scatter_density() use x, y args as axis labels if strings

update examples/mp_bimodal_e_form.ipynb with correct entry counts
(MP API was at one point returning all entries twice)
upgrade pre-commit hooks and apply new black format

.pre-commit-config.yaml

@@ -7,12 +7,12 @@ default_install_hook_types: [pre-commit, commit-msg]
 
 repos:
   - repo: https://github.com/PyCQA/isort
-    rev: 5.11.4
+    rev: 5.12.0
     hooks:
       - id: isort
 
   - repo: https://github.com/psf/black
-    rev: 22.12.0
+    rev: 23.1.0
     hooks:
       - id: black-jupyter
 
@@ -59,23 +59,23 @@ repos:
         exclude_types: [csv, svg, html, yaml, jupyter]
 
   - repo: https://github.com/PyCQA/autoflake
-    rev: v2.0.0
+    rev: v2.0.1
     hooks:
       - id: autoflake
 
   - repo: https://github.com/PyCQA/pydocstyle
-    rev: 6.1.1
+    rev: 6.3.0
     hooks:
       - id: pydocstyle
         exclude: tests
 
   - repo: https://github.com/PyCQA/docformatter
-    rev: v1.5.1
+    rev: v1.6.0.rc1
     hooks:
       - id: docformatter
 
   - repo: https://github.com/nbQA-dev/nbQA
-    rev: 1.6.0
+    rev: 1.6.1
     hooks:
       - id: nbqa-pyupgrade
         args: [--py38-plus]
@@ -90,7 +90,7 @@ repos:
         args: [--drop-empty-cells, --keep-output]
 
   - repo: https://github.com/jendrikseipp/vulture
-    rev: v2.6
+    rev: v2.7
     hooks:
       - id: vulture
         args: [., assets/vulture_whitelist]

pymatviz/histograms.py

@@ -112,7 +112,6 @@ def true_pred_hist(
     )
 
     for xmin, xmax, rect in zip(bin_edges, bin_edges[1:], bars.patches):
-
         y_preds_in_rect = np.logical_and(y_pred > xmin, y_pred < xmax).nonzero()
 
         color_value = y_std[y_preds_in_rect].mean()

pymatviz/parity.py

@@ -60,8 +60,8 @@ def density_scatter(
     sort: bool = True,
     log_cmap: bool = True,
     density_bins: int = 100,
-    xlabel: str = "Actual",
-    ylabel: str = "Predicted",
+    xlabel: str = None,
+    ylabel: str = None,
     identity: bool = True,
     stats: bool | dict[str, Any] = True,
     **kwargs: Any,
@@ -90,6 +90,11 @@ def density_scatter(
     Returns:
         ax: The plot's matplotlib Axes.
     """
+    if xlabel is None:
+        xlabel = getattr(x, "name", x if isinstance(x, str) else "Actual")
+    if ylabel is None:
+        ylabel = getattr(y, "name", y if isinstance(y, str) else "Predicted")
+
     x, y = df_to_arrays(df, x, y)
     ax = ax or plt.gca()
 

pymatviz/ptable.py

@@ -210,7 +210,6 @@ def ptable_heatmap(
     text_style = dict(horizontalalignment="center", fontsize=16, fontweight="semibold")
 
     for symbol, row, column, *_ in df_ptable.itertuples():
-
         row = n_rows - row  # makes periodic table right side up
         heat_val = elem_values.get(symbol)
 
@@ -272,7 +271,6 @@ def ptable_heatmap(
         ax.add_patch(rect)
 
     if heat_mode is not None:
-
         # colorbar position and size: [x, y, width, height]
         # anchored at lower left corner
         cb_ax = ax.inset_axes([0.18, 0.8, 0.42, 0.05], transform=ax.transAxes)

pymatviz/uncertainty.py

@@ -55,7 +55,6 @@ def qq_gaussian(
 
     lines = []  # collect plotted lines to show second legend with miscalibration areas
     for key, std in y_std.items():
-
         z_scored = (np.array(res) / std).reshape(-1, 1)
 
         exp_proportions = np.linspace(0, 1, resolution)

pymatviz/utils.py

@@ -114,7 +114,6 @@ def annotate_bars(
     y_max = 0
 
     for rect, label in zip(ax.patches, labels):
-
         y_pos = rect.get_height()
         x_pos = rect.get_x() + rect.get_width() / 2 + h_offset
 
