tango_simlib modules

• tango_simlib modules
  • tango_simlib.main module
  • tango_simlib.model module
  • tango_simlib.quantities module
  • tango_simlib.sim_sdd_xml_parser module
  • tango_simlib.sim_test_interface module
  • tango_simlib.sim_xmi_parser module
  • tango_simlib.simdd_json_parser module
  • tango_simlib.tango_launcher module
  • tango_simlib.tango_sim_generator module
  • tango_simlib.testutils module
  • tango_simlib.helper_module module

tango_simlib.main module

tango_simlib.main.simulator_main(sim_class, sim_control_class=<Mock name=’mock.DeviceMeta()’ id=‘140049637011920’>)

Main function for a simulator with class sim_class

sim_class is a tango.server.Device subclass

tango_simlib.model module

class tango_simlib.model.Model(name, start_time=None, min_update_period=0.99, time_func=<built-in function time>)

Bases: object

Tango Device main model with quantities and actions

name : str

Model name identifier

start_time : float

Time at instantiation of the model

min_update_period : float

Minimun update period of the quantites in the model

time_func : time function

Function that return current time i.e. time.time

set_sim_action(name, handler)

Add an action handler function

name : str

Name of the action

handler : callable(model_instance, action_args)

Callable that handles action (name). Is called with the model instance as the first parameter.

set_test_sim_action(name, handler)

Add an action handler function

name : str

Name of the action

handler : callable(model_instance, action_args)

Callable that handles action (name). Is called with the model instance as the first parameter.

setup_sim_quantities()

Set up self.sim_quantities with simulated quantities

Subclasses should implement this method. Should place simulated quantities in self.sim_quantities dict. Keyed by name of quantity, value must be instances satisfying the quantites.Quantity interface.

• Must use self.start_time to set initial time values.
• Must call super method after setting up sim_quantities

update()

class tango_simlib.model.PopulateModelActions(parser_instance, tango_device_name, model_instance=None)

Bases: object

Used to populate/update model actions

Populates the model actions using the data from the TANGO device information captured in the POGO generated xmi file.

command_info : dict

A dictionary of all the device commands together with their metadata specified in the POGO generated XMI file. The key represents the name of the command and the value is a dictionary of all the attribute’s metadata.

sim_model : Model instance

An instance of the Model class which is used for simulation of simple attributes and/or commands.

add_actions()

generate_action_handler(action_name, action_output_type, actions=None)

Generates and returns an action handler to manage tango commands

action_name : str

Name of action handler to generate

action_output_type : PyTango._PyTango.CmdArgType

Tango command argument type

actions : list

List of actions that the handler will provide

action_handler : function

action handler, taking command input argument in case of tango commands with input arguments.

class tango_simlib.model.PopulateModelQuantities(parser_instance, tango_device_name, sim_model=None)

Bases: object

Used to populate/update model quantities

Populates the model quantities using the data from the TANGO device information captured in the POGO generated xmi file.

parser_instance : Parser instance

The Parser object which reads an xmi/xml/json file and parses it into device attributes, commands, and properties.

sim_model : Model instance

An instance of the Model class which is used for simulation of simple attributes.

setup_sim_quantities()

Set up self.sim_quantities from Model with simulated quantities

Places simulated quantities in sim_quantities dict. Keyed by name of quantity, value must be instances satifying the quantities.Quantity interface

• Must use self.start_time to set initial time values.
• Must call super method after setting up sim_quantities

sim_attribute_quantities(min_bound, max_bound, max_slew_rate, mean, std_dev)

Simulate attribute quantities with a Guassian value distribution

min_value : float

minimum attribute value to be simulated

max_value : float

maximum attribute value to be simulated

max_slew_rate : float

maximum changing rate of the simulated quantities between min and max values

mean : float

average value of the simulated quantity

std_dev : float

starndard deviation value of the simulated quantity

sim_attribute_quantities : dict

Dict of Gaussian simulated quantities

exception tango_simlib.model.SimModelException(message)

Bases: exceptions.Exception

tango_simlib.quantities module

class tango_simlib.quantities.ConstantQuantity(start_value=None, start_time=None, meta=None)

