Fix ruff NPY002

.pre-commit-config.yaml

@@ -8,7 +8,7 @@ default_install_hook_types: [pre-commit, commit-msg]
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.4.9
+    rev: v0.4.10
     hooks:
       - id: ruff
         args: [--fix]
@@ -87,6 +87,6 @@ repos:
           - typescript-eslint
 
   - repo: https://github.com/RobertCraigie/pyright-python
-    rev: v1.1.367
+    rev: v1.1.368
     hooks:
       - id: pyright

examples/make_assets/correlation.py

@@ -10,7 +10,8 @@
 # i.e. the correlation matrix contains no significant correlations
 # beyond the spurious correlation that occurs randomly
 n_rows, n_cols = 500, 1000
-rand_wide_mat = np.random.normal(0, 1, size=(n_rows, n_cols))
+np_rng = np.random.default_rng(seed=0)
+rand_wide_mat = np_rng.normal(0, 1, size=(n_rows, n_cols))
 
 corr_mat = np.corrcoef(rand_wide_mat)
 
@@ -29,7 +30,7 @@
 
 # Plot eigenvalue distribution of a rank-deficient correlation matrix
 n_rows, n_cols = 600, 500
-rand_tall_mat = np.random.normal(0, 1, size=(n_rows, n_cols))
+rand_tall_mat = np_rng.normal(0, 1, size=(n_rows, n_cols))
 
 corr_mat_rank_deficient = np.corrcoef(rand_tall_mat)
 

examples/make_assets/histogram.py

@@ -24,9 +24,10 @@
 
 # Random regression data
 rand_regression_size = 500
-y_true = np.random.normal(5, 4, rand_regression_size)
-y_pred = 1.2 * y_true - 2 * np.random.normal(0, 1, rand_regression_size)
-y_std = (y_true - y_pred) * 10 * np.random.normal(0, 0.1, rand_regression_size)
+np_rng = np.random.default_rng(seed=0)
+y_true = np_rng.normal(5, 4, rand_regression_size)
+y_pred = 1.2 * y_true - 2 * np_rng.normal(0, 1, rand_regression_size)
+y_std = (y_true - y_pred) * 10 * np_rng.normal(0, 0.1, rand_regression_size)
 
 
 # %% Histogram Plots

examples/make_assets/ptable.py

@@ -19,6 +19,7 @@
 
 
 # %%
+np_rng = np.random.default_rng(seed=0)
 df_expt_gap = load_dataset("matbench_expt_gap")
 df_steels = load_dataset("matbench_steels")
 
@@ -86,9 +87,7 @@
 
 # %% Histograms laid out in as a periodic table
 # Generate random parity data with y \approx x with some noise
-data_dict = {
-    elem.symbol: np.random.randn(100) + np.random.randn(100) for elem in Element
-}
+data_dict = {elem.symbol: np_rng.randn(100) + np_rng.randn(100) for elem in Element}
 fig = ptable_hists(
     data_dict,
     colormap="coolwarm",
@@ -104,9 +103,9 @@
 # %% Scatter plots laid out as a periodic table
 data_dict = {  # random parity data with y = x + noise
     elem.symbol: [
-        np.arange(10) + np.random.normal(0, 1, 10),
-        np.arange(10) + np.random.normal(0, 1, 10),
-        np.arange(10) + np.random.normal(0, 1, 10),
+        np.arange(10) + np_rng.normal(0, 1, 10),
+        np.arange(10) + np_rng.normal(0, 1, 10),
+        np.arange(10) + np_rng.normal(0, 1, 10),
     ]
     for elem in Element
 }
@@ -126,8 +125,8 @@
 data_dict = {  # random parabola data with y = x^2 + noise
     elem.symbol: [
         np.arange(10),
-        (np.arange(10) - 4) ** 2 + np.random.normal(0, 1, 10),
-        np.arange(10) + np.random.normal(0, 10, 10),
+        (np.arange(10) - 4) ** 2 + np_rng.normal(0, 1, 10),
+        np.arange(10) + np_rng.normal(0, 10, 10),
     ]
     for elem in Element
 }
@@ -148,7 +147,7 @@
 data_dict = {
     elem.symbol: [
         np.linspace(0, 10, 10),
-        np.sin(2 * np.pi * np.linspace(0, 10, 10)) + np.random.normal(0, 0.2, 10),
+        np.sin(2 * np.pi * np.linspace(0, 10, 10)) + np_rng.normal(0, 0.2, 10),
     ]
     for elem in Element
 }

examples/make_assets/relevance.py

@@ -6,12 +6,10 @@
 
 
 # Random classification data
-np.random.seed(42)
+np_rng = np.random.default_rng(seed=0)
 rand_clf_size = 100
-y_binary = np.random.choice([0, 1], size=rand_clf_size)
-y_proba = np.clip(
-    y_binary - 0.1 * np.random.normal(scale=5, size=rand_clf_size), 0.2, 0.9
-)
+y_binary = np_rng.choice([0, 1], size=rand_clf_size)
+y_proba = np.clip(y_binary - 0.1 * np_rng.normal(scale=5, size=rand_clf_size), 0.2, 0.9)
 
