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Merge pull request #7346 from samantha-ho/samanthaho/self_registering_parameters
Allow Parameter subclasses to define and unpack their own implicit dependencies and inferences
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‎docs/changes/newsfragments/7346.breaking‎

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Registration and Unpacking interfaces created in ParameterBse
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``ParameterBase`` now implements new `depends_on``, ``is_controlled_by``, and ``has_control_of``properties that allow subclasses to define ``InterDependencies_`` relationships directly
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``ParameterBase.unpack_self`` allows subclasses to unpack themselves during ``DataSaver.add_result``, which removes the requirement for users to add pre-defined ``InterDependencies_`` results explicitly
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``Measurement.register_parameter`` has been refactored to follow the relationship links defined in parameter subclasses and automatically register related parameters with the appropriate relationships
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``DataSaver.add_result`` has been refactored to take advantage of the new ``unpack_self`` method
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Breaking Changes
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- A dependent parameter registered with an independent parameter as its ``setpoints`` no longer requires that the independent parameter be registered first, if the independent parameter is ParameterBase subclass and not a str
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- Previously, a ParameterWithSetpoints whose setpoints values were explicitly added in add_result would use the explicit version. Now, an error is raised if the explicit values are not within some tolerance of the internal values (as with other duplication).
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- ``DataSaver.add_result`` signature has changed from ``*res_tuple`` to ``*result_tuples``

