skill_table also for plotting.scatter

modelskill/comparison/_collection_plotter.py

@@ -198,7 +198,7 @@ def _scatter_one_model(
         title = title or f"{mod_name} vs {cc_sel_mod.name}"
 
         skill = None
-        units = None
+        skill_score_unit = None
         if skill_table:
             metrics = None if skill_table is True else skill_table
 
@@ -212,15 +212,17 @@ def _scatter_one_model(
                 skill = cc_sel_mod.mean_skill(metrics=metrics)  # type: ignore
             # TODO improve this
             try:
-                units = unit_text.split("[")[1].split("]")[0]
+                skill_score_unit = unit_text.split("[")[1].split("]")[0]
             except IndexError:
-                units = ""  # Dimensionless
+                skill_score_unit = ""  # Dimensionless
 
         if self.is_directional:
             # hide quantiles and regression line
             quantiles = 0
             reg_method = False
 
+        skill_scores = skill.iloc[0].to_dict() if skill is not None else None
+
         ax = scatter(
             x=x,
             y=y,
@@ -238,8 +240,8 @@ def _scatter_one_model(
             title=title,
             xlabel=xlabel,
             ylabel=ylabel,
-            skill_df=skill,
-            units=units,
+            skill_scores=skill_scores,
+            skill_score_unit=skill_score_unit,
             ax=ax,
             **kwargs,
         )

modelskill/comparison/_comparer_plotter.py

@@ -604,21 +604,23 @@ def _scatter_one_model(
         title = title or f"{mod_name} vs {cmp.name}"
 
         skill = None
-        units = None
+        skill_score_unit = None
 
         if skill_table:
             metrics = None if skill_table is True else skill_table
             skill = cmp_sel_mod.skill(metrics=metrics)  # type: ignore
             try:
-                units = unit_text.split("[")[1].split("]")[0]
+                skill_score_unit = unit_text.split("[")[1].split("]")[0]
             except IndexError:
-                units = ""  # Dimensionless
+                skill_score_unit = ""  # Dimensionless
 
         if self.is_directional:
             # hide quantiles and regression line
             quantiles = 0
             reg_method = False
 
+        skill_scores = skill.iloc[0].to_dict() if skill is not None else None
+
         ax = scatter(
             x=x,
             y=y,
@@ -637,8 +639,8 @@ def _scatter_one_model(
             title=title,
             xlabel=xlabel,
             ylabel=ylabel,
-            skill_df=skill,
-            units=units,
+            skill_scores=skill_scores,
+            skill_score_unit=skill_score_unit,
             **kwargs,
         )
 

modelskill/metrics.py

@@ -1114,6 +1114,8 @@ def metric_has_units(metric: Union[str, Callable]) -> bool:
         "Optional",
         "Set",
         "Tuple",
+        "List",
+        "Iterable",
         "Union",
         "_c_residual",
         "_linear_regression",

modelskill/plotting/_misc.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 import warnings
-from typing import Optional, Sequence, Tuple, Union
+from typing import Optional, Sequence, Tuple, Union, Mapping
 
 import matplotlib.pyplot as plt
 from matplotlib.axes import Axes
@@ -149,15 +149,12 @@ def quantiles_xy(
     return np.quantile(x, q=q), np.quantile(y, q=q)
 
 
-def format_skill_df(df: pd.DataFrame, units: str) -> pd.DataFrame:
+def format_skill_table(skill_scores: Mapping[str, float], unit: str) -> pd.DataFrame:
     # select metrics columns
     accepted_columns = defined_metrics | {"n"}
+    kv = {k: v for k, v in skill_scores.items() if k in accepted_columns}
 
