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    "# Finding conjunctions\n",
    "\n",
    "> Abstract: VirES currently has the ability to search for conjunctions between only Swarm Alpha & Bravo. These are times when the spacecraft are flying near to each other. Here we show how the Python interface can be used to find these conjunctions. VirES defines conjunctions as times when the angular separation between two spacecraft (based on geographic latitude & longitude) is below a given threshold.\n",
    "\n",
    "See also:\n",
    "\n",
    "- API reference: https://viresclient.readthedocs.io/en/latest/api.html#viresclient.SwarmRequest.get_conjunctions\n",
    "- Point-and-click dashboard: `Swarm_notebooks/dashboards/04_Conjunctions.ipynb` (link TBD)\n",
    "- https://nbviewer.org/github/pacesm/jupyter_notebooks/blob/master/SwarmAB_conjunctions/SwarmAB_conjunctions_VirES_API.ipynb\n",
    "- https://nbviewer.org/github/pacesm/jupyter_notebooks/blob/master/SwarmAB_conjunctions/SwarmAB_conjunctions_VirES_API_and_MAG_measurements.ipynb\n",
    "\n",
    ":::{admonition} Using other tools?\n",
    ":class: seealso\n",
    "\n",
    "For auroral-related studies, you will probably want to use the [AuroraX Conjunction Search](https://aurorax.space/conjunctionSearch/dropdowns) which lets you search conjunctions between multiple ground and space programs. You can use the [`PyAuroraX` package](https://github.com/aurorax-space/pyaurorax) to do this programmatically, which is currently not installed in VRE. You can install it temporarily on the fly from within a notebook with, e.g.:\n",
    "\n",
    "```python\n",
    "!pip install pyaurorax\n",
    "\n",
    "from aurorax import conjunctions\n",
    "```\n",
    "\n",
    "[(see example)](https://nbviewer.org/github/aurorax-space/pyaurorax/blob/main/examples/notebooks/search_conjunctions.ipynb)\n",
    ":::\n",
    "\n",
    "\n",
    "## Interface example"
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      "Python implementation: CPython\n",
      "Python version       : 3.11.6\n",
      "IPython version      : 8.18.0\n",
      "\n",
      "viresclient: 0.12.3\n",
      "pandas     : 2.1.3\n",
      "xarray     : 2023.12.0\n",
      "matplotlib : 3.8.2\n",
      "\n"
     ]
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   "source": [
    "# Display current version numbers used\n",
    "%load_ext watermark\n",
    "%watermark -i -v -p viresclient,pandas,xarray,matplotlib"
   ]
  },
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   "source": [
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from viresclient import SwarmRequest"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdde19ca",
   "metadata": {},
   "source": [
    "Use the [`.get_conjunctions()`](https://viresclient.readthedocs.io/en/latest/api.html#viresclient.SwarmRequest.get_conjunctions) method to search for conjunctions. This takes as its inputs:\n",
    "- `start_time`, `end_time`: the search inteval (as ISO-8601 strings, or as `datetime` objects)\n",
    "- `threshold`: the maximum allowable angular separation in degrees\n",
    "- `mission1`, `mission2`: mission name of the first/second spacecraft (currently only Swarm is allowed)\n",
    "- `spacecraft1`, `spacecraft2`: the spacecraft identifiers (currently only A/B allowed)\n",
    "\n",
    "The object returned from `.get_conjunctions()` can be loaded as a Pandas Dataframe or as an Xarray Dataset, just as with other data queries. For example, searching within September 2021:"
   ]
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       "                     AngularSeparation\n",
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       "2021-09-04 14:16:01           0.531057\n",
       "2021-09-04 15:02:58           0.379247\n",
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    "request = SwarmRequest()\n",
    "conjs = request.get_conjunctions(\n",
    "    start_time=\"2021-09-01\",\n",
    "    end_time=\"2021-10-01\",\n",