‎docs/examples/Parameters/Parameter_defined_InterDependencies.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "a338885a",
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"metadata": {},
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"outputs": [],
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"source": [
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"from typing import TYPE_CHECKING\n",
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"\n",
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"import numpy as np\n",
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"\n",
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"from qcodes.dataset import (\n",
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" Measurement,\n",
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" initialise_or_create_database_at,\n",
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" load_or_create_experiment,\n",
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")\n",
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"from qcodes.parameters import (\n",
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" ManualParameter,\n",
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" Parameter,\n",
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" ParameterBase,\n",
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")\n",
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"\n",
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"if TYPE_CHECKING:\n",
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" from qcodes.dataset.data_set_protocol import ValuesType\n",
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" from qcodes.parameters import ParameterBase, ParamRawDataType"
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]
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},
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{
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"cell_type": "markdown",
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"id": "fd4cb8f8",
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"metadata": {},
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"source": [
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"# Parameter-defined InterDependencies\n",
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"\n",
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"This example demonstrates how to use the `depends_on`, `has_control_of`, and `is_controlled_by` properties to define granular implicit interdependencies between Parameters. These are described in greater detail in the [Interdependent Parameters](../../dataset/interdependentparams.rst)."
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]
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},
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{
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"cell_type": "markdown",
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"id": "a7967c55",
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"metadata": {},
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"source": [
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"## Interdependency Definitions:\n",
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"- `depends_on`: (also `setpoints`) An experimental relationship, usually the focus of the measurement. A dependent parameter will generally `depend_on` one or more independent parameters\n",
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"- `is_controlled_by`: (also `basis` and `inferred_from`) A well-known or defined relationship, with an explicit mathematical function to describe it. The directionality is important: We say a parameter A is inferred from B if there exists a function f such that f(B) = A.\n",
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"- `has_control_of`: The opposite direction of the `is_controlled_by` relationship\n",
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"\n",
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"In this example, we will first create a `ControllingParameter` class that operates two component parameters in tandem according to simple linear equations. We will look at how it uses the `has_control_of` and `is_controlled_by` properties to ensure that these components are properly registered in a `Measurement`. Finally, we will examine its custom `unpack_self` method which allows `datasaver.add_result` to add component results even if they are not explicitly added.\n",
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"\n",
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"Then we will show how to bind a `depends_on` relationship to a parameter, and demonstrate how this simplifies handling of fixed and constant dependencies."
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]
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},
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{
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"cell_type": "markdown",
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"id": "7a188625",
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"metadata": {},
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"source": [
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"# ControllingParameter Example"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "32651dfa",
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"metadata": {},
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"outputs": [],
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"source": [
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"class ControllingParameter(Parameter):\n",
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" def __init__(\n",
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" self, name: str, components: dict[Parameter, tuple[float, float]]\n",
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" ) -> None:\n",
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" super().__init__(name=name, get_cmd=False)\n",
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" # dict of Parameter to (slope, offset) of components\n",
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" self._components_dict: dict[Parameter, tuple[float, float]] = components\n",
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" for param in self._components_dict.keys():\n",
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" self._has_control_of.add(param)\n",
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" param.is_controlled_by.add(self)\n",
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"\n",
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" def set_raw(self, value: \"ParamRawDataType\") -> None:\n",
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" # Set all dependent parameters based on their slope and offsets\n",
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" for param, slope_offset in self._components_dict.items():\n",
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" param(value * slope_offset[0] + slope_offset[1])\n",
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"\n",
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" def get_raw(self) -> \"ParamRawDataType\":\n",
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" return self.cache.get()\n",
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"\n",
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" def unpack_self(\n",
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" self, value: \"ValuesType\"\n",
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" ) -> list[tuple[\"ParameterBase\", \"ValuesType\"]]:\n",
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" assert isinstance(value, float)\n",
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" unpacked_results = super().unpack_self(value)\n",
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" for param, slope_offset in self._components_dict.items():\n",
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" unpacked_results.append((param, value * slope_offset[0] + slope_offset[1]))\n",
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" return unpacked_results"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "9af7d477",
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"metadata": {},
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"outputs": [],
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"source": [
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"param1 = ManualParameter(\"param1\", initial_value=0)\n",
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"param2 = ManualParameter(\"param2\", initial_value=0)\n",
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"control = ControllingParameter(\"control\", components={param1: (1, 0), param2: (-1, 10)})\n",
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"\n",
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"meas_param = Parameter(\"meas\", get_cmd=lambda: param1() + param2() - 5.0)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9241ee47",
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"metadata": {},
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"source": [
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"## ControllingParameter self-registration of components\n",
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"\n",
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"In the ``__init__`` method of the `ControllingParameter`, we use two new attributes to define its built-in InterDependencies. The `has_control_of` property is an ordered set of its internal components. We also add the `ControllingParameter` instance to the `is_controlled_by` sets of the components. This lets us register just _one_ of the set `param1, param2, control` and get the other two for free."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "bb26c0f0",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'control': ParamSpecBase('control', 'numeric', 'control', ''),\n",
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" 'param1': ParamSpecBase('param1', 'numeric', 'param1', ''),\n",
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" 'param2': ParamSpecBase('param2', 'numeric', 'param2', '')}"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"initialise_or_create_database_at(\"experiments.db\")\n",
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"exp = load_or_create_experiment(\"InterDependencies_ examples\")\n",
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"meas = Measurement(exp=exp, name=\"self registration example\")\n",
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"meas.register_parameter(control)\n",
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"\n",
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"meas.parameters"
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]
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},
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{
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"cell_type": "markdown",
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"id": "dce92a39",
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"metadata": {},
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"source": [
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"In addition to the `has_control_of` and `is_controlled_by` properties, there is also a similar `depends_on` property that can be used to flexibly create something like the `ParameterWithSetpoints`. The `setpoints` of a `ParameterWithSetpoints` are now added to its internal `depends_on` set, where they are automatically self-registered with the same machinery as we demonstrated above."
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]
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},
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{
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"cell_type": "markdown",
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"id": "870e9165",
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"metadata": {},
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"source": [
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"## ControllingParameter self-unpacking\n",
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"\n",
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"For qcodes measurements, parameter registration is only the first part of the story. Inside the measurement loop itself, we use `datasaver.add_result` to save new data to the resulting database. The `unpack_self` method defined in the `ControllingParameter` class handles unpacking a `ControllingParameter` result tuple, so that the data for its components is also saved."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "1eb9f5e2",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Starting experimental run with id: 6. \n"
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]
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}
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],
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"source": [
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"with meas.run() as datasaver:\n",
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" for i in np.linspace(0, 1, 11):\n",
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" control(i)\n",
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" datasaver.add_result((control, control()))\n",
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" ds = datasaver.dataset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "c48afcbe",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'param1': {'param1': array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
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" 'control': array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ])},\n",
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" 'param2': {'param2': array([10. , 9.9, 9.8, 9.7, 9.6, 9.5, 9.4, 9.3, 9.2, 9.1, 9. ]),\n",
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" 'control': array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ])}}"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"ds.get_parameter_data()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "95f34917",
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"metadata": {},
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"source": [
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"### But does it work with dond?\n",
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"\n",
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"Yes."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "73823b84",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Starting experimental run with id: 8. Using 'qcodes.dataset.dond'\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"{'meas': {'meas': array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.]),\n",
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" 'control': array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
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" 'param1': array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
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" 'param2': array([10. , 9.9, 9.8, 9.7, 9.6, 9.5, 9.4, 9.3, 9.2, 9.1, 9. ])}}"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from qcodes.dataset import LinSweep, dond\n",
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"\n",
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"ds, _, _ = dond(LinSweep(control, 0, 1, 11), meas_param)\n",
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"ds.get_parameter_data()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "py311",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎docs/examples/plotting/auto_color_scale.ipynb‎