-    df = df.loc[:, df.columns.isin(accepted_columns)]
-
-    kv = df.iloc[0].to_dict()
-
-    lines = [_format_skill_line(key, value, units) for key, value in kv.items()]
+    lines = [_format_skill_line(key, value, unit) for key, value in kv.items()]
 
     df = pd.DataFrame(lines, columns=["name", "sep", "value"])
     return df
@@ -166,7 +163,7 @@ def format_skill_df(df: pd.DataFrame, units: str) -> pd.DataFrame:
 def _format_skill_line(
     name: str,
     value: float | int,
-    units: str,
+    unit: str,
 ) -> Tuple[str, str, str]:
     precision: int = 2
     item_unit = " "
@@ -175,7 +172,7 @@ def _format_skill_line(
     if name != "n":
         if metric_has_units(metric=name):
             # if statistic has dimensions, then add units
-            item_unit = unit_display_name(units)
+            item_unit = unit_display_name(unit)
 
         rounded_value = np.round(value, precision)
         fmt = f".{precision}f"

modelskill/plotting/_scatter.py

@@ -1,24 +1,25 @@
 from __future__ import annotations
-from typing import Optional, Sequence, Tuple, Callable, TYPE_CHECKING
+from typing import Optional, Sequence, Tuple, Callable, TYPE_CHECKING, Mapping
+import warnings
 
 if TYPE_CHECKING:
     import matplotlib.axes
 
 import matplotlib.colors as colors
 import matplotlib.pyplot as plt
 import numpy as np
-import pandas as pd
 from matplotlib.cm import ScalarMappable
 from matplotlib import patches
 from matplotlib.axes import Axes
 from matplotlib.ticker import MaxNLocator
 from scipy import interpolate
+import pandas as pd
 
 import modelskill.settings as settings
 from modelskill.settings import options
 
 from ..metrics import _linear_regression
-from ._misc import quantiles_xy, sample_points, format_skill_df, _get_fig_ax
+from ._misc import quantiles_xy, sample_points, format_skill_table, _get_fig_ax
 
 
 def scatter(
@@ -40,8 +41,9 @@ def scatter(
     title: str = "",
     xlabel: str = "",
     ylabel: str = "",
-    skill_df: pd.DataFrame | None = None,
-    units: Optional[str] = "",
+    skill_table: Optional[str | Sequence[str] | bool] = False,
+    skill_scores: Mapping[str, float] | None = None,
+    skill_score_unit: Optional[str] = "",
     ax: Optional[Axes] = None,
     **kwargs,
 ) -> Axes:
@@ -63,9 +65,9 @@ def scatter(
         number of quantiles for QQ-plot, by default None and will depend on the scatter data length (10, 100 or 1000)
         if int, this is the number of points
         if sequence (list of floats), represents the desired quantiles (from 0 to 1)
-    fit_to_quantiles: bool, optional, by default False
+    fit_to_quantiles: bool, optional
         by default the regression line is fitted to all data, if True, it is fitted to the quantiles
-        which can be useful to represent the extremes of the distribution
+        which can be useful to represent the extremes of the distribution, by default False
     show_points : (bool, int, float), optional
         Should the scatter points be displayed?
         None means: show all points if fewer than 1e4, otherwise show 1e4 sample points, by default None.
@@ -100,10 +102,17 @@ def scatter(
         x-label text on plot, by default None
     ylabel : str, optional
         y-label text on plot, by default None
-    skill_df : dataframe, optional
-        dataframe with skill (stats) results to be added to plot, by default None
-    units : str, optional
-        user default units to override default units, eg 'metre', by default None
+    skill_table: str, List[str], bool, optional
+        calculate skill scores and show in box next to the plot,
+        True will show default metrics, list of metrics will show
+        these skill scores, by default False,
+        Note: cannot be used together with skill_scores argument
+    skill_scores : dict[str, float], optional
+        dictionary with skill scores to be shown in box next to
+        the plot, by default None
+        Note: cannot be used together with skill_table argument
+    skill_score_unit : str, optional
+        unit for skill_scores, by default None
     ax : matplotlib.axes.Axes, optional
         axes to plot on, by default None
     **kwargs
@@ -113,6 +122,13 @@ def scatter(
     matplotlib.axes.Axes
         The axes on which the scatter plot was drawn.
     """
+    if "skill_df" in kwargs:
+        warnings.warn(
+            "The `skill_df` keyword argument is deprecated. Use `skill_scores` instead.",
+            FutureWarning,
+        )
+        skill_scores = kwargs.pop("skill_df").to_dict("records")[0]
+
     if show_hist is None and show_density is None:
         # Default: points density
         show_density = True
@@ -121,7 +137,6 @@ def scatter(
         raise ValueError("x & y are not of equal length")
 
     if norm is None:
-        # Default: PowerNorm with gamma of 0.5
         norm = colors.PowerNorm(vmin=1, gamma=0.5)
 
     x_sample, y_sample = sample_points(x, y, show_points)
@@ -170,6 +185,19 @@ def scatter(
     if backend not in PLOTTING_BACKENDS:
         raise ValueError(f"backend must be one of {list(PLOTTING_BACKENDS.keys())}")
 
