fix "COM812", # trailing comma missing

examples/dataset_exploration/boltztrap_mp/explore_boltztrap_mp.py

@@ -42,7 +42,9 @@
 
 # %%
 fig = ptable_heatmap(
-    count_elements(df_boltz[Key.formula]), log=True, return_type="figure"
+    count_elements(df_boltz[Key.formula]),
+    log=True,
+    return_type="figure",
 )
 fig.suptitle("Elements in BoltzTraP MP dataset")
 pmv.save_fig(fig, "boltztrap_mp-ptable-heatmap.pdf")
@@ -65,6 +67,9 @@
 
 # %%
 df_boltz.sort_values("pf_n", ascending=False).head(1000).hist(
-    bins=50, log=True, layout=[2, 3], figsize=[18, 8]
+    bins=50,
+    log=True,
+    layout=[2, 3],
+    figsize=[18, 8],
 )
 plt.suptitle("BoltzTraP MP")

examples/dataset_exploration/matbench/dielectric/explore_dielectric.py

@@ -40,7 +40,7 @@
 df_diel[Key.n_wyckoff] = df_diel.wyckoff.map(count_wyckoff_positions)
 
 df_diel[Key.crystal_system] = df_diel[Key.spg_num].map(
-    pmv.utils.crystal_sys_from_spg_num
+    pmv.utils.crystal_sys_from_spg_num,
 )
 
 df_diel[Key.volume] = [x.volume for x in df_diel[Key.structure]]
@@ -49,7 +49,9 @@
 
 # %%
 fig = pmv.ptable_heatmap(
-    pmv.count_elements(df_diel[Key.formula]), log=True, return_type="figure"
+    pmv.count_elements(df_diel[Key.formula]),
+    log=True,
+    return_type="figure",
 )
 fig.suptitle("Elemental prevalence in the Matbench dielectric dataset")
 pmv.save_fig(fig, "dielectric-ptable-heatmap.pdf")
@@ -88,7 +90,8 @@
 
 x_ticks = {}  # custom x axis tick labels
 for cry_sys, df_group in sorted(
-    df_diel.groupby(Key.crystal_system), key=lambda x: pmv.crystal_sys_order.index(x[0])
+    df_diel.groupby(Key.crystal_system),
+    key=lambda x: pmv.crystal_sys_order.index(x[0]),
 ):
     x_ticks[cry_sys] = (
         f"<b>{cry_sys}</b><br>"
@@ -101,7 +104,8 @@
 fig.layout.margin = dict(b=10, l=10, r=10, t=50)
 fig.layout.showlegend = False
 fig.layout.xaxis = reusable_x_axis = dict(
-    tickvals=list(range(len(pmv.crystal_sys_order))), ticktext=list(x_ticks.values())
+    tickvals=list(range(len(pmv.crystal_sys_order))),
+    ticktext=list(x_ticks.values()),
 )
 
 
@@ -130,7 +134,8 @@ def rgb_color(val: float, max_val: float) -> str:
 
 x_ticks = {}
 for cry_sys, df_group in sorted(
-    df_diel.groupby(Key.crystal_system), key=lambda x: pmv.crystal_sys_order.index(x[0])
+    df_diel.groupby(Key.crystal_system),
+    key=lambda x: pmv.crystal_sys_order.index(x[0]),
 ):
     n_wyckoff = df_group[Key.n_wyckoff].mean()
     clr = rgb_color(n_wyckoff, 14)

examples/dataset_exploration/matbench/log_g+kvrh/explore_log_g+krvh.py

@@ -41,18 +41,20 @@
 df_grvh[[Key.spg_symbol, Key.spg_num]] = [
     struct.get_space_group_info()
     for struct in tqdm(
-        df_grvh[Key.structure], desc="Getting matbench_log_gvrh spacegroups"
+        df_grvh[Key.structure],
+        desc="Getting matbench_log_gvrh spacegroups",
     )
 ]
 df_grvh[Key.crystal_system] = df_grvh[Key.spg_num].map(
-    pmv.utils.crystal_sys_from_spg_num
+    pmv.utils.crystal_sys_from_spg_num,
 )
 