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"import numpy as np\n",
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"\n",
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"from qcodes.dataset.descriptions.dependencies import InterDependencies_\n",
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"from qcodes.dataset.descriptions.param_spec import ParamSpecBase\n",
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"from qcodes.parameters import ParamSpecBase\n",
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"\n",
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"\n",
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"def dataset_with_outliers_generator(\n",

‎src/qcodes/dataset/data_export.py‎

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if TYPE_CHECKING:
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from qcodes.dataset.data_set_protocol import DataSetProtocol
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from qcodes.dataset.descriptions.param_spec import ParamSpecBase
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from qcodes.parameters import ParamSpecBase
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log = logging.getLogger(__name__)
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‎src/qcodes/dataset/data_set.py‎

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import pandas as pd
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import xarray as xr
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from qcodes.dataset.descriptions.param_spec import ParamSpec, ParamSpecBase
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from qcodes.dataset.descriptions.param_spec import ParamSpec
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from qcodes.dataset.descriptions.versioning.rundescribertypes import Shapes
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from qcodes.parameters import ParameterBase
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from qcodes.parameters import ParameterBase, ParamSpecBase
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log = logging.getLogger(__name__)

‎src/qcodes/dataset/data_set_in_memory.py‎

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import pandas as pd
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import xarray as xr
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from qcodes.dataset.descriptions.param_spec import ParamSpec, ParamSpecBase
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from qcodes.dataset.descriptions.param_spec import ParamSpec
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from qcodes.dataset.descriptions.versioning.rundescribertypes import Shapes
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from qcodes.parameters import ParamSpecBase
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from ..parameters import ParameterBase
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‎src/qcodes/dataset/data_set_protocol.py‎

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import numpy.typing as npt
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from qcodes.dataset.descriptions.dependencies import InterDependencies_
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from qcodes.dataset.descriptions.param_spec import ParamSpec, ParamSpecBase
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from qcodes.dataset.descriptions.param_spec import ParamSpec
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from qcodes.dataset.export_config import (
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DataExportType,
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get_data_export_name_elements,
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from qcodes.dataset.descriptions.rundescriber import RunDescriber
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from qcodes.dataset.descriptions.versioning.rundescribertypes import Shapes
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from qcodes.dataset.linked_datasets.links import Link
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from qcodes.parameters import ParameterBase
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from qcodes.parameters import ParameterBase, ParamSpecBase
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from .data_set_cache import DataSetCache
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from .exporters.export_info import ExportInfo

‎src/qcodes/dataset/descriptions/dependencies.py‎

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import networkx as nx
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from typing_extensions import deprecated
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from qcodes.parameters import ParamSpecBase
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from qcodes.utils import QCoDeSDeprecationWarning
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from .param_spec import ParamSpecBase
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if TYPE_CHECKING:
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from collections.abc import Sequence
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