Bases: tango_simlib.quantities.Quantity

A quantity that does not change unless explicitly set

default_val(t)

Set a default value of True to the quantity

t : float

Time to update quantity

next_val(t)

Returns the last value as the next simulated value

t : float

Time to update quantity

class tango_simlib.quantities.GaussianSlewLimited(mean, std_dev, max_slew_rate=inf, meta=None, min_bound=-inf, max_bound=inf, start_value=None, start_time=None)

Bases: tango_simlib.quantities.Quantity

A Gaussian random variable a slew-rate limit and clipping

mean : float

Gaussian mean value

std_dev : float

Gaussian standard deviation

max_slew_rate : float

Maximum quantity slew rate in amount per second. Random values will be clipped to satisfy this condition.

min_bound : float

Minimum quantity value, random values will be clipped if needed.

max_bound : float

Maximum quantity value, random values will be clipped if needed.

adjustable_attributes = frozenset([‘last_update_time’, ‘max_slew_rate’, ‘last_val’, ‘min_bound’, ‘max_bound’, ‘std_dev’, ‘mean’])

next_val(t)

Returns the next value of the simulation

t : float

Time to update quantity

class tango_simlib.quantities.Quantity(start_value=None, start_time=None, meta=None)

Bases: object

Attributes that should be adjustable via a simulation control interface

start_time : float

The initial time when a quantity is updated.

start_value : float

The initial value of a quantity.

meta : dict

This data structure must contain all the attribute desciption data of all quantities that represent tango device simulator attributes. List of all available tango attibute description data: abs_change, archive_abs_change, archive_period, archive_rel_change, label, max_alarm, max_value, max_warning, min_alarm, min_value, delta_t, delta_val, description, display_unit, format, min_warning, period, rel_change e.g. meta=dict(label=”Outside Temperature”, dtype=float) TODO (AR) 2016-07-27 : Ideally these properties should not be TANGO specific as is at the moment.

Subclasses should add all the attributes to this set that users should be able to adjust via a user interface at simulation runtime, also initialise the last_val attribute with the initial quantity value.

adjustable_attributes = frozenset([‘last_update_time’, ‘last_val’])

default_val(t)

Set a default value of 0 to the quantity

t : float

Time to update quantity

next_val(t)

Return the next simulated value for simulation time at t seconds

Must update attributes last_val with the new value and last_update_time with the simulation time

t : float

Time to update quantity

set_val(val, t)

Set a value to the quantity

t : float

Time to update quantity

val : int/float/string

Value to update quantity

tango_simlib.quantities.register_quantity_class(cls)

tango_simlib.sim_sdd_xml_parser module

This module performs the parsing of the SKA Self-Description Data XML schema file generated from the DSL.

class tango_simlib.sim_sdd_xml_parser.SDDParser

Bases: object

Parses the SDD XML file generated from DSL.

monitoring_points: dict

commands: dict

extract_command_info(cmd_info)

Extracts all the information of the XML element ‘CommandList’

cmd_info: xml.etree.ElementTree.Element e.g.

<CommandList>

<Command>

<CommandID></CommandID> <CommandName>ON</CommandName> <CommandDescription></CommandDescription> <CommandType></CommandType> <Timeout></Timeout> <MaxRetry></MaxRetry> <TimeForExecution></TimeForExecution> <AvailableInModes>

<Mode></Mde> <Mode></Mode>

</AvailableInModes> <CommandParameters></CommandParameters> <ResponseList>

<Response>

<ResponseID></ResponseID> <ResponseName>RES_ON</ResponseName> <ResponseType></ResponseType> <ResponseParameters>

<Parameter>

<ParameterID></ParameterID> <ParameterName>msg</ParameterName> <ParameterValue></ParameterValue>

</Parameter>

</ResponseParameters>

</Response>

</ResponseList>

</Command>

</CommandList>

cmds: dict

A dictionary of all the commands and their metadata. e.g. {

‘cmd_name’: {

‘CommandID’: ”, ‘Command_Name: ”, ‘CommandDescription: ”, ‘CommandType: ”, ‘Timeout’: ”, ‘MaxRetry’: ”, ‘TimeForExecution’: ”, ‘AvailableInModes’: {}, ‘CommandParameters’: {}, ‘ResponseList’: {