    "    threshold=1,\n",
    "    spacecraft1=\"A\",\n",
    "    spacecraft2=\"B\",\n",
    "    mission1=\"Swarm\",\n",
    "    mission2=\"Swarm\"\n",
    ")\n",
    "conjs = conjs.as_dataframe()\n",
    "conjs"
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   "source": [
    "## Using identified conjunctions"
   ]
  },
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   "id": "c1eb4af7",
   "metadata": {},
   "source": [
    "We can now use the identified time instances to extract data from around those moments. Let's pick the first conjunction found and create a one-minute time window around it:"
   ]
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       "(datetime.datetime(2021, 9, 4, 11, 54, 36),\n",
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    "time = conjs.index[0].to_pydatetime()\n",
    "time0 = time - dt.timedelta(seconds=30)\n",
    "time1 = time + dt.timedelta(seconds=30)\n",
    "time0, time1"
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   "source": [
    "Now let's pull the magnetic high rate (50Hz) measurements from this period:"
   ]
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   "id": "736c79b4",
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    "# Make three consecutive requests and store the data within the dictionary, ds_set\n",
    "ds_set = {}\n",
    "for spacecraft in (\"A\", \"B\", \"C\"):\n",
    "    request = SwarmRequest()\n",
    "    request.set_collection(f\"SW_OPER_MAG{spacecraft}_HR_1B\")\n",
    "    request.set_products(measurements=[\"B_NEC\"])\n",
    "    data = request.get_between(time0, time1, asynchronous=False, show_progress=False)\n",
    "    ds_set[spacecraft] = data.as_xarray()"
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       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\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",
       "}\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",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\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-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",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:     (Timestamp: 3000, NEC: 3)\n",
       "Coordinates:\n",
       "  * Timestamp   (Timestamp) datetime64[ns] 2021-09-04T11:54:36.017820416 ... ...\n",
       "  * NEC         (NEC) &lt;U1 &#x27;N&#x27; &#x27;E&#x27; &#x27;C&#x27;\n",
       "Data variables:\n",
       "    Spacecraft  (Timestamp) object &#x27;A&#x27; &#x27;A&#x27; &#x27;A&#x27; &#x27;A&#x27; &#x27;A&#x27; ... &#x27;A&#x27; &#x27;A&#x27; &#x27;A&#x27; &#x27;A&#x27; &#x27;A&#x27;\n",
       "    B_NEC       (Timestamp, NEC) float64 2.351e+04 -1.799e+03 ... 1.986e+04\n",
       "    Radius      (Timestamp) float64 6.803e+06 6.803e+06 ... 6.804e+06 6.804e+06\n",
       "    Longitude   (Timestamp) float64 -15.23 -15.23 -15.23 ... -15.24 -15.24\n",
       "    Latitude    (Timestamp) float64 32.94 32.93 32.93 ... 29.08 29.08 29.07\n",
       "Attributes:\n",
       "    Sources:         [&#x27;SW_OPER_MAGA_HR_1B_20210904T000000_20210904T235959_060...\n",
       "    MagneticModels:  []\n",
       "    AppliedFilters:  []</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-41d3579e-b649-4cd4-81ff-0446797ce430' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-41d3579e-b649-4cd4-81ff-0446797ce430' 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'>Timestamp</span>: 3000</li><li><span class='xr-has-index'>NEC</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e8388b15-3c95-4f5f-abd6-adbe6fe2c492' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e8388b15-3c95-4f5f-abd6-adbe6fe2c492' 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'>Timestamp</span></div><div class='xr-var-dims'>(Timestamp)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-09-04T11:54:36.017820416 .....</div><input id='attrs-e617046d-922d-4044-963b-db30fb154771' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e617046d-922d-4044-963b-db30fb154771' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b03db270-7ceb-4b14-93ba-2afb13d0d9fe' class='xr-var-data-in' type='checkbox'><label for='data-b03db270-7ceb-4b14-93ba-2afb13d0d9fe' 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'><dt><span>description :</span></dt><dd>Time stamp</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-09-04T11:54:36.017820416&#x27;, &#x27;2021-09-04T11:54:36.037820416&#x27;,\n",
