diff --git a/README.md b/README.md index 082b37cb..4f930b8c 100644 --- a/README.md +++ b/README.md @@ -129,7 +129,7 @@ colormap. ```python import geovista as gv -from geovista.pantry import ww3_global_tri +from geovista.pantry.data import ww3_global_tri import geovista.theme # Load the sample data. @@ -167,7 +167,7 @@ from an [FVCOM](https://www.fvcom.org/) **unstructured** mesh, as kindly provide ```python import geovista as gv -from geovista.pantry import fvcom_tamar +from geovista.pantry.data import fvcom_tamar import geovista.theme # Load the sample data. @@ -218,7 +218,7 @@ base layer. ```python import geovista as gv -from geovista.pantry import lam_pacific +from geovista.pantry.data import lam_pacific import geovista.theme # Load the sample data. @@ -262,7 +262,7 @@ base layer. import cartopy.crs as ccrs import geovista as gv -from geovista.pantry import lam_pacific +from geovista.pantry.data import lam_pacific import geovista.theme # Load the sample data. @@ -304,7 +304,7 @@ Now render a [Met Office LFRic](https://www.metoffice.gov.uk/research/approach/m ```python import geovista as gv -from geovista.pantry import lfric_sst +from geovista.pantry.data import lfric_sst import geovista.theme # Load the sample data. @@ -343,7 +343,7 @@ using Nucleus for European Modelling of the Ocean (NEMO) ORCA2 Sea Water Potenti ```python import geovista as gv -from geovista.pantry import nemo_orca2 +from geovista.pantry.data import nemo_orca2 import geovista.theme # Load sample data. @@ -381,7 +381,7 @@ Now let's render a [NOAA/NCEI Optimum Interpolation SST](https://www.ncei.noaa.g ```python import geovista as gv -from geovista.pantry import oisst_avhrr_sst +from geovista.pantry.data import oisst_avhrr_sst import geovista.theme # Load sample data. @@ -421,7 +421,7 @@ model uses hexagonal and pentagonal cells, and is a new dynamical core for ```python import geovista as gv -from geovista.pantry import dynamico +from geovista.pantry.data import dynamico import geovista.theme # Load sample data.