4Impl.always_executed_hook: **arg is not a Python function**

Do not worry since sphinx is able to generate the proper documentation.

### Navigation

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- home|
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