atomiq.components.lock

Classes

Lock	This is a very generic class to describe a laser lock. It is characterized by some means
DetunableLock	This is a very generic class to describe a laser lock where the lock offset can be
SpectroscopyLock	This is a very generic class to describe a laser lock. It is characterized by some means
SidebandLock	Lock on a sideband modulated onto the laser
OFCLock	Lock on an optical frequency comb
OffsetLock	Lock on a reference laser via beating signal

Module Contents

class atomiq.components.lock.Lock(reference_frequency, lock_offset=0.0, harmonic=1, *args, **kwargs)

Bases: atomiq.components.primitives.Component

This is a very generic class to describe a laser lock. It is characterized by some means of a reference frequency and an offset to that frequency at which the lock tries to stabilize.

Most likely you want to use a more specific class inherited from this one.

Parameters:

• reference_frequency (artiq.language.types.TFloat) -- The frequency in Hz that the lock references to

• lock_offset (artiq.language.types.TFloat) -- The frequency offset in Hz at which the lock stabilizes

• harmonic (artiq.language.types.TInt32) -- Use this option if the laser is frequency converted but locked to the fundamental. Use 1 for no conversion, 2 for SHG, 4 for FHG ,... (default 1)

kernel_invariants

_lock_offset = 0.0

reference_frequency

harmonic = 1

get_frequency()

Return type:

artiq.language.types.TFloat

experiment

identifier

debug_output = False

core

_kernel_invariants

_prepare_done = False

_build_done = False

_hooks_done = []

_recursive_prepare()

_prepare()

Specify here what should be done for this component in the prepare phase

_recursive_build()

_build()

Specify here what should be done for this component in the build phase

_do_prerun()

required_components(ancestors=[])

_prerun()

Specify here what should be done for this component before the run starts. In contrast to the _build() method, the _prerun() routine is executed on the core device before the actual experiment starts.

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

build()

Should be implemented by the user to request arguments.

Other initialization steps such as requesting devices may also be performed here.

There are two situations where the requested devices are replaced by DummyDevice() and arguments are set to their defaults (or None) instead: when the repository is scanned to build the list of available experiments and when the dataset browser artiq_browser is used to open or run the analysis stage of an experiment. Do not rely on being able to operate on devices or arguments in build().

Datasets are read-only in this method.

Leftover positional and keyword arguments from the constructor are forwarded to this method. This is intended for experiments that are only meant to be executed programmatically (not from the GUI).

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.components.lock.DetunableLock(settle_time=0, blind=False, *args, **kwargs)

Bases: Lock, atomiq.components.primitives.Parametrizable

This is a very generic class to describe a laser lock where the lock offset can be changed. It is characterized by some means of a reference frequency and a detunable offset to that frequency at which the lock tries to stabilize.

Most likely you want to use a more specific class inherited from this one.

Parameters:

• settle_time (artiq.language.types.TFloat) -- Time in s the lock needs to settle after a detuning of the lock point

• blind (artiq.language.types.TBool) -- Whether the lock is set to its default values during each prerun phase. If True, the lock is unchanged in the prerun phase. Defaults to False.

kernel_invariants

settle_time = 0

_prerun()

Specify here what should be done for this component before the run starts. In contrast to the _build() method, the _prerun() routine is executed on the core device before the actual experiment starts.

abstractmethod _set_lock_frequency(detuning)

Parameters:

detuning (artiq.language.types.TFloat)

abstractmethod _ramp_lock_frequency(duration, detuning_start=float('nan'), detuning_end=float('nan'))

Parameters:

• duration (artiq.language.types.TFloat)

• detuning_start (artiq.language.types.TFloat)

• detuning_end (artiq.language.types.TFloat)

set_frequency(frequency)

Set the absolute frequency of the locked line

Parameters:

frequency (artiq.language.types.TFloat) -- The absolute frequency at which the system should lock

set_detuning(offset_frequency)

Set the frequency relative to the reference frequency, i.e. set the lock offset.

