defmk_iq_shots(\n",
" num_shots:int=128,\n",
" sigmas:Union[Tuple[float],np.ndarray]=(0.1,0.1),\n",
" centers:Union[Tuple[complex],np.ndarray]=(-0.2+0.65j,0.7+4j),\n",
" probabilities:Union[Tuple[float],np.ndarray]=(0.4,0.6),\n",
" seed:Union[int,None]=112233,\n",
")->np.ndarray:\n",
" """\n",
" Generates clusters of (I + 1j*Q) points with a Gaussian distribution with the\n",
" specified sigmas and centers according to the probabilities of each cluster\n",
"\n",
" .. admonition:: Examples\n",
" :class: dropdown\n",
"\n",
" .. include:: examples/utilities.examples_support.mk_iq_shots.rst.txt\n",
"\n",
" Parameters\n",
" ----------\n",
" num_shots\n",
" The number of shot to generate.\n",
" sigma\n",
" The sigma of the Gaussian distribution used for both real and imaginary parts.\n",
" centers\n",
" The center of each cluster on the imaginary plane.\n",
" probabilities\n",
" The probabilities of each cluster being randomly selected for each shot.\n",
" seed\n",
" Random number generator seed passed to ``numpy.random.default_rng``.\n",
" """\n",
" ifnotlen(sigmas)==len(centers)==len(probabilities):\n",
" raiseValueError(\n",
" f"Incorrect input. sigmas={sigmas}, centers={centers} and "\n",
" f"probabilities={probabilities} must have the same length."\n",
" )\n",
"\n",
" rng=np.random.default_rng(seed=seed)\n",
"\n",
" cluster_indices=tuple(range(len(centers)))\n",
" choices=rng.choice(a=cluster_indices,size=num_shots,p=probabilities)\n",
"\n",
" shots=[]\n",
" foridxincluster_indices:\n",
" num_shots_this_cluster=np.sum(choices==idx)\n",
" i_data=rng.normal(\n",
" loc=centers[idx].real,\n",
" scale=sigmas[idx],\n",
" size=num_shots_this_cluster,\n",
" )\n",
" q_data=rng.normal(\n",
" loc=centers[idx].imag,\n",
" scale=sigmas[idx],\n",
" size=num_shots_this_cluster,\n",
" )\n",
" shots.append(i_data+1j*q_data)\n",
" returnnp.concatenate(shots)\n",
"
\n"
],
"text/latex": [
"\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
"\\PY{k}{def} \\PY{n+nf}{mk\\PYZus{}iq\\PYZus{}shots}\\PY{p}{(}\n",
" \\PY{n}{num\\PYZus{}shots}\\PY{p}{:} \\PY{n+nb}{int} \\PY{o}{=} \\PY{l+m+mi}{128}\\PY{p}{,}\n",
" \\PY{n}{sigmas}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n}{Tuple}\\PY{p}{[}\\PY{n+nb}{float}\\PY{p}{]}\\PY{p}{,} \\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{]} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mf}{0.1}\\PY{p}{,} \\PY{l+m+mf}{0.1}\\PY{p}{)}\\PY{p}{,}\n",
" \\PY{n}{centers}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n}{Tuple}\\PY{p}{[}\\PY{n+nb}{complex}\\PY{p}{]}\\PY{p}{,} \\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{]} \\PY{o}{=} \\PY{p}{(}\\PY{o}{\\PYZhy{}}\\PY{l+m+mf}{0.2} \\PY{o}{+} \\PY{l+m+mf}{0.65}\\PY{n}{j}\\PY{p}{,} \\PY{l+m+mf}{0.7} \\PY{o}{+} \\PY{l+m+mi}{4}\\PY{n}{j}\\PY{p}{)}\\PY{p}{,}\n",
" \\PY{n}{probabilities}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n}{Tuple}\\PY{p}{[}\\PY{n+nb}{float}\\PY{p}{]}\\PY{p}{,} \\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{]} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mf}{0.4}\\PY{p}{,} \\PY{l+m+mf}{0.6}\\PY{p}{)}\\PY{p}{,}\n",
" \\PY{n}{seed}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n+nb}{int}\\PY{p}{,} \\PY{k+kc}{None}\\PY{p}{]} \\PY{o}{=} \\PY{l+m+mi}{112233}\\PY{p}{,}\n",
"\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{:}\n",
" \\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
"\\PY{l+s+sd}{ Generates clusters of (I + 1j*Q) points with a Gaussian distribution with the}\n",
"\\PY{l+s+sd}{ specified sigmas and centers according to the probabilities of each cluster}\n",
"\n",
"\\PY{l+s+sd}{ .. admonition:: Examples}\n",
"\\PY{l+s+sd}{ :class: dropdown}\n",
"\n",
"\\PY{l+s+sd}{ .. include:: examples/utilities.examples\\PYZus{}support.mk\\PYZus{}iq\\PYZus{}shots.rst.txt}\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}{ num\\PYZus{}shots}\n",
"\\PY{l+s+sd}{ The number of shot to generate.}\n",
"\\PY{l+s+sd}{ sigma}\n",
"\\PY{l+s+sd}{ The sigma of the Gaussian distribution used for both real and imaginary parts.}\n",
"\\PY{l+s+sd}{ centers}\n",
"\\PY{l+s+sd}{ The center of each cluster on the imaginary plane.}\n",
"\\PY{l+s+sd}{ probabilities}\n",
"\\PY{l+s+sd}{ The probabilities of each cluster being randomly selected for each shot.}\n",
"\\PY{l+s+sd}{ seed}\n",
"\\PY{l+s+sd}{ Random number generator seed passed to ``numpy.random.default\\PYZus{}rng``.}\n",
"\\PY{l+s+sd}{ \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
" \\PY{k}{if} \\PY{o+ow}{not} \\PY{n+nb}{len}\\PY{p}{(}\\PY{n}{sigmas}\\PY{p}{)} \\PY{o}{==} \\PY{n+nb}{len}\\PY{p}{(}\\PY{n}{centers}\\PY{p}{)} \\PY{o}{==} \\PY{n+nb}{len}\\PY{p}{(}\\PY{n}{probabilities}\\PY{p}{)}\\PY{p}{:}\n",
" \\PY{k}{raise} \\PY{n+ne}{ValueError}\\PY{p}{(}\n",
" \\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Incorrect input. sigmas=}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{sigmas}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{, centers=}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{centers}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{ and }\\PY{l+s+s2}{\\PYZdq{}}\n",
" \\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{probabilities=}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{probabilities}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{ must have the same length.}\\PY{l+s+s2}{\\PYZdq{}}\n",
" \\PY{p}{)}\n",
"\n",
" \\PY{n}{rng} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{random}\\PY{o}{.}\\PY{n}{default\\PYZus{}rng}\\PY{p}{(}\\PY{n}{seed}\\PY{o}{=}\\PY{n}{seed}\\PY{p}{)}\n",
"\n",
" \\PY{n}{cluster\\PYZus{}indices} \\PY{o}{=} \\PY{n+nb}{tuple}\\PY{p}{(}\\PY{n+nb}{range}\\PY{p}{(}\\PY{n+nb}{len}\\PY{p}{(}\\PY{n}{centers}\\PY{p}{)}\\PY{p}{)}\\PY{p}{)}\n",
" \\PY{n}{choices} \\PY{o}{=} \\PY{n}{rng}\\PY{o}{.}\\PY{n}{choice}\\PY{p}{(}\\PY{n}{a}\\PY{o}{=}\\PY{n}{cluster\\PYZus{}indices}\\PY{p}{,} \\PY{n}{size}\\PY{o}{=}\\PY{n}{num\\PYZus{}shots}\\PY{p}{,} \\PY{n}{p}\\PY{o}{=}\\PY{n}{probabilities}\\PY{p}{)}\n",
"\n",
" \\PY{n}{shots} \\PY{o}{=} \\PY{p}{[}\\PY{p}{]}\n",
" \\PY{k}{for} \\PY{n}{idx} \\PY{o+ow}{in} \\PY{n}{cluster\\PYZus{}indices}\\PY{p}{:}\n",
" \\PY{n}{num\\PYZus{}shots\\PYZus{}this\\PYZus{}cluster} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{sum}\\PY{p}{(}\\PY{n}{choices} \\PY{o}{==} \\PY{n}{idx}\\PY{p}{)}\n",
" \\PY{n}{i\\PYZus{}data} \\PY{o}{=} \\PY{n}{rng}\\PY{o}{.}\\PY{n}{normal}\\PY{p}{(}\n",
" \\PY{n}{loc}\\PY{o}{=}\\PY{n}{centers}\\PY{p}{[}\\PY{n}{idx}\\PY{p}{]}\\PY{o}{.}\\PY{n}{real}\\PY{p}{,}\n",
" \\PY{n}{scale}\\PY{o}{=}\\PY{n}{sigmas}\\PY{p}{[}\\PY{n}{idx}\\PY{p}{]}\\PY{p}{,}\n",
" \\PY{n}{size}\\PY{o}{=}\\PY{n}{num\\PYZus{}shots\\PYZus{}this\\PYZus{}cluster}\\PY{p}{,}\n",
" \\PY{p}{)}\n",
" \\PY{n}{q\\PYZus{}data} \\PY{o}{=} \\PY{n}{rng}\\PY{o}{.}\\PY{n}{normal}\\PY{p}{(}\n",
" \\PY{n}{loc}\\PY{o}{=}\\PY{n}{centers}\\PY{p}{[}\\PY{n}{idx}\\PY{p}{]}\\PY{o}{.}\\PY{n}{imag}\\PY{p}{,}\n",
" \\PY{n}{scale}\\PY{o}{=}\\PY{n}{sigmas}\\PY{p}{[}\\PY{n}{idx}\\PY{p}{]}\\PY{p}{,}\n",
" \\PY{n}{size}\\PY{o}{=}\\PY{n}{num\\PYZus{}shots\\PYZus{}this\\PYZus{}cluster}\\PY{p}{,}\n",
" \\PY{p}{)}\n",
" \\PY{n}{shots}\\PY{o}{.}\\PY{n}{append}\\PY{p}{(}\\PY{n}{i\\PYZus{}data} \\PY{o}{+} \\PY{l+m+mi}{1}\\PY{n}{j} \\PY{o}{*} \\PY{n}{q\\PYZus{}data}\\PY{p}{)}\n",
" \\PY{k}{return} \\PY{n}{np}\\PY{o}{.}\\PY{n}{concatenate}\\PY{p}{(}\\PY{n}{shots}\\PY{p}{)}\n",
"\\end{Verbatim}\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"
defmk_trace_time(sampling_rate:float=1e9,duration:float=0.3e-6)->np.ndarray:\n",
" """\n",
" Generates a :obj:`~numpy.arange` in which the entries correspond to time instants\n",
" up to ``duration`` seconds sampled according to ``sampling_rate`` in Hz.\n",
"\n",
" See :func:`~.mk_trace_for_iq_shot` for an usage example.\n",
"\n",
" Parameters\n",
" ----------\n",
" sampling_rate\n",
" The sampling rate in Hz.\n",
" duration\n",
" Total duration in seconds.\n",
"\n",
" Returns\n",
" -------\n",
" :\n",
" An array with the time instants.\n",
" """\n",
" trace_length=sampling_rate*duration\n",
" returnnp.arange(0,trace_length,1)/sampling_rate\n",
"
\n"
],
"text/latex": [
"\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
"\\PY{k}{def} \\PY{n+nf}{mk\\PYZus{}trace\\PYZus{}time}\\PY{p}{(}\\PY{n}{sampling\\PYZus{}rate}\\PY{p}{:} \\PY{n+nb}{float} \\PY{o}{=} \\PY{l+m+mf}{1e9}\\PY{p}{,} \\PY{n}{duration}\\PY{p}{:} \\PY{n+nb}{float} \\PY{o}{=} \\PY{l+m+mf}{0.3e\\PYZhy{}6}\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{:}\n",
" \\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
"\\PY{l+s+sd}{ Generates a :obj:`\\PYZti{}numpy.arange` in which the entries correspond to time instants}\n",
