{ "cells": [ { "cell_type": "markdown", "id": "c80cd461", "metadata": {}, "source": [ "(analysis-framework-tutorial)=\n", "# Tutorial 3. Building custom analyses - the data analysis framework\n", "\n", "```{seealso}\n", "\n", "The complete source code of this tutorial can be found in\n", "\n", "{nb-download}`Tutorial 3. Building custom analyses - the data analysis framework.ipynb`\n", "\n", "```\n", "\n", "Quantify provides an analysis framework in the form of a {class}`~quantify_core.analysis.base_analysis.BaseAnalysis` class and several subclasses for simple cases (e.g., {class}`~quantify_core.analysis.base_analysis.BasicAnalysis`, {class}`~quantify_core.analysis.base_analysis.Basic2DAnalysis`, {class}`~quantify_core.analysis.spectroscopy_analysis.ResonatorSpectroscopyAnalysis`). The framework provides a structured, yet flexible, flow of the analysis steps. We encourage all users to adopt the framework by sub-classing the {class}`~quantify_core.analysis.base_analysis.BaseAnalysis`.\n", "\n", "To give insight into the concepts and ideas behind the analysis framework, we first write analysis scripts to *\"manually\"* analyze the data as if we had a new type of experiment in our hands.\n", "Next, we encapsulate these steps into reusable functions packing everything together into a simple python class.\n", "\n", "We conclude by showing how the same class is implemented much more easily by extending the {class}`~quantify_core.analysis.base_analysis.BaseAnalysis` and making use of the quantify framework." ] }, { "cell_type": "code", "execution_count": 1, "id": "114e888a", "metadata": { "mystnb": { "code_prompt_show": "Imports and auxiliary utilities" }, "tags": [ "hide-cell" ] }, "outputs": [], "source": [ "import json\n", "import logging\n", "from pathlib import Path\n", "from typing import Tuple\n", "\n", "import lmfit\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import xarray as xr\n", "\n", "import quantify_core.visualization.pyqt_plotmon as pqm\n", "from quantify_core.analysis.cosine_analysis import CosineAnalysis\n", "from quantify_core.analysis.fitting_models import CosineModel, cos_func\n", "from quantify_core.data.handling import (\n", " default_datadir,\n", " get_latest_tuid,\n", " load_dataset,\n", " locate_experiment_container,\n", " set_datadir,\n", ")\n", "from quantify_core.measurement import MeasurementControl\n", "from quantify_core.utilities.examples_support import mk_cosine_instrument\n", "from quantify_core.utilities.inspect_utils import display_source_code\n", "from quantify_core.visualization.SI_utilities import set_xlabel, set_ylabel" ] }, { "cell_type": "markdown", "id": "97036a87", "metadata": {}, "source": [ "Before instantiating any instruments or starting a measurement we change the\n", "directory in which the experiments are saved using the\n", "{meth}`~quantify_core.data.handling.set_datadir`\n", "\\[{meth}`~quantify_core.data.handling.get_datadir`\\] functions.\n", "\n", "----------------------------------------------------------------------------------------\n", "\n", "⚠️ **Warning!**\n", "\n", "We recommend always setting the directory at the start of the python kernel and stick\n", "to a single common data directory for all notebooks/experiments within your\n", "measurement setup/PC.\n", "\n", "The cell below sets a default data directory (`~/quantify-data` on Linux/macOS or\n", "`$env:USERPROFILE\\\\quantify-data` on Windows) for tutorial purposes. Change it to your\n", "desired data directory. The utilities to find/search/extract data only work if\n", "all the experiment containers are located within the same directory.\n", "\n", "----------------------------------------------------------------------------------------" ] }, { "cell_type": "code", "execution_count": 2, "id": "efe3fa65", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data will be saved in:\n", "/root/quantify-data\n" ] } ], "source": [ "set_datadir(default_datadir()) # change me!" ] }, { "cell_type": "markdown", "id": "6795b2b8", "metadata": {}, "source": [ "## Run an experiment\n", "\n", "We mock an experiment in order to generate a toy dataset to use in this tutorial." ] }, { "cell_type": "code", "execution_count": 3, "id": "881bb888", "metadata": { "mystnb": { "code_prompt_show": "Source code of a mock instrument" }, "tags": [ "hide-cell" ] }, "outputs": [ { "data": { "text/html": [ "
def mk_cosine_instrument() -> Instrument:\n",
       "    """A container of parameters (mock instrument) providing a cosine model."""\n",
       "\n",
       "    instr = Instrument("ParameterHolder")\n",
       "\n",
       "    # ManualParameter's is a handy class that preserves the QCoDeS' Parameter\n",
       "    # structure without necessarily having a connection to the physical world\n",
       "    instr.add_parameter(\n",
       "        "amp",\n",
       "        initial_value=0.5,\n",
       "        unit="V",\n",
       "        label="Amplitude",\n",
       "        parameter_class=ManualParameter,\n",
       "    )\n",
       "    instr.add_parameter(\n",
       "        "freq",\n",
       "        initial_value=1,\n",
       "        unit="Hz",\n",
       "        label="Frequency",\n",
       "        parameter_class=ManualParameter,\n",
       "    )\n",
       "    instr.add_parameter(\n",
       "        "t", initial_value=1, unit="s", label="Time", parameter_class=ManualParameter\n",
       "    )\n",
       "    instr.add_parameter(\n",
       "        "phi",\n",
       "        initial_value=0,\n",
       "        unit="Rad",\n",
       "        label="Phase",\n",
       "        parameter_class=ManualParameter,\n",
       "    )\n",
       "    instr.add_parameter(\n",
       "        "noise_level",\n",
       "        initial_value=0.05,\n",
       "        unit="V",\n",
       "        label="Noise level",\n",
       "        parameter_class=ManualParameter,\n",
       "    )\n",
       "    instr.add_parameter(\n",
       "        "acq_delay", initial_value=0.02, unit="s", parameter_class=ManualParameter\n",
       "    )\n",
       "\n",
       "    def cosine_model():\n",
       "        sleep(instr.acq_delay())  # simulates the acquisition delay of an instrument\n",
       "        return (\n",
       "            cos_func(instr.t(), instr.freq(), instr.amp(), phase=instr.phi(), offset=0)\n",
       "            + np.random.randn() * instr.noise_level()\n",
       "        )\n",
       "\n",
       "    # Wrap our function in a Parameter to be able to associate metadata to it, e.g. unit\n",
       "    instr.add_parameter(\n",
       "        name="sig", label="Signal level", unit="V", get_cmd=cosine_model\n",
       "    )\n",
       "\n",
       "    return instr\n",
       "
