{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "fa33d6d8",
"metadata": {
"execution": {
"iopub.execute_input": "2023-09-26T17:43:39.605913Z",
"iopub.status.busy": "2023-09-26T17:43:39.605733Z",
"iopub.status.idle": "2023-09-26T17:43:40.784239Z",
"shell.execute_reply": "2023-09-26T17:43:40.783572Z"
}
},
"outputs": [],
"source": [
"from pathlib import Path\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import xarray as xr\n",
"from rich import pretty\n",
"\n",
"import quantify_core.data.dataset_adapters as dadapters\n",
"import quantify_core.data.dataset_attrs as dattrs\n",
"from quantify_core.data import handling as dh\n",
"from quantify_core.utilities import dataset_examples\n",
"from quantify_core.utilities.examples_support import round_trip_dataset\n",
"from quantify_core.utilities.inspect_utils import display_source_code\n",
"\n",
"pretty.install()\n",
"\n",
"dh.set_datadir(Path.home() / \"quantify-data\") # change me!"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "95aa4f0e",
"metadata": {
"execution": {
"iopub.execute_input": "2023-09-26T17:43:40.787086Z",
"iopub.status.busy": "2023-09-26T17:43:40.786758Z",
"iopub.status.idle": "2023-09-26T17:43:40.843101Z",
"shell.execute_reply": "2023-09-26T17:43:40.842370Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"def mk_two_qubit_chevron_dataset ( ** kwargs ) -> xr . Dataset : \n",
" """ \n",
" Generates a dataset that look similar to a two-qubit Chevron experiment. \n",
"\n",
" Parameters \n",
" ---------- \n",
" **kwargs \n",
" Keyword arguments passed to :func:`~.mk_two_qubit_chevron_data`. \n",
"\n",
" Returns \n",
" ------- \n",
" : \n",
" A mock Quantify dataset. \n",
" """ \n",
" amp_values , time_values , pop_q0 , pop_q1 = mk_two_qubit_chevron_data ( ** kwargs ) \n",
"\n",
" dims_q0 = dims_q1 = ( "repetitions" , "main_dim" ) \n",
" pop_q0_attrs = mk_main_var_attrs ( \n",
" long_name = "Population Q0" , unit = "" , has_repetitions = True \n",
" ) \n",
" pop_q1_attrs = mk_main_var_attrs ( \n",
" long_name = "Population Q1" , unit = "" , has_repetitions = True \n",
" ) \n",
" data_vars = dict ( \n",
" pop_q0 = ( dims_q0 , pop_q0 , pop_q0_attrs ), \n",
" pop_q1 = ( dims_q1 , pop_q1 , pop_q1_attrs ), \n",
" ) \n",
"\n",
" dims_amp = dims_time = ( "main_dim" ,) \n",
" amp_attrs = mk_main_coord_attrs ( long_name = "Amplitude" , unit = "V" ) \n",
" time_attrs = mk_main_coord_attrs ( long_name = "Time" , unit = "s" ) \n",
" coords = dict ( \n",
" amp = ( dims_amp , amp_values , amp_attrs ), \n",
" time = ( dims_time , time_values , time_attrs ), \n",
" ) \n",
"\n",
" dataset_attrs = mk_dataset_attrs () \n",
" dataset = xr . Dataset ( data_vars = data_vars , coords = coords , attrs = dataset_attrs ) \n",
"\n",
" return dataset \n",
"\n",
"\n",
"\n",
" \n",
"\n",
" \n",
" \n",
" \n",
"
<xarray.Dataset>\n",
"Dimensions: (repetitions: 5, main_dim: 1200)\n",
"Coordinates:\n",
" amp (main_dim) float64 0.45 0.4534 0.4569 0.4603 ... 0.5431 0.5466 0.55\n",
" time (main_dim) float64 0.0 0.0 0.0 0.0 0.0 ... 1e-07 1e-07 1e-07 1e-07\n",
"Dimensions without coordinates: repetitions, main_dim\n",
"Data variables:\n",
" pop_q0 (repetitions, main_dim) float64 0.5 0.5 0.5 ... 0.4886 0.4818 0.5\n",
" pop_q1 (repetitions, main_dim) float64 0.5 0.5 0.5 ... 0.5243 0.5371 0.5\n",
