Welcome to pangaea’s documentation!

An xarray extension for gridded land surface & weather model output.

GitHub: https://github.com/snowman2/pangaea

xarray extension

class pangaea.LSMGridReader(xarray_obj)[source]

This is an extension for xarray specifically designed for land surface models.

Read with pangaea example:

import pangaea as pa

with pa.open_mfdataset('/path/to/ncfiles/*.nc',
                       lat_var='lat',
                       lon_var='lon',
                       time_var='time',
                       lat_dim='lat',
                       lon_dim='lon',
                       time_dim='time') as xds:
    print(xds.lsm.projection)

Read with xarray example:

import xarray as xr

with pa.open_dataset('/path/to/file.nc') as xds:
    print(xds.lsm.projection)
affine

Affine() – The affine for the transformation.

center

Return the geographic center point of this dataset.

coords

Returns y, x coordinate arrays

Warning

The grids always be returned with [0,0] as Northeast and [-1,-1] as Southwest.

datetime

Get datetime object for time variable

dx

float – Pixel size in x direction.

dy

float – Pixel size in y direction.

epsg

str – EPSG code

geotransform

tuple – The geotransform for grid.

getvar(variable, yslice=slice(None, None, None), xslice=slice(None, None, None), calc_4d_method=None, calc_4d_dim=None)[source]

Get variable from model with subset options.

Warning

The grids will always be returned with [0,0] as Northeast and [-1,-1] as Southwest.

Parameters:
  • variable (str) – Name of variable in dataset.
  • yslice (slice, optional) – Slice in y-direction of grid to extract data from.
  • xslice (slice, optional) – Slice in x-direction of grid to extract data from.
  • calc_4d_method (str) – Method to convert 4D variables to 3D variables (Ex. ‘mean’, ‘min’, or ‘max’).
  • calc_4d_dim (str) – Dimension to reduce grid from 4D to 3D (Ex. ‘top_bottom’).
Returns:
Return type:

xarray.DataArray()
latlon

Returns lat,lon arrays

Warning

The grids always be returned with [0,0] as Northeast and [-1,-1] as Southwest.

projection

osgeo.osr.SpatialReference() The projection for the dataset.

resample(variable, match_grid)[source]

Resample data to grid.

Parameters:
  • variable (str) – Name of variable in dataset.
  • match_grid (gdal.Dataset() or sloot.grid.GDALGrid()) – Grid you want the data resampled to match resolution. You can also pass the path to the grid.
to_datetime()[source]

Converts time to datetime.

to_projection(variable, projection)[source]

Convert Grid to New Projection.

Parameters:
  • variable (str) – Name of variable in dataset.
  • projection (osr.SpatialReference()) – Projection to convert data to.
Returns:
Return type:

xarray.Dataset()
to_tif(variable, time_index, out_path)[source]

Dump a variable at a time index to a geotiff.

Parameters:
  • variable (str) – Name of variable in dataset.
  • time_index (int) – 0-based time index,
  • out_path (str) – Path to output geotiff file,
to_utm(variable)[source]

Convert Grid to UTM projection at center of grid.

Parameters:variable (str) – Name of variable in dataset.
Returns:
Return type:xarray.Dataset()
x_size

int – Number of columns in the dataset.

y_inverted

Is the y-coord inverted

y_size

int – Number of rows in the dataset.

Read in LSM files

pangaea.open_mfdataset(path_to_lsm_files, lat_var, lon_var, time_var, lat_dim, lon_dim, time_dim, lon_to_180=False, coords_projected=False, loader=None, engine=None, autoclose=True)[source]

Wrapper to open land surface model netcdf files using xarray.open_mfdataset().

Warning

The time dimension and variable will both be renamed to ‘time’ to enable slicing.

Parameters:
  • path_to_lsm_files (str) – Path to land surface model files with wildcard. (Ex. ‘/path/to/files/*.nc’)
  • lat_var (str) – Latitude variable (Ex. lat).
  • lon_var (str) – Longitude variable (Ex. lon).
  • time_var (str) – Time variable (Ex. time).
  • lat_dim (str) – Latitude dimension (Ex. lat).
  • lon_dim (str) – Longitude dimension (Ex. lon).
  • time_dim (str) – Time dimension (ex. time).
  • lon_to_180 (bool, optional, default=False) – It True, will convert longitude from [0 to 360] to [-180 to 180].
  • coords_projected (bool, optional, default=False) – It True, it will assume the coordinates are already in the projected coordinate system.
  • loader (str, optional, default=None) – If ‘hrrr’, it will load in the HRRR dataset.
  • engine (str, optional) – See: xarray.open_mfdataset() documentation.
  • autoclose (str, optional, default=True) – If True, will use autoclose option with xarray.open_mfdataset().
Returns:
Return type:

xarray.Dataset()

Read with pangaea example:

import pangaea as pa

with pa.open_mfdataset('/path/to/ncfiles/*.nc',
                       lat_var='lat',
                       lon_var='lon',
                       time_var='time',
                       lat_dim='lat',
                       lon_dim='lon',
                       time_dim='time') as xds:
    print(xds.lsm.projection)

Logging

pangaea.log_to_console(status=True, level=None)[source]

Log events to the console.

Parameters:
pangaea.log_to_file(status=True, filename='/home/docs/.cache/pangaea/log/pangaea.log', level=None)[source]

Log events to a file.

Parameters:
  • status (bool, Optional, Default=True) – whether logging to file should be turned on(True) or off(False)
  • filename (string, Optional, Default=None) – path of file to log to
  • level (string, Optional, Default=None) – level of logging; whichever level is chosen all higher levels will be logged. See: https://docs.python.org/2/library/logging.html#levels

Indices and tables