 
 df_grvh[Key.wyckoff] = [
     get_protostructure_label_from_spglib(struct)
     for struct in tqdm(
-        df_grvh[Key.structure], desc="Getting matbench_log_gvrh Wyckoff strings"
+        df_grvh[Key.structure],
+        desc="Getting matbench_log_gvrh Wyckoff strings",
     )
 ]
 df_grvh[Key.n_wyckoff] = df_grvh.wyckoff.map(count_wyckoff_positions)
@@ -133,7 +135,9 @@ def has_isolated_atom(crystal: Structure, radius: float = 5) -> bool:
 df_grvh[Key.formula] = df_grvh[Key.structure].map(lambda struct: struct.formula)
 
 fig = ptable_heatmap(
-    count_elements(df_grvh[Key.formula]), log=True, return_type="figure"
+    count_elements(df_grvh[Key.formula]),
+    log=True,
+    return_type="figure",
 )
 fig.suptitle("Elemental prevalence in the Matbench bulk/shear modulus datasets")
 pmv.save_fig(fig, "log_gvrh-ptable-heatmap.pdf")
@@ -172,7 +176,8 @@ def rgb_color(val: float, max_val: float) -> str:
 
 x_ticks = {}
 for cry_sys, df_group in sorted(
-    df_grvh.groupby(Key.crystal_system), key=lambda x: crystal_sys_order.index(x[0])
+    df_grvh.groupby(Key.crystal_system),
+    key=lambda x: crystal_sys_order.index(x[0]),
 ):
     n_wyckoff_top = df_group[Key.n_wyckoff].mean()
     clr = rgb_color(n_wyckoff_top, 14)

examples/dataset_exploration/matbench/perovskites/explore_perovskites.py

@@ -33,7 +33,7 @@
 df_perov[Key.formula] = df_perov[Key.structure].map(lambda cryst: cryst.formula)
 
 df_perov[Key.crystal_system] = df_perov[Key.spg_num].map(
-    pmv.utils.crystal_sys_from_spg_num
+    pmv.utils.crystal_sys_from_spg_num,
 )
 
 
@@ -49,7 +49,9 @@
 
 # %%
 fig = ptable_heatmap(
-    count_elements(df_perov[Key.formula]), log=True, return_type="figure"
+    count_elements(df_perov[Key.formula]),
+    log=True,
+    return_type="figure",
 )
 fig.suptitle("Elements in Matbench Perovskites dataset")
 pmv.save_fig(fig, "perovskites-ptable-heatmap.pdf")

examples/dataset_exploration/matbench/phonons/explore_phonons.py

@@ -43,7 +43,9 @@
 df_phonon[Key.volume] = df_phonon[Key.structure].map(lambda cryst: cryst.volume)
 
 fig = ptable_heatmap(
-    count_elements(df_phonon[Key.formula]), log=True, return_type="figure"
+    count_elements(df_phonon[Key.formula]),
+    log=True,
+    return_type="figure",
 )
 fig.suptitle("Elemental prevalence in the Matbench phonons dataset")
 pmv.save_fig(fig, "phonons-ptable-heatmap.pdf")

examples/dataset_exploration/matbench/steels/explore_steels.py

@@ -26,7 +26,9 @@
 
 # %%
 fig = ptable_heatmap(
-    count_elements(df_steels[Key.composition]), log=True, return_type="figure"
+    count_elements(df_steels[Key.composition]),
+    log=True,
+    return_type="figure",
 )
 fig.suptitle("Elemental prevalence in the Matbench steels dataset")
 pmv.save_fig(fig, "steels-ptable-heatmap.pdf")

examples/dataset_exploration/matpes/eda.py

@@ -85,7 +85,7 @@
 total_force_col = "Σ|force<sub>i</sub>| (eV/Å)"
 df_pbe[total_force_col] = df_pbe[Key.forces].map(lambda arr: np.abs(arr).sum(axis=1))
 df_r2scan[total_force_col] = df_r2scan[Key.forces].map(
-    lambda arr: np.abs(arr).sum(axis=1)
+    lambda arr: np.abs(arr).sum(axis=1),
 )
 
 fig = go.Figure()
@@ -149,7 +149,8 @@
 }
 
 fig = pmv.ptable_heatmap_splits(
-    per_elem_cohesive_energy, cbar_title=f"{col_name.label} (eV)"
+    per_elem_cohesive_energy,
+    cbar_title=f"{col_name.label} (eV)",
 )
 