       "       &#x27;2021-09-04T11:54:36.057820416&#x27;, ..., &#x27;2021-09-04T11:55:35.954812416&#x27;,\n",
       "       &#x27;2021-09-04T11:55:35.974812416&#x27;, &#x27;2021-09-04T11:55:35.994812416&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>NEC</span></div><div class='xr-var-dims'>(NEC)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;N&#x27; &#x27;E&#x27; &#x27;C&#x27;</div><input id='attrs-cbc4c245-bc55-4bbd-9d32-eb6b52e5e471' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cbc4c245-bc55-4bbd-9d32-eb6b52e5e471' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fdbc6cee-5411-4ba6-95f9-f2f5238c2f98' class='xr-var-data-in' type='checkbox'><label for='data-fdbc6cee-5411-4ba6-95f9-f2f5238c2f98' 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'><dt><span>units :</span></dt><dd></dd><dt><span>description :</span></dt><dd>NEC frame - North, East, Centre (down)</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;N&#x27;, &#x27;E&#x27;, &#x27;C&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0d5bc21a-047d-4f0c-b9bb-963252a29699' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0d5bc21a-047d-4f0c-b9bb-963252a29699' class='xr-section-summary' >Data variables: <span>(5)</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>Spacecraft</span></div><div class='xr-var-dims'>(Timestamp)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;A&#x27; &#x27;A&#x27; &#x27;A&#x27; &#x27;A&#x27; ... &#x27;A&#x27; &#x27;A&#x27; &#x27;A&#x27; &#x27;A&#x27;</div><input id='attrs-a40ba868-447d-47cf-a048-e63bbfde99e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a40ba868-447d-47cf-a048-e63bbfde99e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba6874a1-1cc1-4839-b6cf-a82295ec7585' class='xr-var-data-in' type='checkbox'><label for='data-ba6874a1-1cc1-4839-b6cf-a82295ec7585' 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'><dt><span>units :</span></dt><dd>-</dd><dt><span>description :</span></dt><dd>Spacecraft identifier (values: &#x27;A&#x27;, &#x27;B&#x27;, &#x27;C&#x27; or &#x27;-&#x27; if not available).</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;A&#x27;, &#x27;A&#x27;, &#x27;A&#x27;, ..., &#x27;A&#x27;, &#x27;A&#x27;, &#x27;A&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B_NEC</span></div><div class='xr-var-dims'>(Timestamp, NEC)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.351e+04 -1.799e+03 ... 1.986e+04</div><input id='attrs-9c5a5e7b-6883-42d3-bfce-e5c4adaf4b91' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9c5a5e7b-6883-42d3-bfce-e5c4adaf4b91' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ebe3a16-d9cc-49b5-8602-b8eca6efea80' class='xr-var-data-in' type='checkbox'><label for='data-7ebe3a16-d9cc-49b5-8602-b8eca6efea80' 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'><dt><span>units :</span></dt><dd>nT</dd><dt><span>description :</span></dt><dd>Magnetic field vector, NEC frame</dd></dl></div><div class='xr-var-data'><pre>array([[23512.5704, -1799.1802, 23591.9077],\n",
       "       [23512.9906, -1799.2255, 23590.7271],\n",
       "       [23513.4181, -1799.282 , 23589.5603],\n",
       "       ...,\n",
       "       [24633.3981, -1958.8724, 19858.2203],\n",
       "       [24633.7116, -1958.9472, 19856.9496],\n",
