Hatchmap and scattermap fixes

CHANGELOG.rst

@@ -17,6 +17,7 @@ New features and enhancements
 * ``fg.taylordiagram`` can now accept datasets with many dimensions (not only `taylor_params`), provided that they all share the same `ref_std` (e.g. normalized taylor diagrams)  (:pull:`214`).
 * A new optional way to organize points in a ``fg.taylordiagram``  with  `colors_key`, `markers_key`  : DataArrays with a common dimension value or a common attribute are grouped with the same color/marker (:pull:`214`).
 * Heatmap (``fg.matplotlib.heatmap``) now supports `row,col` arguments in `plot_kw`, allowing to plot a grid of heatmaps. (:issue:`208`, :pull:`219`).
+* No-legend option in ``hatchmap``; use ``edgecolor`` and ``edgecolors`` as aliases (:pull:`195`)
 
 Breaking changes
 ^^^^^^^^^^^^^^^^

docs/notebooks/figanos_multiplots.ipynb

@@ -181,7 +181,7 @@
     "im = fg.hatchmap({'sup_305k': sup_305k, 'inf_300k': inf_300k},\n",
     "            plot_kw={\n",
     "                'sup_305k': {\n",
-    "                    'hatches': '*',\n",
+    "                    'hatches': ['////'],  # hatches must be passed as a list\n",
     "                    'col': 'time',\n",
     "                    \"x\": \"lon\",\n",
     "                    \"y\": \"lat\"\n",
@@ -199,7 +199,7 @@
     "            enumerate_subplots=True,    \n",
     "                )\n",
     "\n",
-    "im.fig.suptitle(\"Multiple hatchmaps\", y=1.08)\n"
+    "im.fig.suptitle(\"Multiple hatchmaps\", y=1.08)"
    ]
   },
   {

src/figanos/matplotlib/plot.py

@@ -1617,17 +1617,15 @@ def scattermap(
     if "row" not in plot_kw and "col" not in plot_kw:
         use_attrs.setdefault("title", "description")
 
-    plot_kw_pop = copy.deepcopy(plot_kw)  # copy plot_kw to modify and pop info in it
-
     # extract plot_kw from dict if needed
     if isinstance(data, dict) and plot_kw and list(data.keys())[0] in plot_kw.keys():
-        plot_kw_pop = plot_kw_pop[list(data.keys())[0]]
+        plot_kw = plot_kw[list(data.keys())[0]]
 
     # figanos does not use xr.plot.scatter default markersize
     if "markersize" in plot_kw.keys():
         if not sizes:
             sizes = plot_kw["markersize"]
-        plot_kw_pop.pop("markersize")
+        plot_kw.pop("markersize")
 
     # if data is dict, extract
     if isinstance(data, dict):
@@ -1667,13 +1665,13 @@ def scattermap(
     elif ax is not None and ("col" in plot_kw or "row" in plot_kw):
         raise ValueError("Cannot use 'ax' and 'col'/'row' at the same time.")
     elif ax is None:
-        plot_kw_pop = {"subplot_kws": {"projection": projection}} | plot_kw_pop
+        plot_kw = {"subplot_kws": {"projection": projection}} | plot_kw
         cfig_kw = fig_kw.copy()
         if "figsize" in fig_kw:  # add figsize to plot_kw for facetgrid
-            plot_kw_pop.setdefault("figsize", fig_kw["figsize"])
+            plot_kw.setdefault("figsize", fig_kw["figsize"])
             cfig_kw.pop("figsize")
         if len(cfig_kw) >= 1:
-            plot_kw_pop = {"subplot_kws": {"projection": projection}} | plot_kw_pop
+            plot_kw = {"subplot_kws": {"projection": projection}} | plot_kw
             warnings.warn(
                 "Only figsize and figure.add_subplot() arguments can be passed to fig_kw when using facetgrid."
             )
@@ -1693,9 +1691,9 @@ def scattermap(
         cbar_label = get_attributes(use_attrs["cbar_label"], data)
 