\n" ], "text/latex": [ "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n", "\\PY{k}{def} \\PY{n+nf}{mk\\PYZus{}cosine\\PYZus{}instrument}\\PY{p}{(}\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{Instrument}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}A container of parameters (mock instrument) providing a cosine model.\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n", "\n", " \\PY{n}{instr} \\PY{o}{=} \\PY{n}{Instrument}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{ParameterHolder}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n", "\n", " \\PY{c+c1}{\\PYZsh{} ManualParameter\\PYZsq{}s is a handy class that preserves the QCoDeS\\PYZsq{} Parameter}\n", " \\PY{c+c1}{\\PYZsh{} structure without necessarily having a connection to the physical world}\n", " \\PY{n}{instr}\\PY{o}{.}\\PY{n}{add\\PYZus{}parameter}\\PY{p}{(}\n", " \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{amp}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n", " \\PY{n}{initial\\PYZus{}value}\\PY{o}{=}\\PY{l+m+mf}{0.5}\\PY{p}{,}\n", " 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\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{noise\\PYZus{}level}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n", " \\PY{n}{initial\\PYZus{}value}\\PY{o}{=}\\PY{l+m+mf}{0.05}\\PY{p}{,}\n", " \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{V}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n", " \\PY{n}{label}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Noise level}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n", " \\PY{n}{parameter\\PYZus{}class}\\PY{o}{=}\\PY{n}{ManualParameter}\\PY{p}{,}\n", " \\PY{p}{)}\n", " \\PY{n}{instr}\\PY{o}{.}\\PY{n}{add\\PYZus{}parameter}\\PY{p}{(}\n", " \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{acq\\PYZus{}delay}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{initial\\PYZus{}value}\\PY{o}{=}\\PY{l+m+mf}{0.02}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{s}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{parameter\\PYZus{}class}\\PY{o}{=}\\PY{n}{ManualParameter}\n", " \\PY{p}{)}\n", "\n", " \\PY{k}{def} \\PY{n+nf}{cosine\\PYZus{}model}\\PY{p}{(}\\PY{p}{)}\\PY{p}{:}\n", " \\PY{n}{sleep}\\PY{p}{(}\\PY{n}{instr}\\PY{o}{.}\\PY{n}{acq\\PYZus{}delay}\\PY{p}{(}\\PY{p}{)}\\PY{p}{)} \\PY{c+c1}{\\PYZsh{} simulates the acquisition delay of an instrument}\n", " \\PY{k}{return} \\PY{p}{(}\n", " \\PY{n}{cos\\PYZus{}func}\\PY{p}{(}\\PY{n}{instr}\\PY{o}{.}\\PY{n}{t}\\PY{p}{(}\\PY{p}{)}\\PY{p}{,} \\PY{n}{instr}\\PY{o}{.}\\PY{n}{freq}\\PY{p}{(}\\PY{p}{)}\\PY{p}{,} \\PY{n}{instr}\\PY{o}{.}\\PY{n}{amp}\\PY{p}{(}\\PY{p}{)}\\PY{p}{,} \\PY{n}{phase}\\PY{o}{=}\\PY{n}{instr}\\PY{o}{.}\\PY{n}{phi}\\PY{p}{(}\\PY{p}{)}\\PY{p}{,} \\PY{n}{offset}\\PY{o}{=}\\PY{l+m+mi}{0}\\PY{p}{)}\n", " \\PY{o}{+} \\PY{n}{np}\\PY{o}{.}\\PY{n}{random}\\PY{o}{.}\\PY{n}{randn}\\PY{p}{(}\\PY{p}{)} \\PY{o}{*} \\PY{n}{instr}\\PY{o}{.}\\PY{n}{noise\\PYZus{}level}\\PY{p}{(}\\PY{p}{)}\n", " \\PY{p}{)}\n", "\n", " \\PY{c+c1}{\\PYZsh{} Wrap our function in a Parameter to be able to associate metadata to it, e.g. unit}\n", " \\PY{n}{instr}\\PY{o}{.}\\PY{n}{add\\PYZus{}parameter}\\PY{p}{(}\n", " \\PY{n}{name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{sig}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{label}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Signal level}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{V}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{get\\PYZus{}cmd}\\PY{o}{=}\\PY{n}{cosine\\PYZus{}model}\n", " \\PY{p}{)}\n", "\n", " \\PY{k}{return} \\PY{n}{instr}\n", "\\end{Verbatim}\n" ], "text/plain": [ "def mk_cosine_instrument() -> Instrument:\n", " \"\"\"A container of parameters (mock instrument) providing a cosine model.\"\"\"\n", "\n", " instr = Instrument(\"ParameterHolder\")\n", "\n", " # ManualParameter's is a handy class that preserves the QCoDeS' Parameter\n", " # structure without necessarily having a connection to the physical world\n", " instr.add_parameter(\n", " \"amp\",\n", " initial_value=0.5,\n", " unit=\"V\",\n", " label=\"Amplitude\",\n", " parameter_class=ManualParameter,\n", " )\n", " instr.add_parameter(\n", " \"freq\",\n", " initial_value=1,\n", " unit=\"Hz\",\n", " label=\"Frequency\",\n", " parameter_class=ManualParameter,\n", " )\n", " instr.add_parameter(\n", " \"t\", initial_value=1, unit=\"s\", label=\"Time\", parameter_class=ManualParameter\n", " )\n", " instr.add_parameter(\n", " \"phi\",\n", " initial_value=0,\n", " unit=\"Rad\",\n", " label=\"Phase\",\n", " parameter_class=ManualParameter,\n", " )\n", " instr.add_parameter(\n", " \"noise_level\",\n", " initial_value=0.05,\n", " unit=\"V\",\n", " label=\"Noise level\",\n", " parameter_class=ManualParameter,\n", " )\n", " instr.add_parameter(\n", " \"acq_delay\", initial_value=0.02, unit=\"s\", parameter_class=ManualParameter\n", " )\n", "\n", " def cosine_model():\n", " sleep(instr.acq_delay()) # simulates the acquisition delay of an instrument\n", " return (\n", " cos_func(instr.t(), instr.freq(), instr.amp(), phase=instr.phi(), offset=0)\n", " + np.random.randn() * instr.noise_level()\n", " )\n", "\n", " # Wrap our function in a Parameter to be able to associate metadata to it, e.g. unit\n", " instr.add_parameter(\n", " name=\"sig\", label=\"Signal level\", unit=\"V\", get_cmd=cosine_model\n", " )\n", "\n", " return instr" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display_source_code(mk_cosine_instrument)" ] }, { "cell_type": "code", "execution_count": 4, "id": "f58b3e02", "metadata": { "mystnb": { "remove-output": true } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting iterative measurement...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f008087219eb4598a30ed26184a3b256", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Completed: 0%| [ elapsed time: 00:00 | time left: ? ] it" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "meas_ctrl = MeasurementControl(\"meas_ctrl\")\n", "plotmon = pqm.PlotMonitor_pyqt(\"plotmon\")\n", "meas_ctrl.instr_plotmon(plotmon.name)\n", "pars = mk_cosine_instrument()\n", "\n", "meas_ctrl.settables(pars.t)\n", "meas_ctrl.setpoints(np.linspace(0, 2, 30))\n", "meas_ctrl.gettables(pars.sig)\n", "dataset = meas_ctrl.run(\"Cosine experiment\")" ] }, { "cell_type": "code", "execution_count": 5, "id": "0e3dbd26", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plotmon.main_QtPlot" ] }, { "cell_type": "markdown", "id": "b2e180c6", "metadata": {}, "source": [ "## Manual analysis steps\n", "\n", "### Loading the data\n", "\n", "The {class}`~xarray.Dataset` contains all the information required to perform a basic analysis of the experiment.\n", "We can alternatively load the dataset from disk based on its {class}`~quantify_core.data.types.TUID`, a timestamp-based unique identifier. If you do not know the tuid of the experiment you can find the latest tuid containing a certain string in the experiment name using {meth}`~quantify_core.data.handling.get_latest_tuid`.\n", "See the {ref}`data-storage` documentation for more details on the folder structure and files contained in the data directory." ] }, { "cell_type": "code", "execution_count": 6, "id": "6210845e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset> Size: 480B\n",
       "Dimensions:  (dim_0: 30)\n",
       "Coordinates:\n",
       "    x0       (dim_0) float64 240B 0.0 0.06897 0.1379 0.2069 ... 1.862 1.931 2.0\n",
       "Dimensions without coordinates: dim_0\n",
       "Data variables:\n",
       "    y0       (dim_0) float64 240B 0.5776 0.4609 0.3737 ... 0.2673 0.4383 0.4626\n",
       "Attributes:\n",
       "    tuid:                             20241014-175651-504-4131bc\n",
       "    name:                             Cosine experiment\n",
       "    grid_2d:                          False\n",
       "    grid_2d_uniformly_spaced:         False\n",
       "    1d_2_settables_uniformly_spaced:  False
" ], "text/plain": [ " Size: 480B\n", "Dimensions: (dim_0: 30)\n", "Coordinates:\n", " x0 (dim_0) float64 240B 0.0 0.06897 0.1379 0.2069 ... 1.862 1.931 2.0\n", "Dimensions without coordinates: dim_0\n", "Data variables:\n", " y0 (dim_0) float64 240B 0.5776 0.4609 0.3737 ... 0.2673 0.4383 0.4626\n", "Attributes:\n", " tuid: 20241014-175651-504-4131bc\n", " name: Cosine experiment\n", " grid_2d: False\n", " grid_2d_uniformly_spaced: False\n", " 1d_2_settables_uniformly_spaced: False" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tuid = get_latest_tuid(contains=\"Cosine experiment\")\n", "dataset = load_dataset(tuid)\n", "dataset" ] }, { "cell_type": "markdown", "id": "868ba095", "metadata": {}, "source": [ "### Performing a fit\n", "\n", "We have a sinusoidal signal in the experiment dataset, the goal is to find the underlying parameters.\n", "We extract these parameters by performing a fit to a model, a cosine function in this case.\n", "For fitting we recommend using the lmfit library. See [the lmfit documentation](https://lmfit.github.io/lmfit-py/model.html) on how to fit data to a custom model." ] }, { "cell_type": "code", "execution_count": 7, "id": "e8f19380", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# create a fitting model based on a cosine function\n", "fitting_model = lmfit.Model(cos_func)\n", "\n", "# specify initial guesses for each parameter\n", "fitting_model.set_param_hint(\"amplitude\", value=0.5, min=0.1, max=2, vary=True)\n", "fitting_model.set_param_hint(\"frequency\", value=0.8, vary=True)\n", "fitting_model.set_param_hint(\"phase\", value=0)\n", "fitting_model.set_param_hint(\"offset\", value=0)\n", "params = fitting_model.make_params()\n", "\n", "# here we run the fit\n", "fit_result = fitting_model.fit(dataset.y0.values, x=dataset.x0.values, params=params)\n", "\n", "# It is possible to get a quick visualization of our fit using a build-in method of lmfit\n", "_ = fit_result.plot_fit(show_init=True)" ] }, { "cell_type": "markdown", "id": "488679bd", "metadata": {}, "source": [ "The summary of the fit result can be nicely printed in a Jupyter-like notebook:" ] }, { "cell_type": "code", "execution_count": 8, "id": "e6f191c1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "

Fit Result

Model: Model(cos_func)

Fit Statistics
fitting methodleastsq
# function evals41
# data points30
# variables4
chi-square 0.07952922
reduced chi-square 0.00305882
Akaike info crit.-169.984843
Bayesian info crit.-164.380054
R-squared 0.97882395
Parameters
namevaluestandard errorrelative errorinitial valueminmaxvary
frequency 0.98668701 0.00896805(0.91%)0.8 -inf infTrue
amplitude 0.49020586 0.01427535(2.91%)0.5 0.10000000 2.00000000True
offset 0.02069294 0.01098747(53.10%)0 -inf infTrue
phase 0.07832459 0.06361375(81.22%)0 -inf infTrue
Correlations (unreported values are < 0.100)
Parameter1Parameter 2Correlation
frequencyphase-0.8865
frequencyoffset-0.3934
offsetphase+0.3487
frequencyamplitude-0.1372
amplitudephase+0.1215
" ], "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fit_result" ] }, { "cell_type": "markdown", "id": "3a6641e6", "metadata": {}, "source": [ "### Analyzing the fit result and saving key quantities" ] }, { "cell_type": "code", "execution_count": 9, "id": "4c8a7ea6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'amplitude': np.float64(0.49020586177215186),\n", " 'frequency': np.float64(0.9866870068017611)}" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quantities_of_interest = {\n", " \"amplitude\": fit_result.params[\"amplitude\"].value,\n", " \"frequency\": fit_result.params[\"frequency\"].value,\n", "}\n", "quantities_of_interest" ] }, { "cell_type": "markdown", "id": "54821380", "metadata": {}, "source": [ "Now that we have the relevant quantities, we want to store them in the same\n", "`experiment directory` where the raw dataset is stored.\n", "\n", "First, we determine the experiment directory on the file system." ] }, { "cell_type": "code", "execution_count": 10, "id": "2084197a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "PosixPath('/root/quantify-data/20241014/20241014-175651-504-4131bc-Cosine experiment')" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# the experiment folder is retrieved with a convenience function\n", "exp_folder = Path(locate_experiment_container(dataset.tuid))\n", "exp_folder" ] }, { "cell_type": "markdown", "id": "033c7543", "metadata": {}, "source": [ "Then, we save the quantities of interest to disk in the human-readable JSON format." ] }, { "cell_type": "code", "execution_count": 11, "id": "57d7ca8f", "metadata": {}, "outputs": [], "source": [ "with open(exp_folder / \"quantities_of_interest.json\", \"w\", encoding=\"utf-8\") as file:\n", " json.dump(quantities_of_interest, file)" ] }, { "cell_type": "markdown", "id": "9054cdd5", "metadata": {}, "source": [ "### Plotting and saving figures\n", "\n", "We would like to save a plot of our data and the fit in our lab logbook but the figure above is not fully satisfactory: there are no units and no reference to the original dataset.\n", "\n", "Below we create our own plot for full control over the appearance and we store it on disk in the same `experiment directory`.\n", "For plotting, we use the ubiquitous matplotlib and some visualization utilities." ] }, { "cell_type": "code", "execution_count": 12, "id": "81af206d", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# create matplotlib figure\n", "fig, ax = plt.subplots()\n", "\n", "# plot data\n", "dataset.y0.plot.line(ax=ax, x=\"x0\", marker=\"o\", label=\"Data\")\n", "\n", "# plot fit\n", "x_fit = np.linspace(dataset[\"x0\"][0].values, dataset[\"x0\"][-1].values, 1000)\n", "y_fit = cos_func(x=x_fit, **fit_result.best_values)\n", "ax.plot(x_fit, y_fit, label=\"Fit\")\n", "ax.legend()\n", "\n", "# set units-aware tick labels\n", "set_xlabel(dataset.x0.long_name, dataset.x0.units)\n", "set_ylabel(dataset.y0.long_name, dataset.y0.units)\n", "\n", "# add a reference to the origal dataset in the figure title\n", "fig.suptitle(f\"{dataset.attrs['name']}\\ntuid: {dataset.attrs['tuid']}\")\n", "\n", "# Save figure\n", "fig.savefig(exp_folder / \"Cosine fit.png\", dpi=300, bbox_inches=\"tight\")" ] }, { "cell_type": "markdown", "id": "ccfab7e1", "metadata": {}, "source": [ "## Reusable fitting model and analysis steps\n", "\n", "The previous steps achieve our goal, however, the code above is not easily reusable and hard to maintain or debug.\n", "We can do better than this! We can package our code in functions that perform specific tasks.\n", "In addition, we will use the objected-oriented interface of `lmfit` to further structure our code.\n", "We explore the details of the object-oriented approach later in this tutorial." ] }, { "cell_type": "code", "execution_count": 13, "id": "652768c7", "metadata": {}, "outputs": [], "source": [ "class MyCosineModel(lmfit.model.Model):\n", " \"\"\"\n", " `lmfit` model with a guess for a cosine fit.\n", " \"\"\"\n", "\n", " def __init__(self, *args, **kwargs):\n", " \"\"\"Configures the constraints of the model.\"\"\"\n", " # pass in the model's equation\n", " super().__init__(cos_func, *args, **kwargs)\n", "\n", " # configure constraints that are independent from the data to be fitted\n", "\n", " self.set_param_hint(\"frequency\", min=0, vary=True) # enforce positive frequency\n", " self.set_param_hint(\"amplitude\", min=0, vary=True) # enforce positive amplitude\n", " self.set_param_hint(\"offset\", vary=True)\n", " self.set_param_hint(\n", " \"phase\", vary=True, min=-np.pi, max=np.pi\n", " ) # enforce phase range\n", "\n", " def guess(self, data, **kws) -> lmfit.parameter.Parameters:\n", " \"\"\"Guess parameters based on the data.\"\"\"\n", "\n", " self.set_param_hint(\"offset\", value=np.average(data))\n", " self.set_param_hint(\"amplitude\", value=(np.max(data) - np.min(data)) / 2)\n", " # a simple educated guess based on experiment type\n", " # a more elaborate but general approach is to use a Fourier transform\n", " self.set_param_hint(\"frequency\", value=1.2)\n", "\n", " params_ = self.make_params()\n", " return lmfit.models.update_param_vals(params_, self.prefix, **kws)" ] }, { "cell_type": "markdown", "id": "47143c62", "metadata": {}, "source": [ "Most of the code related to the fitting model is now packed in a single object, while the analysis steps are split into functions that take care of specific tasks." ] }, { "cell_type": "code", "execution_count": 14, "id": "d288a58c", "metadata": {}, "outputs": [], "source": [ "def extract_data(label: str) -> xr.Dataset:\n", " \"\"\"Loads a dataset from its label.\"\"\"\n", " tuid_ = get_latest_tuid(contains=label)\n", " dataset_ = load_dataset(tuid_)\n", " return dataset_\n", "\n", "\n", "def run_fitting(dataset_: xr.Dataset) -> lmfit.model.ModelResult:\n", " \"\"\"Executes fitting.