+    if skill_table:
+        from modelskill import from_matched
+
+        if skill_scores is not None:
+            raise ValueError(
+                "Cannot pass skill_scores and skill_table at the same time"
+            )
+        df = pd.DataFrame({"obs": x, "model": y})
+        cmp = from_matched(df)
+        metrics = None if skill_table is True else skill_table
+        skill = cmp.skill(metrics=metrics)
+        skill_scores = skill.to_dict("records")[0]
+
     return PLOTTING_BACKENDS[backend](
         x=x,
         y=y,
@@ -191,8 +219,8 @@ def scatter(
         xlim=xlim,
         ylim=ylim,
         title=title,
-        skill_df=skill_df,
-        units=units,
+        skill_scores=skill_scores,
+        skill_score_unit=skill_score_unit,
         fit_to_quantiles=fit_to_quantiles,
         ax=ax,
         **kwargs,
@@ -221,8 +249,8 @@ def _scatter_matplotlib(
     xlim,
     ylim,
     title,
-    skill_df,
-    units,
+    skill_scores,
+    skill_score_unit,
     fit_to_quantiles,
     ax,
     **kwargs,
@@ -325,10 +353,8 @@ def _scatter_matplotlib(
 
     ax.set_title(title)
     # Add skill table
-    if skill_df is not None:
-        df = skill_df.to_dataframe()
-        assert isinstance(df, pd.DataFrame)
-        _plot_summary_table(df, units, max_cbar=max_cbar)
+    if skill_scores is not None:
+        _plot_summary_table(skill_scores, skill_score_unit, max_cbar=max_cbar)
     return ax
 
 
@@ -354,9 +380,9 @@ def _scatter_plotly(
     xlim,
     ylim,
     title,
-    skill_df,  # TODO implement
-    units,  # TODO implement
-    fit_to_quantiles,  # TODO implement
+    skill_scores,  # TODO implement
+    skill_score_unit,  # TODO implement
+    fit_to_quantiles,
     **kwargs,
 ):
     import plotly.graph_objects as go
@@ -516,9 +542,9 @@ def _plot_summary_border(
 
 
 def _plot_summary_table(
-    df: pd.DataFrame, units: str, max_cbar: Optional[float] = None
+    skill_scores: Mapping[str, float], units: str, max_cbar: Optional[float] = None
 ) -> None:
-    table = format_skill_df(df, units)
+    table = format_skill_table(skill_scores, units)
     cols = ["name", "sep", "value"]
     text_cols = ["\n".join(table[col]) for col in cols]
 

tests/plot/test_plot.py

@@ -3,7 +3,7 @@
 import pandas as pd
 import pytest
 import modelskill as ms
-from modelskill.plotting._misc import format_skill_df
+from modelskill.plotting._misc import format_skill_table
 from modelskill.plotting._misc import sample_points
 
 
@@ -69,7 +69,7 @@ def test_plot_spatial_overview(o1, o2, o3, mr1):
     plt.close()
 
 
-def test_format_skill_df():
+def test_format_skill_table():
     #
     #    	            n	bias	rmse	urmse	mae	cc	si	r2
     # observation
@@ -89,7 +89,9 @@ def test_format_skill_df():
         index=["smhi_2095_klagshamn"],
     )
 
-    df = format_skill_df(skill_df, units="degC")
+    skill_scores = skill_df.iloc[0].to_dict()
+
+    df = format_skill_table(skill_scores, unit="degC")
     assert "N" in df.iloc[0, 0]
     assert "167" in df.iloc[0, 2]
     assert "BIAS" in df.iloc[1, 0]
@@ -103,7 +105,7 @@ def test_format_skill_df():
     assert "CC" in df.iloc[5, 0]
     assert "0.84" in df.iloc[5, 2]
 
-    df_short = format_skill_df(skill_df, units="meter")
+    df_short = format_skill_table(skill_scores, unit="meter")
 
     assert "N" in df_short.iloc[0, 0]
     assert "167" in df_short.iloc[0, 2]