       "       [24634.0515, -1958.9906, 19855.6718]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Radius</span></div><div class='xr-var-dims'>(Timestamp)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.803e+06 6.803e+06 ... 6.804e+06</div><input id='attrs-a8890a35-6135-412b-a7fe-ad2da87e3a3a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8890a35-6135-412b-a7fe-ad2da87e3a3a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-131a6b12-9ae3-49e5-a371-bcfc58fd2839' class='xr-var-data-in' type='checkbox'><label for='data-131a6b12-9ae3-49e5-a371-bcfc58fd2839' 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'><dt><span>units :</span></dt><dd>m</dd><dt><span>description :</span></dt><dd>Position in ITRF – Radius</dd></dl></div><div class='xr-var-data'><pre>array([6803478.96, 6803479.22, 6803479.47, ..., 6804236.61, 6804236.86,\n",
       "       6804237.11])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Longitude</span></div><div class='xr-var-dims'>(Timestamp)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-15.23 -15.23 ... -15.24 -15.24</div><input id='attrs-6a64e9b2-1280-4d4e-9195-f7678beddf4d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6a64e9b2-1280-4d4e-9195-f7678beddf4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8907495e-d425-4eca-8061-d07eb8e2d27d' class='xr-var-data-in' type='checkbox'><label for='data-8907495e-d425-4eca-8061-d07eb8e2d27d' 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'><dt><span>units :</span></dt><dd>deg</dd><dt><span>description :</span></dt><dd>Position in ITRF – Longitude</dd></dl></div><div class='xr-var-data'><pre>array([-15.2331998, -15.2331993, -15.2331987, ..., -15.241775 ,\n",
       "       -15.241781 , -15.241787 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Latitude</span></div><div class='xr-var-dims'>(Timestamp)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>32.94 32.93 32.93 ... 29.08 29.07</div><input id='attrs-19379bbc-74c4-43ea-8d73-e822c39100d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-19379bbc-74c4-43ea-8d73-e822c39100d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3d23e13-3327-4805-b5be-047c6cacd564' class='xr-var-data-in' type='checkbox'><label for='data-b3d23e13-3327-4805-b5be-047c6cacd564' 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'><dt><span>units :</span></dt><dd>deg</dd><dt><span>description :</span></dt><dd>Position in ITRF – Latitude</dd></dl></div><div class='xr-var-data'><pre>array([32.9352431, 32.9339555, 32.9326678, ..., 29.0763461, 29.0750585,\n",
       "       29.0737709])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-78708830-969e-45ae-ba25-f0c84f69c435' class='xr-section-summary-in' type='checkbox'  ><label for='section-78708830-969e-45ae-ba25-f0c84f69c435' 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>Timestamp</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9f2235fe-7b01-4615-a437-34c1ccea3a54' class='xr-index-data-in' type='checkbox'/><label for='index-9f2235fe-7b01-4615-a437-34c1ccea3a54' 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(DatetimeIndex([&#x27;2021-09-04 11:54:36.017820416&#x27;,\n",
       "               &#x27;2021-09-04 11:54:36.037820416&#x27;,\n",
       "               &#x27;2021-09-04 11:54:36.057820416&#x27;,\n",
       "               &#x27;2021-09-04 11:54:36.077820416&#x27;,\n",
       "               &#x27;2021-09-04 11:54:36.097820160&#x27;,\n",
       "               &#x27;2021-09-04 11:54:36.117820160&#x27;,\n",
       "               &#x27;2021-09-04 11:54:36.137820160&#x27;,\n",
       "               &#x27;2021-09-04 11:54:36.157820160&#x27;,\n",
       "               &#x27;2021-09-04 11:54:36.177820160&#x27;,\n",
       "               &#x27;2021-09-04 11:54:36.197820416&#x27;,\n",
       "               ...\n",
       "               &#x27;2021-09-04 11:55:35.814812416&#x27;,\n",
       "               &#x27;2021-09-04 11:55:35.834812416&#x27;,\n",
       "               &#x27;2021-09-04 11:55:35.854812416&#x27;,\n",
       "               &#x27;2021-09-04 11:55:35.874812672&#x27;,\n",
       "               &#x27;2021-09-04 11:55:35.894812672&#x27;,\n",
       "               &#x27;2021-09-04 11:55:35.914812672&#x27;,\n",
       "               &#x27;2021-09-04 11:55:35.934812672&#x27;,\n",