"Attributes:\n",
" tuid: 20230926-194340-851-8a0f58\n",
" dataset_name: \n",
" dataset_state: None\n",
" timestamp_start: None\n",
" timestamp_end: None\n",
" quantify_dataset_version: 2.0.0\n",
" software_versions: {}\n",
" relationships: []\n",
" json_serialize_exclude: [] \n",
"\n",
"\n",
" \n",
"\n",
" \n",
" \n",
" \n",
"
<xarray.Dataset>\n",
"Dimensions: (repetitions: 5)\n",
"Coordinates:\n",
" * repetitions (repetitions) <U1 'A' 'B' 'C' 'D' 'E'\n",
"Data variables:\n",
" *empty* \n",
"\n",
"\n",
" \n",
"\n",
" \n",
" \n",
" \n",
"
<xarray.Dataset>\n",
"Dimensions: (repetitions: 5, main_dim: 1200)\n",
"Coordinates:\n",
" amp (main_dim) float64 0.45 0.4534 0.4569 ... 0.5431 0.5466 0.55\n",
" time (main_dim) float64 0.0 0.0 0.0 0.0 ... 1e-07 1e-07 1e-07 1e-07\n",
" * repetitions (repetitions) <U1 'A' 'B' 'C' 'D' 'E'\n",
"Dimensions without coordinates: main_dim\n",
"Data variables:\n",
" pop_q0 (repetitions, main_dim) float64 0.5 0.5 0.5 ... 0.4818 0.5\n",
" pop_q1 (repetitions, main_dim) float64 0.5 0.5 0.5 ... 0.5371 0.5\n",
"Attributes:\n",
" tuid: 20230926-194340-851-8a0f58\n",
" dataset_name: \n",
" dataset_state: None\n",
" timestamp_start: None\n",
" timestamp_end: None\n",
" quantify_dataset_version: 2.0.0\n",
" software_versions: {}\n",
" relationships: []\n",
" json_serialize_exclude: [] \n",
"\n",
"\n",
" \n",
"\n",
" \n",
" \n",
" \n",
"
<xarray.Dataset>\n",
"Dimensions: (amp: 30, time: 40, repetitions: 5)\n",
"Coordinates:\n",
" * amp (amp) float64 0.45 0.4534 0.4569 0.4603 ... 0.5431 0.5466 0.55\n",
" * time (time) float64 0.0 2.564e-09 5.128e-09 ... 9.744e-08 1e-07\n",
" * repetitions (repetitions) <U1 'A' 'B' 'C' 'D' 'E'\n",
"Data variables:\n",
" pop_q0 (repetitions, amp, time) float64 0.5 0.5 0.5 ... 0.5 0.5 0.5\n",
" pop_q1 (repetitions, amp, time) float64 0.5 0.5 0.5 ... 0.5 0.5 0.5\n",
"Attributes:\n",
" tuid: 20230926-194340-851-8a0f58\n",
" dataset_name: \n",
" dataset_state: None\n",
" timestamp_start: None\n",
" timestamp_end: None\n",
" quantify_dataset_version: 2.0.0\n",
" software_versions: {}\n",
" relationships: []\n",
" json_serialize_exclude: [] \n",
"{ \n",
" 'tuid' : '20230926-194343-406-e5bcea' ,\n",
" 'dataset_name' : 'Bias scan' ,\n",
" 'dataset_state' : 'done' ,\n",
" 'timestamp_start' : '2023-09-26T19:43:43.406197+02:00' ,\n",
" 'timestamp_end' : '2023-09-26T19:45:43.406238+02:00' ,\n",
" 'quantify_dataset_version' : '2.0.0' ,\n",
" 'software_versions' : {} ,\n",
" 'relationships' : [] ,\n",
" 'json_serialize_exclude' : [] \n",
"} \n",
" \n"
],
"text/plain": [
"\n",
"\u001b[1m{\u001b[0m\n",
" \u001b[32m'tuid'\u001b[0m: \u001b[32m'20230926-194343-406-e5bcea'\u001b[0m,\n",
" \u001b[32m'dataset_name'\u001b[0m: \u001b[32m'Bias scan'\u001b[0m,\n",
" \u001b[32m'dataset_state'\u001b[0m: \u001b[32m'done'\u001b[0m,\n",
" \u001b[32m'timestamp_start'\u001b[0m: \u001b[32m'2023-09-26T19:43:43.406197+02:00'\u001b[0m,\n",
" \u001b[32m'timestamp_end'\u001b[0m: \u001b[32m'2023-09-26T19:45:43.406238+02:00'\u001b[0m,\n",
" \u001b[32m'quantify_dataset_version'\u001b[0m: \u001b[32m'2.0.0'\u001b[0m,\n",
" \u001b[32m'software_versions'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m,\n",
" \u001b[32m'relationships'\u001b[0m: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m,\n",
