atomiq.components.electronics.adc ================================= .. py:module:: atomiq.components.electronics.adc .. autoapi-nested-parse:: All analog voltages have to go through an ADC at some point to enter the digital world. Often ADCs appear as multi-channel devices that sample several channels at once. Thus the ADC itself and its channels are represented by separate classes. The :class:`ADC` class provides a :func:`ADC.measure` function to sample all channels at once while the :class:`ADCChannel` provides a :func:`ADCChannel.measure` method to extract only a single channel from the ADC. Classes ------- .. autoapisummary:: atomiq.components.electronics.adc.ADC atomiq.components.electronics.adc.ADCChannel Module Contents --------------- .. py:class:: ADC(num_chan, *args, **kwargs) Bases: :py:obj:`atomiq.components.primitives.Component`, :py:obj:`atomiq.components.primitives.Measurable` An analog-to-ditigal converter (ADC) with multiple channels Many ADCs come with multiple channels that are always sampled together. This class represents such ADCs :param num_chan: Number of channels the ADC has. .. py:attribute:: kernel_invariants .. py:attribute:: num_chan .. py:attribute:: invalid_return .. py:method:: _build() Specify here what should be done for this component in the build phase .. py:method:: _measure(target_arr) :abstractmethod: .. py:method:: measure(samples = 1, channel = '') Measures all channels and returns the mean for a given number of subsequent samples. This method advances the timeline by samples * sampling_time :param samples: number of samples to take and average .. 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:: _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:method:: measurement_channels() .. py:class:: ADCChannel(adc_device, channel, *args, **kwargs) Bases: :py:obj:`atomiq.components.primitives.Component`, :py:obj:`atomiq.components.primitives.Measurable` An atomiq Component Every component in atomiq inherits from this class. It provides basic functionality for automatic and recursive building and initialization of components (prepare, build, prerun). It also takes care for joining kernel invariants along the inheritance tree. .. Note:: The arguments `parent` and `identifier` are automatically passed to the component object by the atomiq object builder. :param parent: The parent context of the component. Usually this is the experiment that uses the component :param identifier: A unique name to identify the component. :param debug_output: Set whether the component should show debug output. Using this switch rather than the debug kernel logger can allow the compiler to not include the debug commands in the kernel code if the output is not needed. .. py:attribute:: kernel_invariants .. py:attribute:: adc_device .. py:attribute:: channel .. py:method:: measure(samples = 1, cached = False, channel = '') .. 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:method:: measurement_channels()