API Reference#

Profile#

create_profile(method: str, start: Optional[numpy.ndarray] = None, stop: Optional[numpy.ndarray] = None, shapefile: Optional[str] = None, num=100, shp_num_points: Optional[int] = None, **kwargs)[source]#

Create a pandas DataFrame of points along a line with multiple methods.

Parameters

  • method (str) – Choose sampling method, either “points” or “shapefile”

  • start (np.ndarray, optional) – Coordinates for starting point of profile, by default None

  • stop (np.ndarray, optional) – Coordinates for eding point of profile, by default None

  • num (int, optional) – Number of points between start and stop, by default 100

  • shp_num_points (int, optional) – Number of points to resample shapefile to, by default None

  • shapefile (str, optional) – shapefile file name to create points along, by default None

Returns

Dataframe with ‘x’, ‘y’, and ‘dist’ columns for points along line or shapefile path.

Return type

pd.Dataframe

default_data(region=None) → dict[source]#

Fetch default gravity and magnetic datasets.

Parameters

region (str or list[int], optional) – region of Antarctic to load, by default is entire Antarctic region.

Returns

Nested dictionary of data and attributes

Return type

dict[dict]

default_layers() → dict[source]#

Fetch default Bedmap2 layers.

Returns

Nested dictionary of Bedmap2 layers and attributes

Return type

dict[dict]

fill_nans(df)[source]#

Fill NaN’s in sampled layer with values from above layer.

Parameters

df (pd.DataFrame) – First 3 columns as they are assumed to by x, y, dist.

Returns

Dataframe with NaN’s of lower layers filled

Return type

pd.DataFrame

make_data_dict(names: list, grids: list, colors: list) → dict[source]#

Create nested dictionary of data and attributes

Parameters

  • names (list[str]) – data names

  • grids (list[str or xarray.DataArray]) – files or xarray.DataArray’s

  • colors (list[str]) – colors to plot data

Returns

Nested dictionaries of grids and attributes

Return type

dict[dict]

plot_profile(method: str, layers_dict: Optional[dict] = None, data_dict: Optional[dict] = None, add_map: bool = False, **kwargs)[source]#

Show sampled layers and/or data on a cross section, with an optional location map.

Parameters

  • method (str) – Choose the sample method, either ‘points’, or ‘shapefile’.

  • layers_dict (dict, optional) – nested dictionary of layers to include in cross-section, construct with profile.make_data_dict, by default is Bedmap2 layers.

  • data_dict (dict, optional) – nested dictionary of data to include in option graph, construct with profile.make_data_dict, by default is gravity and magnetic anomalies.

  • add_map (bool = False) – Choose whether to add a location map, by default is False.

  • fillnans (bool) – Choose whether to fill nans in layers, defaults to True

  • clip (bool) – Choose whether to clip the profile based on distance

  • max_dist (int) – Clip all distances greater than.

  • min_dist (int) – Clip all distances less than.

  • **kwargs (dict) –

    map_background: str or xarray.DataArray

    Change the map background by passing a filename string or grid, by default is imagery.

    map_cmap: str

    Change the map colorscale by passing a valid GMT cmap string, by default is ‘earth’.

    map_buffer: float (0-1)

    Change map zoom as relative percentage of profile length, by default is 0.2

    layer_buffer: float (0-1)

    Change vertical white space within cross-section, by default is 0.1

    data_buffer: float (0-1)

    Change vertical white space within data graph, by default is 0.1

    save: bool

    Choose to save the image, by default is False

    path: str

    Filename for saving image, by default is None.

sample_grids(df, grid, name: Optional[str] = None)[source]#

Sample data at every point along a line

Parameters

  • df (pd.DataFrame) – Dataframe containing columns ‘x’, ‘y’

  • grid (str or xr.DataArray) – Grid to sample, either file name or xr.DataArray

  • name (str, optional) – Name for sampled column, by default is str(grid)

Returns

Dataframe with new column (name) of sample values from (grid)

Return type

pd.DataFrame

shorten(df, max_dist=None, min_dist=None, **kwargs)[source]#

Shorten a dataframe at either end based on distance column.