" \u001b[32m'json_serialize_exclude'\u001b[0m: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
"\u001b[1m}\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pendulum\n",
"\n",
"from quantify_core.utilities import examples_support\n",
"\n",
"examples_support.mk_dataset_attrs(\n",
" dataset_name=\"Bias scan\",\n",
" timestamp_start=pendulum.now().to_iso8601_string(),\n",
" timestamp_end=pendulum.now().add(minutes=2).to_iso8601_string(),\n",
" dataset_state=\"done\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "08efb357",
"metadata": {
"execution": {
"iopub.execute_input": "2023-09-26T17:43:43.417293Z",
"iopub.status.busy": "2023-09-26T17:43:43.416609Z",
"iopub.status.idle": "2023-09-26T17:43:43.419971Z",
"shell.execute_reply": "2023-09-26T17:43:43.419348Z"
}
},
"outputs": [],
"source": [
"# pylint: disable=line-too-long\n",
"# pylint: disable=wrong-import-order\n",
"# pylint: disable=wrong-import-position\n",
"# pylint: disable=pointless-string-statement\n",
"# pylint: disable=duplicate-code"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "b5875ec8",
"metadata": {
"execution": {
"iopub.execute_input": "2023-09-26T17:43:43.422218Z",
"iopub.status.busy": "2023-09-26T17:43:43.422030Z",
"iopub.status.idle": "2023-09-26T17:43:43.430051Z",
"shell.execute_reply": "2023-09-26T17:43:43.429347Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"{ \n",
" 'tuid' : '20230926-194343-423-7ca250' ,\n",
" 'dataset_name' : 'My experiment' ,\n",
" 'dataset_state' : None ,\n",
" 'timestamp_start' : '2023-09-26T19:43:43.423773+02:00' ,\n",
" 'timestamp_end' : '2023-09-26T19:45:43.423819+02:00' ,\n",
" 'quantify_dataset_version' : '2.0.0' ,\n",
" 'software_versions' : { \n",
" 'lab_fridge_magnet_driver' : 'v1.4.2' ,\n",
" 'my_lab_repo' : '9d8acf63f48c469c1b9fa9f2c3cf230845f67b18' \n",
" } ,\n",
" 'relationships' : [] ,\n",
" 'json_serialize_exclude' : [] \n",
"} \n",
" \n"
],
"text/plain": [
"\n",
"\u001b[1m{\u001b[0m\n",
" \u001b[32m'tuid'\u001b[0m: \u001b[32m'20230926-194343-423-7ca250'\u001b[0m,\n",
" \u001b[32m'dataset_name'\u001b[0m: \u001b[32m'My experiment'\u001b[0m,\n",
" \u001b[32m'dataset_state'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n",
" \u001b[32m'timestamp_start'\u001b[0m: \u001b[32m'2023-09-26T19:43:43.423773+02:00'\u001b[0m,\n",
" \u001b[32m'timestamp_end'\u001b[0m: \u001b[32m'2023-09-26T19:45:43.423819+02:00'\u001b[0m,\n",
" \u001b[32m'quantify_dataset_version'\u001b[0m: \u001b[32m'2.0.0'\u001b[0m,\n",
" \u001b[32m'software_versions'\u001b[0m: \u001b[1m{\u001b[0m\n",
" \u001b[32m'lab_fridge_magnet_driver'\u001b[0m: \u001b[32m'v1.4.2'\u001b[0m,\n",
" \u001b[32m'my_lab_repo'\u001b[0m: \u001b[32m'9d8acf63f48c469c1b9fa9f2c3cf230845f67b18'\u001b[0m\n",
" \u001b[1m}\u001b[0m,\n",
" \u001b[32m'relationships'\u001b[0m: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m,\n",
" \u001b[32m'json_serialize_exclude'\u001b[0m: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
"\u001b[1m}\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pendulum\n",
"\n",
"from quantify_core.utilities import examples_support\n",
"\n",
"examples_support.mk_dataset_attrs(\n",
" dataset_name=\"My experiment\",\n",
" timestamp_start=pendulum.now().to_iso8601_string(),\n",
