API Reference#

Maps#

add_box(fig: pygmt.figure, box: list, pen='2p,black')[source]#

Plot a GMT region as a box.

Parameters

fig (pygmt.figure) – Figure to plot on
box (Union[list or np.ndarray]) – region in EPSG3031 in format [e,w,n,s] in meters
pen (str, optional) – GMT pen string used for the box, by default “2p,black”

add_coast(fig: pygmt.figure, region: str, projection: str, no_coast: bool = False, pen: str = '0.6p,black')[source]#

add coastline and groundingline to figure.

Parameters

fig (pygmt.figure) –
region (Union[str or np.ndarray]) – region for the figure
projection (str) – GMT projection string
no_coast (bool) – If True, only plot groundingline, not coastline, by default is False
pen (str, optional) – GMT pen string, by default “0.6p,black”

add_gridlines(fig: pygmt.figure, region: str, projection: str, x_annots: int = 30, y_annots: int = 4)[source]#

add lat lon grid lines and annotations to a figure.

Parameters

fig (PyGMT.figure instance) –
region (np.ndarray) – region for the figure
projection (str) – GMT projection string in lat lon
x_annots (int, optional) – interval for longitude lines in degrees, by default 30
y_annots (int, optional) – interval for latitude lines in degrees, by default 4

add_inset(fig: pygmt.figure, region: str, fig_width: Union[int, float], inset_pos: str = 'TL')[source]#

add an inset map showing the figure region relative to the Antarctic continent.

Parameters

fig (PyGMT.figure instance) –
region (np.ndarray) – region for the figure
fig_width (float or int) – width of figure in cm
inset_pos (str, optional) – GMT location string for inset map, by default ‘TL’ (top left)

add_scalebar(fig: pygmt.figure, region: str, projection: str, **kwargs)[source]#

add lat lon grid lines and annotations to a figure.

Parameters

fig (PyGMT.figure instance) –
region (np.ndarray) – region for the figure
projection (str) – GMT projection string in lat lon

interactive_map(center_xy=[0, 0], zoom=0, display_xy=True, show=True)[source]#

Plot an interactive map with satellite imagery. Clicking gives the cursor location in EPSG:3031 [x,y]. Requires ipyleaflet

Parameters

center_xy (list, optional) – choose center coordinates in EPSG3031 [x,y], by default [0,0]
zoom (int, optional) – choose zoom level, by default 0
display_xy (bool, optional) – choose if you want clicks to show the xy location, by default True
show (bool, optional) – choose whether to displat the map, by default True

plot_grd(grid: str, cmap: str = 'viridis', plot_region: Optional[str] = None, coast: bool = False, origin_shift: str = 'initialize', **kwargs)[source]#

Helps easily create PyGMT maps, individually or as subplots.

Parameters

grid (Union[str or xr.DataArray]) – grid file to plot, either loaded xr.DataArray or string of a filename
cmap (str, optional) – GMT color scale to use, by default ‘viridis’
plot_region (Union[str or np.ndarray], optional) – region to plot, by default is extent of input grid
coast (bool, optional) – choose whether to plot Antarctic coastline and grounding line, by default False
origin_shift (str, optional) – automatically will create a new figure, set to ‘xshift’ to instead add plot to right of previous plot, or ‘yshift’ to add plot above previous plot, by default ‘initialize’.

