Repository: janosh/pymatviz (language: python)

Issue:
Split `make_assets` scripts by plot function
Currently, `make_assets` scripts are named after the file whose plot function they import and demo. i think it would be better to split those scripts into one demo file per plot function. would make it easier to check that each plot function has at least 2 or 3 examples. it would also allow linking from the readme table directly to the examples we have for each function. right now, the readme table instead links to the source code which is less helpful

@DanielYang59 is this something you're interested in?


Discussion / hints:
Sure I would do that (was looking into the asset maker seconds ago for [the isotopes in `pymatgen` `Element`](https://github.com/materialsproject/pymatgen/discussions/4178) which I mentioned in one `pymatviz` comment somewhere). That would make assert maker much easier to access (I was having a hard time trying to find the corresponding assert maker almost every time).
And I was thinking about this seconds before you opened this issue: what about moving `make_assets` under `assets` instead of `examples` as we're linking to it anyway.

I found myself going to `asset` directory to look for the scripts almost every time 🙈 
> what about moving make_assets under assets instead of examples as we're linking to it anyway.

i view them more for the user's benefit than purely as dev code which is why i put them in `examples/` originally. that said, i suppose it would be more useful to users if they were jupyter notebooks with the generated figures displayed inline which makes much easier to associate code with output. we could put the scripts in `assets/` and convert them to notebooks for the docs site on the fly

Fix patch:
diff --git a/assets/density-scatter-with-hist.svg b/assets/density-scatter-with-hist.svg
deleted file mode 100644
index 5c1e35c6..00000000
--- a/assets/density-scatter-with-hist.svg
+++ /dev/null
@@ -1,1 +0,0 @@
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diff --git a/examples/make_assets/coordination.py b/assets/scripts/coordination/coordination_hist.py
similarity index 82%
rename from examples/make_assets/coordination.py
rename to assets/scripts/coordination/coordination_hist.py
index ba40a6d6..cd76a5f3 100644
--- a/examples/make_assets/coordination.py
+++ b/assets/scripts/coordination/coordination_hist.py
@@ -8,7 +8,6 @@
 
 import pymatviz as pmv
 from pymatviz.coordination import CnSplitMode
-from pymatviz.enums import Key
 from pymatviz.utils import TEST_FILES
 
 
@@ -190,44 +189,3 @@
 )
 fig.show()
 pmv.io.save_and_compress_svg(fig, "coordination-hist-all-combined-side-by-side")
-
-
-# %% Coordination vs Cutoff example for a single structure
-fig = pmv.coordination_vs_cutoff_line(structures[key1])
-fig.layout.title = dict(text=f"Coordination vs Cutoff: {key1}", x=0.5, y=0.98)
-fig.layout.margin.t = 50
-fig.show()
-pmv.io.save_and_compress_svg(fig, "coordination-vs-cutoff-single")
-
-
-# %% Coordination vs Cutoff example for multiple structures
-fig = pmv.coordination_vs_cutoff_line(
-    {key1: structures[key1], key2: structures[key2], key3: structures[key3]},
-    strategy=(1.0, 6.0),
-    num_points=100,
-)
-fig.layout.title = dict(
-    text="Coordination vs Cutoff: Multiple Structures", x=0.5, y=0.98
-)
-fig.layout.legend.update(x=0, y=1, bgcolor="rgba(0,0,0,0)")
-fig.layout.margin.t = 50
-fig.show()
-pmv.io.save_and_compress_svg(fig, "coordination-vs-cutoff-multiple")
-
-
-# %% Coordination vs Cutoff example with custom color scheme
-custom_colors = {"Pd": "red", "Zr": "blue"}
-fig = pmv.coordination_vs_cutoff_line(
-    structures[key1], element_color_scheme=custom_colors
-)
-fig.layout.margin.t = 25
-fig.layout.legend.update(x=0, y=1)
-fig.show()
-pmv.io.save_and_compress_svg(fig, "coordination-vs-cutoff-custom-colors")
-
-
-# %% plot first 10 structures
-fig = pmv.coordination_vs_cutoff_line(df_phonon[Key.structure][:4].tolist())
-fig.layout.margin.t = 25
-fig.layout.legend.update(x=0, y=1)
-fig.show()
diff --git a/assets/scripts/coordination/coordination_vs_cutoff_line.py b/assets/scripts/coordination/coordination_vs_cutoff_line.py
new file mode 100644
index 00000000..8835be5a
--- /dev/null
+++ b/assets/scripts/coordination/coordination_vs_cutoff_line.py
@@ -0,0 +1,85 @@
+# %%
+from glob import glob
+
+from matminer.datasets import load_dataset
+from pymatgen.core import Structure
+from pymatgen.symmetry.analyzer import SpacegroupAnalyzer
+
+import pymatviz as pmv
+from pymatviz.enums import Key
+from pymatviz.utils import TEST_FILES
+
+
+pmv.set_plotly_template("pymatviz_white")
+
+df_phonon = load_dataset(data_name := "matbench_phonons")
+
+
+# %%
+formula_spg_str = (
+    lambda struct: f"{struct.formula} ({struct.get_space_group_info()[1]})"
+)
+structures = {
+    formula_spg_str(struct := Structure.from_file(file)): struct
+    for file in (
+        glob(f"{TEST_FILES}/structures/*.json.gz") + glob(f"{TEST_FILES}/xrd/*.cif")
+    )
+}
+key1, key2, key3, *_ = structures
+
+for struct in structures.values():
+    spga = SpacegroupAnalyzer(struct)
+    sym_struct = spga.get_symmetrized_structure()
+    # add wyckoff symbols to each site
+    struct.add_oxidation_state_by_guess()
+    wyckoff_symbols = ["n/a"] * len(struct)
+    for indices, symbol in zip(
+        sym_struct.equivalent_indices, sym_struct.wyckoff_symbols, strict=True
+    ):
+        for index in indices:
+            wyckoff_symbols[index] = symbol
+    if any(sym == "n/a" for sym in wyckoff_symbols):
+        raise ValueError(f"{struct.formula} has n/a {wyckoff_symbols=}")
+
+    struct.add_site_property("wyckoff", wyckoff_symbols)
+
+
+# %% Coordination vs Cutoff example for a single structure
+fig = pmv.coordination_vs_cutoff_line(structures[key1])
+fig.layout.title = dict(text=f"Coordination vs Cutoff: {key1}", x=0.5, y=0.98)
+fig.layout.margin.t = 50
+fig.show()
+pmv.io.save_and_compress_svg(fig, "coordination-vs-cutoff-single")
+
+
+# %% Coordination vs Cutoff example for multiple structures
+fig = pmv.coordination_vs_cutoff_line(
+    {key1: structures[key1], key2: structures[key2], key3: structures[key3]},
+    strategy=(1.0, 6.0),
+    num_points=100,
+)
+fig.layout.title = dict(
+    text="Coordination vs Cutoff: Multiple Structures", x=0.5, y=0.98
+)
+fig.layout.legend.update(x=0, y=1, bgcolor="rgba(0,0,0,0)")
+fig.layout.margin.t = 50
+fig.show()
+pmv.io.save_and_compress_svg(fig, "coordination-vs-cutoff-multiple")
+
+
+# %% Coordination vs Cutoff example with custom color scheme
+custom_colors = {"Pd": "red", "Zr": "blue"}
+fig = pmv.coordination_vs_cutoff_line(
+    structures[key1], element_color_scheme=custom_colors
+)
+fig.layout.margin.t = 25
+fig.layout.legend.update(x=0, y=1)
+fig.show()
+pmv.io.save_and_compress_svg(fig, "coordination-vs-cutoff-custom-colors")
+
+
+# %% plot first 10 structures
+fig = pmv.coordination_vs_cutoff_line(df_phonon[Key.structure][:4].tolist())
+fig.layout.margin.t = 25
+fig.layout.legend.update(x=0, y=1)
+fig.show()
diff --git a/assets/scripts/histogram/elements_hist.py b/assets/scripts/histogram/elements_hist.py
new file mode 100644
index 00000000..e18bc3f8
--- /dev/null
+++ b/assets/scripts/histogram/elements_hist.py
@@ -0,0 +1,14 @@
+# %%
+from matminer.datasets import load_dataset
+
+import pymatviz as pmv
+from pymatviz.enums import Key
+
+
+# %% Histogram Plots
+df_expt_gap = load_dataset("matbench_expt_gap")
+
+ax = pmv.elements_hist(
+    df_expt_gap[Key.composition], keep_top=15, v_offset=200, rotation=0, fontsize=12
+)
+pmv.io.save_and_compress_svg(ax, "elements-hist")
diff --git a/assets/scripts/histogram/histogram.py b/assets/scripts/histogram/histogram.py
new file mode 100644
index 00000000..759c07da
--- /dev/null
+++ b/assets/scripts/histogram/histogram.py
@@ -0,0 +1,21 @@
+# %%
+import numpy as np
+
+import pymatviz as pmv
+
+
+pmv.set_plotly_template("pymatviz_white")
+
+
+# %% plot 2 Gaussians and their cumulative distribution functions
+rand_regression_size = 500
+np_rng = np.random.default_rng(seed=0)
+gauss1 = np_rng.normal(5, 4, rand_regression_size)
+gauss2 = np_rng.normal(10, 2, rand_regression_size)
+
+fig = pmv.histogram({"Gaussian 1": gauss1, "Gaussian 2": gauss2}, bins=100)
+for idx in range(len(fig.data)):
+    pmv.powerups.add_ecdf_line(fig, trace_idx=idx)
+fig.show()
+
+pmv.io.save_and_compress_svg(fig, "histogram-ecdf")
diff --git a/assets/scripts/histogram/spacegroup_bar.py b/assets/scripts/histogram/spacegroup_bar.py
new file mode 100644
index 00000000..8479f735
--- /dev/null
+++ b/assets/scripts/histogram/spacegroup_bar.py
@@ -0,0 +1,27 @@
+# %%
+from matminer.datasets import load_dataset
+from tqdm import tqdm
+
+import pymatviz as pmv
+from pymatviz.enums import Key
+
+
+pmv.set_plotly_template("pymatviz_white")
+
+
+# %%
+df_phonons = load_dataset("matbench_phonons")
+
+df_phonons[[Key.spg_symbol, Key.spg_num]] = [
+    struct.get_space_group_info()
+    for struct in tqdm(df_phonons[Key.structure], desc="Getting spacegroups")
+]
+
+
+# %% Spacegroup histograms
+for backend in pmv.BACKENDS:
+    fig = pmv.spacegroup_bar(df_phonons[Key.spg_num], backend=backend)
+    pmv.io.save_and_compress_svg(fig, f"spg-num-hist-{backend}")
+
+    fig = pmv.spacegroup_bar(df_phonons[Key.spg_symbol], backend=backend)
+    pmv.io.save_and_compress_svg(fig, f"spg-symbol-hist-{backend}")
diff --git a/assets/scripts/phonons/phonon_bands.py b/assets/scripts/phonons/phonon_bands.py
new file mode 100644
index 00000000..4e7c11c2
--- /dev/null
+++ b/assets/scripts/phonons/phonon_bands.py
@@ -0,0 +1,38 @@
+# %%
+import json
+from glob import glob
+
+from monty.io import zopen
+from monty.json import MontyDecoder
+from pymatgen.phonon.bandstructure import PhononBandStructureSymmLine as PhononBands
+
+import pymatviz as pmv
+from pymatviz.enums import Key
+
+
+# TODO: ffonons not working properly (see #195)
+try:
+    import ffonons  # noqa: F401
+except ImportError:
+    raise SystemExit(0) from None  # install ffonons to run this script
+
+
+# %% Plot phonon bands and DOS
+for mp_id, formula in (
+    ("mp-2758", "Sr4Se4"),
+    ("mp-23907", "H2"),
+):
+    docs = {}
+    for path in glob(f"{pmv.utils.TEST_FILES}/phonons/{mp_id}-{formula}-*.json.lzma"):
+        key = path.split("-")[-1].split(".")[0]
+        with zopen(path) as file:
+            docs[key] = json.loads(file.read(), cls=MontyDecoder)
+
+    ph_bands: dict[str, PhononBands] = {
+        key: getattr(doc, Key.ph_band_structure) for key, doc in docs.items()
+    }
+
+    fig = pmv.phonon_bands(ph_bands)
+    fig.layout.title = dict(text=f"Phonon Bands 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-{mp_id}")
diff --git a/examples/make_assets/phonons.py b/assets/scripts/phonons/phonon_bands_and_dos.py
similarity index 74%
rename from examples/make_assets/phonons.py
rename to assets/scripts/phonons/phonon_bands_and_dos.py
index 3f03ab72..7c1e666b 100644
--- a/examples/make_assets/phonons.py
+++ b/assets/scripts/phonons/phonon_bands_and_dos.py
@@ -36,16 +36,6 @@
         key: getattr(doc, Key.ph_dos) for key, doc in docs.items()
     }
 