" timestamp_end=pendulum.now().add(minutes=2).to_iso8601_string(),\n",
" software_versions={\n",
" \"lab_fridge_magnet_driver\": \"v1.4.2\", # software version/tag\n",
" \"my_lab_repo\": \"9d8acf63f48c469c1b9fa9f2c3cf230845f67b18\", # git commit hash\n",
" },\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "ea04d5a6",
"metadata": {
"execution": {
"iopub.execute_input": "2023-09-26T17:43:43.432596Z",
"iopub.status.busy": "2023-09-26T17:43:43.432183Z",
"iopub.status.idle": "2023-09-26T17:43:43.435128Z",
"shell.execute_reply": "2023-09-26T17:43:43.434423Z"
}
},
"outputs": [],
"source": [
"# pylint: disable=duplicate-code\n",
"# pylint: disable=wrong-import-position"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "5de80ea5",
"metadata": {
"execution": {
"iopub.execute_input": "2023-09-26T17:43:43.437429Z",
"iopub.status.busy": "2023-09-26T17:43:43.437239Z",
"iopub.status.idle": "2023-09-26T17:43:43.441210Z",
"shell.execute_reply": "2023-09-26T17:43:43.440608Z"
}
},
"outputs": [],
"source": [
"from quantify_core.data.dataset_attrs import QDatasetIntraRelationship\n",
"from quantify_core.utilities import examples_support\n",
"\n",
"attrs = examples_support.mk_dataset_attrs(\n",
" relationships=[\n",
" QDatasetIntraRelationship(\n",
" item_name=\"q0\",\n",
" relation_type=\"calibration\",\n",
" related_names=[\"q0_cal\"],\n",
" ).to_dict()\n",
" ]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "fa12dcbf",
"metadata": {
"execution": {
"iopub.execute_input": "2023-09-26T17:43:43.443538Z",
"iopub.status.busy": "2023-09-26T17:43:43.443344Z",
"iopub.status.idle": "2023-09-26T17:43:43.449935Z",
"shell.execute_reply": "2023-09-26T17:43:43.449320Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"{ \n",
" 'tuid' : None ,\n",
" 'dataset_name' : '' ,\n",
" 'dataset_state' : None ,\n",
" 'timestamp_start' : None ,\n",
" 'timestamp_end' : None ,\n",
" 'quantify_dataset_version' : '2.0.0' ,\n",
" 'software_versions' : {} ,\n",
" 'relationships' : [] ,\n",
" 'json_serialize_exclude' : [] \n",
"} \n",
" \n"
],
"text/plain": [
"\n",
"\u001b[1m{\u001b[0m\n",
" \u001b[32m'tuid'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n",
" \u001b[32m'dataset_name'\u001b[0m: \u001b[32m''\u001b[0m,\n",
" \u001b[32m'dataset_state'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n",
" \u001b[32m'timestamp_start'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n",
" \u001b[32m'timestamp_end'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n",
" \u001b[32m'quantify_dataset_version'\u001b[0m: \u001b[32m'2.0.0'\u001b[0m,\n",
" \u001b[32m'software_versions'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m,\n",
" \u001b[32m'relationships'\u001b[0m: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m,\n",
" \u001b[32m'json_serialize_exclude'\u001b[0m: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
"\u001b[1m}\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from quantify_core.data.dataset_attrs import QDatasetAttrs\n",
"\n",
"# tip: to_json and from_dict, from_json are also available\n",
"dataset.attrs = QDatasetAttrs().to_dict()\n",
"dataset.attrs"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "372c9538",
"metadata": {
"execution": {
"iopub.execute_input": "2023-09-26T17:43:43.452223Z",
"iopub.status.busy": "2023-09-26T17:43:43.452032Z",