"\\PY{l+s+sd}{ up to ``duration`` seconds sampled according to ``sampling\\PYZus{}rate`` in Hz.}\n",
"\n",
"\\PY{l+s+sd}{ See :func:`\\PYZti{}.mk\\PYZus{}trace\\PYZus{}for\\PYZus{}iq\\PYZus{}shot` for an usage example.}\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}{ sampling\\PYZus{}rate}\n",
"\\PY{l+s+sd}{ The sampling rate in Hz.}\n",
"\\PY{l+s+sd}{ duration}\n",
"\\PY{l+s+sd}{ Total duration in seconds.}\n",
"\n",
"\\PY{l+s+sd}{ Returns}\n",
"\\PY{l+s+sd}{ \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
"\\PY{l+s+sd}{ :}\n",
"\\PY{l+s+sd}{ An array with the time instants.}\n",
"\\PY{l+s+sd}{ \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
" \\PY{n}{trace\\PYZus{}length} \\PY{o}{=} \\PY{n}{sampling\\PYZus{}rate} \\PY{o}{*} \\PY{n}{duration}\n",
" \\PY{k}{return} \\PY{n}{np}\\PY{o}{.}\\PY{n}{arange}\\PY{p}{(}\\PY{l+m+mi}{0}\\PY{p}{,} \\PY{n}{trace\\PYZus{}length}\\PY{p}{,} \\PY{l+m+mi}{1}\\PY{p}{)} \\PY{o}{/} \\PY{n}{sampling\\PYZus{}rate}\n",
"\\end{Verbatim}\n"
]
},
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{
"data": {
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"
defmk_trace_for_iq_shot(\n",
" iq_point:complex,\n",
" time_values:np.ndarray=mk_trace_time(),\n",
" intermediate_freq:float=50e6,\n",
")->np.ndarray:\n",
" """\n",
" Generates mock "traces" that a physical instrument would digitize for the readout of\n",
" a transmon qubit when using a down-converting IQ mixer.\n",
"\n",
" .. admonition:: Examples\n",
" :class: dropdown\n",
"\n",
" .. include:: /examples/utilities.examples_support.mk_trace_for_iq_shot.rst.txt\n",
"\n",
" Parameters\n",
" ----------\n",
" iq_point\n",
" A complex number representing a point on the IQ-plane.\n",
" time_values\n",
" The time instants at which the mock intermediate-frequency signal is sampled.\n",
" intermediate_freq\n",
" The intermediate frequency used in the down-conversion scheme.\n",
"\n",
" Returns\n",
" -------\n",
" :\n",
" An array of complex numbers.\n",
" """# pylint: disable=line-too-long\n",
"\n",
" returniq_point*np.exp(2.0j*np.pi*intermediate_freq*time_values)\n",
"
\n"
],
"text/latex": [
"\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
"\\PY{k}{def} \\PY{n+nf}{mk\\PYZus{}trace\\PYZus{}for\\PYZus{}iq\\PYZus{}shot}\\PY{p}{(}\n",
" \\PY{n}{iq\\PYZus{}point}\\PY{p}{:} \\PY{n+nb}{complex}\\PY{p}{,}\n",
" \\PY{n}{time\\PYZus{}values}\\PY{p}{:} \\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray} \\PY{o}{=} \\PY{n}{mk\\PYZus{}trace\\PYZus{}time}\\PY{p}{(}\\PY{p}{)}\\PY{p}{,}\n",
" \\PY{n}{intermediate\\PYZus{}freq}\\PY{p}{:} \\PY{n+nb}{float} \\PY{o}{=} \\PY{l+m+mf}{50e6}\\PY{p}{,}\n",
"\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{:}\n",
" \\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
"\\PY{l+s+sd}{ Generates mock \\PYZdq{}traces\\PYZdq{} that a physical instrument would digitize for the readout of}\n",
"\\PY{l+s+sd}{ a transmon qubit when using a down\\PYZhy{}converting IQ mixer.}\n",
"\n",
"\\PY{l+s+sd}{ .. admonition:: Examples}\n",
"\\PY{l+s+sd}{ :class: dropdown}\n",
"\n",
"\\PY{l+s+sd}{ .. include:: /examples/utilities.examples\\PYZus{}support.mk\\PYZus{}trace\\PYZus{}for\\PYZus{}iq\\PYZus{}shot.rst.txt}\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}{ iq\\PYZus{}point}\n",
"\\PY{l+s+sd}{ A complex number representing a point on the IQ\\PYZhy{}plane.}\n",
"\\PY{l+s+sd}{ time\\PYZus{}values}\n",
"\\PY{l+s+sd}{ The time instants at which the mock intermediate\\PYZhy{}frequency signal is sampled.}\n",
"\\PY{l+s+sd}{ intermediate\\PYZus{}freq}\n",
"\\PY{l+s+sd}{ The intermediate frequency used in the down\\PYZhy{}conversion scheme.}\n",
"\n",
"\\PY{l+s+sd}{ Returns}\n",
"\\PY{l+s+sd}{ \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
"\\PY{l+s+sd}{ :}\n",
"\\PY{l+s+sd}{ An array of complex numbers.}\n",
"\\PY{l+s+sd}{ \\PYZdq{}\\PYZdq{}\\PYZdq{}} \\PY{c+c1}{\\PYZsh{} pylint: disable=line\\PYZhy{}too\\PYZhy{}long}\n",
"\n",
" \\PY{k}{return} \\PY{n}{iq\\PYZus{}point} \\PY{o}{*} \\PY{n}{np}\\PY{o}{.}\\PY{n}{exp}\\PY{p}{(}\\PY{l+m+mf}{2.0}\\PY{n}{j} \\PY{o}{*} \\PY{n}{np}\\PY{o}{.}\\PY{n}{pi} \\PY{o}{*} \\PY{n}{intermediate\\PYZus{}freq} \\PY{o}{*} \\PY{n}{time\\PYZus{}values}\\PY{p}{)}\n",
"\\end{Verbatim}\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for func in (mk_iq_shots, mk_trace_time, mk_trace_for_iq_shot):\n",
" display_source_code(func)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "1ab58d73",
"metadata": {
"execution": {
"iopub.execute_input": "2023-09-26T17:43:33.905939Z",
"iopub.status.busy": "2023-09-26T17:43:33.905730Z",
"iopub.status.idle": "2023-09-26T17:43:34.263966Z",
"shell.execute_reply": "2023-09-26T17:43:34.263349Z"
}
},
"outputs": [
{
"data": {
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\n"
},
"metadata": {
"filenames": {
"image/png": "/home/slavoutich/code/orangeqs/quantify-core/docs/_build/jupyter_execute/technical_notes/dataset_design/Quantify dataset - examples_9_0.png"
}
},
"output_type": "display_data"
}
],
"source": [
"ground = -0.2 + 0.65j\n",
"excited = 0.7 - 0.4j\n",
"centers = ground, excited\n",
"sigmas = [0.1] * 2\n",
"\n",
"shots = mk_iq_shots(\n",
" num_shots=256,\n",
" sigmas=sigmas,\n",
" centers=centers,\n",
" probabilities=[0.4, 1 - 0.4],\n",
")\n",
"\n",
"plt.hexbin(shots.real, shots.imag)\n",
"plt.xlabel(\"I\")\n",
"plt.ylabel(\"Q\")\n",
"_ = plot_complex_points(centers, ax=plt.gca())"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "b4030153",
"metadata": {
"execution": {
"iopub.execute_input": "2023-09-26T17:43:34.266282Z",
"iopub.status.busy": "2023-09-26T17:43:34.266069Z",
"iopub.status.idle": "2023-09-26T17:43:34.462387Z",
"shell.execute_reply": "2023-09-26T17:43:34.461771Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n"
},
"metadata": {
"filenames": {
"image/png": "/home/slavoutich/code/orangeqs/quantify-core/docs/_build/jupyter_execute/technical_notes/dataset_design/Quantify dataset - examples_10_0.png"
}
},
"output_type": "display_data"
}
],
"source": [
"time = mk_trace_time()\n",
"trace = mk_trace_for_iq_shot(shots[0])\n",
"\n",
"fig, ax = plt.subplots(1, 1, figsize=(12, 12 / 1.61 / 2))\n",
"ax.plot(time * 1e6, trace.imag, \".-\", label=\"I-quadrature\")\n",
"ax.plot(time * 1e6, trace.real, \".-\", label=\"Q-quadrature\")\n",
"ax.set_xlabel(\"Time [µs]\")\n",
"ax.set_ylabel(\"Amplitude [V]\")\n",
"_ = ax.legend()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "20947c3b",
"metadata": {
"execution": {
"iopub.execute_input": "2023-09-26T17:43:34.465562Z",
"iopub.status.busy": "2023-09-26T17:43:34.465342Z",
"iopub.status.idle": "2023-09-26T17:43:34.600088Z",
"shell.execute_reply": "2023-09-26T17:43:34.599428Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n"
},
"metadata": {
"filenames": {
"image/png": "/home/slavoutich/code/orangeqs/quantify-core/docs/_build/jupyter_execute/technical_notes/dataset_design/Quantify dataset - examples_11_0.png"
}
},
"output_type": "display_data"
}
],
"source": [
"# parameters of our qubit model\n",
"tau = 30e-6\n",
"ground = -0.2 + 0.65j # ground state on the IQ-plane\n",
"excited = 0.7 - 0.4j # excited state on the IQ-plane\n",
"centers = ground, excited\n",
"sigmas = [0.1] * 2 # sigma, NB in general not the same for both state\n",
"\n",
"# mock of data acquisition configuration\n",
"# NB usually at least 1000+ shots are taken, here we use less for faster code execution\n",
"num_shots = 256\n",
"# time delays between exciting the qubit and measuring its state\n",
"t1_times = np.linspace(0, 120e-6, 30)\n",
"\n",
"# NB this are the ideal probabilities from repeating the measurement many times for a\n",
"# qubit with a lifetime given by tau\n",
"probabilities = exp_decay_func(t=t1_times, tau=tau, offset=0, n_factor=1, amplitude=1)\n",
"\n",
"# Ideal experiment result\n",
"plt.ylabel(\"|1> probability\")\n",
"plt.suptitle(\"Typical processed data of a T1 experiment\")\n",
"plt.plot(t1_times * 1e6, probabilities, \".-\")\n",
"_ = plt.xlabel(\"Time [µs]\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "ad5453e6",
"metadata": {
"execution": {
"iopub.execute_input": "2023-09-26T17:43:34.602426Z",
"iopub.status.busy": "2023-09-26T17:43:34.602217Z",
"iopub.status.idle": "2023-09-26T17:43:34.605398Z",
"shell.execute_reply": "2023-09-26T17:43:34.604947Z"
}
},
"outputs": [],
"source": [
"# convenience dict with the mock parameters\n",
"mock_conf = dict(\n",
" num_shots=num_shots,\n",
" centers=centers,\n",
" sigmas=sigmas,\n",
" t1_times=t1_times,\n",
" probabilities=probabilities,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "cf7d9ab5",
"metadata": {
"execution": {
"iopub.execute_input": "2023-09-26T17:43:34.607559Z",
