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"# AUX_OBS Ground Observatory Data\n",
"\n",
"> Abstract: We Demonstrate geomagnetic ground observatory data access through VirES - this is the AUX_OBS product distributed by BGS to support the Swarm mission, and contains data from [INTERMAGNET](http://intermagnet.github.io) and the [World Data Centre (WDC) for Geomagnetism](http://www.wdc.bgs.ac.uk). Data are available as three collections: 1 second and 1 minute cadences (INTERMAGNET definitive & quasi-definitive data), as well as specially derived hourly means over the past century (WDC).\n",
"\n",
"See also:\n",
"- https://book.magneticearth.org/geomag-obs-models/01a_visualising-geomagnetic-observatory-data"
]
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"## About the data\n",
"\n",
"The data are also available from the BGS FTP server (`ftp.nerc-murchison.ac.uk/geomag/Swarm/AUX_OBS/`). If that is more useful to you, you can refer to [this older notebook demonstration](https://nbviewer.jupyter.org/urls/gist.githubusercontent.com/smithara/fd17d31fa4901fb9c663b81f12d4950c/raw/aaef54458f837c30a139b76903b43a50b7ae406d/04c1_Geomag-Ground-Data-FTP.ipynb) of access to the FTP server.\n",
"\n",
"Please note the data are under different usage terms than the Swarm data:\n",
"- If you use the 1-second or 1-minute data, please refer to the [INTERMAGNET data conditions](https://intermagnet.github.io/data_conditions.html)\n",
"- If you use the 1-hour data, please also refer to the [WDC usage rules](http://www.wdc.bgs.ac.uk/usage.html) and cite the article about the preparation of these data: \n",
" [Macmillan, S., Olsen, N. Observatory data and the Swarm mission. Earth Planet Sp 65, 15 (2013). https://doi.org/10.5047/eps.2013.07.011](https://doi.org/10.5047/eps.2013.07.011)\n",
"\n",
"*Caution*: The magnetic vector components have been rotated into the geocentric (NEC) frame rather than the geodetic frame, so that they are consistent with the Swarm data. This is in contrast with the data provided directly from observatories."
]
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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 important package versions used\n",
"%load_ext watermark\n",
"%watermark -i -v -p viresclient,pandas,xarray,matplotlib"
]
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"source": [
"from viresclient import SwarmRequest\n",
"import matplotlib.pyplot as plt"
]
},
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"source": [
"## Organisation of data in VirES"
]
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"### AUX_OBS collection types\n",
"\n",
"Data are organised into AUX_OBSH (hour), AUX_OBSM (minute), AUX_OBSS (second) types. For example, to access the hourly data, use the *collection name* `SW_OPER_AUX_OBSH2_`."
]
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"text": [
"{'AUX_OBSH': ['SW_OPER_AUX_OBSH2_']}\n",
"{'AUX_OBSM': ['SW_OPER_AUX_OBSM2_']}\n",
"{'AUX_OBSS': ['SW_OPER_AUX_OBSS2_']}\n"
]
}
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"source": [
"request = SwarmRequest()\n",
"print(request.available_collections(\"AUX_OBSH\", details=False))\n",
"print(request.available_collections(\"AUX_OBSM\", details=False))\n",
"print(request.available_collections(\"AUX_OBSS\", details=False))"
]
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"Within each collection, the following variables are available:"
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"['B_NEC', 'F', 'IAGA_code', 'Quality', 'ObsIndex']\n",
"['B_NEC', 'F', 'IAGA_code', 'Quality']\n",
"['B_NEC', 'F', 'IAGA_code', 'Quality']\n"
]
}
],
"source": [
"print(request.available_measurements(\"SW_OPER_AUX_OBSH2_\"))\n",
"print(request.available_measurements(\"SW_OPER_AUX_OBSM2_\"))\n",
"print(request.available_measurements(\"SW_OPER_AUX_OBSS2_\"))"
]
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"- `B_NEC` and `F` are the magnetic field vector and intensity\n",
"- `IAGA_code` gives the official three-letter IAGA codes that identify each observatory\n",
"- `Quality` is either \"D\" or \"Q\" to indicate whether data is definitive (D) or quasi-definitive (Q)\n",
"- `ObsIndex` is an increasing integer (0, 1, 2...) attached to the hourly data - this indicates a change in the observatory (e.g. of precise location) while the 3-letter IAGA code remained the same"
]
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"### Data can be requested similarly to Swarm MAG products\n",
"\n",
"**Note that the `IAGA_code` variable is necessary in order to distinguish records from each observatory**\n",
"\n",
"**Note that there is a special message issued regarding the data terms**\n",
"\n",
"Let's fetch all the variables available within the 1-minute data, from two days:"
]
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"\n",
"Accessing INTERMAGNET and/or WDC data\n",
"Check usage terms at ftp://ftp.nerc-murchison.ac.uk/geomag/Swarm/AUX_OBS/minute/README\n",
"\n"
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"</style><pre class='xr-text-repr-fallback'><xarray.Dataset>\n",
"Dimensions: (Timestamp: 295200, NEC: 3)\n",
"Coordinates:\n",
" * Timestamp (Timestamp) datetime64[ns] 2016-01-01 ... 2016-01-02T23:59:00\n",
" * NEC (NEC) <U1 'N' 'E' 'C'\n",
"Data variables:\n",
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" B_NEC (Timestamp, NEC) float64 3.804e+04 197.1 ... 764.8 4.623e+04\n",
" IAGA_code (Timestamp) <U3 'ABG' 'ABK' 'API' 'ARS' ... 'VSS' 'WIC' 'WNG'\n",
" Latitude (Timestamp) float64 18.5 68.23 -13.73 ... -22.26 47.74 53.56\n",
" F (Timestamp) float64 4.297e+04 5.33e+04 ... 4.85e+04 4.962e+04\n",
" Longitude (Timestamp) float64 72.87 18.82 171.8 58.6 ... 316.4 15.86 9.073\n",
" Radius (Timestamp) float64 6.376e+06 6.36e+06 ... 6.367e+06 6.364e+06\n",
"Attributes:\n",
" Sources: ['SW_OPER_AUX_OBSM2__20160101T000000_20160101T235959_010...\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-ac52f224-91c2-4bb1-b001-a671d0c310b6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ac52f224-91c2-4bb1-b001-a671d0c310b6' 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>: 295200</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-b3f83e10-c3d6-423e-b846-39df0f41f435' class='xr-section-summary-in' type='checkbox' checked><label for='section-b3f83e10-c3d6-423e-b846-39df0f41f435' 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'>2016-01-01 ... 2016-01-02T23:59:00</div><input id='attrs-e9df85bd-56d3-4e5d-9a73-37e7a98a7f37' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e9df85bd-56d3-4e5d-9a73-37e7a98a7f37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0871aba4-fc2a-4198-9731-5d231273ab08' class='xr-var-data-in' type='checkbox'><label for='data-0871aba4-fc2a-4198-9731-5d231273ab08' 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>Date and time</dd></dl></div><div class='xr-var-data'><pre>array(['2016-01-01T00:00:00.000000000', '2016-01-01T00:00:00.000000000',\n",