 
@@ -201,7 +202,9 @@
 
 # %% spacegroup histogram
 fig = pmv.spacegroup_bar(
-    df_r2scan[Key.spg_num], title="r2SCAN spacegroup histogram", log=True
+    df_r2scan[Key.spg_num],
+    title="r2SCAN spacegroup histogram",
+    log=True,
 )
 fig.show()
 pmv.save_fig(fig, "r2scan-spacegroup-hist.pdf")
@@ -230,7 +233,9 @@
         for site, force in zip(struct, forces, strict=True)
     }
     for struct, forces in zip(
-        df_r2scan[Key.structure], df_r2scan[Key.forces], strict=True
+        df_r2scan[Key.structure],
+        df_r2scan[Key.forces],
+        strict=True,
     )
 ).mean()
 

examples/dataset_exploration/ricci_carrier_transport/convert_dtype+add_strucs.py

@@ -15,7 +15,8 @@
 
 # %%
 df_carrier = pd.concat(
-    [df_carrier, pd.json_normalize(df_carrier.data)], axis="columns"
+    [df_carrier, pd.json_normalize(df_carrier.data)],
+    axis="columns",
 ).drop(columns=["data", "is_public", "project"])
 
 df_carrier = df_carrier.set_index("identifier")
@@ -99,5 +100,6 @@
 
 # %%
 df_carrier.to_json(
-    "cleaned_ricci_boltztrap_mp_tabular.json.gz", default_handler=lambda x: x.as_dict()
+    "cleaned_ricci_boltztrap_mp_tabular.json.gz",
+    default_handler=lambda x: x.as_dict(),
 )

examples/dataset_exploration/ricci_carrier_transport/explore_carrier_transport.py

@@ -40,7 +40,9 @@
 
 # %%
 fig = ptable_heatmap(
-    count_elements(df_carrier.pretty_formula.dropna()), log=True, return_type="figure"
+    count_elements(df_carrier.pretty_formula.dropna()),
+    log=True,
+    return_type="figure",
 )
 fig.suptitle("Elemental prevalence in the Ricci Carrier Transport dataset")
 pmv.save_fig(fig, "carrier-transport-ptable-heatmap.pdf")
@@ -55,7 +57,7 @@
 # %%
 ax = df_carrier[["S.p [µV/K]", "S.n [µV/K]"]].hist(bins=50, log=True, figsize=[18, 8])
 plt.suptitle(
-    "Ricci Carrier Transport dataset histograms for n- and p-type Seebeck coefficients"
+    "Ricci Carrier Transport dataset histograms for n- and p-type Seebeck coefficients",
 )
 pmv.save_fig(ax, "carrier-transport-seebeck-n+p.pdf")
 

examples/dataset_exploration/wbm/explore_wbm.py

@@ -13,7 +13,8 @@
 
 # %% download wbm-summary.csv (12 MB)
 df_wbm = pd.read_csv("https://figshare.com/ndownloader/files/44225498").set_index(
-    Key.mat_id, drop=False
+    Key.mat_id,
+    drop=False,
 )
 
 df_wbm["batch_idx"] = df_wbm.index.str.split("-").str[2].astype(int)
@@ -69,7 +70,8 @@
 fig.layout.margin = dict(b=10, l=10, r=10, t=50)
 fig.layout.showlegend = False
 fig.layout.xaxis = dict(
-    tickvals=list(range(len(crystal_sys_order))), ticktext=list(x_ticks.values())
+    tickvals=list(range(len(crystal_sys_order))),
+    ticktext=list(x_ticks.values()),
 )
 fig.update_traces(hoverinfo="skip", hovertemplate=None)
 

examples/diatomics/mace_pair_repulsion.py

@@ -37,7 +37,9 @@ def timer(label: str = "") -> Generator[None, None, None]:
 
 
 def generate_diatomics(
-    symbol0: str, symbol1: str, distances: list[float] | np.ndarray
+    symbol0: str,
+    symbol1: str,
+    distances: list[float] | np.ndarray,
 ) -> list[Atoms]:
     """Build diatomic molecules in vacuum for given distances.
 
@@ -56,7 +58,10 @@ def generate_diatomics(
 
 
 def calc_one_pair(
-    z0: int, z1: int, calc: MACECalculator, distances: list[float] | np.ndarray
+    z0: int,
+    z1: int,
+    calc: MACECalculator,
+    distances: list[float] | np.ndarray,
 ) -> list[float]:
     """Calculate potential energy for a pair of elements at given distances.
 