"iopub.status.busy": "2023-09-26T17:43:34.607356Z",
"iopub.status.idle": "2023-09-26T17:43:34.624611Z",
"shell.execute_reply": "2023-09-26T17:43:34.624050Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"
defmk_t1_av_dataset(\n",
" t1_times:Optional[np.ndarray]=None,\n",
" probabilities:Optional[np.ndarray]=None,\n",
" **kwargs,\n",
")->xr.Dataset:\n",
" """\n",
" Generates a dataset with mock data of a T1 experiment for a single qubit.\n",
"\n",
" Parameters\n",
" ----------\n",
" t1_times\n",
" Array with the T1 times corresponding to each probability in ``probabilities``.\n",
" probabilities\n",
" The probabilities of finding the qubit in the excited state.\n",
" **kwargs\n",
" Keyword arguments passed to\n",
" :func:`~quantify_core.utilities.examples_support.mk_iq_shots`.\n",
" """\n",
" ift1_timesisNone:\n",
" t1_times=np.linspace(0,120e-6,30)\n",
"\n",
" ifprobabilitiesisNone:\n",
" probabilities=exp_decay_func(\n",
" t=t1_times,tau=50e-6,offset=0,n_factor=1,amplitude=1\n",
" )\n",
"\n",
" q0_iq_av=mk_shots_from_probabilities(probabilities,**kwargs).mean(axis=0)\n",
"\n",
" main_dims=("main_dim",)\n",
" q0_attrs=mk_main_var_attrs(unit="V",long_name="Q0 IQ amplitude")\n",
" t1_time_attrs=mk_main_coord_attrs(unit="s",long_name="T1 Time")\n",
"\n",
" data_vars=dict(q0_iq_av=(main_dims,q0_iq_av,q0_attrs))\n",
" coords=dict(t1_time=(main_dims,t1_times,t1_time_attrs))\n",
"\n",
" dataset=xr.Dataset(\n",
" data_vars=data_vars,\n",
" coords=coords,\n",
" attrs=mk_dataset_attrs(),\n",
" )\n",
" returndataset\n",
"
\n"
],
"text/latex": [
"\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
"\\PY{k}{def} \\PY{n+nf}{mk\\PYZus{}t1\\PYZus{}av\\PYZus{}dataset}\\PY{p}{(}\n",
" \\PY{n}{t1\\PYZus{}times}\\PY{p}{:} \\PY{n}{Optional}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{]} \\PY{o}{=} \\PY{k+kc}{None}\\PY{p}{,}\n",
" \\PY{n}{probabilities}\\PY{p}{:} \\PY{n}{Optional}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{]} \\PY{o}{=} \\PY{k+kc}{None}\\PY{p}{,}\n",
" \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{,}\n",
"\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{:}\n",
" \\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
"\\PY{l+s+sd}{ Generates a dataset with mock data of a T1 experiment for a single qubit.}\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}{ t1\\PYZus{}times}\n",
"\\PY{l+s+sd}{ Array with the T1 times corresponding to each probability in ``probabilities``.}\n",
"\\PY{l+s+sd}{ probabilities}\n",
"\\PY{l+s+sd}{ The probabilities of finding the qubit in the excited state.}\n",
"\\PY{l+s+sd}{ **kwargs}\n",
"\\PY{l+s+sd}{ Keyword arguments passed to}\n",
"\\PY{l+s+sd}{ :func:`\\PYZti{}quantify\\PYZus{}core.utilities.examples\\PYZus{}support.mk\\PYZus{}iq\\PYZus{}shots`.}\n",
"\\PY{l+s+sd}{ \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
" \\PY{k}{if} \\PY{n}{t1\\PYZus{}times} \\PY{o+ow}{is} \\PY{k+kc}{None}\\PY{p}{:}\n",
" \\PY{n}{t1\\PYZus{}times} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{linspace}\\PY{p}{(}\\PY{l+m+mi}{0}\\PY{p}{,} \\PY{l+m+mf}{120e\\PYZhy{}6}\\PY{p}{,} \\PY{l+m+mi}{30}\\PY{p}{)}\n",
"\n",
" \\PY{k}{if} \\PY{n}{probabilities} \\PY{o+ow}{is} \\PY{k+kc}{None}\\PY{p}{:}\n",
" \\PY{n}{probabilities} \\PY{o}{=} \\PY{n}{exp\\PYZus{}decay\\PYZus{}func}\\PY{p}{(}\n",
" \\PY{n}{t}\\PY{o}{=}\\PY{n}{t1\\PYZus{}times}\\PY{p}{,} \\PY{n}{tau}\\PY{o}{=}\\PY{l+m+mf}{50e\\PYZhy{}6}\\PY{p}{,} \\PY{n}{offset}\\PY{o}{=}\\PY{l+m+mi}{0}\\PY{p}{,} \\PY{n}{n\\PYZus{}factor}\\PY{o}{=}\\PY{l+m+mi}{1}\\PY{p}{,} \\PY{n}{amplitude}\\PY{o}{=}\\PY{l+m+mi}{1}\n",
" \\PY{p}{)}\n",
"\n",
" \\PY{n}{q0\\PYZus{}iq\\PYZus{}av} \\PY{o}{=} \\PY{n}{mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities}\\PY{p}{(}\\PY{n}{probabilities}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\\PY{o}{.}\\PY{n}{mean}\\PY{p}{(}\\PY{n}{axis}\\PY{o}{=}\\PY{l+m+mi}{0}\\PY{p}{)}\n",
"\n",
" \\PY{n}{main\\PYZus{}dims} \\PY{o}{=} \\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\\PY{p}{)}\n",
" \\PY{n}{q0\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\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}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Q0 IQ amplitude}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
" \\PY{n}{t1\\PYZus{}time\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\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}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{T1 Time}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
"\n",
" \\PY{n}{data\\PYZus{}vars} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\\PY{n}{q0\\PYZus{}iq\\PYZus{}av}\\PY{o}{=}\\PY{p}{(}\\PY{n}{main\\PYZus{}dims}\\PY{p}{,} \\PY{n}{q0\\PYZus{}iq\\PYZus{}av}\\PY{p}{,} \\PY{n}{q0\\PYZus{}attrs}\\PY{p}{)}\\PY{p}{)}\n",
" \\PY{n}{coords} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\\PY{n}{t1\\PYZus{}time}\\PY{o}{=}\\PY{p}{(}\\PY{n}{main\\PYZus{}dims}\\PY{p}{,} \\PY{n}{t1\\PYZus{}times}\\PY{p}{,} \\PY{n}{t1\\PYZus{}time\\PYZus{}attrs}\\PY{p}{)}\\PY{p}{)}\n",
"\n",
" \\PY{n}{dataset} \\PY{o}{=} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{(}\n",
" \\PY{n}{data\\PYZus{}vars}\\PY{o}{=}\\PY{n}{data\\PYZus{}vars}\\PY{p}{,}\n",
" \\PY{n}{coords}\\PY{o}{=}\\PY{n}{coords}\\PY{p}{,}\n",
" \\PY{n}{attrs}\\PY{o}{=}\\PY{n}{mk\\PYZus{}dataset\\PYZus{}attrs}\\PY{p}{(}\\PY{p}{)}\\PY{p}{,}\n",
" \\PY{p}{)}\n",
" \\PY{k}{return} \\PY{n}{dataset}\n",
"\\end{Verbatim}\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display_source_code(dataset_examples.mk_t1_av_dataset)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "416a70ef",
"metadata": {
"execution": {
"iopub.execute_input": "2023-09-26T17:43:34.626919Z",
"iopub.status.busy": "2023-09-26T17:43:34.626718Z",
"iopub.status.idle": "2023-09-26T17:43:34.668491Z",
"shell.execute_reply": "2023-09-26T17:43:34.667905Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"
defplot_xr_complex(\n",
" var:xr.DataArray,\n",
" marker_scatter:str="o",\n",
" label_real:str="Real",\n",
" label_imag:str="Imag",\n",
" cmap:str="viridis",\n",
" c:np.ndarray=None,\n",
" kwargs_line:dict=None,\n",
" kwargs_scatter:dict=None,\n",
" title:str="{} [{}]; shape = {}",\n",
" legend:bool=True,\n",
" ax:object=None,\n",
")->Tuple[Figure,Axes]:\n",
" """Plots the real and imaginary parts of complex data. Points are colored by default\n",
" according to their order in the array.\n",
"\n",
" Parameters\n",
" ----------\n",
" var\n",
" 1D array of complex data.\n",
" marker_scatter\n",
" Marker used for the scatter plot.\n",
" label_real\n",
" Label for legend.\n",
" label_imag\n",
" Label for legend.\n",
" cmap\n",
" The colormap to use for coloring the points.\n",
" c\n",
" Color of the points. Defaults to an array of integers.\n",
" kwargs_line\n",
" Keyword arguments passed to :meth:`matplotlib.axes.Axes.plot`.\n",
" kwargs_scatter\n",
" Keyword arguments passed to :meth:`matplotlib.axes.Axes.scatter`.\n",
" title\n",
" Axes title. By default gets formatted with ``var.long_name``, ``var.name`` and\n",
" var.shape``.\n",
" legend\n",
" Calls :meth:`~matplotlib.axes.Axes.legend` if ``True``.\n",
" ax\n",
" The matplotlib axes. If ``None`` a new axes (and figure) is created.\n",
" """\n",
"\n",
" ifaxisNone:\n",
" _,ax=plt.subplots()\n",
"\n",
" ifcisNone:\n",
" c=np.arange(len(var))\n",
"\n",
" ifkwargs_lineisNone:\n",
" kwargs_line={}\n",
"\n",
" ifkwargs_scatterisNone:\n",
" kwargs_scatter={}\n",
"\n",
" if"marker"notinkwargs_line:\n",
" kwargs_line["marker"]=""\n",
"\n",
" var.real.plot(ax=ax,label=label_real,**kwargs_line)\n",
" var.imag.plot(ax=ax,label=label_imag,**kwargs_line)\n",
"\n",
" forvalsin(var.real,var.imag):\n",
" ax.scatter(\n",
" next(iter(var.coords.values())).values,\n",
" vals,\n",
" marker=marker_scatter,\n",
" c=c,\n",
" cmap=cmap,\n",
" **kwargs_scatter,\n",
" )\n",
"\n",
" ax.set_title(title.format(var.long_name,var.name,var.shape))\n",
"\n",
" iflegend:\n",
" ax.legend()\n",
"\n",
" returnax.get_figure(),ax\n",
"
\n"
],
"text/latex": [
"\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
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" \\PY{n}{kwargs\\PYZus{}scatter}\\PY{p}{:} \\PY{n+nb}{dict} \\PY{o}{=} \\PY{k+kc}{None}\\PY{p}{,}\n",
" \\PY{n}{title}\\PY{p}{:} \\PY{n+nb}{str} \\PY{o}{=} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+si}{\\PYZob{}\\PYZcb{}}\\PY{l+s+s2}{ [}\\PY{l+s+si}{\\PYZob{}\\PYZcb{}}\\PY{l+s+s2}{]; shape = }\\PY{l+s+si}{\\PYZob{}\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{n}{legend}\\PY{p}{:} \\PY{n+nb}{bool} \\PY{o}{=} \\PY{k+kc}{True}\\PY{p}{,}\n",
" \\PY{n}{ax}\\PY{p}{:} \\PY{n+nb}{object} \\PY{o}{=} \\PY{k+kc}{None}\\PY{p}{,}\n",