\"\"\"\n", " model = MyCosineModel() # create the fitting model\n", " params_guess = model.guess(data=dataset_.y0.values)\n", " result = model.fit(\n", " data=dataset_.y0.values, x=dataset_.x0.values, params=params_guess\n", " )\n", " return result\n", "\n", "\n", "def analyze_fit_results(fit_result_: lmfit.model.ModelResult) -> dict:\n", " \"\"\"Analyzes the fit results and saves quantities of interest.\"\"\"\n", " quantities = {\n", " \"amplitude\": fit_result_.params[\"amplitude\"].value,\n", " \"frequency\": fit_result_.params[\"frequency\"].value,\n", " }\n", " return quantities\n", "\n", "\n", "def plot_fit(\n", " fig_: matplotlib.figure.Figure,\n", " ax_: matplotlib.axes.Axes,\n", " dataset_: xr.Dataset,\n", " fit_result_: lmfit.model.ModelResult,\n", ") -> Tuple[matplotlib.figure.Figure, matplotlib.axes.Axes]:\n", " \"\"\"Plots a fit result.\"\"\"\n", " dataset_.y0.plot.line(ax=ax_, x=\"x0\", marker=\"o\", label=\"Data\") # plot data\n", "\n", " x_fit_ = np.linspace(dataset_[\"x0\"][0].values, dataset_[\"x0\"][-1].values, 1000)\n", " y_fit_ = cos_func(x=x_fit_, **fit_result_.best_values)\n", " ax_.plot(x_fit, y_fit_, label=\"Fit\") # plot fit\n", " ax_.legend()\n", "\n", " # set units-aware tick labels\n", " set_xlabel(dataset_.x0.long_name, dataset_.x0.units, ax_)\n", " set_ylabel(dataset_.y0.long_name, dataset_.y0.units, ax_)\n", "\n", " # add a reference to the original dataset_ in the figure title\n", " fig_.suptitle(f\"{dataset_.attrs['name']}\\ntuid: {dataset_.attrs['tuid']}\")\n", "\n", "\n", "def save_quantities_of_interest(tuid_: str, quantities_of_interest_: dict) -> None:\n", " \"\"\"Saves the quantities of interest to disk in JSON format.\"\"\"\n", " exp_folder_ = Path(locate_experiment_container(tuid_))\n", " # Save fit results\n", " with open(exp_folder_ / \"quantities_of_interest.json\", \"w\", encoding=\"utf-8\") as f_:\n", " json.dump(quantities_of_interest_, f_)\n", "\n", "\n", "def save_mpl_figure(tuid_: str, fig_: matplotlib.figure.Figure) -> None:\n", " \"\"\"Saves a matplotlib figure as PNG.\"\"\"\n", " exp_folder_ = Path(locate_experiment_container(tuid_))\n", " fig_.savefig(exp_folder_ / \"Cosine fit.png\", dpi=300, bbox_inches=\"tight\")\n", " plt.close(fig_)" ] }, { "cell_type": "markdown", "id": "c9d139bd", "metadata": {}, "source": [ "Now the execution of the entire analysis becomes much more readable and clean:" ] }, { "cell_type": "code", "execution_count": 15, "id": "358959d4", "metadata": {}, "outputs": [], "source": [ "dataset = extract_data(label=\"Cosine experiment\")\n", "fit_result = run_fitting(dataset)\n", "quantities_of_interest = analyze_fit_results(fit_result)\n", "save_quantities_of_interest(dataset.tuid, quantities_of_interest)\n", "fig, ax = plt.subplots()\n", "plot_fit(fig_=fig, ax_=ax, dataset_=dataset, fit_result_=fit_result)\n", "save_mpl_figure(dataset.tuid, fig)" ] }, { "cell_type": "markdown", "id": "31482522", "metadata": {}, "source": [ "If we inspect the experiment directory, we will find a structure that looks like the following:\n", "\n", "```{code-block}\n", "20230125-172712-018-87b9bf-Cosine experiment/\n", "├── Cosine fit.png\n", "├── dataset.hdf5\n", "├── quantities_of_interest.json\n", "└── snapshot.json\n", "```\n", "\n", "## Creating a simple analysis class\n", "\n", "Even though we have improved code structure greatly, in order to execute the same analysis against some other dataset we would have to copy-paste a significant portion of code (the analysis steps).\n", "\n", "We tackle this by taking advantage of the Object Oriented Programming (OOP) in python.\n", "We will create a python class that serves as a structured container for data (attributes) and the methods (functions) that act on the information.\n", "\n", "Some of the advantages of OOP are:\n", "\n", "- the same class can be instantiated multiple times to act on different data while reusing the same methods;\n", "- all the methods have access to all the data (attributes) associated with a particular instance of the class;\n", "- subclasses can inherit from other classes and extend their functionalities.\n", "\n", "Let's now observe what such a class could look like.\n", "\n", "```{warning}\n", "This analysis class is intended for educational purposes only.\n", "It is not intended to be used as a template!\n", "See the end of the tutorial for the recommended usage of the analysis framework.\n", "```" ] }, { "cell_type": "code", "execution_count": 16, "id": "da4a3264", "metadata": {}, "outputs": [], "source": [ "class MyCosineAnalysis:\n", " \"\"\"Analysis as a class.\"\"\"\n", "\n", " def __init__(self, label: str):\n", " \"\"\"This is a special method that python calls when an instance of this class is\n", " created.\"\"\"\n", "\n", " self.label = label\n", "\n", " # objects to be filled up later when running the analysis\n", " self.tuid = None\n", " self.dataset = None\n", " self.fit_results = {}\n", " self.quantities_of_interest = {}\n", " self.figs_mpl = {}\n", " self.axs_mpl = {}\n", "\n", " # with just slight modification our functions become methods\n", " # with the advantage that we have access to all the necessary information from self\n", " def run(self):\n", " \"\"\"Execute the analysis steps.\"\"\"\n", " self.extract_data()\n", " self.run_fitting()\n", " self.analyze_fit_results()\n", " self.create_figures()\n", " self.save_quantities_of_interest()\n", " self.save_figures()\n", "\n", " def extract_data(self):\n", " \"\"\"Load data from disk.\"\"\"\n", " self.tuid = get_latest_tuid(contains=self.label)\n", " self.dataset = load_dataset(tuid)\n", "\n", " def run_fitting(self):\n", " \"\"\"Fits the model to the data.\"\"\"\n", " model = MyCosineModel()\n", " guess = model.guess(self.dataset.y0.values)\n", " result = model.fit(\n", " self.dataset.y0.values, x=self.dataset.x0.values, params=guess\n", " )\n", " self.fit_results.update({\"cosine\": result})\n", "\n", " def analyze_fit_results(self):\n", " \"\"\"Analyzes the fit results and saves quantities of interest.\"\"\"\n", " self.quantities_of_interest.update(\n", " {\n", " \"amplitude\": self.fit_results[\"cosine\"].params[\"amplitude\"].value,\n", " \"frequency\": self.fit_results[\"cosine\"].params[\"frequency\"].value,\n", " }\n", " )\n", "\n", " def save_quantities_of_interest(self):\n", " \"\"\"Save quantities of interest to disk.\"\"\"\n", " exp_folder_ = Path(locate_experiment_container(self.tuid))\n", " with open(\n", " exp_folder_ / \"quantities_of_interest.json\", \"w\", encoding=\"utf-8\"\n", " ) as file_:\n", " json.dump(self.quantities_of_interest, file_)\n", "\n", " def plot_fit(self, fig_: matplotlib.figure.Figure, ax_: matplotlib.axes.Axes):\n", " \"\"\"Plot the fit result.\"\"\"\n", "\n", " self.dataset.y0.plot.line(ax=ax_, x=\"x0\", marker=\"o\", label=\"Data\") # plot data\n", "\n", " x_fit_ = np.linspace(\n", " self.dataset[\"x0\"][0].values, self.dataset[\"x0\"][-1].values, 1000\n", " )\n", " y_fit_ = cos_func(x=x_fit_, **self.fit_results[\"cosine\"].best_values)\n", " ax_.plot(x_fit_, y_fit_, label=\"Fit\") # plot fit\n", " ax_.legend()\n", "\n", " # set units-aware tick labels\n", " set_xlabel(self.dataset.x0.long_name, self.dataset.x0.attrs[\"units\"], ax_)\n", " set_ylabel(self.dataset.y0.long_name, self.dataset.y0.attrs[\"units\"], ax_)\n", "\n", " # add a reference to the original dataset in the figure title\n", " fig_.suptitle(f\"{dataset.attrs['name']}\\ntuid: {dataset.attrs['tuid']}\")\n", "\n", " def create_figures(self):\n", " \"\"\"Create figures.\"\"\"\n", " fig_, ax_ = plt.subplots()\n", " self.plot_fit(fig_, ax_)\n", "\n", " fig_id = \"cos-data-and-fit\"\n", " self.figs_mpl.update({fig_id: fig_})\n", " # keep a reference to `ax` as well\n", " # it can be accessed later to apply modifications (e.g., in a notebook)\n", " self.axs_mpl.update({fig_id: ax_})\n", "\n", " def save_figures(self):\n", " \"\"\"Save figures to disk.