‘response_name’: {

‘ResponseID’: ”, ‘ResponseName’: ”, ‘ResponseType’: ”, ‘ResponseDescription’: ”, ‘ResponseParameters’: {

‘parameter_name’: {

‘ParameterID’: ”, ‘ParameterName’: ”, ‘ParameterValue’: ”, }

}

}

}

}

}

extract_monitoring_point_info(mp_info)

Extracts all the information of the XML element ‘MonitoringPointsList’

mp_info: xml.etree.ElementTree.Element

e.g. <MonitoringPointsList>

<MonitoringPoint id=”” name=”Temperature” mandatory=”TRUE/FALSE”>

<Description></Description> <DataType>float</DataType> <Size>0</Size> <RWType></RWType> <PossibleValues>

<PossibleValue></PossibleValue> <PossibleValue></PossibleValue>

</PossibleValues> <ValueRange>

<MinValue>-10</MinValue> <MaxValue>55</MaxValue>

</ValueRange> <SamplingFrequency>

<DefaultValue></DefaultValue> <MaxValue></MaxValue>

</SamplingFrequency> <LoggingLevel></LoggingLevel>

</MonitoringPoint>

</MonitoringPointsList>

dev_mnt_pts: dict

A dictionary of all the element’s commands and their metadata. e.g. {

‘monitoring_pt_name’: {

‘id’: ”, ‘name’: ”, ‘Description’: ”, ‘DataType’: ”, ‘Size’: ”, ‘RWType’: ”, ‘PossibleValues’: [], ‘ValueRange’: {

‘MinValue’: ”, ‘MaxValue’: ”, },

‘SamplingFrequecy’: {

‘DefaultValue’: ”, ‘MaxValue’: ”, }

‘LoggingLevel’ :” }

}

get_reformatted_cmd_metadata()

get_reformatted_device_attr_metadata()

get_reformatted_override_metadata()

parse(sdd_xml_file)

tango_simlib.sim_test_interface module

@author MeerKAT CAM team <cam@ska.ac.za>

tango_simlib.sim_xmi_parser module

Simlib library generic simulator generator utility to be used to generate an actual TANGO device that exhibits the behaviour defined in the data description file. @author MeerKAT CAM team <cam@ska.ac.za>

class tango_simlib.sim_xmi_parser.XmiParser

Bases: object

class_properties = None

Data structure format is a list containing class property info in a dict

e.g. [{

“classProperties”: {

“type”: DevString, “mandatory”: “true”, “description”: “Path to the pogo generate xmi file”, “name”: “sim_xmi_description_file”, “DefaultPropValue”: “<any object>”

}

}]

device_attributes = None

The Data structure format is a list containing attribute info in a dict

e.g. [{

“attribute”: {

“displayLevel”: “OPERATOR”, “maxX”: “”, “maxY”: “”, “attType”: “Scalar”, “polledPeriod”: “1000”, “dataType”: DevDouble, “isDynamic”: “true”, “rwType”: “READ”, “allocReadMember”: “true”, “name”: “temperature”

}, “eventCriteria”: {

“relChange”: “10”, “absChange”: “0.5”, “period”: “1000”

}, “evArchiveCriteria”: {

“relChange”: “10”, “absChange”: “0.5”, “period”: “1000”

}, “properties”: {

“description”: “Current temperature outside near the telescope.”, “deltaValue”: “”, “maxAlarm”: “50”, “maxValue”: “51”, “minValue”: “-10”, “standardUnit”: “”, “minAlarm”: “-9”, “maxWarning”: “45”, “unit”: “Degrees Centrigrade”, “displayUnit”: “”, “format”: “”, “deltaTime”: “”, “label”: “Outside Temperature”, “minWarning”: “-5”

}

}]

device_commands = None

The Data structure format is a list containing command info in a dict

e.g. [{

“name”: “On”, “arginDescription”: “”, “arginType”: tango._tango.CmdArgType.DevVoid, “argoutDescription”: “ok | Device ON”, “argoutType”: tango._tango.CmdArgType.DevString, “description”: “Turn On Device”

