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API

Top-level functions

monet.dataset_to_monet(dset[, lat_name, ...])	Rename xarray DataArray or Dataset coordinate variables for use with monet functions, returning a new xarray object.
monet.rename_to_monet_latlon(ds)	Rename latitude/longitude variants to lat/lon, returning a new xarray object.
monet.rename_latlon(ds)	Rename latitude/longitude variants to lat/lon, returning a new xarray object.

Modules

monet.met_funcs	This package contains the main routines for estimating variables related to the Monin-Obukhov (MO) Similarity Theory, such as MO length, adiabatic correctors for heat and momentum transport.
monet.plots
monet.util

DataArray Accessor

DataArray.monet.wrap_longitudes([lon_name])	Ensures longitudes are from -180 -> 180.
DataArray.monet.tidy([lon_name])	Tidy's DataArray–wraps longitudes and sorts lats and lons.
DataArray.monet.is_land([return_xarray])	Check the mask of land and ocean, returning corresponding boolean mask.
DataArray.monet.is_ocean([return_xarray])	Check the mask of land and ocean, returning corresponding boolean mask.
DataArray.monet.cftime_to_datetime64([name])	Convert to datetime64.
DataArray.monet.structure_for_monet([...])	This will attempt to restructure a given DataArray for use within MONET.
DataArray.monet.stratify(levels, vertical[, ...])	Resample in the vertical with stratify.
DataArray.monet.window([lat_min, lon_min, ...])	Function to window, ie select a specific region, given the lower left latitude and longitude and the upper right latitude and longitude
DataArray.monet.interp_constant_lat([lat, ...])	Interpolates the data array to constant longitude.
DataArray.monet.interp_constant_lon([lon])	Interpolates the data array to constant longitude.
DataArray.monet.nearest_ij([lat, lon])	Uses pyresample to interpolate to find the i, j index of grid with respect to the given lat lon.
DataArray.monet.nearest_latlon([lat, lon, ...])	Uses xesmf to interpolate to a given latitude and longitude.
DataArray.monet.quick_imshow([map_kws, ...])	This function takes an xarray DataArray and quickly creates a figure using cartopy and matplotlib imshow.
DataArray.monet.quick_map([map_kws, ...])	This function takes an xarray DataArray and quickly creates a figure using cartopy and matplotlib pcolormesh.
DataArray.monet.quick_contourf([map_kws, ...])	This function takes an xarray DataArray and quickly creates a figure using cartopy and matplotlib contourf.
DataArray.monet.remap_nearest(data, **kwargs)	Remap data from another grid to the current self grid using pyresample nearest-neighbor interpolation.
DataArray.monet.remap_xesmf(data, **kwargs)	Remap data from another grid to the current grid of self using xESMF.
DataArray.monet.combine_point(data[, ...])	Combine self data with point data in dataframe data.

Dataset Accessor

Dataset.monet.wrap_longitudes([lon_name])	Ensures longitudes are from -180 -> 180.
Dataset.monet.tidy([lon_name])	Tidy's Dataset–wraps longitudes and sorts lats and lons.
Dataset.monet.is_land([return_xarray])	checks the mask of land and ocean if the global_land_mask libra.
Dataset.monet.is_ocean([return_xarray])	checks the mask of land and ocean.
Dataset.monet.cftime_to_datetime64([name])	Convert to datetime64.
Dataset.monet.stratify(levels, vertical[, axis])	Resample in the vertical with stratify.
Dataset.monet.window(lat_min, lon_min, ...)	Function to window, ie select a specific region, given the lower left latitude and longitude and the upper right latitude and longitude
Dataset.monet.interp_constant_lat([lat, ...])	Interpolates the data array to constant longitude.
Dataset.monet.interp_constant_lon([lon])	Interpolates the data array to constant longitude.
Dataset.monet.nearest_ij([lat, lon])	Uses pyresample to interpolate to find the i, j index of grid with respect to the given lat lon.
Dataset.monet.nearest_latlon([lat, lon, ...])	Uses xesmf to interpolate to a given latitude and longitude.
Dataset.monet.remap_nearest(data[, ...])	Remap data from another grid to the current self grid using pyresample nearest-neighbor interpolation.
Dataset.monet.remap_nearest_unstructured(data)	Find the closest model data (data) to the observation (self) for unstructured grid model data.
Dataset.monet.remap_xesmf(data, **kwargs)	Remap data from another grid to the current grid of self using xESMF.
Dataset.monet.combine_point(data[, suffix, ...])	Combine self data with point data in dataframe data.

DataFrame Accessor

DataFrame.monet.to_ascii2nc_df([grib_code, ...])
DataFrame.monet.to_ascii2nc_list(**kwargs)
DataFrame.monet.rename_for_monet([df])	Rename latitude and longitude columns in the DataFrame.
DataFrame.monet.get_sparse_SwathDefinition()	Creates a pyreample.geometry.SwathDefinition for a single point.
DataFrame.monet.remap_nearest(df[, ...])	Remap data in df to nearest points in self.
DataFrame.monet.cftime_to_datetime64([col])	Convert to datetime64.

DataFrame.monet.center

The geographic center point of this DataFrame.