-    fig = pmv.phonon_bands(ph_bands)
-    fig.layout.title = dict(text=f"Phonon Bands 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-{mp_id}")
-
-    fig = pmv.phonon_dos(ph_doses)
-    fig.layout.title = dict(text=f"Phonon 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-dos-{mp_id}")
-
     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
diff --git a/assets/scripts/phonons/phonon_dos.py b/assets/scripts/phonons/phonon_dos.py
new file mode 100644
index 00000000..eece02b7
--- /dev/null
+++ b/assets/scripts/phonons/phonon_dos.py
@@ -0,0 +1,38 @@
+# %%
+import json
+from glob import glob
+
+from monty.io import zopen
+from monty.json import MontyDecoder
+from pymatgen.phonon.dos import PhononDos
+
+import pymatviz as pmv
+from pymatviz.enums import Key
+
+
+# TODO: ffonons not working properly (see #195)
+try:
+    import ffonons  # noqa: F401
+except ImportError:
+    raise SystemExit(0) from None  # install ffonons to run this script
+
+
+# %% Plot phonon bands and DOS
+for mp_id, formula in (
+    ("mp-2758", "Sr4Se4"),
+    ("mp-23907", "H2"),
+):
+    docs = {}
+    for path in glob(f"{pmv.utils.TEST_FILES}/phonons/{mp_id}-{formula}-*.json.lzma"):
+        key = path.split("-")[-1].split(".")[0]
+        with zopen(path) as file:
+            docs[key] = json.loads(file.read(), cls=MontyDecoder)
+
+    ph_doses: dict[str, PhononDos] = {
+        key: getattr(doc, Key.ph_dos) for key, doc in docs.items()
+    }
+
+    fig = pmv.phonon_dos(ph_doses)
+    fig.layout.title = dict(text=f"Phonon 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-dos-{mp_id}")
diff --git a/examples/make_assets/powerups.py b/assets/scripts/powerups/plotly_powerups.py
similarity index 100%
rename from examples/make_assets/powerups.py
rename to assets/scripts/powerups/plotly_powerups.py
diff --git a/assets/scripts/ptable_matplotlib/ptable_heatmap.py b/assets/scripts/ptable_matplotlib/ptable_heatmap.py
new file mode 100644
index 00000000..c8d9ccf7
--- /dev/null
+++ b/assets/scripts/ptable_matplotlib/ptable_heatmap.py
@@ -0,0 +1,44 @@
+# %%
+from matminer.datasets import load_dataset
+
+import pymatviz as pmv
+from pymatviz.enums import Key
+
+
+# %%
+df_expt_gap = load_dataset("matbench_expt_gap")
+
+
+# %% Elemental Plots
+ax = pmv.ptable_heatmap(
+    pmv.count_elements(df_expt_gap[Key.composition]),
+    log=True,
+    return_type="axes",  # TODO: change to return Figure after 2025-07-01
+)
+title = (
+    f"Elements in Matbench Experimental Band Gap ({len(df_expt_gap):,} compositions)"
+)
+ax.set_title(title, x=0.75, y=2.5, fontsize=18, fontweight="bold")
+pmv.io.save_and_compress_svg(ax, "ptable-heatmap")
+
+
+# %%
+fig = pmv.ptable_heatmap(pmv.df_ptable[Key.atomic_mass], return_type="figure")
+fig.suptitle("Atomic Mass Heatmap", y=0.96, fontsize=20, fontweight="bold")
+pmv.io.save_and_compress_svg(fig, "ptable-heatmap-atomic-mass")
+
+
+# %%
+# Filter out near-zero entries
+ptable_data = pmv.count_elements(df_expt_gap[Key.composition])
+ptable_data = ptable_data[ptable_data > 0.01]
+
+fig = pmv.ptable_heatmap(
+    ptable_data,
+    value_show_mode="percent",
+    exclude_elements=["O"],
+    return_type="figure",
+)
+title = "Elements in Matbench Experimental Band Gap (percent)"
+fig.suptitle(title, y=0.96, fontsize=20, fontweight="bold")
+pmv.io.save_and_compress_svg(fig, "ptable-heatmap-percent")
diff --git a/assets/scripts/ptable_matplotlib/ptable_heatmap_ratio.py b/assets/scripts/ptable_matplotlib/ptable_heatmap_ratio.py
new file mode 100644
index 00000000..1c335662
--- /dev/null
+++ b/assets/scripts/ptable_matplotlib/ptable_heatmap_ratio.py
@@ -0,0 +1,19 @@
+# %%
+from matminer.datasets import load_dataset
+
+import pymatviz as pmv
+from pymatviz.enums import Key
+
+
+# %%
+df_expt_gap = load_dataset("matbench_expt_gap")
+df_steels = load_dataset("matbench_steels")
+
+
+# %%
+fig = pmv.ptable_heatmap_ratio(
+    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")
+pmv.io.save_and_compress_svg(fig, "ptable-heatmap-ratio")
diff --git a/assets/scripts/ptable_matplotlib/ptable_heatmap_splits.py b/assets/scripts/ptable_matplotlib/ptable_heatmap_splits.py
new file mode 100644
index 00000000..9664ad73
--- /dev/null
+++ b/assets/scripts/ptable_matplotlib/ptable_heatmap_splits.py
@@ -0,0 +1,24 @@
+# %%
+import numpy as np
+from pymatgen.core.periodic_table import Element
+
+import pymatviz as pmv
+
+
+# %% Evenly-split tile plots laid out as a periodic table
+rng = np.random.default_rng(seed=0)
+for n_splits in (2, 3, 4):
+    data_dict = {
+        elem.symbol: rng.integers(10 * n_splits, 20 * (n_splits + 1), size=n_splits)
+        for elem in Element
+    }
+
+    fig = pmv.ptable_heatmap_splits(
+        data=data_dict,
+        colormap="coolwarm",
+        start_angle=135 if n_splits % 2 == 0 else 90,
+        cbar_title="Periodic Table Evenly-Split Heatmap Plots",
+        hide_f_block=True,
+    )
+    fig.show()
+    pmv.io.save_and_compress_svg(fig, f"ptable-heatmap-splits-{n_splits}")
diff --git a/assets/scripts/ptable_matplotlib/ptable_hists.py b/assets/scripts/ptable_matplotlib/ptable_hists.py
new file mode 100644
index 00000000..b72ede7a
--- /dev/null
+++ b/assets/scripts/ptable_matplotlib/ptable_hists.py
@@ -0,0 +1,29 @@
+# %%
+import numpy as np
+from pymatgen.core.periodic_table import Element
+
+import pymatviz as pmv
+
+
+# %%
+np_rng = np.random.default_rng(seed=0)
+
+
+# %% Histograms laid out in as a periodic table
+# Generate random parity data with y \approx x with some noise
+data_dict = {
+    elem.symbol: np_rng.standard_normal(100) + np_rng.standard_normal(100)
+    for elem in Element
+}
+fig = pmv.ptable_hists(
+    data_dict,
+    colormap="coolwarm",
+    cbar_title="Periodic Table Histograms",
+    cbar_axis="x",
+    color_elem_strategy="background",
+    add_elem_type_legend=True,
+    symbol_pos=(0.25, 0.8),
+    anno_text={elem.symbol: str(idx - 1) for idx, elem in enumerate(Element)},
+    anno_pos=(0.75, 0.8),
+)
+pmv.io.save_and_compress_svg(fig, "ptable-hists", transparent_bg=False)
diff --git a/assets/scripts/ptable_matplotlib/ptable_lines.py b/assets/scripts/ptable_matplotlib/ptable_lines.py
new file mode 100644
index 00000000..6f4ae4fc
--- /dev/null
+++ b/assets/scripts/ptable_matplotlib/ptable_lines.py
@@ -0,0 +1,29 @@
+# %%
+import numpy as np
+from pymatgen.core.periodic_table import Element
+
+import pymatviz as pmv
+
+
+# %%
+np_rng = np.random.default_rng(seed=0)
+
+
+# %% Line plots laid out as a periodic table
+data_dict = {
+    elem.symbol: [
+        np.linspace(0, 10, 10),
+        np.sin(2 * np.pi * np.linspace(0, 10, 10)) + np_rng.normal(0, 0.2, 10),
+    ]
+    for elem in Element
+}
+
+fig = pmv.ptable_lines(
+    data_dict,
+    symbol_pos=(0.5, 1.2),
+    symbol_kwargs=dict(fontsize=14),
+    anno_text={elem.symbol: str(idx - 1) for idx, elem in enumerate(Element)},
+    anno_pos=(0.25, 0.2),
+    anno_kwargs={"fontsize": 6},
+)
+pmv.io.save_and_compress_svg(fig, "ptable-lines")
diff --git a/assets/scripts/ptable_matplotlib/ptable_scatters.py b/assets/scripts/ptable_matplotlib/ptable_scatters.py
new file mode 100644
index 00000000..73b875a4
--- /dev/null
+++ b/assets/scripts/ptable_matplotlib/ptable_scatters.py
@@ -0,0 +1,55 @@
+# %%
+import numpy as np
+from pymatgen.core.periodic_table import Element
+
+import pymatviz as pmv
+
+
+# %%
+np_rng = np.random.default_rng(seed=0)
+
+
+# %% Scatter plots laid out as a periodic table
+data_dict = {  # random parity data with y = x + noise
+    elem.symbol: [
+        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
+}
+
+fig = pmv.ptable_scatters(
+    data_dict,
+    colormap="coolwarm",
+    cbar_title="Periodic Table Scatter Plots",
+    child_kwargs=dict(marker="o", linestyle="", s=10),
+    symbol_pos=(0.5, 1.2),
+    symbol_kwargs=dict(fontsize=14),
+    anno_text={elem.symbol: str(idx - 1) for idx, elem in enumerate(Element)},
+    anno_pos=(0.75, 0.2),
+    anno_kwargs={"fontsize": 10},
+)
+pmv.io.save_and_compress_svg(fig, "ptable-scatters-parity")
+
+
+# %% 2nd pmv.ptable_scatters example
+data_dict = {  # random parabola data with y = x^2 + noise
+    elem.symbol: [
+        np.arange(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
+}
+
+fig = pmv.ptable_scatters(
+    data_dict,
+    colormap="inferno",
+    cbar_title="Periodic Table Scatter Plots",
+    child_kwargs=dict(marker="o", linestyle="", s=8, alpha=1),
+    symbol_pos=(0.5, 1.2),
+    symbol_kwargs=dict(fontsize=14),
+    color_elem_strategy="off",
+)
+pmv.io.save_and_compress_svg(fig, "ptable-scatters-parabola")
diff --git a/examples/make_assets/ptable/ptable_plotly.py b/assets/scripts/ptable_plotly/ptable_heatmap_plotly.py
similarity index 77%
rename from examples/make_assets/ptable/ptable_plotly.py
rename to assets/scripts/ptable_plotly/ptable_heatmap_plotly.py
index 4898aef0..5eff200e 100644
--- a/examples/make_assets/ptable/ptable_plotly.py
+++ b/assets/scripts/ptable_plotly/ptable_heatmap_plotly.py
@@ -1,11 +1,9 @@
 # %%
-import itertools
 import os
 
 import numpy as np
 import yaml
 from matminer.datasets import load_dataset
-from pymatgen.core.periodic_table import Element
 
 import pymatviz as pmv
 from pymatviz import df_ptable
@@ -14,7 +12,6 @@
 
 df_expt_gap = load_dataset("matbench_expt_gap")
 module_dir = os.path.dirname(__file__)
-np_rng = np.random.default_rng(seed=0)
 