     if "add_colorbar" not in plot_kw or plot_kw["add_colorbar"] is not False:
-        plot_kw_pop.setdefault("cbar_kwargs", {})
-        plot_kw_pop["cbar_kwargs"].setdefault("label", wrap_text(cbar_label))
-        plot_kw_pop["cbar_kwargs"].setdefault("pad", 0.015)
+        plot_kw.setdefault("cbar_kwargs", {})
+        plot_kw["cbar_kwargs"].setdefault("label", wrap_text(cbar_label))
+        plot_kw["cbar_kwargs"].setdefault("pad", 0.015)
 
     # colormap
     if isinstance(cmap, str):
@@ -1748,12 +1746,15 @@ def scattermap(
             target_range=size_range,
             data_range=None,
         )
-        plot_kw_pop.setdefault("add_legend", False)
+        plot_kw.setdefault("add_legend", False)
         if ax:
-            plot_kw_pop.setdefault("s", pt_sizes)
+            plot_kw.setdefault("s", pt_sizes)
         else:
-            plot_kw_pop.setdefault("s", pt_sizes[0])
+            plot_kw.setdefault("s", pt_sizes[0])
 
+    # norm
+    plot_kw.setdefault("vmin", np.nanmin(plot_data.values[mask]))
+    plot_kw.setdefault("vmax", np.nanmax(plot_data.values[mask]))
     if levels is not None:
         if isinstance(levels, Iterable):
             lin = levels
@@ -1766,7 +1767,7 @@ def scattermap(
                 divergent=divergent,
                 linspace_out=True,
             )
-        plot_kw_pop.setdefault("levels", lin)
+        plot_kw.setdefault("levels", lin)
 
     elif (divergent is not False) and ("levels" not in plot_kw):
         norm = custom_cmap_norm(
@@ -1776,42 +1777,51 @@ def scattermap(
             levels=levels,
             divergent=divergent,
         )
-        plot_kw_pop.setdefault("norm", norm)
+        plot_kw.setdefault("norm", norm)
+
+    # matplotlib.pyplot.scatter treats "edgecolor" and "edgecolors" as aliases so we accept "edgecolor" and convert it
+    if "edgecolor" in plot_kw and "edgecolors" not in plot_kw:
+        plot_kw["edgecolors"] = plot_kw["edgecolor"]
+        plot_kw.pop("edgecolor")
 
-    # set defaults and
-    plot_kw_pop = {
+    # set defaults and create copy without vmin, vmax (conflicts with norm)
+    plot_kw = {
         "cmap": cmap,
         "transform": transform,
         "zorder": 8,
         "marker": "o",
-    } | plot_kw_pop
+        "edgecolors": "none",
+    } | plot_kw
 
     # chek if edgecolors in plot_kw and match len of plot_data
     if "edgecolors" in plot_kw:
         if matplotlib.colors.is_color_like(plot_kw["edgecolors"]):
-            plot_kw_pop["edgecolors"] = np.repeat(
+            plot_kw["edgecolors"] = np.repeat(
                 plot_kw["edgecolors"], len(plot_data.where(mask).values)
             )
         elif len(plot_kw["edgecolors"]) != len(plot_data.values):
-            plot_kw_pop["edgecolors"] = np.repeat(
+            plot_kw["edgecolors"] = np.repeat(
                 plot_kw["edgecolors"][0], len(plot_data.where(mask).values)
             )
             warnings.warn(
                 "Length of edgecolors does not match length of data. Only first edgecolor is used for plotting."
             )
         else:
             if isinstance(plot_kw["edgecolors"], list):
-                plot_kw_pop["edgecolors"] = np.array(plot_kw["edgecolors"])
-            plot_kw_pop["edgecolors"] = plot_kw_pop["edgecolors"][mask]
+                plot_kw["edgecolors"] = np.array(plot_kw["edgecolors"])
+            plot_kw["edgecolors"] = plot_kw["edgecolors"][mask]
     else:
-        plot_kw_pop.setdefault("edgecolor", "none")
+        plot_kw.setdefault("edgecolors", "none")
 