Parameters

  • df (pd.DataFrame) – Dataframe to shorten and recalculate distance, must contain ‘x’, ‘y’, ‘dist’

  • max_dist (int, optional) – remove rows with dist>max_dist, by default None

  • min_dist (int, optional) – remove rows with dist<min_dist, by default None

Returns

Shortened dataframe

Return type

pd.DataFrame

Fetch#

basement(plot: bool = False, info: bool = False) → xarray.core.dataarray.DataArray[source]#

Load a grid of basement topography. Offshore and sub-Ross Ice Shelf basement topography. from Tankersley et al. 2022: https://onlinelibrary.wiley.com/doi/10.1029/2021GL097371 offshore data from Lindeque et al. 2016: https://doi.org/10.1002/2016GC006401

Parameters

  • plot (bool, optional) – plot the fetched grid, by default False

  • info (bool, optional) – print info on the fetched grid, by default False

Returns

dataarray of basement depths

Return type

xr.DataArray

bedmap2(layer: str, plot: bool = False, info: bool = False) → xarray.core.dataarray.DataArray[source]#

Load bedmap2 data. Note, nan’s in surface grid are set to 0. from https://doi.org/10.5194/tc-7-375-2013.

Parameters

  • layer (str) – choose which layer to fetch, ‘thickness’, ‘bed’, ‘surface’, or ‘geiod_to_WGS84’

  • plot (bool, optional) – choose to plot grid, by default False

  • info (bool, optional) – choose to print info on grid, by default False

Returns

Returns a bedmap2 grid

Return type

xr.DataArray

deepbedmap(plot: bool = False, info: bool = False, region=None, spacing=10000.0) → xarray.core.dataarray.DataArray[source]#

Load DeepBedMap data, from Leong and Horgan, 2020: https://doi.org/10.5194/tc-14-3687-2020 Accessed from https://zenodo.org/record/4054246#.Ysy344RByp0

Parameters

  • plot (bool, optional) – choose to plot grid, by default False

  • info (bool, optional) – choose to print info on grid, by default False

  • region (str or np.ndarray, optional) – GMT-format region to clip the loaded grid to, by default doesn’t clip

  • spacing (str or int, optional) – grid spacing to resample the loaded grid to, by default 10e3

Returns

Returns a loaded, and optional clip/resampled grid of DeepBedMap.

Return type

xr.DataArray

gravity(type: str, plot: bool = False, info: bool = False, region=None, spacing=10000.0) → xarray.core.dataarray.DataArray[source]#

Loads an Antarctic gravity grid Preliminary compilation of Antarctica gravity and gravity gradient data. Updates on 2016 AntGG compilation. Accessed from https://ftp.space.dtu.dk/pub/RF/4D-ANTARCTICA/.

Parameters

  • type (str) – either ‘FA’ or ‘BA’, for free-air and bouguer anomalies, respectively.

  • plot (bool, optional) – choose to plot grid, by default False

  • info (bool, optional) – choose to print info on grid, by default False

  • region (str or np.ndarray, optional) – GMT-format region to clip the loaded grid to, by default doesn’t clip

  • spacing (str or int, optional) – grid spacing to resample the loaded grid to, by default 10e3

Returns

Returns a loaded, and optional clip/resampled grid of either free-air or Bouguer gravity anomalies.

Return type

xr.DataArray

groundingline() → str[source]#

Load the file path of a groundingline shapefile Antarctic groundingline shape file, from https://doi.pangaea.de/10.1594/PANGAEA.819147 Supplement to Depoorter et al. 2013: https://doi.org/10.1038/nature12567

Returns

file path

Return type

str

imagery() → str[source]#

Load the file path of Antarctic imagery geotiff from LIMA: https://lima.usgs.gov/fullcontinent.php will replace with below once figured out login issue with pooch MODIS Mosaic of Antarctica: https://doi.org/10.5067/68TBT0CGJSOJ Assessed from https://daacdata.apps.nsidc.org/pub/DATASETS/nsidc0730_MEASURES_MOA2014_v01/geotiff/ # noqa

Returns

file path

Return type

str

magnetics(version: str, plot: bool = False, info: bool = False, region=None, spacing=10000.0) → xarray.core.dataarray.DataArray[source]#

Load 1 of 4 ‘versions’ of Antarctic magnetic anomaly grid. version=’admap2’ ADMAP2 magnetic anomaly compilation of Antarctica. Non-geosoft specific files provide from Sasha Golynsky. version=’admap1’ ADMAP-2001 magnetic anomaly compilation of Antarctica. https://admap.kongju.ac.kr/databases.html version=’admap2_eq_src’ ADMAP2 eqivalent sources, from https://admap.kongju.ac.kr/admapdata/ version=’admap2_gdb’ Geosoft-specific .gdb abridged files. Accessed from https://doi.pangaea.de/10.1594/PANGAEA.892722?format=html#download

Parameters

  • version (str) – Either ‘admap2’, ‘admap1’, ‘admap2_eq_src’, or ‘admap2_gdb’

  • plot (bool, optional) – choose to plot grid, by default False

  • info (bool, optional) – choose to print info on grid, by default False

  • region (str or np.ndarray, optional) – GMT-format region to clip the loaded grid to, by default doesn’t clip

  • spacing (str or int, optional) – grid spacing to resample the loaded grid to, by default 10e3

Returns

Returns a loaded, and optional clip/resampled grid of magnetic anomalies.