 
 # %% Plotly interactive periodic table heatmap
@@ -145,43 +142,3 @@
 fig.show()
 
 pmv.io.save_and_compress_svg(fig, "ptable-heatmap-plotly-vasp-psp-n-valence-electrons")
-
-
-# %%
-data_dict = {
-    elem.symbol: np_rng.standard_normal(100) + np_rng.standard_normal(100)
-    for elem in Element
-}
-
-fig = pmv.ptable_hists_plotly(
-    data_dict, bins=30, colorbar=dict(title="Element Distributions")
-)
-fig.show()
-pmv.io.save_and_compress_svg(fig, "ptable-hists-plotly")
-
-
-# %% Examples of ptable_heatmap_splits_plotly with different numbers of splits
-for n_splits, orientation in itertools.product(
-    range(2, 5),
-    ("diagonal", "horizontal", "vertical", "grid"),
-):
-    if orientation == "grid" and n_splits != 4:
-        continue
-
-    data_dict = {
-        elem.symbol: np_rng.integers(10 * n_splits, 20 * (n_splits + 1), size=n_splits)
-        for elem in Element
-    }
-
-    fig = pmv.ptable_heatmap_splits_plotly(
-        data=data_dict,
-        orientation=orientation,  # type: ignore[arg-type]
-        colorscale="RdYlBu",
-        colorbar=dict(
-            title=f"Periodic Table Heatmap with {n_splits}-fold split",
-        ),
-    )
-
-    fig.show()
-    if orientation == "diagonal":
-        pmv.io.save_and_compress_svg(fig, f"ptable-heatmap-splits-plotly-{n_splits}")
diff --git a/assets/scripts/ptable_plotly/ptable_heatmap_splits_plotly.py b/assets/scripts/ptable_plotly/ptable_heatmap_splits_plotly.py
new file mode 100644
index 00000000..bfac78f5
--- /dev/null
+++ b/assets/scripts/ptable_plotly/ptable_heatmap_splits_plotly.py
@@ -0,0 +1,36 @@
+# %%
+import itertools
+
+import numpy as np
+from pymatgen.core.periodic_table import Element
+
+import pymatviz as pmv
+
+
+np_rng = np.random.default_rng(seed=0)
+
+
+# %% Examples of ptable_heatmap_splits_plotly with different numbers of splits
+for n_splits, orientation in itertools.product(
+    range(2, 5),
+    ("diagonal", "horizontal", "vertical", "grid"),
+):
+    if orientation == "grid" and n_splits != 4:
+        continue
+
+    data_dict = {
+        elem.symbol: np_rng.integers(10 * n_splits, 20 * (n_splits + 1), size=n_splits)
+        for elem in Element
+    }
+
+    cbar_title = f"Periodic Table Heatmap with {n_splits}-fold split"
+    fig = pmv.ptable_heatmap_splits_plotly(
+        data=data_dict,
+        orientation=orientation,  # type: ignore[arg-type]
+        colorscale="RdYlBu",
+        colorbar=dict(title=cbar_title),
+    )
+
+    fig.show()
+    if orientation == "diagonal":
+        pmv.io.save_and_compress_svg(fig, f"ptable-heatmap-splits-plotly-{n_splits}")
diff --git a/assets/scripts/ptable_plotly/ptable_hists_plotly.py b/assets/scripts/ptable_plotly/ptable_hists_plotly.py
new file mode 100644
index 00000000..aca996e8
--- /dev/null
+++ b/assets/scripts/ptable_plotly/ptable_hists_plotly.py
@@ -0,0 +1,21 @@
+# %%
+import numpy as np
+from pymatgen.core.periodic_table import Element
+
+import pymatviz as pmv
+
+
+np_rng = np.random.default_rng(seed=0)
+
+
+# %%
+data_dict = {
+    elem.symbol: np_rng.standard_normal(100) + np_rng.standard_normal(100)
+    for elem in Element
+}
+
+fig = pmv.ptable_hists_plotly(
+    data_dict, bins=30, colorbar=dict(title="Element Distributions")
+)
+fig.show()
+pmv.io.save_and_compress_svg(fig, "ptable-hists-plotly")
diff --git a/examples/make_assets/ptable/vasp-pbe-64-n-val-elecs.yml b/assets/scripts/ptable_plotly/vasp-pbe-64-n-val-elecs.yml
similarity index 100%
rename from examples/make_assets/ptable/vasp-pbe-64-n-val-elecs.yml
rename to assets/scripts/ptable_plotly/vasp-pbe-64-n-val-elecs.yml
diff --git a/examples/make_assets/rainclouds.py b/assets/scripts/rainclouds/rainclouds.py
similarity index 100%
rename from examples/make_assets/rainclouds.py
rename to assets/scripts/rainclouds/rainclouds.py
diff --git a/examples/make_assets/rdf.py b/assets/scripts/rdf/element_pair_rdfs.py
similarity index 89%
rename from examples/make_assets/rdf.py
rename to assets/scripts/rdf/element_pair_rdfs.py
index afda150c..1c561187 100644
--- a/examples/make_assets/rdf.py
+++ b/assets/scripts/rdf/element_pair_rdfs.py
@@ -60,10 +60,3 @@
 # pmv.io.save_and_compress_svg(
 #     fig_crys_vs_amorph, "element-pair-rdfs-crystal-vs-amorphous"
 # )
-
-
-# %% compare full RDFs for crystalline LiPO4 and randomly-placed "amorphous" LiPO4
-fig_crys_vs_amorph = pmv.full_rdf(
-    structures={"Crystal": crystal_lipo4, "Amorphous": amorphous_lipo4}
-)
-fig_crys_vs_amorph.show()
diff --git a/assets/scripts/rdf/full_rdf.py b/assets/scripts/rdf/full_rdf.py
new file mode 100644
index 00000000..52efa8f6
--- /dev/null
+++ b/assets/scripts/rdf/full_rdf.py
@@ -0,0 +1,35 @@
+# %%
+import numpy as np
+from pymatgen.core import Lattice, Structure
+
+import pymatviz as pmv
+
+
+pmv.set_plotly_template("pymatviz_white")
+
+
+# %% compare RDFs for crystalline LiPO4 and randomly-placed "amorphous" LiPO4
+lattice = Lattice.orthorhombic(4.7, 6.0, 4.7)
+species = ["Li", "P", "O", "O", "O", "O"]
+coords = [
+    [0, 0, 0],  # Li
+    [0.5, 0.5, 0.5],  # P
+    [0.25, 0.5, 0.25],  # O
+    [0.75, 0.5, 0.75],  # O
+    [0.5, 0.25, 0.5],  # O
+    [0.5, 0.75, 0.5],  # O
+]
+crystal_lipo4 = Structure(lattice, species, coords)
+
+n_formula_units = 50
+lattice = Lattice.cubic(20)
+n_atoms = n_formula_units * 6
+coords = np.random.default_rng(seed=0).uniform(size=(n_atoms, 3))
+amorphous_lipo4 = Structure(lattice, species * n_formula_units, coords)
+
+
+# %% compare full RDFs for crystalline LiPO4 and randomly-placed "amorphous" LiPO4
+fig_crys_vs_amorph = pmv.full_rdf(
+    structures={"Crystal": crystal_lipo4, "Amorphous": amorphous_lipo4}
+)
+fig_crys_vs_amorph.show()
diff --git a/examples/make_assets/readme.md b/assets/scripts/readme.md
similarity index 100%
rename from examples/make_assets/readme.md
rename to assets/scripts/readme.md
diff --git a/examples/make_assets/relevance.py b/assets/scripts/relevance/precision_recall_curve.py
similarity index 82%
rename from examples/make_assets/relevance.py
rename to assets/scripts/relevance/precision_recall_curve.py
index b61643b6..08527f8a 100644
--- a/examples/make_assets/relevance.py
+++ b/assets/scripts/relevance/precision_recall_curve.py
@@ -12,9 +12,5 @@
 
 
 # %% Relevance Plots
-ax = pmv.roc_curve(y_binary, y_proba)
-pmv.io.save_and_compress_svg(ax, "roc-curve")
-
-
 ax = pmv.precision_recall_curve(y_binary, y_proba)
 pmv.io.save_and_compress_svg(ax, "precision-recall-curve")
diff --git a/assets/scripts/relevance/roc_curve.py b/assets/scripts/relevance/roc_curve.py
new file mode 100644
index 00000000..e7b6158c
--- /dev/null
+++ b/assets/scripts/relevance/roc_curve.py
@@ -0,0 +1,16 @@
+# %%
+import numpy as np
+
+import pymatviz as pmv
+
+
+# Random classification data
+np_rng = np.random.default_rng(seed=0)
+rand_clf_size = 100
+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
+ax = pmv.roc_curve(y_binary, y_proba)
+pmv.io.save_and_compress_svg(ax, "roc-curve")
diff --git a/examples/make_assets/sankey.py b/assets/scripts/sankey/sankey_from_2_df_cols.py
similarity index 100%
rename from examples/make_assets/sankey.py
rename to assets/scripts/sankey/sankey_from_2_df_cols.py
diff --git a/assets/scripts/scatter/density_hexbin.py b/assets/scripts/scatter/density_hexbin.py
new file mode 100644
index 00000000..47a409d5
--- /dev/null
+++ b/assets/scripts/scatter/density_hexbin.py
@@ -0,0 +1,17 @@
+# %%
+from __future__ import annotations
+
+import pymatviz as pmv
+
+
+dummy_data = pmv.data.regression()
+pmv.utils.apply_matplotlib_template()
+
+
+# %% density hexbin
+ax = pmv.density_hexbin(
+    dummy_data.y_pred,
+    dummy_data.y_true,
+    best_fit_line={"annotate_params": {"loc": "lower center"}},
+)
+pmv.io.save_and_compress_svg(ax, "density-scatter-hex")
diff --git a/assets/scripts/scatter/density_hexbin_with_hist.py b/assets/scripts/scatter/density_hexbin_with_hist.py
new file mode 100644
index 00000000..7cc984d7
--- /dev/null
+++ b/assets/scripts/scatter/density_hexbin_with_hist.py
@@ -0,0 +1,17 @@
+# %%
+from __future__ import annotations
+
+import pymatviz as pmv
+
+
+dummy_data = pmv.data.regression()
+pmv.utils.apply_matplotlib_template()
+
+
+# %% density hexbin with hist
+ax = pmv.density_hexbin_with_hist(
+    dummy_data.y_pred,
+    dummy_data.y_true,
+    best_fit_line={"annotate_params": {"loc": "lower center"}},
+)
+pmv.io.save_and_compress_svg(ax, "density-scatter-hex-with-hist")
diff --git a/assets/scripts/scatter/density_scatter.py b/assets/scripts/scatter/density_scatter.py
new file mode 100644
index 00000000..84735d3c
--- /dev/null
+++ b/assets/scripts/scatter/density_scatter.py
@@ -0,0 +1,13 @@
+# %%
+from __future__ import annotations
+
+import pymatviz as pmv
+
+
+dummy_data = pmv.data.regression()
+pmv.utils.apply_matplotlib_template()
+
+
+# %% density scatter
+ax = pmv.density_scatter(dummy_data.y_pred, dummy_data.y_true)
+pmv.io.save_and_compress_svg(ax, "density-scatter")
diff --git a/assets/scripts/scatter/density_scatter_plotly.py b/assets/scripts/scatter/density_scatter_plotly.py
new file mode 100644
index 00000000..8eb99d74
--- /dev/null
+++ b/assets/scripts/scatter/density_scatter_plotly.py
@@ -0,0 +1,16 @@
+# %%
+from __future__ import annotations
+
+import plotly.express as px
+
+import pymatviz as pmv
+
+
+pmv.set_plotly_template("pymatviz_white")
+
+
+# %% density scatter plotly
+df_tips = px.data.tips().rename(columns={"total_bill": "X", "tip": "Y", "size": "Z"})
+fig = pmv.density_scatter_plotly(df_tips, x="X", y="Y", size="Z", facet_col="smoker")
+fig.show()
+pmv.io.save_and_compress_svg(fig, "density-scatter-plotly")
diff --git a/assets/scripts/scatter/density_scatter_with_hist.py b/assets/scripts/scatter/density_scatter_with_hist.py
new file mode 100644
index 00000000..c8666787
--- /dev/null
+++ b/assets/scripts/scatter/density_scatter_with_hist.py
@@ -0,0 +1,13 @@
+# %%
+from __future__ import annotations
+
+import pymatviz as pmv
+
+
+dummy_data = pmv.data.regression()
+pmv.utils.apply_matplotlib_template()
+
+
+# %% density scatter with hist
+ax = pmv.density_scatter_with_hist(dummy_data.y_pred, dummy_data.y_true)
+pmv.io.save_and_compress_svg(ax, "density-scatter-with-hist")
diff --git a/assets/scripts/scatter/residual_vs_actual.py b/assets/scripts/scatter/residual_vs_actual.py
new file mode 100644
index 00000000..650f6430
--- /dev/null
+++ b/assets/scripts/scatter/residual_vs_actual.py
@@ -0,0 +1,13 @@
+# %%
+from __future__ import annotations
+
+import pymatviz as pmv
+
+
+dummy_data = pmv.data.regression()
+pmv.utils.apply_matplotlib_template()
+
+
+# %% residual vs actual
+ax = pmv.residual_vs_actual(dummy_data.y_true, dummy_data.y_pred)
+pmv.io.save_and_compress_svg(ax, "residual-vs-actual")
diff --git a/assets/scripts/scatter/scatter_with_err_bar.py b/assets/scripts/scatter/scatter_with_err_bar.py
new file mode 100644
index 00000000..909dbdbd
--- /dev/null
+++ b/assets/scripts/scatter/scatter_with_err_bar.py
@@ -0,0 +1,15 @@
+# %%
+from __future__ import annotations
+
+import pymatviz as pmv
+
+
+dummy_data = pmv.data.regression()
+pmv.utils.apply_matplotlib_template()
+
+
+# %% scatter with error bar
+ax = pmv.scatter_with_err_bar(
+    dummy_data.y_pred, dummy_data.y_true, yerr=abs(dummy_data.y_std)
+)
+pmv.io.save_and_compress_svg(ax, "scatter-with-err-bar")
diff --git a/examples/make_assets/structure_viz.py b/assets/scripts/structure_viz/structure_2d.py
similarity index 70%
rename from examples/make_assets/structure_viz.py
rename to assets/scripts/structure_viz/structure_2d.py
index 8c4707a5..7ff8bae1 100644
--- a/examples/make_assets/structure_viz.py
+++ b/assets/scripts/structure_viz/structure_2d.py
@@ -8,11 +8,10 @@
 from pymatgen.core import Structure
 
 import pymatviz as pmv
-from pymatviz.enums import ElemColorScheme, Key, SiteCoords
+from pymatviz.enums import ElemColorScheme, Key
 from pymatviz.utils import TEST_FILES
 
 
-df_steels = load_dataset("matbench_steels")
 df_phonons = load_dataset("matbench_phonons")
 