" '2016-01-01T00:00:00.000000000', ..., '2016-01-02T23:59:00.000000000',\n",
" '2016-01-02T23:59:00.000000000', '2016-01-02T23:59:00.000000000'],\n",
" dtype='datetime64[ns]')</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'><U1</div><div class='xr-var-preview xr-preview'>'N' 'E' 'C'</div><input id='attrs-9c07816e-42df-4c2e-b811-c6a765ca7400' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9c07816e-42df-4c2e-b811-c6a765ca7400' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40008901-41c1-43fb-9cfa-7bb048ef4d5e' class='xr-var-data-in' type='checkbox'><label for='data-40008901-41c1-43fb-9cfa-7bb048ef4d5e' 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(['N', 'E', 'C'], dtype='<U1')</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a25febfe-0459-48c2-8d9e-686e19677f95' class='xr-section-summary-in' type='checkbox' checked><label for='section-a25febfe-0459-48c2-8d9e-686e19677f95' class='xr-section-summary' >Data variables: <span>(7)</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>Quality</span></div><div class='xr-var-dims'>(Timestamp)</div><div class='xr-var-dtype'><U1</div><div class='xr-var-preview xr-preview'>'D' 'D' 'D' 'Q' ... 'D' 'Q' 'D' 'D'</div><input id='attrs-34674c98-1cd9-4052-b0b2-a65aae9ddd87' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34674c98-1cd9-4052-b0b2-a65aae9ddd87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12e4d6b1-2061-4e38-b160-bf36952ffa80' class='xr-var-data-in' type='checkbox'><label for='data-12e4d6b1-2061-4e38-b160-bf36952ffa80' 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>Data quality: D for definitive and Q for quasi-definitive</dd></dl></div><div class='xr-var-data'><pre>array(['D', 'D', 'D', ..., 'Q', 'D', 'D'], dtype='<U1')</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'>3.804e+04 197.1 ... 764.8 4.623e+04</div><input id='attrs-6d61f822-bb3e-4816-bc02-7d7c1e1031f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d61f822-bb3e-4816-bc02-7d7c1e1031f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82c38267-ce4a-4e79-b420-a419436ecfcf' class='xr-var-data-in' type='checkbox'><label for='data-82c38267-ce4a-4e79-b420-a419436ecfcf' 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>Geocentric-north, east, and geocentric-down component of magnetic field. NaN values are used as placeholders for missing data.</dd></dl></div><div class='xr-var-data'><pre>array([[ 38043.06564896, 197.1 , 19986.65351001],\n",
" [ 10737.69800464, 1715.9 , 52176.82292725],\n",
" [ 32474.84988752, 6910.6 , -20084.45460506],\n",
" ...,\n",
" [ 16765.21273152, -6976.26 , -14582.1182441 ],\n",
" [ 20844.84980744, 1427.5 , 43772.59764733],\n",
" [ 18016.45057574, 764.8 , 46232.00707099]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>IAGA_code</span></div><div class='xr-var-dims'>(Timestamp)</div><div class='xr-var-dtype'><U3</div><div class='xr-var-preview xr-preview'>'ABG' 'ABK' 'API' ... 'WIC' 'WNG'</div><input id='attrs-1367ade0-a402-45b7-928a-c6e4907f1f15' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1367ade0-a402-45b7-928a-c6e4907f1f15' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c610665-4145-4d04-b259-2d915a695430' class='xr-var-data-in' type='checkbox'><label for='data-9c610665-4145-4d04-b259-2d915a695430' 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>IAGA three letter observatory identification code associated with datum</dd></dl></div><div class='xr-var-data'><pre>array(['ABG', 'ABK', 'API', ..., 'VSS', 'WIC', 'WNG'], dtype='<U3')</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'>18.5 68.23 -13.73 ... 47.74 53.56</div><input id='attrs-053c5aa3-d4a8-4b48-b76e-b14803f7f27d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-053c5aa3-d4a8-4b48-b76e-b14803f7f27d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-abcbfb47-d6d0-4cfb-bb62-065e15ba1572' class='xr-var-data-in' type='checkbox'><label for='data-abcbfb47-d6d0-4cfb-bb62-065e15ba1572' 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>Geocentric latitude</dd></dl></div><div class='xr-var-data'><pre>array([ 18.50386265, 68.2257541 , -13.72602501, ..., -22.26474182,\n",
" 47.73654523, 53.55928187])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>F</span></div><div class='xr-var-dims'>(Timestamp)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.297e+04 5.33e+04 ... 4.962e+04</div><input id='attrs-eacd68ba-d7e1-43f0-b6f5-57c88c18f1d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eacd68ba-d7e1-43f0-b6f5-57c88c18f1d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3325aa6c-c2b9-4f0e-b3dd-0105fb0a0040' class='xr-var-data-in' type='checkbox'><label for='data-3325aa6c-c2b9-4f0e-b3dd-0105fb0a0040' 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>Total magnetic field strength</dd></dl></div><div class='xr-var-data'><pre>array([42974.17842042, 53297.87352259, 38804.09236614, ...,\n",
" 23289.02604245, 48503.4619851 , 49624.34773576])</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'>72.87 18.82 171.8 ... 15.86 9.073</div><input id='attrs-b5418c9c-4505-4336-adb0-26fe080b845f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5418c9c-4505-4336-adb0-26fe080b845f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3e9c24a-e6b7-4602-b2da-2f7815ea54aa' class='xr-var-data-in' type='checkbox'><label for='data-f3e9c24a-e6b7-4602-b2da-2f7815ea54aa' 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>Longitude</dd></dl></div><div class='xr-var-data'><pre>array([ 72.87 , 18.823, 171.781, ..., 316.35 , 15.862, 9.073])</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'>6.376e+06 6.36e+06 ... 6.364e+06</div><input id='attrs-5408aedc-404c-40fb-b67d-73a64a6139fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5408aedc-404c-40fb-b67d-73a64a6139fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-202538bf-8619-4a42-b665-b98c80b69929' class='xr-var-data-in' type='checkbox'><label for='data-202538bf-8619-4a42-b665-b98c80b69929' 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>Radius</dd></dl></div><div class='xr-var-data'><pre>array([6375973.26325604, 6360061.90983027, 6376929.2544302 , ...,\n",
" 6375510.80308448, 6367485.9941623 , 6364322.58465254])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-90293843-116b-4aa9-9c0b-de48a2f3ba55' class='xr-section-summary-in' type='checkbox' ><label for='section-90293843-116b-4aa9-9c0b-de48a2f3ba55' 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-de2415aa-3bb9-48e2-a623-2b7c97e853ef' class='xr-index-data-in' type='checkbox'/><label for='index-de2415aa-3bb9-48e2-a623-2b7c97e853ef' 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(['2016-01-01 00:00:00', '2016-01-01 00:00:00',\n",
" '2016-01-01 00:00:00', '2016-01-01 00:00:00',\n",
