atomiq

Submodules

• atomiq.arguments
• atomiq.atomiq
• atomiq.components
• atomiq.frontend
• atomiq.helper
• atomiq.heros
• atomiq.hooks
• atomiq.tools

Attributes

__version__
old_globals

Classes

AtomiqExperiment	Base class for top-level experiments that use the
AtomiqBlock	Base class for top-level experiments that use the

Functions

is_experiment(o)

Checks if a Python object is a top-level experiment class.

Package Contents

atomiq.__version__ = '0.5.14'

class atomiq.AtomiqExperiment(managers_or_parent, name=None, arg_provider=None, component_map=None, *args, **kwargs)

Bases: artiq.language.environment.EnvExperiment

Base class for top-level experiments that use the HasEnvironment environment manager.

Most experiments should derive from this class.

CHUNKSIZE = 10

TAGS = []

components = ['log']

heros_realm = 'heros'

arg_provider

_components = []

_blocks = []

name

component_map = None

clock_at_start

identifier

step_counter = 0

run_id = 0

__terminate_asap__ = False

_sequence_error = False

_create_atomiq_hero()

_getmro()

_import_from_block(blkdata)

This uses monkey patching of the class to make members available to the current experiment

Parameters:

blkdata (dict) -- Block data with the keys "class", "map", "alias"

_add_to_hook_handler(obj_list)

Add the hooks the objects in obj_list to the experiment hooks such that they are called when processing the hooks. Only if the child object posses a method with the according name of the hook, it will be called.

Parameters:

obj_list -- list of child objects relative to self (e.g. 'modulator.rfsource.switch') for which the hook should be called

_build_blocks()

_build_components()

_build_arguments()

get_multiscanmanager()

Get the MultiScanManager of all arguments.

Return type:

MultiScanManager

prepare()

Prepares components and structure. Called by ARTIQ in the prepare phase, see ARTIQ documentation for more information on experiment phases.

Note

If you overwrite this method in your experiment, make sure to call super().prepare().

_build_core()

_build()

build()

Initializes arguments. Called by ARTIQ in the build phase, see ARTIQ documentation for more information on experiment phases.

Note

If you overwrite this method in your experiment, make sure to call super().build()

_prerun_core(reset=False)

_set_identifiers_in_master(identifier, run_id, step_counter)

makes an rpc call to set the identifier in the master

chunker(mult_scan, size=100)

Generator to call a kernel with chunks of scan points.

Parameters:

size (artiq.language.types.TInt32)

Return type:

artiq.language.types.TList

_loop_chunks(chunk_list)

Loop over a list of chunks in the host, i.e. iterate over starting kernels holding CHUNKSIZE experiments

Parameters:

• chunks -- (list) list of chunks. Each entry is again an iterable with CHUNKSIZE Scanpoint objects inside

• chunk_list (artiq.language.types.TList)

_run_init()

run()

Run entry point for ARTIQ, see ARTIQ documentation for more information on experiment phases.

Warning

Do not implement this entry point in your experiment. Use the provided sub-phases (prerun, step, etc.) More information can be found in the Phases and Chunking documentation.

_runchunk(points)

terminate()

Terminate the running experiment as soon as possible. Normally this means after the current chunk.

prerun()

Kernel entry point, run once at the beginning of the run phase of an experiment. This method can be overloaded by the user. Details can be found in the Phases and Chunking documentation.

prerun_host()

Host entry point, run once at the beginning of the run phase of an experiment. This method can be overloaded by the user. Details can be found in the Phases and Chunking documentation.

postfail()

postfail_host()

postrun()

Kernel entry point, run once at the end of the run phase of an experiment. This method can be overloaded by the user. Details can be found in the Phases and Chunking documentation.

postrun_host()

Host entry point, run once at the end of the run phase of an experiment. This method can be overloaded by the user. Details can be found in the Phases and Chunking documentation.

prestep(point)

Kernel entry point, run before every step. This method can be overloaded by the user. Details can be found in the Phases and Chunking documentation.

poststep(point)

Kernel entry point, run after every step. This method can be overloaded by the user. Details can be found in the Phases and Chunking documentation.

prechunk(points)

Kernel entry point, run once at the beginning of a chunk. This method can be overloaded by the user. Details can be found in the Phases and Chunking documentation.

postchunk(points)

Kernel entry point, run once at the end of a chunk. This method can be overloaded by the user. Details can be found in the Phases and Chunking documentation.

prechunk_host(points)

Host entry point, run once at the beginning of a chunk. This method can be overloaded by the user. Details can be found in the Phases and Chunking documentation.

postchunk_host(points)

Host entry point, run once at the end of a chunk. This method can be overloaded by the user. Details can be found in the Phases and Chunking documentation.

abstractmethod step(point)

Kernel entry point, for the main experiment sequence code. This method must be overloaded by the user. Details can be found in the Phases and Chunking documentation.

analyze()

Entry point for analyzing the results of the experiment.

