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    "result": {"data":{"site":{"siteMetadata":{"salvusDocVersions":{"current":"2026.5.0"}}},"jupyterNotebook":{"slug":"/2025.1.3/examples/tutorials/data_analysis/structured_data_interface/tutorial","notebook_widgets_root_path":"/jupyter_notebook_widgets","notebook_json":"{\"cells\":[{\"cell_type\":\"markdown\",\"id\":\"7c7773c0\",\"metadata\":{},\"source\":[\"# Using the xarray structured data interface within Salvus\\n\"]},{\"cell_type\":\"markdown\",\"id\":\"90d285aa\",\"metadata\":{},\"source\":[\"Originally, Salvus entirely relied on `obspy.Stream` objects to\\n\",\"internally represent simulated and observed data. While `obspy.Stream`'s are\\n\",\"a great representation type for seismological data and come with convenient\\n\",\"processing and filtering functions, there are a few limitations to the\\n\",\"original data interface used within Salvus.\\n\",\"\\n\",\"- Data could only be retrieved per station. If an event contained multiple\\n\",\"  receivers, the data for each receiver had to be retrieved separately in a\\n\",\"  loop, which could become slow in cases where a high number of receivers was\\n\",\"  used.\\n\",\"- All data processing, windowing, and the computation of misfits was\\n\",\"  performed trace-by-trace. Besides the already-mentioned performance\\n\",\"  aspects, this also meant that more sophisticated time-and-space-dependent\\n\",\"  processing and misfit computations (e.g. in the fk-domain) were not\\n\",\"  supported.\\n\",\"- `obspy.Stream` objects are mainly used within the seismological community.\\n\",\"  For non-seismology users, learning how to work with these objects would\\n\",\"  provide an unnecessary extra challenge.\\n\",\"\\n\",\"For these reasons, with salvus release `2025.1.0`, we have changed the way\\n\",\"that data is represented internally within Salvus. Salvus now uses\\n\",\"[xarray](https://xarray.pydata.org/en/v0.13.0/index.html#) data structures\\n\",\"with a specific format to represent data internally. In this tutorial, you\\n\",\"will learn what this format looks like and how you can start using it within\\n\",\"Salvus. If you prefer to continue to use the old interface based on\\n\",\"`obspy.Stream` objects, do not worry, all the old functionality is still\\n\",\"supported!\\n\",\"\\n\",\"This tutorial is structured in the following way:\\n\",\"\\n\",\"1. Introduction to xarray (if you are already familiar with xarray, you can\\n\",\"   skip this step).\\n\",\"2. Retrieval of xarray data from a Salvus project.\\n\",\"3. Custom data processing, data selection, and misfit functions using Salvus'\\n\",\"   xarray interface.\"]},{\"cell_type\":\"code\",\"execution_count\":1,\"id\":\"9681c0f2\",\"metadata\":{},\"outputs\":[{\"name\":\"stdout\",\"output_type\":\"stream\",\"text\":[]}],\"source\":[\"import salvus.namespace as sn\\n\",\"\\n\",\"import matplotlib.pyplot as plt\\n\",\"import numpy as np\\n\",\"import xarray as xr\\n\",\"\\n\",\"import scipy.signal\\n\"]},{\"cell_type\":\"markdown\",\"id\":\"cf89764e\",\"metadata\":{},\"source\":[\"## 1. Introduction to xarray\\n\",\"\\n\",\"[Xarray](https://xarray.pydata.org/en/v0.13.0/index.html#) provides a\\n\",\"convenient interface to represent structured data. In a nutshell, an xarray\\n\",\"data structure is simply a multidimensional array with a label for each\\n\",\"dimension. It can optionally store additional information on the coordinates\\n\",\"of each data point. Additionally, xarray comes with some convenient features\\n\",\"like data selection, plotting, and sorting methods.\\n\",\"\\n\",\"To familiarize ourselves with the xarray interface, let us generate a simple\\n\",\"2D data set analogous to a seismic data set. In this simple example, we model\\n\",\"a space- and time-dependent seismic wavefield with the following simple\\n\",\"monochromatic plane-wave solution to the wave equation\\n\",\"\\n\",\"$u(x, t) = \\\\sin(\\\\omega t - k x)$,\\n\",\"\\n\",\"with $x$ being the space coordinate, $t$ the time coordinate, $\\\\omega$ the\\n\",\"angular frequency, and $k$ the wavenumber, which can be expressed in terms of\\n\",\"the velocity $c$, as $k=\\\\omega/c$.\\n\",\"\\n\",\"Let us use this function to generate a discrete seismic data set for a\\n\",\"monochromatic plane wavefield at 10 Hz where we sample the wavefield along\\n\",\"$x$ with a linear array of 25 sensors spaced at 10 meters. The time axis is\\n\",\"resolved with 20 samples per period.\\n\"]},{\"cell_type\":\"code\",\"execution_count\":2,\"id\":\"98ea812f\",\"metadata\":{},\"outputs\":[{\"data\":{\"text/plain\":[\"Text(0, 0.5, 'x (# of samples)')\"]},\"execution_count\":2,\"metadata\":{},\"output_type\":\"execute_result\"},{\"data\":{\"image/png\":\"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size 640x480 with 1 Axes>\"]},\"metadata\":{},\"output_type\":\"display_data\"}],\"source\":[\"# Frequency of the plane-harmonic wave in Hz.\\n\",\"f = 10.0\\n\",\"# Velocity in m/s.\\n\",\"velocity = 3000.0\\n\",\"\\n\",\"# Wavenumber\\n\",\"k = 2 * np.pi * f / velocity\\n\",\"\\n\",\"# Total number of periods included in the data.\\n\",\"number_of_periods = 4\\n\",\"# Temporal resolution of the data\\n\",\"samples_per_period = 20\\n\",\"\\n\",\"# Time axis\\n\",\"t = np.linspace(\\n\",\"    0,\\n\",\"    number_of_periods * (1 / f),\\n\",\"    int(number_of_periods * (1 / f) / (1 / (samples_per_period * f))),\\n\",\")\\n\",\"# Space axis\\n\",\"x = np.arange(25) * 10\\n\",\"\\n\",\"# Generate plane-harmonic data at the sampling points\\n\",\"u = np.sin(2 * np.pi * f * t[np.newaxis, :] - k * x[:, np.newaxis])\\n\",\"\\n\",\"# Let's visualize the data\\n\",\"plt.imshow(u, aspect=\\\"auto\\\")\\n\",\"plt.xlabel(\\\"t (# of samples)\\\")\\n\",\"plt.ylabel(\\\"x (# of samples)\\\")\\n\",\"\\n\",\"# What it would look like to include axis data manually\\n\",\"# plt.imshow(u,extent=[t.min(), t.max(), x.min(), x.max()], aspect=\\\"auto\\\")\\n\",\"# The axes are now labeled in whatever units the dimensions are defined.\\n\",\"# plt.xlabel(\\\"t\\\")\\n\",\"# plt.ylabel(\\\"x\\\")\"]},{\"cell_type\":\"markdown\",\"id\":\"1031f1a8\",\"metadata\":{},\"source\":[\"Note that we obtained our data as a 2D numpy array, which does not contain\\n\",\"any information on the time and the space coordinates. Here, `xarray` can\\n\",\"help us to additionally store this information in a single data structure.\\n\",\"Let us therefore wrap our data into an `xarray.DataArray` structure. Upon\\n\",\"creation of the `xarray` object can provide the following information:\\n\",\"\\n\",\"- `data`: The numpy array containing our data.\\n\",\"- `dims`: A list with the labels for each dimension of our dataset. Our data\\n\",\"  is 2D, so we add two labels, one for the time and one for the space\\n\",\"  dimension.\\n\",\"- `coords`: Here, we can optionally provide the coordinates for each\\n\",\"  dimension. We pass the time and space axis as a dictionary. The keys of the\\n\",\"  dictionary have to agree with the dimensions the we set in the previous\\n\",\"  row.\\n\",\"- `name`: An optional name for our data.\"]},{\"cell_type\":\"code\",\"execution_count\":3,\"id\":\"74f85961\",\"metadata\":{},\"outputs\":[{\"data\":{\"text/html\":[\"<div><svg style=\\\"position: absolute; width: 0; height: 0; overflow: hidden\\\">\\n\",\"<defs>\\n\",\"<symbol id=\\\"icon-database\\\" viewBox=\\\"0 0 32 32\\\">\\n\",\"<path d=\\\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\\\"></path>\\n\",\"<path d=\\\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\\\"></path>\\n\",\"<path d=\\\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\\\"></path>\\n\",\"</symbol>\\n\",\"<symbol id=\\\"icon-file-text2\\\" viewBox=\\\"0 0 32 32\\\">\\n\",\"<path d=\\\"M28.681 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5))\\n\",\"  );\\n\",\"  --xr-background-color-row-odd: var(\\n\",\"    --jp-layout-color2,\\n\",\"    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\\n\",\"  );\\n\",\"}\\n\",\"\\n\",\"html[theme=\\\"dark\\\"],\\n\",\"html[data-theme=\\\"dark\\\"],\\n\",\"body[data-theme=\\\"dark\\\"],\\n\",\"body.vscode-dark {\\n\",\"  --xr-font-color0: var(\\n\",\"    --jp-content-font-color0,\\n\",\"    var(--pst-color-text-base, rgba(255, 255, 255, 1))\\n\",\"  );\\n\",\"  --xr-font-color2: var(\\n\",\"    --jp-content-font-color2,\\n\",\"    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\\n\",\"  );\\n\",\"  --xr-font-color3: var(\\n\",\"    --jp-content-font-color3,\\n\",\"    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\\n\",\"  );\\n\",\"  --xr-border-color: var(\\n\",\"    --jp-border-color2,\\n\",\"    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\\n\",\"  );\\n\",\"  --xr-disabled-color: var(\\n\",\"    --jp-layout-color3,\\n\",\"    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\\n\",\"  );\\n\",\"  --xr-background-color: var(\\n\",\"    --jp-layout-color0,\\n\",\"    var(--pst-color-on-background, #111111)\\n\",\"  );\\n\",\"  --xr-background-color-row-even: var(\\n\",\"    --jp-layout-color1,\\n\",\"    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\\n\",\"  );\\n\",\"  --xr-background-color-row-odd: var(\\n\",\"    --jp-layout-color2,\\n\",\"    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\\n\",\"  );\\n\",\"}\\n\",\"\\n\",\".xr-wrap {\\n\",\"  display: block !important;\\n\",\"  min-width: 300px;\\n\",\"  max-width: 700px;\\n\",\"}\\n\",\"\\n\",\".xr-text-repr-fallback {\\n\",\"  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\\n\",\"  display: none;\\n\",\"}\\n\",\"\\n\",\".xr-header {\\n\",\"  padding-top: 6px;\\n\",\"  padding-bottom: 6px;\\n\",\"  margin-bottom: 4px;\\n\",\"  border-bottom: solid 1px var(--xr-border-color);\\n\",\"}\\n\",\"\\n\",\".xr-header > div,\\n\",\".xr-header > ul {\\n\",\"  display: inline;\\n\",\"  margin-top: 0;\\n\",\"  margin-bottom: 0;\\n\",\"}\\n\",\"\\n\",\".xr-obj-type,\\n\",\".xr-array-name {\\n\",\"  margin-left: 2px;\\n\",\"  margin-right: 10px;\\n\",\"}\\n\",\"\\n\",\".xr-obj-type {\\n\",\"  color: var(--xr-font-color2);\\n\",\"}\\n\",\"\\n\",\".xr-sections {\\n\",\"  padding-left: 0 !important;\\n\",\"  display: grid;\\n\",\"  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\\n\",\"}\\n\",\"\\n\",\".xr-section-item {\\n\",\"  display: contents;\\n\",\"}\\n\",\"\\n\",\".xr-section-item input {\\n\",\"  display: inline-block;\\n\",\"  opacity: 0;\\n\",\"  height: 0;\\n\",\"}\\n\",\"\\n\",\".xr-section-item input + label {\\n\",\"  color: var(--xr-disabled-color);\\n\",\"  border: 2px solid transparent !important;\\n\",\"}\\n\",\"\\n\",\".xr-section-item input:enabled + label {\\n\",\"  cursor: pointer;\\n\",\"  color: var(--xr-font-color2);\\n\",\"}\\n\",\"\\n\",\".xr-section-item input:focus + label {\\n\",\"  border: 2px solid var(--xr-font-color0) !important;\\n\",\"}\\n\",\"\\n\",\".xr-section-item input:enabled + label:hover {\\n\",\"  color: var(--xr-font-color0);\\n\",\"}\\n\",\"\\n\",\".xr-section-summary {\\n\",\"  grid-column: 1;\\n\",\"  color: var(--xr-font-color2);\\n\",\"  font-weight: 500;\\n\",\"}\\n\",\"\\n\",\".xr-section-summary > span {\\n\",\"  display: inline-block;\\n\",\"  padding-left: 0.5em;\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in:disabled + label {\\n\",\"  color: var(--xr-font-color2);\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in + label:before {\\n\",\"  display: inline-block;\\n\",\"  content: \\\"►\\\";\\n\",\"  font-size: 11px;\\n\",\"  width: 15px;\\n\",\"  text-align: center;\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in:disabled + label:before {\\n\",\"  color: var(--xr-disabled-color);\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in:checked + label:before {\\n\",\"  content: \\\"▼\\\";\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in:checked + label > span {\\n\",\"  display: none;\\n\",\"}\\n\",\"\\n\",\".xr-section-summary,\\n\",\".xr-section-inline-details {\\n\",\"  padding-top: 4px;\\n\",\"  padding-bottom: 4px;\\n\",\"}\\n\",\"\\n\",\".xr-section-inline-details {\\n\",\"  grid-column: 2 / -1;\\n\",\"}\\n\",\"\\n\",\".xr-section-details {\\n\",\"  display: none;\\n\",\"  grid-column: 1 / -1;\\n\",\"  margin-bottom: 5px;\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in:checked ~ .xr-section-details {\\n\",\"  display: contents;\\n\",\"}\\n\",\"\\n\",\".xr-array-wrap {\\n\",\"  grid-column: 1 / -1;\\n\",\"  display: grid;\\n\",\"  grid-template-columns: 20px auto;\\n\",\"}\\n\",\"\\n\",\".xr-array-wrap > label {\\n\",\"  grid-column: 1;\\n\",\"  vertical-align: top;\\n\",\"}\\n\",\"\\n\",\".xr-preview {\\n\",\"  color: var(--xr-font-color3);\\n\",\"}\\n\",\"\\n\",\".xr-array-preview,\\n\",\".xr-array-data {\\n\",\"  padding: 0 5px !important;\\n\",\"  grid-column: 2;\\n\",\"}\\n\",\"\\n\",\".xr-array-data,\\n\",\".xr-array-in:checked ~ .xr-array-preview {\\n\",\"  display: none;\\n\",\"}\\n\",\"\\n\",\".xr-array-in:checked ~ .xr-array-data,\\n\",\".xr-array-preview {\\n\",\"  display: inline-block;\\n\",\"}\\n\",\"\\n\",\".xr-dim-list {\\n\",\"  display: inline-block !important;\\n\",\"  list-style: none;\\n\",\"  padding: 0 !important;\\n\",\"  margin: 0;\\n\",\"}\\n\",\"\\n\",\".xr-dim-list li {\\n\",\"  display: inline-block;\\n\",\"  padding: 0;\\n\",\"  margin: 0;\\n\",\"}\\n\",\"\\n\",\".xr-dim-list:before {\\n\",\"  content: \\\"(\\\";\\n\",\"}\\n\",\"\\n\",\".xr-dim-list:after {\\n\",\"  content: \\\")\\\";\\n\",\"}\\n\",\"\\n\",\".xr-dim-list li:not(:last-child):after {\\n\",\"  content: \\\",\\\";\\n\",\"  padding-right: 5px;\\n\",\"}\\n\",\"\\n\",\".xr-has-index {\\n\",\"  font-weight: bold;\\n\",\"}\\n\",\"\\n\",\".xr-var-list,\\n\",\".xr-var-item {\\n\",\"  display: contents;\\n\",\"}\\n\",\"\\n\",\".xr-var-item > div,\\n\",\".xr-var-item label,\\n\",\".xr-var-item > .xr-var-name span {\\n\",\"  background-color: var(--xr-background-color-row-even);\\n\",\"  border-color: var(--xr-background-color-row-odd);\\n\",\"  margin-bottom: 0;\\n\",\"  padding-top: 2px;\\n\",\"}\\n\",\"\\n\",\".xr-var-item > .xr-var-name:hover span {\\n\",\"  padding-right: 5px;\\n\",\"}\\n\",\"\\n\",\".xr-var-list > li:nth-child(odd) > div,\\n\",\".xr-var-list > li:nth-child(odd) > label,\\n\",\".xr-var-list > li:nth-child(odd) > .xr-var-name span {\\n\",\"  background-color: var(--xr-background-color-row-odd);\\n\",\"  border-color: var(--xr-background-color-row-even);\\n\",\"}\\n\",\"\\n\",\".xr-var-name {\\n\",\"  grid-column: 1;\\n\",\"}\\n\",\"\\n\",\".xr-var-dims {\\n\",\"  grid-column: 2;\\n\",\"}\\n\",\"\\n\",\".xr-var-dtype {\\n\",\"  grid-column: 3;\\n\",\"  text-align: right;\\n\",\"  color: var(--xr-font-color2);\\n\",\"}\\n\",\"\\n\",\".xr-var-preview {\\n\",\"  grid-column: 4;\\n\",\"}\\n\",\"\\n\",\".xr-index-preview {\\n\",\"  grid-column: 2 / 5;\\n\",\"  color: var(--xr-font-color2);\\n\",\"}\\n\",\"\\n\",\".xr-var-name,\\n\",\".xr-var-dims,\\n\",\".xr-var-dtype,\\n\",\".xr-preview,\\n\",\".xr-attrs dt {\\n\",\"  white-space: nowrap;\\n\",\"  overflow: hidden;\\n\",\"  text-overflow: ellipsis;\\n\",\"  padding-right: 10px;\\n\",\"}\\n\",\"\\n\",\".xr-var-name:hover,\\n\",\".xr-var-dims:hover,\\n\",\".xr-var-dtype:hover,\\n\",\".xr-attrs dt:hover {\\n\",\"  overflow: visible;\\n\",\"  width: auto;\\n\",\"  z-index: 1;\\n\",\"}\\n\",\"\\n\",\".xr-var-attrs,\\n\",\".xr-var-data,\\n\",\".xr-index-data {\\n\",\"  display: none;\\n\",\"  border-top: 2px dotted var(--xr-background-color);\\n\",\"  padding-bottom: 20px !important;\\n\",\"  padding-top: 10px !important;\\n\",\"}\\n\",\"\\n\",\".xr-var-attrs-in + label,\\n\",\".xr-var-data-in + label,\\n\",\".xr-index-data-in + label {\\n\",\"  padding: 0 1px;\\n\",\"}\\n\",\"\\n\",\".xr-var-attrs-in:checked ~ .xr-var-attrs,\\n\",\".xr-var-data-in:checked ~ .xr-var-data,\\n\",\".xr-index-data-in:checked ~ .xr-index-data {\\n\",\"  display: block;\\n\",\"}\\n\",\"\\n\",\".xr-var-data > table {\\n\",\"  float: right;\\n\",\"}\\n\",\"\\n\",\".xr-var-data > pre,\\n\",\".xr-index-data > pre,\\n\",\".xr-var-data > table > tbody > tr {\\n\",\"  background-color: transparent !important;\\n\",\"}\\n\",\"\\n\",\".xr-var-name span,\\n\",\".xr-var-data,\\n\",\".xr-index-name div,\\n\",\".xr-index-data,\\n\",\".xr-attrs {\\n\",\"  padding-left: 25px !important;\\n\",\"}\\n\",\"\\n\",\".xr-attrs,\\n\",\".xr-var-attrs,\\n\",\".xr-var-data,\\n\",\".xr-index-data {\\n\",\"  grid-column: 1 / -1;\\n\",\"}\\n\",\"\\n\",\"dl.xr-attrs {\\n\",\"  padding: 0;\\n\",\"  margin: 0;\\n\",\"  display: grid;\\n\",\"  grid-template-columns: 125px auto;\\n\",\"}\\n\",\"\\n\",\".xr-attrs dt,\\n\",\".xr-attrs dd {\\n\",\"  padding: 0;\\n\",\"  margin: 0;\\n\",\"  float: left;\\n\",\"  padding-right: 10px;\\n\",\"  width: auto;\\n\",\"}\\n\",\"\\n\",\".xr-attrs dt {\\n\",\"  font-weight: normal;\\n\",\"  grid-column: 1;\\n\",\"}\\n\",\"\\n\",\".xr-attrs dt:hover span {\\n\",\"  display: inline-block;\\n\",\"  background: var(--xr-background-color);\\n\",\"  padding-right: 10px;\\n\",\"}\\n\",\"\\n\",\".xr-attrs dd {\\n\",\"  grid-column: 2;\\n\",\"  white-space: pre-wrap;\\n\",\"  word-break: break-all;\\n\",\"}\\n\",\"\\n\",\".xr-icon-database,\\n\",\".xr-icon-file-text2,\\n\",\".xr-no-icon {\\n\",\"  display: inline-block;\\n\",\"  vertical-align: middle;\\n\",\"  width: 1em;\\n\",\"  height: 1.5em !important;\\n\",\"  stroke-width: 0;\\n\",\"  stroke: currentColor;\\n\",\"  fill: currentColor;\\n\",\"}\\n\",\"\\n\",\".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\\n\",\".xr-var-data-in:checked + label > .xr-icon-database,\\n\",\".xr-index-data-in:checked + label > .xr-icon-database {\\n\",\"  color: var(--xr-font-color0);\\n\",\"  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\\n\",\"  stroke-width: 0.8px;\\n\",\"}\\n\",\"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;u(x, t)&#x27; (x: 25, t: 80)&gt; Size: 16kB\\n\",\"array([[ 0.00000000e+00,  3.12796607e-01,  5.94201029e-01, ...,\\n\",\"        -5.94201029e-01, -3.12796607e-01, -9.79717439e-16],\\n\",\"       [-2.07911691e-01,  1.08482541e-01,  4.13989494e-01, ...,\\n\",\"        -7.48443128e-01, -5.03439960e-01, -2.07911691e-01],\\n\",\"       [-4.06736643e-01, -1.00572732e-01,  2.15684631e-01, ...,\\n\",\"        -8.69974671e-01, -6.72080571e-01, -4.06736643e-01],\\n\",\"       ...,\\n\",\"       [ 9.94521895e-01,  9.11920769e-01,  7.37799515e-01, ...,\\n\",\"         8.62021355e-01,  9.77313066e-01,  9.94521895e-01],\\n\",\"       [ 9.94521895e-01,  9.77313066e-01,  8.62021355e-01, ...,\\n\",\"         7.37799515e-01,  9.11920769e-01,  9.94521895e-01],\\n\",\"       [ 9.51056516e-01,  9.99992093e-01,  9.48568727e-01, ...,\\n\",\"         5.81332295e-01,  8.06673158e-01,  9.51056516e-01]])\\n\",\"Coordinates:\\n\",\"  * t        (t) float64 640B 0.0 0.005063 0.01013 0.01519 ... 0.3899 0.3949 0.4\\n\",\"  * x        (x) int64 200B 0 10 20 30 40 50 60 ... 180 190 200 210 220 230 240</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'u(x, t)'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>x</span>: 25</li><li><span class='xr-has-index'>t</span>: 80</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-34184f42-8f97-4822-b53d-7f9f42230e4b' class='xr-array-in' type='checkbox' checked><label for='section-34184f42-8f97-4822-b53d-7f9f42230e4b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 0.3128 0.5942 0.816 0.9559 ... 0.2976 0.5813 0.8067 0.9511</span></div><div class='xr-array-data'><pre>array([[ 0.00000000e+00,  3.12796607e-01,  5.94201029e-01, ...,\\n\",\"        -5.94201029e-01, -3.12796607e-01, -9.79717439e-16],\\n\",\"       [-2.07911691e-01,  1.08482541e-01,  4.13989494e-01, ...,\\n\",\"        -7.48443128e-01, -5.03439960e-01, -2.07911691e-01],\\n\",\"       [-4.06736643e-01, -1.00572732e-01,  2.15684631e-01, ...,\\n\",\"        -8.69974671e-01, -6.72080571e-01, -4.06736643e-01],\\n\",\"       ...,\\n\",\"       [ 9.94521895e-01,  9.11920769e-01,  7.37799515e-01, ...,\\n\",\"         8.62021355e-01,  9.77313066e-01,  9.94521895e-01],\\n\",\"       [ 9.94521895e-01,  9.77313066e-01,  8.62021355e-01, ...,\\n\",\"         7.37799515e-01,  9.11920769e-01,  9.94521895e-01],\\n\",\"       [ 9.51056516e-01,  9.99992093e-01,  9.48568727e-01, ...,\\n\",\"         5.81332295e-01,  8.06673158e-01,  9.51056516e-01]])</pre></div></div></li><li class='xr-section-item'><input id='section-07532009-8261-4354-b08a-e80277a70bdd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-07532009-8261-4354-b08a-e80277a70bdd' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.005063 0.01013 ... 0.3949 0.4</div><input id='attrs-166ac11f-86d3-44ef-8159-d7fd8f317d8d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-166ac11f-86d3-44ef-8159-d7fd8f317d8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2a74584-a91c-45f8-8cc7-2aaa5f5cbcb8' class='xr-var-data-in' type='checkbox'><label for='data-c2a74584-a91c-45f8-8cc7-2aaa5f5cbcb8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.      , 0.005063, 0.010127, 0.01519 , 0.020253, 0.025316, 0.03038 ,\\n\",\"       0.035443, 0.040506, 0.04557 , 0.050633, 0.055696, 0.060759, 0.065823,\\n\",\"       0.070886, 0.075949, 0.081013, 0.086076, 0.091139, 0.096203, 0.101266,\\n\",\"       0.106329, 0.111392, 0.116456, 0.121519, 0.126582, 0.131646, 0.136709,\\n\",\"       0.141772, 0.146835, 0.151899, 0.156962, 0.162025, 0.167089, 0.172152,\\n\",\"       0.177215, 0.182278, 0.187342, 0.192405, 0.197468, 0.202532, 0.207595,\\n\",\"       0.212658, 0.217722, 0.222785, 0.227848, 0.232911, 0.237975, 0.243038,\\n\",\"       0.248101, 0.253165, 0.258228, 0.263291, 0.268354, 0.273418, 0.278481,\\n\",\"       0.283544, 0.288608, 0.293671, 0.298734, 0.303797, 0.308861, 0.313924,\\n\",\"       0.318987, 0.324051, 0.329114, 0.334177, 0.339241, 0.344304, 0.349367,\\n\",\"       0.35443 , 0.359494, 0.364557, 0.36962 , 0.374684, 0.379747, 0.38481 ,\\n\",\"       0.389873, 0.394937, 0.4     ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 10 20 30 40 ... 210 220 230 240</div><input id='attrs-8320de0c-1cba-4975-b40d-e0f459bbd804' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8320de0c-1cba-4975-b40d-e0f459bbd804' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec477f99-f275-4403-8f46-08e6c8223d20' class='xr-var-data-in' type='checkbox'><label for='data-ec477f99-f275-4403-8f46-08e6c8223d20' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,  10,  20,  30,  40,  50,  60,  70,  80,  90, 100, 110, 120, 130,\\n\",\"       140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b687f7b6-24ea-432f-b6d0-caf8eb06a999' class='xr-section-summary-in' type='checkbox'  ><label for='section-b687f7b6-24ea-432f-b6d0-caf8eb06a999' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>t</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-cb0f7f8b-95f7-4ade-b7cf-6a01b65340ca' class='xr-index-data-in' type='checkbox'/><label for='index-cb0f7f8b-95f7-4ade-b7cf-6a01b65340ca' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([                 0.0, 0.005063291139240506, 0.010126582278481013,\\n\",\"        0.01518987341772152, 0.020253164556962026,  0.02531645569620253,\\n\",\"        0.03037974683544304, 0.035443037974683546,  0.04050632911392405,\\n\",\"        0.04556962025316456,  0.05063291139240506,  0.05569620253164557,\\n\",\"        0.06075949367088608,  0.06582278481012659,  0.07088607594936709,\\n\",\"         0.0759493670886076,   0.0810126582278481,  0.08607594936708861,\\n\",\"        0.09113924050632911,  0.09620253164556962,  0.10126582278481013,\\n\",\"        0.10632911392405063,  0.11139240506329114,  0.11645569620253164,\\n\",\"        0.12151898734177216,  0.12658227848101267,  0.13164556962025317,\\n\",\"        0.13670886075949368,  0.14177215189873418,   0.1468354430379747,\\n\",\"         0.1518987341772152,   0.1569620253164557,   0.1620253164556962,\\n\",\"         0.1670886075949367,  0.17215189873417722,  0.17721518987341772,\\n\",\"        0.18227848101265823,  0.18734177215189873,  0.19240506329113924,\\n\",\"        0.19746835443037974,  0.20253164556962025,  0.20759493670886076,\\n\",\"        0.21265822784810126,  0.21772151898734177,  0.22278481012658227,\\n\",\"        0.22784810126582278,  0.23291139240506328,   0.2379746835443038,\\n\",\"        0.24303797468354432,  0.24810126582278483,  0.25316455696202533,\\n\",\"         0.2582278481012658,  0.26329113924050634,   0.2683544303797468,\\n\",\"        0.27341772151898736,  0.27848101265822783,  0.28354430379746837,\\n\",\"        0.28860759493670884,   0.2936708860759494,  0.29873417721518986,\\n\",\"         0.3037974683544304,  0.30886075949367087,   0.3139240506329114,\\n\",\"         0.3189873417721519,   0.3240506329113924,  0.32911392405063294,\\n\",\"         0.3341772151898734,  0.33924050632911396,  0.34430379746835443,\\n\",\"        0.34936708860759497,  0.35443037974683544,    0.359493670886076,\\n\",\"        0.36455696202531646,    0.369620253164557,  0.37468354430379747,\\n\",\"          0.379746835443038,   0.3848101265822785,    0.389873417721519,\\n\",\"         0.3949367088607595,                  0.4],\\n\",\"      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-fc4752f2-b2ce-40f3-a642-9ebf1ef36230' class='xr-index-data-in' type='checkbox'/><label for='index-fc4752f2-b2ce-40f3-a642-9ebf1ef36230' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,  10,  20,  30,  40,  50,  60,  70,  80,  90, 100, 110, 120, 130,\\n\",\"       140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240],\\n\",\"      dtype=&#x27;int64&#x27;, name=&#x27;x&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8b38cbe8-2119-45f6-ba40-f6fdef694347' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8b38cbe8-2119-45f6-ba40-f6fdef694347' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>\"],\"text/plain\":[\"<xarray.DataArray 'u(x, t)' (x: 25, t: 80)> Size: 16kB\\n\",\"array([[ 0.00000000e+00,  3.12796607e-01,  5.94201029e-01, ...,\\n\",\"        -5.94201029e-01, -3.12796607e-01, -9.79717439e-16],\\n\",\"       [-2.07911691e-01,  1.08482541e-01,  4.13989494e-01, ...,\\n\",\"        -7.48443128e-01, -5.03439960e-01, -2.07911691e-01],\\n\",\"       [-4.06736643e-01, -1.00572732e-01,  2.15684631e-01, ...,\\n\",\"        -8.69974671e-01, -6.72080571e-01, -4.06736643e-01],\\n\",\"       ...,\\n\",\"       [ 9.94521895e-01,  9.11920769e-01,  7.37799515e-01, ...,\\n\",\"         8.62021355e-01,  9.77313066e-01,  9.94521895e-01],\\n\",\"       [ 9.94521895e-01,  9.77313066e-01,  8.62021355e-01, ...,\\n\",\"         7.37799515e-01,  9.11920769e-01,  9.94521895e-01],\\n\",\"       [ 9.51056516e-01,  9.99992093e-01,  9.48568727e-01, ...,\\n\",\"         5.81332295e-01,  8.06673158e-01,  9.51056516e-01]])\\n\",\"Coordinates:\\n\",\"  * t        (t) float64 640B 0.0 0.005063 0.01013 0.01519 ... 