+    for key in ["vmin", "vmax"]:
+        plot_kw.pop(key)
     # plot
-    plot_kw_pop = {"x": "lon", "y": "lat", "hue": plot_data.name} | plot_kw_pop
+    plot_kw = {"x": "lon", "y": "lat", "hue": plot_data.name} | plot_kw
     if ax:
-        plot_kw_pop.setdefault("ax", ax)
-    v = plot_data.where(mask).to_dataset()
-    im = v.plot.scatter(**plot_kw_pop)
+        plot_kw.setdefault("ax", ax)
+
+    plot_data_masked = plot_data.where(mask).to_dataset()
+    im = plot_data_masked.plot.scatter(**plot_kw)
 
     # add features
     if ax:
@@ -1874,7 +1884,7 @@ def scattermap(
             np.resize(sdata.values[mask], (sdata.values[mask].size, 1)),
             np.resize(pt_sizes[mask], (pt_sizes[mask].size, 1)),
             max_entries=6,
-            marker=plot_kw_pop["marker"],
+            marker=plot_kw["marker"],
         )
         # legend spacing
         if size_range[1] > 200:
@@ -2281,7 +2291,7 @@ def hatchmap(
     features: list[str] | dict[str, dict[str, Any]] | None = None,
     geometries_kw: dict[str, Any] | None = None,
     levels: int | None = None,
-    legend_kw: dict[str, Any] | None = None,
+    legend_kw: dict[str, Any] | bool = True,
     show_time: bool | str | int | tuple[float, float] = False,
     frame: bool = False,
     enumerate_subplots: bool = False,
@@ -2313,8 +2323,8 @@ def hatchmap(
         cartopy.feature: ['coastline', 'borders', 'lakes', 'land', 'ocean', 'rivers', 'states'].
     geometries_kw : dict, optional
         Arguments passed to cartopy ax.add_geometry() which adds given geometries (GeoDataFrame geometry) to axis.
-    legend_kw : dict, optional
-        Arguments to pass to `ax.legend()`.
+    legend_kw : dict or boolean, optional
+        Arguments to pass to `ax.legend()`. No legend is added if legend_kw == False.
     show_time : bool, tuple, string or int.
         If True, show time (as date) at the bottom right of the figure.
         Can be a tuple of axis coordinates (0 to 1, as a fraction of the axis length) representing the location
@@ -2377,24 +2387,23 @@ def hatchmap(
 
     dattrs = None
     plot_data = {}
-    dc = plot_kw.copy()
 
     # convert data to dict (if not one)
     if not isinstance(data, dict):
         if isinstance(data, xr.DataArray):
             plot_data = {data.name: data}
-            if list(data.keys())[0] not in plot_kw.keys():
-                plot_kw = {list(plot_data.keys())[0]: dc}
+            if data.name not in plot_kw.keys():
+                plot_kw = {data.name: plot_kw}
         elif isinstance(data, xr.Dataset):
             dattrs = data
             plot_data = {var: data[var] for var in data.data_vars}
             for v in plot_data.keys():
                 if v not in plot_kw.keys():
-                    plot_kw[v] = dc
+                    plot_kw[v] = plot_kw
     else:
         for k, v in data.items():
             if k not in plot_kw.keys():
-                plot_kw[k] = dc
+                plot_kw[k] = plot_kw
             if isinstance(v, xr.Dataset):
                 dattrs = k
                 plot_data[k] = v[list(v.data_vars)[0]]
@@ -2416,28 +2425,25 @@ def hatchmap(
     if transform and (
         "xlim" in list(plot_kw.values())[0] and "ylim" in list(plot_kw.values())[0]
     ):
-        extend = [
+        extent = [
             list(plot_kw.values())[0]["xlim"][0],
             list(plot_kw.values())[0]["xlim"][1],
             list(plot_kw.values())[0]["ylim"][0],
             list(plot_kw.values())[0]["ylim"][1],
         ]
-        {v.pop("xlim") for v in plot_kw.values()}
-        {v.pop("ylim") for v in plot_kw.values()}
+        [v.pop(lim) for lim in ["xlim", "ylim"] for v in plot_kw.values() if lim in v]
 