" '2016-01-01 00:00:00', '2016-01-01 00:00:00',\n",
" '2016-01-01 00:00:00', '2016-01-01 00:00:00',\n",
" '2016-01-01 00:00:00', '2016-01-01 00:00:00',\n",
" ...\n",
" '2016-01-02 23:59:00', '2016-01-02 23:59:00',\n",
" '2016-01-02 23:59:00', '2016-01-02 23:59:00',\n",
" '2016-01-02 23:59:00', '2016-01-02 23:59:00',\n",
" '2016-01-02 23:59:00', '2016-01-02 23:59:00',\n",
" '2016-01-02 23:59:00', '2016-01-02 23:59:00'],\n",
" dtype='datetime64[ns]', name='Timestamp', length=295200, 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-cd055daa-cd46-48d0-9e97-7c7ffee3b4ac' class='xr-index-data-in' type='checkbox'/><label for='index-cd055daa-cd46-48d0-9e97-7c7ffee3b4ac' 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(['N', 'E', 'C'], dtype='object', name='NEC'))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7f88527f-cc01-416d-bd60-b6c4a35a7f4e' class='xr-section-summary-in' type='checkbox' checked><label for='section-7f88527f-cc01-416d-bd60-b6c4a35a7f4e' 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>['SW_OPER_AUX_OBSM2__20160101T000000_20160101T235959_0108', 'SW_OPER_AUX_OBSM2__20160102T000000_20160102T235959_0108']</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: 295200, NEC: 3)\n",
"Coordinates:\n",
" * Timestamp (Timestamp) datetime64[ns] 2016-01-01 ... 2016-01-02T23:59:00\n",
" * NEC (NEC) <U1 'N' 'E' 'C'\n",
"Data variables:\n",
" Quality (Timestamp) <U1 'D' 'D' 'D' 'Q' 'D' 'D' ... 'D' 'D' 'Q' 'D' 'D'\n",
" B_NEC (Timestamp, NEC) float64 3.804e+04 197.1 ... 764.8 4.623e+04\n",
" IAGA_code (Timestamp) <U3 'ABG' 'ABK' 'API' 'ARS' ... 'VSS' 'WIC' 'WNG'\n",
" Latitude (Timestamp) float64 18.5 68.23 -13.73 ... -22.26 47.74 53.56\n",
" F (Timestamp) float64 4.297e+04 5.33e+04 ... 4.85e+04 4.962e+04\n",
" Longitude (Timestamp) float64 72.87 18.82 171.8 58.6 ... 316.4 15.86 9.073\n",
" Radius (Timestamp) float64 6.376e+06 6.36e+06 ... 6.367e+06 6.364e+06\n",
"Attributes:\n",
" Sources: ['SW_OPER_AUX_OBSM2__20160101T000000_20160101T235959_010...\n",
" MagneticModels: []\n",
" AppliedFilters: []"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"request = SwarmRequest()\n",
"request.set_collection(\"SW_OPER_AUX_OBSM2_\")\n",
"request.set_products([\"IAGA_code\", \"B_NEC\", \"F\", \"Quality\"])\n",
"data = request.get_between(\"2016-01-01\", \"2016-01-03\")\n",
"ds = data.as_xarray()\n",
"ds"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Above, we loaded the data as an xarray `Dataset`, but we could also load the data as a pandas `DataFrame` - note that we should use `expand=True` to separate the vector components of `B_NEC` into distinct columns:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2025-06-21T21:49:12.399248Z",
"iopub.status.busy": "2025-06-21T21:49:12.399087Z",
"iopub.status.idle": "2025-06-21T21:49:12.948506Z",
"shell.execute_reply": "2025-06-21T21:49:12.947891Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Quality</th>\n",
" <th>IAGA_code</th>\n",
" <th>Latitude</th>\n",
" <th>F</th>\n",
" <th>Longitude</th>\n",
" <th>Radius</th>\n",
" <th>B_NEC_N</th>\n",
" <th>B_NEC_E</th>\n",
" <th>B_NEC_C</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Timestamp</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2016-01-01 00:00:00</th>\n",
" <td>D</td>\n",
" <td>ABG</td>\n",
" <td>18.503863</td>\n",
" <td>42974.178420</td>\n",
" <td>72.870</td>\n",
" <td>6.375973e+06</td>\n",
" <td>38043.065649</td>\n",
" <td>197.10</td>\n",
" <td>19986.653510</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-01 00:00:00</th>\n",
" <td>D</td>\n",
" <td>ABK</td>\n",
" <td>68.225754</td>\n",
" <td>53297.873523</td>\n",
" <td>18.823</td>\n",
" <td>6.360062e+06</td>\n",
" <td>10737.698005</td>\n",
" <td>1715.90</td>\n",
" <td>52176.822927</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-01 00:00:00</th>\n",
" <td>D</td>\n",
" <td>API</td>\n",
" <td>-13.726025</td>\n",
" <td>38804.092366</td>\n",
" <td>171.781</td>\n",
" <td>6.376929e+06</td>\n",
" <td>32474.849888</td>\n",
" <td>6910.60</td>\n",
" <td>-20084.454605</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-01 00:00:00</th>\n",
" <td>Q</td>\n",
" <td>ARS</td>\n",
" <td>59.231110</td>\n",
" <td>56202.047989</td>\n",
" <td>58.600</td>\n",
" <td>6.362618e+06</td>\n",
" <td>15439.829840</td>\n",
" <td>3848.14</td>\n",
" <td>53902.445874</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-01 00:00:00</th>\n",
" <td>D</td>\n",
" <td>ASC</td>\n",
" <td>-7.896461</td>\n",
" <td>28516.997217</td>\n",
" <td>345.624</td>\n",
" <td>6.377908e+06</td>\n",
" <td>19779.633516</td>\n",
" <td>-5398.80</td>\n",
" <td>-19820.145984</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-02 23:59:00</th>\n",
" <td>D</td>\n",
" <td>VIC</td>\n",
" <td>48.325950</td>\n",
" <td>53761.882434</td>\n",
" <td>236.583</td>\n",
" <td>6.366376e+06</td>\n",
" <td>17905.402463</td>\n",
" <td>5324.30</td>\n",
" <td>50412.184986</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-02 23:59:00</th>\n",
" <td>D</td>\n",
" <td>VNA</td>\n",
" <td>-70.562545</td>\n",
" <td>38373.585718</td>\n",
" <td>351.718</td>\n",
" <td>6.359150e+06</td>\n",
" <td>18053.865463</td>\n",
" <td>-4436.60</td>\n",
" <td>-33569.429593</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-02 23:59:00</th>\n",
" <td>Q</td>\n",
" <td>VSS</td>\n",
" <td>-22.264742</td>\n",
" <td>23289.026042</td>\n",
" <td>316.350</td>\n",
" <td>6.375511e+06</td>\n",
" <td>16765.212732</td>\n",
" <td>-6976.26</td>\n",
" <td>-14582.118244</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-02 23:59:00</th>\n",
" <td>D</td>\n",
" <td>WIC</td>\n",
" <td>47.736545</td>\n",
" <td>48503.461985</td>\n",
" <td>15.862</td>\n",
" <td>6.367486e+06</td>\n",
" <td>20844.849807</td>\n",
" <td>1427.50</td>\n",
" <td>43772.597647</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-02 23:59:00</th>\n",
" <td>D</td>\n",
" <td>WNG</td>\n",
" <td>53.559282</td>\n",
" <td>49624.347736</td>\n",
" <td>9.073</td>\n",
" <td>6.364323e+06</td>\n",
" <td>18016.450576</td>\n",
" <td>764.80</td>\n",
" <td>46232.007071</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>295200 rows × 9 columns</p>\n",
"</div>"
],
"text/plain": [
" Quality IAGA_code Latitude F Longitude \\\n",
"Timestamp \n",
"2016-01-01 00:00:00 D ABG 18.503863 42974.178420 72.870 \n",
"2016-01-01 00:00:00 D ABK 68.225754 53297.873523 18.823 \n",
"2016-01-01 00:00:00 D API -13.726025 38804.092366 171.781 \n",
"2016-01-01 00:00:00 Q ARS 59.231110 56202.047989 58.600 \n",
"2016-01-01 00:00:00 D ASC -7.896461 28516.997217 345.624 \n",
"... ... ... ... ... ... \n",
"2016-01-02 23:59:00 D VIC 48.325950 53761.882434 236.583 \n",
"2016-01-02 23:59:00 D VNA -70.562545 38373.585718 351.718 \n",
"2016-01-02 23:59:00 Q VSS -22.264742 23289.026042 316.350 \n",
"2016-01-02 23:59:00 D WIC 47.736545 48503.461985 15.862 \n",
