from collections import OrderedDict from inspect import isclass __all__ = ["NoDefault", "FreeValue", "BooleanValue", "EnumerationValue", "NumberValue", "StringValue", "HasEnvironment", "Experiment", "EnvExperiment", "is_experiment"] class NoDefault: """Represents the absence of a default value.""" pass class DefaultMissing(Exception): """Raised by the ``default`` method of argument processors when no default value is available.""" pass class _SimpleArgProcessor: def __init__(self, default=NoDefault): if default is not NoDefault: self.default_value = default def default(self): if not hasattr(self, "default_value"): raise DefaultMissing return self.default_value def process(self, x): return x def describe(self): d = {"ty": self.__class__.__name__} if hasattr(self, "default_value"): d["default"] = self.default_value return d class FreeValue(_SimpleArgProcessor): """An argument that can be an arbitrary Python value.""" pass class BooleanValue(_SimpleArgProcessor): """A boolean argument.""" pass class EnumerationValue(_SimpleArgProcessor): """An argument that can take a string value among a predefined set of values. :param choices: A list of string representing the possible values of the argument. """ def __init__(self, choices, default=NoDefault): _SimpleArgProcessor.__init__(self, default) assert default is NoDefault or default in choices self.choices = choices def describe(self): d = _SimpleArgProcessor.describe(self) d["choices"] = self.choices return d class NumberValue(_SimpleArgProcessor): """An argument that can take a numerical value (typically floating point). :param unit: A string representing the unit of the value, for user interface (UI) purposes. :param scale: The scale of value for UI purposes. The displayed value is divided by the scale. :param step: The step with which the value should be modified by up/down buttons in a UI. The default is the scale divided by 10. :param min: The minimum value of the argument. :param max: The maximum value of the argument. :param ndecimals: The number of decimals a UI should use. """ def __init__(self, default=NoDefault, unit="", scale=1.0, step=None, min=None, max=None, ndecimals=2): if step is None: step = scale/10.0 _SimpleArgProcessor.__init__(self, default) self.unit = unit self.scale = scale self.step = step self.min = min self.max = max self.ndecimals = ndecimals def describe(self): d = _SimpleArgProcessor.describe(self) d["unit"] = self.unit d["scale"] = self.scale d["step"] = self.step d["min"] = self.min d["max"] = self.max d["ndecimals"] = self.ndecimals return d class StringValue(_SimpleArgProcessor): """A string argument.""" pass class HasEnvironment: """Provides methods to manage the environment of an experiment (devices, parameters, results, arguments).""" def __init__(self, device_mgr=None, dataset_mgr=None, *, parent=None, default_arg_none=False, **kwargs): self.requested_args = OrderedDict() self.__device_mgr = device_mgr self.__dataset_mgr = dataset_mgr self.__parent = parent self.__default_arg_none = default_arg_none self.__kwargs = kwargs self.__in_build = True self.build() self.__in_build = False for key in self.__kwargs.keys(): if key not in self.requested_args: raise TypeError("Got unexpected argument: " + key) del self.__kwargs def build(self): """Must be implemented by the user to request arguments. Other initialization steps such as requesting devices and parameters or initializing real-time results may also be performed here. When the repository is scanned, any requested devices and parameters are set to ``None``.""" raise NotImplementedError def managers(self): """Returns the device manager and the dataset manager, in this order. This is the same order that the constructor takes them, allowing sub-objects to be created with this idiom to pass the environment around: :: sub_object = SomeLibrary(*self.managers()) """ return self.__device_mgr, self.__dataset_mgr def get_argument(self, key, processor=None, group=None): """Retrieves and returns the value of an argument. :param key: Name of the argument. :param processor: A description of how to process the argument, such as instances of ``BooleanValue`` and ``NumberValue``. :param group: An optional string that defines what group the argument belongs to, for user interface purposes. """ if not self.__in_build: raise TypeError("get_argument() should only " "be called from build()") if self.__parent is not None and key not in self.__kwargs: return self.__parent.get_argument(key, processor, group) if processor is None: processor = FreeValue() self.requested_args[key] = processor, group try: argval = self.__kwargs[key] except KeyError: try: return processor.default() except DefaultMissing: if self.__default_arg_none: return None else: raise return processor.process(argval) def setattr_argument(self, key, processor=None, group=None): """Sets an argument as attribute. The names of the argument and of the attribute are the same.""" setattr(self, key, self.get_argument(key, processor, group)) def get_device_db(self): """Returns the full contents of the device database.""" if self.__parent is not None: return self.__parent.get_device_db() return self.__device_mgr.get_device_db() def get_device(self, key): """Creates and returns a device driver.""" if self.__parent is not None: return self.__parent.get_device(key) if self.__device_mgr is None: raise ValueError("Device manager not present") return self.__device_mgr.get(key) def setattr_device(self, key): """Sets a device driver as attribute. The names of the device driver and of the attribute are the same.""" setattr(self, key, self.get_device(key)) def set_dataset(self, key, value, broadcast=False, persist=False, save=True): """Sets the contents and handling modes of a dataset. If the dataset is broadcasted, it must be PYON-serializable. If the dataset is saved, it must be a scalar (``bool``, ``int``, ``float`` or NumPy scalar) or a NumPy array. :param broadcast: the data is sent in real-time to the master, which dispatches it. Returns a Notifier that can be used to mutate the dataset. :param persist: the master should store the data on-disk. Implies broadcast. :param save: the data is saved into the local storage of the current run (archived as a HDF5 file). """ if self.__parent is not None: self.__parent.set_dataset(key, value, broadcast, persist, save) return if self.__dataset_mgr is None: raise ValueError("Dataset manager not present") return self.__dataset_mgr.set(key, value, broadcast, persist, save) def get_dataset(self, key, default=NoDefault): """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``. """ if self.__parent is not None: return self.__parent.get_dataset(key, default) if self.__dataset_mgr is None: raise ValueError("Dataset manager not present") try: return self.__dataset_mgr.get(key) except KeyError: if default is NoDefault: raise else: return default def setattr_dataset(self, key, default=NoDefault): """Sets the contents of a dataset as attribute. The names of the dataset and of the attribute are the same.""" setattr(self, key, self.get_dataset(key, default)) class Experiment: """Base class for experiments. Deriving from this class enables automatic experiment discovery in Python modules. """ def prepare(self): """Entry point for pre-computing data necessary for running the experiment. Doing such computations outside of ``run`` enables more efficient scheduling of multiple experiments that need to access the shared hardware during part of their execution. This method must not interact with the hardware. """ pass def run(self): """The main entry point of the experiment. This method must be overloaded by the user to implement the main control flow of the experiment. This method may interact with the hardware. The experiment may call the scheduler's ``pause`` method while in ``run``. """ raise NotImplementedError def analyze(self): """Entry point for analyzing the results of the experiment. This method may be overloaded by the user to implement the analysis phase of the experiment, for example fitting curves. Splitting this phase from ``run`` enables tweaking the analysis algorithm on pre-existing data, and CPU-bound analyses to be run overlapped with the next experiment in a pipelined manner. This method must not interact with the hardware. """ pass class EnvExperiment(Experiment, HasEnvironment): """Base class for experiments that use the ``HasEnvironment`` environment manager. Most experiment should derive from this class.""" pass def is_experiment(o): """Checks if a Python object is an instantiable user experiment.""" return (isclass(o) and issubclass(o, Experiment) and o is not Experiment and o is not EnvExperiment)