"iopub.status.idle": "2023-09-26T17:43:43.457522Z",
"shell.execute_reply": "2023-09-26T17:43:43.456898Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"( '2.0.0' , None ) \n",
"\n",
"{ \n",
" 'unit' : '' ,\n",
" 'long_name' : '' ,\n",
" 'is_main_coord' : True ,\n",
" 'uniformly_spaced' : True ,\n",
" 'is_dataset_ref' : False ,\n",
" 'json_serialize_exclude' : [] \n",
"} \n",
" \n"
],
"text/plain": [
"\n",
"\u001b[1m{\u001b[0m\n",
" \u001b[32m'unit'\u001b[0m: \u001b[32m''\u001b[0m,\n",
" \u001b[32m'long_name'\u001b[0m: \u001b[32m''\u001b[0m,\n",
" \u001b[32m'is_main_coord'\u001b[0m: \u001b[3;92mTrue\u001b[0m,\n",
" \u001b[32m'uniformly_spaced'\u001b[0m: \u001b[3;92mTrue\u001b[0m,\n",
" \u001b[32m'is_dataset_ref'\u001b[0m: \u001b[3;91mFalse\u001b[0m,\n",
" \u001b[32m'json_serialize_exclude'\u001b[0m: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
"\u001b[1m}\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from quantify_core.utilities import examples_support\n",
"\n",
"examples_support.mk_main_coord_attrs()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "cd9db098",
"metadata": {
"execution": {
"iopub.execute_input": "2023-09-26T17:43:43.472949Z",
"iopub.status.busy": "2023-09-26T17:43:43.472759Z",
"iopub.status.idle": "2023-09-26T17:43:43.478776Z",
"shell.execute_reply": "2023-09-26T17:43:43.478137Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"{ \n",
" 'unit' : '' ,\n",
" 'long_name' : '' ,\n",
" 'is_main_coord' : False ,\n",
" 'uniformly_spaced' : True ,\n",
" 'is_dataset_ref' : False ,\n",
" 'json_serialize_exclude' : [] \n",
"} \n",
" \n"
],
"text/plain": [
"\n",
"\u001b[1m{\u001b[0m\n",
" \u001b[32m'unit'\u001b[0m: \u001b[32m''\u001b[0m,\n",
" \u001b[32m'long_name'\u001b[0m: \u001b[32m''\u001b[0m,\n",
" \u001b[32m'is_main_coord'\u001b[0m: \u001b[3;91mFalse\u001b[0m,\n",
" \u001b[32m'uniformly_spaced'\u001b[0m: \u001b[3;92mTrue\u001b[0m,\n",
" \u001b[32m'is_dataset_ref'\u001b[0m: \u001b[3;91mFalse\u001b[0m,\n",
" \u001b[32m'json_serialize_exclude'\u001b[0m: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
"\u001b[1m}\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"examples_support.mk_secondary_coord_attrs()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "c4f8dd4b",
"metadata": {
"execution": {
"iopub.execute_input": "2023-09-26T17:43:43.481091Z",
"iopub.status.busy": "2023-09-26T17:43:43.480903Z",
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{
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"\n",
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" 'is_dataset_ref' : False ,\n",
" 'json_serialize_exclude' : [] \n",
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" \n"
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" \u001b[32m'long_name'\u001b[0m: \u001b[32m'Amplitude'\u001b[0m,\n",
" \u001b[32m'is_main_coord'\u001b[0m: \u001b[3;92mTrue\u001b[0m,\n",
" \u001b[32m'uniformly_spaced'\u001b[0m: \u001b[3;92mTrue\u001b[0m,\n",
" \u001b[32m'is_dataset_ref'\u001b[0m: \u001b[3;91mFalse\u001b[0m,\n",
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"\u001b[1m}\u001b[0m\n"
]
},
"metadata": {},
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}
],
"source": [
"dataset.amp.attrs"
]
},
{
"cell_type": "code",
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"id": "f27f5caa",