0.3899 0.3949 0.4\\n\",\"  * x        (x) int64 200B 0 10 20 30 40 50 60 ... 180 190 200 210 220 230 240\"]},\"execution_count\":3,\"metadata\":{},\"output_type\":\"execute_result\"}],\"source\":[\"u_xr = xr.DataArray(\\n\",\"    data=u,\\n\",\"    dims=[\\\"x\\\", \\\"t\\\"],\\n\",\"    coords={\\\"t\\\": t, \\\"x\\\": x},\\n\",\"    name=\\\"u(x, t)\\\",\\n\",\")\\n\",\"\\n\",\"u_xr\"]},{\"cell_type\":\"markdown\",\"id\":\"b46613f0\",\"metadata\":{},\"source\":[\"Once we have stored our data in an `xarray.DataArray`, we can use the\\n\",\"`plot()` method for a quick visualization. Note that the time and space\\n\",\"coordinates are now represented correctly, as well as adding an appropriate\\n\",\"color scale automatically.\"]},{\"cell_type\":\"code\",\"execution_count\":4,\"id\":\"ef6c5037\",\"metadata\":{},\"outputs\":[{\"data\":{\"text/plain\":[\"<matplotlib.collections.QuadMesh at 0x79eeec9eb3d0>\"]},\"execution_count\":4,\"metadata\":{},\"output_type\":\"execute_result\"},{\"data\":{\"image/png\":\"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\",\"text/plain\":[\"<Figure size 640x480 with 2 Axes>\"]},\"metadata\":{},\"output_type\":\"display_data\"}],\"source\":[\"# Note how we can perform conventional numpy\\n\",\"# operations on the xarray data arrays, such as\\n\",\"# the applying the transform operator here.\\n\",\"u_xr.T.plot()\"]},{\"cell_type\":\"markdown\",\"id\":\"48a06873\",\"metadata\":{},\"source\":[\"We can easily retrieve the underlying data as numpy arrays in the following\\n\",\"way using the `data` attribute:\"]},{\"cell_type\":\"code\",\"execution_count\":5,\"id\":\"c9dee8c3\",\"metadata\":{\"lines_to_next_cell\":2},\"outputs\":[{\"data\":{\"text/plain\":[\"array([[ 0.00000000e+00,  3.12796607e-01,  5.94201029e-01, ...,\\n\",\"        -5.94201029e-01, -3.12796607e-01, -9.79717439e-16],\\n\",\"       [-2.07911691e-01,  1.08482541e-01,  4.13989494e-01, ...,\\n\",\"        -7.48443128e-01, -5.03439960e-01, -2.07911691e-01],\\n\",\"       [-4.06736643e-01, -1.00572732e-01,  2.15684631e-01, ...,\\n\",\"        -8.69974671e-01, -6.72080571e-01, -4.06736643e-01],\\n\",\"       ...,\\n\",\"       [ 9.94521895e-01,  9.11920769e-01,  7.37799515e-01, ...,\\n\",\"         8.62021355e-01,  9.77313066e-01,  9.94521895e-01],\\n\",\"       [ 9.94521895e-01,  9.77313066e-01,  8.62021355e-01, ...,\\n\",\"         7.37799515e-01,  9.11920769e-01,  9.94521895e-01],\\n\",\"       [ 9.51056516e-01,  9.99992093e-01,  9.48568727e-01, ...,\\n\",\"         5.81332295e-01,  8.06673158e-01,  9.51056516e-01]])\"]},\"execution_count\":5,\"metadata\":{},\"output_type\":\"execute_result\"}],\"source\":[\"# Get the data as a numpy array\\n\",\"u = u_xr.data\\n\",\"u\"]},{\"cell_type\":\"code\",\"execution_count\":6,\"id\":\"35fc7d4c\",\"metadata\":{\"lines_to_next_cell\":2},\"outputs\":[{\"data\":{\"text/plain\":[\"array([0.        , 0.00506329, 0.01012658, 0.01518987, 0.02025316,\\n\",\"       0.02531646, 0.03037975, 0.03544304, 0.04050633, 0.04556962,\\n\",\"       0.05063291, 0.0556962 , 0.06075949, 0.06582278, 0.07088608,\\n\",\"       0.07594937, 0.08101266, 0.08607595, 0.09113924, 0.09620253,\\n\",\"       0.10126582, 0.10632911, 0.11139241, 0.1164557 , 0.12151899,\\n\",\"       0.12658228, 0.13164557, 0.13670886, 0.14177215, 0.14683544,\\n\",\"       0.15189873, 0.15696203, 0.16202532, 0.16708861, 0.1721519 ,\\n\",\"       0.17721519, 0.18227848, 0.18734177, 0.19240506, 0.19746835,\\n\",\"       0.20253165, 0.20759494, 0.21265823, 0.21772152, 0.22278481,\\n\",\"       0.2278481 , 0.23291139, 0.23797468, 0.24303797, 0.24810127,\\n\",\"       0.25316456, 0.25822785, 0.26329114, 0.26835443, 0.27341772,\\n\",\"       0.27848101, 0.2835443 , 0.28860759, 0.29367089, 0.29873418,\\n\",\"       0.30379747, 0.30886076, 0.31392405, 0.31898734, 0.32405063,\\n\",\"       0.32911392, 0.33417722, 0.33924051, 0.3443038 , 0.34936709,\\n\",\"       0.35443038, 0.35949367, 0.36455696, 0.36962025, 0.37468354,\\n\",\"       0.37974684, 0.38481013, 0.38987342, 0.39493671, 0.4       ])\"]},\"execution_count\":6,\"metadata\":{},\"output_type\":\"execute_result\"}],\"source\":[\"# Get the time axis as a numpy array\\n\",\"t = u_xr.t.data\\n\",\"t\"]},{\"cell_type\":\"code\",\"execution_count\":7,\"id\":\"163186c5\",\"metadata\":{\"lines_to_next_cell\":2},\"outputs\":[{\"data\":{\"text/plain\":[\"array([  0,  10,  20,  30,  40,  50,  60,  70,  80,  90, 100, 110, 120,\\n\",\"       130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240])\"]},\"execution_count\":7,\"metadata\":{},\"output_type\":\"execute_result\"}],\"source\":[\"# Get the space axis as a numpy array\\n\",\"x = u_xr.x.data\\n\",\"x\"]},{\"cell_type\":\"markdown\",\"id\":\"d9ebd64e\",\"metadata\":{},\"source\":[\"Coordinates do not necessarily need to be numerical values. For example,\\n\",\"instead of the x-coordinate, we might want to store something like a receiver\\n\",\"name that uniquely identifies the receiver that recorded the data.\"]},{\"cell_type\":\"code\",\"execution_count\":8,\"id\":\"50141555\",\"metadata\":{},\"outputs\":[{\"name\":\"stdout\",\"output_type\":\"stream\",\"text\":[\"`receiver_id` is a list of strings, of length: 25\\n\",\"['R00', 'R01', 'R02', 'R03', 'R04', 'R05', 'R06', 'R07', 'R08', 'R09', 'R10', 'R11', 'R12', 'R13', 'R14', 'R15', 'R16', 'R17', 'R18', 'R19', 'R20', 'R21', 'R22', 'R23', 'R24'] \\n\",\"\\n\"]},{\"data\":{\"text/plain\":[\"<matplotlib.collections.QuadMesh at 0x79eee477f150>\"]},\"execution_count\":8,\"metadata\":{},\"output_type\":\"execute_result\"},{\"data\":{\"image/png\":\"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\",\"text/plain\":[\"<Figure size 640x480 with 2 Axes>\"]},\"metadata\":{},\"output_type\":\"display_data\"}],\"source\":[\"# Generate a list of unique receiver identifiers\\n\",\"receiver_id = [f\\\"R{i:02d}\\\" for i in range(u.shape[0])]\\n\",\"\\n\",\"print(f\\\"`receiver_id` is a list of strings, of length: {len(receiver_id)}\\\")\\n\",\"print(receiver_id, \\\"\\\\n\\\")\\n\",\"\\n\",\"data_array = xr.DataArray(\\n\",\"    data=u,\\n\",\"    dims=[\\\"receiver_id\\\", \\\"time\\\"],\\n\",\"    coords={\\\"time\\\": t, \\\"receiver_id\\\": receiver_id},\\n\",\"    name=\\\"wave_recording\\\",  # Only used for visualization & labelling\\n\",\")\\n\",\"\\n\",\"data_array.plot()\"]},{\"cell_type\":\"markdown\",\"id\":\"47147cc6\",\"metadata\":{},\"source\":[\"To select data of a specific receiver, we can use the `sel()`  method on the\\n\",\"`\\\"receiver_id\\\"` dimension:\"]},{\"cell_type\":\"code\",\"execution_count\":9,\"id\":\"3e7f9c29\",\"metadata\":{\"lines_to_next_cell\":2},\"outputs\":[{\"data\":{\"text/plain\":[\"[<matplotlib.lines.Line2D at 0x79eee46e5690>]\"]},\"execution_count\":9,\"metadata\":{},\"output_type\":\"execute_result\"},{\"data\":{\"image/png\":\"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\",\"text/plain\":[\"<Figure size 640x480 with 1 Axes>\"]},\"metadata\":{},\"output_type\":\"display_data\"}],\"source\":[\"# Select the receiver \\\"R15\\\"\\n\",\"data_array.sel(receiver_id=\\\"R15\\\").plot()\"]},{\"cell_type\":\"markdown\",\"id\":\"327a60cf\",\"metadata\":{},\"source\":[\"For index based selection, we can use the `isel()` method:\"]},{\"cell_type\":\"code\",\"execution_count\":10,\"id\":\"0eeb4116\",\"metadata\":{},\"outputs\":[{\"data\":{\"text/plain\":[\"<matplotlib.collections.QuadMesh at 0x79eee4547150>\"]},\"execution_count\":10,\"metadata\":{},\"output_type\":\"execute_result\"},{\"data\":{\"image/png\":\"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size 640x480 with 2 Axes>\"]},\"metadata\":{},\"output_type\":\"display_data\"}],\"source\":[\"# Select the three receivers by their index\\n\",\"data_array.isel(receiver_id=[15, 18, 21]).plot()\"]},{\"cell_type\":\"markdown\",\"id\":\"ca24eca9\",\"metadata\":{},\"source\":[\"Finally, it might be interesting to know that one can construct\\n\",\"[Datasets](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html)\\n\",\"from multiple DataArrays. These objects can be useful when dealing with\\n\",\"multiple quantities together, such as medium models or multiple recording\\n\",\"fields.\"]},{\"cell_type\":\"code\",\"execution_count\":11,\"id\":\"f94cb0fb\",\"metadata\":{},\"outputs\":[{\"data\":{\"text/html\":[\"<div><svg style=\\\"position: absolute; width: 0; height: 0; overflow: hidden\\\">\\n\",\"<defs>\\n\",\"<symbol id=\\\"icon-database\\\" viewBox=\\\"0 0 32 32\\\">\\n\",\"<path d=\\\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\\\"></path>\\n\",\"<path d=\\\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\\\"></path>\\n\",\"<path d=\\\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\\\"></path>\\n\",\"</symbol>\\n\",\"<symbol id=\\\"icon-file-text2\\\" viewBox=\\\"0 0 32 32\\\">\\n\",\"<path d=\\\"M28.681 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5))\\n\",\"  );\\n\",\"  --xr-background-color-row-odd: var(\\n\",\"    --jp-layout-color2,\\n\",\"    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\\n\",\"  );\\n\",\"}\\n\",\"\\n\",\"html[theme=\\\"dark\\\"],\\n\",\"html[data-theme=\\\"dark\\\"],\\n\",\"body[data-theme=\\\"dark\\\"],\\n\",\"body.vscode-dark {\\n\",\"  --xr-font-color0: var(\\n\",\"    --jp-content-font-color0,\\n\",\"    var(--pst-color-text-base, rgba(255, 255, 255, 1))\\n\",\"  );\\n\",\"  --xr-font-color2: var(\\n\",\"    --jp-content-font-color2,\\n\",\"    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\\n\",\"  );\\n\",\"  --xr-font-color3: var(\\n\",\"    --jp-content-font-color3,\\n\",\"    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\\n\",\"  );\\n\",\"  --xr-border-color: var(\\n\",\"    --jp-border-color2,\\n\",\"    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\\n\",\"  );\\n\",\"  --xr-disabled-color: var(\\n\",\"    --jp-layout-color3,\\n\",\"    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\\n\",\"  );\\n\",\"  --xr-background-color: var(\\n\",\"    --jp-layout-color0,\\n\",\"    var(--pst-color-on-background, #111111)\\n\",\"  );\\n\",\"  --xr-background-color-row-even: var(\\n\",\"    --jp-layout-color1,\\n\",\"    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\\n\",\"  );\\n\",\"  --xr-background-color-row-odd: var(\\n\",\"    --jp-layout-color2,\\n\",\"    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\\n\",\"  );\\n\",\"}\\n\",\"\\n\",\".xr-wrap {\\n\",\"  display: block !important;\\n\",\"  min-width: 300px;\\n\",\"  max-width: 700px;\\n\",\"}\\n\",\"\\n\",\".xr-text-repr-fallback {\\n\",\"  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\\n\",\"  display: none;\\n\",\"}\\n\",\"\\n\",\".xr-header {\\n\",\"  padding-top: 6px;\\n\",\"  padding-bottom: 6px;\\n\",\"  margin-bottom: 4px;\\n\",\"  border-bottom: solid 1px var(--xr-border-color);\\n\",\"}\\n\",\"\\n\",\".xr-header > div,\\n\",\".xr-header > ul {\\n\",\"  display: inline;\\n\",\"  margin-top: 0;\\n\",\"  margin-bottom: 0;\\n\",\"}\\n\",\"\\n\",\".xr-obj-type,\\n\",\".xr-array-name {\\n\",\"  margin-left: 2px;\\n\",\"  margin-right: 10px;\\n\",\"}\\n\",\"\\n\",\".xr-obj-type {\\n\",\"  color: var(--xr-font-color2);\\n\",\"}\\n\",\"\\n\",\".xr-sections {\\n\",\"  padding-left: 0 !important;\\n\",\"  display: grid;\\n\",\"  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\\n\",\"}\\n\",\"\\n\",\".xr-section-item {\\n\",\"  display: contents;\\n\",\"}\\n\",\"\\n\",\".xr-section-item input {\\n\",\"  display: inline-block;\\n\",\"  opacity: 0;\\n\",\"  height: 0;\\n\",\"}\\n\",\"\\n\",\".xr-section-item input + label {\\n\",\"  color: var(--xr-disabled-color);\\n\",\"  border: 2px solid transparent !important;\\n\",\"}\\n\",\"\\n\",\".xr-section-item input:enabled + label {\\n\",\"  cursor: pointer;\\n\",\"  color: var(--xr-font-color2);\\n\",\"}\\n\",\"\\n\",\".xr-section-item input:focus + label {\\n\",\"  border: 2px solid var(--xr-font-color0) !important;\\n\",\"}\\n\",\"\\n\",\".xr-section-item input:enabled + label:hover {\\n\",\"  color: var(--xr-font-color0);\\n\",\"}\\n\",\"\\n\",\".xr-section-summary {\\n\",\"  grid-column: 1;\\n\",\"  color: var(--xr-font-color2);\\n\",\"  font-weight: 500;\\n\",\"}\\n\",\"\\n\",\".xr-section-summary > span {\\n\",\"  display: inline-block;\\n\",\"  padding-left: 0.5em;\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in:disabled + label {\\n\",\"  color: var(--xr-font-color2);\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in + label:before {\\n\",\"  display: inline-block;\\n\",\"  content: \\\"►\\\";\\n\",\"  font-size: 11px;\\n\",\"  width: 15px;\\n\",\"  text-align: center;\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in:disabled + label:before {\\n\",\"  color: var(--xr-disabled-color);\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in:checked + label:before {\\n\",\"  content: \\\"▼\\\";\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in:checked + label > span {\\n\",\"  display: none;\\n\",\"}\\n\",\"\\n\",\".xr-section-summary,\\n\",\".xr-section-inline-details {\\n\",\"  padding-top: 4px;\\n\",\"  padding-bottom: 4px;\\n\",\"}\\n\",\"\\n\",\".xr-section-inline-details {\\n\",\"  grid-column: 2 / -1;\\n\",\"}\\n\",\"\\n\",\".xr-section-details {\\n\",\"  display: none;\\n\",\"  grid-column: 1 / -1;\\n\",\"  margin-bottom: 5px;\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in:checked ~ .xr-section-details {\\n\",\"  display: contents;\\n\",\"}\\n\",\"\\n\",\".xr-array-wrap {\\n\",\"  grid-column: 1 / -1;\\n\",\"  display: grid;\\n\",\"  grid-template-columns: 20px auto;\\n\",\"}\\n\",\"\\n\",\".xr-array-wrap > label {\\n\",\"  grid-column: 1;\\n\",\"  vertical-align: top;\\n\",\"}\\n\",\"\\n\",\".xr-preview {\\n\",\"  color: var(--xr-font-color3);\\n\",\"}\\n\",\"\\n\",\".xr-array-preview,\\n\",\".xr-array-data {\\n\",\"  padding: 0 5px !important;\\n\",\"  grid-column: 2;\\n\",\"}\\n\",\"\\n\",\".xr-array-data,\\n\",\".xr-array-in:checked ~ .xr-array-preview {\\n\",\"  display: none;\\n\",\"}\\n\",\"\\n\",\".xr-array-in:checked ~ .xr-array-data,\\n\",\".xr-array-preview {\\n\",\"  display: inline-block;\\n\",\"}\\n\",\"\\n\",\".xr-dim-list {\\n\",\"  display: inline-block !important;\\n\",\"  list-style: none;\\n\",\"  padding: 0 !important;\\n\",\"  margin: 0;\\n\",\"}\\n\",\"\\n\",\".xr-dim-list li {\\n\",\"  display: inline-block;\\n\",\"  padding: 0;\\n\",\"  margin: 0;\\n\",\"}\\n\",\"\\n\",\".xr-dim-list:before {\\n\",\"  content: \\\"(\\\";\\n\",\"}\\n\",\"\\n\",\".xr-dim-list:after {\\n\",\"  content: \\\")\\\";\\n\",\"}\\n\",\"\\n\",\".xr-dim-list li:not(:last-child):after {\\n\",\"  content: \\\",\\\";\\n\",\"  padding-right: 5px;\\n\",\"}\\n\",\"\\n\",\".xr-has-index {\\n\",\"  font-weight: bold;\\n\",\"}\\n\",\"\\n\",\".xr-var-list,\\n\",\".xr-var-item {\\n\",\"  display: contents;\\n\",\"}\\n\",\"\\n\",\".xr-var-item > div,\\n\",\".xr-var-item label,\\n\",\".xr-var-item > .xr-var-name span {\\n\",\"  background-color: var(--xr-background-color-row-even);\\n\",\"  border-color: var(--xr-background-color-row-odd);\\n\",\"  margin-bottom: 0;\\n\",\"  padding-top: 2px;\\n\",\"}\\n\",\"\\n\",\".xr-var-item > .xr-var-name:hover span {\\n\",\"  padding-right: 5px;\\n\",\"}\\n\",\"\\n\",\".xr-var-list > li:nth-child(odd) > div,\\n\",\".xr-var-list > li:nth-child(odd) > label,\\n\",\".xr-var-list > li:nth-child(odd) > .xr-var-name span {\\n\",\"  background-color: var(--xr-background-color-row-odd);\\n\",\"  border-color: var(--xr-background-color-row-even);\\n\",\"}\\n\",\"\\n\",\".xr-var-name {\\n\",\"  grid-column: 1;\\n\",\"}\\n\",\"\\n\",\".xr-var-dims {\\n\",\"  grid-column: 2;\\n\",\"}\\n\",\"\\n\",\".xr-var-dtype {\\n\",\"  grid-column: 3;\\n\",\"  text-align: right;\\n\",\"  color: var(--xr-font-color2);\\n\",\"}\\n\",\"\\n\",\".xr-var-preview {\\n\",\"  grid-column: 4;\\n\",\"}\\n\",\"\\n\",\".xr-index-preview {\\n\",\"  grid-column: 2 / 5;\\n\",\"  color: var(--xr-font-color2);\\n\",\"}\\n\",\"\\n\",\".xr-var-name,\\n\",\".xr-var-dims,\\n\",\".xr-var-dtype,\\n\",\".xr-preview,\\n\",\".xr-attrs dt {\\n\",\"  white-space: nowrap;\\n\",\"  overflow: hidden;\\n\",\"  text-overflow: ellipsis;\\n\",\"  padding-right: 10px;\\n\",\"}\\n\",\"\\n\",\".xr-var-name:hover,\\n\",\".xr-var-dims:hover,\\n\",\".xr-var-dtype:hover,\\n\",\".xr-attrs dt:hover {\\n\",\"  overflow: visible;\\n\",\"  width: auto;\\n\",\"  z-index: 1;\\n\",\"}\\n\",\"\\n\",\".xr-var-attrs,\\n\",\".xr-var-data,\\n\",\".xr-index-data {\\n\",\"  display: none;\\n\",\"  border-top: 2px dotted var(--xr-background-color);\\n\",\"  padding-bottom: 20px !important;\\n\",\"  padding-top: 10px !important;\\n\",\"}\\n\",\"\\n\",\".xr-var-attrs-in + label,\\n\",\".xr-var-data-in + label,\\n\",\".xr-index-data-in + label {\\n\",\"  padding: 0 1px;\\n\",\"}\\n\",\"\\n\",\".xr-var-attrs-in:checked ~ .xr-var-attrs,\\n\",\".xr-var-data-in:checked ~ .xr-var-data,\\n\",\".xr-index-data-in:checked ~ .xr-index-data {\\n\",\"  display: block;\\n\",\"}\\n\",\"\\n\",\".xr-var-data > table {\\n\",\"  float: right;\\n\",\"}\\n\",\"\\n\",\".xr-var-data > pre,\\n\",\".xr-index-data > pre,\\n\",\".xr-var-data > table > tbody > tr {\\n\",\"  background-color: transparent !important;\\n\",\"}\\n\",\"\\n\",\".xr-var-name span,\\n\",\".xr-var-data,\\n\",\".xr-index-name div,\\n\",\".xr-index-data,\\n\",\".xr-attrs {\\n\",\"  padding-left: 25px !important;\\n\",\"}\\n\",\"\\n\",\".xr-attrs,\\n\",\".xr-var-attrs,\\n\",\".xr-var-data,\\n\",\".xr-index-data {\\n\",\"  grid-column: 1 / -1;\\n\",\"}\\n\",\"\\n\",\"dl.xr-attrs {\\n\",\"  padding: 0;\\n\",\"  margin: 0;\\n\",\"  display: grid;\\n\",\"  grid-template-columns: 125px auto;\\n\",\"}\\n\",\"\\n\",\".xr-attrs dt,\\n\",\".xr-attrs dd {\\n\",\"  padding: 0;\\n\",\"  margin: 0;\\n\",\"  float: left;\\n\",\"  padding-right: 10px;\\n\",\"  width: auto;\\n\",\"}\\n\",\"\\n\",\".xr-attrs dt {\\n\",\"  font-weight: normal;\\n\",\"  grid-column: 1;\\n\",\"}\\n\",\"\\n\",\".xr-attrs dt:hover span {\\n\",\"  display: inline-block;\\n\",\"  background: var(--xr-background-color);\\n\",\"  padding-right: 10px;\\n\",\"}\\n\",\"\\n\",\".xr-attrs dd {\\n\",\"  grid-column: 2;\\n\",\"  white-space: pre-wrap;\\n\",\"  word-break: break-all;\\n\",\"}\\n\",\"\\n\",\".xr-icon-database,\\n\",\".xr-icon-file-text2,\\n\",\".xr-no-icon {\\n\",\"  display: inline-block;\\n\",\"  vertical-align: middle;\\n\",\"  width: 1em;\\n\",\"  height: 1.5em !important;\\n\",\"  stroke-width: 0;\\n\",\"  stroke: currentColor;\\n\",\"  fill: currentColor;\\n\",\"}\\n\",\"\\n\",\".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\\n\",\".xr-var-data-in:checked + label > .xr-icon-database,\\n\",\".xr-index-data-in:checked + label > .xr-icon-database {\\n\",\"  color: var(--xr-font-color0);\\n\",\"  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\\n\",\"  stroke-width: 0.8px;\\n\",\"}\\n\",\"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 49kB\\n\",\"Dimensions:      (time: 80, receiver_id: 25)\\n\",\"Coordinates:\\n\",\"  * time         (time) float64 640B 0.0 0.005063 0.01013 ... 0.3899 0.3949 0.4\\n\",\"  * receiver_id  (receiver_id) &lt;U3 300B &#x27;R00&#x27; &#x27;R01&#x27; &#x27;R02&#x27; ... &#x27;R22&#x27; &#x27;R23&#x27; &#x27;R24&#x27;\\n\",\"Data variables:\\n\",\"    var_1        (receiver_id, time) float64 16kB 0.0 0.3128 ... 0.8067 0.9511\\n\",\"    var_2        (receiver_id, time) float64 16kB 0.0 0.09784 ... 0.6507 0.9045\\n\",\"    var_3        (receiver_id, time) float64 16kB -0.5 -0.1872 ... 0.3067 0.4511</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-96344454-4fd8-4af9-8348-779e0ab0d51a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-96344454-4fd8-4af9-8348-779e0ab0d51a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 80</li><li><span class='xr-has-index'>receiver_id</span>: 25</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ef04725c-f462-4698-a468-f4ebc5899dff' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ef04725c-f462-4698-a468-f4ebc5899dff' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.005063 0.01013 ... 0.3949 0.4</div><input id='attrs-4b99a1e2-9e41-4c87-a5de-7d43e72e1b6b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4b99a1e2-9e41-4c87-a5de-7d43e72e1b6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6babb80-623e-49ab-93bd-92ba6bc46e23' class='xr-var-data-in' type='checkbox'><label for='data-b6babb80-623e-49ab-93bd-92ba6bc46e23' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.      , 0.005063, 0.010127, 0.01519 , 0.020253, 0.025316, 0.03038 ,\\n\",\"       0.035443, 0.040506, 0.04557 , 0.050633, 0.055696, 0.060759, 0.065823,\\n\",\"       0.070886, 0.075949, 0.081013, 0.086076, 0.091139, 0.096203, 0.101266,\\n\",\"       0.106329, 0.111392, 0.116456, 0.121519, 0.126582, 0.131646, 0.136709,\\n\",\"       0.141772, 0.146835, 0.151899, 0.156962, 0.162025, 0.167089, 0.172152,\\n\",\"       0.177215, 0.182278, 0.187342, 0.192405, 0.197468, 0.202532, 0.207595,\\n\",\"       0.212658, 0.217722, 0.222785, 0.227848, 0.232911, 0.237975, 0.243038,\\n\",\"       0.248101, 0.253165, 0.258228, 0.263291, 0.268354, 0.273418, 0.278481,\\n\",\"       0.283544, 0.288608, 0.293671, 0.298734, 0.303797, 0.308861, 0.313924,\\n\",\"       0.318987, 0.324051, 0.329114, 0.334177, 0.339241, 0.344304, 0.349367,\\n\",\"       0.35443 , 0.359494, 0.364557, 0.36962 , 0.374684, 0.379747, 0.38481 ,\\n\",\"       0.389873, 0.394937, 0.4     ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>receiver_id</span></div><div class='xr-var-dims'>(receiver_id)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;R00&#x27; &#x27;R01&#x27; &#x27;R02&#x27; ... &#x27;R23&#x27; &#x27;R24&#x27;</div><input id='attrs-a32885ba-f91c-44b0-8f5b-b4faa873840e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a32885ba-f91c-44b0-8f5b-b4faa873840e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed9cab5a-0d06-43c6-95fd-fcb00a1013c8' class='xr-var-data-in' type='checkbox'><label for='data-ed9cab5a-0d06-43c6-95fd-fcb00a1013c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;R00&#x27;, &#x27;R01&#x27;, &#x27;R02&#x27;, &#x27;R03&#x27;, &#x27;R04&#x27;, &#x27;R05&#x27;, &#x27;R06&#x27;, &#x27;R07&#x27;, &#x27;R08&#x27;, &#x27;R09&#x27;,\\n\",\"       &#x27;R10&#x27;, &#x27;R11&#x27;, &#x27;R12&#x27;, &#x27;R13&#x27;, &#x27;R14&#x27;, &#x27;R15&#x27;, &#x27;R16&#x27;, &#x27;R17&#x27;, &#x27;R18&#x27;, &#x27;R19&#x27;,\\n\",\"       &#x27;R20&#x27;, &#x27;R21&#x27;, &#x27;R22&#x27;, &#x27;R23&#x27;, &#x27;R24&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e3fda643-6b7b-4143-bf56-0d4c69a6d9d7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e3fda643-6b7b-4143-bf56-0d4c69a6d9d7' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>var_1</span></div><div class='xr-var-dims'>(receiver_id, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.3128 0.5942 ... 0.8067 0.9511</div><input id='attrs-f548f48a-0ef8-4097-a61d-b635bc6a70a9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f548f48a-0ef8-4097-a61d-b635bc6a70a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20ce3cd3-43ef-4b86-80ff-86a9f3351d3d' class='xr-var-data-in' type='checkbox'><label for='data-20ce3cd3-43ef-4b86-80ff-86a9f3351d3d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.00000000e+00,  3.12796607e-01,  5.94201029e-01, ...,\\n\",\"        -5.94201029e-01, -3.12796607e-01, -9.79717439e-16],\\n\",\"       [-2.07911691e-01,  1.08482541e-01,  4.13989494e-01, ...,\\n\",\"        -7.48443128e-01, -5.03439960e-01, -2.07911691e-01],\\n\",\"       [-4.06736643e-01, -1.00572732e-01,  2.15684631e-01, ...,\\n\",\"        -8.69974671e-01, -6.72080571e-01, -4.06736643e-01],\\n\",\"       ...,\\n\",\"       [ 9.94521895e-01,  9.11920769e-01,  7.37799515e-01, ...,\\n\",\"         8.62021355e-01,  9.77313066e-01,  9.94521895e-01],\\n\",\"       [ 9.94521895e-01,  9.77313066e-01,  8.62021355e-01, ...,\\n\",\"         7.37799515e-01,  9.11920769e-01,  9.94521895e-01],\\n\",\"       [ 9.51056516e-01,  9.99992093e-01,  9.48568727e-01, ...,\\n\",\"         5.81332295e-01,  8.06673158e-01,  9.51056516e-01]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>var_2</span></div><div class='xr-var-dims'>(receiver_id, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.09784 ... 0.6507 0.9045</div><input id='attrs-0316849c-2f3e-42d4-bd95-a5da31c33f0a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0316849c-2f3e-42d4-bd95-a5da31c33f0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e143157-f01a-498d-921c-dfc6c6f3b961' class='xr-var-data-in' type='checkbox'><label for='data-6e143157-f01a-498d-921c-dfc6c6f3b961' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.00000000e+00, 9.78417174e-02, 3.53074863e-01, ...,\\n\",\"        3.53074863e-01, 9.78417174e-02, 9.59846261e-31],\\n\",\"       [4.32272712e-02, 1.17684617e-02, 1.71387301e-01, ...,\\n\",\"        5.60167116e-01, 2.53451794e-01, 4.32272712e-02],\\n\",\"       [1.65434697e-01, 1.01148745e-02, 4.65198601e-02, ...,\\n\",\"        7.56855928e-01, 4.51692294e-01, 1.65434697e-01],\\n\",\"       ...,\\n\",\"       [9.89073800e-01, 8.31599489e-01, 5.44348124e-01, ...,\\n\",\"        7.43080817e-01, 9.55140830e-01, 9.89073800e-01],\\n\",\"       [9.89073800e-01, 9.55140830e-01, 7.43080817e-01, ...,\\n\",\"        5.44348124e-01, 8.31599489e-01, 9.89073800e-01],\\n\",\"       [9.04508497e-01, 9.99984186e-01, 8.99782629e-01, ...,\\n\",\"        3.37947237e-01, 6.50721584e-01, 9.04508497e-01]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>var_3</span></div><div class='xr-var-dims'>(receiver_id, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.5 -0.1872 ... 