Return type

xr.DataArray

sample_shp(name: str) → str[source]#

Load the file path of sample shapefiles

Parameters

name (str) – chose which sample shapefile to load, either ‘Disco_deep_transect’ or ‘Roosevelt_Island’

Returns

file path as a string

Return type

str

Utils#

GMT_reg_xy_to_ll(input)[source]#

Convert GMT region string [e, w, n, s] in EPSG:3031 to deg:min:sec

Parameters

input (np.ndarray) – Array of 4 strings in GMT format; [e, w, n, s] in meters

Returns

Array of 4 strings in GMT format; [e, w, n, s] in lat, lon

Return type

np.ndarray

dd2dms(dd: float)[source]#

Convert decimal degrees to minutes, seconds. Modified from https://stackoverflow.com/a/10286690/18686384

Parameters

dd (float) – input decimal degrees

Returns

degrees in the format “DD:MM:SS”

Return type

str

epsg3031_to_latlon(df, reg: bool = False, input=['x', 'y'], output=['lon', 'lat'])[source]#

Convert coordinates from EPSG:3031 Antarctic Polar Stereographic in meters to EPSG:4326 WGS84 in decimal degrees.

Parameters

  • df (pd.DataFrame) – input dataframe with easting and northing columns

  • reg (bool, optional) – if true, returns a GMT formatted region string, by default False

  • input (list, optional) – set names for input columns, by default [“x”, “y”]

  • output (list, optional) – set names for output columns, by default [“lon”, “lat”]

Returns

Updated dataframe with new latitude and longitude columns or np.ndarray in format [e, w, n, s]

Return type

pd.DataFrame or np.ndarray

get_grid_info(grid)[source]#

Returns the spacing and region of an input grid.

Parameters

grid (str or xarray.DataArray) – Input grid to get info from. Filename string or loaded grid.

Returns

tuple, first item is a string of grid spacing, second item is an array with the region boundaries

Return type

tuple

latlon_to_epsg3031(df, reg: bool = False, input=['lon', 'lat'], output=['x', 'y'])[source]#

Convert coordinates from EPSG:4326 WGS84 in decimal degrees to EPSG:3031 Antarctic Polar Stereographic in meters.

Parameters

  • df (pd.DataFrame) – input dataframe with latitude and longitude columns

  • reg (bool, optional) – if true, returns a GMT formatted region string, by default False

  • input (list, optional) – set names for input columns, by default [“lon”, “lat”]

  • output (list, optional) – set names for output columns, by default [“x”, “y”]

Returns

Updated dataframe with new easting and northing columns or np.ndarray in format [e, w, n, s]

Return type

pd.DataFrame or np.ndarray

mask_from_shp(shapefile: str, invert: bool = True, xr_grid=None, grid_file: Optional[str] = None, region=None, spacing=None, masked: bool = False, crs: str = 'epsg:3031')[source]#

Create a mask or a masked grid from area inside or outside of a closed shapefile.

Parameters

  • shapefile (str) – path to .shp filename, must by in same directory as accompanying files : .shx, .prj, .dbf, should be a closed polygon file.

  • invert (bool, optional) – choose whether to mask data outside the shape (False) or inside the shape (True), by default True (masks inside of shape)

  • xr_grid (xarray.DataArray, optional) – _xarray.DataArray; to use to define region, or to mask, by default None

  • grid_file (str, optional) – path to a .nc or .tif file to use to define region or to mask, by default None

  • region (str or np.ndarray, optional) – GMT region string or 1x4 ndarray in meters to create a dummy grid if none are supplied, by default None

  • spacing (str or int, optional) – grid spacing in meters to create a dummy grid if none are supplied, by default None

  • masked (bool, optional) – choose whether to return the masked grid (True) or the mask itself (False), by default False

  • crs (str, optional) – if grid is provided, rasterio needs to assign a coordinate reference system via an epsg code, by default “epsg:3031”

Returns

Returns either a masked grid, or the mask grid itself.

Return type

xarray.DataArray

reg_str_to_df(input, names=['x', 'y'])[source]#

Convert GMT region string [e, w, n, s] to pandas dataframe with coordinates of region corners

Parameters

  • input (np.ndarray) – Array of 4 strings in GMT format; [e, w, n, s]

  • names (list, optional) – Names of names to use for easting and northing, by default [“x”, “y”]

Returns

Dataframe with easting and northing columns, and a row for each corner of the region.

Return type

pd.DataFrame