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Heterogeneity-Index

import itertools

import matplotlib.pyplot as plt
import xarray as xr


def plot(
    sst: xr.DataArray,
    components: xr.Dataset,
    hi: xr.DataArray,
    input_kw: dict | None = None,
) -> plt.Figure:
    if input_kw is None:
        input_kw = {}

    fig = plt.figure(figsize=(6, 4.2), layout="constrained", dpi=150)

    grid = plt.GridSpec(2, 1, figure=fig, height_ratios=(2.7, 2))
    axes_top = grid[0].subgridspec(1, 2).subplots()
    axes_bot = grid[1].subgridspec(1, 3).subplots()

    for ax in itertools.chain(axes_top[1:], axes_bot):
        ax.sharex(axes_top[0])
        ax.sharey(axes_top[0])

    im_kw = dict(
        add_labels=False,
        cbar_kwargs=dict(location="right", pad=0.05),
    )

    sst.plot.imshow(
        ax=axes_top[0],
        cmap="inferno",
        center=False,
        **input_kw,
        **im_kw,
    )
    axes_top[0].set_title("SST", weight="bold")

    hi.plot.imshow(ax=axes_top[1], cmap="viridis", **im_kw)
    axes_top[1].set_title("HI", weight="bold")

    for ax, c in zip(axes_bot, components, strict=True):
        components[c].plot.imshow(ax=ax, cmap="viridis", **im_kw)
        ax.set_title(c, weight="bold")

    for ax in itertools.chain(axes_top, axes_bot):
        ax.set_aspect("equal")
    for ax in itertools.chain(axes_bot[1:], axes_top[1:]):
        ax.tick_params(labelleft=False)

    return fig

Ideal Jet

A idealized meandering jets with rings, as a 2D numpy array (embeded in Xarray).

import xarray as xr
from fronts_toolbox._fields import ideal_jet, sample
from fronts_toolbox.heterogeneity_index import (
    apply_coefficients,
    coefficient_hi,
    coefficients_components,
    components_xarray,
)

sst = xr.DataArray(ideal_jet(), name="sst", dims=("lat", "lon"))
components = components_xarray(sst, window_size=5, bins_shift=False)
coefs = coefficients_components(components)
coefs["HI"] = coefficient_hi(components, coefs)
hi = apply_coefficients(components, coefs)

fig = plot(sst, components, hi)

ESA-SST-CCI / C3S Data

SST of the North Atlantic, as a Dask array embeded in Xarray.

sst = (
    sample("ESA-CCI-C3S")
    .analysed_sst.sel(lat=slice(15, 55), lon=slice(-82, -40))
    .chunk(lat=256, lon=256)
)
components = components_xarray(sst, window_size=5, bins_shift=False)
coefs = coefficients_components(components)
coefs["HI"] = coefficient_hi(components, coefs)
hi = apply_coefficients(components, coefs)

fig = plot(
    sst.isel(time=0),
    components.isel(time=0),
    hi.isel(time=0),
)

/home/docs/checkouts/readthedocs.org/user_builds/fronts-toolbox/envs/latest/lib/python3.11/site-packages/fronts_toolbox/_fields.py:236: FutureWarning: In a future version of xarray the default value for data_vars will change from data_vars='all' to data_vars=None. This is likely to lead to different results when multiple datasets have matching variables with overlapping values. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set data_vars explicitly.
  return xr.open_mfdataset(files, **kwargs)