0.3067 0.4511</div><input id='attrs-6c255eae-80ab-4fee-b62d-3ebe8e770673' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6c255eae-80ab-4fee-b62d-3ebe8e770673' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f2bb4cf-137f-4325-a137-7bc40f56fede' class='xr-var-data-in' type='checkbox'><label for='data-0f2bb4cf-137f-4325-a137-7bc40f56fede' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-0.5       , -0.18720339,  0.09420103, ..., -1.09420103,\\n\",\"        -0.81279661, -0.5       ],\\n\",\"       [-0.70791169, -0.39151746, -0.08601051, ..., -1.24844313,\\n\",\"        -1.00343996, -0.70791169],\\n\",\"       [-0.90673664, -0.60057273, -0.28431537, ..., -1.36997467,\\n\",\"        -1.17208057, -0.90673664],\\n\",\"       ...,\\n\",\"       [ 0.4945219 ,  0.41192077,  0.23779951, ...,  0.36202136,\\n\",\"         0.47731307,  0.4945219 ],\\n\",\"       [ 0.4945219 ,  0.47731307,  0.36202136, ...,  0.23779951,\\n\",\"         0.41192077,  0.4945219 ],\\n\",\"       [ 0.45105652,  0.49999209,  0.44856873, ...,  0.08133229,\\n\",\"         0.30667316,  0.45105652]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9e834396-c386-48f3-a70c-39cc3253ca83' class='xr-section-summary-in' type='checkbox'  ><label for='section-9e834396-c386-48f3-a70c-39cc3253ca83' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-8c09f17e-be6f-4e24-a596-b262911e9fdc' class='xr-index-data-in' type='checkbox'/><label for='index-8c09f17e-be6f-4e24-a596-b262911e9fdc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([                 0.0, 0.005063291139240506, 0.010126582278481013,\\n\",\"        0.01518987341772152, 0.020253164556962026,  0.02531645569620253,\\n\",\"        0.03037974683544304, 0.035443037974683546,  0.04050632911392405,\\n\",\"        0.04556962025316456,  0.05063291139240506,  0.05569620253164557,\\n\",\"        0.06075949367088608,  0.06582278481012659,  0.07088607594936709,\\n\",\"         0.0759493670886076,   0.0810126582278481,  0.08607594936708861,\\n\",\"        0.09113924050632911,  0.09620253164556962,  0.10126582278481013,\\n\",\"        0.10632911392405063,  0.11139240506329114,  0.11645569620253164,\\n\",\"        0.12151898734177216,  0.12658227848101267,  0.13164556962025317,\\n\",\"        0.13670886075949368,  0.14177215189873418,   0.1468354430379747,\\n\",\"         0.1518987341772152,   0.1569620253164557,   0.1620253164556962,\\n\",\"         0.1670886075949367,  0.17215189873417722,  0.17721518987341772,\\n\",\"        0.18227848101265823,  0.18734177215189873,  0.19240506329113924,\\n\",\"        0.19746835443037974,  0.20253164556962025,  0.20759493670886076,\\n\",\"        0.21265822784810126,  0.21772151898734177,  0.22278481012658227,\\n\",\"        0.22784810126582278,  0.23291139240506328,   0.2379746835443038,\\n\",\"        0.24303797468354432,  0.24810126582278483,  0.25316455696202533,\\n\",\"         0.2582278481012658,  0.26329113924050634,   0.2683544303797468,\\n\",\"        0.27341772151898736,  0.27848101265822783,  0.28354430379746837,\\n\",\"        0.28860759493670884,   0.2936708860759494,  0.29873417721518986,\\n\",\"         0.3037974683544304,  0.30886075949367087,   0.3139240506329114,\\n\",\"         0.3189873417721519,   0.3240506329113924,  0.32911392405063294,\\n\",\"         0.3341772151898734,  0.33924050632911396,  0.34430379746835443,\\n\",\"        0.34936708860759497,  0.35443037974683544,    0.359493670886076,\\n\",\"        0.36455696202531646,    0.369620253164557,  0.37468354430379747,\\n\",\"          0.379746835443038,   0.3848101265822785,    0.389873417721519,\\n\",\"         0.3949367088607595,                  0.4],\\n\",\"      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>receiver_id</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-688340ce-265b-430f-bd12-e67aeecf31c3' class='xr-index-data-in' type='checkbox'/><label for='index-688340ce-265b-430f-bd12-e67aeecf31c3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;R00&#x27;, &#x27;R01&#x27;, &#x27;R02&#x27;, &#x27;R03&#x27;, &#x27;R04&#x27;, &#x27;R05&#x27;, &#x27;R06&#x27;, &#x27;R07&#x27;, &#x27;R08&#x27;, &#x27;R09&#x27;,\\n\",\"       &#x27;R10&#x27;, &#x27;R11&#x27;, &#x27;R12&#x27;, &#x27;R13&#x27;, &#x27;R14&#x27;, &#x27;R15&#x27;, &#x27;R16&#x27;, &#x27;R17&#x27;, &#x27;R18&#x27;, &#x27;R19&#x27;,\\n\",\"       &#x27;R20&#x27;, &#x27;R21&#x27;, &#x27;R22&#x27;, &#x27;R23&#x27;, &#x27;R24&#x27;],\\n\",\"      dtype=&#x27;object&#x27;, name=&#x27;receiver_id&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1cba1b9b-2452-4d2d-a26b-95d9bacb274a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1cba1b9b-2452-4d2d-a26b-95d9bacb274a' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>\"],\"text/plain\":[\"<xarray.Dataset> Size: 49kB\\n\",\"Dimensions:      (time: 80, receiver_id: 25)\\n\",\"Coordinates:\\n\",\"  * time         (time) float64 640B 0.0 0.005063 0.01013 ... 0.3899 0.3949 0.4\\n\",\"  * receiver_id  (receiver_id) <U3 300B 'R00' 'R01' 'R02' ... 'R22' 'R23' 'R24'\\n\",\"Data variables:\\n\",\"    var_1        (receiver_id, time) float64 16kB 0.0 0.3128 ... 0.8067 0.9511\\n\",\"    var_2        (receiver_id, time) float64 16kB 0.0 0.09784 ... 0.6507 0.9045\\n\",\"    var_3        (receiver_id, time) float64 16kB -0.5 -0.1872 ... 0.3067 0.4511\"]},\"execution_count\":11,\"metadata\":{},\"output_type\":\"execute_result\"}],\"source\":[\"ds = xr.Dataset(\\n\",\"    {\\n\",\"        \\\"var_1\\\": data_array,\\n\",\"        \\\"var_2\\\": data_array**2,\\n\",\"        \\\"var_3\\\": data_array - 0.5,\\n\",\"    }\\n\",\")\\n\",\"\\n\",\"ds\"]},{\"cell_type\":\"markdown\",\"id\":\"65964f91\",\"metadata\":{},\"source\":[\"### Multi-index dimensions\\n\",\"\\n\",\"In certain cases, our data will have more dimensions than just the space and\\n\",\"time dimension. For example, elastic seismic wavefields are vector fields of\\n\",\"which we might record multiple components. Our data will thus have an\\n\",\"additional dimension corresponding to the component of the wavefield. For\\n\",\"various reasons, data within Salvus are always represented as 2D xarray data\\n\",\"structures. In order to keep track of information on additional dimensions\\n\",\"such as the component, we make use a so-called multi-index dimension that can\\n\",\"store information on both the `\\\"receiver_id\\\"` and the `\\\"component\\\"`. Let us\\n\",\"see how this works in practice.\\n\",\"\\n\",\"First, we create a 3D data array, where the first dimension corresponds to\\n\",\"the `\\\"receiver_id\\\"`, the second dimension to the `\\\"component\\\"`, and the third\\n\",\"dimension to `\\\"time\\\"`.\"]},{\"cell_type\":\"code\",\"execution_count\":12,\"id\":\"49140031\",\"metadata\":{},\"outputs\":[{\"data\":{\"text/plain\":[\"(25, 2, 80)\"]},\"execution_count\":12,\"metadata\":{},\"output_type\":\"execute_result\"}],\"source\":[\"# Let's mock some multicomponent data\\n\",\"# where each component corresponds to a\\n\",\"# scaled version of our original data.\\n\",\"theta = np.pi / 8\\n\",\"# X component\\n\",\"u_x = np.cos(theta) * u\\n\",\"\\n\",\"# Y component\\n\",\"u_y = np.sin(theta) * u\\n\",\"\\n\",\"# 3D array with multicomponent data,\\n\",\"# where the first dimension corresponds\\n\",\"# to the receiver id, the second to\\n\",\"# the component, and the third to time.\\n\",\"u_multicomponent = np.stack([u_x, u_y], axis=1)\\n\",\"u_multicomponent.shape\\n\"]},{\"cell_type\":\"markdown\",\"id\":\"75c5c0fe\",\"metadata\":{},\"source\":[\"Now, we set up a 3D `xarray.DataArray` from that.\"]},{\"cell_type\":\"code\",\"execution_count\":13,\"id\":\"7203698c\",\"metadata\":{},\"outputs\":[{\"data\":{\"text/html\":[\"<div><svg style=\\\"position: absolute; width: 0; height: 0; overflow: hidden\\\">\\n\",\"<defs>\\n\",\"<symbol id=\\\"icon-database\\\" viewBox=\\\"0 0 32 32\\\">\\n\",\"<path d=\\\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\\\"></path>\\n\",\"<path d=\\\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\\\"></path>\\n\",\"<path d=\\\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\\\"></path>\\n\",\"</symbol>\\n\",\"<symbol id=\\\"icon-file-text2\\\" viewBox=\\\"0 0 32 32\\\">\\n\",\"<path d=\\\"M28.681 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{\\n\",\"  --xr-font-color0: var(\\n\",\"    --jp-content-font-color0,\\n\",\"    var(--pst-color-text-base rgba(0, 0, 0, 1))\\n\",\"  );\\n\",\"  --xr-font-color2: var(\\n\",\"    --jp-content-font-color2,\\n\",\"    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\\n\",\"  );\\n\",\"  --xr-font-color3: var(\\n\",\"    --jp-content-font-color3,\\n\",\"    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\\n\",\"  );\\n\",\"  --xr-border-color: var(\\n\",\"    --jp-border-color2,\\n\",\"    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\\n\",\"  );\\n\",\"  --xr-disabled-color: var(\\n\",\"    --jp-layout-color3,\\n\",\"    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\\n\",\"  );\\n\",\"  --xr-background-color: var(\\n\",\"    --jp-layout-color0,\\n\",\"    var(--pst-color-on-background, white)\\n\",\"  );\\n\",\"  --xr-background-color-row-even: var(\\n\",\"    --jp-layout-color1,\\n\",\"    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\\n\",\"  );\\n\",\"  --xr-background-color-row-odd: var(\\n\",\"    --jp-layout-color2,\\n\",\"    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\\n\",\"  );\\n\",\"}\\n\",\"\\n\",\"html[theme=\\\"dark\\\"],\\n\",\"html[data-theme=\\\"dark\\\"],\\n\",\"body[data-theme=\\\"dark\\\"],\\n\",\"body.vscode-dark {\\n\",\"  --xr-font-color0: var(\\n\",\"    --jp-content-font-color0,\\n\",\"    var(--pst-color-text-base, rgba(255, 255, 255, 1))\\n\",\"  );\\n\",\"  --xr-font-color2: var(\\n\",\"    --jp-content-font-color2,\\n\",\"    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\\n\",\"  );\\n\",\"  --xr-font-color3: var(\\n\",\"    --jp-content-font-color3,\\n\",\"    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\\n\",\"  );\\n\",\"  --xr-border-color: var(\\n\",\"    --jp-border-color2,\\n\",\"    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\\n\",\"  );\\n\",\"  --xr-disabled-color: var(\\n\",\"    --jp-layout-color3,\\n\",\"    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\\n\",\"  );\\n\",\"  --xr-background-color: var(\\n\",\"    --jp-layout-color0,\\n\",\"    var(--pst-color-on-background, #111111)\\n\",\"  );\\n\",\"  --xr-background-color-row-even: var(\\n\",\"    --jp-layout-color1,\\n\",\"    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\\n\",\"  );\\n\",\"  --xr-background-color-row-odd: var(\\n\",\"    --jp-layout-color2,\\n\",\"    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\\n\",\"  );\\n\",\"}\\n\",\"\\n\",\".xr-wrap {\\n\",\"  display: block !important;\\n\",\"  min-width: 300px;\\n\",\"  max-width: 700px;\\n\",\"}\\n\",\"\\n\",\".xr-text-repr-fallback {\\n\",\"  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\\n\",\"  display: none;\\n\",\"}\\n\",\"\\n\",\".xr-header {\\n\",\"  padding-top: 6px;\\n\",\"  padding-bottom: 6px;\\n\",\"  margin-bottom: 4px;\\n\",\"  border-bottom: solid 1px var(--xr-border-color);\\n\",\"}\\n\",\"\\n\",\".xr-header > div,\\n\",\".xr-header > ul {\\n\",\"  display: inline;\\n\",\"  margin-top: 0;\\n\",\"  margin-bottom: 0;\\n\",\"}\\n\",\"\\n\",\".xr-obj-type,\\n\",\".xr-array-name {\\n\",\"  margin-left: 2px;\\n\",\"  margin-right: 10px;\\n\",\"}\\n\",\"\\n\",\".xr-obj-type {\\n\",\"  color: var(--xr-font-color2);\\n\",\"}\\n\",\"\\n\",\".xr-sections {\\n\",\"  padding-left: 0 !important;\\n\",\"  display: grid;\\n\",\"  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\\n\",\"}\\n\",\"\\n\",\".xr-section-item {\\n\",\"  display: contents;\\n\",\"}\\n\",\"\\n\",\".xr-section-item input {\\n\",\"  display: inline-block;\\n\",\"  opacity: 0;\\n\",\"  height: 0;\\n\",\"}\\n\",\"\\n\",\".xr-section-item input + label {\\n\",\"  color: var(--xr-disabled-color);\\n\",\"  border: 2px solid transparent !important;\\n\",\"}\\n\",\"\\n\",\".xr-section-item input:enabled + label {\\n\",\"  cursor: pointer;\\n\",\"  color: var(--xr-font-color2);\\n\",\"}\\n\",\"\\n\",\".xr-section-item input:focus + label {\\n\",\"  border: 2px solid var(--xr-font-color0) !important;\\n\",\"}\\n\",\"\\n\",\".xr-section-item input:enabled + label:hover {\\n\",\"  color: var(--xr-font-color0);\\n\",\"}\\n\",\"\\n\",\".xr-section-summary {\\n\",\"  grid-column: 1;\\n\",\"  color: var(--xr-font-color2);\\n\",\"  font-weight: 500;\\n\",\"}\\n\",\"\\n\",\".xr-section-summary > span {\\n\",\"  display: inline-block;\\n\",\"  padding-left: 0.5em;\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in:disabled + label {\\n\",\"  color: var(--xr-font-color2);\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in + label:before {\\n\",\"  display: inline-block;\\n\",\"  content: \\\"►\\\";\\n\",\"  font-size: 11px;\\n\",\"  width: 15px;\\n\",\"  text-align: center;\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in:disabled + label:before {\\n\",\"  color: var(--xr-disabled-color);\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in:checked + label:before {\\n\",\"  content: \\\"▼\\\";\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in:checked + label > span {\\n\",\"  display: none;\\n\",\"}\\n\",\"\\n\",\".xr-section-summary,\\n\",\".xr-section-inline-details {\\n\",\"  padding-top: 4px;\\n\",\"  padding-bottom: 4px;\\n\",\"}\\n\",\"\\n\",\".xr-section-inline-details {\\n\",\"  grid-column: 2 / -1;\\n\",\"}\\n\",\"\\n\",\".xr-section-details {\\n\",\"  display: none;\\n\",\"  grid-column: 1 / -1;\\n\",\"  margin-bottom: 5px;\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in:checked ~ .xr-section-details {\\n\",\"  display: contents;\\n\",\"}\\n\",\"\\n\",\".xr-array-wrap {\\n\",\"  grid-column: 1 / -1;\\n\",\"  display: grid;\\n\",\"  grid-template-columns: 20px auto;\\n\",\"}\\n\",\"\\n\",\".xr-array-wrap > label {\\n\",\"  grid-column: 1;\\n\",\"  vertical-align: top;\\n\",\"}\\n\",\"\\n\",\".xr-preview {\\n\",\"  color: var(--xr-font-color3);\\n\",\"}\\n\",\"\\n\",\".xr-array-preview,\\n\",\".xr-array-data {\\n\",\"  padding: 0 5px !important;\\n\",\"  grid-column: 2;\\n\",\"}\\n\",\"\\n\",\".xr-array-data,\\n\",\".xr-array-in:checked ~ .xr-array-preview {\\n\",\"  display: none;\\n\",\"}\\n\",\"\\n\",\".xr-array-in:checked ~ .xr-array-data,\\n\",\".xr-array-preview {\\n\",\"  display: inline-block;\\n\",\"}\\n\",\"\\n\",\".xr-dim-list {\\n\",\"  display: inline-block !important;\\n\",\"  list-style: none;\\n\",\"  padding: 0 !important;\\n\",\"  margin: 0;\\n\",\"}\\n\",\"\\n\",\".xr-dim-list li {\\n\",\"  display: inline-block;\\n\",\"  padding: 0;\\n\",\"  margin: 0;\\n\",\"}\\n\",\"\\n\",\".xr-dim-list:before {\\n\",\"  content: \\\"(\\\";\\n\",\"}\\n\",\"\\n\",\".xr-dim-list:after {\\n\",\"  content: \\\")\\\";\\n\",\"}\\n\",\"\\n\",\".xr-dim-list li:not(:last-child):after {\\n\",\"  content: \\\",\\\";\\n\",\"  padding-right: 5px;\\n\",\"}\\n\",\"\\n\",\".xr-has-index {\\n\",\"  font-weight: bold;\\n\",\"}\\n\",\"\\n\",\".xr-var-list,\\n\",\".xr-var-item {\\n\",\"  display: contents;\\n\",\"}\\n\",\"\\n\",\".xr-var-item > div,\\n\",\".xr-var-item label,\\n\",\".xr-var-item > .xr-var-name span {\\n\",\"  background-color: var(--xr-background-color-row-even);\\n\",\"  border-color: var(--xr-background-color-row-odd);\\n\",\"  margin-bottom: 0;\\n\",\"  padding-top: 2px;\\n\",\"}\\n\",\"\\n\",\".xr-var-item > .xr-var-name:hover span {\\n\",\"  padding-right: 5px;\\n\",\"}\\n\",\"\\n\",\".xr-var-list > li:nth-child(odd) > div,\\n\",\".xr-var-list > li:nth-child(odd) > label,\\n\",\".xr-var-list > li:nth-child(odd) > .xr-var-name span {\\n\",\"  background-color: var(--xr-background-color-row-odd);\\n\",\"  border-color: var(--xr-background-color-row-even);\\n\",\"}\\n\",\"\\n\",\".xr-var-name {\\n\",\"  grid-column: 1;\\n\",\"}\\n\",\"\\n\",\".xr-var-dims {\\n\",\"  grid-column: 2;\\n\",\"}\\n\",\"\\n\",\".xr-var-dtype {\\n\",\"  grid-column: 3;\\n\",\"  text-align: right;\\n\",\"  color: var(--xr-font-color2);\\n\",\"}\\n\",\"\\n\",\".xr-var-preview {\\n\",\"  grid-column: 4;\\n\",\"}\\n\",\"\\n\",\".xr-index-preview {\\n\",\"  grid-column: 2 / 5;\\n\",\"  color: var(--xr-font-color2);\\n\",\"}\\n\",\"\\n\",\".xr-var-name,\\n\",\".xr-var-dims,\\n\",\".xr-var-dtype,\\n\",\".xr-preview,\\n\",\".xr-attrs dt {\\n\",\"  white-space: nowrap;\\n\",\"  overflow: hidden;\\n\",\"  text-overflow: ellipsis;\\n\",\"  padding-right: 10px;\\n\",\"}\\n\",\"\\n\",\".xr-var-name:hover,\\n\",\".xr-var-dims:hover,\\n\",\".xr-var-dtype:hover,\\n\",\".xr-attrs dt:hover {\\n\",\"  overflow: visible;\\n\",\"  width: auto;\\n\",\"  z-index: 1;\\n\",\"}\\n\",\"\\n\",\".xr-var-attrs,\\n\",\".xr-var-data,\\n\",\".xr-index-data {\\n\",\"  display: none;\\n\",\"  border-top: 2px dotted var(--xr-background-color);\\n\",\"  padding-bottom: 20px !important;\\n\",\"  padding-top: 10px !important;\\n\",\"}\\n\",\"\\n\",\".xr-var-attrs-in + label,\\n\",\".xr-var-data-in + label,\\n\",\".xr-index-data-in + label {\\n\",\"  padding: 0 1px;\\n\",\"}\\n\",\"\\n\",\".xr-var-attrs-in:checked ~ .xr-var-attrs,\\n\",\".xr-var-data-in:checked ~ .xr-var-data,\\n\",\".xr-index-data-in:checked ~ .xr-index-data {\\n\",\"  display: block;\\n\",\"}\\n\",\"\\n\",\".xr-var-data > table {\\n\",\"  float: right;\\n\",\"}\\n\",\"\\n\",\".xr-var-data > pre,\\n\",\".xr-index-data > pre,\\n\",\".xr-var-data > table > tbody > tr {\\n\",\"  background-color: transparent !important;\\n\",\"}\\n\",\"\\n\",\".xr-var-name span,\\n\",\".xr-var-data,\\n\",\".xr-index-name div,\\n\",\".xr-index-data,\\n\",\".xr-attrs {\\n\",\"  padding-left: 25px !important;\\n\",\"}\\n\",\"\\n\",\".xr-attrs,\\n\",\".xr-var-attrs,\\n\",\".xr-var-data,\\n\",\".xr-index-data {\\n\",\"  grid-column: 1 / -1;\\n\",\"}\\n\",\"\\n\",\"dl.xr-attrs {\\n\",\"  padding: 0;\\n\",\"  margin: 0;\\n\",\"  display: grid;\\n\",\"  grid-template-columns: 125px auto;\\n\",\"}\\n\",\"\\n\",\".xr-attrs dt,\\n\",\".xr-attrs dd {\\n\",\"  padding: 0;\\n\",\"  margin: 0;\\n\",\"  float: left;\\n\",\"  padding-right: 10px;\\n\",\"  width: auto;\\n\",\"}\\n\",\"\\n\",\".xr-attrs dt {\\n\",\"  font-weight: normal;\\n\",\"  grid-column: 1;\\n\",\"}\\n\",\"\\n\",\".xr-attrs dt:hover span {\\n\",\"  display: inline-block;\\n\",\"  background: var(--xr-background-color);\\n\",\"  padding-right: 10px;\\n\",\"}\\n\",\"\\n\",\".xr-attrs dd {\\n\",\"  grid-column: 2;\\n\",\"  white-space: pre-wrap;\\n\",\"  word-break: break-all;\\n\",\"}\\n\",\"\\n\",\".xr-icon-database,\\n\",\".xr-icon-file-text2,\\n\",\".xr-no-icon {\\n\",\"  display: inline-block;\\n\",\"  vertical-align: middle;\\n\",\"  width: 1em;\\n\",\"  height: 1.5em !important;\\n\",\"  stroke-width: 0;\\n\",\"  stroke: currentColor;\\n\",\"  fill: currentColor;\\n\",\"}\\n\",\"\\n\",\".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\\n\",\".xr-var-data-in:checked + label > .xr-icon-database,\\n\",\".xr-index-data-in:checked + label > .xr-icon-database {\\n\",\"  color: var(--xr-font-color0);\\n\",\"  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\\n\",\"  stroke-width: 0.8px;\\n\",\"}\\n\",\"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (receiver_id: 25, component: 2, time: 80)&gt; Size: 32kB\\n\",\"array([[[ 0.00000000e+00,  2.88986383e-01,  5.48970169e-01, ...,\\n\",\"         -5.48970169e-01, -2.88986383e-01, -9.05140890e-16],\\n\",\"        [ 0.00000000e+00,  1.19702079e-01,  2.27390889e-01, ...,\\n\",\"         -2.27390889e-01, -1.19702079e-01, -3.74921632e-16]],\\n\",\"\\n\",\"       [[-1.92085356e-01,  1.00224799e-01,  3.82476420e-01, ...,\\n\",\"         -6.91471287e-01, -4.65117875e-01, -1.92085356e-01],\\n\",\"        [-7.95643595e-02,  4.15144712e-02,  1.58426920e-01, ...,\\n\",\"         -2.86416785e-01, -1.92658132e-01, -7.95643595e-02]],\\n\",\"\\n\",\"       [[-3.75775660e-01, -9.29170890e-02,  1.99266616e-01, ...,\\n\",\"         -8.03751792e-01, -6.20921484e-01, -3.75775660e-01],\\n\",\"        [-1.55651375e-01, -3.84875184e-02,  8.25389350e-02, ...,\\n\",\"         -3.32924893e-01, -2.57194100e-01, -1.55651375e-01]],\\n\",\"\\n\",\"       ...,\\n\",\"\\n\",\"       [[ 9.18818424e-01,  8.42504934e-01,  6.81637871e-01, ...,\\n\",\"          7.96403887e-01,  9.02919539e-01,  9.18818424e-01],\\n\",\"        [ 3.80587052e-01,  3.48976970e-01,  2.82343651e-01, ...,\\n\",\"          3.29881291e-01,  3.74001519e-01,  3.80587052e-01]],\\n\",\"\\n\",\"       [[ 9.18818424e-01,  9.02919539e-01,  7.96403887e-01, ...,\\n\",\"          6.81637871e-01,  8.42504934e-01,  9.18818424e-01],\\n\",\"        [ 3.80587052e-01,  3.74001519e-01,  3.29881291e-01, ...,\\n\",\"          2.82343651e-01,  3.48976970e-01,  3.80587052e-01]],\\n\",\"\\n\",\"       [[ 8.78661650e-01,  9.23872227e-01,  8.76363232e-01, ...,\\n\",\"          5.37081009e-01,  7.45268820e-01,  8.78661650e-01],\\n\",\"        [ 3.63953572e-01,  3.82680406e-01,  3.63001536e-01, ...,\\n\",\"          2.22466238e-01,  3.08700453e-01,  3.63953572e-01]]])\\n\",\"Coordinates:\\n\",\"  * receiver_id  (receiver_id) &lt;U3 300B &#x27;R00&#x27; &#x27;R01&#x27; &#x27;R02&#x27; ... &#x27;R22&#x27; &#x27;R23&#x27; &#x27;R24&#x27;\\n\",\"  * component    (component) &lt;U1 8B &#x27;X&#x27; &#x27;Y&#x27;\\n\",\"  * time         (time) float64 640B 0.0 0.005063 0.01013 ... 0.3899 0.3949 0.4</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>receiver_id</span>: 25</li><li><span class='xr-has-index'>component</span>: 2</li><li><span class='xr-has-index'>time</span>: 80</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-1ae97add-4622-4d36-ba45-6f76481c89ef' class='xr-array-in' type='checkbox' checked><label for='section-1ae97add-4622-4d36-ba45-6f76481c89ef' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 0.289 0.549 0.7539 0.8831 ... -0.006087 0.1139 0.2225 0.3087 0.364</span></div><div class='xr-array-data'><pre>array([[[ 0.00000000e+00,  2.88986383e-01,  5.48970169e-01, ...,\\n\",\"         -5.48970169e-01, -2.88986383e-01, -9.05140890e-16],\\n\",\"        [ 0.00000000e+00,  1.19702079e-01,  2.27390889e-01, ...,\\n\",\"         -2.27390889e-01, -1.19702079e-01, -3.74921632e-16]],\\n\",\"\\n\",\"       [[-1.92085356e-01,  1.00224799e-01,  3.82476420e-01, ...,\\n\",\"         -6.91471287e-01, -4.65117875e-01, -1.92085356e-01],\\n\",\"        [-7.95643595e-02,  4.15144712e-02,  1.58426920e-01, ...,\\n\",\"         -2.86416785e-01, -1.92658132e-01, -7.95643595e-02]],\\n\",\"\\n\",\"       [[-3.75775660e-01, -9.29170890e-02,  1.99266616e-01, ...,\\n\",\"         -8.03751792e-01, -6.20921484e-01, -3.75775660e-01],\\n\",\"        [-1.55651375e-01, -3.84875184e-02,  8.25389350e-02, ...,\\n\",\"         -3.32924893e-01, -2.57194100e-01, -1.55651375e-01]],\\n\",\"\\n\",\"       ...,\\n\",\"\\n\",\"       [[ 9.18818424e-01,  8.42504934e-01,  6.81637871e-01, ...,\\n\",\"          7.96403887e-01,  9.02919539e-01,  9.18818424e-01],\\n\",\"        [ 3.80587052e-01,  3.48976970e-01,  2.82343651e-01, ...,\\n\",\"          3.29881291e-01,  3.74001519e-01,  3.80587052e-01]],\\n\",\"\\n\",\"       [[ 9.18818424e-01,  9.02919539e-01,  7.96403887e-01, ...,\\n\",\"          6.81637871e-01,  8.42504934e-01,  9.18818424e-01],\\n\",\"        [ 3.80587052e-01,  3.74001519e-01,  3.29881291e-01, ...,\\n\",\"          2.82343651e-01,  3.48976970e-01,  3.80587052e-01]],\\n\",\"\\n\",\"       [[ 8.78661650e-01,  9.23872227e-01,  8.76363232e-01, ...,\\n\",\"          5.37081009e-01,  7.45268820e-01,  8.78661650e-01],\\n\",\"        [ 3.63953572e-01,  3.82680406e-01,  3.63001536e-01, ...,\\n\",\"          2.22466238e-01,  3.08700453e-01,  3.63953572e-01]]])</pre></div></div></li><li class='xr-section-item'><input id='section-2dc75297-13ff-4c6d-83f6-698b71530c57' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2dc75297-13ff-4c6d-83f6-698b71530c57' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>receiver_id</span></div><div class='xr-var-dims'>(receiver_id)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;R00&#x27; &#x27;R01&#x27; &#x27;R02&#x27; ... &#x27;R23&#x27; &#x27;R24&#x27;</div><input id='attrs-0436d751-1c09-4861-bbc3-cffa1fc592f4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0436d751-1c09-4861-bbc3-cffa1fc592f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5427917-da5a-4e4b-9533-5325c5f32dfb' class='xr-var-data-in' type='checkbox'><label for='data-e5427917-da5a-4e4b-9533-5325c5f32dfb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;R00&#x27;, &#x27;R01&#x27;, &#x27;R02&#x27;, &#x27;R03&#x27;, &#x27;R04&#x27;, &#x27;R05&#x27;, &#x27;R06&#x27;, &#x27;R07&#x27;, &#x27;R08&#x27;, &#x27;R09&#x27;,\\n\",\"       &#x27;R10&#x27;, &#x27;R11&#x27;, &#x27;R12&#x27;, &#x27;R13&#x27;, &#x27;R14&#x27;, &#x27;R15&#x27;, &#x27;R16&#x27;, &#x27;R17&#x27;, &#x27;R18&#x27;, &#x27;R19&#x27;,\\n\",\"       &#x27;R20&#x27;, &#x27;R21&#x27;, &#x27;R22&#x27;, &#x27;R23&#x27;, &#x27;R24&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;X&#x27; &#x27;Y&#x27;</div><input id='attrs-a3111cd5-8a4c-4045-b0fc-b8e671d2ac87' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a3111cd5-8a4c-4045-b0fc-b8e671d2ac87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ab4e63b-757d-47b3-900e-6e0f99a08b32' class='xr-var-data-in' type='checkbox'><label for='data-1ab4e63b-757d-47b3-900e-6e0f99a08b32' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;X&#x27;, &#x27;Y&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.005063 0.01013 ... 0.3949 0.4</div><input id='attrs-301cc95d-f049-4f25-8f62-ddde1a2825ba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-301cc95d-f049-4f25-8f62-ddde1a2825ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b43a15c9-9869-4a89-923a-8eed664a7ccf' class='xr-var-data-in' type='checkbox'><label for='data-b43a15c9-9869-4a89-923a-8eed664a7ccf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.      , 0.005063, 0.010127, 0.01519 , 0.020253, 0.025316, 0.03038 ,\\n\",\"       0.035443, 0.040506, 0.04557 , 0.050633, 0.055696, 0.060759, 0.065823,\\n\",\"       0.070886, 0.075949, 0.081013, 0.086076, 0.091139, 0.096203, 0.101266,\\n\",\"       0.106329, 0.111392, 0.116456, 0.121519, 0.126582, 0.131646, 0.136709,\\n\",\"       0.141772, 0.146835, 0.151899, 0.156962, 0.162025, 0.167089, 0.172152,\\n\",\"       0.177215, 0.182278, 0.187342, 0.192405, 0.197468, 0.202532, 0.207595,\\n\",\"       0.212658, 0.217722, 0.222785, 0.227848, 0.232911, 0.237975, 0.243038,\\n\",\"       0.248101, 0.253165, 0.258228, 0.263291, 0.268354, 0.273418, 0.278481,\\n\",\"       0.283544, 0.288608, 0.293671, 0.298734, 0.303797, 0.308861, 0.313924,\\n\",\"       0.318987, 0.324051, 0.329114, 0.334177, 0.339241, 0.344304, 0.349367,\\n\",\"       0.35443 , 0.359494, 0.364557, 0.36962 , 0.374684, 0.379747, 0.38481 ,\\n\",\"       0.389873, 0.394937, 0.4     ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f37a9600-8961-4ae9-b5c9-2de9b3d09240' class='xr-section-summary-in' type='checkbox'  ><label for='section-f37a9600-8961-4ae9-b5c9-2de9b3d09240' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>receiver_id</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-c3297474-acd4-4505-8435-8b850d82d056' class='xr-index-data-in' type='checkbox'/><label for='index-c3297474-acd4-4505-8435-8b850d82d056' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;R00&#x27;, &#x27;R01&#x27;, &#x27;R02&#x27;, &#x27;R03&#x27;, &#x27;R04&#x27;, &#x27;R05&#x27;, &#x27;R06&#x27;, &#x27;R07&#x27;, &#x27;R08&#x27;, &#x27;R09&#x27;,\\n\",\"       &#x27;R10&#x27;, &#x27;R11&#x27;, &#x27;R12&#x27;, &#x27;R13&#x27;, &#x27;R14&#x27;, &#x27;R15&#x27;, &#x27;R16&#x27;, &#x27;R17&#x27;, &#x27;R18&#x27;, &#x27;R19&#x27;,\\n\",\"       &#x27;R20&#x27;, &#x27;R21&#x27;, &#x27;R22&#x27;, &#x27;R23&#x27;, &#x27;R24&#x27;],\\n\",\"      dtype=&#x27;object&#x27;, name=&#x27;receiver_id&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-f22812b9-3cba-4169-97b0-fe34b3551457' class='xr-index-data-in' type='checkbox'/><label for='index-f22812b9-3cba-4169-97b0-fe34b3551457' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;X&#x27;, &#x27;Y&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-39d4cca3-307f-4e9a-b8f7-564de8a52713' class='xr-index-data-in' type='checkbox'/><label for='index-39d4cca3-307f-4e9a-b8f7-564de8a52713' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([                 0.0, 0.005063291139240506, 0.010126582278481013,\\n\",\"        0.01518987341772152, 0.020253164556962026,  0.02531645569620253,\\n\",\"        0.03037974683544304, 0.035443037974683546,  0.04050632911392405,\\n\",\"        0.04556962025316456,  0.05063291139240506,  0.05569620253164557,\\n\",\"        0.06075949367088608,  0.06582278481012659,  0.07088607594936709,\\n\",\"         0.0759493670886076,   0.0810126582278481,  0.08607594936708861,\\n\",\"        0.09113924050632911,  0.09620253164556962,  0.10126582278481013,\\n\",\"        0.10632911392405063,  0.11139240506329114,  0.11645569620253164,\\n\",\"        0.12151898734177216,  0.12658227848101267,  0.13164556962025317,\\n\",\"        0.13670886075949368,  0.14177215189873418,   0.1468354430379747,\\n\",\"         0.1518987341772152,   0.1569620253164557,   0.1620253164556962,\\n\",\"         0.1670886075949367,  0.17215189873417722,  0.17721518987341772,\\n\",\"        0.18227848101265823,  0.18734177215189873,  0.19240506329113924,\\n\",\"        0.19746835443037974,  0.20253164556962025,  0.20759493670886076,\\n\",\"        0.21265822784810126,  0.21772151898734177,  0.22278481012658227,\\n\",\"        0.22784810126582278,  0.23291139240506328,   0.2379746835443038,\\n\",\"        0.24303797468354432,  0.24810126582278483,  0.25316455696202533,\\n\",\"         0.2582278481012658,  0.26329113924050634,   0.2683544303797468,\\n\",\"        0.27341772151898736,  0.27848101265822783,  0.28354430379746837,\\n\",\"        0.28860759493670884,   0.2936708860759494,  0.29873417721518986,\\n\",\"         0.3037974683544304,  0.30886075949367087,   0.3139240506329114,\\n\",\"         0.3189873417721519,   0.3240506329113924,  0.32911392405063294,\\n\",\"         0.3341772151898734,  0.33924050632911396,  0.34430379746835443,\\n\",\"        0.34936708860759497,  0.35443037974683544,    0.359493670886076,\\n\",\"        0.36455696202531646,    0.369620253164557,  0.37468354430379747,\\n\",\"          0.379746835443038,   0.3848101265822785,    0.389873417721519,\\n\",\"         0.3949367088607595,                  0.4],\\n\",\"      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-52e02292-ce8f-4eba-af60-3e3d4a26f396' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-52e02292-ce8f-4eba-af60-3e3d4a26f396' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>\"],\"text/plain\":[\"<xarray.DataArray (receiver_id: 25, component: 2, time: 80)> Size: 32kB\\n\",\"array([[[ 0.00000000e+00,  2.88986383e-01,  5.48970169e-01, ...,\\n\",\"         -5.48970169e-01, -2.88986383e-01, -9.05140890e-16],\\n\",\"        [ 0.00000000e+00,  1.19702079e-01,  2.27390889e-01, ...,\\n\",\"         -2.27390889e-01, -1.19702079e-01, -3.74921632e-16]],\\n\",\"\\n\",\"       [[-1.92085356e-01,  1.00224799e-01,  3.82476420e-01, ...,\\n\",\"         -6.91471287e-01, -4.65117875e-01, -1.92085356e-01],\\n\",\"        [-7.95643595e-02,  4.15144712e-02,  1.58426920e-01, ...,\\n\",\"         -2.86416785e-01, -1.92658132e-01, -7.95643595e-02]],\\n\",\"\\n\",\"       [[-3.75775660e-01, -9.29170890e-02,  1.99266616e-01, ...,\\n\",\"         -8.03751792e-01, -6.20921484e-01, -3.75775660e-01],\\n\",\"        [-1.55651375e-01, -3.84875184e-02,  8.25389350e-02, ...,\\n\",\"         -3.32924893e-01, -2.57194100e-01, -1.55651375e-01]],\\n\",\"\\n\",\"       ...,\\n\",\"\\n\",\"       [[ 9.18818424e-01,  8.42504934e-01,  6.81637871e-01, ...,\\n\",\"          7.96403887e-01,  9.02919539e-01,  9.18818424e-01],\\n\",\"        [ 3.80587052e-01,  3.48976970e-01,  2.82343651e-01, ...,\\n\",\"          3.29881291e-01,  3.74001519e-01,  3.80587052e-01]],\\n\",\"\\n\",\"       [[ 9.18818424e-01,  9.02919539e-01,  7.96403887e-01, ...,\\n\",\"          6.81637871e-01,  8.42504934e-01,  9.18818424e-01],\\n\",\"        [ 3.80587052e-01,  3.74001519e-01,  3.29881291e-01, ...,\\n\",\"          2.82343651e-01,  3.48976970e-01,  3.80587052e-01]],\\n\",\"\\n\",\"       [[ 8.78661650e-01,  9.23872227e-01,  8.76363232e-01, ...,\\n\",\"          5.37081009e-01,  7.45268820e-01,  8.78661650e-01],\\n\",\"        [ 3.63953572e-01,  3.82680406e-01,  3.63001536e-01, ...,\\n\",\"          2.22466238e-01,  3.08700453e-01,  3.63953572e-01]]])\\n\",\"Coordinates:\\n\",\"  * receiver_id  (receiver_id) <U3 300B 'R00' 'R01' 'R02' ... 'R22' 'R23' 'R24'\\n\",\"  * component    (component) <U1 8B 'X' 'Y'\\n\",\"  * time         (time) float64 640B 0.0 0.005063 0.01013 ... 0.3899 0.3949 0.4\"]},\"execution_count\":13,\"metadata\":{},\"output_type\":\"execute_result\"}],\"source\":[\"components = [\\\"X\\\", \\\"Y\\\"]\\n\",\"\\n\",\"# 3D data structure\\n\",\"data_array_multicomponent = xr.DataArray(\\n\",\"    # Data as 3D numpy array\\n\",\"    data=u_multicomponent,\\n\",\"    # Labels for each dimension\\n\",\"    dims=[\\\"receiver_id\\\", \\\"component\\\", \\\"time\\\"],\\n\",\"    # Coordinates for each dimension\\n\",\"    coords={\\n\",\"        \\\"receiver_id\\\": receiver_id,\\n\",\"        \\\"component\\\": components,\\n\",\"        \\\"time\\\": t,\\n\",\"    },\\n\",\")\\n\",\"\\n\",\"data_array_multicomponent\\n\"]},{\"cell_type\":\"markdown\",\"id\":\"e17be8cc\",\"metadata\":{},\"source\":[\"We now convert this 3D array to a 2D array by combining the `\\\"receiver_id\\\"`\\n\",\"and `\\\"component\\\"` dimensions into a multi-indexed `\\\"trace\\\"` dimension. To do\\n\",\"so, we use the `stack()` method. This is exactly how data is represented\\n\",\"internally within Salvus: any dimension that is not `time` is stacked into\\n\",\"`trace`.\"]},{\"cell_type\":\"code\",\"execution_count\":14,\"id\":\"2d85ded3\",\"metadata\":{},\"outputs\":[{\"data\":{\"text/html\":[\"<div><svg style=\\\"position: absolute; width: 0; height: 0; overflow: hidden\\\">\\n\",\"<defs>\\n\",\"<symbol id=\\\"icon-database\\\" viewBox=\\\"0 0 32 32\\\">\\n\",\"<path d=\\\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\\\"></path>\\n\",\"<path d=\\\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\\\"></path>\\n\",\"<path d=\\\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\\\"></path>\\n\",\"</symbol>\\n\",\"<symbol id=\\\"icon-file-text2\\\" viewBox=\\\"0 0 32 32\\\">\\n\",\"<path d=\\\"M28.681 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5))\\n\",\"  );\\n\",\"  --xr-background-color-row-odd: var(\\n\",\"    --jp-layout-color2,\\n\",\"    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\\n\",\"  );\\n\",\"}\\n\",\"\\n\",\"html[theme=\\\"dark\\\"],\\n\",\"html[data-theme=\\\"dark\\\"],\\n\",\"body[data-theme=\\\"dark\\\"],\\n\",\"body.vscode-dark {\\n\",\"  --xr-font-color0: var(\\n\",\"    --jp-content-font-color0,\\n\",\"    var(--pst-color-text-base, rgba(255, 255, 255, 1))\\n\",\"  );\\n\",\"  --xr-font-color2: var(\\n\",\"    --jp-content-font-color2,\\n\",\"    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\\n\",\"  );\\n\",\"  --xr-font-color3: var(\\n\",\"    --jp-content-font-color3,\\n\",\"    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\\n\",\"  );\\n\",\"  --xr-border-color: var(\\n\",\"    --jp-border-color2,\\n\",\"    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\\n\",\"  );\\n\",\"  --xr-disabled-color: var(\\n\",\"    --jp-layout-color3,\\n\",\"    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\\n\",\"  );\\n\",\"  --xr-background-color: var(\\n\",\"    --jp-layout-color0,\\n\",\"    var(--pst-color-on-background, #111111)\\n\",\"  );\\n\",\"  --xr-background-color-row-even: var(\\n\",\"    --jp-layout-color1,\\n\",\"    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\\n\",\"  );\\n\",\"  --xr-background-color-row-odd: var(\\n\",\"    --jp-layout-color2,\\n\",\"    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\\n\",\"  );\\n\",\"}\\n\",\"\\n\",\".xr-wrap {\\n\",\"  display: block !important;\\n\",\"  min-width: 300px;\\n\",\"  max-width: 700px;\\n\",\"}\\n\",\"\\n\",\".xr-text-repr-fallback {\\n\",\"  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\\n\",\"  display: none;\\n\",\"}\\n\",\"\\n\",\".xr-header {\\n\",\"  padding-top: 6px;\\n\",\"  padding-bottom: 6px;\\n\",\"  margin-bottom: 4px;\\n\",\"  border-bottom: solid 1px var(--xr-border-color);\\n\",\"}\\n\",\"\\n\",\".xr-header > div,\\n\",\".xr-header > ul {\\n\",\"  display: inline;\\n\",\"  margin-top: 0;\\n\",\"  margin-bottom: 0;\\n\",\"}\\n\",\"\\n\",\".xr-obj-type,\\n\",\".xr-array-name {\\n\",\"  margin-left: 2px;\\n\",\"  margin-right: 10px;\\n\",\"}\\n\",\"\\n\",\".xr-obj-type {\\n\",\"  color: var(--xr-font-color2);\\n\",\"}\\n\",\"\\n\",\".xr-sections {\\n\",\"  padding-left: 0 !important;\\n\",\"  display: grid;\\n\",\"  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\\n\",\"}\\n\",\"\\n\",\".xr-section-item {\\n\",\"  display: contents;\\n\",\"}\\n\",\"\\n\",\".xr-section-item input {\\n\",\"  display: inline-block;\\n\",\"  opacity: 0;\\n\",\"  height: 0;\\n\",\"}\\n\",\"\\n\",\".xr-section-item input + label {\\n\",\"  color: var(--xr-disabled-color);\\n\",\"  border: 2px solid transparent !important;\\n\",\"}\\n\",\"\\n\",\".xr-section-item input:enabled + label {\\n\",\"  cursor: pointer;\\n\",\"  color: var(--xr-font-color2);\\n\",\"}\\n\",\"\\n\",\".xr-section-item input:focus + label {\\n\",\"  border: 2px solid var(--xr-font-color0) !important;\\n\",\"}\\n\",\"\\n\",\".xr-section-item input:enabled + label:hover {\\n\",\"  color: var(--xr-font-color0);\\n\",\"}\\n\",\"\\n\",\".xr-section-summary {\\n\",\"  grid-column: 1;\\n\",\"  color: var(--xr-font-color2);\\n\",\"  font-weight: 500;\\n\",\"}\\n\",\"\\n\",\".xr-section-summary > span {\\n\",\"  display: inline-block;\\n\",\"  padding-left: 0.5em;\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in:disabled + label {\\n\",\"  color: var(--xr-font-color2);\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in + label:before {\\n\",\"  display: inline-block;\\n\",\"  content: \\\"►\\\";\\n\",\"  font-size: 11px;\\n\",\"  width: 15px;\\n\",\"  text-align: center;\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in:disabled + label:before {\\n\",\"  color: var(--xr-disabled-color);\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in:checked + label:before {\\n\",\"  content: \\\"▼\\\";\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in:checked + label > span {\\n\",\"  display: none;\\n\",\"}\\n\",\"\\n\",\".xr-section-summary,\\n\",\".xr-section-inline-details {\\n\",\"  padding-top: 4px;\\n\",\"  padding-bottom: 4px;\\n\",\"}\\n\",\"\\n\",\".xr-section-inline-details {\\n\",\"  grid-column: 2 / -1;\\n\",\"}\\n\",\"\\n\",\".xr-section-details {\\n\",\"  display: none;\\n\",\"  grid-column: 1 / -1;\\n\",\"  margin-bottom: 5px;\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in:checked ~ .xr-section-details {\\n\",\"  display: contents;\\n\",\"}\\n\",\"\\n\",\".xr-array-wrap {\\n\",\"  grid-column: 1 / -1;\\n\",\"  display: grid;\\n\",\"  grid-template-columns: 20px auto;\\n\",\"}\\n\",\"\\n\",\".xr-array-wrap > label {\\n\",\"  grid-column: 1;\\n\",\"  vertical-align: top;\\n\",\"}\\n\",\"\\n\",\".xr-preview {\\n\",\"  color: var(--xr-font-color3);\\n\",\"}\\n\",\"\\n\",\".xr-array-preview,\\n\",\".xr-array-data {\\n\",\"  padding: 0 5px !important;\\n\",\"  grid-column: 2;\\n\",\"}\\n\",\"\\n\",\".xr-array-data,\\n\",\".xr-array-in:checked ~ .xr-array-preview {\\n\",\"  display: none;\\n\",\"}\\n\",\"\\n\",\".xr-array-in:checked ~ .xr-array-data,\\n\",\".xr-array-preview {\\n\",\"  display: inline-block;\\n\",\"}\\n\",\"\\n\",\".xr-dim-list {\\n\",\"  display: inline-block !important;\\n\",\"  list-style: none;\\n\",\"  padding: 0 !important;\\n\",\"  margin: 0;\\n\",\"}\\n\",\"\\n\",\".xr-dim-list li {\\n\",\"  display: inline-block;\\n\",\"  padding: 0;\\n\",\"  margin: 0;\\n\",\"}\\n\",\"\\n\",\".xr-dim-list:before {\\n\",\"  content: \\\"(\\\";\\n\",\"}\\n\",\"\\n\",\".xr-dim-list:after {\\n\",\"  content: \\\")\\\";\\n\",\"}\\n\",\"\\n\",\".xr-dim-list li:not(:last-child):after {\\n\",\"  content: \\\",\\\";\\n\",\"  padding-right: 5px;\\n\",\"}\\n\",\"\\n\",\".xr-has-index {\\n\",\"  font-weight: bold;\\n\",\"}\\n\",\"\\n\",\".xr-var-list,\\n\",\".xr-var-item {\\n\",\"  display: contents;\\n\",\"}\\n\",\"\\n\",\".xr-var-item > div,\\n\",\".xr-var-item label,\\n\",\".xr-var-item > .xr-var-name span {\\n\",\"  background-color: var(--xr-background-color-row-even);\\n\",\"  border-color: var(--xr-background-color-row-odd);\\n\",\"  margin-bottom: 0;\\n\",\"  padding-top: 2px;\\n\",\"}\\n\",\"\\n\",\".xr-var-item > .xr-var-name:hover span {\\n\",\"  padding-right: 5px;\\n\",\"}\\n\",\"\\n\",\".xr-var-list > li:nth-child(odd) > div,\\n\",\".xr-var-list > li:nth-child(odd) > label,\\n\",\".xr-var-list > li:nth-child(odd) > .xr-var-name span {\\n\",\"  background-color: var(--xr-background-color-row-odd);\\n\",\"  border-color: var(--xr-background-color-row-even);\\n\",\"}\\n\",\"\\n\",\".xr-var-name {\\n\",\"  grid-column: 1;\\n\",\"}\\n\",\"\\n\",\".xr-var-dims {\\n\",\"  grid-column: 2;\\n\",\"}\\n\",\"\\n\",\".xr-var-dtype {\\n\",\"  grid-column: 3;\\n\",\"  text-align: right;\\n\",\"  color: var(--xr-font-color2);\\n\",\"}\\n\",\"\\n\",\".xr-var-preview {\\n\",\"  grid-column: 4;\\n\",\"}\\n\",\"\\n\",\".xr-index-preview {\\n\",\"  grid-column: 2 / 5;\\n\",\"  color: var(--xr-font-color2);\\n\",\"}\\n\",\"\\n\",\".xr-var-name,\\n\",\".xr-var-dims,\\n\",\".xr-var-dtype,\\n\",\".xr-preview,\\n\",\".xr-attrs dt {\\n\",\"  white-space: nowrap;\\n\",\"  overflow: hidden;\\n\",\"  text-overflow: ellipsis;\\n\",\"  padding-right: 10px;\\n\",\"}\\n\",\"\\n\",\".xr-var-name:hover,\\n\",\".xr-var-dims:hover,\\n\",\".xr-var-dtype:hover,\\n\",\".xr-attrs dt:hover {\\n\",\"  overflow: visible;\\n\",\"  width: auto;\\n\",\"  z-index: 1;\\n\",\"}\\n\",\"\\n\",\".xr-var-attrs,\\n\",\".xr-var-data,\\n\",\".xr-index-data {\\n\",\"  display: none;\\n\",\"  border-top: 2px dotted var(--xr-background-color);\\n\",\"  padding-bottom: 20px !important;\\n\",\"  padding-top: 10px !important;\\n\",\"}\\n\",\"\\n\",\".xr-var-attrs-in + label,\\n\",\".xr-var-data-in + label,\\n\",\".xr-index-data-in + label {\\n\",\"  padding: 0 1px;\\n\",\"}\\n\",\"\\n\",\".xr-var-attrs-in:checked ~ .xr-var-attrs,\\n\",\".xr-var-data-in:checked ~ .xr-var-data,\\n\",\".xr-index-data-in:checked ~ .xr-index-data {\\n\",\"  display: block;\\n\",\"}\\n\",\"\\n\",\".xr-var-data > table {\\n\",\"  float: right;\\n\",\"}\\n\",\"\\n\",\".xr-var-data > pre,\\n\",\".xr-index-data > pre,\\n\",\".xr-var-data > table > tbody > tr {\\n\",\"  background-color: transparent !important;\\n\",\"}\\n\",\"\\n\",\".xr-var-name span,\\n\",\".xr-var-data,\\n\",\".xr-index-name div,\\n\",\".xr-index-data,\\n\",\".xr-attrs {\\n\",\"  padding-left: 25px !important;\\n\",\"}\\n\",\"\\n\",\".xr-attrs,\\n\",\".xr-var-attrs,\\n\",\".xr-var-data,\\n\",\".xr-index-data {\\n\",\"  grid-column: 1 / -1;\\n\",\"}\\n\",\"\\n\",\"dl.xr-attrs {\\n\",\"  padding: 0;\\n\",\"  margin: 0;\\n\",\"  display: grid;\\n\",\"  grid-template-columns: 125px auto;\\n\",\"}\\n\",\"\\n\",\".xr-attrs dt,\\n\",\".xr-attrs dd {\\n\",\"  padding: 0;\\n\",\"  margin: 0;\\n\",\"  float: left;\\n\",\"  padding-right: 10px;\\n\",\"  width: auto;\\n\",\"}\\n\",\"\\n\",\".xr-attrs dt {\\n\",\"  font-weight: normal;\\n\",\"  grid-column: 1;\\n\",\"}\\n\",\"\\n\",\".xr-attrs dt:hover span {\\n\",\"  display: inline-block;\\n\",\"  background: var(--xr-background-color);\\n\",\"  padding-right: 10px;\\n\",\"}\\n\",\"\\n\",\".xr-attrs dd {\\n\",\"  grid-column: 2;\\n\",\"  white-space: pre-wrap;\\n\",\"  word-break: break-all;\\n\",\"}\\n\",\"\\n\",\".xr-icon-database,\\n\",\".xr-icon-file-text2,\\n\",\".xr-no-icon {\\n\",\"  display: inline-block;\\n\",\"  vertical-align: middle;\\n\",\"  width: 1em;\\n\",\"  height: 1.5em !important;\\n\",\"  stroke-width: 0;\\n\",\"  stroke: currentColor;\\n\",\"  fill: currentColor;\\n\",\"}\\n\",\"\\n\",\".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\\n\",\".xr-var-data-in:checked + label > .xr-icon-database,\\n\",\".xr-index-data-in:checked + label > .xr-icon-database {\\n\",\"  color: var(--xr-font-color0);\\n\",\"  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\\n\",\"  stroke-width: 0.8px;\\n\",\"}\\n\",\"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (time: 80, trace: 50)&gt; Size: 32kB\\n\",\"array([[ 0.00000000e+00,  0.00000000e+00, -1.92085356e-01, ...,\\n\",\"         3.80587052e-01,  8.78661650e-01,  3.63953572e-01],\\n\",\"       [ 2.88986383e-01,  1.19702079e-01,  1.00224799e-01, ...,\\n\",\"         3.74001519e-01,  9.23872227e-01,  3.82680406e-01],\\n\",\"       [ 5.48970169e-01,  2.27390889e-01,  3.82476420e-01, ...,\\n\",\"         3.29881291e-01,  8.76363232e-01,  3.63001536e-01],\\n\",\"       ...,\\n\",\"       [-5.48970169e-01, -2.27390889e-01, -6.91471287e-01, ...,\\n\",\"         2.82343651e-01,  5.37081009e-01,  2.22466238e-01],\\n\",\"       [-2.88986383e-01, -1.19702079e-01, -4.65117875e-01, ...,\\n\",\"         3.48976970e-01,  7.45268820e-01,  3.08700453e-01],\\n\",\"       [-9.05140890e-16, -3.74921632e-16, -1.92085356e-01, ...,\\n\",\"         3.80587052e-01,  8.78661650e-01,  3.63953572e-01]])\\n\",\"Coordinates:\\n\",\"  * time         (time) float64 640B 0.0 0.005063 0.01013 ... 0.3899 0.3949 0.4\\n\",\"  * trace        (trace) object 400B MultiIndex\\n\",\"  * receiver_id  (trace) &lt;U3 600B &#x27;R00&#x27; &#x27;R00&#x27; &#x27;R01&#x27; &#x27;R01&#x27; ... &#x27;R23&#x27; &#x27;R24&#x27; &#x27;R24&#x27;\\n\",\"  * component    (trace) &lt;U1 200B &#x27;X&#x27; &#x27;Y&#x27; &#x27;X&#x27; &#x27;Y&#x27; &#x27;X&#x27; ... &#x27;Y&#x27; &#x27;X&#x27; &#x27;Y&#x27; &#x27;X&#x27; &#x27;Y&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 80</li><li><span class='xr-has-index'>trace</span>: 50</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9d809bb7-13dc-4088-b148-0631ee6c302d' class='xr-array-in' type='checkbox' checked><label for='section-9d809bb7-13dc-4088-b148-0631ee6c302d' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 0.0 -0.1921 -0.07956 -0.3758 ... 0.3806 0.9188 0.3806 0.8787 0.364</span></div><div class='xr-array-data'><pre>array([[ 0.00000000e+00,  0.00000000e+00, -1.92085356e-01, ...,\\n\",\"         3.80587052e-01,  8.78661650e-01,  3.63953572e-01],\\n\",\"       [ 2.88986383e-01,  1.19702079e-01,  1.00224799e-01, ...,\\n\",\"         3.74001519e-01,  9.23872227e-01,  3.82680406e-01],\\n\",\"       [ 5.48970169e-01,  2.27390889e-01,  3.82476420e-01, ...,\\n\",\"         3.29881291e-01,  8.76363232e-01,  3.63001536e-01],\\n\",\"       ...,\\n\",\"       [-5.48970169e-01, -2.27390889e-01, -6.91471287e-01, ...,\\n\",\"         2.82343651e-01,  5.37081009e-01,  2.22466238e-01],\\n\",\"       [-2.88986383e-01, -1.19702079e-01, -4.65117875e-01, ...,\\n\",\"         3.48976970e-01,  7.45268820e-01,  3.08700453e-01],\\n\",\"       [-9.05140890e-16, -3.74921632e-16, -1.92085356e-01, ...,\\n\",\"         3.80587052e-01,  8.78661650e-01,  3.63953572e-01]])</pre></div></div></li><li class='xr-section-item'><input id='section-3c490fc6-7ce3-4d65-bbc5-9f24544bbb0c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3c490fc6-7ce3-4d65-bbc5-9f24544bbb0c' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.005063 0.01013 ... 0.3949 0.4</div><input id='attrs-e7a86462-bd30-4b1e-affb-5c26620a1a3c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e7a86462-bd30-4b1e-affb-5c26620a1a3c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08b730a1-9308-4e64-b581-2dd601155f7f' class='xr-var-data-in' type='checkbox'><label for='data-08b730a1-9308-4e64-b581-2dd601155f7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.      , 0.005063, 0.010127, 0.01519 , 0.020253, 0.025316, 0.03038 ,\\n\",\"       0.035443, 0.040506, 0.04557 , 0.050633, 0.055696, 0.060759, 0.065823,\\n\",\"       0.070886, 0.075949, 0.081013, 0.086076, 0.091139, 0.096203, 0.101266,\\n\",\"       0.106329, 0.111392, 0.116456, 0.121519, 0.126582, 0.131646, 0.136709,\\n\",\"       0.141772, 0.146835, 0.151899, 0.156962, 0.162025, 0.167089, 0.172152,\\n\",\"       0.177215, 0.182278, 0.187342, 0.192405, 0.197468, 0.202532, 0.207595,\\n\",\"       0.212658, 0.217722, 0.222785, 0.227848, 0.232911, 0.237975, 0.243038,\\n\",\"       0.248101, 0.253165, 0.258228, 0.263291, 0.268354, 0.273418, 0.278481,\\n\",\"       0.283544, 0.288608, 0.293671, 0.298734, 0.303797, 0.308861, 0.313924,\\n\",\"       0.318987, 0.324051, 0.329114, 0.334177, 0.339241, 0.344304, 0.349367,\\n\",\"       0.35443 , 0.359494, 0.364557, 0.36962 , 0.374684, 0.379747, 0.38481 ,\\n\",\"       0.389873, 0.394937, 0.4     ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>trace</span></div><div class='xr-var-dims'>(trace)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>MultiIndex</div><input id='attrs-b0ee200b-ad86-4ed5-b5c5-503bee0f8a0b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b0ee200b-ad86-4ed5-b5c5-503bee0f8a0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b194958-550a-4827-9b1d-a8307be29bcb' class='xr-var-data-in' type='checkbox'><label for='data-5b194958-550a-4827-9b1d-a8307be29bcb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[50 values with dtype=object]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>receiver_id</span></div><div class='xr-var-dims'>(trace)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;R00&#x27; &#x27;R00&#x27; &#x27;R01&#x27; ... &#x27;R24&#x27; &#x27;R24&#x27;</div><input id='attrs-d7fd95b2-e7c9-48e4-ae93-2e89058c691f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d7fd95b2-e7c9-48e4-ae93-2e89058c691f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6fa565e5-a92e-43fa-9283-965600f877f5' class='xr-var-data-in' type='checkbox'><label for='data-6fa565e5-a92e-43fa-9283-965600f877f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[50 values with dtype=&lt;U3]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(trace)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;X&#x27; &#x27;Y&#x27; &#x27;X&#x27; &#x27;Y&#x27; ... &#x27;X&#x27; &#x27;Y&#x27; &#x27;X&#x27; &#x27;Y&#x27;</div><input id='attrs-7132ab35-146e-4865-9699-e8f977d03b14' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7132ab35-146e-4865-9699-e8f977d03b14' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25cdc6e8-7230-47c8-8d71-f6b283730fca' class='xr-var-data-in' type='checkbox'><label for='data-25cdc6e8-7230-47c8-8d71-f6b283730fca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[50 values with dtype=&lt;U1]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-624f2720-b48b-4746-a1a9-2f97e1599e9a' class='xr-section-summary-in' type='checkbox'  ><label for='section-624f2720-b48b-4746-a1a9-2f97e1599e9a' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-d7e45a8e-75b0-42f1-9c0b-1a29810ba156' class='xr-index-data-in' type='checkbox'/><label for='index-d7e45a8e-75b0-42f1-9c0b-1a29810ba156' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([                 0.0, 0.005063291139240506, 0.010126582278481013,\\n\",\"        0.01518987341772152, 0.020253164556962026,  0.02531645569620253,\\n\",\"        0.03037974683544304, 0.035443037974683546,  0.04050632911392405,\\n\",\"        0.04556962025316456,  0.05063291139240506,  0.05569620253164557,\\n\",\"        0.06075949367088608,  0.06582278481012659,  0.07088607594936709,\\n\",\"         0.0759493670886076,   0.0810126582278481,  0.08607594936708861,\\n\",\"        0.09113924050632911,  0.09620253164556962,  0.10126582278481013,\\n\",\"        0.10632911392405063,  0.11139240506329114,  0.11645569620253164,\\n\",\"        0.12151898734177216,  0.12658227848101267,  0.13164556962025317,\\n\",\"        0.13670886075949368,  0.14177215189873418,   0.1468354430379747,\\n\",\"         0.1518987341772152,   0.1569620253164557,   0.1620253164556962,\\n\",\"         0.1670886075949367,  0.17215189873417722,  0.17721518987341772,\\n\",\"        0.18227848101265823,  0.18734177215189873,  0.19240506329113924,\\n\",\"        0.19746835443037974,  0.20253164556962025,  0.20759493670886076,\\n\",\"        0.21265822784810126,  0.21772151898734177,  0.22278481012658227,\\n\",\"        0.22784810126582278,  0.23291139240506328,   0.2379746835443038,\\n\",\"        0.24303797468354432,  0.24810126582278483,  0.25316455696202533,\\n\",\"         0.2582278481012658,  0.26329113924050634,   0.2683544303797468,\\n\",\"        0.27341772151898736,  0.27848101265822783,  0.28354430379746837,\\n\",\"        0.28860759493670884,   0.2936708860759494,  0.29873417721518986,\\n\",\"         0.3037974683544304,  0.30886075949367087,   0.3139240506329114,\\n\",\"         0.3189873417721519,   0.3240506329113924,  0.32911392405063294,\\n\",\"         0.3341772151898734,  0.33924050632911396,  0.34430379746835443,\\n\",\"        0.34936708860759497,  0.35443037974683544,    0.359493670886076,\\n\",\"        0.36455696202531646,    0.369620253164557,  0.37468354430379747,\\n\",\"          0.379746835443038,   0.3848101265822785,    0.389873417721519,\\n\",\"         0.3949367088607595,                  0.4],\\n\",\"      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>trace<br>receiver_id<br>component</div></div><div class='xr-index-preview'>PandasMultiIndex</div><input type='checkbox' disabled/><label></label><input id='index-daebf5b2-25c6-4991-8a6f-8ce0535a2286' class='xr-index-data-in' type='checkbox'/><label for='index-daebf5b2-25c6-4991-8a6f-8ce0535a2286' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(MultiIndex([(&#x27;R00&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;R00&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;R01&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;R01&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;R02&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;R02&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;R03&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;R03&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;R04&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;R04&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;R05&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;R05&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;R06&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;R06&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;R07&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;R07&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;R08&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;R08&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;R09&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;R09&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;R10&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;R10&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;R11&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;R11&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;R12&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;R12&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;R13&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;R13&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;R14&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;R14&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;R15&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;R15&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;R16&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;R16&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;R17&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;R17&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;R18&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;R18&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;R19&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;R19&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;R20&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;R20&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;R21&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;R21&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;R22&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;R22&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;R23&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;R23&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;R24&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;R24&#x27;, &#x27;Y&#x27;)],\\n\",\"           name=&#x27;trace&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7d7244a8-8c7a-4dbd-9bb1-c73f447eb941' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7d7244a8-8c7a-4dbd-9bb1-c73f447eb941' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>\"],\"text/plain\":[\"<xarray.DataArray (time: 80, trace: 50)> Size: 32kB\\n\",\"array([[ 0.00000000e+00,  0.00000000e+00, -1.92085356e-01, ...,\\n\",\"         3.80587052e-01,  8.78661650e-01,  3.63953572e-01],\\n\",\"       [ 2.88986383e-01,  1.19702079e-01,  1.00224799e-01, ...,\\n\",\"         3.74001519e-01,  9.23872227e-01,  3.82680406e-01],\\n\",\"       [ 5.48970169e-01,  2.27390889e-01,  3.82476420e-01, ...,\\n\",\"         3.29881291e-01,  8.76363232e-01,  3.63001536e-01],\\n\",\"       ...,\\n\",\"       [-5.48970169e-01, -2.27390889e-01, -6.91471287e-01, ...,\\n\",\"         2.82343651e-01,  5.37081009e-01,  2.22466238e-01],\\n\",\"       [-2.88986383e-01, -1.19702079e-01, -4.65117875e-01, ...,\\n\",\"         3.48976970e-01,  7.45268820e-01,  3.08700453e-01],\\n\",\"       [-9.05140890e-16, -3.74921632e-16, -1.92085356e-01, ...,\\n\",\"         3.80587052e-01,  8.78661650e-01,  3.63953572e-01]])\\n\",\"Coordinates:\\n\",\"  * time         (time) float64 640B 0.0 0.005063 0.01013 ... 