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"execution": {
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"outputs": [],
"source": [
"# pylint: disable=line-too-long\n",
"# pylint: disable=wrong-import-order\n",
"# pylint: disable=wrong-import-position\n",
"# pylint: disable=pointless-string-statement\n",
"# pylint: disable=duplicate-code"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "60a7875c",
"metadata": {
"execution": {
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{
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"\n",
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" 'long_name' : '' ,\n",
" 'is_main_var' : True ,\n",
" 'uniformly_spaced' : True ,\n",
" 'grid' : True ,\n",
" 'is_dataset_ref' : False ,\n",
" 'has_repetitions' : False ,\n",
" 'json_serialize_exclude' : [] ,\n",
" 'coords' : [ 'time' ] \n",
"} \n",
" \n"
],
"text/plain": [
"\n",
"\u001b[1m{\u001b[0m\n",
" \u001b[32m'unit'\u001b[0m: \u001b[32m''\u001b[0m,\n",
" \u001b[32m'long_name'\u001b[0m: \u001b[32m''\u001b[0m,\n",
" \u001b[32m'is_main_var'\u001b[0m: \u001b[3;92mTrue\u001b[0m,\n",
" \u001b[32m'uniformly_spaced'\u001b[0m: \u001b[3;92mTrue\u001b[0m,\n",
" \u001b[32m'grid'\u001b[0m: \u001b[3;92mTrue\u001b[0m,\n",
" \u001b[32m'is_dataset_ref'\u001b[0m: \u001b[3;91mFalse\u001b[0m,\n",
" \u001b[32m'has_repetitions'\u001b[0m: \u001b[3;91mFalse\u001b[0m,\n",
" \u001b[32m'json_serialize_exclude'\u001b[0m: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m,\n",
" \u001b[32m'coords'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'time'\u001b[0m\u001b[1m]\u001b[0m\n",
"\u001b[1m}\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from quantify_core.utilities import examples_support\n",
"\n",
"examples_support.mk_main_var_attrs(coords=[\"time\"])"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "fa0c44fc",
"metadata": {
"execution": {
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{
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" 'grid' : True ,\n",
" 'is_dataset_ref' : False ,\n",
" 'has_repetitions' : False ,\n",
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" 'coords' : [ 'cal' ] \n",
"} \n",
" \n"
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"text/plain": [
"\n",
"\u001b[1m{\u001b[0m\n",
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" \u001b[32m'long_name'\u001b[0m: \u001b[32m''\u001b[0m,\n",
" \u001b[32m'is_main_var'\u001b[0m: \u001b[3;91mFalse\u001b[0m,\n",
" \u001b[32m'uniformly_spaced'\u001b[0m: \u001b[3;92mTrue\u001b[0m,\n",
" \u001b[32m'grid'\u001b[0m: \u001b[3;92mTrue\u001b[0m,\n",
" \u001b[32m'is_dataset_ref'\u001b[0m: \u001b[3;91mFalse\u001b[0m,\n",
" \u001b[32m'has_repetitions'\u001b[0m: \u001b[3;91mFalse\u001b[0m,\n",
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" \u001b[32m'coords'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'cal'\u001b[0m\u001b[1m]\u001b[0m\n",
"\u001b[1m}\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"examples_support.mk_secondary_var_attrs(coords=[\"cal\"])"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "1b1ee042",
"metadata": {
"execution": {
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"outputs": [
{
"data": {
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" 'grid' : True ,\n",