 
@@ -71,29 +70,3 @@
 
     plt.show()
     pmv.io.save_and_compress_svg(ax, f"struct-2d-{mp_id}-{formula}-disordered")
-
-
-# %% Plot Matbench phonon structures with plotly
-fig = pmv.structure_2d_plotly(
-    df_phonons[Key.structure].head(6).to_dict(),
-    # show_unit_cell={"edge": dict(color="white", width=1.5)},
-    # show_sites=dict(line=None),
-    elem_colors=ElemColorScheme.jmol,
-    n_cols=3,
-    subplot_title=lambda _struct, _key: dict(font=dict(color="black")),
-    hover_text=lambda site: f"<b>{site.frac_coords}</b>",
-)
-
-fig.show()
-pmv.io.save_and_compress_svg(fig, "matbench-phonons-structures-2d-plotly")
-
-
-# %% 3d example
-fig = pmv.structure_3d_plotly(
-    df_phonons[Key.structure].head(6).to_dict(),
-    elem_colors=ElemColorScheme.jmol,
-    # show_unit_cell={"edge": dict(color="white", width=1.5)},
-    hover_text=SiteCoords.cartesian_fractional,
-)
-fig.show()
-pmv.io.save_and_compress_svg(fig, "matbench-phonons-structures-3d-plotly")
diff --git a/assets/scripts/structure_viz/structure_2d_plotly.py b/assets/scripts/structure_viz/structure_2d_plotly.py
new file mode 100644
index 00000000..1c77ace2
--- /dev/null
+++ b/assets/scripts/structure_viz/structure_2d_plotly.py
@@ -0,0 +1,23 @@
+# %%
+from matminer.datasets import load_dataset
+
+import pymatviz as pmv
+from pymatviz.enums import ElemColorScheme, Key
+
+
+df_phonons = load_dataset("matbench_phonons")
+
+
+# %% Plot Matbench phonon structures with plotly
+fig = pmv.structure_2d_plotly(
+    df_phonons[Key.structure].head(6).to_dict(),
+    # show_unit_cell={"edge": dict(color="white", width=1.5)},
+    # show_sites=dict(line=None),
+    elem_colors=ElemColorScheme.jmol,
+    n_cols=3,
+    subplot_title=lambda _struct, _key: dict(font=dict(color="black")),
+    hover_text=lambda site: f"<b>{site.frac_coords}</b>",
+)
+
+fig.show()
+pmv.io.save_and_compress_svg(fig, "matbench-phonons-structures-2d-plotly")
diff --git a/assets/scripts/structure_viz/structure_3d_plotly.py b/assets/scripts/structure_viz/structure_3d_plotly.py
new file mode 100644
index 00000000..b3f18200
--- /dev/null
+++ b/assets/scripts/structure_viz/structure_3d_plotly.py
@@ -0,0 +1,19 @@
+# %%
+from matminer.datasets import load_dataset
+
+import pymatviz as pmv
+from pymatviz.enums import ElemColorScheme, Key, SiteCoords
+
+
+df_phonons = load_dataset("matbench_phonons")
+
+
+# %% 3d example
+fig = pmv.structure_3d_plotly(
+    df_phonons[Key.structure].head(6).to_dict(),
+    elem_colors=ElemColorScheme.jmol,
+    # show_unit_cell={"edge": dict(color="white", width=1.5)},
+    hover_text=SiteCoords.cartesian_fractional,
+)
+fig.show()
+pmv.io.save_and_compress_svg(fig, "matbench-phonons-structures-3d-plotly")
diff --git a/examples/make_assets/symmetry.py b/assets/scripts/symmetry/spacegroup_sunburst.py
similarity index 100%
rename from examples/make_assets/symmetry.py
rename to assets/scripts/symmetry/spacegroup_sunburst.py
diff --git a/assets/scripts/uncertainty/cumulative.py b/assets/scripts/uncertainty/cumulative.py
new file mode 100644
index 00000000..bce14e1c
--- /dev/null
+++ b/assets/scripts/uncertainty/cumulative.py
@@ -0,0 +1,14 @@
+# %%
+import pymatviz as pmv
+
+
+dummy_data = pmv.data.regression()
+
+
+# %% Cumulative Plots
+ax = pmv.cumulative_error(dummy_data.y_pred - dummy_data.y_true)
+pmv.io.save_and_compress_svg(ax, "cumulative-error")
+
+
+ax = pmv.cumulative_residual(dummy_data.y_pred - dummy_data.y_true)
+pmv.io.save_and_compress_svg(ax, "cumulative-residual")
diff --git a/assets/scripts/uncertainty/error_decay_with_uncert.py b/assets/scripts/uncertainty/error_decay_with_uncert.py
new file mode 100644
index 00000000..71e352be
--- /dev/null
+++ b/assets/scripts/uncertainty/error_decay_with_uncert.py
@@ -0,0 +1,22 @@
+# %%
+import numpy as np
+
+import pymatviz as pmv
+
+
+np_rng = np.random.default_rng(seed=0)
+dummy_data = pmv.data.regression()
+
+
+# %% Uncertainty Plots
+ax = pmv.error_decay_with_uncert(dummy_data.y_true, dummy_data.y_pred, dummy_data.y_std)
+pmv.io.save_and_compress_svg(ax, "error-decay-with-uncert")
+
+eps = 0.2 * np_rng.standard_normal(*dummy_data.y_std.shape)
+
+ax = pmv.error_decay_with_uncert(
+    dummy_data.y_true,
+    dummy_data.y_pred,
+    {"better": dummy_data.y_std, "worse": dummy_data.y_std + eps},
+)
+pmv.io.save_and_compress_svg(ax, "error-decay-with-uncert-multiple")
diff --git a/assets/scripts/uncertainty/qq_gaussian.py b/assets/scripts/uncertainty/qq_gaussian.py
new file mode 100644
index 00000000..940d0220
--- /dev/null
+++ b/assets/scripts/uncertainty/qq_gaussian.py
@@ -0,0 +1,23 @@
+# %%
+import pymatviz as pmv
+
+
+dummy_data = pmv.data.regression()
+
+
+# %% Uncertainty Plots
+ax = pmv.qq_gaussian(
+    dummy_data.y_pred,
+    dummy_data.y_true,
+    dummy_data.y_std,
+    identity_line={"line_kwargs": {"color": "red"}},
+)
+pmv.io.save_and_compress_svg(ax, "normal-prob-plot")
+
+
+ax = pmv.qq_gaussian(
+    dummy_data.y_pred,
+    dummy_data.y_true,
+    {"over-confident": dummy_data.y_std, "under-confident": 1.5 * dummy_data.y_std},
+)
+pmv.io.save_and_compress_svg(ax, "normal-prob-plot-multiple")
diff --git a/examples/make_assets/xrd.py b/assets/scripts/xrd/xrd_pattern.py
similarity index 100%
rename from examples/make_assets/xrd.py
rename to assets/scripts/xrd/xrd_pattern.py
diff --git a/assets/coordination-hist-by-structure-and-element.svg b/assets/svg/coordination-hist-by-structure-and-element.svg
similarity index 100%
rename from assets/coordination-hist-by-structure-and-element.svg
rename to assets/svg/coordination-hist-by-structure-and-element.svg
diff --git a/assets/coordination-hist-single.svg b/assets/svg/coordination-hist-single.svg
similarity index 100%
rename from assets/coordination-hist-single.svg
rename to assets/svg/coordination-hist-single.svg
diff --git a/assets/coordination-vs-cutoff-multiple.svg b/assets/svg/coordination-vs-cutoff-multiple.svg
similarity index 100%
rename from assets/coordination-vs-cutoff-multiple.svg
rename to assets/svg/coordination-vs-cutoff-multiple.svg
diff --git a/assets/coordination-vs-cutoff-single.svg b/assets/svg/coordination-vs-cutoff-single.svg
similarity index 100%
rename from assets/coordination-vs-cutoff-single.svg
rename to assets/svg/coordination-vs-cutoff-single.svg
diff --git a/assets/cumulative-error.svg b/assets/svg/cumulative-error.svg
similarity index 100%
rename from assets/cumulative-error.svg
rename to assets/svg/cumulative-error.svg
diff --git a/assets/cumulative-residual.svg b/assets/svg/cumulative-residual.svg
similarity index 100%
rename from assets/cumulative-residual.svg
rename to assets/svg/cumulative-residual.svg
diff --git a/assets/density-hexbin-with-hist.svg b/assets/svg/density-hexbin-with-hist.svg
similarity index 100%
rename from assets/density-hexbin-with-hist.svg
rename to assets/svg/density-hexbin-with-hist.svg
diff --git a/assets/density-hexbin.svg b/assets/svg/density-hexbin.svg
similarity index 100%
rename from assets/density-hexbin.svg
rename to assets/svg/density-hexbin.svg
diff --git a/assets/svg/density-scatter-hex-with-hist.svg b/assets/svg/density-scatter-hex-with-hist.svg
new file mode 100644
index 00000000..c0907a35
--- /dev/null
+++ b/assets/svg/density-scatter-hex-with-hist.svg
@@ -0,0 +1,1 @@
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diff --git a/assets/density-scatter-hex.svg b/assets/svg/density-scatter-hex.svg
similarity index 100%
rename from assets/density-scatter-hex.svg
rename to assets/svg/density-scatter-hex.svg
diff --git a/assets/density-scatter-plotly-blobs.svg b/assets/svg/density-scatter-plotly-blobs.svg
similarity index 100%
rename from assets/density-scatter-plotly-blobs.svg
rename to assets/svg/density-scatter-plotly-blobs.svg
diff --git a/assets/density-scatter-plotly.svg b/assets/svg/density-scatter-plotly.svg
similarity index 100%
rename from assets/density-scatter-plotly.svg
rename to assets/svg/density-scatter-plotly.svg
diff --git a/assets/svg/density-scatter-with-hist.svg b/assets/svg/density-scatter-with-hist.svg
new file mode 100644
index 00000000..4c0f765d
--- /dev/null
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diff --git a/examples/make_assets/histogram.py b/examples/make_assets/histogram.py
deleted file mode 100644
index 122b194a..00000000
--- a/examples/make_assets/histogram.py
+++ /dev/null
@@ -1,57 +0,0 @@
-# %%
-import numpy as np
-from matminer.datasets import load_dataset
-from tqdm import tqdm
-
-import pymatviz as pmv
-from pymatviz.enums import Key
-
-
-pmv.set_plotly_template("pymatviz_white")
-
-
-# %%
-df_phonons = load_dataset("matbench_phonons")
-df_expt_gap = load_dataset("matbench_expt_gap")
-
-df_phonons[[Key.spg_symbol, Key.spg_num]] = [
-    struct.get_space_group_info()
-    for struct in tqdm(df_phonons[Key.structure], desc="Getting spacegroups")
-]
-
-# Random regression data
-rand_regression_size = 500
-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
-ax = pmv.elements_hist(
-    df_expt_gap[Key.composition], keep_top=15, v_offset=200, rotation=0, fontsize=12
-)
-pmv.io.save_and_compress_svg(ax, "elements-hist")
-
-
-# %% Spacegroup histograms
-for backend in pmv.BACKENDS:
-    fig = pmv.spacegroup_bar(df_phonons[Key.spg_num], backend=backend)
-    pmv.io.save_and_compress_svg(fig, f"spg-num-hist-{backend}")
-
-    fig = pmv.spacegroup_bar(df_phonons[Key.spg_symbol], backend=backend)
-    pmv.io.save_and_compress_svg(fig, f"spg-symbol-hist-{backend}")
-
-
-# %% plot 2 Gaussians and their cumulative distribution functions
-rand_regression_size = 500
-np_rng = np.random.default_rng(seed=0)
-gauss1 = np_rng.normal(5, 4, rand_regression_size)
-gauss2 = np_rng.normal(10, 2, rand_regression_size)
-
-fig = pmv.histogram({"Gaussian 1": gauss1, "Gaussian 2": gauss2}, bins=100)
-for idx in range(len(fig.data)):
-    pmv.powerups.add_ecdf_line(fig, trace_idx=idx)
-fig.show()
-
-pmv.io.save_and_compress_svg(fig, "histogram-ecdf")
diff --git a/examples/make_assets/ptable/ptable_matplotlib.py b/examples/make_assets/ptable/ptable_matplotlib.py
deleted file mode 100644
index 75b26d05..00000000
--- a/examples/make_assets/ptable/ptable_matplotlib.py
+++ /dev/null
@@ -1,164 +0,0 @@
-# %%
-import numpy as np
-from matminer.datasets import load_dataset
-from pymatgen.core.periodic_table import Element
-
-import pymatviz as pmv
-from pymatviz.enums import Key
-
-
-# %%
-np_rng = np.random.default_rng(seed=0)
-df_expt_gap = load_dataset("matbench_expt_gap")
-df_steels = load_dataset("matbench_steels")
-
-
-# %% Elemental Plots
-ax = pmv.ptable_heatmap(
-    pmv.count_elements(df_expt_gap[Key.composition]),
-    log=True,
-    return_type="axes",  # TODO: change to return Figure after 2025-07-01
-)
-title = (
-    f"Elements in Matbench Experimental Band Gap ({len(df_expt_gap):,} compositions)"
-)
-ax.set_title(title, x=0.75, y=2.5, fontsize=18, fontweight="bold")
-pmv.io.save_and_compress_svg(ax, "ptable-heatmap")
-
-
-# %%
-fig = pmv.ptable_heatmap(pmv.df_ptable[Key.atomic_mass], return_type="figure")
-fig.suptitle("Atomic Mass Heatmap", y=0.96, fontsize=20, fontweight="bold")
-pmv.io.save_and_compress_svg(fig, "ptable-heatmap-atomic-mass")
-
-
-# %%
-# Filter out near-zero entries
-ptable_data = pmv.count_elements(df_expt_gap[Key.composition])
-ptable_data = ptable_data[ptable_data > 0.01]
-
-fig = pmv.ptable_heatmap(
-    ptable_data,
-    value_show_mode="percent",
-    exclude_elements=["O"],
-    return_type="figure",
-)
-title = "Elements in Matbench Experimental Band Gap (percent)"
-fig.suptitle(title, y=0.96, fontsize=20, fontweight="bold")
-pmv.io.save_and_compress_svg(fig, "ptable-heatmap-percent")
-
-
-# %%
-fig = pmv.ptable_heatmap_ratio(
-    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")
-pmv.io.save_and_compress_svg(fig, "ptable-heatmap-ratio")
-
-
-# %% Histograms laid out in as a periodic table
-# Generate random parity data with y \approx x with some noise
-data_dict = {
-    elem.symbol: np_rng.standard_normal(100) + np_rng.standard_normal(100)
-    for elem in Element
-}
-fig = pmv.ptable_hists(
-    data_dict,
-    colormap="coolwarm",
-    cbar_title="Periodic Table Histograms",
-    cbar_axis="x",
-    color_elem_strategy="background",
-    add_elem_type_legend=True,
-    symbol_pos=(0.25, 0.8),
-    anno_text={elem.symbol: str(idx - 1) for idx, elem in enumerate(Element)},
-    anno_pos=(0.75, 0.8),
-)
-pmv.io.save_and_compress_svg(fig, "ptable-hists", transparent_bg=False)
-
-
-# %% Scatter plots laid out as a periodic table
-data_dict = {  # random parity data with y = x + noise
-    elem.symbol: [
-        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
-}
-
-fig = pmv.ptable_scatters(
-    data_dict,
-    colormap="coolwarm",
-    cbar_title="Periodic Table Scatter Plots",
-    child_kwargs=dict(marker="o", linestyle="", s=10),
-    symbol_pos=(0.5, 1.2),
-    symbol_kwargs=dict(fontsize=14),
-    anno_text={elem.symbol: str(idx - 1) for idx, elem in enumerate(Element)},
-    anno_pos=(0.75, 0.2),
-    anno_kwargs={"fontsize": 10},
-)
-pmv.io.save_and_compress_svg(fig, "ptable-scatters-parity")
-
-
-# %% 2nd pmv.ptable_scatters example
-data_dict = {  # random parabola data with y = x^2 + noise
-    elem.symbol: [
-        np.arange(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
-}
-
-fig = pmv.ptable_scatters(
-    data_dict,
-    colormap="inferno",
-    cbar_title="Periodic Table Scatter Plots",
-    child_kwargs=dict(marker="o", linestyle="", s=8, alpha=1),
-    symbol_pos=(0.5, 1.2),
-    symbol_kwargs=dict(fontsize=14),
-    color_elem_strategy="off",
-)
-pmv.io.save_and_compress_svg(fig, "ptable-scatters-parabola")
-
-
-# %% Line plots laid out as a periodic table
-data_dict = {
-    elem.symbol: [
-        np.linspace(0, 10, 10),
-        np.sin(2 * np.pi * np.linspace(0, 10, 10)) + np_rng.normal(0, 0.2, 10),
-    ]
-    for elem in Element
-}
-
-fig = pmv.ptable_lines(
-    data_dict,
-    symbol_pos=(0.5, 1.2),
-    symbol_kwargs=dict(fontsize=14),
-    anno_text={elem.symbol: str(idx - 1) for idx, elem in enumerate(Element)},
-    anno_pos=(0.25, 0.2),
-    anno_kwargs={"fontsize": 6},
-)
-pmv.io.save_and_compress_svg(fig, "ptable-lines")
-
-
-# %% Evenly-split tile plots laid out as a periodic table
-rng = np.random.default_rng(seed=0)
-for n_splits in (2, 3, 4):
-    data_dict = {
-        elem.symbol: rng.integers(10 * n_splits, 20 * (n_splits + 1), size=n_splits)
-        for elem in Element
-    }
-
-    fig = pmv.ptable_heatmap_splits_plotly(
-        data=data_dict,
-        colorscale="agsunset",
-        hover_data=data_dict,
-        # start_angle=135 if n_splits % 2 == 0 else 90,
-        colorbar=dict(title="Periodic Table Evenly-Split Heatmap Plots"),
-        hide_f_block=True,
-    )
-    fig.layout.paper_bgcolor = "white"
-    fig.show()
-    # pmv.io.save_and_compress_svg(fig, f"ptable-heatmap-splits-{n_splits}")
diff --git a/examples/make_assets/scatter.py b/examples/make_assets/scatter.py
deleted file mode 100644
index c6d7f502..00000000
--- a/examples/make_assets/scatter.py
+++ /dev/null
@@ -1,83 +0,0 @@
-# %%
-from __future__ import annotations
-
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import plotly.express as px
-import plotly.io as pio
-from sklearn.datasets import make_blobs
-
-import pymatviz as pmv
-
-
-px.defaults.template = "pymatviz_white"
-pio.templates.default = "pymatviz_white"
-
-
-# %% Configure matplotlib and load test data
-plt.rc("font", size=14)
-plt.rc("savefig", bbox="tight", dpi=200)
-plt.rc("axes", titlesize=16, titleweight="bold")
-plt.rc("figure", dpi=200, titlesize=20, titleweight="bold")
-plt.rcParams["figure.constrained_layout.use"] = True
-
-
-# Random regression data
-rand_regression_size = 500
-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 = abs((y_true - y_pred) * 10 * np_rng.normal(0, 0.1, rand_regression_size))
-
-
-# %% density scatter plotly
-df_tips = px.data.tips().rename(columns={"total_bill": "X", "tip": "Y", "size": "Z"})
-fig = pmv.density_scatter_plotly(df_tips, x="X", y="Y", size="Z", facet_col="smoker")
-fig.show()
-pmv.io.save_and_compress_svg(fig, "density-scatter-plotly")
-
-
-# %% density scatter plotly blobs
-xs, ys = make_blobs(n_samples=100_000, centers=3, n_features=2, random_state=42)
-
-x_col, y_col, target_col = "feature1", "feature2", "target"
-df_blobs = pd.DataFrame(dict(zip([x_col, y_col], xs.T, strict=True)) | {target_col: ys})
-
-fig = pmv.density_scatter_plotly(df=df_blobs, x=x_col, y=y_col)
-fig.show()
-pmv.io.save_and_compress_svg(fig, "density-scatter-plotly-blobs")
-
-
-# %% density scatter
-ax = pmv.density_scatter(y_pred, y_true)
-pmv.io.save_and_compress_svg(ax, "density-scatter")
-
-
-# %% density scatter with hist
-ax = pmv.density_scatter_with_hist(y_pred, y_true)
-pmv.io.save_and_compress_svg(ax, "density-scatter-with-hist")
-
-
-# %% density hexbin
-ax = pmv.density_hexbin(
-    y_pred, y_true, best_fit_line={"annotate_params": {"loc": "lower center"}}
-)
-pmv.io.save_and_compress_svg(ax, "density-scatter-hex")
-
-
-# %% density hexbin with hist
-ax = pmv.density_hexbin_with_hist(
-    y_pred, y_true, best_fit_line={"annotate_params": {"loc": "lower center"}}
-)
-pmv.io.save_and_compress_svg(ax, "density-scatter-hex-with-hist")
-
-
-# %% scatter with error bar
-ax = pmv.scatter_with_err_bar(y_pred, y_true, yerr=y_std)
-pmv.io.save_and_compress_svg(ax, "scatter-with-err-bar")
-
-
-# %% residual vs actual
-ax = pmv.residual_vs_actual(y_true, y_pred)
-pmv.io.save_and_compress_svg(ax, "residual-vs-actual")
diff --git a/examples/make_assets/uncertainty.py b/examples/make_assets/uncertainty.py
deleted file mode 100644
index 1644aaf9..00000000
--- a/examples/make_assets/uncertainty.py
+++ /dev/null
@@ -1,45 +0,0 @@
-# %%
-import numpy as np
-
-import pymatviz as pmv
-
-
-# %% Random regression data
-rand_regression_size = 500
-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
-ax = pmv.qq_gaussian(
-    y_pred, y_true, y_std, identity_line={"line_kwargs": {"color": "red"}}
-)
-pmv.io.save_and_compress_svg(ax, "normal-prob-plot")
-
-
-ax = pmv.qq_gaussian(
-    y_pred, y_true, {"over-confident": y_std, "under-confident": 1.5 * y_std}
-)
-pmv.io.save_and_compress_svg(ax, "normal-prob-plot-multiple")
-
-
-ax = pmv.error_decay_with_uncert(y_true, y_pred, y_std)
-pmv.io.save_and_compress_svg(ax, "error-decay-with-uncert")
-
-eps = 0.2 * np_rng.standard_normal(*y_std.shape)
-
-ax = pmv.error_decay_with_uncert(
-    y_true, y_pred, {"better": y_std, "worse": y_std + eps}
-)
-pmv.io.save_and_compress_svg(ax, "error-decay-with-uncert-multiple")
-
-
-# %% Cumulative Plots
-ax = pmv.cumulative_error(y_pred - y_true)
-pmv.io.save_and_compress_svg(ax, "cumulative-error")
-
-
-ax = pmv.cumulative_residual(y_pred - y_true)
-pmv.io.save_and_compress_svg(ax, "cumulative-residual")
diff --git a/pymatviz/__init__.py b/pymatviz/__init__.py
index 585c794b..8042807a 100644
--- a/pymatviz/__init__.py
+++ b/pymatviz/__init__.py
@@ -21,6 +21,7 @@
     colors,
     coordination,
     cumulative,
+    data,
     enums,
     io,
     phonons,
diff --git a/pymatviz/data/__init__.py b/pymatviz/data/__init__.py
new file mode 100644
index 00000000..67318d07
--- /dev/null
+++ b/pymatviz/data/__init__.py
@@ -0,0 +1,50 @@
+"""Generate dummy data for testing and prototyping."""
+
+from typing import NamedTuple
+
+import numpy as np
+from numpy.typing import NDArray
+
+
+class RegressionData(NamedTuple):
+    """Regression data containing: y_true, y_pred and y_std."""
+
+    y_true: NDArray[np.float64]
+    y_pred: NDArray[np.float64]
+    y_std: NDArray[np.float64]
+
+
+def regression(
+    n_samples: int = 500,
+    true_mean: float = 5,
+    true_std: float = 4,
+    pred_slope: float = 1.2,
+    pred_intercept: float = -2,
+    seed: int = 0,
+) -> RegressionData:
+    """Generate dummy regression data for testing and prototyping.
+
+    This function creates synthetic data to simulate a regression task:
+    - `y_true`: Sampled from a normal distribution with mean 5 and
+        standard deviation 4.
+    - `y_pred`: Linearly related to `y_true` with a slope of 1.2 and
+        additional Gaussian noise.
+    - `y_std`: Residuals scaled by random noise, representing variability
+        in predictions.
+
+    Parameters:
+        n_samples (int): Number of samples to generate. Default is 500.
+
+    Returns:
+        RegressionData: A named tuple containing y_true, y_pred, and y_std.
+    """
+    np_rng = np.random.default_rng(seed=seed)
+
+    y_true = np_rng.normal(loc=true_mean, scale=true_std, size=n_samples)
+
+    noise = np_rng.normal(loc=0, scale=1, size=n_samples)
+    y_pred = pred_slope * y_true + pred_intercept + noise
+
+    y_std = (y_true - y_pred) * 10 * np_rng.normal(loc=0, scale=0.1, size=n_samples)
+
+    return RegressionData(y_true=y_true, y_pred=y_pred, y_std=y_std)
diff --git a/pymatviz/io.py b/pymatviz/io.py
index 55da44a3..f25ed489 100644
--- a/pymatviz/io.py
+++ b/pymatviz/io.py
@@ -227,7 +227,7 @@ def save_and_compress_svg(
         raise ValueError("Passed fig contains no axes. Nothing to plot!")
 