0.3899 0.3949 0.4\\n\",\"  * trace        (trace) object 400B MultiIndex\\n\",\"  * receiver_id  (trace) <U3 600B 'R00' 'R00' 'R01' 'R01' ... 'R23' 'R24' 'R24'\\n\",\"  * component    (trace) <U1 200B 'X' 'Y' 'X' 'Y' 'X' ... 'Y' 'X' 'Y' 'X' 'Y'\"]},\"execution_count\":14,\"metadata\":{},\"output_type\":\"execute_result\"}],\"source\":[\"da_multi_indexed = data_array_multicomponent.stack(\\n\",\"    trace=(\\\"receiver_id\\\", \\\"component\\\")\\n\",\")\\n\",\"da_multi_indexed\"]},{\"cell_type\":\"code\",\"execution_count\":15,\"id\":\"a307849a\",\"metadata\":{},\"outputs\":[{\"name\":\"stdout\",\"output_type\":\"stream\",\"text\":[\"Original shape:\\t(25, 2, 80)\\n\",\"Stacked shape:\\t(80, 50)\\n\"]}],\"source\":[\"# The shape of this new array is now 2D\\n\",\"print(f\\\"Original shape:\\\\t{data_array_multicomponent.shape}\\\")\\n\",\"print(f\\\"Stacked shape:\\\\t{da_multi_indexed.shape}\\\")\"]},{\"cell_type\":\"markdown\",\"id\":\"6186d9d7\",\"metadata\":{},\"source\":[\"We can still select data of one of the original dimensions, like a single\\n\",\"component, the same way:\"]},{\"cell_type\":\"code\",\"execution_count\":16,\"id\":\"6c090511\",\"metadata\":{},\"outputs\":[{\"data\":{\"text/plain\":[\"<matplotlib.collections.QuadMesh at 0x79eee440ffd0>\"]},\"execution_count\":16,\"metadata\":{},\"output_type\":\"execute_result\"},{\"data\":{\"image/png\":\"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\",\"text/plain\":[\"<Figure size 640x480 with 2 Axes>\"]},\"metadata\":{},\"output_type\":\"display_data\"}],\"source\":[\"da_multi_indexed.sel(component=\\\"X\\\").T.plot()\"]},{\"cell_type\":\"markdown\",\"id\":\"3c608866\",\"metadata\":{},\"source\":[\"Or, we can directly make a selection on the new combined `\\\"trace\\\"` dimension\\n\",\"for a specific receiver and component pair like this.\"]},{\"cell_type\":\"code\",\"execution_count\":17,\"id\":\"e1b2d1d7\",\"metadata\":{},\"outputs\":[{\"data\":{\"text/plain\":[\"[<matplotlib.lines.Line2D at 0x79eee432bb50>]\"]},\"execution_count\":17,\"metadata\":{},\"output_type\":\"execute_result\"},{\"data\":{\"image/png\":\"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size 640x480 with 1 Axes>\"]},\"metadata\":{},\"output_type\":\"display_data\"}],\"source\":[\"# Select the Y component of receiver R11.\\n\",\"da_multi_indexed.sel(trace=(\\\"R11\\\", \\\"Y\\\")).plot()\"]},{\"cell_type\":\"markdown\",\"id\":\"19f928ac\",\"metadata\":{},\"source\":[\"This won't work, as it is not the correct order of coordinates.\\n\",\"da_multi_indexed.sel(trace=(\\\"Y\\\", \\\"R11\\\")).plot()\"]},{\"cell_type\":\"markdown\",\"id\":\"49225f1c\",\"metadata\":{},\"source\":[\"To go back to a 3D representation of the data, we can use the `unstack`\\n\",\"method, which gives us our original 3D array.\"]},{\"cell_type\":\"code\",\"execution_count\":18,\"id\":\"79feaaf2\",\"metadata\":{},\"outputs\":[{\"data\":{\"text/html\":[\"<div><svg style=\\\"position: absolute; width: 0; height: 0; overflow: hidden\\\">\\n\",\"<defs>\\n\",\"<symbol id=\\\"icon-database\\\" viewBox=\\\"0 0 32 32\\\">\\n\",\"<path d=\\\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 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--xr-disabled-color: var(\\n\",\"    --jp-layout-color3,\\n\",\"    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\\n\",\"  );\\n\",\"  --xr-background-color: var(\\n\",\"    --jp-layout-color0,\\n\",\"    var(--pst-color-on-background, white)\\n\",\"  );\\n\",\"  --xr-background-color-row-even: var(\\n\",\"    --jp-layout-color1,\\n\",\"    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\\n\",\"  );\\n\",\"  --xr-background-color-row-odd: var(\\n\",\"    --jp-layout-color2,\\n\",\"    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\\n\",\"  );\\n\",\"}\\n\",\"\\n\",\"html[theme=\\\"dark\\\"],\\n\",\"html[data-theme=\\\"dark\\\"],\\n\",\"body[data-theme=\\\"dark\\\"],\\n\",\"body.vscode-dark {\\n\",\"  --xr-font-color0: var(\\n\",\"    --jp-content-font-color0,\\n\",\"    var(--pst-color-text-base, rgba(255, 255, 255, 1))\\n\",\"  );\\n\",\"  --xr-font-color2: var(\\n\",\"    --jp-content-font-color2,\\n\",\"    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\\n\",\"  );\\n\",\"  --xr-font-color3: var(\\n\",\"    --jp-content-font-color3,\\n\",\"    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\\n\",\"  );\\n\",\"  --xr-border-color: var(\\n\",\"    --jp-border-color2,\\n\",\"    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\\n\",\"  );\\n\",\"  --xr-disabled-color: var(\\n\",\"    --jp-layout-color3,\\n\",\"    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\\n\",\"  );\\n\",\"  --xr-background-color: var(\\n\",\"    --jp-layout-color0,\\n\",\"    var(--pst-color-on-background, #111111)\\n\",\"  );\\n\",\"  --xr-background-color-row-even: var(\\n\",\"    --jp-layout-color1,\\n\",\"    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\\n\",\"  );\\n\",\"  --xr-background-color-row-odd: var(\\n\",\"    --jp-layout-color2,\\n\",\"    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\\n\",\"  );\\n\",\"}\\n\",\"\\n\",\".xr-wrap {\\n\",\"  display: block !important;\\n\",\"  min-width: 300px;\\n\",\"  max-width: 700px;\\n\",\"}\\n\",\"\\n\",\".xr-text-repr-fallback {\\n\",\"  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\\n\",\"  display: none;\\n\",\"}\\n\",\"\\n\",\".xr-header {\\n\",\"  padding-top: 6px;\\n\",\"  padding-bottom: 6px;\\n\",\"  margin-bottom: 4px;\\n\",\"  border-bottom: solid 1px var(--xr-border-color);\\n\",\"}\\n\",\"\\n\",\".xr-header > div,\\n\",\".xr-header > ul {\\n\",\"  display: inline;\\n\",\"  margin-top: 0;\\n\",\"  margin-bottom: 0;\\n\",\"}\\n\",\"\\n\",\".xr-obj-type,\\n\",\".xr-array-name {\\n\",\"  margin-left: 2px;\\n\",\"  margin-right: 10px;\\n\",\"}\\n\",\"\\n\",\".xr-obj-type {\\n\",\"  color: var(--xr-font-color2);\\n\",\"}\\n\",\"\\n\",\".xr-sections {\\n\",\"  padding-left: 0 !important;\\n\",\"  display: grid;\\n\",\"  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\\n\",\"}\\n\",\"\\n\",\".xr-section-item {\\n\",\"  display: contents;\\n\",\"}\\n\",\"\\n\",\".xr-section-item input {\\n\",\"  display: inline-block;\\n\",\"  opacity: 0;\\n\",\"  height: 0;\\n\",\"}\\n\",\"\\n\",\".xr-section-item input + label {\\n\",\"  color: var(--xr-disabled-color);\\n\",\"  border: 2px solid transparent !important;\\n\",\"}\\n\",\"\\n\",\".xr-section-item input:enabled + label {\\n\",\"  cursor: pointer;\\n\",\"  color: var(--xr-font-color2);\\n\",\"}\\n\",\"\\n\",\".xr-section-item input:focus + label {\\n\",\"  border: 2px solid var(--xr-font-color0) !important;\\n\",\"}\\n\",\"\\n\",\".xr-section-item input:enabled + label:hover {\\n\",\"  color: var(--xr-font-color0);\\n\",\"}\\n\",\"\\n\",\".xr-section-summary {\\n\",\"  grid-column: 1;\\n\",\"  color: var(--xr-font-color2);\\n\",\"  font-weight: 500;\\n\",\"}\\n\",\"\\n\",\".xr-section-summary > span {\\n\",\"  display: inline-block;\\n\",\"  padding-left: 0.5em;\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in:disabled + label {\\n\",\"  color: var(--xr-font-color2);\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in + label:before {\\n\",\"  display: inline-block;\\n\",\"  content: \\\"►\\\";\\n\",\"  font-size: 11px;\\n\",\"  width: 15px;\\n\",\"  text-align: center;\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in:disabled + label:before {\\n\",\"  color: var(--xr-disabled-color);\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in:checked + label:before {\\n\",\"  content: \\\"▼\\\";\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in:checked + label > span {\\n\",\"  display: none;\\n\",\"}\\n\",\"\\n\",\".xr-section-summary,\\n\",\".xr-section-inline-details {\\n\",\"  padding-top: 4px;\\n\",\"  padding-bottom: 4px;\\n\",\"}\\n\",\"\\n\",\".xr-section-inline-details {\\n\",\"  grid-column: 2 / -1;\\n\",\"}\\n\",\"\\n\",\".xr-section-details {\\n\",\"  display: none;\\n\",\"  grid-column: 1 / -1;\\n\",\"  margin-bottom: 5px;\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in:checked ~ .xr-section-details {\\n\",\"  display: contents;\\n\",\"}\\n\",\"\\n\",\".xr-array-wrap {\\n\",\"  grid-column: 1 / -1;\\n\",\"  display: grid;\\n\",\"  grid-template-columns: 20px auto;\\n\",\"}\\n\",\"\\n\",\".xr-array-wrap > label {\\n\",\"  grid-column: 1;\\n\",\"  vertical-align: top;\\n\",\"}\\n\",\"\\n\",\".xr-preview {\\n\",\"  color: var(--xr-font-color3);\\n\",\"}\\n\",\"\\n\",\".xr-array-preview,\\n\",\".xr-array-data {\\n\",\"  padding: 0 5px !important;\\n\",\"  grid-column: 2;\\n\",\"}\\n\",\"\\n\",\".xr-array-data,\\n\",\".xr-array-in:checked ~ .xr-array-preview {\\n\",\"  display: none;\\n\",\"}\\n\",\"\\n\",\".xr-array-in:checked ~ .xr-array-data,\\n\",\".xr-array-preview {\\n\",\"  display: inline-block;\\n\",\"}\\n\",\"\\n\",\".xr-dim-list {\\n\",\"  display: inline-block !important;\\n\",\"  list-style: none;\\n\",\"  padding: 0 !important;\\n\",\"  margin: 0;\\n\",\"}\\n\",\"\\n\",\".xr-dim-list li {\\n\",\"  display: inline-block;\\n\",\"  padding: 0;\\n\",\"  margin: 0;\\n\",\"}\\n\",\"\\n\",\".xr-dim-list:before {\\n\",\"  content: \\\"(\\\";\\n\",\"}\\n\",\"\\n\",\".xr-dim-list:after {\\n\",\"  content: \\\")\\\";\\n\",\"}\\n\",\"\\n\",\".xr-dim-list li:not(:last-child):after {\\n\",\"  content: \\\",\\\";\\n\",\"  padding-right: 5px;\\n\",\"}\\n\",\"\\n\",\".xr-has-index {\\n\",\"  font-weight: bold;\\n\",\"}\\n\",\"\\n\",\".xr-var-list,\\n\",\".xr-var-item {\\n\",\"  display: contents;\\n\",\"}\\n\",\"\\n\",\".xr-var-item > div,\\n\",\".xr-var-item label,\\n\",\".xr-var-item > .xr-var-name span {\\n\",\"  background-color: var(--xr-background-color-row-even);\\n\",\"  border-color: var(--xr-background-color-row-odd);\\n\",\"  margin-bottom: 0;\\n\",\"  padding-top: 2px;\\n\",\"}\\n\",\"\\n\",\".xr-var-item > .xr-var-name:hover span {\\n\",\"  padding-right: 5px;\\n\",\"}\\n\",\"\\n\",\".xr-var-list > li:nth-child(odd) > div,\\n\",\".xr-var-list > li:nth-child(odd) > label,\\n\",\".xr-var-list > li:nth-child(odd) > .xr-var-name span {\\n\",\"  background-color: var(--xr-background-color-row-odd);\\n\",\"  border-color: var(--xr-background-color-row-even);\\n\",\"}\\n\",\"\\n\",\".xr-var-name {\\n\",\"  grid-column: 1;\\n\",\"}\\n\",\"\\n\",\".xr-var-dims {\\n\",\"  grid-column: 2;\\n\",\"}\\n\",\"\\n\",\".xr-var-dtype {\\n\",\"  grid-column: 3;\\n\",\"  text-align: right;\\n\",\"  color: var(--xr-font-color2);\\n\",\"}\\n\",\"\\n\",\".xr-var-preview {\\n\",\"  grid-column: 4;\\n\",\"}\\n\",\"\\n\",\".xr-index-preview {\\n\",\"  grid-column: 2 / 5;\\n\",\"  color: var(--xr-font-color2);\\n\",\"}\\n\",\"\\n\",\".xr-var-name,\\n\",\".xr-var-dims,\\n\",\".xr-var-dtype,\\n\",\".xr-preview,\\n\",\".xr-attrs dt {\\n\",\"  white-space: nowrap;\\n\",\"  overflow: hidden;\\n\",\"  text-overflow: ellipsis;\\n\",\"  padding-right: 10px;\\n\",\"}\\n\",\"\\n\",\".xr-var-name:hover,\\n\",\".xr-var-dims:hover,\\n\",\".xr-var-dtype:hover,\\n\",\".xr-attrs dt:hover {\\n\",\"  overflow: visible;\\n\",\"  width: auto;\\n\",\"  z-index: 1;\\n\",\"}\\n\",\"\\n\",\".xr-var-attrs,\\n\",\".xr-var-data,\\n\",\".xr-index-data {\\n\",\"  display: none;\\n\",\"  border-top: 2px dotted var(--xr-background-color);\\n\",\"  padding-bottom: 20px !important;\\n\",\"  padding-top: 10px !important;\\n\",\"}\\n\",\"\\n\",\".xr-var-attrs-in + label,\\n\",\".xr-var-data-in + label,\\n\",\".xr-index-data-in + label {\\n\",\"  padding: 0 1px;\\n\",\"}\\n\",\"\\n\",\".xr-var-attrs-in:checked ~ .xr-var-attrs,\\n\",\".xr-var-data-in:checked ~ .xr-var-data,\\n\",\".xr-index-data-in:checked ~ .xr-index-data {\\n\",\"  display: block;\\n\",\"}\\n\",\"\\n\",\".xr-var-data > table {\\n\",\"  float: right;\\n\",\"}\\n\",\"\\n\",\".xr-var-data > pre,\\n\",\".xr-index-data > pre,\\n\",\".xr-var-data > table > tbody > tr {\\n\",\"  background-color: transparent !important;\\n\",\"}\\n\",\"\\n\",\".xr-var-name span,\\n\",\".xr-var-data,\\n\",\".xr-index-name div,\\n\",\".xr-index-data,\\n\",\".xr-attrs {\\n\",\"  padding-left: 25px !important;\\n\",\"}\\n\",\"\\n\",\".xr-attrs,\\n\",\".xr-var-attrs,\\n\",\".xr-var-data,\\n\",\".xr-index-data {\\n\",\"  grid-column: 1 / -1;\\n\",\"}\\n\",\"\\n\",\"dl.xr-attrs {\\n\",\"  padding: 0;\\n\",\"  margin: 0;\\n\",\"  display: grid;\\n\",\"  grid-template-columns: 125px auto;\\n\",\"}\\n\",\"\\n\",\".xr-attrs dt,\\n\",\".xr-attrs dd {\\n\",\"  padding: 0;\\n\",\"  margin: 0;\\n\",\"  float: left;\\n\",\"  padding-right: 10px;\\n\",\"  width: auto;\\n\",\"}\\n\",\"\\n\",\".xr-attrs dt {\\n\",\"  font-weight: normal;\\n\",\"  grid-column: 1;\\n\",\"}\\n\",\"\\n\",\".xr-attrs dt:hover span {\\n\",\"  display: inline-block;\\n\",\"  background: var(--xr-background-color);\\n\",\"  padding-right: 10px;\\n\",\"}\\n\",\"\\n\",\".xr-attrs dd {\\n\",\"  grid-column: 2;\\n\",\"  white-space: pre-wrap;\\n\",\"  word-break: break-all;\\n\",\"}\\n\",\"\\n\",\".xr-icon-database,\\n\",\".xr-icon-file-text2,\\n\",\".xr-no-icon {\\n\",\"  display: inline-block;\\n\",\"  vertical-align: middle;\\n\",\"  width: 1em;\\n\",\"  height: 1.5em !important;\\n\",\"  stroke-width: 0;\\n\",\"  stroke: currentColor;\\n\",\"  fill: currentColor;\\n\",\"}\\n\",\"\\n\",\".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\\n\",\".xr-var-data-in:checked + label > .xr-icon-database,\\n\",\".xr-index-data-in:checked + label > .xr-icon-database {\\n\",\"  color: var(--xr-font-color0);\\n\",\"  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\\n\",\"  stroke-width: 0.8px;\\n\",\"}\\n\",\"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (time: 80, receiver_id: 25, component: 2)&gt; Size: 32kB\\n\",\"array([[[ 0.00000000e+00,  0.00000000e+00],\\n\",\"        [-1.92085356e-01, -7.95643595e-02],\\n\",\"        [-3.75775660e-01, -1.55651375e-01],\\n\",\"        ...,\\n\",\"        [ 9.18818424e-01,  3.80587052e-01],\\n\",\"        [ 9.18818424e-01,  3.80587052e-01],\\n\",\"        [ 8.78661650e-01,  3.63953572e-01]],\\n\",\"\\n\",\"       [[ 2.88986383e-01,  1.19702079e-01],\\n\",\"        [ 1.00224799e-01,  4.15144712e-02],\\n\",\"        [-9.29170890e-02, -3.84875184e-02],\\n\",\"        ...,\\n\",\"        [ 8.42504934e-01,  3.48976970e-01],\\n\",\"        [ 9.02919539e-01,  3.74001519e-01],\\n\",\"        [ 9.23872227e-01,  3.82680406e-01]],\\n\",\"\\n\",\"       [[ 5.48970169e-01,  2.27390889e-01],\\n\",\"        [ 3.82476420e-01,  1.58426920e-01],\\n\",\"        [ 1.99266616e-01,  8.25389350e-02],\\n\",\"        ...,\\n\",\"...\\n\",\"        ...,\\n\",\"        [ 7.96403887e-01,  3.29881291e-01],\\n\",\"        [ 6.81637871e-01,  2.82343651e-01],\\n\",\"        [ 5.37081009e-01,  2.22466238e-01]],\\n\",\"\\n\",\"       [[-2.88986383e-01, -1.19702079e-01],\\n\",\"        [-4.65117875e-01, -1.92658132e-01],\\n\",\"        [-6.20921484e-01, -2.57194100e-01],\\n\",\"        ...,\\n\",\"        [ 9.02919539e-01,  3.74001519e-01],\\n\",\"        [ 8.42504934e-01,  3.48976970e-01],\\n\",\"        [ 7.45268820e-01,  3.08700453e-01]],\\n\",\"\\n\",\"       [[-9.05140890e-16, -3.74921632e-16],\\n\",\"        [-1.92085356e-01, -7.95643595e-02],\\n\",\"        [-3.75775660e-01, -1.55651375e-01],\\n\",\"        ...,\\n\",\"        [ 9.18818424e-01,  3.80587052e-01],\\n\",\"        [ 9.18818424e-01,  3.80587052e-01],\\n\",\"        [ 8.78661650e-01,  3.63953572e-01]]])\\n\",\"Coordinates:\\n\",\"  * receiver_id  (receiver_id) &lt;U3 300B &#x27;R00&#x27; &#x27;R01&#x27; &#x27;R02&#x27; ... &#x27;R22&#x27; &#x27;R23&#x27; &#x27;R24&#x27;\\n\",\"  * component    (component) &lt;U1 8B &#x27;X&#x27; &#x27;Y&#x27;\\n\",\"  * time         (time) float64 640B 0.0 0.005063 0.01013 ... 0.3899 0.3949 0.4</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 80</li><li><span class='xr-has-index'>receiver_id</span>: 25</li><li><span class='xr-has-index'>component</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-0fe2bbe6-5952-43af-b3e7-fecf4485506c' class='xr-array-in' type='checkbox' checked><label for='section-0fe2bbe6-5952-43af-b3e7-fecf4485506c' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 0.0 -0.1921 -0.07956 -0.3758 ... 0.3806 0.9188 0.3806 0.8787 0.364</span></div><div class='xr-array-data'><pre>array([[[ 0.00000000e+00,  0.00000000e+00],\\n\",\"        [-1.92085356e-01, -7.95643595e-02],\\n\",\"        [-3.75775660e-01, -1.55651375e-01],\\n\",\"        ...,\\n\",\"        [ 9.18818424e-01,  3.80587052e-01],\\n\",\"        [ 9.18818424e-01,  3.80587052e-01],\\n\",\"        [ 8.78661650e-01,  3.63953572e-01]],\\n\",\"\\n\",\"       [[ 2.88986383e-01,  1.19702079e-01],\\n\",\"        [ 1.00224799e-01,  4.15144712e-02],\\n\",\"        [-9.29170890e-02, -3.84875184e-02],\\n\",\"        ...,\\n\",\"        [ 8.42504934e-01,  3.48976970e-01],\\n\",\"        [ 9.02919539e-01,  3.74001519e-01],\\n\",\"        [ 9.23872227e-01,  3.82680406e-01]],\\n\",\"\\n\",\"       [[ 5.48970169e-01,  2.27390889e-01],\\n\",\"        [ 3.82476420e-01,  1.58426920e-01],\\n\",\"        [ 1.99266616e-01,  8.25389350e-02],\\n\",\"        ...,\\n\",\"...\\n\",\"        ...,\\n\",\"        [ 7.96403887e-01,  3.29881291e-01],\\n\",\"        [ 6.81637871e-01,  2.82343651e-01],\\n\",\"        [ 5.37081009e-01,  2.22466238e-01]],\\n\",\"\\n\",\"       [[-2.88986383e-01, -1.19702079e-01],\\n\",\"        [-4.65117875e-01, -1.92658132e-01],\\n\",\"        [-6.20921484e-01, -2.57194100e-01],\\n\",\"        ...,\\n\",\"        [ 9.02919539e-01,  3.74001519e-01],\\n\",\"        [ 8.42504934e-01,  3.48976970e-01],\\n\",\"        [ 7.45268820e-01,  3.08700453e-01]],\\n\",\"\\n\",\"       [[-9.05140890e-16, -3.74921632e-16],\\n\",\"        [-1.92085356e-01, -7.95643595e-02],\\n\",\"        [-3.75775660e-01, -1.55651375e-01],\\n\",\"        ...,\\n\",\"        [ 9.18818424e-01,  3.80587052e-01],\\n\",\"        [ 9.18818424e-01,  3.80587052e-01],\\n\",\"        [ 8.78661650e-01,  3.63953572e-01]]])</pre></div></div></li><li class='xr-section-item'><input id='section-ccb19f90-516b-4bdc-adff-d117cb99d85b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ccb19f90-516b-4bdc-adff-d117cb99d85b' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>receiver_id</span></div><div class='xr-var-dims'>(receiver_id)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;R00&#x27; &#x27;R01&#x27; &#x27;R02&#x27; ... &#x27;R23&#x27; &#x27;R24&#x27;</div><input id='attrs-5bdda513-670b-4da2-a9fc-9a1e22c715bd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5bdda513-670b-4da2-a9fc-9a1e22c715bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69962f6a-33a0-4f5e-9c4f-752520c445c3' class='xr-var-data-in' type='checkbox'><label for='data-69962f6a-33a0-4f5e-9c4f-752520c445c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;R00&#x27;, &#x27;R01&#x27;, &#x27;R02&#x27;, &#x27;R03&#x27;, &#x27;R04&#x27;, &#x27;R05&#x27;, &#x27;R06&#x27;, &#x27;R07&#x27;, &#x27;R08&#x27;, &#x27;R09&#x27;,\\n\",\"       &#x27;R10&#x27;, &#x27;R11&#x27;, &#x27;R12&#x27;, &#x27;R13&#x27;, &#x27;R14&#x27;, &#x27;R15&#x27;, &#x27;R16&#x27;, &#x27;R17&#x27;, &#x27;R18&#x27;, &#x27;R19&#x27;,\\n\",\"       &#x27;R20&#x27;, &#x27;R21&#x27;, &#x27;R22&#x27;, &#x27;R23&#x27;, &#x27;R24&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;X&#x27; &#x27;Y&#x27;</div><input id='attrs-588da946-04b8-45e2-97e1-6a6f260b6343' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-588da946-04b8-45e2-97e1-6a6f260b6343' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11edee50-664b-4846-8cd7-d4b8c55f4106' class='xr-var-data-in' type='checkbox'><label for='data-11edee50-664b-4846-8cd7-d4b8c55f4106' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;X&#x27;, &#x27;Y&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.005063 0.01013 ... 0.3949 0.4</div><input id='attrs-894ea918-0ab9-40e6-b5bd-a915ea7d247f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-894ea918-0ab9-40e6-b5bd-a915ea7d247f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7a4b25c-5f68-477d-8aff-125cfab33cc2' class='xr-var-data-in' type='checkbox'><label for='data-a7a4b25c-5f68-477d-8aff-125cfab33cc2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.      , 0.005063, 0.010127, 0.01519 , 0.020253, 0.025316, 0.03038 ,\\n\",\"       0.035443, 0.040506, 0.04557 , 0.050633, 0.055696, 0.060759, 0.065823,\\n\",\"       0.070886, 0.075949, 0.081013, 0.086076, 0.091139, 0.096203, 0.101266,\\n\",\"       0.106329, 0.111392, 0.116456, 0.121519, 0.126582, 0.131646, 0.136709,\\n\",\"       0.141772, 0.146835, 0.151899, 0.156962, 0.162025, 0.167089, 0.172152,\\n\",\"       0.177215, 0.182278, 0.187342, 0.192405, 0.197468, 0.202532, 0.207595,\\n\",\"       0.212658, 0.217722, 0.222785, 0.227848, 0.232911, 0.237975, 0.243038,\\n\",\"       0.248101, 0.253165, 0.258228, 0.263291, 0.268354, 0.273418, 0.278481,\\n\",\"       0.283544, 0.288608, 0.293671, 0.298734, 0.303797, 0.308861, 0.313924,\\n\",\"       0.318987, 0.324051, 0.329114, 0.334177, 0.339241, 0.344304, 0.349367,\\n\",\"       0.35443 , 0.359494, 0.364557, 0.36962 , 0.374684, 0.379747, 0.38481 ,\\n\",\"       0.389873, 0.394937, 0.4     ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-92356bef-ceb6-43cb-abb5-4c030f94d110' class='xr-section-summary-in' type='checkbox'  ><label for='section-92356bef-ceb6-43cb-abb5-4c030f94d110' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-c958ffd4-2494-4a59-9fca-e3ba4a2b7f9b' class='xr-index-data-in' type='checkbox'/><label for='index-c958ffd4-2494-4a59-9fca-e3ba4a2b7f9b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([                 0.0, 0.005063291139240506, 0.010126582278481013,\\n\",\"        0.01518987341772152, 0.020253164556962026,  0.02531645569620253,\\n\",\"        0.03037974683544304, 0.035443037974683546,  0.04050632911392405,\\n\",\"        0.04556962025316456,  0.05063291139240506,  0.05569620253164557,\\n\",\"        0.06075949367088608,  0.06582278481012659,  0.07088607594936709,\\n\",\"         0.0759493670886076,   0.0810126582278481,  0.08607594936708861,\\n\",\"        0.09113924050632911,  0.09620253164556962,  0.10126582278481013,\\n\",\"        0.10632911392405063,  0.11139240506329114,  0.11645569620253164,\\n\",\"        0.12151898734177216,  0.12658227848101267,  0.13164556962025317,\\n\",\"        0.13670886075949368,  0.14177215189873418,   0.1468354430379747,\\n\",\"         0.1518987341772152,   0.1569620253164557,   0.1620253164556962,\\n\",\"         0.1670886075949367,  0.17215189873417722,  0.17721518987341772,\\n\",\"        0.18227848101265823,  0.18734177215189873,  0.19240506329113924,\\n\",\"        0.19746835443037974,  0.20253164556962025,  0.20759493670886076,\\n\",\"        0.21265822784810126,  0.21772151898734177,  0.22278481012658227,\\n\",\"        0.22784810126582278,  0.23291139240506328,   0.2379746835443038,\\n\",\"        0.24303797468354432,  0.24810126582278483,  0.25316455696202533,\\n\",\"         0.2582278481012658,  0.26329113924050634,   0.2683544303797468,\\n\",\"        0.27341772151898736,  0.27848101265822783,  0.28354430379746837,\\n\",\"        0.28860759493670884,   0.2936708860759494,  0.29873417721518986,\\n\",\"         0.3037974683544304,  0.30886075949367087,   0.3139240506329114,\\n\",\"         0.3189873417721519,   0.3240506329113924,  0.32911392405063294,\\n\",\"         0.3341772151898734,  0.33924050632911396,  0.34430379746835443,\\n\",\"        0.34936708860759497,  0.35443037974683544,    0.359493670886076,\\n\",\"        0.36455696202531646,    0.369620253164557,  0.37468354430379747,\\n\",\"          0.379746835443038,   0.3848101265822785,    0.389873417721519,\\n\",\"         0.3949367088607595,                  0.4],\\n\",\"      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>receiver_id</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-143a7c69-001e-4491-9ba8-5d4e95b71b1e' class='xr-index-data-in' type='checkbox'/><label for='index-143a7c69-001e-4491-9ba8-5d4e95b71b1e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;R00&#x27;, &#x27;R01&#x27;, &#x27;R02&#x27;, &#x27;R03&#x27;, &#x27;R04&#x27;, &#x27;R05&#x27;, &#x27;R06&#x27;, &#x27;R07&#x27;, &#x27;R08&#x27;, &#x27;R09&#x27;,\\n\",\"       &#x27;R10&#x27;, &#x27;R11&#x27;, &#x27;R12&#x27;, &#x27;R13&#x27;, &#x27;R14&#x27;, &#x27;R15&#x27;, &#x27;R16&#x27;, &#x27;R17&#x27;, &#x27;R18&#x27;, &#x27;R19&#x27;,\\n\",\"       &#x27;R20&#x27;, &#x27;R21&#x27;, &#x27;R22&#x27;, &#x27;R23&#x27;, &#x27;R24&#x27;],\\n\",\"      dtype=&#x27;object&#x27;, name=&#x27;receiver_id&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-2c00220b-e87c-40ed-9bc6-28e0a5cae267' class='xr-index-data-in' type='checkbox'/><label for='index-2c00220b-e87c-40ed-9bc6-28e0a5cae267' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;X&#x27;, &#x27;Y&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6bc199a7-d6b0-4c15-8cc4-2e4c8bfe1679' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6bc199a7-d6b0-4c15-8cc4-2e4c8bfe1679' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>\"],\"text/plain\":[\"<xarray.DataArray (time: 80, receiver_id: 25, component: 2)> Size: 32kB\\n\",\"array([[[ 0.00000000e+00,  0.00000000e+00],\\n\",\"        [-1.92085356e-01, -7.95643595e-02],\\n\",\"        [-3.75775660e-01, -1.55651375e-01],\\n\",\"        ...,\\n\",\"        [ 9.18818424e-01,  3.80587052e-01],\\n\",\"        [ 9.18818424e-01,  3.80587052e-01],\\n\",\"        [ 8.78661650e-01,  3.63953572e-01]],\\n\",\"\\n\",\"       [[ 2.88986383e-01,  1.19702079e-01],\\n\",\"        [ 1.00224799e-01,  4.15144712e-02],\\n\",\"        [-9.29170890e-02, -3.84875184e-02],\\n\",\"        ...,\\n\",\"        [ 8.42504934e-01,  3.48976970e-01],\\n\",\"        [ 9.02919539e-01,  3.74001519e-01],\\n\",\"        [ 9.23872227e-01,  3.82680406e-01]],\\n\",\"\\n\",\"       [[ 5.48970169e-01,  2.27390889e-01],\\n\",\"        [ 3.82476420e-01,  1.58426920e-01],\\n\",\"        [ 1.99266616e-01,  8.25389350e-02],\\n\",\"        ...,\\n\",\"...