{
"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": [
"
defmk_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",
" defcosine_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",
" returninstr\n",
"
\n"
],
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"\n",
" \\PY{c+c1}{\\PYZsh{} ManualParameter\\PYZsq{}s is a handy class that preserves the QCoDeS\\PYZsq{} Parameter}\n",
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" \\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",
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" \\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",
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" \\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": {
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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": [
"
"
],
"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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",
"text/plain": [
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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 method
leastsq
# function evals
41
# data points
30
# variables
4
chi-square
0.07952922
reduced chi-square
0.00305882
Akaike info crit.
-169.984843
Bayesian info crit.
-164.380054
R-squared
0.97882395
Parameters
name
value
standard error
relative error
initial value
min
max
vary
frequency
0.98668701
0.00896805
(0.91%)
0.8
-inf
inf
True
amplitude
0.49020586
0.01427535
(2.91%)
0.5
0.10000000
2.00000000
True
offset
0.02069294
0.01098747
(53.10%)
0
-inf
inf
True
phase
0.07832459
0.06361375
(81.22%)
0
-inf
inf
True
Correlations (unreported values are < 0.100)
Parameter1
Parameter 2
Correlation
frequency
phase
-0.8865
frequency
offset
-0.3934
offset
phase
+0.3487
frequency
amplitude
-0.1372
amplitude
phase
+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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