@@ -72,7 +77,9 @@ def calc_one_pair(
     return [
         calc.get_potential_energy(at)
         for at in generate_diatomics(
-            chemical_symbols[z0], chemical_symbols[z1], distances
+            chemical_symbols[z0],
+            chemical_symbols[z1],
+            distances,
         )
     ]
 

examples/diatomics/plot.py

@@ -26,7 +26,10 @@
 
 # %%
 def plot_on_ax(
-    ax: plt.Axes, distances: np.ndarray, energy: np.ndarray, formula: str
+    ax: plt.Axes,
+    distances: np.ndarray,
+    energy: np.ndarray,
+    formula: str,
 ) -> None:
     """Plot pair repulsion curve on a given axes.
 
@@ -54,7 +57,7 @@ def plot_homo_nuclear(model_size: str) -> None:
     n_rows, n_columns, size_factor = 10, 18, 3
 
     fig = plt.figure(
-        figsize=(0.75 * n_columns * size_factor, 0.7 * n_rows * size_factor)
+        figsize=(0.75 * n_columns * size_factor, 0.7 * n_rows * size_factor),
     )
     gs = plt.GridSpec(figure=fig, nrows=n_rows, ncols=n_columns)
 
@@ -93,12 +96,12 @@ def plot_hetero_nuclear(model_size: str) -> None:
         [
             int(fn.name.split("-")[2])
             for fn in Path("simulations/").glob(f"results-{model_size}-*-X.json")
-        ]
+        ],
     )
     with PdfPages(f"{model_size}-hetero-nuclear.pdf") as pdf:
         for z_main in z_calculated:
             fig = plt.figure(
-                figsize=(0.75 * n_columns * size_factor, 0.7 * n_rows * size_factor)
+                figsize=(0.75 * n_columns * size_factor, 0.7 * n_rows * size_factor),
             )
             gs = plt.GridSpec(figure=fig, nrows=n_rows, ncols=n_columns)
             plot_element_heteronuclear(fig, gs, model_size, z_main)
@@ -113,7 +116,10 @@ def plot_hetero_nuclear(model_size: str) -> None:
 
 
 def plot_element_heteronuclear(
-    fig: plt.Figure, gs: plt.GridSpec, model_size: str, atomic_number: int
+    fig: plt.Figure,
+    gs: plt.GridSpec,
+    model_size: str,
+    atomic_number: int,
 ) -> None:
     """Plot heteronuclear pair repulsion curves for a specific element.
 

examples/make_assets/histogram.py

@@ -29,7 +29,11 @@
 
 # %% Histogram Plots
 ax = pmv.elements_hist(
-    df_expt_gap[Key.composition], keep_top=15, v_offset=200, rotation=0, fontsize=12
+    df_expt_gap[Key.composition],
+    keep_top=15,
+    v_offset=200,
+    rotation=0,
+    fontsize=12,
 )
 pmv.io.save_and_compress_svg(ax, "elements-hist")
 

examples/make_assets/phonons.py

@@ -48,7 +48,9 @@
 
     fig = pmv.phonon_bands_and_dos(ph_bands, ph_doses)
     fig.layout.title = dict(
-        text=f"Phonon Bands and DOS of {formula} ({mp_id})", x=0.5, y=0.98
+        text=f"Phonon Bands and DOS of {formula} ({mp_id})",
+        x=0.5,
+        y=0.98,
     )
     fig.layout.margin = dict(l=0, r=0, b=0, t=40)
     pmv.io.save_and_compress_svg(fig, f"phonon-bands-and-dos-{mp_id}")

examples/make_assets/ptable/ptable_matplotlib.py

@@ -49,7 +49,10 @@
 
 # %%
 fig = pmv.ptable_heatmap_ratio(
-    df_expt_gap[Key.composition], df_steels[Key.composition], log=True, value_fmt=".4g"
+    df_expt_gap[Key.composition],
+    df_steels[Key.composition],
+    log=True,
+    value_fmt=".4g",
 )
 title = "Element ratios in Matbench Experimental Band Gap vs Matbench Steel"
 fig.suptitle(title, y=0.96, fontsize=16, fontweight="bold")

examples/make_assets/ptable/ptable_plotly.py

@@ -32,7 +32,9 @@
 
 # %%
 fig = pmv.ptable_heatmap_plotly(
-    df_expt_gap[Key.composition], log=True, colorscale="viridis"
+    df_expt_gap[Key.composition],
+    log=True,
+    colorscale="viridis",
 )
 title = "Elements in Matbench Experimental Bandgap (log scale)"
 fig.layout.title = dict(text=f"<b>{title}</b>", x=0.45, y=0.94, font_size=20)