       "               &#x27;2021-09-04 11:55:35.954812416&#x27;,\n",
       "               &#x27;2021-09-04 11:55:35.974812416&#x27;,\n",
       "               &#x27;2021-09-04 11:55:35.994812416&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;Timestamp&#x27;, length=3000, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>NEC</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-114e9198-a5d0-4b75-8313-b97f67c31cdf' class='xr-index-data-in' type='checkbox'/><label for='index-114e9198-a5d0-4b75-8313-b97f67c31cdf' 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;N&#x27;, &#x27;E&#x27;, &#x27;C&#x27;], dtype=&#x27;object&#x27;, name=&#x27;NEC&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ca04f0e7-1edd-446f-9e73-1f8758446031' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ca04f0e7-1edd-446f-9e73-1f8758446031' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Sources :</span></dt><dd>[&#x27;SW_OPER_MAGA_HR_1B_20210904T000000_20210904T235959_0605_MDR_MAG_HR&#x27;]</dd><dt><span>MagneticModels :</span></dt><dd>[]</dd><dt><span>AppliedFilters :</span></dt><dd>[]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:     (Timestamp: 3000, NEC: 3)\n",
       "Coordinates:\n",
       "  * Timestamp   (Timestamp) datetime64[ns] 2021-09-04T11:54:36.017820416 ... ...\n",
       "  * NEC         (NEC) <U1 'N' 'E' 'C'\n",
       "Data variables:\n",
       "    Spacecraft  (Timestamp) object 'A' 'A' 'A' 'A' 'A' ... 'A' 'A' 'A' 'A' 'A'\n",
       "    B_NEC       (Timestamp, NEC) float64 2.351e+04 -1.799e+03 ... 1.986e+04\n",
       "    Radius      (Timestamp) float64 6.803e+06 6.803e+06 ... 6.804e+06 6.804e+06\n",
       "    Longitude   (Timestamp) float64 -15.23 -15.23 -15.23 ... -15.24 -15.24\n",
       "    Latitude    (Timestamp) float64 32.94 32.93 32.93 ... 29.08 29.08 29.07\n",
       "Attributes:\n",
       "    Sources:         ['SW_OPER_MAGA_HR_1B_20210904T000000_20210904T235959_060...\n",
       "    MagneticModels:  []\n",
       "    AppliedFilters:  []"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_set[\"A\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1933db9b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-21T21:47:43.558656Z",
     "iopub.status.busy": "2025-06-21T21:47:43.558477Z",
     "iopub.status.idle": "2025-06-21T21:47:44.282380Z",
     "shell.execute_reply": "2025-06-21T21:47:44.281753Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spacecraft = (\"A\", \"B\", \"C\")\n",
    "colors = (\"tab:blue\", \"tab:orange\", \"tab:green\")\n",
    "# Loop through each spacecraft and plot in a different colour\n",
    "fig, axes = plt.subplots(nrows=3, sharex=True, figsize=(10,5))\n",
    "for sc, color in zip(spacecraft, colors):\n",
    "    # Extract latitude and B_NEC vector for each spacecraft\n",
    "    lat = ds_set[sc][\"Latitude\"]\n",
    "    B_N = ds_set[sc][\"B_NEC\"].sel(NEC=\"N\")\n",
    "    B_E = ds_set[sc][\"B_NEC\"].sel(NEC=\"E\")\n",
    "    B_C = ds_set[sc][\"B_NEC\"].sel(NEC=\"C\")\n",
    "    axes[0].plot(lat, B_N, color=color, label=f\"Swarm {sc}\")\n",
    "    axes[1].plot(lat, B_E, color=color)\n",
    "    axes[2].plot(lat, B_C, color=color)\n",
    "\n",
    "# Adjust labelling\n",
    "axes[0].legend(loc=\"upper right\")\n",
    "axes[0].set_ylabel(\"$B_N$ [nT]\")\n",
    "axes[1].set_ylabel(\"$B_E$ [nT]\")\n",
    "axes[2].set_ylabel(\"$B_C$ [nT]\")\n",
    "axes[2].set_xlabel(\"Latitude\")\n",
    "for ax in axes:\n",
    "    ax.grid()\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73f5f07d",
   "metadata": {},
   "source": [
    "The pair of Alpha and Charlie fly together at the same altitude so measure a very similar field. Bravo, in this instance, is flying in the *opposite* direction (in this part of the mission, the orbits are counter-rotating so there are many conjunctions with the spacecraft flying towards each other), but at a *higher altitude* and so measuring a weaker field."
   ]
  }
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