 
 # %% Relevance Plots

examples/make_assets/sankey.py

@@ -8,15 +8,16 @@
 
 # %% Sankey diagram of random integers
 cols = ["col_a", "col_b"]
-df_rand_ints = pd.DataFrame(np.random.randint(1, 6, size=(100, 2)), columns=cols)
+np_rng = np.random.default_rng(seed=0)
+df_rand_ints = pd.DataFrame(np_rng.randint(1, 6, size=(100, 2)), columns=cols)
 fig = sankey_from_2_df_cols(df_rand_ints, cols, labels_with_counts="percent")
 rand_int_title = "Two sets of 100 random integers from 1 to 5"
 fig.update_layout(title=dict(text=rand_int_title, x=0.5, y=0.87))
 code_anno = dict(
     x=0.5,
     y=-0.2,
     text="<span style='font-family: monospace;'>df = pd.DataFrame("
-    "np.random.randint(1, 6, size=(100, 2)), columns=['col_a','col_b'])<br>"
+    "np_rng.randint(1, 6, size=(100, 2)), columns=['col_a','col_b'])<br>"
     "fig = sankey_from_2_df_cols(df, df.columns)</span>",
     font_size=12,
     showarrow=False,

examples/make_assets/scatter.py

@@ -34,9 +34,10 @@
 
 # Random regression data
 rand_regression_size = 500
-y_true = np.random.normal(5, 4, rand_regression_size)
-y_pred = 1.2 * y_true - 2 * np.random.normal(0, 1, rand_regression_size)
-y_std = (y_true - y_pred) * 10 * np.random.normal(0, 0.1, rand_regression_size)
+np_rng = np.random.default_rng(seed=0)
+y_true = np_rng.normal(5, 4, rand_regression_size)
+y_pred = 1.2 * y_true - 2 * np_rng.normal(0, 1, rand_regression_size)
+y_std = (y_true - y_pred) * 10 * np_rng.normal(0, 0.1, rand_regression_size)
 
 
 # %% density scatter plotly

examples/make_assets/uncertainty.py

@@ -8,9 +8,10 @@
 
 # %% Random regression data
 rand_regression_size = 500
-y_true = np.random.normal(5, 4, rand_regression_size)
-y_pred = 1.2 * y_true - 2 * np.random.normal(0, 1, rand_regression_size)
-y_std = (y_true - y_pred) * 10 * np.random.normal(0, 0.1, rand_regression_size)
+np_rng = np.random.default_rng(seed=0)
+y_true = np_rng.normal(5, 4, rand_regression_size)
+y_pred = 1.2 * y_true - 2 * np_rng.normal(0, 1, rand_regression_size)
+y_std = (y_true - y_pred) * 10 * np_rng.normal(0, 0.1, rand_regression_size)
 
 
 # %% Uncertainty Plots
@@ -27,7 +28,7 @@
 ax = error_decay_with_uncert(y_true, y_pred, y_std)
 save_and_compress_svg(ax, "error-decay-with-uncert")
 
-eps = 0.2 * np.random.randn(*y_std.shape)
+eps = 0.2 * np_rng.randn(*y_std.shape)
 
 ax = error_decay_with_uncert(y_true, y_pred, {"better": y_std, "worse": y_std + eps})
 save_and_compress_svg(ax, "error-decay-with-uncert-multiple")

pymatviz/uncertainty.py

@@ -152,12 +152,12 @@ def get_err_decay(
     decay_by_err = np.sort(abs_err).cumsum() / n_inc
 
     # error decay for random exclusion of samples
-    ae_tile = np.tile(abs_err, [n_rand, 1])
+    abs_err_tile = np.tile(abs_err, [n_rand, 1])
 
-    for row in ae_tile:
-        np.random.shuffle(row)  # shuffle rows of ae_tile in place
+    for row in abs_err_tile:
+        np.random.default_rng(seed=0).shuffle(row)  # shuffle rows of ae_tile in place
 
-    rand = ae_tile.cumsum(1) / n_inc
+    rand = abs_err_tile.cumsum(1) / n_inc
 
     return decay_by_err, rand.std(0)
 

pyproject.toml

@@ -84,32 +84,31 @@ target-version = "py39"
 [tool.ruff.lint]
 select = ["ALL"]
 ignore = [
-    "ANN101",
-    "ANN102",
-    "ANN401",
+    "ANN101",  # missing-type-self
+    "ANN102",  # missing-type-cls
+    "ANN401",  # any-type
     "C408",    # unnecessary-collection-call
-    "C901",
+    "C901",    # complex-structure
     "COM812",  # trailing comma missing
     "D205",    # 1 blank line required between summary line and description
     "E731",    # do not assign a lambda expression, use a def
-    "EM101",
-    "EM102",
-    "ERA001",
+    "EM101",   # raw-string-in-exception
+    "EM102",   # f-string-in-exception
+    "ERA001",  # dead code
     "FIX002",
     "ISC001",
     "N806",    # non-lowercase-variable-in-function
-    "NPY002",
     "PLR0911", # too-many-return-statements
     "PLR0912", # too-many-branches
     "PLR0913", # function-with-too-many-arguments
     "PLR0915", # too-many-statements
     "PLR2004", # magic-number-comparison
     "PT006",   # pytest-parametrize-names-wrong-type
-    "PTH",
+    "PTH",     # prefer pathlib over os.path
     "S311",
     "SIM105",  # Use contextlib.suppress() instead of try-except-pass
     "TD",
-    "TRY003",
+    "TRY003",  # raise-vanilla-args
 ]
 pydocstyle.convention = "google"
 isort.lines-after-imports = 2