"2016-01-02 23:59:00 D WNG 53.559282 49624.347736 9.073 \n",
"\n",
" Radius B_NEC_N B_NEC_E B_NEC_C \n",
"Timestamp \n",
"2016-01-01 00:00:00 6.375973e+06 38043.065649 197.10 19986.653510 \n",
"2016-01-01 00:00:00 6.360062e+06 10737.698005 1715.90 52176.822927 \n",
"2016-01-01 00:00:00 6.376929e+06 32474.849888 6910.60 -20084.454605 \n",
"2016-01-01 00:00:00 6.362618e+06 15439.829840 3848.14 53902.445874 \n",
"2016-01-01 00:00:00 6.377908e+06 19779.633516 -5398.80 -19820.145984 \n",
"... ... ... ... ... \n",
"2016-01-02 23:59:00 6.366376e+06 17905.402463 5324.30 50412.184986 \n",
"2016-01-02 23:59:00 6.359150e+06 18053.865463 -4436.60 -33569.429593 \n",
"2016-01-02 23:59:00 6.375511e+06 16765.212732 -6976.26 -14582.118244 \n",
"2016-01-02 23:59:00 6.367486e+06 20844.849807 1427.50 43772.597647 \n",
"2016-01-02 23:59:00 6.364323e+06 18016.450576 764.80 46232.007071 \n",
"\n",
"[295200 rows x 9 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = data.as_dataframe(expand=True)\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Use `available_observatories` to find possible IAGA codes\n",
"\n",
"We can get a dataframe containing the availability times of data from all the available observatories for a given collection:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2025-06-21T21:49:12.950620Z",
"iopub.status.busy": "2025-06-21T21:49:12.950259Z",
"iopub.status.idle": "2025-06-21T21:49:21.157196Z",
"shell.execute_reply": "2025-06-21T21:49:21.156684Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Accessing INTERMAGNET and/or WDC data\n",
"Check usage terms at ftp://ftp.nerc-murchison.ac.uk/geomag/Swarm/AUX_OBS/minute/README\n",
"\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>site</th>\n",
" <th>startTime</th>\n",
" <th>endTime</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" <th>Radius</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>AAA</td>\n",
" <td>2005-01-01T00:00:00Z</td>\n",
" <td>2015-12-31T23:59:00Z</td>\n",
" <td>43.058011</td>\n",
" <td>76.920</td>\n",
" <td>6.369442e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>AAE</td>\n",
" <td>1998-01-01T00:00:00Z</td>\n",
" <td>2013-12-31T23:59:00Z</td>\n",
" <td>8.975526</td>\n",
" <td>38.766</td>\n",
" <td>6.380055e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>ABG</td>\n",
" <td>1997-01-02T00:00:00Z</td>\n",
" <td>2023-12-31T23:59:00Z</td>\n",
" <td>18.503863</td>\n",
" <td>72.870</td>\n",
" <td>6.375980e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>ABK</td>\n",
" <td>1997-01-02T00:00:00Z</td>\n",
" <td>2025-05-22T23:59:00Z</td>\n",
" <td>68.267960</td>\n",
" <td>18.800</td>\n",
" <td>6.360051e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>AIA</td>\n",
" <td>2004-01-01T00:00:00Z</td>\n",
" <td>2022-12-31T23:59:00Z</td>\n",
" <td>-65.103358</td>\n",
" <td>295.730</td>\n",
" <td>6.360536e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145</th>\n",
" <td>VSS</td>\n",
" <td>1999-01-01T00:00:00Z</td>\n",
" <td>2025-05-22T23:59:00Z</td>\n",
" <td>-22.264742</td>\n",
" <td>316.350</td>\n",
" <td>6.375511e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>146</th>\n",
" <td>WIC</td>\n",
" <td>2015-01-01T00:00:00Z</td>\n",
" <td>2025-05-22T23:59:00Z</td>\n",
" <td>47.736545</td>\n",
" <td>15.866</td>\n",
" <td>6.367486e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>147</th>\n",
" <td>WNG</td>\n",
" <td>1997-01-02T00:00:00Z</td>\n",
" <td>2025-05-22T23:59:00Z</td>\n",
" <td>53.541246</td>\n",
" <td>9.053</td>\n",
" <td>6.364345e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>148</th>\n",
" <td>YAK</td>\n",
" <td>2009-01-01T00:00:00Z</td>\n",
" <td>2018-12-31T23:59:00Z</td>\n",
" <td>61.800024</td>\n",
" <td>129.660</td>\n",
" <td>6.361609e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>149</th>\n",
" <td>YKC</td>\n",
" <td>1997-01-02T00:00:00Z</td>\n",
" <td>2023-12-31T23:59:00Z</td>\n",
" <td>62.324005</td>\n",
" <td>245.518</td>\n",
" <td>6.361545e+06</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>150 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" site startTime endTime Latitude Longitude \\\n",
"0 AAA 2005-01-01T00:00:00Z 2015-12-31T23:59:00Z 43.058011 76.920 \n",
"1 AAE 1998-01-01T00:00:00Z 2013-12-31T23:59:00Z 8.975526 38.766 \n",
"2 ABG 1997-01-02T00:00:00Z 2023-12-31T23:59:00Z 18.503863 72.870 \n",
"3 ABK 1997-01-02T00:00:00Z 2025-05-22T23:59:00Z 68.267960 18.800 \n",
"4 AIA 2004-01-01T00:00:00Z 2022-12-31T23:59:00Z -65.103358 295.730 \n",
".. ... ... ... ... ... \n",
"145 VSS 1999-01-01T00:00:00Z 2025-05-22T23:59:00Z -22.264742 316.350 \n",
"146 WIC 2015-01-01T00:00:00Z 2025-05-22T23:59:00Z 47.736545 15.866 \n",
"147 WNG 1997-01-02T00:00:00Z 2025-05-22T23:59:00Z 53.541246 9.053 \n",
"148 YAK 2009-01-01T00:00:00Z 2018-12-31T23:59:00Z 61.800024 129.660 \n",
"149 YKC 1997-01-02T00:00:00Z 2023-12-31T23:59:00Z 62.324005 245.518 \n",
"\n",
" Radius \n",
"0 6.369442e+06 \n",
"1 6.380055e+06 \n",
"2 6.375980e+06 \n",
"3 6.360051e+06 \n",
"4 6.360536e+06 \n",
".. ... \n",
"145 6.375511e+06 \n",
"146 6.367486e+06 \n",
"147 6.364345e+06 \n",
"148 6.361609e+06 \n",
"149 6.361545e+06 \n",
"\n",
"[150 rows x 6 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"request.available_observatories(\"SW_OPER_AUX_OBSM2_\", details=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also get a list of only the available observatories during a given time window:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2025-06-21T21:49:21.159615Z",
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"shell.execute_reply": "2025-06-21T21:49:21.631382Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Accessing INTERMAGNET and/or WDC data\n",
"Check usage terms at ftp://ftp.nerc-murchison.ac.uk/geomag/Swarm/AUX_OBS/minute/README\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"['ABG', 'ABK', 'API', 'ARS', 'ASC', 'ASP', 'BDV', 'BEL', 'BFO', 'BLC', 'BMT', 'BOU', 'BOX', 'BRD', 'BRW', 'BSL', 'CBB', 'CKI', 'CLF', 'CMO', 'CNB', 'CSY', 'CTA', 'CYG', 'DED', 'DLT', 'DMC', 'DUR', 'EBR', 'ESK', 'EYR', 'FCC', 'FRD', 'FRN', 'FUR', 'GAN', 'GCK', 'GDH', 'GNG', 'GUA', 'GUI', 'HAD', 'HBK', 'HER', 'HLP', 'HON', 'HRB', 'HRN', 'HUA', 'HYB', 'IPM', 'IRT', 'IZN', 'JCO', 'KAK', 'KDU', 'KEP', 'KHB', 'KNY', 'KOU', 'LER', 'LON', 'LRM', 'LYC', 'LZH', 'MAB', 'MAW', 'MBO', 'MCQ', 'MGD', 'MMB', 'NAQ', 'NEW', 'NGK', 'NVS', 'OTT', 'PAG', 'PET', 'PST', 'RES', 'SBA', 'SBL', 'SFS', 'SHU', 'SIT', 'SJG', 'SOD', 'SPG', 'SPT', 'STJ', 'SUA', 'TAM', 'TDC', 'THL', 'THY', 'TSU', 'TUC', 'UPS', 'VIC', 'VNA', 'VSS', 'WIC', 'WNG']\n"
]
}
],
"source": [
"print(request.available_observatories(\"SW_OPER_AUX_OBSM2_\", '2016-01-01', '2016-01-02'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Use `IAGA_code` to specify a particular observatory\n",
"\n",