\"\"\"\n", " exp_folder_ = Path(locate_experiment_container(self.tuid))\n", " for fig_name, fig_ in self.figs_mpl.items():\n", " fig_.savefig(exp_folder_ / f\"{fig_name}.png\", dpi=300, bbox_inches=\"tight\")\n", " plt.close(fig_)" ] }, { "cell_type": "markdown", "id": "b56c4016", "metadata": {}, "source": [ "Running the analysis is now as simple as:" ] }, { "cell_type": "code", "execution_count": 17, "id": "ba6ee364", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a_obj = MyCosineAnalysis(label=\"Cosine experiment\")\n", "a_obj.run()\n", "a_obj.figs_mpl[\"cos-data-and-fit\"]" ] }, { "cell_type": "markdown", "id": "6b1d19bb", "metadata": {}, "source": [ "The first line will instantiate the class by calling the {code}`.__init__()` method.\n", "\n", "As expected this will save similar files into the `experiment directory`:\n", "\n", "```{code-block}\n", "20230125-172712-018-87b9bf-Cosine experiment/\n", "├── cos-data-and-fit.png\n", "├── Cosine fit.png\n", "├── dataset.hdf5\n", "├── quantities_of_interest.json\n", "└── snapshot.json\n", "```\n", "\n", "## Extending the BaseAnalysis\n", "\n", "While the above stand-alone class provides the gist of an analysis, we can do even better by defining a structured framework that all analyses need to adhere to and factoring out the pieces of code that are common to most analyses.\n", "Besides that, the overall functionality can be improved.\n", "\n", "Here is where the {class}`~quantify_core.analysis.base_analysis.BaseAnalysis` enters the scene.\n", "It allows us to focus only on the particular aspect of our custom analysis by implementing only the relevant methods. Take a look at how the above class is implemented where we are making use of the analysis framework. For completeness, a fully documented {class}`~quantify_core.analysis.fitting_models.CosineModel` which can serve as a template is shown as well." ] }, { "cell_type": "code", "execution_count": 18, "id": "0909e0d6", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
class CosineModel(lmfit.model.Model):\n",
       "    """\n",
       "    Exemplary lmfit model with a guess for a cosine.\n",
       "\n",
       "    .. note::\n",
       "\n",
       "        The :mod:`lmfit.models` module provides several fitting models that might fit\n",
       "        your needs out of the box.\n",
       "    """\n",
       "\n",
       "    def __init__(self, *args, **kwargs):\n",
       "        # pass in the model's equation\n",
       "        super().__init__(cos_func, *args, **kwargs)\n",
       "\n",
       "        # configure constraints that are independent from the data to be fitted\n",
       "        self.set_param_hint("frequency", min=0, vary=True)  # enforce positive frequency\n",
       "        self.set_param_hint("amplitude", min=0, vary=True)  # enforce positive amplitude\n",
       "        self.set_param_hint("offset", vary=True)\n",
       "        self.set_param_hint(\n",
       "            "phase", vary=True, min=-np.pi, max=np.pi\n",
       "        )  # enforce phase range\n",
       "\n",
       "    # pylint: disable=missing-function-docstring\n",
       "    def guess(self, data, x, **kws) -> lmfit.parameter.Parameters:\n",
       "        """\n",
       "        guess parameters based on the data\n",
       "\n",
       "        Parameters\n",
       "        ----------\n",
       "        data: np.ndarray\n",
       "            Data to fit to\n",
       "        x: np.ndarray\n",
       "            Independet variable\n",
       "        """\n",
       "\n",
       "        self.set_param_hint("offset", value=np.average(data))\n",
       "        self.set_param_hint("amplitude", value=(np.max(data) - np.min(data)) / 2)\n",
       "\n",
       "        # Guess frequency and phase using Fourier Transform\n",
       "        freq_guess, phase_guess = fft_freq_phase_guess(data, x)\n",
       "        phase_wrap = (phase_guess + np.pi) % (2 * np.pi) - np.pi\n",
       "        self.set_param_hint("frequency", value=freq_guess)\n",
       "        self.set_param_hint("phase", value=phase_wrap)\n",
       "\n",
       "        params = self.make_params()\n",
       "        return lmfit.models.update_param_vals(params, self.prefix, **kws)\n",
       "\n",
       "    # Same design patter is used in lmfit.models to inherit common docstrings.\n",
       "    # We adjust these common docstrings to our docs build pipeline\n",
       "    __init__.__doc__ = get_model_common_doc() + mk_seealso("cos_func")\n",
       "    guess.__doc__ = get_guess_common_doc()\n",
       "
\n" ], "text/latex": [ "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n", "\\PY{k}{class} \\PY{n+nc}{CosineModel}\\PY{p}{(}\\PY{n}{lmfit}\\PY{o}{.}\\PY{n}{model}\\PY{o}{.}\\PY{n}{Model}\\PY{p}{)}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n", "\\PY{l+s+sd}{ Exemplary lmfit model with a guess for a cosine.}\n", "\n", "\\PY{l+s+sd}{ .. note::}\n", "\n", "\\PY{l+s+sd}{ The :mod:`lmfit.models` module provides several fitting models that might fit}\n", "\\PY{l+s+sd}{ your needs out of the box.}\n", "\\PY{l+s+sd}{ \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n", "\n", " \\PY{k}{def} \\PY{n+nf+fm}{\\PYZus{}\\PYZus{}init\\PYZus{}\\PYZus{}}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{p}{,} \\PY{o}{*}\\PY{n}{args}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\\PY{p}{:}\n", " \\PY{c+c1}{\\PYZsh{} pass in the model\\PYZsq{}s equation}\n", " \\PY{n+nb}{super}\\PY{p}{(}\\PY{p}{)}\\PY{o}{.}\\PY{n+nf+fm}{\\PYZus{}\\PYZus{}init\\PYZus{}\\PYZus{}}\\PY{p}{(}\\PY{n}{cos\\PYZus{}func}\\PY{p}{,} \\PY{o}{*}\\PY{n}{args}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\n", "\n", " \\PY{c+c1}{\\PYZsh{} configure constraints that are independent from the data to be fitted}\n", " \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{set\\PYZus{}param\\PYZus{}hint}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{frequency}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n+nb}{min}\\PY{o}{=}\\PY{l+m+mi}{0}\\PY{p}{,} \\PY{n}{vary}\\PY{o}{=}\\PY{k+kc}{True}\\PY{p}{)} \\PY{c+c1}{\\PYZsh{} enforce positive frequency}\n", " \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{set\\PYZus{}param\\PYZus{}hint}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{amplitude}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n+nb}{min}\\PY{o}{=}\\PY{l+m+mi}{0}\\PY{p}{,} \\PY{n}{vary}\\PY{o}{=}\\PY{k+kc}{True}\\PY{p}{)} \\PY{c+c1}{\\PYZsh{} enforce positive amplitude}\n", " \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{set\\PYZus{}param\\PYZus{}hint}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{offset}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{vary}\\PY{o}{=}\\PY{k+kc}{True}\\PY{p}{)}\n", " \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{set\\PYZus{}param\\PYZus{}hint}\\PY{p}{(}\n", " \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{phase}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{vary}\\PY{o}{=}\\PY{k+kc}{True}\\PY{p}{,} \\PY{n+nb}{min}\\PY{o}{=}\\PY{o}{\\PYZhy{}}\\PY{n}{np}\\PY{o}{.}\\PY{n}{pi}\\PY{p}{,} \\PY{n+nb}{max}\\PY{o}{=}\\PY{n}{np}\\PY{o}{.}\\PY{n}{pi}\n", " \\PY{p}{)} \\PY{c+c1}{\\PYZsh{} enforce phase range}\n", "\n", " \\PY{c+c1}{\\PYZsh{} pylint: disable=missing\\PYZhy{}function\\PYZhy{}docstring}\n", " \\PY{k}{def} \\PY{n+nf}{guess}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{p}{,} \\PY{n}{data}\\PY{p}{,} \\PY{n}{x}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kws}\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{lmfit}\\PY{o}{.}\\PY{n}{parameter}\\PY{o}{.}\\PY{n}{Parameters}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n", "\\PY{l+s+sd}{ guess parameters based on the data}\n", "\n", "\\PY{l+s+sd}{ Parameters}\n", "\\PY{l+s+sd}{ \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n", "\\PY{l+s+sd}{ data: np.ndarray}\n", "\\PY{l+s+sd}{ Data to fit to}\n", "\\PY{l+s+sd}{ x: np.ndarray}\n", "\\PY{l+s+sd}{ Independet variable}\n", "\\PY{l+s+sd}{ \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n", "\n", " \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{set\\PYZus{}param\\PYZus{}hint}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{offset}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{value}\\PY{o}{=}\\PY{n}{np}\\PY{o}{.