}]

device_properties = None

Data structure format is a list containing device property info in a dict

e.g. [{

“deviceProperties”: {

“type”: DevString, “mandatory”: “true”, “description”: “Path to the pogo generate xmi file”, “name”: “sim_xmi_description_file”, “DefaultPropValue”: “<any object>”

}

}]

extract_attributes_description_data(description_data)

Extract attribute description data from the xmi tree element.

description_data: xml.etree.ElementTree.Element

XMI tree element with attribute data

Expected element tag(s) are (i.e. description_data.tag) ‘dynamicAttributes’

description_data.find(‘properties’).attrib contains {

“description”: “”, “deltaValue”: “”, “maxAlarm”: “”, “maxValue”: “”, “minValue”: “”, “standardUnit”: “”, “minAlarm”: “”, “maxWarning”: “”, “unit”: “”, “displayUnit”: “”, “format”: “”, “deltaTime”: “”, “label”: “”, “minWarning”: “”

}

and

description_data.attrib contains {

“maxX”: “”, “maxY”: “”, “attType”: “Scalar”, “polledPeriod”: “0”, “displayLevel”: “OPERATOR”, “isDynamic”: “false”, “rwType”: “WRITE”, “allocReadMember”: “false”, “name”: “Constant”

}

description_data.find(‘eventCriteria’).attrib contains {

“relChange”: “10”, “absChange”: “0.5”, “period”: “1000”

}

description_data.find(‘evArchiveCriteria’).attrib contains {

“relChange”: “10”, “absChange”: “0.5”, “period”: “1000”

}

attribute_data: dict

Dictionary of all attribute data required to create a tango attribute

extract_command_description_data(description_data)

Extract command description data from the xmi tree element.

description_data: xml.etree.ElementTree.Element

XMI tree element with command data, where expected element tag(s) are (i.e. description_data.tag) [‘argin’, ‘argout’] and description_data.attrib contains {

“description”: “Turn On Device”, “displayLevel”: “OPERATOR”, “isDynamic”: “false”, “execMethod”: “on”, “polledPeriod”: “0”, “name”: “On”

}

command_data: dict

Dictionary of all the command data required to create a tango command

extract_property_description_data(description_data, property_group)

Extract device/class property description data from the xmi tree element.

description_data: xml.etree.ElementTree.Element

XMI tree element with device property data

Expected element tag(s) are (i.e. description_data.tag) [‘DefaultPropValue’]

description_data.attrib contains {

‘description’: ”, ‘name’: ‘katcp_address’, ‘type’: ”

}

property_group: str

A string representing a group to which the property belongs to, either device properties or class properties.

device_property_data: dict

Dictionary of all device property data required to create a tango device property

get_reformatted_cmd_metadata()

Converts the device_commands data structure into a dictionary that makes searching easier.

commands : dict

A dictionary of all the device commands together with their metadata specified in the POGO generated XMI file. The key represents the name of the command and the value is a dictionary of all the attribute’s metadata.

e.g. { ‘cmd_name’: {cmd_properties}

}

get_reformatted_device_attr_metadata()

Converts the device_attributes data structure into a dictionary to make searching easier.

attributes: dict

A dictionary of all the device attributes together with their metadata specified in the POGO generated XMI file. The key represents the name of the attribute and the value is a dictionary of all the attribute’s metadata.

e.g. {‘input_comms_ok’: {

‘abs_change’: ”, ‘archive_abs_change’: ”, ‘archive_period’: ‘1000’, ‘archive_rel_change’: ”, ‘data_type’: PyTango._PyTango.CmdArgType.DevBoolean, ‘data_format: PyTango._PyTango.AttrDataFormat.SCALAR, ‘delta_t’: ”, ‘delta_val’: ”, ‘description’: ‘Communications with all weather sensors are nominal.’, ‘display_unit’: ”, ‘event_period’: ‘1000’, ‘format’: ”, ‘label’: ‘Input communication OK’, ‘max_alarm’: ”, ‘max_value’: ”, ‘max_warning’: ”, ‘min_alarm’: ”, ‘min_value’: ”, ‘min_warning’: ”, ‘name’: ‘input_comms_ok’, ‘period’: ‘1000’, ‘rel_change’: ”, ‘standard_unit’: ”, ‘unit’: ”, ‘writable’: ‘READ’, ‘enum_labels’: []}, # If attribute data type is DevEnum