Keyword Arguments

image (bool) – set to True if plotting imagery to correctly set colorscale.
grd2cpt (bool) – use GMT module grd2cpt to set color scale from grid values, by default is False
cmap_region (Union[str or np.ndarray]) – region to use to define color scale if grd2cpt is True, by default is plot_region
cbar_label (str) – label to add to colorbar.
points (pd.DataFrame) – points to plot on map, must contain columns ‘x’ and ‘y’.
show_region (np.ndarray) – GMT-format region to use to plot a bounding regions.
cpt_lims (Union[str or tuple]) – limits to use for color scale max and min, by default is max and min of data.
fig (pygmt.Figure()) – if adding subplots, set the first returned figure to a variable, and add that variable as the kwargs ‘fig’ to subsequent calls to plot_grd.
grid_lines (bool) – choose to plot lat/long grid lines, by default is False
inset (bool) – choose to plot inset map showing figure location, by default is False
inset_pos (str) – position for inset map; either ‘TL’, ‘TR’, BL’, ‘BR’, by default is ‘TL’
fig_height (int or float) – height in cm for figures, by default is 15cm.
scalebar (bool) – choose to add a scalebar to the plot, by default is False. See maps.add_scalebar for additional kwargs.
colorbar (bool) – choose to add a colorbar to the plot, by default is True

Returns

Returns a figure object, which can be passed to the fig kwarg to add subplots or other PyGMT plotting methods.

Return type

PyGMT.Figure()

Example

>>> from antarctic_plots import maps
...
>>> fig = maps.plot_grd('grid1.nc')
>>> fig = maps.plot_grd(
... 'grid2.nc',
... origin_shift = 'xshift',
... fig = fig,
... )
...
>>> fig.show()

Profile#

create_profile(method: str, start: Optional[numpy.ndarray] = None, stop: Optional[numpy.ndarray] = None, num: Optional[int] = None, shapefile: Optional[str] = None, polyline: Optional[pandas.core.frame.DataFrame] = None, **kwargs)[source]#

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

Parameters

method (str) – Choose sampling method, either “points”, “shapefile”, or “polyline”
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 to sample at, for “points” by default is 100, for other methods
num by default is determined by shapefile or dataframe
shapefile (str, optional) – shapefile file name to create points along, by default None
polyline (pd.DataFrame, optional) – pandas dataframe with columns x and y as vertices of a polyline, by default None

Returns

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

Return type

pd.Dataframe

cum_dist(df: pandas.core.frame.DataFrame)[source]#

calculate cumulatine distance of points along a line.

Parameters: df (pd.DataFrame) – Dataframe containing columns x, y, and rel_dist
Returns: Returns orignal dataframe with additional column dist
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 Bedmachine layers.

Returns: Nested dictionary of Bedmachine layers and attributes
Return type: dict[dict]

draw_lines(**kwargs)[source]#

Plot an interactive map, and use the “Draw a Polyline” button to create vertices of a line. Verticles will be returned as the output of the function.

Returns: Returns a tuple of list of vertices for each polyline in lat long.
Return type: tuple

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.

Keyword Arguments

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.
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 0.3.
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.
inset (bool) – choose to plot inset map showing figure location, by default is True
inset_pos (str) – position for inset map; either ‘TL’, ‘TR’, BL’, ‘BR’, by default is ‘TL’
save (bool) – Choose to save the image, by default is False.
path (str) – Filename for saving image, by default is None.

rel_dist(df: pandas.core.frame.DataFrame)[source]#

calculate distance between x,y points in a dataframe, relative to the previous row.

Parameters: df (pd.DataFrame) – Dataframe containing columns x and y in meters.
Returns: Returns original dataframe with additional column rel_dist
Return type: pd.DataFrame

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#

class EarthDataDownloader[source]#: Adapted from IcePack: https://github.com/icepack/icepack/blob/master/icepack/datasets.py # noqa Either pulls login details from pre-set environment variables, or prompts user to input username and password.

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

bedmachine(layer: str, reference: str = 'geoid', plot: bool = False, info: bool = False, region=None, spacing=10000.0) → xarray.core.dataarray.DataArray[source]#

Load BedMachine data, from Morlighem et al. 2020: https://doi.org/10.1038/s41561-019-0510-8

orignally from https://nsidc.org/data/nsidc-0756/versions/1. Added to Google Bucket as described in the following notebook: https://github.com/ldeo-glaciology/pangeo-bedmachine/blob/master/load_plot_bedmachine.ipynb # noqa

Parameters

layer (str) – choose which layer to fetch: ‘surface’, ‘thickness’, ‘bed’, ‘firn’, ‘geoid’, ‘mapping’, ‘mask’, ‘errbed’, ‘source’; ‘icebase’ will give results of surface-thickness
reference (str) – choose whether heights are referenced to ‘geoid’ (EIGEN-6C4) or ‘ellipsoid’ (WGS84), by default is ‘geoid’
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 Bedmachine.