}\\PY{n}{average}\\PY{p}{(}\\PY{n}{data}\\PY{p}{)}\\PY{p}{)}\n", " \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{set\\PYZus{}param\\PYZus{}hint}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{amplitude}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{value}\\PY{o}{=}\\PY{p}{(}\\PY{n}{np}\\PY{o}{.}\\PY{n}{max}\\PY{p}{(}\\PY{n}{data}\\PY{p}{)} \\PY{o}{\\PYZhy{}} \\PY{n}{np}\\PY{o}{.}\\PY{n}{min}\\PY{p}{(}\\PY{n}{data}\\PY{p}{)}\\PY{p}{)} \\PY{o}{/} \\PY{l+m+mi}{2}\\PY{p}{)}\n", "\n", " \\PY{c+c1}{\\PYZsh{} Guess frequency and phase using Fourier Transform}\n", " \\PY{n}{freq\\PYZus{}guess}\\PY{p}{,} \\PY{n}{phase\\PYZus{}guess} \\PY{o}{=} \\PY{n}{fft\\PYZus{}freq\\PYZus{}phase\\PYZus{}guess}\\PY{p}{(}\\PY{n}{data}\\PY{p}{,} \\PY{n}{x}\\PY{p}{)}\n", " \\PY{n}{phase\\PYZus{}wrap} \\PY{o}{=} \\PY{p}{(}\\PY{n}{phase\\PYZus{}guess} \\PY{o}{+} \\PY{n}{np}\\PY{o}{.}\\PY{n}{pi}\\PY{p}{)} \\PY{o}{\\PYZpc{}} \\PY{p}{(}\\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{np}\\PY{o}{.}\\PY{n}{pi}\\PY{p}{)} \\PY{o}{\\PYZhy{}} \\PY{n}{np}\\PY{o}{.}\\PY{n}{pi}\n", " \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{set\\PYZus{}param\\PYZus{}hint}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{frequency}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{value}\\PY{o}{=}\\PY{n}{freq\\PYZus{}guess}\\PY{p}{)}\n", " \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{set\\PYZus{}param\\PYZus{}hint}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{phase}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{value}\\PY{o}{=}\\PY{n}{phase\\PYZus{}wrap}\\PY{p}{)}\n", "\n", " \\PY{n}{params} \\PY{o}{=} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{make\\PYZus{}params}\\PY{p}{(}\\PY{p}{)}\n", " \\PY{k}{return} \\PY{n}{lmfit}\\PY{o}{.}\\PY{n}{models}\\PY{o}{.}\\PY{n}{update\\PYZus{}param\\PYZus{}vals}\\PY{p}{(}\\PY{n}{params}\\PY{p}{,} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{prefix}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kws}\\PY{p}{)}\n", "\n", " \\PY{c+c1}{\\PYZsh{} Same design patter is used in lmfit.models to inherit common docstrings.}\n", " \\PY{c+c1}{\\PYZsh{} We adjust these common docstrings to our docs build pipeline}\n", " \\PY{n+nf+fm}{\\PYZus{}\\PYZus{}init\\PYZus{}\\PYZus{}}\\PY{o}{.}\\PY{n+nv+vm}{\\PYZus{}\\PYZus{}doc\\PYZus{}\\PYZus{}} \\PY{o}{=} \\PY{n}{get\\PYZus{}model\\PYZus{}common\\PYZus{}doc}\\PY{p}{(}\\PY{p}{)} \\PY{o}{+} \\PY{n}{mk\\PYZus{}seealso}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{cos\\PYZus{}func}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n", " \\PY{n}{guess}\\PY{o}{.}\\PY{n+nv+vm}{\\PYZus{}\\PYZus{}doc\\PYZus{}\\PYZus{}} \\PY{o}{=} \\PY{n}{get\\PYZus{}guess\\PYZus{}common\\PYZus{}doc}\\PY{p}{(}\\PY{p}{)}\n", "\\end{Verbatim}\n" ], "text/plain": [ "class CosineModel(lmfit.model.Model):\n", " \"\"\"\n", " Exemplary lmfit model with a guess for a cosine.\n", "\n", " .. note::\n", "\n", " The :mod:`lmfit.models` module provides several fitting models that might fit\n", " your needs out of the box.\n", " \"\"\"\n", "\n", " def __init__(self, *args, **kwargs):\n", " # pass in the model's equation\n", " super().__init__(cos_func, *args, **kwargs)\n", "\n", " # configure constraints that are independent from the data to be fitted\n", " self.set_param_hint(\"frequency\", min=0, vary=True) # enforce positive frequency\n", " self.set_param_hint(\"amplitude\", min=0, vary=True) # enforce positive amplitude\n", " self.set_param_hint(\"offset\", vary=True)\n", " self.set_param_hint(\n", " \"phase\", vary=True, min=-np.pi, max=np.pi\n", " ) # enforce phase range\n", "\n", " # pylint: disable=missing-function-docstring\n", " def guess(self, data, x, **kws) -> lmfit.parameter.Parameters:\n", " \"\"\"\n", " guess parameters based on the data\n", "\n", " Parameters\n", " ----------\n", " data: np.ndarray\n", " Data to fit to\n", " x: np.ndarray\n", " Independet variable\n", " \"\"\"\n", "\n", " self.set_param_hint(\"offset\", value=np.average(data))\n", " self.set_param_hint(\"amplitude\", value=(np.max(data) - np.min(data)) / 2)\n", "\n", " # Guess frequency and phase using Fourier Transform\n", " freq_guess, phase_guess = fft_freq_phase_guess(data, x)\n", " phase_wrap = (phase_guess + np.pi) % (2 * np.pi) - np.pi\n", " self.set_param_hint(\"frequency\", value=freq_guess)\n", " self.set_param_hint(\"phase\", value=phase_wrap)\n", "\n", " params = self.make_params()\n", " return lmfit.models.update_param_vals(params, self.prefix, **kws)\n", "\n", " # Same design patter is used in lmfit.models to inherit common docstrings.\n", " # We adjust these common docstrings to our docs build pipeline\n", " __init__.__doc__ = get_model_common_doc() + mk_seealso(\"cos_func\")\n", " guess.__doc__ = get_guess_common_doc()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
class CosineAnalysis(ba.BaseAnalysis):\n",
       "    """\n",
       "    Exemplary analysis subclass that fits a cosine to a dataset.\n",
       "    """\n",
       "\n",
       "    def process_data(self):\n",
       "        """\n",
       "        In some cases, you might need to process the data, e.g., reshape, filter etc.,\n",
       "        before starting the analysis. This is the method where it should be done.\n",
       "\n",
       "        See :meth:`~quantify_core.analysis.spectroscopy_analysis.ResonatorSpectroscopyAnalysis.process_data`\n",
       "        for an implementation example.\n",
       "        """  # pylint: disable=line-too-long\n",
       "\n",
       "    def run_fitting(self):\n",
       "        """\n",
       "        Fits a :class:`~quantify_core.analysis.fitting_models.CosineModel` to the data.\n",
       "        """\n",
       "        # create a fitting model based on a cosine function\n",
       "        model = CosineModel()\n",
       "        guess = model.guess(self.dataset.y0.values, x=self.dataset.x0.values)\n",
       "        result = model.fit(\n",
       "            self.dataset.y0.values, x=self.dataset.x0.values, params=guess\n",
       "        )\n",
       "        self.fit_results.update({"cosine": result})\n",
       "\n",
       "    def create_figures(self):\n",
       "        """\n",
       "        Creates a figure with the data and the fit.\n",
       "        """\n",
       "        fig, ax = plt.subplots()\n",
       "        fig_id = "cos_fit"\n",
       "        self.figs_mpl.update({fig_id: fig})\n",
       "        self.axs_mpl.update({fig_id: ax})\n",
       "\n",
       "        self.dataset.y0.plot(ax=ax, x="x0", marker="o", linestyle="")\n",
       "        qpl.plot_fit(ax, self.fit_results["cosine"])\n",
       "        qpl.plot_textbox(ax, ba.wrap_text(self.quantities_of_interest["fit_msg"]))\n",
       "\n",
       "        adjust_axeslabels_SI(ax)\n",
       "        qpl.set_suptitle_from_dataset(fig, self.dataset, "x0-y0")\n",
       "        ax.legend()\n",
       "\n",
       "    def analyze_fit_results(self):\n",
       "        """\n",
       "        Checks fit success and populates :code:`quantities_of_interest`.\n",
       "        """\n",
       "        fit_result = self.fit_results["cosine"]\n",
       "        fit_warning = ba.check_lmfit(fit_result)\n",
       "\n",
       "        # If there is a problem with the fit, display an error message in the text box.\n",
       "        # Otherwise, display the parameters as normal.\n",
       "        if fit_warning is None:\n",
       "            self.quantities_of_interest["fit_success"] = True\n",
       "            unit = self.dataset.y0.units\n",
       "            text_msg = "Summary\\n"\n",
       "            text_msg += format_value_string(\n",