"\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{Tuple}\\PY{p}{[}\\PY{n}{Figure}\\PY{p}{,} \\PY{n}{Axes}\\PY{p}{]}\\PY{p}{:}\n",
" \\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}Plots the real and imaginary parts of complex data. Points are colored by default}\n",
"\\PY{l+s+sd}{ according to their order in the array.}\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}{ var}\n",
"\\PY{l+s+sd}{ 1D array of complex data.}\n",
"\\PY{l+s+sd}{ marker\\PYZus{}scatter}\n",
"\\PY{l+s+sd}{ Marker used for the scatter plot.}\n",
"\\PY{l+s+sd}{ label\\PYZus{}real}\n",
"\\PY{l+s+sd}{ Label for legend.}\n",
"\\PY{l+s+sd}{ label\\PYZus{}imag}\n",
"\\PY{l+s+sd}{ Label for legend.}\n",
"\\PY{l+s+sd}{ cmap}\n",
"\\PY{l+s+sd}{ The colormap to use for coloring the points.}\n",
"\\PY{l+s+sd}{ c}\n",
"\\PY{l+s+sd}{ Color of the points. Defaults to an array of integers.}\n",
"\\PY{l+s+sd}{ kwargs\\PYZus{}line}\n",
"\\PY{l+s+sd}{ Keyword arguments passed to :meth:`matplotlib.axes.Axes.plot`.}\n",
"\\PY{l+s+sd}{ kwargs\\PYZus{}scatter}\n",
"\\PY{l+s+sd}{ Keyword arguments passed to :meth:`matplotlib.axes.Axes.scatter`.}\n",
"\\PY{l+s+sd}{ title}\n",
"\\PY{l+s+sd}{ Axes title. By default gets formatted with ``var.long\\PYZus{}name``, ``var.name`` and}\n",
"\\PY{l+s+sd}{ var.shape``.}\n",
"\\PY{l+s+sd}{ legend}\n",
"\\PY{l+s+sd}{ Calls :meth:`\\PYZti{}matplotlib.axes.Axes.legend` if ``True``.}\n",
"\\PY{l+s+sd}{ ax}\n",
"\\PY{l+s+sd}{ The matplotlib axes. If ``None`` a new axes (and figure) is created.}\n",
"\\PY{l+s+sd}{ \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
"\n",
" \\PY{k}{if} \\PY{n}{ax} \\PY{o+ow}{is} \\PY{k+kc}{None}\\PY{p}{:}\n",
" \\PY{n}{\\PYZus{}}\\PY{p}{,} \\PY{n}{ax} \\PY{o}{=} \\PY{n}{plt}\\PY{o}{.}\\PY{n}{subplots}\\PY{p}{(}\\PY{p}{)}\n",
"\n",
" \\PY{k}{if} \\PY{n}{c} \\PY{o+ow}{is} \\PY{k+kc}{None}\\PY{p}{:}\n",
" \\PY{n}{c} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{arange}\\PY{p}{(}\\PY{n+nb}{len}\\PY{p}{(}\\PY{n}{var}\\PY{p}{)}\\PY{p}{)}\n",
"\n",
" \\PY{k}{if} \\PY{n}{kwargs\\PYZus{}line} \\PY{o+ow}{is} \\PY{k+kc}{None}\\PY{p}{:}\n",
" \\PY{n}{kwargs\\PYZus{}line} \\PY{o}{=} \\PY{p}{\\PYZob{}}\\PY{p}{\\PYZcb{}}\n",
"\n",
" \\PY{k}{if} \\PY{n}{kwargs\\PYZus{}scatter} \\PY{o+ow}{is} \\PY{k+kc}{None}\\PY{p}{:}\n",
" \\PY{n}{kwargs\\PYZus{}scatter} \\PY{o}{=} \\PY{p}{\\PYZob{}}\\PY{p}{\\PYZcb{}}\n",
"\n",
" \\PY{k}{if} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{marker}\\PY{l+s+s2}{\\PYZdq{}} \\PY{o+ow}{not} \\PY{o+ow}{in} \\PY{n}{kwargs\\PYZus{}line}\\PY{p}{:}\n",
" \\PY{n}{kwargs\\PYZus{}line}\\PY{p}{[}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{marker}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]} \\PY{o}{=} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{\\PYZdq{}}\n",
"\n",
" \\PY{n}{var}\\PY{o}{.}\\PY{n}{real}\\PY{o}{.}\\PY{n}{plot}\\PY{p}{(}\\PY{n}{ax}\\PY{o}{=}\\PY{n}{ax}\\PY{p}{,} \\PY{n}{label}\\PY{o}{=}\\PY{n}{label\\PYZus{}real}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs\\PYZus{}line}\\PY{p}{)}\n",
" \\PY{n}{var}\\PY{o}{.}\\PY{n}{imag}\\PY{o}{.}\\PY{n}{plot}\\PY{p}{(}\\PY{n}{ax}\\PY{o}{=}\\PY{n}{ax}\\PY{p}{,} \\PY{n}{label}\\PY{o}{=}\\PY{n}{label\\PYZus{}imag}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs\\PYZus{}line}\\PY{p}{)}\n",
"\n",
" \\PY{k}{for} \\PY{n}{vals} \\PY{o+ow}{in} \\PY{p}{(}\\PY{n}{var}\\PY{o}{.}\\PY{n}{real}\\PY{p}{,} \\PY{n}{var}\\PY{o}{.}\\PY{n}{imag}\\PY{p}{)}\\PY{p}{:}\n",
" \\PY{n}{ax}\\PY{o}{.}\\PY{n}{scatter}\\PY{p}{(}\n",
" \\PY{n+nb}{next}\\PY{p}{(}\\PY{n+nb}{iter}\\PY{p}{(}\\PY{n}{var}\\PY{o}{.}\\PY{n}{coords}\\PY{o}{.}\\PY{n}{values}\\PY{p}{(}\\PY{p}{)}\\PY{p}{)}\\PY{p}{)}\\PY{o}{.}\\PY{n}{values}\\PY{p}{,}\n",
" \\PY{n}{vals}\\PY{p}{,}\n",
" \\PY{n}{marker}\\PY{o}{=}\\PY{n}{marker\\PYZus{}scatter}\\PY{p}{,}\n",
" \\PY{n}{c}\\PY{o}{=}\\PY{n}{c}\\PY{p}{,}\n",
" \\PY{n}{cmap}\\PY{o}{=}\\PY{n}{cmap}\\PY{p}{,}\n",
" \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs\\PYZus{}scatter}\\PY{p}{,}\n",
" \\PY{p}{)}\n",
"\n",
" \\PY{n}{ax}\\PY{o}{.}\\PY{n}{set\\PYZus{}title}\\PY{p}{(}\\PY{n}{title}\\PY{o}{.}\\PY{n}{format}\\PY{p}{(}\\PY{n}{var}\\PY{o}{.}\\PY{n}{long\\PYZus{}name}\\PY{p}{,} \\PY{n}{var}\\PY{o}{.}\\PY{n}{name}\\PY{p}{,} \\PY{n}{var}\\PY{o}{.}\\PY{n}{shape}\\PY{p}{)}\\PY{p}{)}\n",
"\n",
" \\PY{k}{if} \\PY{n}{legend}\\PY{p}{:}\n",
" \\PY{n}{ax}\\PY{o}{.}\\PY{n}{legend}\\PY{p}{(}\\PY{p}{)}\n",
"\n",
" \\PY{k}{return} \\PY{n}{ax}\\PY{o}{.}\\PY{n}{get\\PYZus{}figure}\\PY{p}{(}\\PY{p}{)}\\PY{p}{,} \\PY{n}{ax}\n",
"\\end{Verbatim}\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"
defplot_xr_complex_on_plane(\n",
" var:xr.DataArray,\n",
" marker:str="o",\n",
" label:str="Data on imaginary plane",\n",
" cmap:str="viridis",\n",
" c:np.ndarray=None,\n",
" xlabel:str="Real{}{}{}",\n",
" ylabel:str="Imag{}{}{}",\n",
" legend:bool=True,\n",
" ax:object=None,\n",
" **kwargs,\n",
")->Tuple[Figure,Axes]:\n",
" """Plots complex data on the imaginary plane. Points are colored by default\n",
" according to their order in the array.\n",
"\n",
"\n",
" Parameters\n",
" ----------\n",
" var\n",
" 1D array of complex data.\n",
" marker\n",
" Marker used for the scatter plot.\n",
" label\n",
" Data label for the legend.\n",
" cmap\n",
" The colormap to use for coloring the points.\n",
" c\n",
" Color of the points. Defaults to an array of integers.\n",
" xlabel\n",
" Label o x axes.\n",
" ylabel\n",
" Label o y axes.\n",
" legend\n",
" Calls :meth:`~matplotlib.axes.Axes.legend` if ``True``.\n",
" ax\n",
" The matplotlib axes. If ``None`` a new axes (and figure) is created.\n",
" """\n",
"\n",
" ifaxisNone:\n",
" _,ax=plt.subplots()\n",
"\n",
" ifcisNone:\n",
" c=np.arange(0,len(var))\n",
"\n",
" ax.scatter(var.real,var.imag,marker=marker,label=label,c=c,cmap=cmap,**kwargs)\n",
"\n",
" unit_str=get_unit_from_attrs(var)\n",
" ax.set_xlabel(xlabel.format(" ",var.name,unit_str))\n",
" ax.set_ylabel(ylabel.format(" ",var.name,unit_str))\n",
"\n",
" iflegend:\n",
" ax.legend()\n",
"\n",
" returnax.get_figure(),ax\n",
"
\n"
],
"text/latex": [
"\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
"\\PY{k}{def} \\PY{n+nf}{plot\\PYZus{}xr\\PYZus{}complex\\PYZus{}on\\PYZus{}plane}\\PY{p}{(}\n",
" \\PY{n}{var}\\PY{p}{:} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{DataArray}\\PY{p}{,}\n",
" \\PY{n}{marker}\\PY{p}{:} \\PY{n+nb}{str} \\PY{o}{=} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{o}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{n}{label}\\PY{p}{:} \\PY{n+nb}{str} \\PY{o}{=} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Data on imaginary plane}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{n}{cmap}\\PY{p}{:} \\PY{n+nb}{str} \\PY{o}{=} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{viridis}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{n}{c}\\PY{p}{:} \\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray} \\PY{o}{=} \\PY{k+kc}{None}\\PY{p}{,}\n",
" \\PY{n}{xlabel}\\PY{p}{:} \\PY{n+nb}{str} \\PY{o}{=} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Real}\\PY{l+s+si}{\\PYZob{}\\PYZcb{}}\\PY{l+s+si}{\\PYZob{}\\PYZcb{}}\\PY{l+s+si}{\\PYZob{}\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{n}{ylabel}\\PY{p}{:} \\PY{n+nb}{str} \\PY{o}{=} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Imag}\\PY{l+s+si}{\\PYZob{}\\PYZcb{}}\\PY{l+s+si}{\\PYZob{}\\PYZcb{}}\\PY{l+s+si}{\\PYZob{}\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{n}{legend}\\PY{p}{:} \\PY{n+nb}{bool} \\PY{o}{=} \\PY{k+kc}{True}\\PY{p}{,}\n",
" \\PY{n}{ax}\\PY{p}{:} \\PY{n+nb}{object} \\PY{o}{=} \\PY{k+kc}{None}\\PY{p}{,}\n",
" \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{,}\n",
"\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{Tuple}\\PY{p}{[}\\PY{n}{Figure}\\PY{p}{,} \\PY{n}{Axes}\\PY{p}{]}\\PY{p}{:}\n",
" \\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}Plots complex data on the imaginary plane. Points are colored by default}\n",
"\\PY{l+s+sd}{ according to their order in the array.}\n",
"\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}{ var}\n",
"\\PY{l+s+sd}{ 1D array of complex data.}\n",
"\\PY{l+s+sd}{ marker}\n",
"\\PY{l+s+sd}{ Marker used for the scatter plot.}\n",
"\\PY{l+s+sd}{ label}\n",
"\\PY{l+s+sd}{ Data label for the legend.}\n",
"\\PY{l+s+sd}{ cmap}\n",
"\\PY{l+s+sd}{ The colormap to use for coloring the points.}\n",
"\\PY{l+s+sd}{ c}\n",
"\\PY{l+s+sd}{ Color of the points. Defaults to an array of integers.}\n",
"\\PY{l+s+sd}{ xlabel}\n",
"\\PY{l+s+sd}{ Label o x axes.}\n",
"\\PY{l+s+sd}{ ylabel}\n",
"\\PY{l+s+sd}{ Label o y axes.}\n",
"\\PY{l+s+sd}{ legend}\n",
"\\PY{l+s+sd}{ Calls :meth:`\\PYZti{}matplotlib.axes.Axes.legend` if ``True``.}\n",
