use norm explicitly (#76)

* use norm explicitly

* remove debug statement

* add second test

* edge case when norm is string

* removed additional space

* make test into img compare

* updated warning message

* remove debug statement

ultraplot/axes/plot.py

@@ -2275,7 +2275,7 @@ def _parse_cmap(
             Normalize specs.
         extend : optional
             The colormap extend setting.
-        vmin, vmax : flaot, optional
+        vmin, vmax : float, optional
             The normalization range.
         sequential, diverging, cyclic, qualitative : bool, optional
             Toggle various colormap types.
@@ -2295,6 +2295,12 @@ def _parse_cmap(
         # Parse keyword args
         cmap_kw = cmap_kw or {}
         norm_kw = norm_kw or {}
+        # If norm is given we use it to set vmin and vmax
+        if (vmin is not None or vmax is not None) and norm is not None:
+            raise ValueError("If 'norm' is given, 'vmin' and 'vmax' must not be set.")
+        if isinstance(norm, mcolors.Normalize):
+            vmin = norm.vmin
+            vmax = norm.vmax
         vmin = _not_none(vmin=vmin, norm_kw_vmin=norm_kw.pop("vmin", None))
         vmax = _not_none(vmax=vmax, norm_kw_vmax=norm_kw.pop("vmax", None))
         extend = _not_none(extend, "neither")
@@ -2440,7 +2446,6 @@ def _parse_cmap(
             kwargs.update({"levels": levels, "extend": extend})
         else:
             guides._add_guide_kw("colorbar", kwargs, extend=extend)
-
         return kwargs
 
     def _parse_cycle(

ultraplot/tests/test_1dplots.py

@@ -430,3 +430,31 @@ def test_triplot_variants(x, y, z, triangles, use_triangulation, use_datadict):
         else:
             ax.triplot(x, y, "ko-")  # Without specific triangles
     return fig
+
+
+@pytest.mark.mpl_image_compare
+def test_norm_not_modified():
+    """
+    Ensure that norm is correctly passed to pcolor and related functions.
+    """
+    # Create mock data and assign the colors to y
+    # The norm should clip the data and not be modified
+    x = np.arange(10)
+    y = x**2
+    c = y
+    cmap = uplt.Colormap("viridis")
+    norm = uplt.Norm("linear", 0, 10)
+    fig, (left, right) = uplt.subplots(ncols=2, share=0)
+    left.scatter(x, y, c=c, cmap=cmap, norm=norm)
+    assert norm.vmin == 0
+    assert norm.vmax == 10
+
+    arr = np.random.rand(20, 40) * 1000
+    xe = np.linspace(0, 1, num=40, endpoint=True)
+    ye = np.linspace(0, 1, num=20, endpoint=True)
+
+    norm = uplt.Norm("linear", vmin=0, vmax=1)
+    right.pcolor(xe, ye, arr, cmap="viridis", norm=norm)
+    assert norm.vmin == 0
+    assert norm.vmax == 1
+    return fig