\\n\",\"        ...,\\n\",\"        [ 7.96403887e-01,  3.29881291e-01],\\n\",\"        [ 6.81637871e-01,  2.82343651e-01],\\n\",\"        [ 5.37081009e-01,  2.22466238e-01]],\\n\",\"\\n\",\"       [[-2.88986383e-01, -1.19702079e-01],\\n\",\"        [-4.65117875e-01, -1.92658132e-01],\\n\",\"        [-6.20921484e-01, -2.57194100e-01],\\n\",\"        ...,\\n\",\"        [ 9.02919539e-01,  3.74001519e-01],\\n\",\"        [ 8.42504934e-01,  3.48976970e-01],\\n\",\"        [ 7.45268820e-01,  3.08700453e-01]],\\n\",\"\\n\",\"       [[-9.05140890e-16, -3.74921632e-16],\\n\",\"        [-1.92085356e-01, -7.95643595e-02],\\n\",\"        [-3.75775660e-01, -1.55651375e-01],\\n\",\"        ...,\\n\",\"        [ 9.18818424e-01,  3.80587052e-01],\\n\",\"        [ 9.18818424e-01,  3.80587052e-01],\\n\",\"        [ 8.78661650e-01,  3.63953572e-01]]])\\n\",\"Coordinates:\\n\",\"  * receiver_id  (receiver_id) <U3 300B 'R00' 'R01' 'R02' ... 'R22' 'R23' 'R24'\\n\",\"  * component    (component) <U1 8B 'X' 'Y'\\n\",\"  * time         (time) float64 640B 0.0 0.005063 0.01013 ... 0.3899 0.3949 0.4\"]},\"execution_count\":18,\"metadata\":{},\"output_type\":\"execute_result\"}],\"source\":[\"da_3D = da_multi_indexed.unstack()\\n\",\"da_3D\"]},{\"cell_type\":\"markdown\",\"id\":\"5910f3d5\",\"metadata\":{},\"source\":[\"## 2. Retrieval of xarray data structures within Salvus\"]},{\"cell_type\":\"markdown\",\"id\":\"2c5f2a7a\",\"metadata\":{},\"source\":[\"Now that we know some of the basics of xarray data structures, let us have a\\n\",\"closer look at how we can use them within Salvus. In the following example,\\n\",\"we use Salvus to simulate some simple 2D seismic line data as it would be\\n\",\"recorded by a line of geophones at the Earth's surface. The following cell\\n\",\"sets up a simple elastic 2D simulation in a homogeneous domain with the\\n\",\"following elastic properties:\\n\",\"\\n\",\"- P-wave velocity = 3000 m/s\\n\",\"- S-wave velocity = 1500 m/s\\n\",\"- Density = 2000 kg/m$^3$\\n\",\"\\n\",\"We set up a single vertically-directed source and a line of receivers spaced\\n\",\"at 10 meters. Please refer to the tutorial [\\\"My first Salvus\\n\",\"simulation\\\"](https://docs.mondaic.com/examples/tutorials/first_steps/my_first_simulation)\\n\",\"for more details on how to set up a Salvus simulation.\"]},{\"cell_type\":\"code\",\"execution_count\":19,\"id\":\"0e0e72ee\",\"metadata\":{\"lines_to_end_of_cell_marker\":2},\"outputs\":[{\"output_type\":\"display_data\",\"metadata\":{},\"data\":{}},{\"name\":\"stdout\",\"output_type\":\"stream\",\"text\":[\"[2026-05-01 14:39:26,240] \\u001b[34m\\u001b[1mINFO\\u001b[0m: Creating mesh. Hang on.\\n\"]},{\"data\":{\"image/png\":\"iVBORw0KGgoAAAANSUhEUgAAA9IAAAPMCAYAAACuT4E1AAAAAXNSR0IArs4c6QAAAARzQklUCAgI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\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qxBYxzrfvclOtIAAAAAAISBIA0AAAAAQBiY2g0AAAAAUSt09JWTx18N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\\ngg==\\n\"},\"metadata\":{\"needs_background\":\"light\"},\"output_type\":\"display_data\"},{\"data\":{\"text/plain\":[\"<salvus.flow.simple_config.simulation.waveform.Waveform object at 0x79eee43b6c10>\"]},\"execution_count\":19,\"metadata\":{},\"output_type\":\"execute_result\"}],\"source\":[\"# A 2-D Box.\\n\",\"d = sn.domain.dim2.BoxDomain(x0=0.0, x1=350.0, y0=-100.0, y1=0.0)\\n\",\"\\n\",\"# Create a project.\\n\",\"p = sn.Project.from_domain(domain=d, path=\\\"project_2d\\\", load_if_exists=True)\\n\",\"\\n\",\"# Model.\\n\",\"mc = sn.ModelConfiguration(\\n\",\"    background_model=sn.model.background.homogeneous.IsotropicElastic(\\n\",\"        vp=3000.0, vs=1500.0, rho=2000.0\\n\",\"    )\\n\",\")\\n\",\"\\n\",\"# Event specific configuration.\\n\",\"ec = sn.EventConfiguration(\\n\",\"    # 10 Hz Ricker wavelet\\n\",\"    wavelet=sn.simple_config.stf.Ricker(center_frequency=10.0),\\n\",\"    waveform_simulation_configuration=sn.WaveformSimulationConfiguration(\\n\",\"        end_time_in_seconds=0.3\\n\",\"    ),\\n\",\")\\n\",\"\\n\",\"# Combine everything into a complete configuration.\\n\",\"p += sn.SimulationConfiguration(\\n\",\"    name=\\\"simulation\\\",\\n\",\"    elements_per_wavelength=1.2,\\n\",\"    max_frequency_in_hertz=25.0,\\n\",\"    model_configuration=mc,\\n\",\"    event_configuration=ec,\\n\",\"    # Absorbing boundaries everywhere but the top.\\n\",\"    absorbing_boundaries=sn.AbsorbingBoundaryParameters(\\n\",\"        reference_frequency=10.0,\\n\",\"        number_of_wavelengths=0.0,\\n\",\"        reference_velocity=1500.0,\\n\",\"        free_surface=[\\\"y1\\\"],\\n\",\"    ),\\n\",\")\\n\",\"\\n\",\"# Add an event.\\n\",\"p += sn.Event(\\n\",\"    event_name=\\\"event_a\\\",\\n\",\"    # A single source in the center.\\n\",\"    sources=[\\n\",\"        sn.simple_config.source.cartesian.VectorPoint2D(\\n\",\"            x=50.0, y=0.0, fy=-10.0, fx=0.0\\n\",\"        )\\n\",\"    ],\\n\",\"    # Line of 25 receivers spaced @ 10 meters\\n\",\"    receivers=[\\n\",\"        sn.simple_config.receiver.cartesian.Point2D(\\n\",\"            x=50.0 + (i + 1) * 10.0,\\n\",\"            y=0.0,\\n\",\"            station_code=f\\\"AA_{i}\\\",\\n\",\"            fields=[\\\"displacement\\\"],\\n\",\"        )\\n\",\"        for i in range(25)\\n\",\"    ],\\n\",\")\\n\",\"\\n\",\"# Visualize the setup.\\n\",\"p.viz.nb.simulation_setup(\\\"simulation\\\", events=p.events.list())\"]},{\"cell_type\":\"markdown\",\"id\":\"43cabe51\",\"metadata\":{},\"source\":[\"Now we run the simulations.\"]},{\"cell_type\":\"code\",\"execution_count\":20,\"id\":\"5c9cd56c\",\"metadata\":{\"lines_to_next_cell\":2},\"outputs\":[{\"name\":\"stdout\",\"output_type\":\"stream\",\"text\":[\"[2026-05-01 14:39:26,614] \\u001b[34m\\u001b[1mINFO\\u001b[0m: Submitting job ...\\n\",\"Uploading 1 files...\\n\",\"\\n\",\"🚀  Submitted job_2605011439688736_4eb6802495\\u001b[0m@\\u001b[34mlocal\\u001b[0m\\n\"]},{\"data\":{\"image/png\":\"iVBORw0KGgoAAAANSUhEUgAAA9IAAACRCAYAAAAvt4CBAAAAAXNSR0IArs4c6QAAAARzQklUCAgI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\\nREREREScoCAtIiIiIiIi4oT/D73KPHwR4xOwAAAAAElFTkSuQmCC\\n\"},\"metadata\":{\"needs_background\":\"light\"},\"output_type\":\"display_data\"},{\"data\":{\"text/plain\":[\"True\"]},\"execution_count\":20,\"metadata\":{},\"output_type\":\"execute_result\"}],\"source\":[\"# Run it.\\n\",\"p.simulations.launch(\\n\",\"    simulation_configuration=\\\"simulation\\\",\\n\",\"    events=p.events.list(),\\n\",\"    site_name=\\\"local\\\",\\n\",\"    ranks_per_job=1,\\n\",\")\\n\",\"p.simulations.query(block=True)\"]},{\"cell_type\":\"markdown\",\"id\":\"29656fa6\",\"metadata\":{},\"source\":[\"So, how do we now access the data? First, let us revisit how we can retrieve\\n\",\"data of a single receiver as an obspy stream object. To do so, we first\\n\",\"retrieve an `EventData` object from which we can then get the data using the\\n\",\"`get_waveform_data()` method. For example, to retrieve data for the first\\n\",\"receiver with id `XX.AA_1.`, we would do the following.\"]},{\"cell_type\":\"code\",\"execution_count\":21,\"id\":\"271a7457\",\"metadata\":{},\"outputs\":[{\"data\":{\"image/png\":\"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\",\"text/plain\":[\"<Figure size 800x500 with 2 Axes>\"]},\"execution_count\":21,\"metadata\":{},\"output_type\":\"execute_result\"},{\"data\":{\"image/png\":\"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\",\"text/plain\":[\"<Figure size 800x500 with 2 Axes>\"]},\"metadata\":{},\"output_type\":\"display_data\"}],\"source\":[\"# Get the EventData object.\\n\",\"ed = p.waveforms.get(\\\"simulation\\\", \\\"event_a\\\")[0]\\n\",\"# Retrieve an obspy stream object for receiver AA_1\\n\",\"st = ed.get_waveform_data(\\n\",\"    receiver_name=\\\"XX.AA_1.\\\", receiver_field=\\\"displacement\\\"\\n\",\")\\n\",\"st.plot(show=False)\"]},{\"cell_type\":\"markdown\",\"id\":\"e5252f7b\",\"metadata\":{},\"source\":[\"If we want to avoid retrieving the data for each receiver separately by\\n\",\"looping over all receiver names, we can directly access all data of this\\n\",\"event as an `xarray.DataArray` using the function\\n\",\"`get_waveform_data_xarray()` that was introduced with Salvus release\\n\",\"`2025.1.0`.\"]},{\"cell_type\":\"code\",\"execution_count\":22,\"id\":\"96f7aa50\",\"metadata\":{},\"outputs\":[{\"data\":{\"text/html\":[\"<div><svg style=\\\"position: absolute; width: 0; height: 0; overflow: 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transparent !important;\\n\",\"}\\n\",\"\\n\",\".xr-var-name span,\\n\",\".xr-var-data,\\n\",\".xr-index-name div,\\n\",\".xr-index-data,\\n\",\".xr-attrs {\\n\",\"  padding-left: 25px !important;\\n\",\"}\\n\",\"\\n\",\".xr-attrs,\\n\",\".xr-var-attrs,\\n\",\".xr-var-data,\\n\",\".xr-index-data {\\n\",\"  grid-column: 1 / -1;\\n\",\"}\\n\",\"\\n\",\"dl.xr-attrs {\\n\",\"  padding: 0;\\n\",\"  margin: 0;\\n\",\"  display: grid;\\n\",\"  grid-template-columns: 125px auto;\\n\",\"}\\n\",\"\\n\",\".xr-attrs dt,\\n\",\".xr-attrs dd {\\n\",\"  padding: 0;\\n\",\"  margin: 0;\\n\",\"  float: left;\\n\",\"  padding-right: 10px;\\n\",\"  width: auto;\\n\",\"}\\n\",\"\\n\",\".xr-attrs dt {\\n\",\"  font-weight: normal;\\n\",\"  grid-column: 1;\\n\",\"}\\n\",\"\\n\",\".xr-attrs dt:hover span {\\n\",\"  display: inline-block;\\n\",\"  background: var(--xr-background-color);\\n\",\"  padding-right: 10px;\\n\",\"}\\n\",\"\\n\",\".xr-attrs dd {\\n\",\"  grid-column: 2;\\n\",\"  white-space: pre-wrap;\\n\",\"  word-break: break-all;\\n\",\"}\\n\",\"\\n\",\".xr-icon-database,\\n\",\".xr-icon-file-text2,\\n\",\".xr-no-icon {\\n\",\"  display: inline-block;\\n\",\"  vertical-align: middle;\\n\",\"  width: 1em;\\n\",\"  height: 1.5em !important;\\n\",\"  stroke-width: 0;\\n\",\"  stroke: currentColor;\\n\",\"  fill: currentColor;\\n\",\"}\\n\",\"\\n\",\".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\\n\",\".xr-var-data-in:checked + label > .xr-icon-database,\\n\",\".xr-index-data-in:checked + label > .xr-icon-database {\\n\",\"  color: var(--xr-font-color0);\\n\",\"  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\\n\",\"  stroke-width: 0.8px;\\n\",\"}\\n\",\"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;displacement&#x27; (trace: 50, time: 266)&gt; Size: 53kB\\n\",\"array([[ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,\\n\",\"        -4.1478106e-12, -4.4830879e-12, -4.7161940e-12],\\n\",\"       [ 0.0000000e+00,  0.0000000e+00, -2.0031971e-19, ...,\\n\",\"         5.2360204e-12,  5.3063851e-12,  5.2815031e-12],\\n\",\"       [ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,\\n\",\"        -4.4507436e-12, -4.5939767e-12, -4.6323197e-12],\\n\",\"       ...,\\n\",\"       [ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,\\n\",\"        -1.6913061e-11, -1.5586641e-11, -1.4017070e-11],\\n\",\"       [ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,\\n\",\"         5.6616422e-12,  5.1260819e-12,  4.5559602e-12],\\n\",\"       [ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,\\n\",\"        -3.0636119e-11, -2.7980177e-11, -2.5016589e-11]], dtype=float32)\\n\",\"Coordinates:\\n\",\"  * time         (time) float64 2kB -0.1559 -0.1542 -0.1525 ... 0.2983 0.3\\n\",\"  * trace        (trace) object 400B MultiIndex\\n\",\"  * receiver_id  (trace) &lt;U9 2kB &#x27;XX.AA_0.&#x27; &#x27;XX.AA_0.&#x27; ... &#x27;XX.AA_24.&#x27;\\n\",\"  * component    (trace) &lt;U1 200B &#x27;X&#x27; &#x27;Y&#x27; &#x27;X&#x27; &#x27;Y&#x27; &#x27;X&#x27; ... &#x27;Y&#x27; &#x27;X&#x27; &#x27;Y&#x27; &#x27;X&#x27; &#x27;Y&#x27;\\n\",\"Attributes:\\n\",\"    start_time_in_seconds:               -0.15593936024673521\\n\",\"    sampling_rate_in_hertz:              581.217642312331\\n\",\"    start_time_in_seconds_utc_datetime:  1969-12-31T23:59:59.844061Z\\n\",\"    end_time_in_seconds_utc_datetime:    1970-01-01T00:00:00.300000Z\\n\",\"    reference_time_utc:                  1970-01-01T00:00:00.000000Z\\n\",\"    reference_time_in_seconds:           0.0\\n\",\"    npts:                                266\\n\",\"    location:                            [[ 60.   0.]\\\\n [ 70.   0.]\\\\n [ 80.  ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'displacement'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>trace</span>: 50</li><li><span class='xr-has-index'>time</span>: 266</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-23b15b44-25ec-4ec1-b4ca-823da5f1d1fc' class='xr-array-in' type='checkbox' checked><label for='section-23b15b44-25ec-4ec1-b4ca-823da5f1d1fc' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 0.0 0.0 -2.164e-19 ... -3.292e-11 -3.064e-11 -2.798e-11 -2.502e-11</span></div><div class='xr-array-data'><pre>array([[ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,\\n\",\"        -4.1478106e-12, -4.4830879e-12, -4.7161940e-12],\\n\",\"       [ 0.0000000e+00,  0.0000000e+00, -2.0031971e-19, ...,\\n\",\"         5.2360204e-12,  5.3063851e-12,  5.2815031e-12],\\n\",\"       [ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,\\n\",\"        -4.4507436e-12, -4.5939767e-12, -4.6323197e-12],\\n\",\"       ...,\\n\",\"       [ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,\\n\",\"        -1.6913061e-11, -1.5586641e-11, -1.4017070e-11],\\n\",\"       [ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,\\n\",\"         5.6616422e-12,  5.1260819e-12,  4.5559602e-12],\\n\",\"       [ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,\\n\",\"        -3.0636119e-11, -2.7980177e-11, -2.5016589e-11]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-0d6396d7-068c-4db3-9dfc-9962e714d08f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0d6396d7-068c-4db3-9dfc-9962e714d08f' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.1559 -0.1542 ... 0.2983 0.3</div><input id='attrs-c8ea8e28-70f6-4482-99c3-7b2b3acbc486' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c8ea8e28-70f6-4482-99c3-7b2b3acbc486' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e3cc4ab-be8f-4eee-b834-fe0665b471da' class='xr-var-data-in' type='checkbox'><label for='data-1e3cc4ab-be8f-4eee-b834-fe0665b471da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-0.155939, -0.154219, -0.152498, ...,  0.296559,  0.298279,  0.3     ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>trace</span></div><div class='xr-var-dims'>(trace)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>MultiIndex</div><input id='attrs-a0d2f655-ec91-4fc1-bfd5-295b27c44c2c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a0d2f655-ec91-4fc1-bfd5-295b27c44c2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ce02959-dc7a-4218-9f27-8b5c04048523' class='xr-var-data-in' type='checkbox'><label for='data-0ce02959-dc7a-4218-9f27-8b5c04048523' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[50 values with dtype=object]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>receiver_id</span></div><div class='xr-var-dims'>(trace)</div><div class='xr-var-dtype'>&lt;U9</div><div class='xr-var-preview xr-preview'>&#x27;XX.AA_0.&#x27; ... &#x27;XX.AA_24.&#x27;</div><input id='attrs-03a1d57d-d092-4bd9-a0b2-a07e21849176' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-03a1d57d-d092-4bd9-a0b2-a07e21849176' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37277af2-f773-461b-bd70-fc26048c0311' class='xr-var-data-in' type='checkbox'><label for='data-37277af2-f773-461b-bd70-fc26048c0311' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[50 values with dtype=&lt;U9]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(trace)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;X&#x27; &#x27;Y&#x27; &#x27;X&#x27; &#x27;Y&#x27; ... &#x27;X&#x27; &#x27;Y&#x27; &#x27;X&#x27; &#x27;Y&#x27;</div><input id='attrs-3a2cba0d-8523-49c7-9bea-25185f2f2884' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3a2cba0d-8523-49c7-9bea-25185f2f2884' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2fc2d4b0-e5ae-483b-ab92-f97e85e57ac0' class='xr-var-data-in' type='checkbox'><label for='data-2fc2d4b0-e5ae-483b-ab92-f97e85e57ac0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[50 values with dtype=&lt;U1]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d2dc440a-b63e-4ec5-be71-b71ed81288fb' class='xr-section-summary-in' type='checkbox'  ><label for='section-d2dc440a-b63e-4ec5-be71-b71ed81288fb' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-feaaf62f-7da5-4cbb-9207-3f5ed8d49e90' class='xr-index-data-in' type='checkbox'/><label for='index-feaaf62f-7da5-4cbb-9207-3f5ed8d49e90' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-0.15593936024673521, -0.15421883435901168, -0.15249830847128815,\\n\",\"        -0.1507777825835646,  -0.1490572566958411, -0.14733673080811757,\\n\",\"       -0.14561620492039404,  -0.1438956790326705, -0.14217515314494697,\\n\",\"       -0.14045462725722346,\\n\",\"       ...\\n\",\"         0.2845152670104883,   0.2862357928982118,  0.28795631878593536,\\n\",\"         0.2896768446736589,  0.29139737056138243,  0.29311789644910596,\\n\",\"        0.29483842233682944,    0.296558948224553,   0.2982794741122765,\\n\",\"        0.30000000000000004],\\n\",\"      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;, length=266))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>trace<br>receiver_id<br>component</div></div><div class='xr-index-preview'>PandasMultiIndex</div><input type='checkbox' disabled/><label></label><input id='index-f3898871-1f48-432c-a1bf-d9c9a3df3e59' class='xr-index-data-in' type='checkbox'/><label for='index-f3898871-1f48-432c-a1bf-d9c9a3df3e59' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(MultiIndex([( &#x27;XX.AA_0.&#x27;, &#x27;X&#x27;),\\n\",\"            ( &#x27;XX.AA_0.&#x27;, &#x27;Y&#x27;),\\n\",\"            ( &#x27;XX.AA_1.&#x27;, &#x27;X&#x27;),\\n\",\"            ( &#x27;XX.AA_1.&#x27;, &#x27;Y&#x27;),\\n\",\"            ( &#x27;XX.AA_2.&#x27;, &#x27;X&#x27;),\\n\",\"            ( &#x27;XX.AA_2.&#x27;, &#x27;Y&#x27;),\\n\",\"            ( &#x27;XX.AA_3.&#x27;, &#x27;X&#x27;),\\n\",\"            ( &#x27;XX.AA_3.&#x27;, &#x27;Y&#x27;),\\n\",\"            ( &#x27;XX.AA_4.&#x27;, &#x27;X&#x27;),\\n\",\"            ( &#x27;XX.AA_4.&#x27;, &#x27;Y&#x27;),\\n\",\"            ( &#x27;XX.AA_5.&#x27;, &#x27;X&#x27;),\\n\",\"            ( &#x27;XX.AA_5.&#x27;, &#x27;Y&#x27;),\\n\",\"            ( &#x27;XX.AA_6.&#x27;, &#x27;X&#x27;),\\n\",\"            ( &#x27;XX.AA_6.&#x27;, &#x27;Y&#x27;),\\n\",\"            ( &#x27;XX.AA_7.&#x27;, &#x27;X&#x27;),\\n\",\"            ( &#x27;XX.AA_7.&#x27;, &#x27;Y&#x27;),\\n\",\"            ( &#x27;XX.AA_8.&#x27;, &#x27;X&#x27;),\\n\",\"            ( &#x27;XX.AA_8.&#x27;, &#x27;Y&#x27;),\\n\",\"            ( &#x27;XX.AA_9.&#x27;, &#x27;X&#x27;),\\n\",\"            ( &#x27;XX.AA_9.&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;XX.AA_10.&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;XX.AA_10.&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;XX.AA_11.&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;XX.AA_11.&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;XX.AA_12.&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;XX.AA_12.&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;XX.AA_13.&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;XX.AA_13.&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;XX.AA_14.&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;XX.AA_14.&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;XX.AA_15.&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;XX.AA_15.&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;XX.AA_16.&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;XX.AA_16.&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;XX.AA_17.&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;XX.AA_17.&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;XX.AA_18.&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;XX.AA_18.&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;XX.AA_19.&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;XX.AA_19.&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;XX.AA_20.&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;XX.AA_20.&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;XX.AA_21.&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;XX.AA_21.&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;XX.AA_22.&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;XX.AA_22.&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;XX.AA_23.&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;XX.AA_23.&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;XX.AA_24.&#x27;, &#x27;X&#x27;),\\n\",\"            (&#x27;XX.AA_24.&#x27;, &#x27;Y&#x27;)],\\n\",\"           name=&#x27;trace&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f3355306-5382-4b3d-835f-73d5068f613f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f3355306-5382-4b3d-835f-73d5068f613f' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>start_time_in_seconds :</span></dt><dd>-0.15593936024673521</dd><dt><span>sampling_rate_in_hertz :</span></dt><dd>581.217642312331</dd><dt><span>start_time_in_seconds_utc_datetime :</span></dt><dd>1969-12-31T23:59:59.844061Z</dd><dt><span>end_time_in_seconds_utc_datetime :</span></dt><dd>1970-01-01T00:00:00.300000Z</dd><dt><span>reference_time_utc :</span></dt><dd>1970-01-01T00:00:00.000000Z</dd><dt><span>reference_time_in_seconds :</span></dt><dd>0.0</dd><dt><span>npts :</span></dt><dd>266</dd><dt><span>location :</span></dt><dd>[[ 60.   0.]\\n\",\" [ 70.   0.]\\n\",\" [ 80.   0.]\\n\",\" [ 90.   0.]\\n\",\" [100.   0.]\\n\",\" [110.   0.]\\n\",\" [120.   0.]\\n\",\" [130.   0.]\\n\",\" [140.   0.]\\n\",\" [150.   0.]\\n\",\" [160.   0.]\\n\",\" [170.   0.]\\n\",\" [180.   0.]\\n\",\" [190.   0.]\\n\",\" [200.   0.]\\n\",\" [210.   0.]\\n\",\" [220.   0.]\\n\",\" [230.   0.]\\n\",\" [240.   0.]\\n\",\" [250.   0.]\\n\",\" [260.   0.]\\n\",\" [270.   0.]\\n\",\" [280.   0.]\\n\",\" [290.   0.]\\n\",\" [300.   0.]]</dd></dl></div></li></ul></div></div>\"],\"text/plain\":[\"<xarray.DataArray 'displacement' (trace: 50, time: 266)> Size: 53kB\\n\",\"array([[ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,\\n\",\"        -4.1478106e-12, -4.4830879e-12, -4.7161940e-12],\\n\",\"       [ 0.0000000e+00,  0.0000000e+00, -2.0031971e-19, ...,\\n\",\"         5.2360204e-12,  5.3063851e-12,  5.2815031e-12],\\n\",\"       [ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,\\n\",\"        -4.4507436e-12, -4.5939767e-12, -4.6323197e-12],\\n\",\"       ...,\\n\",\"       [ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,\\n\",\"        -1.6913061e-11, -1.5586641e-11, -1.4017070e-11],\\n\",\"       [ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,\\n\",\"         5.6616422e-12,  5.1260819e-12,  4.5559602e-12],\\n\",\"       [ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,\\n\",\"        -3.0636119e-11, -2.7980177e-11, -2.5016589e-11]], dtype=float32)\\n\",\"Coordinates:\\n\",\"  * time         (time) float64 2kB -0.1559 -0.1542 -0.1525 ... 0.2983 0.3\\n\",\"  * trace        (trace) object 400B MultiIndex\\n\",\"  * receiver_id  (trace) <U9 2kB 'XX.AA_0.' 'XX.AA_0.' ... 'XX.AA_24.'\\n\",\"  * component    (trace) <U1 200B 'X' 'Y' 'X' 'Y' 'X' ... 'Y' 'X' 'Y' 'X' 'Y'\\n\",\"Attributes:\\n\",\"    start_time_in_seconds:               -0.15593936024673521\\n\",\"    sampling_rate_in_hertz:              581.217642312331\\n\",\"    start_time_in_seconds_utc_datetime:  1969-12-31T23:59:59.844061Z\\n\",\"    end_time_in_seconds_utc_datetime:    1970-01-01T00:00:00.300000Z\\n\",\"    reference_time_utc:                  1970-01-01T00:00:00.000000Z\\n\",\"    reference_time_in_seconds:           0.0\\n\",\"    npts:                                266\\n\",\"    location:                            [[ 60.   0.]\\\\n [ 70.   0.]\\\\n [ 80.  ...\"]},\"execution_count\":22,\"metadata\":{},\"output_type\":\"execute_result\"}],\"source\":[\"da = ed.get_waveform_data_xarray(receiver_field=\\\"displacement\\\")\\n\",\"da\"]},{\"cell_type\":\"markdown\",\"id\":\"f561f284\",\"metadata\":{},\"source\":[\"Looking at the output, we observe the following structure of the data.