@@ -290,7 +289,15 @@ def save_fig(
     if path.lower().endswith((".svelte", ".html")):
         config = dict(
             showTips=False,
-            modeBarButtonsToRemove=["lasso2d", "select2d", "autoScale2d", "toImage"],
+            modeBarButtonsToRemove=[
+                "lasso2d",
+                "select2d",
+                "autoScale2d",
+                "toImage",
+                "toggleSpikelines",
+                "hoverClosestCartesian",
+                "hoverCompareCartesian",
+            ],
             responsive=True,
             displaylogo=False,
         )

site/package.json

@@ -15,13 +15,13 @@
     "make-api-docs": "python ../assets/make_api_docs.py"
   },
   "devDependencies": {
-    "@iconify/svelte": "^3.0.1",
-    "@sveltejs/adapter-static": "^1.0.4",
-    "@sveltejs/kit": "^1.1.1",
+    "@iconify/svelte": "^3.1.0",
+    "@sveltejs/adapter-static": "^1.0.5",
+    "@sveltejs/kit": "^1.3.9",
     "@sveltejs/vite-plugin-svelte": "^2.0.2",
-    "@typescript-eslint/eslint-plugin": "^5.48.2",
-    "@typescript-eslint/parser": "^5.48.2",
-    "eslint": "^8.32.0",
+    "@typescript-eslint/eslint-plugin": "^5.50.0",
+    "@typescript-eslint/parser": "^5.50.0",
+    "eslint": "^8.33.0",
     "eslint-plugin-svelte3": "^4.0.0",
     "hastscript": "^7.2.0",
     "highlight.js": "^11.7.0",
@@ -31,13 +31,13 @@
     "rehype-autolink-headings": "^6.1.1",
     "rehype-slug": "^5.1.0",
     "svelte": "^3.55.1",
-    "svelte-check": "^3.0.2",
-    "svelte-preprocess": "^5.0.0",
+    "svelte-check": "^3.0.3",
+    "svelte-preprocess": "^5.0.1",
     "svelte-toc": "^0.5.2",
-    "svelte-zoo": "^0.2.2",
-    "svelte2tsx": "^0.6.0",
-    "tslib": "^2.4.1",
-    "typescript": "^4.9.4",
+    "svelte-zoo": "^0.2.4",
+    "svelte2tsx": "^0.6.1",
+    "tslib": "^2.5.0",
+    "typescript": "^4.9.5",
     "vite": "^4.0.4"
   },
   "prettier": {

tests/test_parity.py

@@ -2,6 +2,8 @@
 
 from typing import Any
 
+import matplotlib.pyplot as plt
+import numpy as np
 import pandas as pd
 import pytest
 
@@ -34,9 +36,21 @@ def test_density_scatter(
     cmap: str | None,
     stats: bool | dict[str, Any],
 ) -> None:
-    density_scatter(
+    ax = density_scatter(
         df=df, x=x, y=y, log_cmap=log_cmap, sort=sort, cmap=cmap, stats=stats
     )
+    assert isinstance(ax, plt.Axes)
+    assert ax.get_xlabel() == x if isinstance(x, str) else "Actual"
+    assert ax.get_ylabel() == y if isinstance(y, str) else "Predicted"
+
+
+def test_density_scatter_uses_series_name_as_label() -> None:
+    x = pd.Series(np.random.rand(5), name="x")
+    y = pd.Series(np.random.rand(5), name="y")
+    ax = density_scatter(x=x, y=y)
+
+    assert ax.get_xlabel() == "x"
+    assert ax.get_ylabel() == "y"
 
 
 @pytest.mark.parametrize("df, x, y", df_x_y)