       "                r"$f$", fit_result.params["frequency"], end_char="\\n", unit="Hz"\n",
       "            )\n",
       "            text_msg += format_value_string(\n",
       "                r"$A$", fit_result.params["amplitude"], unit=unit\n",
       "            )\n",
       "        else:\n",
       "            text_msg = fit_warning\n",
       "            self.quantities_of_interest["fit_success"] = False\n",
       "\n",
       "        # save values and fit uncertainty\n",
       "        for parameter_name in ["frequency", "amplitude"]:\n",
       "            self.quantities_of_interest[parameter_name] = ba.lmfit_par_to_ufloat(\n",
       "                fit_result.params[parameter_name]\n",
       "            )\n",
       "        self.quantities_of_interest["fit_msg"] = text_msg\n",
       "
\n" ], "text/latex": [ "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n", "\\PY{k}{class} \\PY{n+nc}{CosineAnalysis}\\PY{p}{(}\\PY{n}{ba}\\PY{o}{.}\\PY{n}{BaseAnalysis}\\PY{p}{)}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n", "\\PY{l+s+sd}{ Exemplary analysis subclass that fits a cosine to a dataset.}\n", "\\PY{l+s+sd}{ \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n", "\n", " \\PY{k}{def} \\PY{n+nf}{process\\PYZus{}data}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{p}{)}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n", "\\PY{l+s+sd}{ In some cases, you might need to process the data, e.g., reshape, filter etc.,}\n", "\\PY{l+s+sd}{ before starting the analysis. This is the method where it should be done.}\n", "\n", "\\PY{l+s+sd}{ See :meth:`\\PYZti{}quantify\\PYZus{}core.analysis.spectroscopy\\PYZus{}analysis.ResonatorSpectroscopyAnalysis.process\\PYZus{}data`}\n", "\\PY{l+s+sd}{ for an implementation example.}\n", "\\PY{l+s+sd}{ \\PYZdq{}\\PYZdq{}\\PYZdq{}} \\PY{c+c1}{\\PYZsh{} pylint: disable=line\\PYZhy{}too\\PYZhy{}long}\n", "\n", " \\PY{k}{def} \\PY{n+nf}{run\\PYZus{}fitting}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{p}{)}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n", "\\PY{l+s+sd}{ Fits a :class:`\\PYZti{}quantify\\PYZus{}core.analysis.fitting\\PYZus{}models.CosineModel` to the data.}\n", "\\PY{l+s+sd}{ \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n", " \\PY{c+c1}{\\PYZsh{} create a fitting model based on a cosine function}\n", " \\PY{n}{model} \\PY{o}{=} \\PY{n}{CosineModel}\\PY{p}{(}\\PY{p}{)}\n", " \\PY{n}{guess} \\PY{o}{=} \\PY{n}{model}\\PY{o}{.}\\PY{n}{guess}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{dataset}\\PY{o}{.}\\PY{n}{y0}\\PY{o}{.}\\PY{n}{values}\\PY{p}{,} \\PY{n}{x}\\PY{o}{=}\\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{dataset}\\PY{o}{.}\\PY{n}{x0}\\PY{o}{.}\\PY{n}{values}\\PY{p}{)}\n", " \\PY{n}{result} \\PY{o}{=} \\PY{n}{model}\\PY{o}{.}\\PY{n}{fit}\\PY{p}{(}\n", " \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{dataset}\\PY{o}{.}\\PY{n}{y0}\\PY{o}{.}\\PY{n}{values}\\PY{p}{,} \\PY{n}{x}\\PY{o}{=}\\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{dataset}\\PY{o}{.}\\PY{n}{x0}\\PY{o}{.}\\PY{n}{values}\\PY{p}{,} \\PY{n}{params}\\PY{o}{=}\\PY{n}{guess}\n", " \\PY{p}{)}\n", " \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{fit\\PYZus{}results}\\PY{o}{.}\\PY{n}{update}\\PY{p}{(}\\PY{p}{\\PYZob{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{cosine}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{:} \\PY{n}{result}\\PY{p}{\\PYZcb{}}\\PY{p}{)}\n", "\n", " \\PY{k}{def} \\PY{n+nf}{create\\PYZus{}figures}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{p}{)}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n", "\\PY{l+s+sd}{ Creates a figure with the data and the fit.}\n", "\\PY{l+s+sd}{ \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n", " \\PY{n}{fig}\\PY{p}{,} \\PY{n}{ax} \\PY{o}{=} \\PY{n}{plt}\\PY{o}{.}\\PY{n}{subplots}\\PY{p}{(}\\PY{p}{)}\n", " \\PY{n}{fig\\PYZus{}id} \\PY{o}{=} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{cos\\PYZus{}fit}\\PY{l+s+s2}{\\PYZdq{}}\n", " \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{figs\\PYZus{}mpl}\\PY{o}{.}\\PY{n}{update}\\PY{p}{(}\\PY{p}{\\PYZob{}}\\PY{n}{fig\\PYZus{}id}\\PY{p}{:} \\PY{n}{fig}\\PY{p}{\\PYZcb{}}\\PY{p}{)}\n", " \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{axs\\PYZus{}mpl}\\PY{o}{.}\\PY{n}{update}\\PY{p}{(}\\PY{p}{\\PYZob{}}\\PY{n}{fig\\PYZus{}id}\\PY{p}{:} \\PY{n}{ax}\\PY{p}{\\PYZcb{}}\\PY{p}{)}\n", "\n", " \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{dataset}\\PY{o}{.}\\PY{n}{y0}\\PY{o}{.}\\PY{n}{plot}\\PY{p}{(}\\PY{n}{ax}\\PY{o}{=}\\PY{n}{ax}\\PY{p}{,} \\PY{n}{x}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{x0}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{marker}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{o}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{linestyle}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n", " \\PY{n}{qpl}\\PY{o}{.}\\PY{n}{plot\\PYZus{}fit}\\PY{p}{(}\\PY{n}{ax}\\PY{p}{,} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{fit\\PYZus{}results}\\PY{p}{[}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{cosine}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]}\\PY{p}{)}\n", " \\PY{n}{qpl}\\PY{o}{.}\\PY{n}{plot\\PYZus{}textbox}\\PY{p}{(}\\PY{n}{ax}\\PY{p}{,} \\PY{n}{ba}\\PY{o}{.}\\PY{n}{wrap\\PYZus{}text}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{quantities\\PYZus{}of\\PYZus{}interest}\\PY{p}{[}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{fit\\PYZus{}msg}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]}\\PY{p}{)}\\PY{p}{)}\n", "\n", " \\PY{n}{adjust\\PYZus{}axeslabels\\PYZus{}SI}\\PY{p}{(}\\PY{n}{ax}\\PY{p}{)}\n", " \\PY{n}{qpl}\\PY{o}{.}\\PY{n}{set\\PYZus{}suptitle\\PYZus{}from\\PYZus{}dataset}\\PY{p}{(}\\PY{n}{fig}\\PY{p}{,} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{dataset}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{x0\\PYZhy{}y0}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n", " \\PY{n}{ax}\\PY{o}{.}\\PY{n}{legend}\\PY{p}{(}\\PY{p}{)}\n", "\n", " \\PY{k}{def} \\PY{n+nf}{analyze\\PYZus{}fit\\PYZus{}results}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{p}{)}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n", "\\PY{l+s+sd}{ Checks fit success and populates :code:`quantities\\PYZus{}of\\PYZus{}interest`.}\n", "\\PY{l+s+sd}{ \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n", " \\PY{n}{fit\\PYZus{}result} \\PY{o}{=} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{fit\\PYZus{}results}\\PY{p}{[}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{cosine}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]}\n", " \\PY{n}{fit\\PYZus{}warning} \\PY{o}{=} \\PY{n}{ba}\\PY{o}{.}\\PY{n}{check\\PYZus{}lmfit}\\PY{p}{(}\\PY{n}{fit\\PYZus{}result}\\PY{p}{)}\n", "\n", " \\PY{c+c1}{\\PYZsh{} If there is a problem with the fit, display an error message in the text box.}\n", " \\PY{c+c1}{\\PYZsh{} Otherwise, display the parameters as normal.}\n", " \\PY{k}{if} \\PY{n}{fit\\PYZus{}warning} \\PY{o+ow}{is} \\PY{k+kc}{None}\\PY{p}{:}\n", " \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{quantities\\PYZus{}of\\PYZus{}interest}\\PY{p}{[}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{fit\\PYZus{}success}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]} \\PY{o}{=} \\PY{k+kc}{True}\n", " \\PY{n}{unit} \\PY{o}{=} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{dataset}\\PY{o}{.}\\PY{n}{y0}\\PY{o}{.}\\PY{n}{units}\n", " \\PY{n}{text\\PYZus{}msg} \\PY{o}{=} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Summary}\\PY{l+s+se}{\\PYZbs{}n}\\PY{l+s+s2}{\\PYZdq{}}\n", " \\PY{n}{text\\PYZus{}msg} \\PY{o}{+}\\PY{o}{=} \\PY{n}{format\\PYZus{}value\\PYZus{}string}\\PY{p}{(}\n", " \\PY{l+s+sa}{r}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{\\PYZdl{}f\\PYZdl{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{fit\\PYZus{}result}\\PY{o}{.