"Subset the collection with a special collection name like `\"SW_OPER_AUX_OBSM2_:<IAGA_code>\"` to get data from only that observatory:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2025-06-21T21:49:21.634306Z",
"iopub.status.busy": "2025-06-21T21:49:21.633889Z",
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},
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},
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{
"name": "stdout",
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"text": [
"\n",
"Accessing INTERMAGNET and/or WDC data\n",
"Check usage terms at ftp://ftp.nerc-murchison.ac.uk/geomag/Swarm/AUX_OBS/minute/README\n",
"\n"
]
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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" display: inline-block;\n",
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"\n",
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"\n",
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"\n",
".xr-dim-list li:not(:last-child):after {\n",
" content: ',';\n",
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"\n",
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"\n",
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"\n",
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".xr-var-list > li:nth-child(odd) > div,\n",
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"\n",
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"</style><pre class='xr-text-repr-fallback'><xarray.Dataset>\n",
"Dimensions: (Timestamp: 2880, NEC: 3)\n",
"Coordinates:\n",
" * Timestamp (Timestamp) datetime64[ns] 2016-01-01 ... 2016-01-02T23:59:00\n",
" * NEC (NEC) <U1 'N' 'E' 'C'\n",
"Data variables:\n",
" Quality (Timestamp) <U1 'D' 'D' 'D' 'D' 'D' 'D' ... 'D' 'D' 'D' 'D' 'D'\n",
" B_NEC (Timestamp, NEC) float64 1.074e+04 1.716e+03 ... 5.198e+04\n",
" IAGA_code (Timestamp) <U3 'ABK' 'ABK' 'ABK' 'ABK' ... 'ABK' 'ABK' 'ABK'\n",
" Latitude (Timestamp) float64 68.23 68.23 68.23 68.23 ... 68.23 68.23 68.23\n",
" F (Timestamp) float64 5.33e+04 5.331e+04 ... 5.32e+04 5.32e+04\n",
" Longitude (Timestamp) float64 18.82 18.82 18.82 18.82 ... 18.82 18.82 18.82\n",
" Radius (Timestamp) float64 6.36e+06 6.36e+06 ... 6.36e+06 6.36e+06\n",
"Attributes:\n",
" Sources: ['SW_OPER_AUX_OBSM2__20160101T000000_20160101T235959_010...\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-b5e80219-daff-4104-ba04-60958c328627' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b5e80219-daff-4104-ba04-60958c328627' 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>: 2880</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-a2658cd7-277e-4fb5-a391-a1b667cdb2ab' class='xr-section-summary-in' type='checkbox' checked><label for='section-a2658cd7-277e-4fb5-a391-a1b667cdb2ab' 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'>2016-01-01 ... 2016-01-02T23:59:00</div><input id='attrs-94a9b1aa-b546-4d81-b872-4b1da56f7511' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94a9b1aa-b546-4d81-b872-4b1da56f7511' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1798eeeb-2d9b-42e4-a85f-69ac39e0356b' class='xr-var-data-in' type='checkbox'><label for='data-1798eeeb-2d9b-42e4-a85f-69ac39e0356b' 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>Date and time</dd></dl></div><div class='xr-var-data'><pre>array(['2016-01-01T00:00:00.000000000', '2016-01-01T00:01:00.000000000',\n",
" '2016-01-01T00:02:00.000000000', ..., '2016-01-02T23:57:00.000000000',\n",
" '2016-01-02T23:58:00.000000000', '2016-01-02T23:59:00.000000000'],\n",
" dtype='datetime64[ns]')</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'><U1</div><div class='xr-var-preview xr-preview'>'N' 'E' 'C'</div><input id='attrs-83699180-7b81-4c46-b0a5-41de265de66c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83699180-7b81-4c46-b0a5-41de265de66c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2e2e90b-215e-457c-a22b-f29686e30c03' class='xr-var-data-in' type='checkbox'><label for='data-d2e2e90b-215e-457c-a22b-f29686e30c03' 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(['N', 'E', 'C'], dtype='<U1')</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-442d60c6-f732-4e62-bfac-bceb87def77e' class='xr-section-summary-in' type='checkbox' checked><label for='section-442d60c6-f732-4e62-bfac-bceb87def77e' class='xr-section-summary' >Data variables: <span>(7)</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>Quality</span></div><div class='xr-var-dims'>(Timestamp)</div><div class='xr-var-dtype'><U1</div><div class='xr-var-preview xr-preview'>'D' 'D' 'D' 'D' ... 'D' 'D' 'D' 'D'</div><input id='attrs-0269fe4c-8235-4c5a-811b-3c61dd94d23f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0269fe4c-8235-4c5a-811b-3c61dd94d23f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a31ef214-dd72-4624-99e2-dfa3c7e4a07c' class='xr-var-data-in' type='checkbox'><label for='data-a31ef214-dd72-4624-99e2-dfa3c7e4a07c' 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>Data quality: D for definitive and Q for quasi-definitive</dd></dl></div><div class='xr-var-data'><pre>array(['D', 'D', 'D', ..., 'D', 'D', 'D'], dtype='<U1')</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'>1.074e+04 1.716e+03 ... 5.198e+04</div><input id='attrs-c0377644-6973-4612-bb39-49af8ec7a861' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0377644-6973-4612-bb39-49af8ec7a861' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7723ee9f-ac18-41b8-b54a-0d991fc6ed68' class='xr-var-data-in' type='checkbox'><label for='data-7723ee9f-ac18-41b8-b54a-0d991fc6ed68' 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>Geocentric-north, east, and geocentric-down component of magnetic field. NaN values are used as placeholders for missing data.</dd></dl></div><div class='xr-var-data'><pre>array([[10737.69800464, 1715.9 , 52176.82292725],\n",
" [10769.49445775, 1720.6 , 52178.39632161],\n",
" [10807.09597328, 1709.2 , 52177.78310895],\n",
" ...,\n",
" [11210.65398547, 1653.9 , 51979.8140437 ],\n",
" [11210.35398627, 1653.6 , 51979.81335127],\n",
" [11210.25375572, 1653.2 , 51979.91312019]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>IAGA_code</span></div><div class='xr-var-dims'>(Timestamp)</div><div class='xr-var-dtype'><U3</div><div class='xr-var-preview xr-preview'>'ABK' 'ABK' 'ABK' ... 'ABK' 'ABK'</div><input id='attrs-6b82e4ec-eb6e-49e8-892d-9d78306c44a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b82e4ec-eb6e-49e8-892d-9d78306c44a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-905aaa22-bb8c-4bba-9d22-740964d3fcb0' class='xr-var-data-in' type='checkbox'><label for='data-905aaa22-bb8c-4bba-9d22-740964d3fcb0' 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>IAGA three letter observatory identification code associated with datum</dd></dl></div><div class='xr-var-data'><pre>array(['ABK', 'ABK', 'ABK', ..., 'ABK', 'ABK', 'ABK'], dtype='<U3')</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'>68.23 68.23 68.23 ... 68.23 68.23</div><input id='attrs-16c6a7ac-d30f-4d46-8caf-d8603572dbeb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-16c6a7ac-d30f-4d46-8caf-d8603572dbeb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b820b195-8de1-480e-9a6a-6a6707078ad7' class='xr-var-data-in' type='checkbox'><label for='data-b820b195-8de1-480e-9a6a-6a6707078ad7' 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>Geocentric latitude</dd></dl></div><div class='xr-var-data'><pre>array([68.2257541, 68.2257541, 68.2257541, ..., 68.2257541, 68.2257541,\n",