}

get_reformatted_override_metadata()

get_reformatted_properties_metadata(property_group)

Creates a dictionary of the device properties and their metadata.

property_group: str

A string representing a group to which the property belongs to, either device properties or class properties (deviceProperties or classProperties).

device_properties: dict

A dictionary of all the device properties together with their metadata specified in the POGO generated XMI file. The keys represent the name of the device property and the value is a dictionary of all the property’s metadata.

e.g. { ‘device_property_name’ : {device_property_metadata}}

property_group: str

A string representing a group to which the property belongs to, either device properties or class properties.

parse(sim_xmi_file)

Read simulator description data from xmi file into self.device_properties

Stores all the simulator description data from the xmi tree into appropriate attribute, command and device property data structures. Loops through the xmi tree class elements and appends description information of dynamic/attributes into self.device_attributes, commands into self.device_commands, and device_properties into self.device_properties.

sim_xmi_file: str

Name of simulator descrition data file

• Data structures, are type list with dictionary elements keyed with description data and values must be the corresponding data value.

tango_simlib.simdd_json_parser module

This module performs the parsing of the Simulator Description Datafile, containing the information needed to instantiate a useful device simulator. @author MeerKAT CAM team <cam@ska.ac.za>

class tango_simlib.simdd_json_parser.SimddParser

Bases: object

get_device_data_components_dict(elements, element_type)

Extract description data from the simdd json element

elements: list

List of device data elements with items in unicode format

e.g. [{

“basicAttributeData”: {

“name”: “temperature”, “unit”: “Degrees Centrigrade”, “label”: “Outside Temperature”, “description”: “Current temperature outside near the telescope.”, “data_type”: “Double”, “data_format”: “”, “delta_t”: “1000”, “delta_val”: “0.5”, “data_shape”: {

“max_dim_x”: “1”, “max_dim_y”: “0”

}, “attributeErrorChecking”: {

“min_value”: “-10”, “max_value”: “51”, “min_alarm”: “-9”, “max_alarm”: “50”

}, “attributeInterlocks”: {

“writable”: “READ”

}, “dataSimulationParameters”: {

“quantity_simulation_type”: “GaussianSlewLimited”, “min_bound”: “-10”, “max_bound”: “50”, “mean”: “25”, “max_slew_rate”: “1”, “update_period”: “1”, “std_dev”: “5”

}, “attributeControlSystem”: {

“display_level”: “OPERATOR”, “period”: “1000”, “EventSettings”: {

“eventArchiveCriteria”: {

“archive_abs_change”: “0.5”, “archive_period”: “1000”, “archive_rel_change”: “10”

}, “eventCrateria”: {

“abs_change”: “0.5”, “event_period”: “1000”, “rel_change”: “10”

}

}

}

}

}]

device_dict: dict

device data dictionary in the format of self._device_attributes or self._device_commands

get_reformated_data(sim_device_element_info, element_type)

Helper function for flattening the data dicts to be more readable

sim_device_info: dict

Data element Dict

e.g. {“basicAttributeData”: {

“name”: “temperature”, “unit”: “Degrees Centrigrade”, “label”: “Outside Temperature”, “description”: “Current temperature outside near the telescope.”, “data_type”: “Double”, “data_format”: “”, “delta_t”: “1000”, “delta_val”: “0.5”, “data_shape”: {

“max_dim_x”: “1”, “max_dim_y”: “0”

}, “attributeErrorChecking”: {

“min_value”: “-10”, “max_value”: “51”, “min_alarm”: “-9”, “max_alarm”: “50”

}, “attributeInterlocks”: {

“writable”: “READ”

}, “dataSimulationParameters”: {

‘quantity_simulation_type’: ‘GaussianSlewLimited’, “min_bound”: “-10”, “max_bound”: “50”, “mean”: “25”, “max_slew_rate”: “1”, “update_period”: “1”

}, “attributeControlSystem”: {

“display_level”: “OPERATOR”, “period”: “1000”, “EventSettings”: {

“eventArchiveCriteria”: {

“archive_abs_change”: “0.5”, “archive_period”: “1000”, “archive_rel_change”: “10”

}, “eventCrateria”: {

“abs_change”: “0.5”, “event_period”: “1000”, “rel_change”: “10”

}

}

}

}

}

items : dict

A more formatted and easy to read dictionary

e.g.

