atomiq.components.optoelectronics.camera

Classes

Camera	A Camera
TriggeredCamera	Camera that is configured via RPC but triggered via a TTL
TriggeredRPCCamera	Camera that is configured via RPC but triggered via a TTL

Module Contents

class atomiq.components.optoelectronics.camera.Camera(config_dict, default_config, *args, **kwargs)

Bases: atomiq.components.primitives.Component

A Camera

This is an abstract class to represent a camera.

A camera needs to have a configuration (exposure time, readout mode, binning, subarray, etc). Since cameras usually need quite some config options to be set, this class works with named configuration sets stored in a dict. An example looks like the following:

config_dict = {
     "camera_usecase1" : {
             "option_xy": 2.3,
             "option_zy": "external",
             ...
     },
     "camera_usecase2": {
         "option_xy": 2e-3,
         "option_zy": "internal",
     },
     ...
}

Parameters:

• config_dict (dict) -- Dictionary holding the configuration sets for the camera

• default_config (artiq.language.types.TStr) -- key from the config dict to use if none is specified in the configure() function

kernel_invariants

config_dict

default_config

abstractmethod _set_config(config)

Parameters:

config (artiq.language.types.TStr)

configure(config='')

Configure the camera, i.e. set exposure time, readout mode, binning, etc. Named configuration options are available from the config_dict provided upon object creation. We load the one with the key given in config. If no key is given, load the default defined upon object creation.

Parameters:

config (artiq.language.types.TStr)

abstractmethod arm()

Arm the camera such that a subsequent call of the start() method can start the exposure immediately. Arming typically cannot meet realtime requirements. However, once armed, the camera can start exposure on a trigger in realtime

abstractmethod start()

Start exposure of the camera

abstractmethod stop()

Stop exposure of the camera

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.optoelectronics.camera.TriggeredCamera(camera, fire_ttl, fire_pulsetime=100 * us, *args, **kwargs)

Bases: Camera, atomiq.components.primitives.Triggerable

Camera that is configured via RPC but triggered via a TTL

Parameters:

• camera (Camera) -- The camera component. It needs to provide the functions configure(identifier: int, configuration: dict, metadata: dict) to configure the camera, start() to arm the camera (i.e. wait for trigger), and abort() to stop waiting for trigger or stop exposure.

• fire_ttl (Switchable) -- The logic signal that triggers the exposure.

• fire_pulsetime (artiq.language.types.TFloat) -- The time the logic signal remains high to trigger the camera (default 100us)

kernel_invariants

camera

fire_ttl

fire_pulsetime

configure(config='')

Configure the camera to a known configuration.

Parameters:

config (artiq.language.types.TStr) -- The name of the configuration.

arm()

Arm the camera such that a subsequent call of the start() method can start the exposure immediately. Arming typically cannot meet realtime requirements. However, once armed, the camera can start exposure on a trigger in realtime

stop()

Stop exposure of the camera

fire(channel='')

Uses the fire_ttl component to trigger the camera. Pulses the TTL for fire_pulsetime (as defined in components) and thereby advances the time cursor by this amount.

Parameters:

channel (artiq.language.types.TStr) -- Not used.

start()

Start exposure of the camera (alias for fire() ).

config_dict

default_config

abstractmethod _set_config(config)

Parameters:

config (artiq.language.types.TStr)

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.optoelectronics.camera.TriggeredRPCCamera(rpc_camera, fire_ttl, fire_pulsetime=100 * us, *args, **kwargs)

Bases: TriggeredCamera

Camera that is configured via RPC but triggered via a TTL

Attention

This legacy implementation is deprecated in favor of TriggeredCamera.

Parameters:

• rpc_camera (Component) -- The ARTIQ RPC object that represents the camera. It needs to provide the RPC functions configure(identifier: int, configuration: dict, metadata: dict) to configure the camera, start() to arm the camera (i.e. wait for trigger), and abort() to stop waiting for trigger or stop exposure.

• fire_ttl (Switchable) -- The logic signal that triggers the exposure.

• fire_pulsetime (artiq.language.types.TFloat) -- The time the logic signal remains high to trigger the camera (default 100us)

kernel_invariants

camera

fire_ttl

fire_pulsetime

_set_config(config)

Parameters:

config (artiq.language.types.TStr)

arm()

Arm the camera such that a subsequent call of the start() method can start the exposure immediately. Arming typically cannot meet realtime requirements. However, once armed, the camera can start exposure on a trigger in realtime

stop()

Stop exposure of the camera

configure(config='')

Configure the camera to a known configuration.

Parameters:

config (artiq.language.types.TStr) -- The name of the configuration.

fire(channel='')

Uses the fire_ttl component to trigger the camera. Pulses the TTL for fire_pulsetime (as defined in components) and thereby advances the time cursor by this amount.

Parameters:

channel (artiq.language.types.TStr) -- Not used.

start()

Start exposure of the camera (alias for fire() ).

config_dict

default_config

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.