\\n\",\"\\n\",\"The data is wrapped in a 2D array with the name corresponding to the field of\\n\",\"the data, in this case `\\\"displacement\\\"`. The array has the following two\\n\",\"dimensions, **always in this order**:\\n\",\"\\n\",\"- `\\\"trace\\\"`: A multi-index dimension that combines the dimensions\\n\",\"  `\\\"receiver_id\\\"` and `\\\"component\\\"`. For details on multi-indices, see\\n\",\"  section 1 of this tutorial.\\n\",\"- `\\\"time`: The time dimension.\\n\",\"\\n\",\"The data contains additional attributes. The most important ones are the\\n\",\"following:\\n\",\"\\n\",\"- `\\\"start_time_in_seconds\\\"`: The time of the first sample.\\n\",\"- `\\\"sampling_rate_in_hertz\\\"`: The sampling rate of the data.\\n\",\"- `\\\"npts\\\"`: The number of time samples.\\n\",\"- `\\\"location\\\"`: The coordinates of each receiver location.\\n\",\"\\n\",\"We can now in turn select the two components `\\\"X\\\"` and `\\\"Y\\\"` and plot them.\"]},{\"cell_type\":\"code\",\"execution_count\":23,\"id\":\"b0cf456b\",\"metadata\":{},\"outputs\":[{\"data\":{\"text/plain\":[\"<matplotlib.collections.QuadMesh at 0x79eee2c1ffd0>\"]},\"execution_count\":23,\"metadata\":{},\"output_type\":\"execute_result\"},{\"data\":{\"image/png\":\"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\",\"text/plain\":[\"<Figure size 640x480 with 2 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\",\"text/plain\":[\"<Figure size 640x480 with 2 Axes>\"]},\"metadata\":{},\"output_type\":\"display_data\"}],\"source\":[\"plt.figure()\\n\",\"# Note that the xarray plotting routines use\\n\",\"# matplotlib under the hood and accept the\\n\",\"# usual input arguments. Here, we make sure\\n\",\"# that both components are plotted with the same\\n\",\"# color range using the vmin and vmax arguments.\\n\",\"da.sel(component=\\\"X\\\").plot(vmin=-6e-10, vmax=6e-10)\\n\",\"plt.figure()\\n\",\"da.sel(component=\\\"Y\\\").plot(vmin=-6e-10, vmax=6e-10)\\n\",\"# Attributes are stored as a dictionary and can be accessed like this (e.g.,\\n\",\"# for the sampling rate):\"]},{\"cell_type\":\"code\",\"execution_count\":24,\"id\":\"f65aa5ab\",\"metadata\":{},\"outputs\":[{\"data\":{\"text/plain\":[\"581.217642312331\"]},\"execution_count\":24,\"metadata\":{},\"output_type\":\"execute_result\"}],\"source\":[\"sr = da.attrs[\\\"sampling_rate_in_hertz\\\"]\\n\",\"sr\"]},{\"cell_type\":\"markdown\",\"id\":\"c527c830\",\"metadata\":{},\"source\":[\"Again, we can use the `unstack()` method to obtain a 3D representation of the\\n\",\"data with dimensions `(\\\"receiver_id\\\", \\\"component\\\", \\\"time\\\")`.\"]},{\"cell_type\":\"code\",\"execution_count\":25,\"id\":\"739c6c29\",\"metadata\":{\"lines_to_next_cell\":2},\"outputs\":[{\"name\":\"stdout\",\"output_type\":\"stream\",\"text\":[\"The unstacked array has a shape of (266, 25, 2), with the dimensions corresponding to ('time', 'receiver_id', 'component').\\n\"]}],\"source\":[\"da_3D = da.unstack()\\n\",\"print(\\n\",\"    f\\\"The unstacked array has a shape of {da_3D.shape}, with the dimensions \\\"\\n\",\"    f\\\"corresponding to {da_3D.dims}.\\\"\\n\",\")\"]},{\"cell_type\":\"markdown\",\"id\":\"7addcdb7\",\"metadata\":{},\"source\":[\"## 3. Custom processing, selection, and misfits using xarrays\"]},{\"cell_type\":\"markdown\",\"id\":\"0a7dea9d\",\"metadata\":{},\"source\":[\"You can use SalvusProject to apply custom processing, data selection, and\\n\",\"misfit functions to you data. To do so, you need to format your custom\\n\",\"functions in a specific way. As of Salvus release `2025.1.0`, you can now\\n\",\"define custom functions that operate on the new `xarray` data structures\\n\",\"directly and add them to a project.   Trace-by-trace based custom functions\\n\",\"operating on `obspy.Stream` objects are still supported as well. However, you\\n\",\"should observe a significant performance improvement when using the `xarray`\\n\",\"interface for custom processing functions, especially when working with data\\n\",\"with a large number of receivers.\\n\",\"\\n\",\"In the following, we briefly describe the format of the new custom functions\\n\",\"and give some examples on how you can use these functions within a Salvus\\n\",\"project.\"]},{\"cell_type\":\"markdown\",\"id\":\"61b6615e\",\"metadata\":{},\"source\":[\"### Data processing / filtering\\n\",\"\\n\",\"First, let us consider the case where we want to apply some filtering to our\\n\",\"data. Here, we consider a simple bandpass filter that filters the data to a\\n\",\"frequency band beteween 10 and 20 Hz. This function will directly operate on\\n\",\"an `xarray.DataArray` object.\\n\",\"\\n\",\"If we want Salvus to recognize our custom filtering function, we need to\\n\",\"specify it in a specific way. The function needs to take exactly two\\n\",\"mandatory input arguments:\\n\",\"\\n\",\"- `data_array`: The Salvus `xarray.DataArray` object on which the filtering\\n\",\"  will be performed.\\n\",\"- `event`: The Salvus `Event` object of the Event that this data corresponds\\n\",\"  to.\\n\"]},{\"cell_type\":\"code\",\"execution_count\":26,\"id\":\"45850f8f\",\"metadata\":{},\"outputs\":[],\"source\":[\"\\n\",\"\\n\",\"def bandpass_filter(\\n\",\"    data_array: xr.DataArray,\\n\",\"    event: sn.Event,\\n\",\") -> xr.DataArray:\\n\",\"    \\\"\\\"\\\"\\n\",\"    A custom bandpass filtering function that operates on an xarray.DataArray\\n\",\"    object.\\n\",\"\\n\",\"    Args:\\n\",\"        data_array: The input data array.\\n\",\"        event: The Salvus event that the data belongs to.\\n\",\"\\n\",\"    Returns:\\n\",\"        data_array: The filtered output data array.\\n\",\"    \\\"\\\"\\\"\\n\",\"\\n\",\"    # Minimum frequency in Hz\\n\",\"    min_frequency = 10.0\\n\",\"    # Maximum frequency in Hz\\n\",\"    max_frequency = 20.0\\n\",\"    # The order of the filter.\\n\",\"    N = 8\\n\",\"    # If true, the resulting filter will be zero phase.\\n\",\"    zerophase = True\\n\",\"\\n\",\"    # Here, we make a copy of the original data\\n\",\"    # to avoid modifying the input data when\\n\",\"    # applying the filter.\\n\",\"    data_array_filtered = data_array.copy()\\n\",\"\\n\",\"    sampling_rate_in_hertz = data_array_filtered.attrs[\\n\",\"        \\\"sampling_rate_in_hertz\\\"\\n\",\"    ]\\n\",\"    nyquist = 0.5 * sampling_rate_in_hertz\\n\",\"    sos = scipy.signal.iirfilter(\\n\",\"        # The order of the filter.\\n\",\"        N=N,\\n\",\"        # The critical frequencies.\\n\",\"        Wn=(min_frequency / nyquist, max_frequency / nyquist),\\n\",\"        btype=\\\"band\\\",\\n\",\"        # Choose a butterworth filter for the linear phase shift.\\n\",\"        ftype=\\\"butter\\\",\\n\",\"        # Directly output the second order section coefficients.\\n\",\"        output=\\\"sos\\\",\\n\",\"    )\\n\",\"    if zerophase:\\n\",\"        data_array_filtered.data[:] = scipy.signal.sosfiltfilt(\\n\",\"            sos, data_array_filtered.data, axis=1\\n\",\"        )\\n\",\"    else:\\n\",\"        data_array_filtered.data[:] = scipy.signal.sosfilt(\\n\",\"            sos, data_array_filtered.data, axis=1\\n\",\"        )\\n\",\"    # Apply the filter to the data\\n\",\"    return data_array_filtered\"]},{\"cell_type\":\"markdown\",\"id\":\"26478920\",\"metadata\":{},\"source\":[\"Let us apply this function to our simulated data to see if this works as\\n\",\"intended.\\n\",\"\\n\",\"<div class=\\\"alert alert-info\\\">\\n\",\"<b>Info</b>: Note that the function, as defined above, creates\\n\",\"a copy of the original data. This way, we avoid that the original\\n\",\"data gets modified in place, which can lead to unexpected results\\n\",\"when running the function multiple times. For large datasets,\\n\",\"creating this copy can consume a significant amount of memory and\\n\",\"the function can become slow.  In such cases, we would advise to\\n\",\"avoid making the copy and recommend directly applying the filter\\n\",\"to the input data!\\n\",\"</div>\\n\"]},{\"cell_type\":\"code\",\"execution_count\":27,\"id\":\"574e3f65\",\"metadata\":{},\"outputs\":[{\"data\":{\"text/plain\":[\"<matplotlib.collections.QuadMesh at 0x79eef6a7eed0>\"]},\"execution_count\":27,\"metadata\":{},\"output_type\":\"execute_result\"},{\"data\":{\"image/png\":\"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\",\"text/plain\":[\"<Figure size 640x480 with 2 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\",\"text/plain\":[\"<Figure size 640x480 with 2 Axes>\"]},\"metadata\":{},\"output_type\":\"display_data\"}],\"source\":[\"# Plot the unfiltered data\\n\",\"plt.figure()\\n\",\"da.sel(component=\\\"X\\\").plot()\\n\",\"\\n\",\"# Plot the filtered data\\n\",\"plt.figure()\\n\",\"da_filt = bandpass_filter(da, ed)\\n\",\"da_filt.sel(component=\\\"X\\\").plot()\"]},{\"cell_type\":\"markdown\",\"id\":\"df57ed50\",\"metadata\":{},\"source\":[\"This looks as expected.\\n\",\"\\n\",\"We can now directly add this function to our project so that Salvus can use\\n\",\"it internally. This is achieved via a `ProcessingConfiguration`.\"]},{\"cell_type\":\"code\",\"execution_count\":28,\"id\":\"0846baf5\",\"metadata\":{\"lines_to_next_cell\":2},\"outputs\":[],\"source\":[\"# Define a processing configuration\\n\",\"pc = sn.processing.ProcessingConfiguration(\\n\",\"    name=\\\"bandpass\\\",\\n\",\"    data_source_name=\\\"SYNTHETIC_DATA:simulation\\\",\\n\",\"    processing_function=bandpass_filter,\\n\",\")\\n\",\"# Add it to the project\\n\",\"p.add_to_project(\\n\",\"    pc,\\n\",\"    overwrite=True,\\n\",\")\"]},{\"cell_type\":\"markdown\",\"id\":\"d98fc2ee\",\"metadata\":{},\"source\":[\"The project is now aware of this function and we can use it, for example, when\\n\",\"defining a new misfit configuration.\\n\",\"\\n\",\"To retrieve filtered data from the project, we can access it in the following\\n\",\"way, adding the function \\\"bandpass\\\" with a `:` to the data that is being\\n\",\"queried:\"]},{\"cell_type\":\"code\",\"execution_count\":29,\"id\":\"29288b4f\",\"metadata\":{\"lines_to_next_cell\":2},\"outputs\":[{\"data\":{\"text/plain\":[\"<matplotlib.collections.QuadMesh at 0x79eef698c9d0>\"]},\"execution_count\":29,\"metadata\":{},\"output_type\":\"execute_result\"},{\"data\":{\"image/png\":\"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\",\"text/plain\":[\"<Figure size 640x480 with 2 Axes>\"]},\"metadata\":{},\"output_type\":\"display_data\"}],\"source\":[\"ed_filt = p.waveforms.get(\\\"PROCESSED_DATA:bandpass\\\", \\\"event_a\\\")[0]\\n\",\"da_filt = ed_filt.get_waveform_data_xarray(receiver_field=\\\"displacement\\\")\\n\",\"da_filt.sel(component=\\\"X\\\").plot()\"]},{\"cell_type\":\"markdown\",\"id\":\"d63e331e\",\"metadata\":{\"lines_to_next_cell\":2},\"source\":[\"### Data selection / windowing\\n\",\"\\n\",\"In a similar fashion, we can define custom data selection and windowing\\n\",\"functions using the `xarray` interface. Here, we want to define a data\\n\",\"selection function that performs a couple of operations on our input data:\\n\",\"\\n\",\"First, the data selection function should only select a subset of the\\n\",\"available receivers. This is important if we want to disregard certain\\n\",\"receivers in an inversion (e.g., receivers that record data that is too\\n\",\"noisy).\\n\",\"\\n\",\"Second, the function should only select a single component. In this case, we\\n\",\"want to select only the vertical component.\\n\",\"\\n\",\"Last, the data selection function should apply a time window to the data.\\n\",\"\\n\",\"Again, our data selection function will need to accept two mandatory input\\n\",\"arguments:\\n\",\"\\n\",\"- `data_array`: The Salvus `xarray.DataArray` object on which the data\\n\",\"  selection will be performed.\\n\",\"- `event`: The Salvus `Event` object of the event that this data corresponds\\n\",\"  to.\\n\",\"\\n\",\"The output of the function should then again be a `DataArray`, with the same\\n\",\"dimensions as the `DataArray` on which the selection was performed. However,\\n\",\"please note that in this case, not all coordinates of the original `DataArray`\\n\",\"need to be present: the omission of e.g. a `receiver_id` or `component`\\n\",\"implicitly sets its weights to zero. The data of the weights array itself\\n\",\"should correspond to the temporal weighting function (window) that will be\\n\",\"applied to the data.\\n\",\"\\n\",\"In the following, we define such a data selection function.\"]},{\"cell_type\":\"code\",\"execution_count\":30,\"id\":\"82302aa9\",\"metadata\":{},\"outputs\":[],\"source\":[\"def select_data(\\n\",\"    data_array: xr.DataArray,\\n\",\"    event: sn.Event,\\n\",\"):\\n\",\"    \\\"\\\"\\\"\\n\",\"    A custom data selection function that operates on an xarray.DataArray\\n\",\"    object.\\n\",\"\\n\",\"    Args:\\n\",\"        data_array: The input data array.\\n\",\"        event: The Salvus event that the data belongs to.\\n\",\"\\n\",\"    Returns:\\n\",\"        weights: A data array with the desired selection applied and data\\n\",\"            corresponding to the temporal weights to be applied to the data.\\n\",\"    \\\"\\\"\\\"\\n\",\"    # Some stations that we wish to keep.\\n\",\"    selected_receiver_ids = [\\\"XX.AA_1.\\\", \\\"XX.AA_2.\\\", \\\"XX.AA_3.\\\"]\\n\",\"    # The component that we wish to keep.\\n\",\"    selected_components = [\\\"Y\\\"]\\n\",\"\\n\",\"    # This is an intersection of the selection (selected stations) and the\\n\",\"    # available (station, component) pairs. Important if not every station\\n\",\"    # has every component.\\n\",\"    selection = [\\n\",\"        (receiver_id, component)\\n\",\"        for receiver_id, component in set(data_array.trace.data)\\n\",\"        if receiver_id in selected_receiver_ids\\n\",\"        and component in selected_components\\n\",\"    ]\\n\",\"    weights = data_array.sel(trace=selection)\\n\",\"\\n\",\"    # Set the weights to 1, if kept like this, no temporal selection on the\\n\",\"    # data is performed\\n\",\"    weights.data[:] = 1\\n\",\"\\n\",\"    # Now we additionally apply a box window on the time dimension,\\n\",\"    # where the first half of the data is disregarded.\\n\",\"    weights.data[:, : weights.data.shape[1] // 2] = 0\\n\",\"\\n\",\"    # With the coordinate \\\"trace_weights\\\", we can optionally give a specific\\n\",\"    # scalar weight to each trace that will be considered in the computation\\n\",\"    # of misfits. You can use it by uncommenting the following line.\\n\",\"\\n\",\"    # weights.coords[\\\"trace_weights\\\"] = (\\\"trace\\\", np.ones((weights.shape[0])))\\n\",\"\\n\",\"    return weights\"]},{\"cell_type\":\"markdown\",\"id\":\"47c7820c\",\"metadata\":{},\"source\":[\"We can now create a custom `DataSelectionConfiguration` and add it to our\\n\",\"project.\"]},{\"cell_type\":\"code\",\"execution_count\":31,\"id\":\"a0b617f0\",\"metadata\":{},\"outputs\":[],\"source\":[\"dsc = sn.DataSelectionConfiguration(\\n\",\"    name=\\\"data_selection\\\",\\n\",\"    receiver_field=\\\"displacement\\\",\\n\",\"    data_selection_function=select_data,\\n\",\")\\n\",\"p.add_to_project(\\n\",\"    dsc,\\n\",\"    overwrite=True,\\n\",\")\"]},{\"cell_type\":\"markdown\",\"id\":\"019019e9\",\"metadata\":{},\"source\":[\"This data selection configuration can now be used when setting up a\\n\",\"`MisfitConfiguration`. To check whether the data selection works as intended,\\n\",\"we retrieve data from the project and specify that we want to use our new\\n\",\"data selection configuration.\"]},{\"cell_type\":\"code\",\"execution_count\":32,\"id\":\"7d6e01f5\",\"metadata\":{\"scrolled\":false},\"outputs\":[{\"data\":{\"text/html\":[\"<div><svg style=\\\"position: absolute; width: 0; height: 0; overflow: hidden\\\">\\n\",\"<defs>\\n\",\"<symbol id=\\\"icon-database\\\" viewBox=\\\"0 0 32 32\\\">\\n\",\"<path d=\\\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\\\"></path>\\n\",\"<path d=\\\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\\\"></path>\\n\",\"<path d=\\\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\\\"></path>\\n\",\"</symbol>\\n\",\"<symbol id=\\\"icon-file-text2\\\" viewBox=\\\"0 0 32 32\\\">\\n\",\"<path d=\\\"M28.681 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{\\n\",\"  --xr-font-color0: var(\\n\",\"    --jp-content-font-color0,\\n\",\"    var(--pst-color-text-base rgba(0, 0, 0, 1))\\n\",\"  );\\n\",\"  --xr-font-color2: var(\\n\",\"    --jp-content-font-color2,\\n\",\"    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\\n\",\"  );\\n\",\"  --xr-font-color3: var(\\n\",\"    --jp-content-font-color3,\\n\",\"    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\\n\",\"  );\\n\",\"  --xr-border-color: var(\\n\",\"    --jp-border-color2,\\n\",\"    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\\n\",\"  );\\n\",\"  --xr-disabled-color: var(\\n\",\"    --jp-layout-color3,\\n\",\"    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\\n\",\"  );\\n\",\"  --xr-background-color: var(\\n\",\"    --jp-layout-color0,\\n\",\"    var(--pst-color-on-background, white)\\n\",\"  );\\n\",\"  --xr-background-color-row-even: var(\\n\",\"    --jp-layout-color1,\\n\",\"    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\\n\",\"  );\\n\",\"  --xr-background-color-row-odd: var(\\n\",\"    --jp-layout-color2,\\n\",\"    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\\n\",\"  );\\n\",\"}\\n\",\"\\n\",\"html[theme=\\\"dark\\\"],\\n\",\"html[data-theme=\\\"dark\\\"],\\n\",\"body[data-theme=\\\"dark\\\"],\\n\",\"body.vscode-dark {\\n\",\"  --xr-font-color0: var(\\n\",\"    --jp-content-font-color0,\\n\",\"    var(--pst-color-text-base, rgba(255, 255, 255, 1))\\n\",\"  );\\n\",\"  --xr-font-color2: var(\\n\",\"    --jp-content-font-color2,\\n\",\"    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\\n\",\"  );\\n\",\"  --xr-font-color3: var(\\n\",\"    --jp-content-font-color3,\\n\",\"    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\\n\",\"  );\\n\",\"  --xr-border-color: var(\\n\",\"    --jp-border-color2,\\n\",\"    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\\n\",\"  );\\n\",\"  --xr-disabled-color: var(\\n\",\"    --jp-layout-color3,\\n\",\"    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\\n\",\"  );\\n\",\"  --xr-background-color: var(\\n\",\"    --jp-layout-color0,\\n\",\"    var(--pst-color-on-background, #111111)\\n\",\"  );\\n\",\"  --xr-background-color-row-even: var(\\n\",\"    --jp-layout-color1,\\n\",\"    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\\n\",\"  );\\n\",\"  --xr-background-color-row-odd: var(\\n\",\"    --jp-layout-color2,\\n\",\"    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\\n\",\"  );\\n\",\"}\\n\",\"\\n\",\".xr-wrap {\\n\",\"  display: block !important;\\n\",\"  min-width: 300px;\\n\",\"  max-width: 700px;\\n\",\"}\\n\",\"\\n\",\".xr-text-repr-fallback {\\n\",\"  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\\n\",\"  display: none;\\n\",\"}\\n\",\"\\n\",\".xr-header {\\n\",\"  padding-top: 6px;\\n\",\"  padding-bottom: 6px;\\n\",\"  margin-bottom: 4px;\\n\",\"  border-bottom: solid 1px var(--xr-border-color);\\n\",\"}\\n\",\"\\n\",\".xr-header > div,\\n\",\".xr-header > ul {\\n\",\"  display: inline;\\n\",\"  margin-top: 0;\\n\",\"  margin-bottom: 0;\\n\",\"}\\n\",\"\\n\",\".xr-obj-type,\\n\",\".xr-array-name {\\n\",\"  margin-left: 2px;\\n\",\"  margin-right: 10px;\\n\",\"}\\n\",\"\\n\",\".xr-obj-type {\\n\",\"  color: var(--xr-font-color2);\\n\",\"}\\n\",\"\\n\",\".xr-sections {\\n\",\"  padding-left: 0 !important;\\n\",\"  display: grid;\\n\",\"  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\\n\",\"}\\n\",\"\\n\",\".xr-section-item {\\n\",\"  display: contents;\\n\",\"}\\n\",\"\\n\",\".xr-section-item input {\\n\",\"  display: inline-block;\\n\",\"  opacity: 0;\\n\",\"  height: 0;\\n\",\"}\\n\",\"\\n\",\".xr-section-item input + label {\\n\",\"  color: var(--xr-disabled-color);\\n\",\"  border: 2px solid transparent !important;\\n\",\"}\\n\",\"\\n\",\".xr-section-item input:enabled + label {\\n\",\"  cursor: pointer;\\n\",\"  color: var(--xr-font-color2);\\n\",\"}\\n\",\"\\n\",\".xr-section-item input:focus + label {\\n\",\"  border: 2px solid var(--xr-font-color0) !important;\\n\",\"}\\n\",\"\\n\",\".xr-section-item input:enabled + label:hover {\\n\",\"  color: var(--xr-font-color0);\\n\",\"}\\n\",\"\\n\",\".xr-section-summary {\\n\",\"  grid-column: 1;\\n\",\"  color: var(--xr-font-color2);\\n\",\"  font-weight: 500;\\n\",\"}\\n\",\"\\n\",\".xr-section-summary > span {\\n\",\"  display: inline-block;\\n\",\"  padding-left: 0.5em;\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in:disabled + label {\\n\",\"  color: var(--xr-font-color2);\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in + label:before {\\n\",\"  display: inline-block;\\n\",\"  content: \\\"►\\\";\\n\",\"  font-size: 11px;\\n\",\"  width: 15px;\\n\",\"  text-align: center;\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in:disabled + label:before {\\n\",\"  color: var(--xr-disabled-color);\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in:checked + label:before {\\n\",\"  content: \\\"▼\\\";\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in:checked + label > span {\\n\",\"  display: none;\\n\",\"}\\n\",\"\\n\",\".xr-section-summary,\\n\",\".xr-section-inline-details {\\n\",\"  padding-top: 4px;\\n\",\"  padding-bottom: 4px;\\n\",\"}\\n\",\"\\n\",\".xr-section-inline-details {\\n\",\"  grid-column: 2 / -1;\\n\",\"}\\n\",\"\\n\",\".xr-section-details {\\n\",\"  display: none;\\n\",\"  grid-column: 1 / -1;\\n\",\"  margin-bottom: 5px;\\n\",\"}\\n\",\"\\n\",\".xr-section-summary-in:checked ~ .xr-section-details {\\n\",\"  display: contents;\\n\",\"}\\n\",\"\\n\",\".xr-array-wrap {\\n\",\"  grid-column: 1 / -1;\\n\",\"  display: grid;\\n\",\"  grid-template-columns: 20px auto;\\n\",\"}\\n\",\"\\n\",\".xr-array-wrap > label {\\n\",\"  grid-column: 1;\\n\",\"  vertical-align: top;\\n\",\"}\\n\",\"\\n\",\".xr-preview {\\n\",\"  color: var(--xr-font-color3);\\n\",\"}\\n\",\"\\n\",\".xr-array-preview,\\n\",\".xr-array-data {\\n\",\"  padding: 0 5px !important;\\n\",\"  grid-column: 2;\\n\",\"}\\n\",\"\\n\",\".xr-array-data,\\n\",\".xr-array-in:checked ~ .xr-array-preview {\\n\",\"  display: none;\\n\",\"}\\n\",\"\\n\",\".xr-array-in:checked ~ .xr-array-data,\\n\",\".xr-array-preview {\\n\",\"  display: inline-block;\\n\",\"}\\n\",\"\\n\",\".xr-dim-list {\\n\",\"  display: inline-block !important;\\n\",\"  list-style: none;\\n\",\"  padding: 0 !important;\\n\",\"  margin: 0;\\n\",\"}\\n\",\"\\n\",\".xr-dim-list li {\\n\",\"  display: inline-block;\\n\",\"  padding: 0;\\n\",\"  margin: 0;\\n\",\"}\\n\",\"\\n\",\".xr-dim-list:before {\\n\",\"  content: \\\"(\\\";\\n\",\"}\\n\",\"\\n\",\".xr-dim-list:after {\\n\",\"  content: \\\")\\\";\\n\",\"}\\n\",\"\\n\",\".xr-dim-list li:not(:last-child):after {\\n\",\"  content: \\\",\\\";\\n\",\"  padding-right: 5px;\\n\",\"}\\n\",\"\\n\",\".xr-has-index {\\n\",\"  font-weight: bold;\\n\",\"}\\n\",\"\\n\",\".xr-var-list,\\n\",\".xr-var-item {\\n\",\"  display: contents;\\n\",\"}\\n\",\"\\n\",\".xr-var-item > div,\\n\",\".xr-var-item label,\\n\",\".xr-var-item > .xr-var-name span {\\n\",\"  background-color: var(--xr-background-color-row-even);\\n\",\"  border-color: var(--xr-background-color-row-odd);\\n\",\"  margin-bottom: 0;\\n\",\"  padding-top: 2px;\\n\",\"}\\n\",\"\\n\",\".xr-var-item > .xr-var-name:hover span {\\n\",\"  padding-right: 5px;\\n\",\"}\\n\",\"\\n\",\".xr-var-list > li:nth-child(odd) > div,\\n\",\".xr-var-list > li:nth-child(odd) > label,\\n\",\".xr-var-list > li:nth-child(odd) > .xr-var-name span {\\n\",\"  background-color: var(--xr-background-color-row-odd);\\n\",\"  border-color: var(--xr-background-color-row-even);\\n\",\"}\\n\",\"\\n\",\".xr-var-name {\\n\",\"  grid-column: 1;\\n\",\"}\\n\",\"\\n\",\".xr-var-dims {\\n\",\"  grid-column: 2;\\n\",\"}\\n\",\"\\n\",\".xr-var-dtype {\\n\",\"  grid-column: 3;\\n\",\"  text-align: right;\\n\",\"  color: var(--xr-font-color2);\\n\",\"}\\n\",\"\\n\",\".xr-var-preview {\\n\",\"  grid-column: 4;\\n\",\"}\\n\",\"\\n\",\".xr-index-preview {\\n\",\"  grid-column: 2 / 5;\\n\",\"  color: var(--xr-font-color2);\\n\",\"}\\n\",\"\\n\",\".xr-var-name,\\n\",\".xr-var-dims,\\n\",\".xr-var-dtype,\\n\",\".xr-preview,\\n\",\".xr-attrs dt {\\n\",\"  white-space: nowrap;\\n\",\"  overflow: hidden;\\n\",\"  text-overflow: ellipsis;\\n\",\"  padding-right: 10px;\\n\",\"}\\n\",\"\\n\",\".xr-var-name:hover,\\n\",\".xr-var-dims:hover,\\n\",\".xr-var-dtype:hover,\\n\",\".xr-attrs dt:hover {\\n\",\"  overflow: visible;\\n\",\"  width: auto;\\n\",\"  z-index: 1;\\n\",\"}\\n\",\"\\n\",\".xr-var-attrs,\\n\",\".xr-var-data,\\n\",\".xr-index-data {\\n\",\"  display: none;\\n\",\"  border-top: 2px dotted var(--xr-background-color);\\n\",\"  padding-bottom: 20px !important;\\n\",\"  padding-top: 10px !important;\\n\",\"}\\n\",\"\\n\",\".xr-var-attrs-in + label,\\n\",\".xr-var-data-in + label,\\n\",\".xr-index-data-in + label {\\n\",\"  padding: 0 1px;\\n\",\"}\\n\",\"\\n\",\".xr-var-attrs-in:checked ~ .xr-var-attrs,\\n\",\".xr-var-data-in:checked ~ .xr-var-data,\\n\",\".xr-index-data-in:checked ~ .xr-index-data {\\n\",\"  display: block;\\n\",\"}\\n\",\"\\n\",\".xr-var-data > table {\\n\",\"  float: right;\\n\",\"}\\n\",\"\\n\",\".xr-var-data > pre,\\n\",\".xr-index-data > pre,\\n\",\".xr-var-data > table > tbody > tr {\\n\",\"  background-color: transparent !important;\\n\",\"}\\n\",\"\\n\",\".xr-var-name span,\\n\",\".xr-var-data,\\n\",\".xr-index-name div,\\n\",\".xr-index-data,\\n\",\".xr-attrs {\\n\",\"  padding-left: 25px !important;\\n\",\"}\\n\",\"\\n\",\".xr-attrs,\\n\",\".xr-var-attrs,\\n\",\".xr-var-data,\\n\",\".xr-index-data {\\n\",\"  grid-column: 1 / -1;\\n\",\"}\\n\",\"\\n\",\"dl.xr-attrs {\\n\",\"  padding: 0;\\n\",\"  margin: 0;\\n\",\"  display: grid;\\n\",\"  grid-template-columns: 125px auto;\\n\",\"}\\n\",\"\\n\",\".xr-attrs dt,\\n\",\".xr-attrs dd {\\n\",\"  padding: 0;\\n\",\"  margin: 0;\\n\",\"  float: left;\\n\",\"  padding-right: 10px;\\n\",\"  width: auto;\\n\",\"}\\n\",\"\\n\",\".xr-attrs dt {\\n\",\"  font-weight: normal;\\n\",\"  grid-column: 1;\\n\",\"}\\n\",\"\\n\",\".xr-attrs dt:hover span {\\n\",\"  display: inline-block;\\n\",\"  background: var(--xr-background-color);\\n\",\"  padding-right: 10px;\\n\",\"}\\n\",\"\\n\",\".xr-attrs dd {\\n\",\"  grid-column: 2;\\n\",\"  white-space: pre-wrap;\\n\",\"  word-break: break-all;\\n\",\"}\\n\",\"\\n\",\".xr-icon-database,\\n\",\".xr-icon-file-text2,\\n\",\".xr-no-icon {\\n\",\"  display: inline-block;\\n\",\"  vertical-align: middle;\\n\",\"  width: 1em;\\n\",\"  height: 1.5em !important;\\n\",\"  stroke-width: 0;\\n\",\"  stroke: currentColor;\\n\",\"  fill: currentColor;\\n\",\"}\\n\",\"\\n\",\".