This method may be overloaded by the user to implement the analysis phase of the experiment, for example fitting curves.

Splitting this phase from run() enables tweaking the analysis algorithm on pre-existing data, and CPU-bound analyses to be run overlapped with the next experiment in a pipelined manner.

This method must not interact with the hardware.

children = []

__in_build = True

register_child(child)

call_child_method(method, *args, **kwargs)

Calls the named method for each child, if it exists for that child, in the order of registration.

Parameters:

• method (str) -- Name of the method to call

• args -- Tuple of positional arguments to pass to all children

• kwargs -- Dict of keyword arguments to pass to all children

get_argument(key, processor, group=None, tooltip=None)

Retrieves and returns the value of an argument.

This function should only be called from build.

Parameters:

• key -- Name of the argument.

• processor -- A description of how to process the argument, such as instances of BooleanValue and NumberValue.

• group -- An optional string that defines what group the argument belongs to, for user interface purposes.

• tooltip -- An optional string to describe the argument in more detail, applied as a tooltip to the argument name in the user interface.

setattr_argument(key, processor=None, group=None, tooltip=None)

Sets an argument as attribute. The names of the argument and of the attribute are the same.

The key is added to the instance's kernel invariants.

interactive(title='')

Request arguments from the user interactively.

This context manager returns a namespace object on which the method setattr_argument() should be called, with the usual semantics.

When the context manager terminates, the experiment is blocked and the user is presented with the requested argument widgets. After the user enters values, the experiment is resumed and the namespace contains the values of the arguments.

If the interactive arguments request is cancelled, raises CancelledArgsError.

get_device_db()

Returns the full contents of the device database.

get_device(key)

Creates and returns a device driver.

setattr_device(key)

Sets a device driver as attribute. The names of the device driver and of the attribute are the same.

The key is added to the instance's kernel invariants.

set_dataset(key, value, *, unit=None, scale=None, precision=None, broadcast=False, persist=False, archive=True)

Sets the contents and handling modes of a dataset.

Datasets must be scalars (bool, int, float or NumPy scalar) or NumPy arrays.

Parameters:

• unit -- A string representing the unit of the value.

• scale -- A numerical factor that is used to adjust the value of the dataset to match the scale or units of the experiment's reference frame when the value is displayed.

• precision -- The maximum number of digits to print after the decimal point. Set precision=None to print as many digits as necessary to uniquely specify the value. Uses IEEE unbiased rounding.

• broadcast -- the data is sent in real-time to the master, which dispatches it.

• persist -- the master should store the data on-disk. Implies broadcast.

• archive -- the data is saved into the local storage of the current run (archived as a HDF5 file).

mutate_dataset(key, index, value)

Mutate an existing dataset at the given index (e.g. set a value at a given position in a NumPy array)

If the dataset was created in broadcast mode, the modification is immediately transmitted.

If the index is a tuple of integers, it is interpreted as slice(*index). If the index is a tuple of tuples, each sub-tuple is interpreted as slice(*sub_tuple) (multi-dimensional slicing).

append_to_dataset(key, value)

Append a value to a dataset.

The target dataset must be a list (i.e. support append()), and must have previously been set from this experiment.

The broadcast/persist/archive mode of the given key remains unchanged from when the dataset was last set. Appended values are transmitted efficiently as incremental modifications in broadcast mode.

get_dataset(key, default=NoDefault, archive=True)

Returns the contents of a dataset.

The local storage is searched first, followed by the master storage (which contains the broadcasted datasets from all experiments) if the key was not found initially.

If the dataset does not exist, returns the default value. If no default is provided, raises KeyError.

By default, datasets obtained by this method are archived into the output HDF5 file of the experiment. If an archived dataset is requested more than one time or is modified, only the value at the time of the first call is archived. This may impact reproducibility of experiments.

Parameters:

archive -- Set to False to prevent archival together with the run's results. Default is True.

get_dataset_metadata(key, default=NoDefault)

Returns the metadata of a dataset.

Returns dictionary with items describing the dataset, including the units, scale and precision.

This function is used to get additional information for displaying the dataset.