     if not filename.endswith(".svg") and not os.path.isabs(filename):
-        filepath = f"{ROOT}/assets/{filename}.svg"
+        filepath = f"{ROOT}/assets/svg/{filename}.svg"
     else:
         filepath = filename
 
diff --git a/pymatviz/powerups/matplotlib.py b/pymatviz/powerups/matplotlib.py
index 0131b58a..1332e1e2 100644
--- a/pymatviz/powerups/matplotlib.py
+++ b/pymatviz/powerups/matplotlib.py
@@ -6,13 +6,13 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
+from matplotlib.gridspec import GridSpec
 
 
 if TYPE_CHECKING:
     from collections.abc import Sequence
     from typing import Any
 
-    from matplotlib.gridspec import GridSpec
     from matplotlib.text import Annotation
     from numpy.typing import ArrayLike
 
@@ -23,6 +23,7 @@ def with_marginal_hist(
     cell: GridSpec | None = None,
     bins: int = 100,
     fig: plt.Figure | plt.Axes | None = None,
+    **kwargs: Any,
 ) -> plt.Axes:
     """Call before creating a matplotlib figure and use the returned `ax_main` for all
     subsequent plotting ops to create a grid of plots with the main plot in the
@@ -37,29 +38,43 @@ def with_marginal_hist(
         bins (int, optional): Resolution/bin count of the histograms. Defaults to 100.
         fig (Figure, optional): matplotlib Figure or Axes to add the marginal histograms
             to. Defaults to None.
+        **kwargs: Additional keywords passed to ax.hist().
 