Parameters:

offset_frequency (artiq.language.types.TFloat) -- new lock offset in Hz

ramp_detuning(duration, offset_frequency_start=float('nan'), offset_frequency_end=float('nan'), ramp_timestep=float('nan'), ramp_steps=-1)

Parameters:

• duration (artiq.language.types.TFloat)

• offset_frequency_start (artiq.language.types.TFloat)

• offset_frequency_end (artiq.language.types.TFloat)

• ramp_timestep (artiq.language.types.TFloat)

• ramp_steps (artiq.language.types.TInt32)

ramp_frequency(duration, frequency_start=float('nan'), frequency_end=float('nan'), ramp_timestep=float('nan'), ramp_steps=-1)

Parameters:

• duration (artiq.language.types.TFloat)

• frequency_start (artiq.language.types.TFloat)

• frequency_end (artiq.language.types.TFloat)

• ramp_timestep (artiq.language.types.TFloat)

• ramp_steps (artiq.language.types.TInt32)

_lock_offset = 0.0

reference_frequency

harmonic = 1

get_frequency()

Return type:

artiq.language.types.TFloat

experiment

identifier

debug_output = False

core

_kernel_invariants

_prepare_done = False

_build_done = False

_hooks_done = []

_recursive_prepare()

_prepare()

Specify here what should be done for this component in the prepare phase

_recursive_build()

_build()

Specify here what should be done for this component in the build phase

_do_prerun()

required_components(ancestors=[])

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

build()

Should be implemented by the user to request arguments.

Other initialization steps such as requesting devices may also be performed here.

There are two situations where the requested devices are replaced by DummyDevice() and arguments are set to their defaults (or None) instead: when the repository is scanned to build the list of available experiments and when the dataset browser artiq_browser is used to open or run the analysis stage of an experiment. Do not rely on being able to operate on devices or arguments in build().

Datasets are read-only in this method.

Leftover positional and keyword arguments from the constructor are forwarded to this method. This is intended for experiments that are only meant to be executed programmatically (not from the GUI).

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.

set_parameter(value, channel=None)

Parameters:

• value (artiq.language.types.TFloat)

• channel (artiq.language.types.TStr)

class atomiq.components.lock.SpectroscopyLock(reference_frequency, lock_offset=0.0, harmonic=1, *args, **kwargs)

Bases: Lock

This is a very generic class to describe a laser lock. It is characterized by some means of a reference frequency and an offset to that frequency at which the lock tries to stabilize.

Most likely you want to use a more specific class inherited from this one.

Parameters:

• reference_frequency (artiq.language.types.TFloat) -- The frequency in Hz that the lock references to

• lock_offset (artiq.language.types.TFloat) -- The frequency offset in Hz at which the lock stabilizes

• harmonic (artiq.language.types.TInt32) -- Use this option if the laser is frequency converted but locked to the fundamental. Use 1 for no conversion, 2 for SHG, 4 for FHG ,... (default 1)

kernel_invariants

_lock_offset = 0.0

reference_frequency

harmonic = 1

get_frequency()

Return type:

artiq.language.types.TFloat

experiment

identifier

debug_output = False

core

_kernel_invariants

_prepare_done = False

_build_done = False

_hooks_done = []

_recursive_prepare()

_prepare()

Specify here what should be done for this component in the prepare phase

_recursive_build()

_build()

Specify here what should be done for this component in the build phase

_do_prerun()

required_components(ancestors=[])

_prerun()

Specify here what should be done for this component before the run starts. In contrast to the _build() method, the _prerun() routine is executed on the core device before the actual experiment starts.

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

build()

Should be implemented by the user to request arguments.

Other initialization steps such as requesting devices may also be performed here.

There are two situations where the requested devices are replaced by DummyDevice() and arguments are set to their defaults (or None) instead: when the repository is scanned to build the list of available experiments and when the dataset browser artiq_browser is used to open or run the analysis stage of an experiment. Do not rely on being able to operate on devices or arguments in build().

Datasets are read-only in this method.

