Load a Time Series of Data From the NEMO Model

This example demonstrates how to load multiple files containing data output by the NEMO model and combine them into a time series in a single cube. The different time dimensions in these files can prevent Iris from concatenating them without the intervention shown here.

Sea surface temperature at 57.157° S 173.500° E
import matplotlib.pyplot as plt

import iris
import iris.plot as iplt
import iris.quickplot as qplt
from iris.util import promote_aux_coord_to_dim_coord

def main():
    # Load the three files of sample NEMO data.
    fname = iris.sample_data_path("NEMO/nemo_1m_*.nc")
    cubes = iris.load(fname)

    # Some attributes are unique to each file and must be blanked
    # to allow concatenation.
    differing_attrs = ["file_name", "name", "timeStamp", "TimeStamp"]
    for cube in cubes:
        for attribute in differing_attrs:
            cube.attributes[attribute] = ""

    # The cubes still cannot be concatenated because their time dimension is
    # time_counter rather than time. time needs to be promoted to allow
    # concatenation.
    for cube in cubes:
        promote_aux_coord_to_dim_coord(cube, "time")

    # The cubes can now be concatenated into a single time series.
    cube = cubes.concatenate_cube()

    # Generate a time series plot of a single point
    y_point_index = 100
    x_point_index = 100
    qplt.plot(cube[:, y_point_index, x_point_index], "o-")

    # Include the point's position in the plot's title
    lat_point = cube.coord("latitude").points[y_point_index, x_point_index]
    lat_string = "{:.3f}\u00B0 {}".format(
        abs(lat_point), "N" if lat_point > 0.0 else "S"
    lon_point = cube.coord("longitude").points[y_point_index, x_point_index]
    lon_string = "{:.3f}\u00B0 {}".format(
        abs(lon_point), "E" if lon_point > 0.0 else "W"
        "{} at {} {}".format(
            cube.long_name.capitalize(), lat_string, lon_string


if __name__ == "__main__":

Total running time of the script: ( 0 minutes 0.286 seconds)

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