<jupyter_start><jupyter_text>Tri-Polar Grid Projected Plotting=================================This example demonstrates cell plots of data on the semi-structured ORCA2 modelgrid.First, the data is projected into the PlateCarree coordinate reference system.Second four pcolormesh plots are created from this projected dataset,using different projections for the output image.<jupyter_code>import matplotlib.pyplot as plt

import cartopy.crs as ccrs
import iris
import iris.analysis.cartography
import iris.plot as iplt
import iris.quickplot as qplt


def main():
    # Load data
    filepath = iris.sample_data_path("orca2_votemper.nc")
    cube = iris.load_cube(filepath)

    # Choose plot projections
    projections = {}
    projections["Mollweide"] = ccrs.Mollweide()
    projections["PlateCarree"] = ccrs.PlateCarree()
    projections["NorthPolarStereo"] = ccrs.NorthPolarStereo()
    projections["Orthographic"] = ccrs.Orthographic(
        central_longitude=-90, central_latitude=45
    )

    pcarree = projections["PlateCarree"]
    # Transform cube to target projection
    new_cube, extent = iris.analysis.cartography.project(
        cube, pcarree, nx=400, ny=200
    )

    # Plot data in each projection
    for name in sorted(projections):
        fig = plt.figure()
        fig.suptitle("ORCA2 Data Projected to {}".format(name))
        # Set up axes and title
        ax = plt.subplot(projection=projections[name])
        # Set limits
        ax.set_global()
        # plot with Iris quickplot pcolormesh
        qplt.pcolormesh(new_cube)
        # Draw coastlines
        ax.coastlines()

        iplt.show()


if __name__ == "__main__":
    main()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10417892878163626, 'fraction_numerical': 0.007651559741024131, 'mean_word_length': 3.607046070460705, 'pattern_counts': {'":': 1, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '633557', 'license': 'MIT', 'n_tokens_mistral': 555, 'n_tokens_neox': 521, 'n_words': 135, 'path': 'gallery/general/plot_orca_projection.ipynb', 'repo_name': 'TomasTorsvik-tutorial/iris_tutorial', 'size': '1699'}