Leftover positional and keyword arguments from the constructor are forwarded to this method. This is intended for experiments that are only meant to be executed programmatically (not from the GUI).

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.components.lock.SidebandLock(rf_source, sideband_order=1, *args, **kwargs)

Bases: DetunableLock

Lock on a sideband modulated onto the laser

This kind of lock is frequently used when locking on tunable sideband to the transmission signal of a stable cavity. The offset frequency for the sideband is generated by an RF source and can be changed at runtime. Thus the lock point can be detuned.

Parameters:

• rf_source (RFSource) -- RF source that determines the sideband frequency.

• sideband_order (artiq.language.types.TInt32) -- Order of the sideband the laser is locked to [.., -2, -1, 1, 2, ...] (default 1)

kernel_invariants

rf_source

sideband_order = 1

_set_lock_frequency(frequency)

Parameters:

frequency (artiq.language.types.TFloat)

_ramp_lock_frequency(duration, frequency_start=float('nan'), frequency_end=float('nan'), ramp_timestep=float('nan'), ramp_steps=-1)

Parameters:

• duration (artiq.language.types.TFloat)

• frequency_start (artiq.language.types.TFloat)

• frequency_end (artiq.language.types.TFloat)

• ramp_timestep (artiq.language.types.TFloat)

• ramp_steps (artiq.language.types.TInt32)

settle_time = 0

_prerun()

Specify here what should be done for this component before the run starts. In contrast to the _build() method, the _prerun() routine is executed on the core device before the actual experiment starts.

set_frequency(frequency)

Set the absolute frequency of the locked line

Parameters:

frequency (artiq.language.types.TFloat) -- The absolute frequency at which the system should lock

set_detuning(offset_frequency)

Set the frequency relative to the reference frequency, i.e. set the lock offset.

Parameters:

offset_frequency (artiq.language.types.TFloat) -- new lock offset in Hz

ramp_detuning(duration, offset_frequency_start=float('nan'), offset_frequency_end=float('nan'), ramp_timestep=float('nan'), ramp_steps=-1)

Parameters:

• duration (artiq.language.types.TFloat)

• offset_frequency_start (artiq.language.types.TFloat)

• offset_frequency_end (artiq.language.types.TFloat)

• ramp_timestep (artiq.language.types.TFloat)

• ramp_steps (artiq.language.types.TInt32)

ramp_frequency(duration, frequency_start=float('nan'), frequency_end=float('nan'), ramp_timestep=float('nan'), ramp_steps=-1)

Parameters:

• duration (artiq.language.types.TFloat)

• frequency_start (artiq.language.types.TFloat)

• frequency_end (artiq.language.types.TFloat)

• ramp_timestep (artiq.language.types.TFloat)

• ramp_steps (artiq.language.types.TInt32)

_lock_offset = 0.0

reference_frequency

harmonic = 1

get_frequency()

Return type:

artiq.language.types.TFloat

experiment

identifier

debug_output = False

core

_kernel_invariants

_prepare_done = False

_build_done = False

_hooks_done = []

_recursive_prepare()

_prepare()

Specify here what should be done for this component in the prepare phase

_recursive_build()

_build()

Specify here what should be done for this component in the build phase

_do_prerun()

required_components(ancestors=[])

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

build()

Should be implemented by the user to request arguments.

Other initialization steps such as requesting devices may also be performed here.

There are two situations where the requested devices are replaced by DummyDevice() and arguments are set to their defaults (or None) instead: when the repository is scanned to build the list of available experiments and when the dataset browser artiq_browser is used to open or run the analysis stage of an experiment. Do not rely on being able to operate on devices or arguments in build().

Datasets are read-only in this method.

Leftover positional and keyword arguments from the constructor are forwarded to this method. This is intended for experiments that are only meant to be executed programmatically (not from the GUI).

