atomiq.components.optoelectronics.camera ======================================== .. py:module:: atomiq.components.optoelectronics.camera Classes ------- .. autoapisummary:: atomiq.components.optoelectronics.camera.Camera atomiq.components.optoelectronics.camera.TriggeredCamera atomiq.components.optoelectronics.camera.TriggeredRPCCamera Module Contents --------------- .. py:class:: Camera(config_dict, default_config, *args, **kwargs) Bases: :py:obj:`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: .. code-block:: python config_dict = { "camera_usecase1" : { "option_xy": 2.3, "option_zy": "external", ... }, "camera_usecase2": { "option_xy": 2e-3, "option_zy": "internal", }, ... } :param config_dict: Dictionary holding the configuration sets for the camera :param default_config: key from the config dict to use if none is specified in the :func:`configure` function .. py:attribute:: kernel_invariants .. py:attribute:: config_dict .. py:attribute:: default_config .. py:method:: _set_config(config) :abstractmethod: .. py:method:: 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. .. py:method:: arm() :abstractmethod: 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 .. py:method:: start() :abstractmethod: Start exposure of the camera .. py:method:: stop() :abstractmethod: Stop exposure of the camera .. py:attribute:: experiment .. py:attribute:: identifier .. py:attribute:: debug_output :value: False .. py:attribute:: core .. py:attribute:: _kernel_invariants .. py:attribute:: _prepare_done :value: False .. py:attribute:: _build_done :value: False .. py:attribute:: _hooks_done :value: [] .. py:method:: _recursive_prepare() .. py:method:: _prepare() Specify here what should be done for this component in the prepare phase .. py:method:: _recursive_build() .. py:method:: _build() Specify here what should be done for this component in the build phase .. py:method:: _do_prerun() .. py:method:: required_components(ancestors=[]) .. py:method:: _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. .. py:attribute:: children :value: [] .. py:attribute:: __in_build :value: True .. py:method:: register_child(child) .. py:method:: call_child_method(method, *args, **kwargs) Calls the named method for each child, if it exists for that child, in the order of registration. :param method: Name of the method to call :type method: str :param args: Tuple of positional arguments to pass to all children :param kwargs: Dict of keyword arguments to pass to all children .. py:method:: 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 :meth:`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). .. py:method:: get_argument(key, processor, group=None, tooltip=None) Retrieves and returns the value of an argument. This function should only be called from ``build``. :param key: Name of the argument. :param processor: A description of how to process the argument, such as instances of :mod:`~artiq.language.environment.BooleanValue` and :mod:`~artiq.language.environment.NumberValue`. :param group: An optional string that defines what group the argument belongs to, for user interface purposes. :param tooltip: An optional string to describe the argument in more detail, applied as a tooltip to the argument name in the user interface. .. py:method:: 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. .. py:method:: interactive(title='') Request arguments from the user interactively. This context manager returns a namespace object on which the method :meth:`~artiq.language.environment.HasEnvironment.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 :exc:`~artiq.language.environment.CancelledArgsError`. .. py:method:: get_device_db() Returns the full contents of the device database. .. py:method:: get_device(key) Creates and returns a device driver. .. py:method:: 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. .. py:method:: 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. :param unit: A string representing the unit of the value. :param 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. :param 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. :param broadcast: the data is sent in real-time to the master, which dispatches it. :param persist: the master should store the data on-disk. Implies broadcast. :param archive: the data is saved into the local storage of the current run (archived as a HDF5 file). .. py:method:: 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). .. py:method:: 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. .. py:method:: 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. :param archive: Set to ``False`` to prevent archival together with the run's results. Default is ``True``. .. py:method:: 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 :meth:`set_dataset` for documentation of metadata items. .. py:method:: 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. .. py:method:: set_default_scheduling(priority=None, pipeline_name=None, flush=None) Sets the default scheduling options. This function should only be called from ``build``. .. py:class:: TriggeredCamera(camera, fire_ttl, fire_pulsetime = 100 * us, *args, **kwargs) Bases: :py:obj:`Camera`, :py:obj:`atomiq.components.primitives.Triggerable` Camera that is configured via RPC but triggered via a TTL :param 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. :param fire_ttl: The logic signal that triggers the exposure. :param fire_pulsetime: The time the logic signal remains high to trigger the camera (default 100us) .. py:attribute:: kernel_invariants .. py:attribute:: camera .. py:attribute:: fire_ttl .. py:attribute:: fire_pulsetime .. py:method:: configure(config = '') Configure the camera to a known configuration. :param config: The name of the configuration. .. py:method:: 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 .. py:method:: stop() Stop exposure of the camera .. py:method:: 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. :param channel: Not used. .. py:method:: start() Start exposure of the camera (alias for :meth:`fire` ). .. py:attribute:: config_dict .. py:attribute:: default_config .. py:method:: _set_config(config) :abstractmethod: .. py:attribute:: experiment .. py:attribute:: identifier .. py:attribute:: debug_output :value: False .. py:attribute:: core .. py:attribute:: _kernel_invariants .. py:attribute:: _prepare_done :value: False .. py:attribute:: _build_done :value: False .. py:attribute:: _hooks_done :value: [] .. py:method:: _recursive_prepare() .. py:method:: _prepare() Specify here what should be done for this component in the prepare phase .. py:method:: _recursive_build() .. py:method:: _build() Specify here what should be done for this component in the build phase .. py:method:: _do_prerun() .. py:method:: required_components(ancestors=[]) .. py:method:: _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. .. py:attribute:: children :value: [] .. py:attribute:: __in_build :value: True .. py:method:: register_child(child) .. py:method:: call_child_method(method, *args, **kwargs) Calls the named method for each child, if it exists for that child, in the order of registration. :param method: Name of the method to call :type method: str :param args: Tuple of positional arguments to pass to all children :param kwargs: Dict of keyword arguments to pass to all children .. py:method:: 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 :meth:`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). .. py:method:: get_argument(key, processor, group=None, tooltip=None) Retrieves and returns the value of an argument. This function should only be called from ``build``. :param key: Name of the argument. :param processor: A description of how to process the argument, such as instances of :mod:`~artiq.language.environment.BooleanValue` and :mod:`~artiq.language.environment.NumberValue`. :param group: An optional string that defines what group the argument belongs to, for user interface purposes. :param tooltip: An optional string to describe the argument in more detail, applied as a tooltip to the argument name in the user interface. .. py:method:: 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. .. py:method:: interactive(title='') Request arguments from the user interactively. This context manager returns a namespace object on which the method :meth:`~artiq.language.environment.HasEnvironment.