"\\PY{l+s+sd}{ ax}\n",
"\\PY{l+s+sd}{ The matplotlib axes. If ``None`` a new axes (and figure) is created.}\n",
"\\PY{l+s+sd}{ \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
"\n",
" \\PY{k}{if} \\PY{n}{ax} \\PY{o+ow}{is} \\PY{k+kc}{None}\\PY{p}{:}\n",
" \\PY{n}{\\PYZus{}}\\PY{p}{,} \\PY{n}{ax} \\PY{o}{=} \\PY{n}{plt}\\PY{o}{.}\\PY{n}{subplots}\\PY{p}{(}\\PY{p}{)}\n",
"\n",
" \\PY{k}{if} \\PY{n}{c} \\PY{o+ow}{is} \\PY{k+kc}{None}\\PY{p}{:}\n",
" \\PY{n}{c} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{arange}\\PY{p}{(}\\PY{l+m+mi}{0}\\PY{p}{,} \\PY{n+nb}{len}\\PY{p}{(}\\PY{n}{var}\\PY{p}{)}\\PY{p}{)}\n",
"\n",
" \\PY{n}{ax}\\PY{o}{.}\\PY{n}{scatter}\\PY{p}{(}\\PY{n}{var}\\PY{o}{.}\\PY{n}{real}\\PY{p}{,} \\PY{n}{var}\\PY{o}{.}\\PY{n}{imag}\\PY{p}{,} \\PY{n}{marker}\\PY{o}{=}\\PY{n}{marker}\\PY{p}{,} \\PY{n}{label}\\PY{o}{=}\\PY{n}{label}\\PY{p}{,} \\PY{n}{c}\\PY{o}{=}\\PY{n}{c}\\PY{p}{,} \\PY{n}{cmap}\\PY{o}{=}\\PY{n}{cmap}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\n",
"\n",
" \\PY{n}{unit\\PYZus{}str} \\PY{o}{=} \\PY{n}{get\\PYZus{}unit\\PYZus{}from\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{var}\\PY{p}{)}\n",
" \\PY{n}{ax}\\PY{o}{.}\\PY{n}{set\\PYZus{}xlabel}\\PY{p}{(}\\PY{n}{xlabel}\\PY{o}{.}\\PY{n}{format}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{ }\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{var}\\PY{o}{.}\\PY{n}{name}\\PY{p}{,} \\PY{n}{unit\\PYZus{}str}\\PY{p}{)}\\PY{p}{)}\n",
" \\PY{n}{ax}\\PY{o}{.}\\PY{n}{set\\PYZus{}ylabel}\\PY{p}{(}\\PY{n}{ylabel}\\PY{o}{.}\\PY{n}{format}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{ }\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{var}\\PY{o}{.}\\PY{n}{name}\\PY{p}{,} \\PY{n}{unit\\PYZus{}str}\\PY{p}{)}\\PY{p}{)}\n",
"\n",
" \\PY{k}{if} \\PY{n}{legend}\\PY{p}{:}\n",
" \\PY{n}{ax}\\PY{o}{.}\\PY{n}{legend}\\PY{p}{(}\\PY{p}{)}\n",
"\n",
" \\PY{k}{return} \\PY{n}{ax}\\PY{o}{.}\\PY{n}{get\\PYZus{}figure}\\PY{p}{(}\\PY{p}{)}\\PY{p}{,} \\PY{n}{ax}\n",
"\\end{Verbatim}\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display_source_code(plot_xr_complex)\n",
"display_source_code(plot_xr_complex_on_plane)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "734a8da8",
"metadata": {
"execution": {
"iopub.execute_input": "2023-09-26T17:43:34.736369Z",
"iopub.status.busy": "2023-09-26T17:43:34.736160Z",
"iopub.status.idle": "2023-09-26T17:43:35.091133Z",
"shell.execute_reply": "2023-09-26T17:43:35.090442Z"
}
},
"outputs": [
{
"data": {
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\n"
},
"metadata": {
"filenames": {
"image/png": "/home/slavoutich/code/orangeqs/quantify-core/docs/_build/jupyter_execute/technical_notes/dataset_design/Quantify dataset - examples_18_0.png"
}
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n"
},
"metadata": {
"filenames": {
"image/png": "/home/slavoutich/code/orangeqs/quantify-core/docs/_build/jupyter_execute/technical_notes/dataset_design/Quantify dataset - examples_18_1.png"
}
},
"output_type": "display_data"
}
],
"source": [
"plot_xr_complex(dataset_gridded.q0_iq_av)\n",
"fig, ax = plot_xr_complex_on_plane(dataset_gridded.q0_iq_av)\n",
"_ = plot_complex_points(centers, ax=ax)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "abc92552",
"metadata": {
"execution": {
"iopub.execute_input": "2023-09-26T17:43:35.093537Z",
"iopub.status.busy": "2023-09-26T17:43:35.093315Z",
"iopub.status.idle": "2023-09-26T17:43:35.112516Z",
"shell.execute_reply": "2023-09-26T17:43:35.111924Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"
defmk_t1_av_with_cal_dataset(\n",
" t1_times:Optional[np.ndarray]=None,\n",
" probabilities:Optional[np.ndarray]=None,\n",
" **kwargs,\n",
")->xr.Dataset:\n",
" """\n",
" Generates a dataset with mock data of a T1 experiment for a single qubit including\n",
" calibration points for the ground and excited states.\n",
"\n",
" Parameters\n",
" ----------\n",
" t1_times\n",
" Array with the T1 times corresponding to each probability in ``probabilities``.\n",
" probabilities\n",
" The probabilities of finding the qubit in the excited state.\n",
" **kwargs\n",
" Keyword arguments passed to\n",
" :func:`~quantify_core.utilities.examples_support.mk_iq_shots`.\n",
" """\n",
" # reuse previous dataset\n",
" dataset_av=mk_t1_av_dataset(t1_times,probabilities,**kwargs)\n",
"\n",
" # generate mock calibration data for the ground and excited states\n",
" q0_iq_av_cal=mk_shots_from_probabilities([0,1],**kwargs).mean(axis=0)\n",
"\n",
" secondary_dims=("cal_dim",)\n",
" q0_cal_attrs=mk_secondary_var_attrs(unit="V",long_name="Q0 IQ Calibration")\n",
" cal_attrs=mk_secondary_coord_attrs(unit="",long_name="Q0 state")\n",
"\n",
" relationships=[\n",
" dattrs.QDatasetIntraRelationship(\n",
" item_name=dataset_av.q0_iq_av.name,# name of a variable in the dataset\n",
" relation_type="calibration",\n",
" related_names=["q0_iq_av_cal"],# the secondary variable in the dataset\n",
" ).to_dict()\n",
" ]\n",
"\n",
" data_vars=dict(\n",
" q0_iq_av=dataset_av.q0_iq_av,# reuse from the other dataset\n",
" q0_iq_av_cal=(secondary_dims,q0_iq_av_cal,q0_cal_attrs),\n",
" )\n",
" coords=dict(\n",
" t1_time=dataset_av.t1_time,# reuse from the other dataset\n",
" cal=(secondary_dims,["|0>","|1>"],cal_attrs),# coords can be strings\n",
" )\n",
"\n",
" dataset=xr.Dataset(\n",
" data_vars=data_vars,\n",
" coords=coords,\n",
" attrs=mk_dataset_attrs(relationships=relationships),# relationships added here\n",
" )\n",
"\n",
" returndataset\n",
"
\n"
],
"text/latex": [
"\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
"\\PY{k}{def} \\PY{n+nf}{mk\\PYZus{}t1\\PYZus{}av\\PYZus{}with\\PYZus{}cal\\PYZus{}dataset}\\PY{p}{(}\n",
" \\PY{n}{t1\\PYZus{}times}\\PY{p}{:} \\PY{n}{Optional}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{]} \\PY{o}{=} \\PY{k+kc}{None}\\PY{p}{,}\n",
" \\PY{n}{probabilities}\\PY{p}{:} \\PY{n}{Optional}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{]} \\PY{o}{=} \\PY{k+kc}{None}\\PY{p}{,}\n",
" \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{,}\n",
"\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{:}\n",
" \\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
"\\PY{l+s+sd}{ Generates a dataset with mock data of a T1 experiment for a single qubit including}\n",
"\\PY{l+s+sd}{ calibration points for the ground and excited states.}\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}{ t1\\PYZus{}times}\n",
"\\PY{l+s+sd}{ Array with the T1 times corresponding to each probability in ``probabilities``.}\n",
"\\PY{l+s+sd}{ probabilities}\n",
"\\PY{l+s+sd}{ The probabilities of finding the qubit in the excited state.}\n",
"\\PY{l+s+sd}{ **kwargs}\n",
"\\PY{l+s+sd}{ Keyword arguments passed to}\n",
"\\PY{l+s+sd}{ :func:`\\PYZti{}quantify\\PYZus{}core.utilities.examples\\PYZus{}support.mk\\PYZus{}iq\\PYZus{}shots`.}\n",
"\\PY{l+s+sd}{ \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
" \\PY{c+c1}{\\PYZsh{} reuse previous dataset}\n",
" \\PY{n}{dataset\\PYZus{}av} \\PY{o}{=} \\PY{n}{mk\\PYZus{}t1\\PYZus{}av\\PYZus{}dataset}\\PY{p}{(}\\PY{n}{t1\\PYZus{}times}\\PY{p}{,} \\PY{n}{probabilities}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\n",
"\n",
" \\PY{c+c1}{\\PYZsh{} generate mock calibration data for the ground and excited states}\n",
" \\PY{n}{q0\\PYZus{}iq\\PYZus{}av\\PYZus{}cal} \\PY{o}{=} \\PY{n}{mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities}\\PY{p}{(}\\PY{p}{[}\\PY{l+m+mi}{0}\\PY{p}{,} \\PY{l+m+mi}{1}\\PY{p}{]}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\\PY{o}{.}\\PY{n}{mean}\\PY{p}{(}\\PY{n}{axis}\\PY{o}{=}\\PY{l+m+mi}{0}\\PY{p}{)}\n",
"\n",
" \\PY{n}{secondary\\PYZus{}dims} \\PY{o}{=} \\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{cal\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\\PY{p}{)}\n",
" \\PY{n}{q0\\PYZus{}cal\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}secondary\\PYZus{}var\\PYZus{}attrs}\\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}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Q0 IQ Calibration}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
" \\PY{n}{cal\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}secondary\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Q0 state}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
"\n",
" \\PY{n}{relationships} \\PY{o}{=} \\PY{p}{[}\n",
" \\PY{n}{dattrs}\\PY{o}{.}\\PY{n}{QDatasetIntraRelationship}\\PY{p}{(}\n",
" \\PY{n}{item\\PYZus{}name}\\PY{o}{=}\\PY{n}{dataset\\PYZus{}av}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}av}\\PY{o}{.}\\PY{n}{name}\\PY{p}{,} \\PY{c+c1}{\\PYZsh{} name of a variable in the dataset}\n",