     Returns:
         plt.Axes: The matplotlib Axes to be used for the main plot.
     """
-    if fig is None or isinstance(fig, plt.Axes):
-        ax_main = fig or plt.gca()
-        fig = ax_main.figure
-
-    gs = (cell.subgridspec if cell else fig.add_gridspec)(
-        2, 2, width_ratios=(6, 1), height_ratios=(1, 5), wspace=0, hspace=0
+    if isinstance(fig, plt.Axes):
+        fig = fig.figure
+    if fig is None:
+        fig = plt.figure(figsize=(8, 8))
+
+    # Create the grid
+    cell_kwargs = dict(
+        nrows=2, ncols=2, width_ratios=(6, 1), height_ratios=(1, 5), wspace=0, hspace=0
     )
-
-    ax_main = fig.add_subplot(gs[1, 0])
-    ax_hist_x = fig.add_subplot(gs[0, 0], sharex=ax_main)
-    ax_hist_y = fig.add_subplot(gs[1, 1], sharey=ax_main)
+    if cell is None:
+        gs = GridSpec(figure=fig, **cell_kwargs)
+    else:
+        # Create a subgridspec within the provided cell
+        gs = cell.subgridspec(**cell_kwargs)
+
+    # Create the three axes
+    ax_main = fig.add_subplot(gs[1, 0])  # Main scatter plot
+    ax_top = fig.add_subplot(gs[0, 0], sharex=ax_main)  # Top histogram
+    ax_right = fig.add_subplot(gs[1, 1], sharey=ax_main)  # Right histogram
+
+    # Plot the histograms
+    hist_defaults = dict(bins=bins, density=True, color="blue")
+    ax_top.hist(xs, **hist_defaults | kwargs)
+    ax_right.hist(ys, **dict(orientation="horizontal") | hist_defaults | kwargs)
 
     # x_hist
-    ax_hist_x.hist(xs, bins=bins, rwidth=0.8)
-    ax_hist_x.axis("off")
+    ax_top.hist(xs, bins=bins, rwidth=0.8)
+    ax_top.axis("off")
 
     # y_hist
-    ax_hist_y.hist(ys, bins=bins, rwidth=0.8, orientation="horizontal")
-    ax_hist_y.axis("off")
+    ax_right.hist(ys, bins=bins, rwidth=0.8, orientation="horizontal")
+    ax_right.axis("off")
 
     return ax_main
 
diff --git a/pymatviz/utils.py b/pymatviz/utils.py
index f363d543..1e07fbc9 100644
--- a/pymatviz/utils.py
+++ b/pymatviz/utils.py
@@ -729,3 +729,18 @@ def normalize_to_dict(
     raise TypeError(
         f"Invalid {inputs=}, expected {cls_name} or dict/list/tuple of {cls_name}"
     )
+
+
+def apply_matplotlib_template() -> None:
+    """Set default matplotlib configurations for consistency.
+    - Font size: 14 for readability.
+    - Savefig: Tight bounding box and 200 DPI for high-quality saved plots.
+    - Axes: Title size 16, bold weight for emphasis.
+    - Figure: DPI 200, title size 20, bold weight for better visibility.
+    - Layout: Enables constrained layout to reduce element overlap.
+    """
+    plt.rc("font", size=14)
+    plt.rc("savefig", bbox="tight", dpi=200)
+    plt.rc("axes", titlesize=16, titleweight="bold")
+    plt.rc("figure", dpi=200, titlesize=20, titleweight="bold")
+    plt.rcParams["figure.constrained_layout.use"] = True
diff --git a/pyproject.toml b/pyproject.toml
index ad65079d..d95acdad 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -32,10 +32,7 @@ dependencies = [
     # see https://github.com/pandas-dev/pandas/blob/-/pyproject.toml
     "pandas[output-formatting]>=2.2",
     "plotly>=5.23",
-    "pymatgen>=2024.7.18",
-    # TODO: pmv doesn't actually depend on monty, however latest monty
-    # includes a critical import patch, remove this after pmg bump dep
-    "monty>=2024.10.21",
+    "pymatgen>=2024.11.13",
     "scikit-learn>=1.5",
     "scipy>=1.14",
 ]
@@ -129,6 +126,11 @@ docstring-code-format = true
     "INP001", # implicit-namespace-package
     "T201",   # print found
 ]
+"assets/scripts/*" = [
+    "D100",   # missing docstring in public module
+    "INP001", # implicit-namespace-package
+    "T201",   # print found
+]
 "site/*" = [
     "D",      # pydocstyle
     "INP001", # implicit-namespace-package
diff --git a/readme.md b/readme.md
index 8ee53633..52aba582 100644
--- a/readme.md
+++ b/readme.md
@@ -30,7 +30,7 @@ pip install pymatviz
 
 ## API Docs
 
-See the [/api] page.
+See the [/api][/api] page.
 
 [/api]: https://janosh.github.io/pymatviz/api
 
@@ -38,13 +38,13 @@ See the [/api] page.
 
 See the Jupyter notebooks under [`examples/`](examples) for how to use `pymatviz`. PRs with additional examples are welcome! 🙏
 
-|                                                                                                                        |                                                                                                                                       |                                      |
-| ---------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------ |
-| [mlff_phonons.ipynb](https://github.com/janosh/pymatviz/blob/main/examples/mlff_phonons.ipynb)                         | [![Open in Google Colab]](https://colab.research.google.com/github/janosh/pymatviz/blob/main/examples/mlff_phonons.ipynb)             | [![Launch Codespace]][codespace url] |
-| [matbench_dielectric_eda.ipynb](https://github.com/janosh/pymatviz/blob/main/examples/matbench_dielectric_eda.ipynb)   | [![Open in Google Colab]](https://colab.research.google.com/github/janosh/pymatviz/blob/main/examples/matbench_dielectric_eda.ipynb)  | [![Launch Codespace]][codespace url] |
-| [mp_bimodal_e_form.ipynb](https://github.com/janosh/pymatviz/blob/main/examples/mp_bimodal_e_form.ipynb)               | [![Open in Google Colab]](https://colab.research.google.com/github/janosh/pymatviz/blob/main/examples/mp_bimodal_e_form.ipynb)        | [![Launch Codespace]][codespace url] |
-| [matbench_perovskites_eda.ipynb](https://github.com/janosh/pymatviz/blob/main/examples/matbench_perovskites_eda.ipynb) | [![Open in Google Colab]](https://colab.research.google.com/github/janosh/pymatviz/blob/main/examples/matbench_perovskites_eda.ipynb) | [![Launch Codespace]][codespace url] |
-| [mprester_ptable.ipynb](https://github.com/janosh/pymatviz/blob/main/examples/mprester_ptable.ipynb)                   | [![Open in Google Colab]](https://colab.research.google.com/github/janosh/pymatviz/blob/main/examples/mprester_ptable.ipynb)          | [![Launch Codespace]][codespace url] |
+|                                                                                                                        |                                                                                                                                                             |                                   |
+| ---------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------- |
+| [mlff_phonons.ipynb](https://github.com/janosh/pymatviz/blob/main/examples/mlff_phonons.ipynb)                         | [![Open in Google Colab][Open in Google Colab]](https://colab.research.google.com/github/janosh/pymatviz/blob/main/examples/mlff_phonons.ipynb)             | [Launch Codespace][codespace url] |
+| [matbench_dielectric_eda.ipynb](https://github.com/janosh/pymatviz/blob/main/examples/matbench_dielectric_eda.ipynb)   | [![Open in Google Colab][Open in Google Colab]](https://colab.research.google.com/github/janosh/pymatviz/blob/main/examples/matbench_dielectric_eda.ipynb)  | [Launch Codespace][codespace url] |
+| [mp_bimodal_e_form.ipynb](https://github.com/janosh/pymatviz/blob/main/examples/mp_bimodal_e_form.ipynb)               | [![Open in Google Colab][Open in Google Colab]](https://colab.research.google.com/github/janosh/pymatviz/blob/main/examples/mp_bimodal_e_form.ipynb)        | [Launch Codespace][codespace url] |
+| [matbench_perovskites_eda.ipynb](https://github.com/janosh/pymatviz/blob/main/examples/matbench_perovskites_eda.ipynb) | [![Open in Google Colab][Open in Google Colab]](https://colab.research.google.com/github/janosh/pymatviz/blob/main/examples/matbench_perovskites_eda.ipynb) | [Launch Codespace][codespace url] |
+| [mprester_ptable.ipynb](https://github.com/janosh/pymatviz/blob/main/examples/mprester_ptable.ipynb)                   | [![Open in Google Colab][Open in Google Colab]](https://colab.research.google.com/github/janosh/pymatviz/blob/main/examples/mprester_ptable.ipynb)          | [Launch Codespace][codespace url] |
 
 [Open in Google Colab]: https://colab.research.google.com/assets/colab-badge.svg
 [Launch Codespace]: https://img.shields.io/badge/Launch-Codespace-darkblue?logo=github
@@ -54,218 +54,218 @@ See the Jupyter notebooks under [`examples/`](examples) for how to use `pymatviz
 
 See [`pymatviz/ptable/ptable_matplotlib.py`](pymatviz/ptable/ptable_matplotlib.py) and [`pymatviz/ptable/ptable_plotly.py`](pymatviz/ptable/ptable_plotly.py). `matplotlib` supports heatmaps, heatmap ratios, heatmap splits (multiple values per element), histograms, scatter plots and line plots. `plotly` currently only supports heatmaps (PRs to port over other `matplotlib` `ptable` variants to `plotly` are very welcome!). The `plotly` heatmap supports displaying additional data on hover or full interactivity through [Dash](https://plotly.com/dash).
 
-|                    [`ptable_heatmap(compositions, log=True)`](pymatviz/ptable/ptable_matplotlib.py)                    |                   [`ptable_heatmap_ratio(comps_a, comps_b)`](pymatviz/ptable/ptable_matplotlib.py)                    |
-| :--------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------: |
-|                                                   ![ptable-heatmap]                                                    |                                                ![ptable-heatmap-ratio]                                                |
-|                       [`ptable_heatmap_plotly(atomic_masses)`](pymatviz/ptable/ptable_plotly.py)                       |                [`ptable_heatmap_plotly(compositions, log=True)`](pymatviz/ptable/ptable_matplotlib.py)                |
-|                                        ![ptable-heatmap-plotly-more-hover-data]                                        |                                             ![ptable-heatmap-plotly-log]                                              |
-|                   [`ptable_hists(data, colormap="coolwarm")`](pymatviz/ptable/ptable_matplotlib.py)                    |                            [`ptable_hists_plotly(data)`](pymatviz/ptable/ptable_plotly.py)                            |
-|                                                    ![ptable-hists]                                                     |                                                ![ptable-hists-plotly]                                                 |
-|                  [`ptable_scatters(data, colormap="coolwarm")`](pymatviz/ptable/ptable_matplotlib.py)                  |                             [`ptable_lines(data)`](pymatviz/ptable/ptable_matplotlib.py)                              |
-|                                               ![ptable-scatters-parity]                                                |                                                    ![ptable-lines]                                                    |
-| [`ptable_heatmap_splits(2_vals_per_elem, colormap="coolwarm", start_angle=135)`](pymatviz/ptable/ptable_matplotlib.py) | [`ptable_heatmap_splits(3_vals_per_elem, colormap="coolwarm", start_angle=90)`](pymatviz/ptable/ptable_matplotlib.py) |
-|                                               ![ptable-heatmap-splits-2]                                               |                                              ![ptable-heatmap-splits-3]                                               |
-|                  [`ptable_heatmap_splits_plotly(2_vals_per_elem)`](pymatviz/ptable/ptable_plotly.py)                   |                  [`ptable_heatmap_splits_plotly(3_vals_per_elem)`](pymatviz/ptable/ptable_plotly.py)                  |
-|                                           ![ptable-heatmap-splits-plotly-2]                                            |                                           ![ptable-heatmap-splits-plotly-3]                                           |
-
-[ptable-hists]: assets/ptable-hists.svg
-[ptable-lines]: assets/homo-nuclear-mace-medium.svg
-[ptable-scatters-parity]: assets/ptable-scatters-parity.svg
-[ptable-scatters-parabola]: assets/ptable-scatters-parabola.svg
-[ptable-heatmap-splits-2]: assets/ptable-heatmap-splits-2.svg
-[ptable-heatmap-splits-3]: assets/ptable-heatmap-splits-3.svg
-[ptable-heatmap-splits-plotly-2]: assets/ptable-heatmap-splits-plotly-2.svg
-[ptable-heatmap-splits-plotly-3]: assets/ptable-heatmap-splits-plotly-3.svg
-[ptable-hists-plotly]: assets/ptable-hists-plotly.svg
+|                       [`ptable_heatmap(compositions, log=True)`](assets/scripts/ptable_matplotlib/ptable_heatmap.py)                        |                    [`ptable_heatmap_ratio(comps_a, comps_b)`](assets/scripts/ptable_matplotlib/ptable_heatmap_ratio.py)                    |
+| :-----------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------: |
+|                                                      ![ptable-heatmap][ptable-heatmap]                                                      |                                               ![ptable-heatmap-ratio][ptable-heatmap-ratio]                                                |
+|                       [`ptable_heatmap_plotly(atomic_masses)`](assets/scripts/ptable_plotly/ptable_heatmap_plotly.py)                       |                  [`ptable_heatmap_plotly(compositions, log=True)`](assets/scripts/ptable_plotly/ptable_heatmap_plotly.py)                  |
+|                               ![ptable-heatmap-plotly-more-hover-data][ptable-heatmap-plotly-more-hover-data]                               |                                          ![ptable-heatmap-plotly-log][ptable-heatmap-plotly-log]                                           |
+|                        [`ptable_hists(data, colormap="coolwarm")`](assets/scripts/ptable_matplotlib/ptable_hists.py)                        |                             [`ptable_hists_plotly(data)`](assets/scripts/ptable_plotly/ptable_hists_plotly.py)                             |
+|                                                        ![ptable-hists][ptable-hists]                                                        |                                                ![ptable-hists-plotly][ptable-hists-plotly]                                                 |
+|                     [`ptable_scatters(data, colormap="coolwarm")`](assets/scripts/ptable_matplotlib/ptable_scatters.py)                     |                                  [`ptable_lines(data)`](assets/scripts/ptable_matplotlib/ptable_lines.py)                                  |
+|                                              ![ptable-scatters-parity][ptable-scatters-parity]                                              |                                                       ![ptable-lines][ptable-lines]                                                        |
+| [`ptable_heatmap_splits(2_vals_per_elem, colormap="coolwarm", start_angle=135)`](assets/scripts/ptable_matplotlib/ptable_heatmap_splits.py) | [`ptable_heatmap_splits(3_vals_per_elem, colormap="coolwarm", start_angle=90)`](assets/scripts/ptable_matplotlib/ptable_heatmap_splits.py) |
+|                                             ![ptable-heatmap-splits-2][ptable-heatmap-splits-2]                                             |                                            ![ptable-heatmap-splits-3][ptable-heatmap-splits-3]                                             |
+|               [`ptable_heatmap_splits_plotly(2_vals_per_elem)`](assets/scripts/ptable_plotly/ptable_heatmap_splits_plotly.py)               |              [`ptable_heatmap_splits_plotly(3_vals_per_elem)`](assets/scripts/ptable_plotly/ptable_heatmap_splits_plotly.py)               |
+|                                      ![ptable-heatmap-splits-plotly-2][ptable-heatmap-splits-plotly-2]                                      |                                     ![ptable-heatmap-splits-plotly-3][ptable-heatmap-splits-plotly-3]                                      |
+
+[ptable-hists]: assets/svg/ptable-hists.svg
+[ptable-lines]: assets/svg/homo-nuclear-mace-medium.svg
+[ptable-scatters-parity]: assets/svg/ptable-scatters-parity.svg
+[ptable-scatters-parabola]: assets/svg/ptable-scatters-parabola.svg
+[ptable-heatmap-splits-2]: assets/svg/ptable-heatmap-splits-2.svg
+[ptable-heatmap-splits-3]: assets/svg/ptable-heatmap-splits-3.svg
+[ptable-heatmap-splits-plotly-2]: assets/svg/ptable-heatmap-splits-plotly-2.svg
+[ptable-heatmap-splits-plotly-3]: assets/svg/ptable-heatmap-splits-plotly-3.svg
+[ptable-hists-plotly]: assets/svg/ptable-hists-plotly.svg
 
-## Phonons
+### Dash app using `ptable_heatmap_plotly()`
 
-See [`examples/mlff_phonons.ipynb`](https://github.com/janosh/pymatviz/blob/main/examples/mlff_phonons.ipynb) for usage example.
+See [`examples/mprester_ptable.ipynb`](https://github.com/janosh/pymatviz/blob/main/examples/mprester_ptable.ipynb).
 