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 :exc:`~artiq.language.environment.CancelledArgsError`. .. py:method:: get_device_db() Returns the full contents of the device database. .. py:method:: get_device(key) Creates and returns a device driver. .. py:method:: 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. .. py:method:: 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. :param unit: A string representing the unit of the value. :param 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. :param 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. :param broadcast: the data is sent in real-time to the master, which dispatches it. :param persist: the master should store the data on-disk. Implies broadcast. :param archive: the data is saved into the local storage of the current run (archived as a HDF5 file). .. py:method:: 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). .. py:method:: 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. .. py:method:: 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. :param archive: Set to ``False`` to prevent archival together with the run's results. Default is ``True``. .. py:method:: 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 :meth:`set_dataset` for documentation of metadata items. .. py:method:: 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. .. py:method:: set_default_scheduling(priority=None, pipeline_name=None, flush=None) Sets the default scheduling options. This function should only be called from ``build``. .. py:class:: TriggeredRPCCamera(rpc_camera, fire_ttl, fire_pulsetime = 100 * us, *args, **kwargs) Bases: :py:obj:`TriggeredCamera` Camera that is configured via RPC but triggered via a TTL .. attention :: This legacy implementation is deprecated in favor of ``TriggeredCamera``. :param rpc_camera: 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. :param fire_ttl: The logic signal that triggers the exposure. :param fire_pulsetime: The time the logic signal remains high to trigger the camera (default 100us) .. py:attribute:: kernel_invariants .. py:attribute:: camera .. py:attribute:: fire_ttl .. py:attribute:: fire_pulsetime .. py:method:: _set_config(config) .. py:method:: 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 .. py:method:: stop() Stop exposure of the camera .. py:method:: configure(config = '') Configure the camera to a known configuration. :param config: The name of the configuration. .. py:method:: 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. :param channel: Not used. .. py:method:: start() Start exposure of the camera (alias for :meth:`fire` ). .. py:attribute:: config_dict .. py:attribute:: default_config .. py:attribute:: experiment .. py:attribute:: identifier .. py:attribute:: debug_output :value: False .. py:attribute:: core .. py:attribute:: _kernel_invariants .. py:attribute:: _prepare_done :value: False .. py:attribute:: _build_done :value: False .. py:attribute:: _hooks_done :value: [] .. py:method:: _recursive_prepare() .. py:method:: _prepare() Specify here what should be done for this component in the prepare phase .. py:method:: _recursive_build() .. py:method:: _build() Specify here what should be done for this component in the build phase .. py:method:: _do_prerun() .. py:method:: required_components(ancestors=[]) .. py:method:: _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. .. py:attribute:: children :value: [] .. py:attribute:: __in_build :value: True .. py:method:: register_child(child) .. py:method:: call_child_method(method, *args, **kwargs) Calls the named method for each child, if it exists for that child, in the order of registration. :param method: Name of the method to call :type method: str :param args: Tuple of positional arguments to pass to all children :param kwargs: Dict of keyword arguments to pass to all children .. py:method:: 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 :meth:`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). .. py:method:: get_argument(key, processor, group=None, tooltip=None) Retrieves and returns the value of an argument. This function should only be called from ``build``. :param key: Name of the argument. :param processor: A description of how to process the argument, such as instances of :mod:`~artiq.language.environment.BooleanValue` and :mod:`~artiq.language.environment.NumberValue`. :param group: An optional string that defines what group the argument belongs to, for user interface purposes. :param tooltip: An optional string to describe the argument in more detail, applied as a tooltip to the argument name in the user interface. .. py:method:: 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. .. py:method:: interactive(title='') Request arguments from the user interactively. This context manager returns a namespace object on which the method :meth:`~artiq.language.environment.HasEnvironment.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 :exc:`~artiq.language.environment.CancelledArgsError`. .. py:method:: get_device_db() Returns the full contents of the device database. .. py:method:: get_device(key) Creates and returns a device driver. .. py:method:: 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. .. py:method:: 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. :param unit: A string representing the unit of the value. :param 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. :param 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. :param broadcast: the data is sent in real-time to the master, which dispatches it. :param persist: the master should store the data on-disk. Implies broadcast. :param archive: the data is saved into the local storage of the current run (archived as a HDF5 file). .. py:method:: 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). .. py:method:: 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. .. py:method:: 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. :param archive: Set to ``False`` to prevent archival together with the run's results. Default is ``True``. .. py:method:: 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 :meth:`set_dataset` for documentation of metadata items. .. py:method:: 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. .. py:method:: set_default_scheduling(priority=None, pipeline_name=None, flush=None) Sets the default scheduling options. This function should only be called from ``build``.