" \\PY{n}{relation\\PYZus{}type}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{calibration}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{n}{related\\PYZus{}names}\\PY{o}{=}\\PY{p}{[}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{q0\\PYZus{}iq\\PYZus{}av\\PYZus{}cal}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]}\\PY{p}{,} \\PY{c+c1}{\\PYZsh{} the secondary variable in the dataset}\n",
" \\PY{p}{)}\\PY{o}{.}\\PY{n}{to\\PYZus{}dict}\\PY{p}{(}\\PY{p}{)}\n",
" \\PY{p}{]}\n",
"\n",
" \\PY{n}{data\\PYZus{}vars} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\n",
" \\PY{n}{q0\\PYZus{}iq\\PYZus{}av}\\PY{o}{=}\\PY{n}{dataset\\PYZus{}av}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}av}\\PY{p}{,} \\PY{c+c1}{\\PYZsh{} reuse from the other dataset}\n",
" \\PY{n}{q0\\PYZus{}iq\\PYZus{}av\\PYZus{}cal}\\PY{o}{=}\\PY{p}{(}\\PY{n}{secondary\\PYZus{}dims}\\PY{p}{,} \\PY{n}{q0\\PYZus{}iq\\PYZus{}av\\PYZus{}cal}\\PY{p}{,} \\PY{n}{q0\\PYZus{}cal\\PYZus{}attrs}\\PY{p}{)}\\PY{p}{,}\n",
" \\PY{p}{)}\n",
" \\PY{n}{coords} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\n",
" \\PY{n}{t1\\PYZus{}time}\\PY{o}{=}\\PY{n}{dataset\\PYZus{}av}\\PY{o}{.}\\PY{n}{t1\\PYZus{}time}\\PY{p}{,} \\PY{c+c1}{\\PYZsh{} reuse from the other dataset}\n",
" \\PY{n}{cal}\\PY{o}{=}\\PY{p}{(}\\PY{n}{secondary\\PYZus{}dims}\\PY{p}{,} \\PY{p}{[}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{|0\\PYZgt{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{|1\\PYZgt{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]}\\PY{p}{,} \\PY{n}{cal\\PYZus{}attrs}\\PY{p}{)}\\PY{p}{,} \\PY{c+c1}{\\PYZsh{} coords can be strings}\n",
" \\PY{p}{)}\n",
"\n",
" \\PY{n}{dataset} \\PY{o}{=} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{(}\n",
" \\PY{n}{data\\PYZus{}vars}\\PY{o}{=}\\PY{n}{data\\PYZus{}vars}\\PY{p}{,}\n",
" \\PY{n}{coords}\\PY{o}{=}\\PY{n}{coords}\\PY{p}{,}\n",
" \\PY{n}{attrs}\\PY{o}{=}\\PY{n}{mk\\PYZus{}dataset\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{relationships}\\PY{o}{=}\\PY{n}{relationships}\\PY{p}{)}\\PY{p}{,} \\PY{c+c1}{\\PYZsh{} relationships added here}\n",
" \\PY{p}{)}\n",
"\n",
" \\PY{k}{return} \\PY{n}{dataset}\n",
"\\end{Verbatim}\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display_source_code(dataset_examples.mk_t1_av_with_cal_dataset)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "41d36e0a",
"metadata": {
"execution": {
"iopub.execute_input": "2023-09-26T17:43:35.114752Z",
"iopub.status.busy": "2023-09-26T17:43:35.114551Z",
"iopub.status.idle": "2023-09-26T17:43:35.179307Z",
"shell.execute_reply": "2023-09-26T17:43:35.178653Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"
defmk_t1_shots_dataset(\n",
" t1_times:Optional[np.ndarray]=None,\n",
" probabilities:Optional[np.ndarray]=None,\n",
" **kwargs,\n",
")->xr.Dataset:\n",
" """\n",
" Generates a dataset with mock data of a T1 experiment for a single qubit including\n",
" calibration points for the ground and excited states, including all the individual\n",
" shots (repeated qubit state measurement for the same exact experiment).\n",
"\n",
" Parameters\n",
" ----------\n",
" t1_times\n",
" Array with the T1 times corresponding to each probability in ``probabilities``.\n",
" probabilities\n",
" The probabilities of finding the qubit in the excited state.\n",
" **kwargs\n",
" Keyword arguments passed to\n",
" :func:`~quantify_core.utilities.examples_support.mk_iq_shots`.\n",
" """\n",
" # reuse previous dataset\n",
" dataset_av_with_cal=mk_t1_av_with_cal_dataset(t1_times,probabilities,**kwargs)\n",
" ifprobabilitiesisNone:\n",
" probabilities=dataset_av_with_cal.q0_iq_av.values\n",
" probabilities=rotate_to_calibrated_axis(\n",
" probabilities,*dataset_av_with_cal.q0_iq_av_cal.values\n",
" ).real\n",
" # generate mock data containing all the shots,\n",
" # NB not the same data that was used for the average above, but this is just a mock\n",
" q0_iq_shots=mk_shots_from_probabilities(probabilities,**kwargs)\n",
" q0_iq_shots_cal=mk_shots_from_probabilities([0,1],**kwargs)\n",
"\n",
" # the xarray dimensions will now require an outer repetitions dimension\n",
" secondary_dims_rep=("repetitions","cal_dim")\n",
" main_dims_rep=("repetitions","main_dim")\n",
"\n",
" relationships=[\n",
" dattrs.QDatasetIntraRelationship(\n",
" item_name=dataset_av_with_cal.q0_iq_av.name,\n",
" relation_type="calibration",\n",
" related_names=[dataset_av_with_cal.q0_iq_av_cal.name],\n",
" ).to_dict(),\n",
" dattrs.QDatasetIntraRelationship(\n",
" item_name="q0_iq_shots",\n",
" relation_type="calibration",\n",
" related_names=["q0_iq_cal_shots"],\n",
" ).to_dict(),\n",
" # suggestion of a custom relationship\n",
" dattrs.QDatasetIntraRelationship(\n",
" item_name=dataset_av_with_cal.q0_iq_av.name,\n",
" relation_type="individual_shots",\n",
" related_names=["q0_iq_shots"],\n",
" ).to_dict(),\n",
" ]\n",
"\n",
" # Flag that these variables use a repetitions dimension\n",
" q0_attrs_rep=dict(dataset_av_with_cal.q0_iq_av.attrs)\n",
" q0_attrs_rep["has_repetitions"]=True\n",
" q0_cal_attrs_rep=dict(dataset_av_with_cal.q0_iq_av_cal.attrs)\n",
" q0_cal_attrs_rep["has_repetitions"]=True\n",
"\n",
" data_vars=dict(\n",
" # variables that are the same as in the previous dataset, and are now redundant,\n",
" # however, we include them to showcase the dataset flexibility\n",
" q0_iq_av=dataset_av_with_cal.q0_iq_av,\n",
" q0_iq_av_cal=dataset_av_with_cal.q0_iq_av_cal,\n",
" # variables that contain all the individual shots\n",
" q0_iq_shots=(main_dims_rep,q0_iq_shots,q0_attrs_rep),\n",
" q0_iq_shots_cal=(secondary_dims_rep,q0_iq_shots_cal,q0_cal_attrs_rep),\n",
" )\n",
"\n",
" dataset=xr.Dataset(\n",
" data_vars=data_vars,\n",
" coords=dataset_av_with_cal.coords,# same coordinates as in previous dataset\n",
" attrs=mk_dataset_attrs(relationships=relationships),# relationships added here\n",
" )\n",
"\n",
" returndataset\n",
"
\n"
],
"text/latex": [
"\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
"\\PY{k}{def} \\PY{n+nf}{mk\\PYZus{}t1\\PYZus{}shots\\PYZus{}dataset}\\PY{p}{(}\n",
" \\PY{n}{t1\\PYZus{}times}\\PY{p}{:} \\PY{n}{Optional}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{]} \\PY{o}{=} \\PY{k+kc}{None}\\PY{p}{,}\n",
" \\PY{n}{probabilities}\\PY{p}{:} \\PY{n}{Optional}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{]} \\PY{o}{=} \\PY{k+kc}{None}\\PY{p}{,}\n",
" \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{,}\n",
"\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{:}\n",
" \\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
"\\PY{l+s+sd}{ Generates a dataset with mock data of a T1 experiment for a single qubit including}\n",
"\\PY{l+s+sd}{ calibration points for the ground and excited states, including all the individual}\n",
"\\PY{l+s+sd}{ shots (repeated qubit state measurement for the same exact experiment).}\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}{ t1\\PYZus{}times}\n",
"\\PY{l+s+sd}{ Array with the T1 times corresponding to each probability in ``probabilities``.}\n",
"\\PY{l+s+sd}{ probabilities}\n",
"\\PY{l+s+sd}{ The probabilities of finding the qubit in the excited state.}\n",
"\\PY{l+s+sd}{ **kwargs}\n",
"\\PY{l+s+sd}{ Keyword arguments passed to}\n",
"\\PY{l+s+sd}{ :func:`\\PYZti{}quantify\\PYZus{}core.utilities.examples\\PYZus{}support.mk\\PYZus{}iq\\PYZus{}shots`.}\n",
"\\PY{l+s+sd}{ \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
" \\PY{c+c1}{\\PYZsh{} reuse previous dataset}\n",
" \\PY{n}{dataset\\PYZus{}av\\PYZus{}with\\PYZus{}cal} \\PY{o}{=} \\PY{n}{mk\\PYZus{}t1\\PYZus{}av\\PYZus{}with\\PYZus{}cal\\PYZus{}dataset}\\PY{p}{(}\\PY{n}{t1\\PYZus{}times}\\PY{p}{,} \\PY{n}{probabilities}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\n",
" \\PY{k}{if} \\PY{n}{probabilities} \\PY{o+ow}{is} \\PY{k+kc}{None}\\PY{p}{:}\n",
" \\PY{n}{probabilities} \\PY{o}{=} \\PY{n}{dataset\\PYZus{}av\\PYZus{}with\\PYZus{}cal}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}av}\\PY{o}{.}\\PY{n}{values}\n",
" \\PY{n}{probabilities} \\PY{o}{=} \\PY{n}{rotate\\PYZus{}to\\PYZus{}calibrated\\PYZus{}axis}\\PY{p}{(}\n",
" \\PY{n}{probabilities}\\PY{p}{,} \\PY{o}{*}\\PY{n}{dataset\\PYZus{}av\\PYZus{}with\\PYZus{}cal}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}av\\PYZus{}cal}\\PY{o}{.}\\PY{n}{values}\n",
" \\PY{p}{)}\\PY{o}{.}\\PY{n}{real}\n",
" \\PY{c+c1}{\\PYZsh{} generate mock data containing all the shots,}\n",
" \\PY{c+c1}{\\PYZsh{} NB not the same data that was used for the average above, but this is just a mock}\n",
" \\PY{n}{q0\\PYZus{}iq\\PYZus{}shots} \\PY{o}{=} \\PY{n}{mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities}\\PY{p}{(}\\PY{n}{probabilities}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\n",