Return type

xr.DataArray

bedmap2(layer: str, reference: str = 'geoid', plot: bool = False, info: bool = False, region=None, spacing=10000.0) → xarray.core.dataarray.DataArray[source]#

Load bedmap2 data. All grids are by default referenced to the g104c geoid. Use the ‘reference’ parameter to convert to the ellipsoid. 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: ‘surface’, ‘thickness’, ‘bed’, ‘gl04c_geiod_to_WGS84’, ‘icebase’ will give results of surface-thickness
reference (str) – choose whether heights are referenced to ‘geoid’ (g104c) or ‘ellipsoid’ (WGS84), by default is ‘geoid’
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 Bedmap2.

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

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

Load 1 of 3 ‘versions’ of Antarctic geothermal heat flux grids. version=’burton-johnson-2020’ From Burton-Johnson et al. 2020: Review article: Geothermal heat flow in Antarctica: current and future directions, https://doi.org/10.5194/tc-14-3843-2020 Accessed from supplementary material version=’losing-ebbing-2021’ From Losing and Ebbing 2021: Predicting Geothermal Heat Flow in Antarctica With a Machine Learning Approach. Journal of Geophysical Research: Solid Earth, 126(6), https://doi.org/10.1029/2020JB021499 Accessed from https://doi.pangaea.de/10.1594/PANGAEA.930237 version=’aq1’ From Stal et al. 2021: Antarctic Geothermal Heat Flow Model: Aq1. DOI: https://doi.org/10.1029/2020GC009428 Accessed from https://doi.pangaea.de/10.1594/PANGAEA.924857 verion=’shen-2020’: From Shen et al. 2020; A Geothermal Heat Flux Map of Antarctica Empirically Constrained by Seismic Structure. https://doi.org/ 10.1029/2020GL086955 Accessed from https://sites.google.com/view/weisen/research-products?authuser=0

Parameters

version (str) – Either ‘burton-johnson-2020’, ‘losing-ebbing-2021’, ‘aq1’,
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 (int, optional) – grid spacing to resample the loaded grid to, by default spacing is read from downloaded files

Returns

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

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

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

MEaSUREs Phase-Based Antarctica Ice Velocity Map, version 1: https://nsidc.org/data/nsidc-0754/versions/1#anchor-1 Data part of https://doi.org/10.1029/2019GL083826

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, original spacing is 450m

Returns

Returns a calculated grid of ice velocity in meters/year.

Return type

xr.DataArray

imagery() → xarray.core.dataarray.DataArray[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 ‘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

modis_moa(version: int = 750) → str[source]#

Load the MODIS MoA imagery in either 750m or 125m resolutions.

Parameters: version (int, optional) – choose between 750m or 125m resolutions, by default 750m
Returns: filepath for either 750m or 125m MODIS MoA Imagery
Return type: str

resample_grid(grid, initial_spacing, initial_region, spacing=None, region=None)[source]#

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_to_bounding_box(input)[source]#

Convert GMT region string [e, w, n, s] to bounding box format used for icepyx: [ lower left long,

lower left lat, upper right long, uper right lat

]

Parameters: input (np.ndarray) – Array of 4 strings in GMT format; [e, w, n, s] in meters or degrees.
Returns: Array of 4 strings in bounding box format.
Return type: np.ndarray

GMT_reg_xy_to_ll(input, decimal_degree=False)[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
decimal_degrees (bool, False) – if True, will return results as decimal degrees instead of deg:min:sec

Returns

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

Return type

np.ndarray

alter_region(starting_region: numpy.ndarray, zoom: float = 0, n_shift: float = 0, w_shift: float = 0, buffer: float = 0, print_reg: bool = False)[source]#

Change a region string by shifting the box east/west or north/south, zooming in or out, or adding a seperate buffer region.