" 68.2257541])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>F</span></div><div class='xr-var-dims'>(Timestamp)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.33e+04 5.331e+04 ... 5.32e+04</div><input id='attrs-cef09610-b2d7-402a-8cc1-2e9c026b3fc0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cef09610-b2d7-402a-8cc1-2e9c026b3fc0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b9bbac0-3c30-487b-b716-d6f880c9c0f5' class='xr-var-data-in' type='checkbox'><label for='data-3b9bbac0-3c30-487b-b716-d6f880c9c0f5' 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>Total magnetic field strength</dd></dl></div><div class='xr-var-data'><pre>array([53297.87352259, 53305.98013291, 53312.62269088, ...,\n",
" 53200.70691269, 53200.63369444, 53200.69762221])</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'>18.82 18.82 18.82 ... 18.82 18.82</div><input id='attrs-d7546bf6-c8bd-4710-9552-c8b74e08c989' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7546bf6-c8bd-4710-9552-c8b74e08c989' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ffa7270-a537-4ae0-96d6-821ce3a6ce55' class='xr-var-data-in' type='checkbox'><label for='data-0ffa7270-a537-4ae0-96d6-821ce3a6ce55' 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>Longitude</dd></dl></div><div class='xr-var-data'><pre>array([18.823, 18.823, 18.823, ..., 18.823, 18.823, 18.823])</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'>6.36e+06 6.36e+06 ... 6.36e+06</div><input id='attrs-70e4dbd5-42d2-4ea9-8f5c-be1912f352d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70e4dbd5-42d2-4ea9-8f5c-be1912f352d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2ea134a-885f-4715-a757-6499aa796137' class='xr-var-data-in' type='checkbox'><label for='data-c2ea134a-885f-4715-a757-6499aa796137' 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>Radius</dd></dl></div><div class='xr-var-data'><pre>array([6360061.90983027, 6360061.90983027, 6360061.90983027, ...,\n",
" 6360061.90983027, 6360061.90983027, 6360061.90983027])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-94cf5952-c33e-48fa-9ff3-dda06ec7d443' class='xr-section-summary-in' type='checkbox' ><label for='section-94cf5952-c33e-48fa-9ff3-dda06ec7d443' 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-798e57fd-a913-444b-9de3-d3d95d15d914' class='xr-index-data-in' type='checkbox'/><label for='index-798e57fd-a913-444b-9de3-d3d95d15d914' 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(['2016-01-01 00:00:00', '2016-01-01 00:01:00',\n",
" '2016-01-01 00:02:00', '2016-01-01 00:03:00',\n",
" '2016-01-01 00:04:00', '2016-01-01 00:05:00',\n",
" '2016-01-01 00:06:00', '2016-01-01 00:07:00',\n",
" '2016-01-01 00:08:00', '2016-01-01 00:09:00',\n",
" ...\n",
" '2016-01-02 23:50:00', '2016-01-02 23:51:00',\n",
" '2016-01-02 23:52:00', '2016-01-02 23:53:00',\n",
" '2016-01-02 23:54:00', '2016-01-02 23:55:00',\n",
" '2016-01-02 23:56:00', '2016-01-02 23:57:00',\n",
" '2016-01-02 23:58:00', '2016-01-02 23:59:00'],\n",
" dtype='datetime64[ns]', name='Timestamp', length=2880, 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-528c5fef-8dd4-4318-b716-8f3f91def3a4' class='xr-index-data-in' type='checkbox'/><label for='index-528c5fef-8dd4-4318-b716-8f3f91def3a4' 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(['N', 'E', 'C'], dtype='object', name='NEC'))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-867ba969-95f9-43d3-9aa9-2c079184bd76' class='xr-section-summary-in' type='checkbox' checked><label for='section-867ba969-95f9-43d3-9aa9-2c079184bd76' 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>['SW_OPER_AUX_OBSM2__20160101T000000_20160101T235959_0108', 'SW_OPER_AUX_OBSM2__20160102T000000_20160102T235959_0108']</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: 2880, NEC: 3)\n",
"Coordinates:\n",
" * Timestamp (Timestamp) datetime64[ns] 2016-01-01 ... 2016-01-02T23:59:00\n",
" * NEC (NEC) <U1 'N' 'E' 'C'\n",
"Data variables:\n",
" Quality (Timestamp) <U1 'D' 'D' 'D' 'D' 'D' 'D' ... 'D' 'D' 'D' 'D' 'D'\n",
" B_NEC (Timestamp, NEC) float64 1.074e+04 1.716e+03 ... 5.198e+04\n",
" IAGA_code (Timestamp) <U3 'ABK' 'ABK' 'ABK' 'ABK' ... 'ABK' 'ABK' 'ABK'\n",
" Latitude (Timestamp) float64 68.23 68.23 68.23 68.23 ... 68.23 68.23 68.23\n",
" F (Timestamp) float64 5.33e+04 5.331e+04 ... 5.32e+04 5.32e+04\n",
" Longitude (Timestamp) float64 18.82 18.82 18.82 18.82 ... 18.82 18.82 18.82\n",
" Radius (Timestamp) float64 6.36e+06 6.36e+06 ... 6.36e+06 6.36e+06\n",
"Attributes:\n",
" Sources: ['SW_OPER_AUX_OBSM2__20160101T000000_20160101T235959_010...\n",
" MagneticModels: []\n",
" AppliedFilters: []"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"request = SwarmRequest()\n",
"request.set_collection(\"SW_OPER_AUX_OBSM2_:ABK\")\n",
"request.set_products([\"IAGA_code\", \"B_NEC\", \"F\", \"Quality\"])\n",
"data = request.get_between(\"2016-01-01\", \"2016-01-03\")\n",
"ds = data.as_xarray()\n",
"ds"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Magnetic models can be evaluated at the same time\n",
"\n",
"The VirES API treats these data similarly to the Swarm MAG products, and so all the same model handling behaviour applies. For example, we can directly remove the CHAOS core and crustal model predictions:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"execution": {
"iopub.execute_input": "2025-06-21T21:49:25.912328Z",
"iopub.status.busy": "2025-06-21T21:49:25.912120Z",
"iopub.status.idle": "2025-06-21T21:49:29.798799Z",
"shell.execute_reply": "2025-06-21T21:49:29.798245Z"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Accessing INTERMAGNET and/or WDC data\n",
"Check usage terms at ftp://ftp.nerc-murchison.ac.uk/geomag/Swarm/AUX_OBS/minute/README\n",
"\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f6c5a910790847e1a077cf55a0d680b8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Processing: 0%| | [ Elapsed: 00:00, Remaining: ? ] [1/1] "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "916710ffb63c4a2c90d7b310cc770595",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading: 0%| | [ Elapsed: 00:00, Remaining: ? ] (0.166MB)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"request = SwarmRequest()\n",
"request.set_collection(\"SW_OPER_AUX_OBSM2_:ABK\")\n",