" \\PY{n}{q0\\PYZus{}iq\\PYZus{}shots\\PYZus{}cal} \\PY{o}{=} \\PY{n}{mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities}\\PY{p}{(}\\PY{p}{[}\\PY{l+m+mi}{0}\\PY{p}{,} \\PY{l+m+mi}{1}\\PY{p}{]}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\n",
"\n",
" \\PY{c+c1}{\\PYZsh{} the xarray dimensions will now require an outer repetitions dimension}\n",
" \\PY{n}{secondary\\PYZus{}dims\\PYZus{}rep} \\PY{o}{=} \\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{repetitions}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{cal\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
" \\PY{n}{main\\PYZus{}dims\\PYZus{}rep} \\PY{o}{=} \\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{repetitions}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
"\n",
" \\PY{n}{relationships} \\PY{o}{=} \\PY{p}{[}\n",
" \\PY{n}{dattrs}\\PY{o}{.}\\PY{n}{QDatasetIntraRelationship}\\PY{p}{(}\n",
" \\PY{n}{item\\PYZus{}name}\\PY{o}{=}\\PY{n}{dataset\\PYZus{}av\\PYZus{}with\\PYZus{}cal}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}av}\\PY{o}{.}\\PY{n}{name}\\PY{p}{,}\n",
" \\PY{n}{relation\\PYZus{}type}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{calibration}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{n}{related\\PYZus{}names}\\PY{o}{=}\\PY{p}{[}\\PY{n}{dataset\\PYZus{}av\\PYZus{}with\\PYZus{}cal}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}av\\PYZus{}cal}\\PY{o}{.}\\PY{n}{name}\\PY{p}{]}\\PY{p}{,}\n",
" \\PY{p}{)}\\PY{o}{.}\\PY{n}{to\\PYZus{}dict}\\PY{p}{(}\\PY{p}{)}\\PY{p}{,}\n",
" \\PY{n}{dattrs}\\PY{o}{.}\\PY{n}{QDatasetIntraRelationship}\\PY{p}{(}\n",
" \\PY{n}{item\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{q0\\PYZus{}iq\\PYZus{}shots}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{n}{relation\\PYZus{}type}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{calibration}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{n}{related\\PYZus{}names}\\PY{o}{=}\\PY{p}{[}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{q0\\PYZus{}iq\\PYZus{}cal\\PYZus{}shots}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]}\\PY{p}{,}\n",
" \\PY{p}{)}\\PY{o}{.}\\PY{n}{to\\PYZus{}dict}\\PY{p}{(}\\PY{p}{)}\\PY{p}{,}\n",
" \\PY{c+c1}{\\PYZsh{} suggestion of a custom relationship}\n",
" \\PY{n}{dattrs}\\PY{o}{.}\\PY{n}{QDatasetIntraRelationship}\\PY{p}{(}\n",
" \\PY{n}{item\\PYZus{}name}\\PY{o}{=}\\PY{n}{dataset\\PYZus{}av\\PYZus{}with\\PYZus{}cal}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}av}\\PY{o}{.}\\PY{n}{name}\\PY{p}{,}\n",
" \\PY{n}{relation\\PYZus{}type}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{individual\\PYZus{}shots}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{n}{related\\PYZus{}names}\\PY{o}{=}\\PY{p}{[}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{q0\\PYZus{}iq\\PYZus{}shots}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]}\\PY{p}{,}\n",
" \\PY{p}{)}\\PY{o}{.}\\PY{n}{to\\PYZus{}dict}\\PY{p}{(}\\PY{p}{)}\\PY{p}{,}\n",
" \\PY{p}{]}\n",
"\n",
" \\PY{c+c1}{\\PYZsh{} Flag that these variables use a repetitions dimension}\n",
" \\PY{n}{q0\\PYZus{}attrs\\PYZus{}rep} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\\PY{n}{dataset\\PYZus{}av\\PYZus{}with\\PYZus{}cal}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}av}\\PY{o}{.}\\PY{n}{attrs}\\PY{p}{)}\n",
" \\PY{n}{q0\\PYZus{}attrs\\PYZus{}rep}\\PY{p}{[}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{has\\PYZus{}repetitions}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]} \\PY{o}{=} \\PY{k+kc}{True}\n",
" \\PY{n}{q0\\PYZus{}cal\\PYZus{}attrs\\PYZus{}rep} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\\PY{n}{dataset\\PYZus{}av\\PYZus{}with\\PYZus{}cal}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}av\\PYZus{}cal}\\PY{o}{.}\\PY{n}{attrs}\\PY{p}{)}\n",
" \\PY{n}{q0\\PYZus{}cal\\PYZus{}attrs\\PYZus{}rep}\\PY{p}{[}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{has\\PYZus{}repetitions}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]} \\PY{o}{=} \\PY{k+kc}{True}\n",
"\n",
" \\PY{n}{data\\PYZus{}vars} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\n",
" \\PY{c+c1}{\\PYZsh{} variables that are the same as in the previous dataset, and are now redundant,}\n",
" \\PY{c+c1}{\\PYZsh{} however, we include them to showcase the dataset flexibility}\n",
" \\PY{n}{q0\\PYZus{}iq\\PYZus{}av}\\PY{o}{=}\\PY{n}{dataset\\PYZus{}av\\PYZus{}with\\PYZus{}cal}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}av}\\PY{p}{,}\n",
" \\PY{n}{q0\\PYZus{}iq\\PYZus{}av\\PYZus{}cal}\\PY{o}{=}\\PY{n}{dataset\\PYZus{}av\\PYZus{}with\\PYZus{}cal}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}av\\PYZus{}cal}\\PY{p}{,}\n",
" \\PY{c+c1}{\\PYZsh{} variables that contain all the individual shots}\n",
" \\PY{n}{q0\\PYZus{}iq\\PYZus{}shots}\\PY{o}{=}\\PY{p}{(}\\PY{n}{main\\PYZus{}dims\\PYZus{}rep}\\PY{p}{,} \\PY{n}{q0\\PYZus{}iq\\PYZus{}shots}\\PY{p}{,} \\PY{n}{q0\\PYZus{}attrs\\PYZus{}rep}\\PY{p}{)}\\PY{p}{,}\n",
" \\PY{n}{q0\\PYZus{}iq\\PYZus{}shots\\PYZus{}cal}\\PY{o}{=}\\PY{p}{(}\\PY{n}{secondary\\PYZus{}dims\\PYZus{}rep}\\PY{p}{,} \\PY{n}{q0\\PYZus{}iq\\PYZus{}shots\\PYZus{}cal}\\PY{p}{,} \\PY{n}{q0\\PYZus{}cal\\PYZus{}attrs\\PYZus{}rep}\\PY{p}{)}\\PY{p}{,}\n",
" \\PY{p}{)}\n",
"\n",
" \\PY{n}{dataset} \\PY{o}{=} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{(}\n",
" \\PY{n}{data\\PYZus{}vars}\\PY{o}{=}\\PY{n}{data\\PYZus{}vars}\\PY{p}{,}\n",
" \\PY{n}{coords}\\PY{o}{=}\\PY{n}{dataset\\PYZus{}av\\PYZus{}with\\PYZus{}cal}\\PY{o}{.}\\PY{n}{coords}\\PY{p}{,} \\PY{c+c1}{\\PYZsh{} same coordinates as in previous dataset}\n",
" \\PY{n}{attrs}\\PY{o}{=}\\PY{n}{mk\\PYZus{}dataset\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{relationships}\\PY{o}{=}\\PY{n}{relationships}\\PY{p}{)}\\PY{p}{,} \\PY{c+c1}{\\PYZsh{} relationships added here}\n",
" \\PY{p}{)}\n",
"\n",
" \\PY{k}{return} \\PY{n}{dataset}\n",
"\\end{Verbatim}\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display_source_code(dataset_examples.mk_t1_shots_dataset)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "b805f845",
"metadata": {
"execution": {
"iopub.execute_input": "2023-09-26T17:43:35.906828Z",
"iopub.status.busy": "2023-09-26T17:43:35.906624Z",
"iopub.status.idle": "2023-09-26T17:43:35.946552Z",
"shell.execute_reply": "2023-09-26T17:43:35.945895Z"
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"outputs": [
{
"data": {
"text/html": [
"
defmk_t1_traces_dataset(\n",
" t1_times:Optional[np.ndarray]=None,\n",
" probabilities:Optional[np.ndarray]=None,\n",
" **kwargs,\n",
")->xr.Dataset:\n",
" """\n",
" Generates a dataset with mock data of a T1 experiment for a single qubit including\n",
" calibration points for the ground and excited states, including all the individual\n",
" shots (repeated qubit state measurement for the same exact experiment); and\n",
" including all the signals that had to be digitized to obtain the rest of the data.\n",
"\n",
" Parameters\n",
" ----------\n",
" t1_times\n",
" Array with the T1 times corresponding to each probability in ``probabilities``.\n",
" probabilities\n",
" The probabilities of finding the qubit in the excited state.\n",
" **kwargs\n",
" Keyword arguments passed to\n",
" :func:`~quantify_core.utilities.examples_support.mk_iq_shots`.\n",
" """\n",
" dataset_shots=mk_t1_shots_dataset(t1_times,probabilities,**kwargs)\n",
" shots=dataset_shots.q0_iq_shots.values\n",
" shots_cal=dataset_shots.q0_iq_shots_cal.values\n",
"\n",
" # generate mock traces for all shots\n",
" q0_traces=np.array(tuple(map(mk_trace_for_iq_shot,shots.flatten())))\n",
" q0_traces=q0_traces.reshape(*shots.shape,q0_traces.shape[-1])\n",
" # generate mock traces for calibration points shots\n",
" q0_traces_cal=np.array(tuple(map(mk_trace_for_iq_shot,shots_cal.flatten())))\n",
" q0_traces_cal=q0_traces_cal.reshape(*shots_cal.shape,q0_traces_cal.shape[-1])\n",
"\n",
" traces_dims=("repetitions","main_dim","trace_dim")\n",
" traces_cal_dims=("repetitions","cal_dim","trace_dim")\n",
" trace_times=mk_trace_time()\n",
" trace_attrs=mk_main_coord_attrs(long_name="Trace time",unit="s")\n",
"\n",
" relationships_with_traces=dataset_shots.relationships+[\n",
" dattrs.QDatasetIntraRelationship(\n",
" item_name="q0_traces",\n",
" related_names=["q0_traces_cal"],\n",
" relation_type="calibration",\n",
" ).to_dict(),\n",
" ]\n",
"\n",
" data_vars=dict(\n",
" q0_iq_av=dataset_shots.q0_iq_av,\n",
" q0_iq_av_cal=dataset_shots.q0_iq_av_cal,\n",
" q0_iq_shots=dataset_shots.q0_iq_shots,\n",
" q0_iq_shots_cal=dataset_shots.q0_iq_shots_cal,\n",
" q0_traces=(traces_dims,q0_traces,dataset_shots.q0_iq_shots.attrs),\n",