Parameters

starting_region (np.ndarray) – Initial GMT formatted region in meters, [e,w,n,s]
zoom (float, optional) – zoom in or out, in meters, by default 0
n_shift (float, optional) – shift north, or south if negative, in meters, by default 0
w_shift (float, optional) – shift west, or eash if negative, in meters, by default 0
buffer (float, optional) – create new region which is zoomed out in all direction, in meters, by default 0
print_reg (bool, optional) – print out the dimensions of the altered region, by default False

Returns

Returns of list of the following variables (region, buffer_region, proj)

Return type

list

coherency(grids: list, label: str, **kwargs)[source]#

Compute and plot the Radially Averaged Power Spectrum input data.

Parameters

grids (list) – list of 2 grids to calculate the cohereny between. grid format can be str (filename), xr.DataArray, or pd.DataFrame.
label (str) – used to label line.

Keyword Arguments

region (Union[str, np.ndarray]) – grid region if input is pd.DataFrame
spacing (float) – grid spacing if input is pd.DataFrame

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 or list) – input dataframe with easting and northing columns, or list [x,y]
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, np.ndarray in format [e, w, n, s], or list in format [lat, lon]

Return type

pd.DataFrame or np.ndarray

get_grid_info(grid)[source]#

Returns information of the specified grid.

Parameters: grid (str or xarray.DataArray) – Input grid to get info from. Filename string or loaded grid.
Returns: (string of grid spacing, array with the region boundaries, data min, data max, grid registration)
Return type: list

get_min_max(grid: xarray.core.dataarray.DataArray, shapefile: Optional[str] = None)[source]#

Get a grids max and min values, optionally just for the region within a shapefile.

Parameters

grid (xr.DataArray) – grid to get values for
shapefile (Union[str or gpd.geodataframe.GeoDataFrame], optional) – path or loaded shapefile to use for a mask, by default None

Returns

returns the min and max values.

Return type

tuple

grd_compare(da1: Union[xarray.core.dataarray.DataArray, str], da2: Union[xarray.core.dataarray.DataArray, str], plot: bool = False, plot_type: str = 'pygmt', **kwargs)[source]#

Find the difference between 2 grids and plot the results, if necessary resample and cut grids to match

Parameters

da1 (xr.DataArray or str) – first grid, loaded grid of filename
da2 (xr.DataArray or str) – second grid, loaded grid of filename
plot (bool, optional) – plot the results, by default False
plot_type (str, optional) – choose the style of plot, by default is pygmt, can choose xarray for faster, simplier plots.

Keyword Arguments

shp_mask (str) – shapefile filename to use to mask the grids for setting the color range.
robust (bool) – use xarray robust color lims instead of min and max, by default is False.

Returns

the result of da1 - da2

Return type

xr.DataArray

grd_trend(da: xarray.core.dataarray.DataArray, coords: list = ['x', 'y', 'z'], deg: int = 1, plot_all: bool = False)[source]#

Fit an arbitrary order trend to a grid and use it to detrend.

Parameters

da (xr.DataArray) – input grid
coords (list, optional) – coordinate names of the supplied grid, by default [‘x’, ‘y’, ‘z’]
deg (int, optional) – trend order to use, by default 1
plot_all (bool, optional) – plot the results, by default False

Returns

returns xr.DataArrays of the fitted surface, and the detrended grid.

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

make_grid(region: Union[str, numpy.ndarray], spacing: float, value: float, name: str)[source]#

Create a grid with 1 variable by defining a region, spacing, name and constant value

Parameters

region (Union[str, np.ndarray]) – GMT format region for the inverion, by default is extent of gravity data,
spacing (float) – spacing for grid
value (float) – constant value to use for variable
name (str) – name for variable

Returns

Returns a xr.DataArray with 1 variable of constant value.

