Merge branch 'main' into feature/add_qq_and_residual_hist_to_comparer…

…_collection

.github/workflows/build_docs.yml

@@ -14,11 +14,11 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-    - uses: actions/checkout@v2
-    - name: Set up Python 3.10
-      uses: actions/setup-python@v2
+    - uses: actions/checkout@v3
+    - name: Set up Python
+      uses: actions/setup-python@v4
       with:
-        python-version: "3.10"
+        python-version: "3.11"
 
     - name: Install modelskill
       run: |

.github/workflows/docs.yml

@@ -11,11 +11,11 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-    - uses: actions/checkout@v2
-    - name: Set up Python 3.10
+    - uses: actions/checkout@v3
+    - name: Set up Python
       uses: actions/setup-python@v4
       with:
-        python-version: "3.10"
+        python-version: "3.11"
 
     - name: Install modelskill
       run: pip install .[dev]

.github/workflows/full_test.yml

@@ -15,11 +15,11 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, windows-latest]
-        python-version: ["3.9", "3.13"]      
+        python-version: ["3.10", "3.13"]      
 
     steps:
     - uses: actions/checkout@v3
-    - uses: chartboost/ruff-action@v1 # Fail fast if there are any linting errors
+    - uses: astral-sh/ruff-action@v2 # Fail fast if there are any linting errors
       with:
         version: 0.6.2
         src: "modelskill"
@@ -35,11 +35,11 @@ jobs:
     - name: Install modelskill
       run: |
         pip install .[test]
-    - name: Static type check
-      run: make typecheck
     - name: Test with pytest
       run: |
         pytest --cov=modelskill --ignore tests/notebooks/
+    - name: Static type check
+      run: make typecheck
     - name: Test docstrings with pytest
       run: |
         pytest ./modelskill/metrics.py --doctest-modules

.github/workflows/notebooks_test.yml

@@ -16,7 +16,7 @@ jobs:
     - name: Set up Python
       uses: actions/setup-python@v4
       with:
-        python-version: '3.9'    
+        python-version: '3.11'    
     - name: Install modelskill
       run: |
         pip install .[test,notebooks]    

.github/workflows/python-publish.yml

@@ -18,7 +18,7 @@ jobs:
     - name: Set up Python
       uses: actions/setup-python@v4
       with:
-        python-version: '3.10'
+        python-version: '3.11'
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip

.github/workflows/scheduled_test.yml

@@ -14,7 +14,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, windows-latest]
-        python-version: [3.9, "3.13"]
+        python-version: ["3.10", "3.13"]
 
     steps:
     - uses: actions/checkout@v3

README.md

@@ -3,6 +3,7 @@
 # ModelSkill: Flexible Model skill evaluation.
  ![Python version](https://img.shields.io/pypi/pyversions/modelskill.svg) 
 ![Python package](https://github.com/DHI/modelskill/actions/workflows/full_test.yml/badge.svg) [![PyPI version](https://badge.fury.io/py/modelskill.svg)](https://badge.fury.io/py/modelskill)
+![Downloads](https://img.shields.io/pypi/dm/modelskill)
 
 [ModelSkill](https://github.com/DHI/modelskill) is a python package for scoring [MIKE](https://www.mikepoweredbydhi.com) models (other models can be evaluated as well). 
 

modelskill/comparison/_collection_plotter.py

@@ -70,7 +70,7 @@ def scatter(
         Parameters
         ----------
         bins: (int, float, sequence), optional
-            bins for the 2D histogram on the background. By default 20 bins.
+            bins for the 2D histogram on the background. By default 120 bins.
             if int, represents the number of bins of 2D
             if float, represents the bin size
             if sequence (list of int or float), represents the bin edges
@@ -94,8 +94,9 @@ def scatter(
         show_density: bool, optional
             show the data density as a colormap of the scatter, by default
             None. If both `show_density` and `show_hist` are None, then
-            `show_density` is used by default. For binning the data, the
-            kword `bins=Float` is used.
+            `show_density` is used by default. If number of points is less
+            than 200, then `show_density` is False as default.
+            For binning the data, the kword `bins=Float` is used.
         norm : matplotlib.colors norm
             colormap normalization. If None, defaults to
             matplotlib.colors.PowerNorm(vmin=1, gamma=0.5)

modelskill/comparison/_comparer_plotter.py

@@ -494,7 +494,7 @@ def scatter(
         Parameters
         ----------
         bins: (int, float, sequence), optional
-            bins for the 2D histogram on the background. By default 20 bins.
+            bins for the 2D histogram on the background. By default 120 bins.
             if int, represents the number of bins of 2D
             if float, represents the bin size
             if sequence (list of int or float), represents the bin edges
@@ -518,8 +518,9 @@ def scatter(
         show_density: bool, optional
             show the data density as a colormap of the scatter, by default
             None. If both `show_density` and `show_hist` are None, then
-            `show_density` is used by default. For binning the data, the
-            kword `bins=Float` is used.
+            `show_density` is used by default. If number of points is less
+            than 200, then `show_density` is False as default.
+            For binning the data, the kword `bins=Float` is used.
         norm : matplotlib.colors norm
             colormap normalization. If None, defaults to
             matplotlib.colors.PowerNorm(vmin=1, gamma=0.5)

modelskill/comparison/_comparison.py

@@ -369,6 +369,27 @@ def _matched_data_to_xarray(
     cols = list(df.columns)
     items = ItemSelection.parse(cols, obs_item, mod_items, aux_items, x_item, y_item)
 
+    # check that x and x_item is not both specified (same for y and y_item)
+    if (x is not None) and (x_item is not None):
+        raise ValueError("x and x_item cannot both be specified")
+    if (y is not None) and (y_item is not None):
+        raise ValueError("y and y_item cannot both be specified")
+
+    if x is not None:
+        try:
+            x = float(x)
+        except TypeError:
+            raise TypeError(
+                f"x must be scalar, not {type(x)}; if x is a coordinate variable, use x_item"
+            )
+    if y is not None:
+        try:
+            y = float(y)
+        except TypeError:
+            raise TypeError(
+                f"y must be scalar, not {type(y)}; if y is a coordinate variable, use y_item"
+            )
+
     # check that items.obs and items.model are numeric
     if not np.issubdtype(df[items.obs].dtype, np.number):
         raise ValueError(

modelskill/matching.py

@@ -84,6 +84,7 @@ def from_matched(
     y_item: str | int | None = None,
 ) -> Comparer:
     """Create a Comparer from observation and model results that are already matched (aligned)
+
     Parameters
     ----------
     data : [pd.DataFrame, str, Path, mikeio.Dfs0, mikeio.Dataset]
@@ -129,6 +130,7 @@ def from_matched(
     Observation: stn_a, n_points=3
         Model: local, rmse=0.100
         Model: global, rmse=0.200
+
     """
     # pre-process if dfs0, or mikeio.Dataset
     if isinstance(data, (str, Path)):