arctichoke.plot.seasonal_cycle

Functions

plot_seasonal_cycle(datasets[, variable_id, ...])

Plot a seasonal cycle of the dataset.

multi_seasonal_cycle([source_id, variable_ids, ...])

Plot multiple seasonal cycles of different datasets.

Module Contents

arctichoke.plot.seasonal_cycle.plot_seasonal_cycle(datasets: [str, xarray.DataArray, xarray.Dataset], variable_id: str = None, take_mean: bool = False, ax: matplotlib.axes.Axes = None, plt_title: str = None, line_labels: [str] = None, line_styles: [str] = '-', xlims: [str, str] = None, ylims: [float, float] = None, c_map: [matplotlib.colors.ListedColormap] = mplcm.viridis_r, c_map_label: str = 'Year', save_as: str = None, test: bool = False, **kwargs)

Plot a seasonal cycle of the dataset.

Plots a seasonal cycle of the given dataset for the given variable, if applicable.

Parameters:

  • datasets (list of str, xarray.DataArray, xarray.Dataset) – A list of datasets for which to make a plot.

  • variable_id (str) – The name of the variable ID to plot.

  • take_mean (bool, optional) – Whether to take the mean for each month across all the years. Default is False.

  • ax (matplotlib.axes.Axes, optional) – The axes on which to plot the data. If None, a new figure is created. Default is None.

  • plt_title (str, None, optional) – The title to use for the plot. Default is None, which uses a default title for the plot.

  • line_labels (list of str, None, optional) – The labels to use for the lines that are plotted if take_mean = True. Default is None.

  • line_styles (list of str, None, optional) – The line styles to use for the lines that are plotted if take_mean = True. Default is ‘-’.

  • xlims (List of float, optional) –

    The limits to use for the x-axis on the plot in the following format:

    • [x_min, x_max]

    where x_min and x_max are strings in the format YYYY-MM-DD Default is None, which expands the x-axis to include all the data.

  • ylims (List of float, optional) –

    The limits to use for the y-axis on the plot in the following format:

    • [y_min, y_max]

    Default is None, which expands the y-axis to include all the data.

  • c_map (matplotlib.colors.ListedColormap, optional) – The color map to use for the different lines so their order is clearer. Default is matplotlib.cm.viridis_r, the reverse of viridis.

  • c_map_label (str, None, optional) – The label to use on the colorbar. If None, then the colorbar will have no label. Default is Year.

  • save_as (str, None, optional) – The name of the file to which to save the plot. Default is None, which doesn’t save the plot to a file.

  • test (bool, optional) – If True, the function exists before making a plot for use in testing. Default is False.

  • **kwargs – Keyword arguments to pass to xr.DataArray.plot().

Returns:

  • If test == False – fig ax

  • If test == True – dataset : xarray.DataArray

Examples

>>> from arctichoke.dataset.field_mean import get_field_mean
>>> fldmean_xr = get_field_mean('example_siconc_dataset.nc')
>>> from arctichoke.plot.seasonal_cycle import plot_seasonal_cycle
>>> plot_seasonal_cycle(dataset = fldmean_xr, variable_id = 'siconc')
arctichoke.plot.seasonal_cycle.multi_seasonal_cycle(source_id: str = 'EC-Earth3P-HR', variable_ids: [str] = ['siconc', 'sithick'], experiment_ids: [str] = ['hist-1950'], time_bounds_lists: dict = {'hist-1950': [[1950, 1959], [2005, 2014]], 'highres-future': [[2015, 2024], [2041, 2050]]}, variant_labels=['r1i1p2f1', 'r2i1p2f1', 'r3i1p2f1'], super_title: str = None, fig_scale: int, float = 2, save_as: str = None, test: bool = False, **kwargs)

Plot multiple seasonal cycles of different datasets.

Plots a seasonal cycle for each combination of parameters. Different experiments get their own axes, as well as different variables. Multiple time bounds and variant labels are plotted on the same axes.

Parameters:

  • source_id (str) – The name of the source_id to specify the model to plot.

  • variable_ids (list of str) – The variable ID(s) to plot.

  • experiment_ids (list of str, optional) – The experiment(s) to plot. Default is [‘hist-1950’].

  • time_bounds_lists (dict, optional) – A dictionary of the time bounds within which to plot. Each key in the dictionary is the name of an experiment and each value is a list of pairs of years. These time bounds will be passed to select_files_by_time(). Default is shown above.

  • variant_labels (list of str, optional) – The variant label(s) to plot for each subplot. Default is shown above.

  • super_title (str, None, optional) – The title for the overall figure. Default is None.

  • fig_scale (int, float, optional) – The scale factor by which to resize the figure. Default is 2.

  • save_as (str, None, optional) – The name of the file to which to save the plot. Default is None, which doesn’t save the plot to a file.

  • test (bool, optional) – If True, the function exists before making a plot for use in testing. Default is False.

  • **kwargs – Keyword arguments to pass to xr.DataArray.plot().

Return type:

None

Examples

>>> from arctichoke.plot.seasonal_cycle import multi_seasonal_cycle
>>> multi_seasonal_cycle(
        source_id = 'EC-Earth3P-HR',
        variable_ids = ['siconc'],
        super_title = 'Seasonal Cycles',
    )