"request.set_products(\n",
" measurements=[\"B_NEC\"],\n",
" models=[\"'CHAOS-internal' = 'CHAOS-Core' + 'CHAOS-Static'\"],\n",
" residuals=True\n",
")\n",
"data = request.get_between(\"2016-01-01\", \"2016-01-03\")\n",
"ds = data.as_xarray()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"execution": {
"iopub.execute_input": "2025-06-21T21:49:29.801633Z",
"iopub.status.busy": "2025-06-21T21:49:29.801437Z",
"iopub.status.idle": "2025-06-21T21:49:30.607556Z",
"shell.execute_reply": "2025-06-21T21:49:30.606827Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.plot.facetgrid.FacetGrid at 0x7f6c1efca5d0>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"<Figure size 1000x300 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ds[\"B_NEC_res_CHAOS-internal\"].plot.line(x=\"Timestamp\", col=\"NEC\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(This roughly shows the disturbance sensed by the observatory due to the magnetospheric and ionospheric sources)"
]
},
{
"cell_type": "markdown",
"metadata": {
"jp-MarkdownHeadingCollapsed": true
},
"source": [
"## Data visualisation\n",
"\n",
"### Three observatories over a year\n",
"\n",
"Let's run through a visualisation of one year of hourly means from three observatories.\n",
"\n",
"First fetch the data from our chosen observatories across the UK: LER (Lerwick), ESK (Eskdalemuir), HAD (Hartland). We can apply a few optional settings to reduce unnecessary output:\n",
"- `verbose=False` to disable the data terms message\n",
"- `asynchronous=False` to enable synchronous processing on the server - it will be slightly faster but only works for smaller data requests\n",
"- `show_progress=False` to hide the progress bars"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"execution": {
"iopub.execute_input": "2025-06-21T21:49:30.610249Z",
"iopub.status.busy": "2025-06-21T21:49:30.609629Z",
"iopub.status.idle": "2025-06-21T21:49:35.772928Z",
"shell.execute_reply": "2025-06-21T21:49:35.772325Z"
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},
"outputs": [],
"source": [
"request = SwarmRequest()\n",
"request.set_collection(\"SW_OPER_AUX_OBSH2_:LER\", verbose=False)\n",
"request.set_products(measurements=[\"B_NEC\"])\n",
"data = request.get_between(\"2013-01-01\", \"2014-01-01\", asynchronous=False, show_progress=False)\n",
"ds_ler = data.as_xarray()\n",
"\n",
"request = SwarmRequest()\n",
"request.set_collection(\"SW_OPER_AUX_OBSH2_:ESK\", verbose=False)\n",
"request.set_products(measurements=[\"B_NEC\"])\n",
"data = request.get_between(\"2013-01-01\", \"2014-01-01\", asynchronous=False, show_progress=False)\n",
"ds_esk = data.as_xarray()\n",
"\n",
"request = SwarmRequest()\n",
"request.set_collection(\"SW_OPER_AUX_OBSH2_:HAD\", verbose=False)\n",
"request.set_products(measurements=[\"B_NEC\"])\n",
"data = request.get_between(\"2013-01-01\", \"2014-01-01\", asynchronous=False, show_progress=False)\n",
"ds_had = data.as_xarray()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now our data is in three objects which look like this:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"execution": {
"iopub.execute_input": "2025-06-21T21:49:35.775704Z",
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"shell.execute_reply": "2025-06-21T21:49:35.790842Z"
}
},
"outputs": [
{
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"</style><pre class='xr-text-repr-fallback'><xarray.Dataset>\n",
"Dimensions: (Timestamp: 8760, NEC: 3)\n",
"Coordinates:\n",
" * Timestamp (Timestamp) datetime64[ns] 2013-01-01T00:30:00 ... 2013-12-31T...\n",
" * NEC (NEC) <U1 'N' 'E' 'C'\n",
"Data variables:\n",
" Latitude (Timestamp) float64 59.97 59.97 59.97 59.97 ... 59.97 59.97 59.97\n",
" B_NEC (Timestamp, NEC) float64 1.487e+04 -616.8 ... -561.9 4.862e+04\n",
" Longitude (Timestamp) float64 358.8 358.8 358.8 358.8 ... 358.8 358.8 358.8\n",
" Radius (Timestamp) float64 6.362e+06 6.362e+06 ... 6.362e+06 6.362e+06\n",
"Attributes:\n",
" Sources: ['SW_OPER_AUX_OBS_2__20130101T000000_20131231T235959_0144']\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-5743ba94-e313-4d12-bdc0-004106ffcea9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5743ba94-e313-4d12-bdc0-004106ffcea9' 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>: 8760</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-d92fed4c-1e00-4426-a6d3-a66fc3b24406' class='xr-section-summary-in' type='checkbox' checked><label for='section-d92fed4c-1e00-4426-a6d3-a66fc3b24406' 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'>2013-01-01T00:30:00 ... 2013-12-...</div><input id='attrs-00e56ccc-3f5a-45d1-acec-421f4e59de47' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-00e56ccc-3f5a-45d1-acec-421f4e59de47' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-496d623d-c7b1-4502-869b-f6c795cb47ae' class='xr-var-data-in' type='checkbox'><label for='data-496d623d-c7b1-4502-869b-f6c795cb47ae' 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>Date and time</dd></dl></div><div class='xr-var-data'><pre>array(['2013-01-01T00:30:00.000000000', '2013-01-01T01:30:00.000000000',\n",
" '2013-01-01T02:30:00.000000000', ..., '2013-12-31T21:30:00.000000000',\n",
" '2013-12-31T22:30:00.000000000', '2013-12-31T23:30:00.000000000'],\n",
" dtype='datetime64[ns]')</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'><U1</div><div class='xr-var-preview xr-preview'>'N' 'E' 'C'</div><input id='attrs-9a62d954-787e-4402-8600-a4e8d5d7801e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a62d954-787e-4402-8600-a4e8d5d7801e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cbd016e3-02ea-484d-9a24-6d68a0f6755f' class='xr-var-data-in' type='checkbox'><label for='data-cbd016e3-02ea-484d-9a24-6d68a0f6755f' 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(['N', 'E', 'C'], dtype='<U1')</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e7a8bbd8-c928-4cb3-a5fc-4197c52e0516' class='xr-section-summary-in' type='checkbox' checked><label for='section-e7a8bbd8-c928-4cb3-a5fc-4197c52e0516' class='xr-section-summary' >Data variables: <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>Latitude</span></div><div class='xr-var-dims'>(Timestamp)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>59.97 59.97 59.97 ... 59.97 59.97</div><input id='attrs-ff17ddba-1675-426e-9788-9581c37dc716' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ff17ddba-1675-426e-9788-9581c37dc716' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e92f1a48-d9b4-41e2-a12c-ad73b6a823c7' class='xr-var-data-in' type='checkbox'><label for='data-e92f1a48-d9b4-41e2-a12c-ad73b6a823c7' 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>Geocentric latitude</dd></dl></div><div class='xr-var-data'><pre>array([59.967, 59.967, 59.967, ..., 59.967, 59.967, 59.967])</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'>1.487e+04 -616.8 ... 4.862e+04</div><input id='attrs-9adb9eaf-9493-4476-ac6d-689d3f3b33fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9adb9eaf-9493-4476-ac6d-689d3f3b33fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b7c47e3-2d7e-492b-bb6a-a0c153e4dc82' class='xr-var-data-in' type='checkbox'><label for='data-4b7c47e3-2d7e-492b-bb6a-a0c153e4dc82' 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>Geocentric-north, east, and geocentric-down component of magnetic field. NaN values are used as placeholders for missing data.</dd></dl></div><div class='xr-var-data'><pre>array([[14865.9, -616.8, 48595.4],\n",