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.

set_parameter(value, channel=None)

Parameters:

• value (artiq.language.types.TFloat)

• channel (artiq.language.types.TStr)

class atomiq.components.lock.OFCLock(rf_source, lock_direction=1, tooth_number=-1, rep_rate=float('nan'), ceo=float('nan'), *args, **kwargs)

Bases: DetunableLock

Lock on an optical frequency comb

This is used to lock the laser on a beat note with an optical frequency comb. It is characterized by the frequency of the closest comb tooth and the offset frequency. The offset frequency is generated by an RF source and can be changed at runtime. Thus the lock point can be detuned.

Parameters:

• rfsource -- RF source that generates the reference beat frequency (i.e. the offset frequency) to which the laser beat note is stabilized.

• reference_frequency -- The frequency in Hz of the comb tooth. Instead ceo, rep_rate and tooth_number) can be passed to automatically calculate the reference frequency. reference_frequency is ignored in this case.

• lock_offset -- The default frequency offset to the comb tooth in Hz at which the lock stabilizes

• settle_time -- Time in s the lock needs to settle after a detuning of the lock point

• lock_direction (artiq.language.types.TInt32) -- Whether the laser is locked to the positive (+1) or negative (-1) beat note (default 1)

• tooth_number (artiq.language.types.TInt32) -- Number of the comb tooth, the laser is beaten with (default -1)

• rep_rate (artiq.language.types.TFloat) -- Repetition frequency of the comb in Hz (default nan)

• ceo (artiq.language.types.TFloat) -- Ceo frequency of the comb in Hz (default nan)

• rf_source (RFSource)

kernel_invariants

rf_source

lock_direction = 1

_set_lock_frequency(frequency)

Parameters:

frequency (artiq.language.types.TFloat)

_ramp_lock_frequency(duration, frequency_start=float('nan'), frequency_end=float('nan'), ramp_timestep=float('nan'), ramp_steps=-1)

Parameters:

• duration (artiq.language.types.TFloat)

• frequency_start (artiq.language.types.TFloat)

• frequency_end (artiq.language.types.TFloat)

• ramp_timestep (artiq.language.types.TFloat)

• ramp_steps (artiq.language.types.TInt32)

get_frequency()

Return type:

artiq.language.types.TFloat

settle_time = 0

_prerun()

Specify here what should be done for this component before the run starts. In contrast to the _build() method, the _prerun() routine is executed on the core device before the actual experiment starts.

set_frequency(frequency)

Set the absolute frequency of the locked line

Parameters:

frequency (artiq.language.types.TFloat) -- The absolute frequency at which the system should lock

set_detuning(offset_frequency)

Set the frequency relative to the reference frequency, i.e. set the lock offset.

Parameters:

offset_frequency (artiq.language.types.TFloat) -- new lock offset in Hz

ramp_detuning(duration, offset_frequency_start=float('nan'), offset_frequency_end=float('nan'), ramp_timestep=float('nan'), ramp_steps=-1)

Parameters:

• duration (artiq.language.types.TFloat)

• offset_frequency_start (artiq.language.types.TFloat)

• offset_frequency_end (artiq.language.types.TFloat)

• ramp_timestep (artiq.language.types.TFloat)

• ramp_steps (artiq.language.types.TInt32)

ramp_frequency(duration, frequency_start=float('nan'), frequency_end=float('nan'), ramp_timestep=float('nan'), ramp_steps=-1)

Parameters:

• duration (artiq.language.types.TFloat)

• frequency_start (artiq.language.types.TFloat)

• frequency_end (artiq.language.types.TFloat)

• ramp_timestep (artiq.language.types.TFloat)

• ramp_steps (artiq.language.types.TInt32)

_lock_offset = 0.0

reference_frequency

harmonic = 1

experiment

identifier

debug_output = False

core

_kernel_invariants

_prepare_done = False

_build_done = False

_hooks_done = []

_recursive_prepare()

_prepare()

Specify here what should be done for this component in the prepare phase

_recursive_build()

_build()

Specify here what should be done for this component in the build phase

_do_prerun()

required_components(ancestors=[])

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

build()

Should be implemented by the user to request arguments.

Other initialization steps such as requesting devices may also be performed here.