-|           [`phonon_bands(bands_dict)`](pymatviz/phonons.py)           |             [`phonon_dos(doses_dict)`](pymatviz/phonons.py)             |
-| :-------------------------------------------------------------------: | :---------------------------------------------------------------------: |
-|                            ![phonon-bands]                            |                              ![phonon-dos]                              |
-| [`phonon_bands_and_dos(bands_dict, doses_dict)`](pymatviz/phonons.py) | [`phonon_bands_and_dos(single_bands, single_dos)`](pymatviz/phonons.py) |
-|                    ![phonon-bands-and-dos-mp-2758]                    |                    ![phonon-bands-and-dos-mp-23907]                     |
+[https://user-images.githubusercontent.com/30958850/181644052-b330f0a2-70fc-451c-8230-20d45d3af72f.mp4](https://user-images.githubusercontent.com/30958850/181644052-b330f0a2-70fc-451c-8230-20d45d3af72f.mp4)
 
-[phonon-bands]: assets/phonon-bands-mp-2758.svg
-[phonon-dos]: assets/phonon-dos-mp-2758.svg
-[phonon-bands-and-dos-mp-2758]: assets/phonon-bands-and-dos-mp-2758.svg
-[phonon-bands-and-dos-mp-23907]: assets/phonon-bands-and-dos-mp-23907.svg
+## Phonons
 
-### Dash app using `ptable_heatmap_plotly()`
+See [`examples/mlff_phonons.ipynb`](https://github.com/janosh/pymatviz/blob/main/examples/mlff_phonons.ipynb) for usage example.
 
-See [`examples/mprester_ptable.ipynb`](https://github.com/janosh/pymatviz/blob/main/examples/mprester_ptable.ipynb).
+|               [`phonon_bands(bands_dict)`](assets/scripts/phonons/phonon_bands.py)               |                  [`phonon_dos(doses_dict)`](assets/scripts/phonons/phonon_dos.py)                  |
+| :----------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------: |
+|                                  ![phonon-bands][phonon-bands]                                   |                                     ![phonon-dos][phonon-dos]                                      |
+| [`phonon_bands_and_dos(bands_dict, doses_dict)`](assets/scripts/phonons/phonon_bands_and_dos.py) | [`phonon_bands_and_dos(single_bands, single_dos)`](assets/scripts/phonons/phonon_bands_and_dos.py) |
+|                  ![phonon-bands-and-dos-mp-2758][phonon-bands-and-dos-mp-2758]                   |                  ![phonon-bands-and-dos-mp-23907][phonon-bands-and-dos-mp-23907]                   |
 
-<https://user-images.githubusercontent.com/30958850/181644052-b330f0a2-70fc-451c-8230-20d45d3af72f.mp4>
+[phonon-bands]: assets/svg/phonon-bands-mp-2758.svg
+[phonon-dos]: assets/svg/phonon-dos-mp-2758.svg
+[phonon-bands-and-dos-mp-2758]: assets/svg/phonon-bands-and-dos-mp-2758.svg
+[phonon-bands-and-dos-mp-23907]: assets/svg/phonon-bands-and-dos-mp-23907.svg
 
 ## Structure
 
 See [`pymatviz/structure_viz/(mpl|plotly).py`](pymatviz/structure_viz/plotly.py). Currently structure plotting is only supported with `matplotlib` in 2d. 3d interactive plots (probably with `plotly`) are on the road map.
 
-|       [`structure_2d(mp_19017)`](pymatviz/structure_viz/mpl.py)        |       [`structure_2d(mp_12712)`](pymatviz/structure_viz/mpl.py)        |
-| :--------------------------------------------------------------------: | :--------------------------------------------------------------------: |
-|        ![struct-2d-mp-19017-Li4Mn0.8Fe1.6P4C1.6O16-disordered]         |              ![struct-2d-mp-12712-Hf9Zr9Pd24-disordered]               |
-| [`structure_2d_plotly(six_structs)`](pymatviz/structure_viz/plotly.py) | [`structure_3d_plotly(six_structs)`](pymatviz/structure_viz/plotly.py) |
-|                ![matbench-phonons-structures-2d-plotly]                |                ![matbench-phonons-structures-3d-plotly]                |
+|                           [`structure_2d(mp_19017)`](pymatviz/structure_viz/mpl.py)                           |                 [`structure_2d(mp_12712)`](pymatviz/structure_viz/mpl.py)                 |
+| :-----------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------: |
+| ![struct-2d-mp-19017-Li4Mn0.8Fe1.6P4C1.6O16-disordered][struct-2d-mp-19017-Li4Mn0.8Fe1.6P4C1.6O16-disordered] |   ![struct-2d-mp-12712-Hf9Zr9Pd24-disordered][struct-2d-mp-12712-Hf9Zr9Pd24-disordered]   |
+|           [`structure_2d_plotly(six_structs)`](assets/scripts/structure_viz/structure_2d_plotly.py)           | [`structure_3d_plotly(six_structs)`](assets/scripts/structure_viz/structure_3d_plotly.py) |
+|                ![matbench-phonons-structures-2d-plotly][matbench-phonons-structures-2d-plotly]                |      ![matbench-phonons-structures-3d-plotly][matbench-phonons-structures-3d-plotly]      |
 
-[matbench-phonons-structures-2d-plotly]: assets/matbench-phonons-structures-2d-plotly.svg
-[matbench-phonons-structures-3d-plotly]: assets/matbench-phonons-structures-3d-plotly.svg
+[matbench-phonons-structures-2d-plotly]: assets/svg/matbench-phonons-structures-2d-plotly.svg
+[matbench-phonons-structures-3d-plotly]: assets/svg/matbench-phonons-structures-3d-plotly.svg
 
 ## X-Ray Diffraction
 
 See [`pymatviz/xrd.py`](pymatviz/xrd.py).
 
-|             [`xrd_pattern(pattern)`](pymatviz/xrd.py)             |             [`xrd_pattern({key1: patt1, key2: patt2})`](pymatviz/xrd.py)              |
-| :---------------------------------------------------------------: | :-----------------------------------------------------------------------------------: |
-|                          ![xrd-pattern]                           |                                ![xrd-pattern-multiple]                                |
-| [`xrd_pattern(struct_dict, stack="horizontal")`](pymatviz/xrd.py) | [`xrd_pattern(struct_dict, stack="vertical", title="Custom Title")`](pymatviz/xrd.py) |
-|                  ![xrd-pattern-horizontal-stack]                  |                             ![xrd-pattern-vertical-stack]                             |
+|             [`xrd_pattern(pattern)`](assets/scripts/xrd/xrd_pattern.py)             |             [`xrd_pattern({key1: patt1, key2: patt2})`](assets/scripts/xrd/xrd_pattern.py)              |
+| :---------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------: |
+|                             ![xrd-pattern][xrd-pattern]                             |                              ![xrd-pattern-multiple][xrd-pattern-multiple]                              |
+| [`xrd_pattern(struct_dict, stack="horizontal")`](assets/scripts/xrd/xrd_pattern.py) | [`xrd_pattern(struct_dict, stack="vertical", title="Custom Title")`](assets/scripts/xrd/xrd_pattern.py) |
+|            ![xrd-pattern-horizontal-stack][xrd-pattern-horizontal-stack]            |                        ![xrd-pattern-vertical-stack][xrd-pattern-vertical-stack]                        |
 
-[xrd-pattern]: assets/xrd-pattern.svg
-[xrd-pattern-multiple]: assets/xrd-pattern-multiple.svg
-[xrd-pattern-horizontal-stack]: assets/xrd-pattern-horizontal-stack.svg
-[xrd-pattern-vertical-stack]: assets/xrd-pattern-vertical-stack.svg
+[xrd-pattern]: assets/svg/xrd-pattern.svg
+[xrd-pattern-multiple]: assets/svg/xrd-pattern-multiple.svg
+[xrd-pattern-horizontal-stack]: assets/svg/xrd-pattern-horizontal-stack.svg
+[xrd-pattern-vertical-stack]: assets/svg/xrd-pattern-vertical-stack.svg
 
 ## Radial Distribution Functions
 
 See [`pymatviz/rdf/plotly.py`](pymatviz/rdf/plotly.py).
 
-| [`rdf_plot(rdf)`](pymatviz/rdf/plotly.py) | [`rdf_plot(rdf, rdf2)`](pymatviz/rdf/plotly.py) |
-| :---------------------------------------: | :---------------------------------------------: |
-|      ![element-pair-rdfs-Na8Nb8O24]       |    ![element-pair-rdfs-crystal-vs-amorphous]    |
+| [`rdf_plot(rdf)`](assets/scripts/rdf/element_pair_rdfs.py)  |         [`rdf_plot(rdf, rdf2)`](assets/scripts/rdf/element_pair_rdfs.py)          |
+| :---------------------------------------------------------: | :-------------------------------------------------------------------------------: |
+| ![element-pair-rdfs-Na8Nb8O24][element-pair-rdfs-Na8Nb8O24] | ![element-pair-rdfs-crystal-vs-amorphous][element-pair-rdfs-crystal-vs-amorphous] |
 
-[element-pair-rdfs-Na8Nb8O24]: assets/element-pair-rdfs-Na8Nb8O24.svg
-[element-pair-rdfs-crystal-vs-amorphous]: assets/element-pair-rdfs-crystal-vs-amorphous.svg
+[element-pair-rdfs-Na8Nb8O24]: assets/svg/element-pair-rdfs-Na8Nb8O24.svg
+[element-pair-rdfs-crystal-vs-amorphous]: assets/svg/element-pair-rdfs-crystal-vs-amorphous.svg
 
 ## Coordination
 
 See [`pymatviz/coordination/plotly.py`](pymatviz/coordination/plotly.py).
 
-|             [`coordination_hist(struct_dict)`](pymatviz/coordination/plotly.py)              |     [`coordination_hist(struct_dict, by_element=True)`](pymatviz/coordination/plotly.py)     |
-| :------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------: |
-|                                 ![coordination-hist-single]                                  |                        ![coordination-hist-by-structure-and-element]                         |
-| [`coordination_vs_cutoff_line(struct_dict, strategy=None)`](pymatviz/coordination/plotly.py) | [`coordination_vs_cutoff_line(struct_dict, strategy=None)`](pymatviz/coordination/plotly.py) |
-|                               ![coordination-vs-cutoff-single]                               |                              ![coordination-vs-cutoff-multiple]                              |
+|                  [`coordination_hist(struct_dict)`](assets/scripts/coordination/coordination_hist.py)                   |          [`coordination_hist(struct_dict, by_element=True)`](assets/scripts/coordination/coordination_hist.py)          |
+| :---------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------: |
+|                                  ![coordination-hist-single][coordination-hist-single]                                  |                ![coordination-hist-by-structure-and-element][coordination-hist-by-structure-and-element]                |
+| [`coordination_vs_cutoff_line(struct_dict, strategy=None)`](assets/scripts/coordination/coordination_vs_cutoff_line.py) | [`coordination_vs_cutoff_line(struct_dict, strategy=None)`](assets/scripts/coordination/coordination_vs_cutoff_line.py) |
+|                             ![coordination-vs-cutoff-single][coordination-vs-cutoff-single]                             |                           ![coordination-vs-cutoff-multiple][coordination-vs-cutoff-multiple]                           |
 
-[coordination-hist-single]: assets/coordination-hist-single.svg
-[coordination-hist-by-structure-and-element]: assets/coordination-hist-by-structure-and-element.svg
-[coordination-vs-cutoff-single]: assets/coordination-vs-cutoff-single.svg
-[coordination-vs-cutoff-multiple]: assets/coordination-vs-cutoff-multiple.svg
+[coordination-hist-single]: assets/svg/coordination-hist-single.svg
+[coordination-hist-by-structure-and-element]: assets/svg/coordination-hist-by-structure-and-element.svg
+[coordination-vs-cutoff-single]: assets/svg/coordination-vs-cutoff-single.svg
+[coordination-vs-cutoff-multiple]: assets/svg/coordination-vs-cutoff-multiple.svg
 
 ## Sunburst
 
 See [`pymatviz/sunburst.py`](pymatviz/sunburst.py).
 
