1. Download polar data

One of the main functionalities of PolarToolkit is to download datasets commonly used for polar research. There is a very simple demonstration of how this works. We will download a grid file, a shapefile, and some point data.

Import the fetch module of PolarToolkit

from polartoolkit import fetch

Download bathymetry and bed elevation data from IBCSO. The first time you call this function, it will download the IBCSO netcdf (.nc) file, reproject it to EPSG 3031 (a mapping projection useful for Antarctica) and save the reprojected file to your computer. Subsequent calls to the function will find this reprojected file and load it for you, without re-downloading. This function returns the grid loaded as an xarray dataarray.

grid = fetch.ibcso(layer="bed")
grid

<xarray.DataArray 'bed' (y: 14001, x: 14001)> Size: 784MB
dask.array<open_dataset-bed, shape=(14001, 14001), dtype=float32, chunksize=(438, 876), chunktype=numpy.ndarray>
Coordinates:
  * x        (x) float64 112kB -3.5e+06 -3.5e+06 -3.499e+06 ... 3.5e+06 3.5e+06
  * y        (y) float64 112kB -3.5e+06 -3.5e+06 -3.499e+06 ... 3.5e+06 3.5e+06
Attributes:
    Conventions:   CF-1.7
    actual_range:  [-8447.6396484375, 4731.80712890625]
    description:
    history:       gmt grdsample @GMTAPI@-S-I-G-M-G-N-000000 -G@GMTAPI@-S-O-G...
    long_name:     z
    title:         

xarray.DataArray

'bed'

• y: 14001
• x: 14001

• dask.array<chunksize=(438, 876), meta=np.ndarray>

  Array Chunk Bytes 747.79 MiB 1.46 MiB Shape (14001, 14001) (438, 876) Dask graph 512 chunks in 2 graph layers Data type float32 numpy.ndarray

• Coordinates: (2)
  • x
    (x)
    float64
    -3.5e+06 -3.5e+06 ... 3.5e+06

    actual_range :

    [-3500000.0, 3500000.0]

    axis :

    X

    long_name :

    x

    array([-3500000., -3499500., -3499000., ...,  3499000.,  3499500.,  3500000.],
          shape=(14001,))

  • y
    (y)
    float64
    -3.5e+06 -3.5e+06 ... 3.5e+06

    actual_range :

    [-3500000.0, 3500000.0]

    axis :

    Y

    long_name :

    y

    array([-3500000., -3499500., -3499000., ...,  3499000.,  3499500.,  3500000.],
          shape=(14001,))

• Attributes: (6)

  Conventions :

  CF-1.7

  actual_range :

  [-8447.6396484375, 4731.80712890625]

  description :

  history :

  gmt grdsample @GMTAPI@-S-I-G-M-G-N-000000 -G@GMTAPI@-S-O-G-G-G-Y-000001 -I500 -R-3500000.0/3500000.0/-3500000.0/3500000.0 -rg

  long_name :

  z

  title :

There are also some shapefiles available to download. Here we will download a shapefile (.shp) of the Greenland grounding line. In this case, the function returns the path to the download shapefile.

filepath = fetch.groundingline(version="measures-greenland")
filepath

'/home/sungw937/.cache/pooch/polartoolkit/shapefiles/measures/mog100_geus_coastline_v02.shp'

There are also some point datasets available. Below we will download the individual point data of topography from Bedmap1 as a .csv file and automatically load it into a pandas dataframe.

df = fetch.bedmap_points(version="bedmap1")
df

/tmp/ipykernel_287521/2057769998.py:1: UserWarning: this file is large, if you only need a subset of data please provide a bounding box region via `region` to subset the data.
  df = fetch.bedmap_points(version="bedmap1")

	trajectory_id	trace_number	longitude (degree_east)	latitude (degree_north)	date	time_UTC	surface_altitude (m)	land_ice_thickness (m)	bedrock_altitude (m)	two_way_travel_time (m)	aircraft_altitude (m)	along_track_distance (m)	easting	northing	project	geometry
0	1	NaN	4.44900	-75.62300	NaN	NaN	NaN	2650.0	NaN	NaN	NaN	NaN	121788.567945	1.565282e+06	NaN	POINT (121788.568 1565282.264)
1	2	NaN	4.43800	-75.62100	NaN	NaN	NaN	2628.0	NaN	NaN	NaN	NaN	121505.125021	1.565526e+06	NaN	POINT (121505.125 1565525.58)
2	3	NaN	4.42500	-75.61900	NaN	NaN	NaN	2620.0	NaN	NaN	NaN	NaN	121166.937514	1.565773e+06	NaN	POINT (121166.938 1565773.075)
3	4	NaN	4.42000	-75.61900	NaN	NaN	NaN	2635.0	NaN	NaN	NaN	NaN	121030.297575	1.565784e+06	NaN	POINT (121030.298 1565783.643)
4	5	NaN	4.40700	-75.61700	NaN	NaN	NaN	2646.0	NaN	NaN	NaN	NaN	120691.982493	1.566031e+06	NaN	POINT (120691.982 1566031.037)
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1905045	1905046	NaN	-53.79618	-83.97787	NaN	NaN	1350.0	779.0	NaN	NaN	NaN	NaN	-528450.753601	3.868216e+05	NaN	POINT (-528450.754 386821.593)
1905046	1905047	NaN	-53.81247	-83.98000	NaN	NaN	1346.0	756.0	NaN	NaN	NaN	NaN	-528373.430978	3.865343e+05	NaN	POINT (-528373.431 386534.325)
1905047	1905048	NaN	-53.88616	-83.98947	NaN	NaN	1342.0	941.0	NaN	NaN	NaN	NaN	-528036.700836	3.852464e+05	NaN	POINT (-528036.701 385246.393)
1905048	1905049	NaN	-53.90617	-83.99211	NaN	NaN	1343.0	705.0	NaN	NaN	NaN	NaN	-527938.815436	3.848925e+05	NaN	POINT (-527938.815 384892.529)
1905049	1905050	NaN	-53.92110	-83.99413	NaN	NaN	1341.0	581.0	NaN	NaN	NaN	NaN	-527861.239735	3.846254e+05	NaN	POINT (-527861.24 384625.356)

1905050 rows × 16 columns