" q0_traces_cal=(\n",
" traces_cal_dims,\n",
" q0_traces_cal,\n",
" dataset_shots.q0_iq_shots_cal.attrs,\n",
" ),\n",
" )\n",
" coords=dict(\n",
" t1_time=dataset_shots.t1_time,\n",
" cal=dataset_shots.cal,\n",
" trace_time=(("trace_dim",),trace_times,trace_attrs),\n",
" )\n",
"\n",
" dataset=xr.Dataset(\n",
" data_vars=data_vars,\n",
" coords=coords,\n",
" attrs=mk_dataset_attrs(relationships=relationships_with_traces),\n",
" )\n",
"\n",
" returndataset\n",
"
\n"
],
"text/latex": [
"\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
"\\PY{k}{def} \\PY{n+nf}{mk\\PYZus{}t1\\PYZus{}traces\\PYZus{}dataset}\\PY{p}{(}\n",
" \\PY{n}{t1\\PYZus{}times}\\PY{p}{:} \\PY{n}{Optional}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{]} \\PY{o}{=} \\PY{k+kc}{None}\\PY{p}{,}\n",
" \\PY{n}{probabilities}\\PY{p}{:} \\PY{n}{Optional}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{]} \\PY{o}{=} \\PY{k+kc}{None}\\PY{p}{,}\n",
" \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{,}\n",
"\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{:}\n",
" \\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
"\\PY{l+s+sd}{ Generates a dataset with mock data of a T1 experiment for a single qubit including}\n",
"\\PY{l+s+sd}{ calibration points for the ground and excited states, including all the individual}\n",
"\\PY{l+s+sd}{ shots (repeated qubit state measurement for the same exact experiment); and}\n",
"\\PY{l+s+sd}{ including all the signals that had to be digitized to obtain the rest of 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}{ t1\\PYZus{}times}\n",
"\\PY{l+s+sd}{ Array with the T1 times corresponding to each probability in ``probabilities``.}\n",
"\\PY{l+s+sd}{ probabilities}\n",
"\\PY{l+s+sd}{ The probabilities of finding the qubit in the excited state.}\n",
"\\PY{l+s+sd}{ **kwargs}\n",
"\\PY{l+s+sd}{ Keyword arguments passed to}\n",
"\\PY{l+s+sd}{ :func:`\\PYZti{}quantify\\PYZus{}core.utilities.examples\\PYZus{}support.mk\\PYZus{}iq\\PYZus{}shots`.}\n",
"\\PY{l+s+sd}{ \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
" \\PY{n}{dataset\\PYZus{}shots} \\PY{o}{=} \\PY{n}{mk\\PYZus{}t1\\PYZus{}shots\\PYZus{}dataset}\\PY{p}{(}\\PY{n}{t1\\PYZus{}times}\\PY{p}{,} \\PY{n}{probabilities}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\n",
" \\PY{n}{shots} \\PY{o}{=} \\PY{n}{dataset\\PYZus{}shots}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}shots}\\PY{o}{.}\\PY{n}{values}\n",
" \\PY{n}{shots\\PYZus{}cal} \\PY{o}{=} \\PY{n}{dataset\\PYZus{}shots}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}shots\\PYZus{}cal}\\PY{o}{.}\\PY{n}{values}\n",
"\n",
" \\PY{c+c1}{\\PYZsh{} generate mock traces for all shots}\n",
" \\PY{n}{q0\\PYZus{}traces} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{array}\\PY{p}{(}\\PY{n+nb}{tuple}\\PY{p}{(}\\PY{n+nb}{map}\\PY{p}{(}\\PY{n}{mk\\PYZus{}trace\\PYZus{}for\\PYZus{}iq\\PYZus{}shot}\\PY{p}{,} \\PY{n}{shots}\\PY{o}{.}\\PY{n}{flatten}\\PY{p}{(}\\PY{p}{)}\\PY{p}{)}\\PY{p}{)}\\PY{p}{)}\n",
" \\PY{n}{q0\\PYZus{}traces} \\PY{o}{=} \\PY{n}{q0\\PYZus{}traces}\\PY{o}{.}\\PY{n}{reshape}\\PY{p}{(}\\PY{o}{*}\\PY{n}{shots}\\PY{o}{.}\\PY{n}{shape}\\PY{p}{,} \\PY{n}{q0\\PYZus{}traces}\\PY{o}{.}\\PY{n}{shape}\\PY{p}{[}\\PY{o}{\\PYZhy{}}\\PY{l+m+mi}{1}\\PY{p}{]}\\PY{p}{)}\n",
" \\PY{c+c1}{\\PYZsh{} generate mock traces for calibration points shots}\n",
" \\PY{n}{q0\\PYZus{}traces\\PYZus{}cal} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{array}\\PY{p}{(}\\PY{n+nb}{tuple}\\PY{p}{(}\\PY{n+nb}{map}\\PY{p}{(}\\PY{n}{mk\\PYZus{}trace\\PYZus{}for\\PYZus{}iq\\PYZus{}shot}\\PY{p}{,} \\PY{n}{shots\\PYZus{}cal}\\PY{o}{.}\\PY{n}{flatten}\\PY{p}{(}\\PY{p}{)}\\PY{p}{)}\\PY{p}{)}\\PY{p}{)}\n",
" \\PY{n}{q0\\PYZus{}traces\\PYZus{}cal} \\PY{o}{=} \\PY{n}{q0\\PYZus{}traces\\PYZus{}cal}\\PY{o}{.}\\PY{n}{reshape}\\PY{p}{(}\\PY{o}{*}\\PY{n}{shots\\PYZus{}cal}\\PY{o}{.}\\PY{n}{shape}\\PY{p}{,} \\PY{n}{q0\\PYZus{}traces\\PYZus{}cal}\\PY{o}{.}\\PY{n}{shape}\\PY{p}{[}\\PY{o}{\\PYZhy{}}\\PY{l+m+mi}{1}\\PY{p}{]}\\PY{p}{)}\n",
"\n",
" \\PY{n}{traces\\PYZus{}dims} \\PY{o}{=} \\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{repetitions}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{trace\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
" \\PY{n}{traces\\PYZus{}cal\\PYZus{}dims} \\PY{o}{=} \\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{repetitions}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{cal\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{trace\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
" \\PY{n}{trace\\PYZus{}times} \\PY{o}{=} \\PY{n}{mk\\PYZus{}trace\\PYZus{}time}\\PY{p}{(}\\PY{p}{)}\n",
" \\PY{n}{trace\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Trace time}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{s}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
"\n",
" \\PY{n}{relationships\\PYZus{}with\\PYZus{}traces} \\PY{o}{=} \\PY{n}{dataset\\PYZus{}shots}\\PY{o}{.}\\PY{n}{relationships} \\PY{o}{+} \\PY{p}{[}\n",
" \\PY{n}{dattrs}\\PY{o}{.}\\PY{n}{QDatasetIntraRelationship}\\PY{p}{(}\n",
" \\PY{n}{item\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{q0\\PYZus{}traces}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{n}{related\\PYZus{}names}\\PY{o}{=}\\PY{p}{[}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{q0\\PYZus{}traces\\PYZus{}cal}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]}\\PY{p}{,}\n",
" \\PY{n}{relation\\PYZus{}type}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{calibration}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{p}{)}\\PY{o}{.}\\PY{n}{to\\PYZus{}dict}\\PY{p}{(}\\PY{p}{)}\\PY{p}{,}\n",
" \\PY{p}{]}\n",
"\n",
" \\PY{n}{data\\PYZus{}vars} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\n",
" \\PY{n}{q0\\PYZus{}iq\\PYZus{}av}\\PY{o}{=}\\PY{n}{dataset\\PYZus{}shots}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}av}\\PY{p}{,}\n",
" \\PY{n}{q0\\PYZus{}iq\\PYZus{}av\\PYZus{}cal}\\PY{o}{=}\\PY{n}{dataset\\PYZus{}shots}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}av\\PYZus{}cal}\\PY{p}{,}\n",
" \\PY{n}{q0\\PYZus{}iq\\PYZus{}shots}\\PY{o}{=}\\PY{n}{dataset\\PYZus{}shots}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}shots}\\PY{p}{,}\n",
" \\PY{n}{q0\\PYZus{}iq\\PYZus{}shots\\PYZus{}cal}\\PY{o}{=}\\PY{n}{dataset\\PYZus{}shots}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}shots\\PYZus{}cal}\\PY{p}{,}\n",
" \\PY{n}{q0\\PYZus{}traces}\\PY{o}{=}\\PY{p}{(}\\PY{n}{traces\\PYZus{}dims}\\PY{p}{,} \\PY{n}{q0\\PYZus{}traces}\\PY{p}{,} \\PY{n}{dataset\\PYZus{}shots}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}shots}\\PY{o}{.}\\PY{n}{attrs}\\PY{p}{)}\\PY{p}{,}\n",
" \\PY{n}{q0\\PYZus{}traces\\PYZus{}cal}\\PY{o}{=}\\PY{p}{(}\n",
" \\PY{n}{traces\\PYZus{}cal\\PYZus{}dims}\\PY{p}{,}\n",
" \\PY{n}{q0\\PYZus{}traces\\PYZus{}cal}\\PY{p}{,}\n",
" \\PY{n}{dataset\\PYZus{}shots}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}shots\\PYZus{}cal}\\PY{o}{.}\\PY{n}{attrs}\\PY{p}{,}\n",
" \\PY{p}{)}\\PY{p}{,}\n",
" \\PY{p}{)}\n",
" \\PY{n}{coords} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\n",
" \\PY{n}{t1\\PYZus{}time}\\PY{o}{=}\\PY{n}{dataset\\PYZus{}shots}\\PY{o}{.}\\PY{n}{t1\\PYZus{}time}\\PY{p}{,}\n",
" \\PY{n}{cal}\\PY{o}{=}\\PY{n}{dataset\\PYZus{}shots}\\PY{o}{.}\\PY{n}{cal}\\PY{p}{,}\n",
" \\PY{n}{trace\\PYZus{}time}\\PY{o}{=}\\PY{p}{(}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{trace\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\\PY{p}{)}\\PY{p}{,} \\PY{n}{trace\\PYZus{}times}\\PY{p}{,} \\PY{n}{trace\\PYZus{}attrs}\\PY{p}{)}\\PY{p}{,}\n",
" \\PY{p}{)}\n",
"\n",
" \\PY{n}{dataset} \\PY{o}{=} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{(}\n",
" \\PY{n}{data\\PYZus{}vars}\\PY{o}{=}\\PY{n}{data\\PYZus{}vars}\\PY{p}{,}\n",
" \\PY{n}{coords}\\PY{o}{=}\\PY{n}{coords}\\PY{p}{,}\n",
" \\PY{n}{attrs}\\PY{o}{=}\\PY{n}{mk\\PYZus{}dataset\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{relationships}\\PY{o}{=}\\PY{n}{relationships\\PYZus{}with\\PYZus{}traces}\\PY{p}{)}\\PY{p}{,}\n",
" \\PY{p}{)}\n",
"\n",
" \\PY{k}{return} \\PY{n}{dataset}\n",
"\\end{Verbatim}\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display_source_code(dataset_examples.mk_t1_traces_dataset)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "0e7be475",
"metadata": {
"execution": {
"iopub.execute_input": "2023-09-26T17:43:37.319139Z",
"iopub.status.busy": "2023-09-26T17:43:37.318933Z",
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"shell.execute_reply": "2023-09-26T17:43:37.632643Z"
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"outputs": [
{
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