See set_dataset() for documentation of metadata items.

setattr_dataset(key, default=NoDefault, archive=True)

Sets the contents of a dataset as attribute. The names of the dataset and of the attribute are the same.

set_default_scheduling(priority=None, pipeline_name=None, flush=None)

Sets the default scheduling options.

This function should only be called from build.

class atomiq.AtomiqBlock(*args, **kwargs)

Bases: AtomiqExperiment

Base class for top-level experiments that use the HasEnvironment environment manager.

Most experiments should derive from this class.

experiment

_getmro()

CHUNKSIZE = 10

TAGS = []

components = ['log']

heros_realm = 'heros'

arg_provider

_components = []

_blocks = []

name

component_map = None

clock_at_start

identifier

step_counter = 0

run_id = 0

__terminate_asap__ = False

_sequence_error = False

_create_atomiq_hero()

_import_from_block(blkdata)

This uses monkey patching of the class to make members available to the current experiment

Parameters:

blkdata (dict) -- Block data with the keys "class", "map", "alias"

_add_to_hook_handler(obj_list)

Add the hooks the objects in obj_list to the experiment hooks such that they are called when processing the hooks. Only if the child object posses a method with the according name of the hook, it will be called.

Parameters:

obj_list -- list of child objects relative to self (e.g. 'modulator.rfsource.switch') for which the hook should be called

_build_blocks()

_build_components()

_build_arguments()

get_multiscanmanager()

Get the MultiScanManager of all arguments.

Return type:

MultiScanManager

prepare()

Prepares components and structure. Called by ARTIQ in the prepare phase, see ARTIQ documentation for more information on experiment phases.

Note

If you overwrite this method in your experiment, make sure to call super().prepare().

_build_core()

_build()

build()

Initializes arguments. Called by ARTIQ in the build phase, see ARTIQ documentation for more information on experiment phases.

Note

If you overwrite this method in your experiment, make sure to call super().build()

_prerun_core(reset=False)

_set_identifiers_in_master(identifier, run_id, step_counter)

makes an rpc call to set the identifier in the master

chunker(mult_scan, size=100)

Generator to call a kernel with chunks of scan points.

Parameters:

size (artiq.language.types.TInt32)

Return type:

artiq.language.types.TList

_loop_chunks(chunk_list)

Loop over a list of chunks in the host, i.e. iterate over starting kernels holding CHUNKSIZE experiments

Parameters:

• chunks -- (list) list of chunks. Each entry is again an iterable with CHUNKSIZE Scanpoint objects inside

• chunk_list (artiq.language.types.TList)

_run_init()

run()

Run entry point for ARTIQ, see ARTIQ documentation for more information on experiment phases.

Warning

Do not implement this entry point in your experiment. Use the provided sub-phases (prerun, step, etc.) More information can be found in the Phases and Chunking documentation.

_runchunk(points)

terminate()

Terminate the running experiment as soon as possible. Normally this means after the current chunk.

prerun()

Kernel entry point, run once at the beginning of the run phase of an experiment. This method can be overloaded by the user. Details can be found in the Phases and Chunking documentation.

prerun_host()

Host entry point, run once at the beginning of the run phase of an experiment. This method can be overloaded by the user. Details can be found in the Phases and Chunking documentation.

postfail()

postfail_host()

postrun()

Kernel entry point, run once at the end of the run phase of an experiment. This method can be overloaded by the user. Details can be found in the Phases and Chunking documentation.

postrun_host()

Host entry point, run once at the end of the run phase of an experiment. This method can be overloaded by the user. Details can be found in the Phases and Chunking documentation.

prestep(point)

Kernel entry point, run before every step. This method can be overloaded by the user. Details can be found in the Phases and Chunking documentation.

poststep(point)

Kernel entry point, run after every step. This method can be overloaded by the user. Details can be found in the Phases and Chunking documentation.

prechunk(points)

Kernel entry point, run once at the beginning of a chunk. This method can be overloaded by the user. Details can be found in the Phases and Chunking documentation.

postchunk(points)

Kernel entry point, run once at the end of a chunk. This method can be overloaded by the user. Details can be found in the Phases and Chunking documentation.

prechunk_host(points)

Host entry point, run once at the beginning of a chunk. This method can be overloaded by the user. Details can be found in the Phases and Chunking documentation.

postchunk_host(points)

Host entry point, run once at the end of a chunk. This method can be overloaded by the user. Details can be found in the Phases and Chunking documentation.

abstractmethod step(point)

Kernel entry point, for the main experiment sequence code. This method must be overloaded by the user. Details can be found in the Phases and Chunking documentation.

analyze()

Entry point for analyzing the results of the experiment.

This method may be overloaded by the user to implement the analysis phase of the experiment, for example fitting curves.