{

‘abs_change’: ‘0.5’, ‘archive_abs_change’: ‘0.5’, ‘archive_period’: ‘1000’, ‘archive_rel_change’: ‘10’, ‘data_format’: ”, ‘data_type’: PyTango._PyTango.CmdArgType.DevDouble, ‘delta_t’: ‘1000’, ‘delta_val’: ‘0.5’, ‘description’: ‘Current temperature outside near the telescope.’, ‘display_level’: ‘OPERATOR’, ‘event_period’: ‘1000’, ‘label’: ‘Outside Temperature’, ‘max_alarm’: ‘50’, ‘max_bound’: ‘50’, ‘max_dim_x’: ‘1’, ‘quantity_simulation_type’: ‘GaussianSlewLimited’, ‘max_dim_y’: ‘0’, ‘max_slew_rate’: ‘1’, ‘max_value’: ‘51’, ‘mean’: ‘25’, ‘min_alarm’: ‘-9’, ‘min_bound’: ‘-10’, ‘min_value’: ‘-10’, ‘name’: ‘temperature’, ‘period’: ‘1000’, ‘rel_change’: ‘10’, ‘unit’: ‘Degrees Centrigrade’, ‘update_period’: ‘1’, ‘writable’: ‘READ’

}

get_reformatted_cmd_metadata()

Returns a more formatted command data structure in a format of dict

get_reformatted_device_attr_metadata()

Returns a more formatted attribute data structure in a format of dict

get_reformatted_override_metadata()

Returns more formatted device override info data structure in dict format

get_reformatted_properties_metadata(property_group)

Returns a more formatted device prop data structure in a format of dict

parse(simdd_json_file)

Read simulator description data from json file into self._device_properties

Stores all the simulator description data from the json file into appropriate attribute, command and device property data structures. Loops through the json object elements and updates description information of dynamic/attributes into self._device_attributes, commands into self._device_commands, and device_properties into self._device_properties.

simdd_json_file: str

Name of simulator descrition data file

• Data structures, are type dict with dictionary elements keyed with element name and values must be the corresponding data value.

tango_simlib.tango_launcher module

Utility to help launch a TANGO device in a KATCP eco-system

Helps by auto-registering a TANGO device if needed

tango_simlib.tango_launcher.main()

tango_simlib.tango_launcher.put_device_property(dev_name, property_name, property_value)

tango_simlib.tango_launcher.register_device(name, device_class, server_name, instance)

tango_simlib.tango_launcher.start_device(opts)

tango_simlib.tango_sim_generator module

Simlib library generic simulator generator utility to be used to generate an actual TANGO device that exhibits the behaviour defined in the data description file. @author MeerKAT CAM team <cam@ska.ac.za>

tango_simlib.tango_sim_generator.configure_device_model(sim_data_file=None, test_device_name=None)

In essence this function should get the xmi file, parse it, take the attribute and command information, populate the model quantities and actions to be simulated and return that model.

sim_datafile : list

A list of direct paths to either xmi/xml/json files.

test_device_name : str

A TANGO device name. This is used for running tests as we want the model instance and the device name to have the same name.

model : model.Model instance

tango_simlib.tango_sim_generator.generate_device_server(server_name, sim_data_files, directory=”)

Create a tango device server python file

server_name: str

Tango device server name

sim_data_files: list

A list of direct paths to either xmi/xml/json data files.

tango_simlib.tango_sim_generator.get_argparser()

tango_simlib.tango_sim_generator.get_device_class(sim_data_files)

Get device class name from specified xmi/simdd description file

sim_data_files: list

A list of direct paths to either xmi/xml/json data files.