Return type

xr.DataArray

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 (Union[str or gpd.geodataframe.GeoDataFrame]) –
either path to .shp filename, must by in same directory as accompanying files : .shx, .prj, .dbf, should be a closed polygon file, or shapefile which as already

been loaded into a geodataframe.
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

polygon_to_region(polygon: list)[source]#

convert the output of regions.draw_region to bounding region in EPSG:3031

Parameters: polyon (list) – list of polygon vertices
Returns: list in format [e,w,n,s]
Return type: list

random_color()[source]#

generate a random color in format R/G/B

Returns: returns a random color string
Return type: str

raps(data: Union[pandas.core.frame.DataFrame, xarray.core.dataarray.DataArray, xarray.core.dataset.Dataset], names: numpy.ndarray, plot_type: str = 'mpl', filter: Optional[str] = None, **kwargs)[source]#

Compute and plot the Radially Averaged Power Spectrum input data.

Parameters

data (Union[pd.DataFrame, str, list, xr.Dataset, xr.Dataarray]) – if dataframe: need with columns ‘x’, ‘y’, and other columns to calc RAPS for. if str: should be a .nc or .tif file. if list: list of grids or filenames.
names (np.ndarray) – names of pd.dataframe columns, xr.dataset variables, xr.dataarray variable, or files to calculate and plot RAPS for.
plot_type (str, optional) – choose whether to plot with PyGMT or matplotlib, by default ‘mpl’
filter (str) – GMT string to use for pre-filtering data, ex. “c100e3+h” is a 100km low-pass cosine filter, by default is None.

Keyword Arguments

region (Union[str, np.ndarray]) – grid region if input is not a grid
spacing (float) – grid spacing if input is not a grid

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

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

Parameters

input (np.ndarray or pd.DataFrame) – Array of 4 strings in GMT format; [e, w, n, s], or, if reverse is True, a DataFrame with easting and northing columns with names set by names
names (list, optional) – List of names to use for easting and northing, by default [“x”, “y”]
reverse (bool, False) – If True, convert from df to region string.

Returns

Dataframe with easting and northing columns, and a row for each corner of the region, or, if reverse is True, a array in the format [e,w,n,s].

Return type

pd.DataFrame or np.ndarray

region_to_bounding_box(input)[source]#

Convert regions in format [e,w,n,s] in EPSG:3031 meters to format [ll lon, ll lat, ur lon, ur lat] to be used as a bounding box in icepyx.

Parameters: input (np.ndarray) – Array of 4 strings in GMT format; [e, w, n, s] in meters
Returns: Array of 4 strings in bounding box format.
Return type: np.ndarray

set_proj(region: str, fig_height: float = 15) → str[source]#

Gives GMT format projection string from region and figure height. Inspired from https://github.com/mrsiegfried/Venturelli2020-GRL.

Parameters

region (Union[str or np.ndarray]) – GMT-format region str or list (e, w, n, s) in meters EPSG:3031
fig_height (float) – desired figure height in cm

Returns

returns a list of the following variables: (proj, proj_latlon, fig_width, fig_height)

Return type

list

shapes_to_df(shapes: list)[source]#

convert the output of regions.draw_region and profile.draw_lines to a dataframe of x and y points

Parameters: shapes (list) – list of vertices
Returns: Dataframe with x, y, and shape_num.
Return type: pd.DataFrame

square_subplots(n)[source]#

From https://github.com/matplotlib/grid-strategy/blob/master/src/grid_strategy/strategies.py # noqa Return an arrangement of rows containing n axes that is as close to square as looks good. :param n:

The number of plots in the subplot

Returns: Returns a tuple of length nrows, where each element represents the number of plots in that row, so for example a 3 x 2 grid would be represented as (3, 3), because there are 2 rows of length 3.

Antarctic-plots

API Reference

Contents

API Reference#

Maps#

Profile#

Fetch#

Utils#