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\\n\",\".xr-var-data-in:checked + label > .xr-icon-database,\\n\",\".xr-index-data-in:checked + label > .xr-icon-database {\\n\",\"  color: var(--xr-font-color0);\\n\",\"  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\\n\",\"  stroke-width: 0.8px;\\n\",\"}\\n\",\"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;displacement&#x27; (trace: 3, time: 266)&gt; Size: 3kB\\n\",\"array([[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\\n\",\"         0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\\n\",\"        -0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\\n\",\"        -0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\\n\",\"        -0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\\n\",\"        -0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\\n\",\"        -0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\\n\",\"        -0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\\n\",\"        -0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\\n\",\"        -0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\\n\",\"         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\\n\",\"         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\\n\",\"         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\\n\",\"         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\\n\",\"         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\\n\",\"         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\\n\",\"         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\\n\",\"         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\\n\",\"         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\\n\",\"         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\\n\",\"...\\n\",\"        -2.90627470e-11, -2.62216759e-11, -2.28313202e-11,\\n\",\"        -1.90024281e-11, -1.48555196e-11, -1.05167706e-11,\\n\",\"        -6.11381345e-12, -1.77160803e-12,  2.39147027e-12,\\n\",\"         6.26719206e-12,  9.76056735e-12,  1.27923731e-11,\\n\",\"         1.53010521e-11,  1.72439216e-11,  1.85977067e-11,\\n\",\"         1.93583587e-11,  1.95402444e-11,  1.91747347e-11,\\n\",\"         1.83082664e-11,  1.70000004e-11,  1.53191470e-11,\\n\",\"         1.33420905e-11,  1.11494321e-11,  8.82304473e-12,\\n\",\"         6.44326042e-12,  4.08628860e-12,  1.82192998e-12,\\n\",\"        -2.88329094e-13, -2.19280518e-12, -3.85065721e-12,\\n\",\"        -5.23246550e-12, -6.32036177e-12, -7.10773064e-12,\\n\",\"        -7.59853031e-12, -7.80627472e-12, -7.75275157e-12,\\n\",\"        -7.46653260e-12, -6.98135820e-12, -6.33446334e-12,\\n\",\"        -5.56491441e-12, -4.71202287e-12, -3.81388757e-12,\\n\",\"        -2.90611784e-12, -2.02076702e-12, -1.18550601e-12,\\n\",\"        -4.23044575e-13,  2.49196741e-13,  8.19172638e-13,\\n\",\"         1.28007793e-12,  1.63001654e-12,  1.87150772e-12,\\n\",\"         2.01086630e-12,  2.05749545e-12,  2.02313123e-12,\\n\",\"         1.92107636e-12,  1.76545714e-12,  1.57053331e-12,\\n\",\"         1.35008703e-12,  1.11690637e-12]], dtype=float32)\\n\",\"Coordinates:\\n\",\"  * time         (time) float64 2kB -0.1559 -0.1542 -0.1525 ... 0.2983 0.3\\n\",\"  * trace        (trace) object 24B MultiIndex\\n\",\"  * receiver_id  (trace) &lt;U9 108B &#x27;XX.AA_3.&#x27; &#x27;XX.AA_1.&#x27; &#x27;XX.AA_2.&#x27;\\n\",\"  * component    (trace) &lt;U1 12B &#x27;Y&#x27; &#x27;Y&#x27; &#x27;Y&#x27;\\n\",\"Attributes:\\n\",\"    start_time_in_seconds:               -0.15593936024673521\\n\",\"    sampling_rate_in_hertz:              581.217642312331\\n\",\"    start_time_in_seconds_utc_datetime:  1969-12-31T23:59:59.844061Z\\n\",\"    end_time_in_seconds_utc_datetime:    1970-01-01T00:00:00.300000Z\\n\",\"    reference_time_utc:                  1970-01-01T00:00:00.000000Z\\n\",\"    reference_time_in_seconds:           0.0\\n\",\"    npts:                                266\\n\",\"    location:                            [[ 60.   0.]\\\\n [ 70.   0.]\\\\n [ 80.  ...\\n\",\"    temporal_weights:                    &lt;xarray.DataArray &#x27;displacement&#x27; (tr...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'displacement'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>trace</span>: 3</li><li><span class='xr-has-index'>time</span>: 266</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-12d344e8-cfe1-4769-8a82-9b7d7a205485' class='xr-array-in' type='checkbox' checked><label for='section-12d344e8-cfe1-4769-8a82-9b7d7a205485' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 0.0 0.0 0.0 -0.0 ... 1.765e-12 1.571e-12 1.35e-12 1.117e-12</span></div><div class='xr-array-data'><pre>array([[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\\n\",\"         0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\\n\",\"        -0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\\n\",\"        -0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\\n\",\"        -0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\\n\",\"        -0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\\n\",\"        -0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\\n\",\"        -0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\\n\",\"        -0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\\n\",\"        -0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\\n\",\"         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\\n\",\"         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\\n\",\"         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\\n\",\"         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\\n\",\"         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\\n\",\"         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\\n\",\"         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\\n\",\"         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\\n\",\"         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\\n\",\"         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\\n\",\"...\\n\",\"        -2.90627470e-11, -2.62216759e-11, -2.28313202e-11,\\n\",\"        -1.90024281e-11, -1.48555196e-11, -1.05167706e-11,\\n\",\"        -6.11381345e-12, -1.77160803e-12,  2.39147027e-12,\\n\",\"         6.26719206e-12,  9.76056735e-12,  1.27923731e-11,\\n\",\"         1.53010521e-11,  1.72439216e-11,  1.85977067e-11,\\n\",\"         1.93583587e-11,  1.95402444e-11,  1.91747347e-11,\\n\",\"         1.83082664e-11,  1.70000004e-11,  1.53191470e-11,\\n\",\"         1.33420905e-11,  1.11494321e-11,  8.82304473e-12,\\n\",\"         6.44326042e-12,  4.08628860e-12,  1.82192998e-12,\\n\",\"        -2.88329094e-13, -2.19280518e-12, -3.85065721e-12,\\n\",\"        -5.23246550e-12, -6.32036177e-12, -7.10773064e-12,\\n\",\"        -7.59853031e-12, -7.80627472e-12, -7.75275157e-12,\\n\",\"        -7.46653260e-12, -6.98135820e-12, -6.33446334e-12,\\n\",\"        -5.56491441e-12, -4.71202287e-12, -3.81388757e-12,\\n\",\"        -2.90611784e-12, -2.02076702e-12, -1.18550601e-12,\\n\",\"        -4.23044575e-13,  2.49196741e-13,  8.19172638e-13,\\n\",\"         1.28007793e-12,  1.63001654e-12,  1.87150772e-12,\\n\",\"         2.01086630e-12,  2.05749545e-12,  2.02313123e-12,\\n\",\"         1.92107636e-12,  1.76545714e-12,  1.57053331e-12,\\n\",\"         1.35008703e-12,  1.11690637e-12]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-45b4bbdd-f683-46fc-9746-da399e4f206e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-45b4bbdd-f683-46fc-9746-da399e4f206e' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.1559 -0.1542 ... 0.2983 0.3</div><input id='attrs-99265ac2-1cb2-4cf0-8e51-d6462605a09a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-99265ac2-1cb2-4cf0-8e51-d6462605a09a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4de6ef46-b3c8-4892-9891-fdd447a8b52d' class='xr-var-data-in' type='checkbox'><label for='data-4de6ef46-b3c8-4892-9891-fdd447a8b52d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-0.155939, -0.154219, -0.152498, ...,  0.296559,  0.298279,  0.3     ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>trace</span></div><div class='xr-var-dims'>(trace)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>MultiIndex</div><input id='attrs-99468940-2c7b-4fc0-a4d6-dc917b4337b4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-99468940-2c7b-4fc0-a4d6-dc917b4337b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72821bab-6930-4a1d-ada1-b5cb88a6df57' class='xr-var-data-in' type='checkbox'><label for='data-72821bab-6930-4a1d-ada1-b5cb88a6df57' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[3 values with dtype=object]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>receiver_id</span></div><div class='xr-var-dims'>(trace)</div><div class='xr-var-dtype'>&lt;U9</div><div class='xr-var-preview xr-preview'>&#x27;XX.AA_3.&#x27; &#x27;XX.AA_1.&#x27; &#x27;XX.AA_2.&#x27;</div><input id='attrs-66582005-4a61-4781-98a2-263ff3d166a9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-66582005-4a61-4781-98a2-263ff3d166a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9a0fe64-337c-474b-9669-22c507aa07c5' class='xr-var-data-in' type='checkbox'><label for='data-c9a0fe64-337c-474b-9669-22c507aa07c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[3 values with dtype=&lt;U9]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(trace)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;Y&#x27; &#x27;Y&#x27; &#x27;Y&#x27;</div><input id='attrs-7fc9a09a-1796-4b66-aac3-1b2847fa372a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7fc9a09a-1796-4b66-aac3-1b2847fa372a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8dc59486-53d2-4265-8ba1-d276f37fb876' class='xr-var-data-in' type='checkbox'><label for='data-8dc59486-53d2-4265-8ba1-d276f37fb876' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[3 values with dtype=&lt;U1]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2e63781e-f7b5-45c8-8c79-a2565fc2aa55' class='xr-section-summary-in' type='checkbox'  ><label for='section-2e63781e-f7b5-45c8-8c79-a2565fc2aa55' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-7e2cafb4-3632-4942-9377-ce8992362bb9' class='xr-index-data-in' type='checkbox'/><label for='index-7e2cafb4-3632-4942-9377-ce8992362bb9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-0.15593936024673521, -0.15421883435901168, -0.15249830847128815,\\n\",\"        -0.1507777825835646,  -0.1490572566958411, -0.14733673080811757,\\n\",\"       -0.14561620492039404,  -0.1438956790326705, -0.14217515314494697,\\n\",\"       -0.14045462725722346,\\n\",\"       ...\\n\",\"         0.2845152670104883,   0.2862357928982118,  0.28795631878593536,\\n\",\"         0.2896768446736589,  0.29139737056138243,  0.29311789644910596,\\n\",\"        0.29483842233682944,    0.296558948224553,   0.2982794741122765,\\n\",\"        0.30000000000000004],\\n\",\"      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;, length=266))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>trace<br>receiver_id<br>component</div></div><div class='xr-index-preview'>PandasMultiIndex</div><input type='checkbox' disabled/><label></label><input id='index-836bca09-7093-4f1e-ab1d-9d8126af7e0e' class='xr-index-data-in' type='checkbox'/><label for='index-836bca09-7093-4f1e-ab1d-9d8126af7e0e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(MultiIndex([(&#x27;XX.AA_3.&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;XX.AA_1.&#x27;, &#x27;Y&#x27;),\\n\",\"            (&#x27;XX.AA_2.&#x27;, &#x27;Y&#x27;)],\\n\",\"           name=&#x27;trace&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5f9c32b8-0e09-4a8d-b0d6-836d90ae5fc5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5f9c32b8-0e09-4a8d-b0d6-836d90ae5fc5' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>start_time_in_seconds :</span></dt><dd>-0.15593936024673521</dd><dt><span>sampling_rate_in_hertz :</span></dt><dd>581.217642312331</dd><dt><span>start_time_in_seconds_utc_datetime :</span></dt><dd>1969-12-31T23:59:59.844061Z</dd><dt><span>end_time_in_seconds_utc_datetime :</span></dt><dd>1970-01-01T00:00:00.300000Z</dd><dt><span>reference_time_utc :</span></dt><dd>1970-01-01T00:00:00.000000Z</dd><dt><span>reference_time_in_seconds :</span></dt><dd>0.0</dd><dt><span>npts :</span></dt><dd>266</dd><dt><span>location :</span></dt><dd>[[ 60.   0.]\\n\",\" [ 70.   0.]\\n\",\" [ 80.   0.]\\n\",\" [ 90.   0.]\\n\",\" [100.   0.]\\n\",\" [110.   0.]\\n\",\" [120.   0.]\\n\",\" [130.   0.]\\n\",\" [140.   0.]\\n\",\" [150.   0.]\\n\",\" [160.   0.]\\n\",\" [170.   0.]\\n\",\" [180.   0.]\\n\",\" [190.   0.]\\n\",\" [200.   0.]\\n\",\" [210.   0.]\\n\",\" [220.   0.]\\n\",\" [230.   0.]\\n\",\" [240.   0.]\\n\",\" [250.   0.]\\n\",\" [260.   0.]\\n\",\" [270.   0.]\\n\",\" [280.   0.]\\n\",\" [290.   0.]\\n\",\" [300.   0.]]</dd><dt><span>temporal_weights :</span></dt><dd>&lt;xarray.DataArray &#x27;displacement&#x27; (trace: 3, time: 266)&gt; Size: 3kB\\n\",\"array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\\n\",\"        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\\n\",\"        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\\n\",\"        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\\n\",\"        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\\n\",\"        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\\n\",\"        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\\n\",\"        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\\n\",\"        0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\\n\",\"        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\\n\",\"        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\\n\",\"        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\\n\",\"        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\\n\",\"        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\\n\",\"        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\\n\",\"        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\\n\",\"        1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\\n\",\"       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\\n\",\"        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\\n\",\"        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\\n\",\"...\\n\",\"        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\\n\",\"        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\\n\",\"        1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\\n\",\"       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\\n\",\"        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\\n\",\"        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\\n\",\"        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\\n\",\"        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\\n\",\"        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\\n\",\"        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\\n\",\"        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\\n\",\"        0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\\n\",\"        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\\n\",\"        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\\n\",\"        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\\n\",\"        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\\n\",\"        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\\n\",\"        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\\n\",\"        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\\n\",\"        1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]], dtype=float32)\\n\",\"Coordinates:\\n\",\"  * time         (time) float64 2kB -0.1559 -0.1542 -0.1525 ... 0.2983 0.3\\n\",\"  * trace        (trace) object 24B MultiIndex\\n\",\"  * receiver_id  (trace) &lt;U9 108B &#x27;XX.AA_3.&#x27; &#x27;XX.AA_1.&#x27; &#x27;XX.AA_2.&#x27;\\n\",\"  * component    (trace) &lt;U1 12B &#x27;Y&#x27; &#x27;Y&#x27; &#x27;Y&#x27;\\n\",\"Attributes:\\n\",\"    start_time_in_seconds:               -0.15593936024673521\\n\",\"    sampling_rate_in_hertz:              581.217642312331\\n\",\"    start_time_in_seconds_utc_datetime:  1969-12-31T23:59:59.844061Z\\n\",\"    end_time_in_seconds_utc_datetime:    1970-01-01T00:00:00.300000Z\\n\",\"    reference_time_utc:                  1970-01-01T00:00:00.000000Z\\n\",\"    reference_time_in_seconds:           0.0\\n\",\"    npts:                                266\\n\",\"    location:                            [[ 60.   0.]\\\\n [ 70.   0.]\\\\n [ 80.  ...</dd></dl></div></li></ul></div></div>\"],\"text/plain\":[\"<xarray.DataArray 'displacement' (trace: 3, time: 266)> Size: 3kB\\n\",\"array([[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\\n\",\"         0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\\n\",\"        -0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\\n\",\"        -0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\\n\",\"        -0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\\n\",\"        -0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\\n\",\"        -0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\\n\",\"        -0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\\n\",\"        -0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\\n\",\"        -0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\\n\",\"         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\\n\",\"         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\\n\",\"         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\\n\",\"         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\\n\",\"         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\\n\",\"         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\\n\",\"         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\\n\",\"         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\\n\",\"         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\\n\",\"         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\\n\",\"...\\n\",\"        -2.90627470e-11, -2.62216759e-11, -2.28313202e-11,\\n\",\"        -1.90024281e-11, -1.48555196e-11, -1.05167706e-11,\\n\",\"        -6.11381345e-12, -1.77160803e-12,  2.39147027e-12,\\n\",\"         6.26719206e-12,  9.76056735e-12,  1.27923731e-11,\\n\",\"         1.53010521e-11,  1.72439216e-11,  1.85977067e-11,\\n\",\"         1.93583587e-11,  1.95402444e-11,  1.91747347e-11,\\n\",\"         1.83082664e-11,  1.70000004e-11,  1.53191470e-11,\\n\",\"         1.33420905e-11,  1.11494321e-11,  8.82304473e-12,\\n\",\"         6.44326042e-12,  4.08628860e-12,  1.82192998e-12,\\n\",\"        -2.88329094e-13, -2.19280518e-12, -3.85065721e-12,\\n\",\"        -5.23246550e-12, -6.32036177e-12, -7.10773064e-12,\\n\",\"        -7.59853031e-12, -7.80627472e-12, -7.75275157e-12,\\n\",\"        -7.46653260e-12, -6.98135820e-12, -6.33446334e-12,\\n\",\"        -5.56491441e-12, -4.71202287e-12, -3.81388757e-12,\\n\",\"        -2.90611784e-12, -2.02076702e-12, -1.18550601e-12,\\n\",\"        -4.23044575e-13,  2.49196741e-13,  8.19172638e-13,\\n\",\"         1.28007793e-12,  1.63001654e-12,  1.87150772e-12,\\n\",\"         2.01086630e-12,  2.05749545e-12,  2.02313123e-12,\\n\",\"         1.92107636e-12,  1.76545714e-12,  1.57053331e-12,\\n\",\"         1.35008703e-12,  1.11690637e-12]], dtype=float32)\\n\",\"Coordinates:\\n\",\"  * time         (time) float64 2kB -0.1559 -0.1542 -0.1525 ... 0.2983 0.3\\n\",\"  * trace        (trace) object 24B MultiIndex\\n\",\"  * receiver_id  (trace) <U9 108B 'XX.AA_3.' 'XX.AA_1.' 'XX.AA_2.'\\n\",\"  * component    (trace) <U1 12B 'Y' 'Y' 'Y'\\n\",\"Attributes:\\n\",\"    start_time_in_seconds:               -0.15593936024673521\\n\",\"    sampling_rate_in_hertz:              581.217642312331\\n\",\"    start_time_in_seconds_utc_datetime:  1969-12-31T23:59:59.844061Z\\n\",\"    end_time_in_seconds_utc_datetime:    1970-01-01T00:00:00.300000Z\\n\",\"    reference_time_utc:                  1970-01-01T00:00:00.000000Z\\n\",\"    reference_time_in_seconds:           0.0\\n\",\"    npts:                                266\\n\",\"    location:                            [[ 60.   0.]\\\\n [ 70.   0.]\\\\n [ 80.  ...\\n\",\"    temporal_weights:                    <xarray.DataArray 'displacement' (tr...\"]},\"execution_count\":32,\"metadata\":{},\"output_type\":\"execute_result\"}],\"source\":[\"ed_filt_sel = p.waveforms.get(\\n\",\"    \\\"PROCESSED_DATA:bandpass\\\",\\n\",\"    \\\"event_a\\\",\\n\",\"    data_selection_configuration=\\\"data_selection\\\",\\n\",\")[0]\\n\",\"da_filt_sel = ed_filt_sel.get_waveform_data_xarray(\\n\",\"    receiver_field=\\\"displacement\\\"\\n\",\")\\n\",\"da_filt_sel\"]},{\"cell_type\":\"markdown\",\"id\":\"599592bc\",\"metadata\":{},\"source\":[\"Note that the resulting DataArray has the temporal weights applied, but any\\n\",\"station or component that wasn't present in the returned object from\\n\",\"`select_data` is not present at all in the retrieved data.\"]},{\"cell_type\":\"code\",\"execution_count\":33,\"id\":\"4892c353\",\"metadata\":{},\"outputs\":[{\"data\":{\"text/plain\":[\"(['Y'], ['XX.AA_3.', 'XX.AA_1.', 'XX.AA_2.'])\"]},\"execution_count\":33,\"metadata\":{},\"output_type\":\"execute_result\"}],\"source\":[\"# Present components and stations\\n\",\"list(da_filt_sel.unstack().component.data), list(\\n\",\"    da_filt_sel.unstack().receiver_id.data\\n\",\")\"]},{\"cell_type\":\"markdown\",\"id\":\"dff101e7\",\"metadata\":{},\"source\":[\"If we now plot the output, you will see that our data selection works as\\n\",\"intended.\"]},{\"cell_type\":\"code\",\"execution_count\":34,\"id\":\"6fc1e7f8\",\"metadata\":{\"lines_to_next_cell\":2},\"outputs\":[{\"data\":{\"text/plain\":[\"<matplotlib.collections.QuadMesh at 0x79eee2db5cd0>\"]},\"execution_count\":34,\"metadata\":{},\"output_type\":\"execute_result\"},{\"data\":{\"image/png\":\"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\",\"text/plain\":[\"<Figure size 640x480 with 2 Axes>\"]},\"metadata\":{},\"output_type\":\"display_data\"}],\"source\":[\"da_filt_sel.sel(component=\\\"Y\\\").plot()\"]},{\"cell_type\":\"markdown\",\"id\":\"ac3d77c1\",\"metadata\":{\"lines_to_next_cell\":2},\"source\":[\"### Custom misfit functions\\n\",\"\\n\",\"In earlier versions of Salvus, misfits were always computed on a\\n\",\"trace-by-trace basis by looping over all receivers and components. Now,\\n\",\"misfits are directly computed on the new `xarray` structures, which allows us\\n\",\"to vectorize the misfit computations. If you want to define a custom misfit\\n\",\"function, you can now take advantage of this. Additionally, it is now\\n\",\"possible to define time-and-space dependent misfit functions.\\n\",\"\\n\",\"Here, we simply review the standard L2 misfit to understand how custom misfit\\n\",\"functions can be implemented within Salvus.\\n\",\"\\n\",\"A custom misfit function needs to take three mandatory input arguments:\\n\",\"\\n\",\"- data_array_synthetic: The synthetic waveforms as an `xarray.DataArray`\\n\",\"  object.\\n\",\"- data_array_observed: The observed waveforms as an `xarray.DataArray`\\n\",\"  object.\\n\",\"- event: The event object.\\n\",\"\\n\",\"The output must be an `xarray.Dataset` object. A `xarray.Dataset` object\\n\",\"combines multiple `xarray.DataArray`'s. The output `Dataset` will contain two\\n\",\"`DataArray`'s, one for the adjoint sources and one for the per-trace misfits.\\n\",\"\\n\",\"The following code snippets demonstrates the computation of the misfits and\\n\",\"adjoint sources for a conventional L2 misfit.\"]},{\"cell_type\":\"code\",\"execution_count\":35,\"id\":\"efb41e27\",\"metadata\":{},\"outputs\":[],\"source\":[\"def l2_misfit_and_adjoint_source(\\n\",\"    data_array_synthetic: xr.DataArray,\\n\",\"    data_array_observed: xr.DataArray,\\n\",\"    event: sn.Event,\\n\",\") -> xr.Dataset:\\n\",\"    \\\"\\\"\\\"\\n\",\"    L2 misfit and adjoint source.\\n\",\"\\n\",\"    Using this only serves for demonstration purposes right now as Salvus\\n\",\"    already comes with this misfit implemented.\\n\",\"\\n\",\"    Both data traces have been resampled to exactly the same points in time.\\n\",\"\\n\",\"    Args:\\n\",\"        data_array_synthetic: synthetic waveforms.\\n\",\"        data_array_observed: observed waveforms.\\n\",\"        event: Event object.\\n\",\"    \\\"\\\"\\\"\\n\",\"    diff = data_array_observed - data_array_synthetic\\n\",\"    misfits = (\\n\",\"        0.5\\n\",\"        * (diff**2).sum(axis=1)\\n\",\"        / data_array_observed.attrs[\\\"sampling_rate_in_hertz\\\"]\\n\",\"    )\\n\",\"    adj_src = diff / data_array_observed.attrs[\\\"sampling_rate_in_hertz\\\"]\\n\",\"\\n\",\"    return xr.Dataset(\\n\",\"        data_vars={\\n\",\"            \\\"adjoint_sources\\\": adj_src,\\n\",\"            \\\"misfits\\\": misfits,\\n\",\"        },\\n\",\"        attrs={\\n\",\"            \\\"sampling_rate_in_hertz\\\": data_array_synthetic.attrs[\\n\",\"                \\\"sampling_rate_in_hertz\\\"\\n\",\"            ],\\n\",\"            \\\"start_time_in_seconds\\\": data_array_synthetic.attrs.get(\\n\",\"                \\\"start_time_in_seconds\\\", 0.0\\n\",\"            ),\\n\",\"        },\\n\",\"    )\"]},{\"cell_type\":\"markdown\",\"id\":\"02859931\",\"metadata\":{},\"source\":[\"We can now set up a new misfit configuration using all of our custom\\n\",\"functions (processing, data_selection, and custom misfit) and add it to our\\n\",\"project in the following way.\"]},{\"cell_type\":\"code\",\"execution_count\":36,\"id\":\"cb5cf0d2\",\"metadata\":{},\"outputs\":[],\"source\":[\"mc = sn.MisfitConfiguration(\\n\",\"    name=\\\"custom_L2\\\",\\n\",\"    observed_data=\\\"PROCESSED_DATA:bandpass\\\",\\n\",\"    misfit_function=l2_misfit_and_adjoint_source,\\n\",\"    receiver_field=\\\"displacement\\\",\\n\",\"    data_selection_configuration=\\\"data_selection\\\",\\n\",\")\\n\",\"\\n\",\"p.add_to_project(mc)\"]},{\"cell_type\":\"markdown\",\"id\":\"50a35b9d\",\"metadata\":{},\"source\":[\"Let us verify that everything was properly added to our project:\"]},{\"cell_type\":\"code\",\"execution_count\":37,\"id\":\"455443dc\",\"metadata\":{},\"outputs\":[{\"data\":{\"image/png\":\"iVBORw0KGgoAAAANSUhEUgAAA9IAAADyCAYAAACh1/11AAAAAXNSR0IArs4c6QAAAARzQklUCAgI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\\ncFBUVITJZOLq1asEAoEHruF2uzly5AgOh4OffvqJvr4+duzYwZEjR1Z8fy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