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" 'has_repetitions' : True ,\n",
" 'json_serialize_exclude' : [] \n",
"} \n",
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"text/plain": [
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" \u001b[32m'long_name'\u001b[0m: \u001b[32m'Population Q0'\u001b[0m,\n",
" \u001b[32m'is_main_var'\u001b[0m: \u001b[3;92mTrue\u001b[0m,\n",
" \u001b[32m'uniformly_spaced'\u001b[0m: \u001b[3;92mTrue\u001b[0m,\n",
" \u001b[32m'grid'\u001b[0m: \u001b[3;92mTrue\u001b[0m,\n",
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"\u001b[1m}\u001b[0m\n"
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},
"metadata": {},
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}
],
"source": [
"dataset.pop_q0.attrs"
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},
{
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"def write_dataset ( path : Union [ Path , str ], dataset : xr . Dataset ) -> None : \n",
" """ \n",
" Writes a :class:`~xarray.Dataset` to a file with the `h5netcdf` engine. \n",
"\n",
" Before writing the \n",
" :meth:`AdapterH5NetCDF.adapt() <quantify_core.data.dataset_adapters.AdapterH5NetCDF.adapt>` \n",
" is applied. \n",
"\n",
" To accommodate for complex-type numbers and arrays ``invalid_netcdf=True`` is used. \n",
"\n",
" Parameters \n",
" ---------- \n",
" path \n",
" Path to the file including filename and extension \n",
" dataset \n",
" The :class:`~xarray.Dataset` to be written to file. \n",
" """ # pylint: disable=line-too-long \n",
" _xarray_numpy_bool_patch ( dataset ) # See issue #161 in quantify-core \n",
" # Only quantify_dataset_version=>2.0.0 requires the adapter \n",
" if "quantify_dataset_version" in dataset . attrs : \n",
" dataset = da . AdapterH5NetCDF . adapt ( dataset ) \n",
" dataset . to_netcdf ( path , engine = "h5netcdf" , invalid_netcdf = True ) \n",
"def load_dataset ( \n",
" tuid : TUID , datadir : str = None , name : str = DATASET_NAME \n",
") -> xr . Dataset : \n",
" """ \n",
" Loads a dataset specified by a tuid. \n",
"\n",
" .. tip:: \n",
"\n",
" This method also works when specifying only the first part of a \n",
" :class:`~quantify_core.data.types.TUID`. \n",
"\n",
" .. note:: \n",
"\n",
" This method uses :func:`~.load_dataset` to ensure the file is closed after \n",
" loading as datasets are intended to be immutable after performing the initial \n",
" experiment. \n",
"\n",
" Parameters \n",
" ---------- \n",
" tuid \n",
" A :class:`~quantify_core.data.types.TUID` string. It is also possible to specify \n",
" only the first part of a tuid. \n",
" datadir \n",
" Path of the data directory. If ``None``, uses :meth:`~get_datadir` to determine \n",
" the data directory. \n",
" Returns \n",
" ------- \n",
" : \n",
" The dataset. \n",
" Raises \n",
" ------ \n",
" FileNotFoundError \n",
" No data found for specified date. \n",
" """ \n",
" return load_dataset_from_path ( _locate_experiment_file ( tuid , datadir , name )) \n",
"class AdapterH5NetCDF ( DatasetAdapterBase ): \n",
" """ \n",
" Quantify dataset adapter for the ``h5netcdf`` engine. \n",
"\n",
" It has the functionality of adapting the Quantify dataset to a format compatible \n",
" with the ``h5netcdf`` xarray backend engine that is used to write and load the \n",