" [14864.9, -615.8, 48596.4],\n",
" [14864.9, -616.8, 48597.4],\n",
" ...,\n",
" [14876.8, -561.9, 48619.4],\n",
" [14875.8, -562.9, 48619.4],\n",
" [14876.8, -561.9, 48617.4]])</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'>358.8 358.8 358.8 ... 358.8 358.8</div><input id='attrs-288c56a5-6b65-421d-9daf-748905a35b7f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-288c56a5-6b65-421d-9daf-748905a35b7f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a697e593-e0e2-437b-b55f-297b665fced5' class='xr-var-data-in' type='checkbox'><label for='data-a697e593-e0e2-437b-b55f-297b665fced5' 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>Longitude</dd></dl></div><div class='xr-var-data'><pre>array([358.817, 358.817, 358.817, ..., 358.817, 358.817, 358.817])</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'>6.362e+06 6.362e+06 ... 6.362e+06</div><input id='attrs-06294221-0c8c-4d77-8071-4a5aa43ffcb9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06294221-0c8c-4d77-8071-4a5aa43ffcb9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb8cc33e-d24e-4473-91b6-cd667e987281' class='xr-var-data-in' type='checkbox'><label for='data-cb8cc33e-d24e-4473-91b6-cd667e987281' 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>Geocentric radius</dd></dl></div><div class='xr-var-data'><pre>array([6362174., 6362174., 6362174., ..., 6362174., 6362174., 6362174.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5b0115a6-1bbc-45e1-ab78-3d88580616fc' class='xr-section-summary-in' type='checkbox' ><label for='section-5b0115a6-1bbc-45e1-ab78-3d88580616fc' 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-2c176433-9564-4b50-ad4e-860dd7771f55' class='xr-index-data-in' type='checkbox'/><label for='index-2c176433-9564-4b50-ad4e-860dd7771f55' 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(['2013-01-01 00:30:00', '2013-01-01 01:30:00',\n",
" '2013-01-01 02:30:00', '2013-01-01 03:30:00',\n",
" '2013-01-01 04:30:00', '2013-01-01 05:30:00',\n",
" '2013-01-01 06:30:00', '2013-01-01 07:30:00',\n",
" '2013-01-01 08:30:00', '2013-01-01 09:30:00',\n",
" ...\n",
" '2013-12-31 14:30:00', '2013-12-31 15:30:00',\n",
" '2013-12-31 16:30:00', '2013-12-31 17:30:00',\n",
" '2013-12-31 18:30:00', '2013-12-31 19:30:00',\n",
" '2013-12-31 20:30:00', '2013-12-31 21:30:00',\n",
" '2013-12-31 22:30:00', '2013-12-31 23:30:00'],\n",
" dtype='datetime64[ns]', name='Timestamp', length=8760, 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-1fc9e3e8-ba3c-4c7b-bdbe-76cf7777a927' class='xr-index-data-in' type='checkbox'/><label for='index-1fc9e3e8-ba3c-4c7b-bdbe-76cf7777a927' 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(['N', 'E', 'C'], dtype='object', name='NEC'))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-54ff0ff7-c448-4685-9ae1-f0a076b22bfd' class='xr-section-summary-in' type='checkbox' checked><label for='section-54ff0ff7-c448-4685-9ae1-f0a076b22bfd' 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>['SW_OPER_AUX_OBS_2__20130101T000000_20131231T235959_0144']</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: 8760, NEC: 3)\n",
"Coordinates:\n",
" * Timestamp (Timestamp) datetime64[ns] 2013-01-01T00:30:00 ... 2013-12-31T...\n",
" * NEC (NEC) <U1 'N' 'E' 'C'\n",
"Data variables:\n",
" Latitude (Timestamp) float64 59.97 59.97 59.97 59.97 ... 59.97 59.97 59.97\n",
" B_NEC (Timestamp, NEC) float64 1.487e+04 -616.8 ... -561.9 4.862e+04\n",
" Longitude (Timestamp) float64 358.8 358.8 358.8 358.8 ... 358.8 358.8 358.8\n",
" Radius (Timestamp) float64 6.362e+06 6.362e+06 ... 6.362e+06 6.362e+06\n",
"Attributes:\n",
" Sources: ['SW_OPER_AUX_OBS_2__20130101T000000_20131231T235959_0144']\n",
" MagneticModels: []\n",
" AppliedFilters: []"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds_ler"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can quickly preview the data using the xarray plotting tools:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"execution": {
"iopub.execute_input": "2025-06-21T21:49:35.793817Z",
"iopub.status.busy": "2025-06-21T21:49:35.793396Z",
"iopub.status.idle": "2025-06-21T21:49:36.333415Z",
"shell.execute_reply": "2025-06-21T21:49:36.332702Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.plot.facetgrid.FacetGrid at 0x7f6c283aa4d0>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1000x300 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ds_ler[\"B_NEC\"].plot.line(x=\"Timestamp\", col=\"NEC\", sharey=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's make a more complex figure to display data from all three observatories together. We can use matplotlib directly now to create the figure and pass the xarray objects to it to fill the contents. Note that we slice out a particular vector component with e.g. `ds_ler[\"B_NEC\"].sel(NEC=\"N\")`."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"execution": {
"iopub.execute_input": "2025-06-21T21:49:36.335571Z",
"iopub.status.busy": "2025-06-21T21:49:36.335373Z",
"iopub.status.idle": "2025-06-21T21:49:38.168026Z",
"shell.execute_reply": "2025-06-21T21:49:38.167419Z"
}
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1500x500 with 9 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(15, 5), sharex=\"all\", sharey=\"row\")\n",
"for i, NEC in enumerate(\"NEC\"):\n",
" axes[i, 0].plot(ds_ler[\"Timestamp\"], ds_ler[\"B_NEC\"].sel(NEC=NEC))\n",
" axes[i, 1].plot(ds_esk[\"Timestamp\"], ds_esk[\"B_NEC\"].sel(NEC=NEC))\n",
" axes[i, 2].plot(ds_had[\"Timestamp\"], ds_had[\"B_NEC\"].sel(NEC=NEC))\n",
" axes[i, 0].set_ylabel(f\"B ({NEC}) [nT]\")\n",
"axes[0, 0].set_title(\"LER: Lerwick (60.0°N)\")\n",
"axes[0, 1].set_title(\"ESK: Eskdalemuir (55.1°N)\")\n",
"axes[0, 2].set_title(\"HAD: Hartland (50.8°N)\")\n",
"fig.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This shows us the difference in the main field between these locations - further North (Lerwick), the field is pointing more downwards so the vertical component (C) is stronger. We can also see a small secular variation over the year as the field changes."
]
}
],
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"language": "python",
"name": "python3"
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