klass_name: str

Tango device class name

tango_simlib.tango_sim_generator.get_parser_instance(sim_datafile)

This method returns an appropriate parser instance to generate a Tango device

sim_datafile : str

A direct path to the xmi/xml/json file.

parser_instance: Parser instance

The Parser object which reads an xmi/xml/json file and parses it into device attributes, commands, and properties.

tango_simlib.tango_sim_generator.get_tango_device_server(model, sim_data_files)

Declares a tango device class that inherits the Device class and then adds tango commands.

model: model.Model instance

Device model instance

sim_data_files: list

A list of direct paths to either xmi/xml/json data files.

TangoDeviceServer : PyTango.Device

Tango device that has the results of the translated KATCP server

tango_simlib.tango_sim_generator.main()

tango_simlib.testutils module

class tango_simlib.testutils.ClassCleanupUnittestMixin

Bases: object

Implement class-level setup/deardown semantics that emulate addCleanup()

Subclasses can define a setUpClassWithCleanup() method that wraps addCleanup such that cls.addCleanup() can be used to add cleanup methods that will be called at class tear-down time.

classmethod addCleanupClass(function, *args, **kwargs)

Add a cleanup that will be called at class tear-down time

classmethod doCleanupsClass()

Run class-level cleanups registered with cls.addCleanupClass()

classmethod setUpClass()

Call setUpClassWithCleanup with cls.addCleanup for class-level cleanup

Any exceptions raised during cls.setUpClassWithCleanup will result in the cleanups registered up to that point being called before logging the exception with traceback.

classmethod setUpClassWithCleanup()

Do class-level setup and ensure that cleanup functions are called

It is inteded that subclasses override this class method

In this method calls to cls.addCleanup is forwarded to cls.addCleanupClass, which means callables registered with cls.addCleanup() is added to the class-level cleanup function stack.

classmethod tearDownClass()

tango_simlib.testutils.cleanup_tempdir(test_instance, *mkdtemp_args, **mkdtemp_kwargs)

Return filname of a new tempfile and add cleanup callback to test_instance.

Will not raise an error if the directory is not present when trying to delete.

Extra args and kwargs are passed on to the tempfile.mkdtemp call

tango_simlib.testutils.cleanup_tempfile(test_instance, unlink=False, *mkstemp_args, **mkstemp_kwargs)

Return filename of a new tempfile and add cleanup callback to test_instance.

Will not raise an error if the file is not present when trying to delete.

If unlink=True the actual temp file will be deleted immediately. This is useful if you want to check behaviour in absence of a named file.

Extra args and kwargs are passed on to the tempfile.mkstemp call.

tango_simlib.testutils.disable_attributes_polling(test_case, device_proxy, device_server, attributes)

Disable polling for a tango device server, en re-eable at end of test

tango_simlib.testutils.set_attributes_polling(test_case, device_proxy, device_server, poll_periods)

Set attribute polling and restore after test

test_case : unittest.TestCase instance

Unit test case class instance

device_proxy : PyTango.DeviceProxy instance

The Tango device proxy instance

device_server : PyTango.Device instance

The instance of the device class device_proxy is talking to

poll_periods : dict {“attribute_name” : poll_period}

poll_poriod in milliseconds as per Tango APIs, 0 or falsy to disable polling.

restore_polling : function

This function can be used to restore polling if it is to happen before the end of the test. Should be idempotent if only one set_attributes_polling() is called per test.

tango_simlib.helper_module module

tango_simlib.helper_module.append_device_to_db_file(server, instance, device, db_file_name, tangoclass=None, properties={})

Generate a database file corresponding to the given arguments.

tango_simlib.helper_module.get_host_address()

tango_simlib.helper_module.get_port()

tango_simlib.helper_module.get_server_name()

Gets the TANGO server name from the command line arguments

server_name : str

tango device server name

Extract the Tango server_name or equivalent executable (i.e.sim_xmi_parser.py -> sim_xmi_parser or /usr/local/bin/mkat-tango-katcpdevice2tango-DS -> mkat-tango-katcpdevice2tango-DS) from the command line arguments passed, where sys.argv[1] is the server instance.