" dataset to/from disk. \n",
"\n",
" .. warning:: \n",
"\n",
" The ``h5netcdf`` engine has minor issues when performing a two-way trip of the \n",
" dataset. The ``type`` of some attributes are not preserved. E.g., list- and \n",
" tuple-like objects are loaded as numpy arrays of ``dtype=object``. \n",
" """ \n",
"\n",
" @classmethod \n",
" def adapt ( cls , dataset : xr . Dataset ) -> xr . Dataset : \n",
" """ \n",
" Serializes to JSON the dataset and variables attributes. \n",
"\n",
" To prevent the JSON serialization for specific items, their names should be \n",
" listed under the attribute named ``json_serialize_exclude`` (for each ``attrs`` \n",
" dictionary). \n",
"\n",
" Parameters \n",
" ---------- \n",
" dataset \n",
" Dataset that needs to be adapted. \n",
"\n",
" Returns \n",
" ------- \n",
" : \n",
" Dataset in which the attributes have been replaced with their JSON strings \n",
" version. \n",
" """ \n",
"\n",
" return cls . _transform ( dataset , vals_converter = json . dumps ) \n",
"\n",
" @classmethod \n",
" def recover ( cls , dataset : xr . Dataset ) -> xr . Dataset : \n",
" """ \n",
" Reverts the action of ``.adapt()``. \n",
"\n",
" To prevent the JSON de-serialization for specific items, their names should be \n",
" listed under the attribute named ``json_serialize_exclude`` \n",
" (for each ``attrs`` dictionary). \n",
"\n",
" Parameters \n",
" ---------- \n",
" dataset \n",
" Dataset from which to recover the original format. \n",
"\n",
" Returns \n",
" ------- \n",
" : \n",
" Dataset in which the attributes have been replaced with their python objects \n",
" version. \n",
" """ \n",
"\n",
" return cls . _transform ( dataset , vals_converter = json . loads ) \n",
"\n",
" @staticmethod \n",
" def attrs_convert ( \n",
" attrs : dict , \n",
" inplace : bool = False , \n",
" vals_converter : Callable [ Any , Any ] = json . dumps , \n",
" ) -> dict : \n",
" """ \n",
" Converts to/from JSON string the values of the keys which are not listed in the \n",
" ``json_serialize_exclude`` list. \n",
"\n",
" Parameters \n",
" ---------- \n",
" attrs \n",
" The input dictionary. \n",
" inplace \n",
" If ``True`` the values are replaced in place, otherwise a deepcopy of \n",
" ``attrs`` is performed first. \n",
" """ \n",
" json_serialize_exclude = attrs . get ( "json_serialize_exclude" , []) \n",
"\n",
" attrs = attrs if inplace else deepcopy ( attrs ) \n",
" for attr_name , attr_val in attrs . items (): \n",
" if attr_name not in json_serialize_exclude : \n",
" attrs [ attr_name ] = vals_converter ( attr_val ) \n",
" return attrs \n",
"\n",
" @classmethod \n",
" def _transform ( \n",
" cls , dataset : xr . Dataset , vals_converter : Callable [ Any , Any ] = json . dumps \n",
" ) -> xr . Dataset : \n",
" dataset = xr . Dataset ( \n",
" dataset , \n",
" attrs = cls . attrs_convert ( \n",
" dataset . attrs , inplace = False , vals_converter = vals_converter \n",
" ), \n",
" ) \n",
"\n",
" for var_name in dataset . variables . keys (): \n",
" # The new dataset generated above has already a deepcopy of the attributes. \n",
" _ = cls . attrs_convert ( \n",
" dataset [ var_name ] . attrs , inplace = True , vals_converter = vals_converter \n",
" ) \n",
"\n",
" return dataset \n",
"