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Merged
jucordero merged 2 commits into from
Jul 29, 2026
Merged

options for land plot #109

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9def94a
options for land plot
jucordero Jul 28, 2026
ddd4a62
updated label values
jucordero Jul 28, 2026
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115 changes: 78 additions & 37 deletions agrifoodpy/land/land.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,18 +16,26 @@ def __init__(self, xarray_obj):

@staticmethod
def _validate(obj):
"""Validate land xarray, checking it is a DataSet and has the minimum
"""Validate land xarray, checking it is a DataArray and has the minimum
set of coordinates
"""
if not isinstance(obj, xr.DataArray):
raise TypeError("Land array must be an xarray.Dataarray")
raise TypeError("Land array must be an xarray.DataArray")

if "x" not in obj.dims or "y" not in obj.dims:
raise AttributeError("Land array must have 'x' and 'y' dimensions")


def plot(self, ax=None, category_dim=None, colors=None, labels=None,
**kwargs):
def plot(
self,
ax=None,
category_dim=None,
colors=None,
labels=None,
legend=False,
force_categorical=False,
**kwargs
):
"""Plot a LandDataArray

Generates a plot of a LandDataArray using matplotlib imshow, without
Expand All @@ -46,10 +54,14 @@ def plot(self, ax=None, category_dim=None, colors=None, labels=None,
colors : list of strings
Dictionary of colors to use for each land class. If not provided,
the default matplotlib colour map is used.
labels : list of strings
Dictionary of labels to use for each land class. If not provided
and the map is a class percentage map, the coordinate values are
used as labels.
labels : list of strings, dict
List or dictionary of labels to use for each land class. If not
provided and the map is a class percentage map, the coordinate
values are used as labels.
legend : bool
If True, and data is determined to be categorical, a legend is
added to the plot. If data is not categorical, a colorbar is
added instead.
**kwargs : dict
Style options to be passed to the imshow function.

Expand All @@ -74,41 +86,70 @@ def plot(self, ax=None, category_dim=None, colors=None, labels=None,
# Check the map type by checking for additional coordinates
extra_coords = [dim for dim in map.dims if dim not in ["x", "y"]]
if len(extra_coords) >= 1:
if labels is None:
labels = map[extra_coords[0]].values
map = self.dominant_category(category_dim=category_dim)
else:
labels = np.unique(map.values)

if colors is None:
colors = [f"C{i}" for i in np.arange(len(labels))]
unique_vals = np.unique(map.values[~np.isnan(map.values)])

is_discrete = True if len(unique_vals) < 20 else False
if force_categorical:
is_discrete = True

# Create a colour map
cmap = mcolors.ListedColormap(colors)
bounds = np.linspace(-0.5, len(colors), len(colors) + 1)
norm = mcolors.BoundaryNorm(bounds, cmap.N)
if is_discrete:
if colors is None:
colors = [f"C{i}" for i in np.arange(len(unique_vals))]

# Get plot ranges
dx_low = (map.x.values[1] - map.x.values[0])/2
dx_high = (map.x.values[-1] - map.x.values[-2])/2
dy_low = (map.y.values[1] - map.y.values[0])/2
dy_high = (map.y.values[-1] - map.y.values[-2])/2
if labels is None:
# labels = [str(val) for val in unique_vals]
labels = {val: str(val) for val in unique_vals}

xmin, xmax = map.x.values[[0, -1]]
ymin, ymax = map.y.values[[0, -1]]
# Create a discrete colour map
cmap = mcolors.ListedColormap(colors)
bounds = np.linspace(-0.5, len(colors), len(colors) + 1)
norm = mcolors.BoundaryNorm(bounds, cmap.N)

ax.imshow(map, interpolation="none", origin="lower",
extent=[xmin-dx_low,
xmax+dx_high,
ymin-dy_low,
ymax+dy_high],
cmap=cmap, norm=norm)
# Get plot ranges
dx_low = (map.x.values[1] - map.x.values[0])/2
dx_high = (map.x.values[-1] - map.x.values[-2])/2
dy_low = (map.y.values[1] - map.y.values[0])/2
dy_high = (map.y.values[-1] - map.y.values[-2])/2

xmin, xmax = map.x.values[[0, -1]]
ymin, ymax = map.y.values[[0, -1]]

patches = [mpatches.Patch(color=colors[i],
label=labels[i])
for i in np.arange(len(labels))]
patches = [mpatches.Patch(color=colors[i],
label=labels[val])
for i, val in enumerate(unique_vals)]

ax.legend(handles=patches, loc="best")
else:
cmap = kwargs.pop("cmap", "viridis")
norm = kwargs.pop("norm", None)

# Get plot ranges
dx_low = (map.x.values[1] - map.x.values[0])/2
dx_high = (map.x.values[-1] - map.x.values[-2])/2
dy_low = (map.y.values[1] - map.y.values[0])/2
dy_high = (map.y.values[-1] - map.y.values[-2])/2

xmin, xmax = map.x.values[[0, -1]]
ymin, ymax = map.y.values[[0, -1]]

ax.imshow(map, interpolation="none", origin="lower",
extent=[xmin-dx_low,
xmax+dx_high,
ymin-dy_low,
ymax+dy_high],
cmap=cmap, norm=norm)

if legend:
if is_discrete:
ax.legend(handles=patches, loc="best")
else:
plt.colorbar(ax.imshow(map, interpolation="none", origin="lower",
extent=[xmin-dx_low,
xmax+dx_high,
ymin-dy_low,
ymax+dy_high],
cmap=cmap, norm=norm), ax=ax)

return ax

Expand All @@ -134,7 +175,7 @@ def area_by_category(

Parameters
----------
cateogries : int, array
categories : int, array
List of categories to return the total area for. If not set,
the function returns areas for all categories found on the map,
excluding nan values.
Expand Down Expand Up @@ -189,7 +230,7 @@ def area_overlap(
----------
map_right : xarray.DataArray
LandDataArray style DataArray to compare overlapping areas with
categories_left
categories_left : int, array
List of land categories from the left map to return the total area
overlaps for.
If not set, all categories are used, except nan values.
Expand Down
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