Splitting this phase from run() enables tweaking the analysis algorithm on pre-existing data, and CPU-bound analyses to be run overlapped with the next experiment in a pipelined manner.

This method must not interact with the hardware.

children = []

__in_build = True

register_child(child)

call_child_method(method, *args, **kwargs)

Calls the named method for each child, if it exists for that child, in the order of registration.

Parameters:

• method (str) -- Name of the method to call

• args -- Tuple of positional arguments to pass to all children

• kwargs -- Dict of keyword arguments to pass to all children

get_argument(key, processor, group=None, tooltip=None)

Retrieves and returns the value of an argument.

This function should only be called from build.

Parameters:

• key -- Name of the argument.

• processor -- A description of how to process the argument, such as instances of BooleanValue and NumberValue.

• group -- An optional string that defines what group the argument belongs to, for user interface purposes.

• tooltip -- An optional string to describe the argument in more detail, applied as a tooltip to the argument name in the user interface.

setattr_argument(key, processor=None, group=None, tooltip=None)

Sets an argument as attribute. The names of the argument and of the attribute are the same.

The key is added to the instance's kernel invariants.

interactive(title='')

Request arguments from the user interactively.

This context manager returns a namespace object on which the method setattr_argument() should be called, with the usual semantics.

When the context manager terminates, the experiment is blocked and the user is presented with the requested argument widgets. After the user enters values, the experiment is resumed and the namespace contains the values of the arguments.

If the interactive arguments request is cancelled, raises CancelledArgsError.

get_device_db()

Returns the full contents of the device database.

get_device(key)

Creates and returns a device driver.

setattr_device(key)

Sets a device driver as attribute. The names of the device driver and of the attribute are the same.

The key is added to the instance's kernel invariants.

set_dataset(key, value, *, unit=None, scale=None, precision=None, broadcast=False, persist=False, archive=True)

Sets the contents and handling modes of a dataset.

Datasets must be scalars (bool, int, float or NumPy scalar) or NumPy arrays.

Parameters:

• unit -- A string representing the unit of the value.

• scale -- A numerical factor that is used to adjust the value of the dataset to match the scale or units of the experiment's reference frame when the value is displayed.

• precision -- The maximum number of digits to print after the decimal point. Set precision=None to print as many digits as necessary to uniquely specify the value. Uses IEEE unbiased rounding.

• broadcast -- the data is sent in real-time to the master, which dispatches it.

• persist -- the master should store the data on-disk. Implies broadcast.

• archive -- the data is saved into the local storage of the current run (archived as a HDF5 file).

mutate_dataset(key, index, value)

Mutate an existing dataset at the given index (e.g. set a value at a given position in a NumPy array)

If the dataset was created in broadcast mode, the modification is immediately transmitted.

If the index is a tuple of integers, it is interpreted as slice(*index). If the index is a tuple of tuples, each sub-tuple is interpreted as slice(*sub_tuple) (multi-dimensional slicing).

append_to_dataset(key, value)

Append a value to a dataset.

The target dataset must be a list (i.e. support append()), and must have previously been set from this experiment.

The broadcast/persist/archive mode of the given key remains unchanged from when the dataset was last set. Appended values are transmitted efficiently as incremental modifications in broadcast mode.

get_dataset(key, default=NoDefault, archive=True)

Returns the contents of a dataset.

The local storage is searched first, followed by the master storage (which contains the broadcasted datasets from all experiments) if the key was not found initially.

If the dataset does not exist, returns the default value. If no default is provided, raises KeyError.

By default, datasets obtained by this method are archived into the output HDF5 file of the experiment. If an archived dataset is requested more than one time or is modified, only the value at the time of the first call is archived. This may impact reproducibility of experiments.

Parameters:

archive -- Set to False to prevent archival together with the run's results. Default is True.

get_dataset_metadata(key, default=NoDefault)

Returns the metadata of a dataset.

Returns dictionary with items describing the dataset, including the units, scale and precision.

This function is used to get additional information for displaying the dataset.

See set_dataset() for documentation of metadata items.

setattr_dataset(key, default=NoDefault, archive=True)

Sets the contents of a dataset as attribute. The names of the dataset and of the attribute are the same.

set_default_scheduling(priority=None, pipeline_name=None, flush=None)

Sets the default scheduling options.

This function should only be called from build.

atomiq.is_experiment(o)

Checks if a Python object is a top-level experiment class. Monkey patch of the artiq_master function 'artiq.language.environment.is_experiment()' to include Atomiq experiments and blocks in the ignore list

atomiq.old_globals