}\\PY{n}{params}\\PY{p}{[}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{frequency}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]}\\PY{p}{,} \\PY{n}{end\\PYZus{}char}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+se}{\\PYZbs{}n}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Hz}\\PY{l+s+s2}{\\PYZdq{}}\n", " \\PY{p}{)}\n", " \\PY{n}{text\\PYZus{}msg} \\PY{o}{+}\\PY{o}{=} \\PY{n}{format\\PYZus{}value\\PYZus{}string}\\PY{p}{(}\n", " \\PY{l+s+sa}{r}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{\\PYZdl{}A\\PYZdl{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{fit\\PYZus{}result}\\PY{o}{.}\\PY{n}{params}\\PY{p}{[}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{amplitude}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{n}{unit}\n", " \\PY{p}{)}\n", " \\PY{k}{else}\\PY{p}{:}\n", " \\PY{n}{text\\PYZus{}msg} \\PY{o}{=} \\PY{n}{fit\\PYZus{}warning}\n", " \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{quantities\\PYZus{}of\\PYZus{}interest}\\PY{p}{[}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{fit\\PYZus{}success}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]} \\PY{o}{=} \\PY{k+kc}{False}\n", "\n", " \\PY{c+c1}{\\PYZsh{} save values and fit uncertainty}\n", " \\PY{k}{for} \\PY{n}{parameter\\PYZus{}name} \\PY{o+ow}{in} \\PY{p}{[}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{frequency}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{amplitude}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]}\\PY{p}{:}\n", " \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{quantities\\PYZus{}of\\PYZus{}interest}\\PY{p}{[}\\PY{n}{parameter\\PYZus{}name}\\PY{p}{]} \\PY{o}{=} \\PY{n}{ba}\\PY{o}{.}\\PY{n}{lmfit\\PYZus{}par\\PYZus{}to\\PYZus{}ufloat}\\PY{p}{(}\n", " \\PY{n}{fit\\PYZus{}result}\\PY{o}{.}\\PY{n}{params}\\PY{p}{[}\\PY{n}{parameter\\PYZus{}name}\\PY{p}{]}\n", " \\PY{p}{)}\n", " \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{quantities\\PYZus{}of\\PYZus{}interest}\\PY{p}{[}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{fit\\PYZus{}msg}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]} \\PY{o}{=} \\PY{n}{text\\PYZus{}msg}\n", "\\end{Verbatim}\n" ], "text/plain": [ "class CosineAnalysis(ba.BaseAnalysis):\n", " \"\"\"\n", " Exemplary analysis subclass that fits a cosine to a dataset.\n", " \"\"\"\n", "\n", " def process_data(self):\n", " \"\"\"\n", " In some cases, you might need to process the data, e.g., reshape, filter etc.,\n", " before starting the analysis. This is the method where it should be done.\n", "\n", " See :meth:`~quantify_core.analysis.spectroscopy_analysis.ResonatorSpectroscopyAnalysis.process_data`\n", " for an implementation example.\n", " \"\"\" # pylint: disable=line-too-long\n", "\n", " def run_fitting(self):\n", " \"\"\"\n", " Fits a :class:`~quantify_core.analysis.fitting_models.CosineModel` to the data.\n", " \"\"\"\n", " # create a fitting model based on a cosine function\n", " model = CosineModel()\n", " guess = model.guess(self.dataset.y0.values, x=self.dataset.x0.values)\n", " result = model.fit(\n", " self.dataset.y0.values, x=self.dataset.x0.values, params=guess\n", " )\n", " self.fit_results.update({\"cosine\": result})\n", "\n", " def create_figures(self):\n", " \"\"\"\n", " Creates a figure with the data and the fit.\n", " \"\"\"\n", " fig, ax = plt.subplots()\n", " fig_id = \"cos_fit\"\n", " self.figs_mpl.update({fig_id: fig})\n", " self.axs_mpl.update({fig_id: ax})\n", "\n", " self.dataset.y0.plot(ax=ax, x=\"x0\", marker=\"o\", linestyle=\"\")\n", " qpl.plot_fit(ax, self.fit_results[\"cosine\"])\n", " qpl.plot_textbox(ax, ba.wrap_text(self.quantities_of_interest[\"fit_msg\"]))\n", "\n", " adjust_axeslabels_SI(ax)\n", " qpl.set_suptitle_from_dataset(fig, self.dataset, \"x0-y0\")\n", " ax.legend()\n", "\n", " def analyze_fit_results(self):\n", " \"\"\"\n", " Checks fit success and populates :code:`quantities_of_interest`.\n", " \"\"\"\n", " fit_result = self.fit_results[\"cosine\"]\n", " fit_warning = ba.check_lmfit(fit_result)\n", "\n", " # If there is a problem with the fit, display an error message in the text box.\n", " # Otherwise, display the parameters as normal.\n", " if fit_warning is None:\n", " self.quantities_of_interest[\"fit_success\"] = True\n", " unit = self.dataset.y0.units\n", " text_msg = \"Summary\\n\"\n", " text_msg += format_value_string(\n", " r\"$f$\", fit_result.params[\"frequency\"], end_char=\"\\n\", unit=\"Hz\"\n", " )\n", " text_msg += format_value_string(\n", " r\"$A$\", fit_result.params[\"amplitude\"], unit=unit\n", " )\n", " else:\n", " text_msg = fit_warning\n", " self.quantities_of_interest[\"fit_success\"] = False\n", "\n", " # save values and fit uncertainty\n", " for parameter_name in [\"frequency\", \"amplitude\"]:\n", " self.quantities_of_interest[parameter_name] = ba.lmfit_par_to_ufloat(\n", " fit_result.params[parameter_name]\n", " )\n", " self.quantities_of_interest[\"fit_msg\"] = text_msg" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display_source_code(CosineModel)\n", "display_source_code(CosineAnalysis)" ] }, { "cell_type": "markdown", "id": "4c1eee01", "metadata": {}, "source": [ "Now we can simply execute it against our latest experiment as follows:" ] }, { "cell_type": "code", "execution_count": 19, "id": "c030ad1e", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "a_obj = CosineAnalysis(label=\"Cosine experiment\").run()\n", "a_obj.display_figs_mpl()" ] }, { "cell_type": "markdown", "id": "5f30a46e", "metadata": {}, "source": [ "Inspecting the `experiment directory` will show something like this:\n", "\n", "```{code-block}\n", "20230125-172712-018-87b9bf-Cosine experiment/\n", "├── analysis_CosineAnalysis/\n", "│ ├── dataset_processed.hdf5\n", "│ ├── figs_mpl/\n", "│ │ ├── cos_fit.png\n", "│ │ └── cos_fit.svg\n", "│ ├── fit_results/\n", "│ │ └── cosine.txt\n", "│ └── quantities_of_interest.json\n", "├── cos-data-and-fit.png\n", "├── Cosine fit.png\n", "├── dataset.hdf5\n", "├── quantities_of_interest.json\n", "└── snapshot.json\n", "```\n", "\n", "As you can conclude from the {class}`!CosineAnalysis` code, we did not implement quite a few methods in there.\n", "These are provided by the {class}`~quantify_core.analysis.base_analysis.BaseAnalysis`.\n", "To gain some insight into what exactly is being executed we can enable the logging module and use the internal logger of the analysis instance:" ] }, { "cell_type": "code", "execution_count": 20, "id": "62be0929", "metadata": { "myst_nb": { "output_stderr": "show" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:CosineAnalysis:Executing `.analysis_steps` of CosineAnalysis\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:CosineAnalysis:extracting data: >\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:CosineAnalysis:executing step 1: >\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:CosineAnalysis:executing step 2: >\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:CosineAnalysis:executing step 3: >\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:CosineAnalysis:executing step 4: >\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:CosineAnalysis:executing step 5: >\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:CosineAnalysis:executing step 6: >\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:CosineAnalysis:executing step 7: >\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:CosineAnalysis:executing step 8: >\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:CosineAnalysis:executing step 9: >\n" ] } ], "source": [ "# activate logging and set global level to show warnings only\n", "logging.basicConfig(level=logging.WARNING)\n", "\n", "# set analysis logger level to info (the logger is inherited from BaseAnalysis)\n", "a_obj.logger.setLevel(level=logging.INFO)\n", "_ = a_obj.run()" ] } ], "metadata": { "file_format": "mystnb", "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": 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