There are two situations where the requested devices are replaced by DummyDevice() and arguments are set to their defaults (or None) instead: when the repository is scanned to build the list of available experiments and when the dataset browser artiq_browser is used to open or run the analysis stage of an experiment. Do not rely on being able to operate on devices or arguments in build().

Datasets are read-only in this method.

Leftover positional and keyword arguments from the constructor are forwarded to this method. This is intended for experiments that are only meant to be executed programmatically (not from the GUI).

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.

set_parameter(value, channel=None)

Parameters:

• value (artiq.language.types.TFloat)

• channel (artiq.language.types.TStr)

class atomiq.components.lock.OffsetLock(reference_laser, *args, **kwargs)

Bases: SidebandLock

Lock on a reference laser via beating signal

Parameters:

reference_laser -- laser that is used to generate the beat note. It's frequency serves as the reference frequency for this lock

kernel_invariants

reference_laser

get_frequency()

Return type:

artiq.language.types.TFloat

set_frequency(frequency)

Set the absolute frequency of the locked line

Parameters:

frequency (artiq.language.types.TFloat) -- The absolute frequency at which the system should lock

ramp_frequency(duration, frequency_start=float('nan'), frequency_end=float('nan'), ramp_timestep=float('nan'), ramp_steps=-1)

Parameters:

• duration (artiq.language.types.TFloat)

• frequency_start (artiq.language.types.TFloat)

• frequency_end (artiq.language.types.TFloat)

• ramp_timestep (artiq.language.types.TFloat)

• ramp_steps (artiq.language.types.TInt32)

rf_source

sideband_order = 1

_set_lock_frequency(frequency)

Parameters:

frequency (artiq.language.types.TFloat)

_ramp_lock_frequency(duration, frequency_start=float('nan'), frequency_end=float('nan'), ramp_timestep=float('nan'), ramp_steps=-1)

Parameters:

• duration (artiq.language.types.TFloat)

• frequency_start (artiq.language.types.TFloat)

• frequency_end (artiq.language.types.TFloat)

• ramp_timestep (artiq.language.types.TFloat)

• ramp_steps (artiq.language.types.TInt32)

settle_time = 0

_prerun()

Specify here what should be done for this component before the run starts. In contrast to the _build() method, the _prerun() routine is executed on the core device before the actual experiment starts.

set_detuning(offset_frequency)

Set the frequency relative to the reference frequency, i.e. set the lock offset.

Parameters:

offset_frequency (artiq.language.types.TFloat) -- new lock offset in Hz

ramp_detuning(duration, offset_frequency_start=float('nan'), offset_frequency_end=float('nan'), ramp_timestep=float('nan'), ramp_steps=-1)

Parameters:

• duration (artiq.language.types.TFloat)

• offset_frequency_start (artiq.language.types.TFloat)

• offset_frequency_end (artiq.language.types.TFloat)

• ramp_timestep (artiq.language.types.TFloat)

• ramp_steps (artiq.language.types.TInt32)

_lock_offset = 0.0

reference_frequency

harmonic = 1

experiment

identifier

debug_output = False

core

_kernel_invariants

_prepare_done = False

_build_done = False

_hooks_done = []

_recursive_prepare()

_prepare()

Specify here what should be done for this component in the prepare phase

_recursive_build()

_build()

Specify here what should be done for this component in the build phase

_do_prerun()

required_components(ancestors=[])

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

build()

Should be implemented by the user to request arguments.

Other initialization steps such as requesting devices may also be performed here.

There are two situations where the requested devices are replaced by DummyDevice() and arguments are set to their defaults (or None) instead: when the repository is scanned to build the list of available experiments and when the dataset browser artiq_browser is used to open or run the analysis stage of an experiment. Do not rely on being able to operate on devices or arguments in build().

Datasets are read-only in this method.

Leftover positional and keyword arguments from the constructor are forwarded to this method. This is intended for experiments that are only meant to be executed programmatically (not from the GUI).

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.

set_parameter(value, channel=None)

Parameters:

• value (artiq.language.types.TFloat)

• channel (artiq.language.types.TStr)