     elif transform and (
         "xlim" in list(plot_kw.values())[0] or "ylim" in list(plot_kw.values())[0]
     ):
-        extend = None
+        extent = None
         warnings.warn(
             "Requires both xlim and ylim with 'transform'. Xlim or ylim was dropped"
         )
-        if "xlim" in list(plot_kw.values())[0].keys():
-            {v.pop("xlim") for v in plot_kw.values()}
-        if "ylim" in list(plot_kw.values())[0].keys():
-            {v.pop("ylim") for v in plot_kw.values()}
+        [v.pop(lim) for lim in ["xlim", "ylim"] for v in plot_kw.values() if lim in v]
+
     else:
-        extend = None
+        extent = None
 
     # setup fig, ax
     if ax is None and (
@@ -2451,11 +2457,11 @@ def hatchmap(
     ):
         raise ValueError("Cannot use 'ax' and 'col'/'row' at the same time.")
     elif ax is None:
-        {
+        [
             v.setdefault("subplot_kws", {}).setdefault("projection", projection)
             for v in plot_kw.values()
-        }
-        cfig_kw = fig_kw.copy()
+        ]
+        cfig_kw = copy.deepcopy(fig_kw)
         if "figsize" in fig_kw:  # add figsize to plot_kw for facetgrid
             plot_kw[0].setdefault("figsize", fig_kw["figsize"])
             cfig_kw.pop("figsize")
@@ -2503,9 +2509,9 @@ def hatchmap(
             im = v.where(mask is not True).plot.contourf(**plot_kw[k])
             artists, labels = im.legend_elements(str_format="{:2.1f}".format)
 
-            if ax:
+            if ax and legend_kw:
                 ax.legend(artists, labels, **legend_kw)
-            else:
+            elif legend_kw:
                 im.figlegend = im.fig.legend(**legend_kw)
 
         elif len(plot_data) > 1 and "levels" in plot_kw[k]:
@@ -2519,6 +2525,13 @@ def hatchmap(
             if "hatches" not in plot_kw[k].keys():
                 plot_kw[k]["hatches"] = dfh[n]
                 n += 1
+            elif isinstance(
+                plot_kw[k]["hatches"], str
+            ):  # make sure the hatches are in a list
+                warnings.warn(
+                    "Hatches argument must be of type 'list'. Wrapping string argument as list."
+                )
+                plot_kw[k]["hatches"] = [plot_kw[k]["hatches"]]
 
             plot_kw[k].setdefault("transform", transform)
             if ax:
@@ -2552,31 +2565,31 @@ def hatchmap(
                             geometries_kw,
                             frame,
                         )
-                        if extend:
-                            fax.set_extent(extend)
+                        if extent:
+                            fax.set_extent(extent)
 
             pat_leg.append(
                 matplotlib.patches.Patch(
-                    hatch=plot_kw[k]["hatches"], fill=False, label=k
+                    hatch=plot_kw[k]["hatches"][0], fill=False, label=k
                 )
             )
 
-    if pat_leg:
+    if pat_leg and legend_kw:
         legend_kw = {
             "loc": "lower right",
             "handleheight": 2,
             "handlelength": 4,
         } | legend_kw
 
-        if ax:
+        if ax and legend_kw:
             ax.legend(handles=pat_leg, **legend_kw)
-        else:
+        elif legend_kw:
             im.figlegend = im.fig.legend(handles=pat_leg, **legend_kw)
 
     # add features
     if ax:
-        if extend:
-            ax.set_extend(extend)
+        if extent:
+            ax.set_extent(extent)
         if dattrs:
             use_attrs.setdefault("title", "description")