-| [`spacegroup_sunburst([65, 134, 225, ...])`](pymatviz/sunburst.py) | [`spacegroup_sunburst(["C2/m", "P-43m", "Fm-3m", ...])`](pymatviz/sunburst.py) |
-| :----------------------------------------------------------------: | :----------------------------------------------------------------------------: |
-|                        ![spg-num-sunburst]                         |                             ![spg-symbol-sunburst]                             |
+| [`spacegroup_sunburst([65, 134, 225, ...])`](assets/scripts/symmetry/spacegroup_sunburst.py) | [`spacegroup_sunburst(["C2/m", "P-43m", "Fm-3m", ...])`](assets/scripts/symmetry/spacegroup_sunburst.py) |
+| :------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------: |
+|                            ![spg-num-sunburst][spg-num-sunburst]                             |                               ![spg-symbol-sunburst][spg-symbol-sunburst]                                |
 
 ## Rainclouds
 
 See [`pymatviz/rainclouds.py`](pymatviz/rainclouds.py).
 
-| [`rainclouds(two_key_dict)`](pymatviz/rainclouds.py) | [`rainclouds(three_key_dict)`](pymatviz/rainclouds.py) |
-| :--------------------------------------------------: | :----------------------------------------------------: |
-|                ![rainclouds-bimodal]                 |                 ![rainclouds-trimodal]                 |
+| [`rainclouds(two_key_dict)`](assets/scripts/rainclouds/rainclouds.py) | [`rainclouds(three_key_dict)`](assets/scripts/rainclouds/rainclouds.py) |
+| :-------------------------------------------------------------------: | :---------------------------------------------------------------------: |
+|               ![rainclouds-bimodal][rainclouds-bimodal]               |               ![rainclouds-trimodal][rainclouds-trimodal]               |
 
-[rainclouds-bimodal]: assets/rainclouds-bimodal.svg
-[rainclouds-trimodal]: assets/rainclouds-trimodal.svg
+[rainclouds-bimodal]: assets/svg/rainclouds-bimodal.svg
+[rainclouds-trimodal]: assets/svg/rainclouds-trimodal.svg
 
 ## Sankey
 
 See [`pymatviz/sankey.py`](pymatviz/sankey.py).
 
-| [`sankey_from_2_df_cols(df_perovskites)`](pymatviz/sankey.py) | [`sankey_from_2_df_cols(df_space_groups)`](pymatviz/sankey.py) |
-| :-----------------------------------------------------------: | :------------------------------------------------------------: |
-|             ![sankey-spglib-vs-aflow-spacegroups]             |              ![sankey-crystal-sys-to-spg-symbol]               |
+| [`sankey_from_2_df_cols(df_perovskites)`](assets/scripts/sankey/sankey_from_2_df_cols.py) | [`sankey_from_2_df_cols(df_space_groups)`](assets/scripts/sankey/sankey_from_2_df_cols.py) |
+| :---------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------: |
+|         ![sankey-spglib-vs-aflow-spacegroups][sankey-spglib-vs-aflow-spacegroups]         |           ![sankey-crystal-sys-to-spg-symbol][sankey-crystal-sys-to-spg-symbol]            |
 
-[sankey-spglib-vs-aflow-spacegroups]: assets/sankey-spglib-vs-aflow-spacegroups.svg
-[sankey-crystal-sys-to-spg-symbol]: assets/sankey-crystal-sys-to-spg-symbol.svg
+[sankey-spglib-vs-aflow-spacegroups]: assets/svg/sankey-spglib-vs-aflow-spacegroups.svg
+[sankey-crystal-sys-to-spg-symbol]: assets/svg/sankey-crystal-sys-to-spg-symbol.svg
 
 ## Histograms
 
 See [`pymatviz/histogram.py`](pymatviz/histogram.py).
 
-| [`spacegroup_bar([65, 134, 225, ...], backend="matplotlib")`](pymatviz/histogram.py) | [`spacegroup_bar(["C2/m", "P-43m", "Fm-3m", ...], backend="matplotlib")`](pymatviz/histogram.py) |
-| :----------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------: |
-|                              ![spg-num-hist-matplotlib]                              |                                  ![spg-symbol-hist-matplotlib]                                   |
-|   [`spacegroup_bar([65, 134, 225, ...], backend="plotly")`](pymatviz/histogram.py)   |   [`spacegroup_bar(["C2/m", "P-43m", "Fm-3m", ...], backend="plotly")`](pymatviz/histogram.py)   |
-|                                ![spg-num-hist-plotly]                                |                                    ![spg-symbol-hist-plotly]                                     |
-| [`elements_hist(compositions, log=True, bar_values='count')`](pymatviz/histogram.py) |             [`histogram({'key1': values1, 'key2': values2})`](pymatviz/histogram.py)             |
-|                                   ![elements-hist]                                   |                                        ![histogram-ecdf]                                         |
+| [`spacegroup_bar([65, 134, 225, ...], backend="matplotlib")`](assets/scripts/histogram/spacegroup_bar.py) | [`spacegroup_bar(["C2/m", "P-43m", "Fm-3m", ...], backend="matplotlib")`](assets/scripts/histogram/spacegroup_bar.py) |
+| :-------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------: |
+|                            ![spg-num-hist-matplotlib][spg-num-hist-matplotlib]                            |                               ![spg-symbol-hist-matplotlib][spg-symbol-hist-matplotlib]                               |
+|   [`spacegroup_bar([65, 134, 225, ...], backend="plotly")`](assets/scripts/histogram/spacegroup_bar.py)   |   [`spacegroup_bar(["C2/m", "P-43m", "Fm-3m", ...], backend="plotly")`](assets/scripts/histogram/spacegroup_bar.py)   |
+|                                ![spg-num-hist-plotly][spg-num-hist-plotly]                                |                                   ![spg-symbol-hist-plotly][spg-symbol-hist-plotly]                                   |
+| [`elements_hist(compositions, log=True, bar_values='count')`](assets/scripts/histogram/elements_hist.py)  |               [`histogram({'key1': values1, 'key2': values2})`](assets/scripts/histogram/histogram.py)                |
+|                                      ![elements-hist][elements-hist]                                      |                                           ![histogram-ecdf][histogram-ecdf]                                           |
 
-[spg-symbol-hist-plotly]: assets/spg-symbol-hist-plotly.svg
-[spg-num-hist-plotly]: assets/spg-num-hist-plotly.svg
-[spg-num-hist-matplotlib]: assets/spg-num-hist-matplotlib.svg
-[spg-symbol-hist-matplotlib]: assets/spg-symbol-hist-matplotlib.svg
-[histogram-ecdf]: assets/histogram-ecdf.svg
+[spg-symbol-hist-plotly]: assets/svg/spg-symbol-hist-plotly.svg
+[spg-num-hist-plotly]: assets/svg/spg-num-hist-plotly.svg
+[spg-num-hist-matplotlib]: assets/svg/spg-num-hist-matplotlib.svg
+[spg-symbol-hist-matplotlib]: assets/svg/spg-symbol-hist-matplotlib.svg
+[histogram-ecdf]: assets/svg/histogram-ecdf.svg
 
 ## Scatter Plots
 
 See [`pymatviz/scatter.py`](pymatviz/scatter.py).
 
-| [`density_scatter_plotly(df, x=x_col, y=y_col, ...)`](pymatviz/scatter.py) | [`density_scatter_plotly(df, x=x_col, y=y_col, ...)`](pymatviz/scatter.py) |
-| :------------------------------------------------------------------------: | :------------------------------------------------------------------------: |
-|                         ![density-scatter-plotly]                          |                      ![density-scatter-plotly-blobs]                       |
-|           [`density_scatter(xs, ys, ...)`](pymatviz/scatter.py)            |      [`density_scatter_with_hist(xs, ys, ...)`](pymatviz/scatter.py)       |
-|                             ![density-scatter]                             |                        ![density-scatter-with-hist]                        |
-|            [`density_hexbin(xs, ys, ...)`](pymatviz/scatter.py)            |       [`density_hexbin_with_hist(xs, ys, ...)`](pymatviz/scatter.py)       |
-|                             ![density-hexbin]                              |                        ![density-hexbin-with-hist]                         |
-
-[density-scatter-plotly]: assets/density-scatter-plotly.svg
-[density-scatter-plotly-blobs]: assets/density-scatter-plotly-blobs.svg
-[density-hexbin-with-hist]: assets/density-hexbin-with-hist.svg
-[density-hexbin]: assets/density-hexbin.svg
-[density-scatter-with-hist]: assets/density-scatter-with-hist.svg
-[density-scatter]: assets/density-scatter.svg
+| [`density_scatter_plotly(df, x=x_col, y=y_col, ...)`](assets/scripts/scatter/density_scatter_plotly.py) | [`density_scatter_plotly(df, x=x_col, y=y_col, ...)`](assets/scripts/scatter/density_scatter_plotly.py) |
+| :-----------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------: |
+|                            ![density-scatter-plotly][density-scatter-plotly]                            |                      ![density-scatter-plotly-blobs][density-scatter-plotly-blobs]                      |
+|               [`density_scatter(xs, ys, ...)`](assets/scripts/scatter/density_scatter.py)               |     [`density_scatter_with_hist(xs, ys, ...)`](assets/scripts/scatter/density_scatter_with_hist.py)     |
+|                                   ![density-scatter][density-scatter]                                   |                         ![density-scatter-with-hist][density-scatter-with-hist]                         |
+|                [`density_hexbin(xs, ys, ...)`](assets/scripts/scatter/density_hexbin.py)                |      [`density_hexbin_with_hist(xs, ys, ...)`](assets/scripts/scatter/density_hexbin_with_hist.py)      |
+|                                    ![density-hexbin][density-hexbin]                                    |                          ![density-hexbin-with-hist][density-hexbin-with-hist]                          |
+
+[density-scatter-plotly]: assets/svg/density-scatter-plotly.svg
+[density-scatter-plotly-blobs]: assets/svg/density-scatter-plotly-blobs.svg
+[density-hexbin-with-hist]: assets/svg/density-hexbin-with-hist.svg
+[density-hexbin]: assets/svg/density-hexbin.svg
+[density-scatter-with-hist]: assets/svg/density-scatter-with-hist.svg
+[density-scatter]: assets/svg/density-scatter.svg
 
 ## Uncertainty
 
 See [`pymatviz/uncertainty.py`](pymatviz/uncertainty.py).
 
-|       [`qq_gaussian(y_true, y_pred, y_std)`](pymatviz/uncertainty.py)       |       [`qq_gaussian(y_true, y_pred, y_std: dict)`](pymatviz/uncertainty.py)       |
-| :-------------------------------------------------------------------------: | :-------------------------------------------------------------------------------: |
-|                             ![normal-prob-plot]                             |                           ![normal-prob-plot-multiple]                            |
-| [`error_decay_with_uncert(y_true, y_pred, y_std)`](pymatviz/uncertainty.py) | [`error_decay_with_uncert(y_true, y_pred, y_std: dict)`](pymatviz/uncertainty.py) |
-|                         ![error-decay-with-uncert]                          |                        ![error-decay-with-uncert-multiple]                        |
+|             [`qq_gaussian(y_true, y_pred, y_std)`](assets/scripts/uncertainty/qq_gaussian.py)             |            [`qq_gaussian(y_true, y_pred, y_std: dict)`](assets/scripts/uncertainty//qq_gaussian.py)             |
+| :-------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------: |
+|                                   ![normal-prob-plot][normal-prob-plot]                                   |                             ![normal-prob-plot-multiple][normal-prob-plot-multiple]                             |
+| [`error_decay_with_uncert(y_true, y_pred, y_std)`](assets/scripts/uncertainty/error_decay_with_uncert.py) | [`error_decay_with_uncert(y_true, y_pred, y_std: dict)`](assets/scripts/uncertainty/error_decay_with_uncert.py) |
+|                            ![error-decay-with-uncert][error-decay-with-uncert]                            |                      ![error-decay-with-uncert-multiple][error-decay-with-uncert-multiple]                      |
 
 ## Cumulative Metrics
 
 See [`pymatviz/cumulative.py`](pymatviz/cumulative.py).
 
-| [`cumulative_error(preds, targets)`](pymatviz/cumulative.py) | [`cumulative_residual(preds, targets)`](pymatviz/cumulative.py) |
-| :----------------------------------------------------------: | :-------------------------------------------------------------: |
-|                     ![cumulative-error]                      |                     ![cumulative-residual]                      |
+| [`cumulative_error(preds, targets)`](assets/scripts/uncertainty/cumulative.py) | [`cumulative_residual(preds, targets)`](assets/scripts/uncertainty/cumulative.py) |
+| :----------------------------------------------------------------------------: | :-------------------------------------------------------------------------------: |
+|                     ![cumulative-error][cumulative-error]                      |                    ![cumulative-residual][cumulative-residual]                    |
 
 ## Classification
 
 See [`pymatviz/relevance.py`](pymatviz/relevance.py).
 
-| [`roc_curve(targets, proba_pos)`](pymatviz/relevance.py) | [`precision_recall_curve(targets, proba_pos)`](pymatviz/relevance.py) |
-| :------------------------------------------------------: | :-------------------------------------------------------------------: |
-|                       ![roc-curve]                       |                       ![precision-recall-curve]                       |
-
-[cumulative-error]: assets/cumulative-error.svg
-[cumulative-residual]: assets/cumulative-residual.svg
-[error-decay-with-uncert-multiple]: assets/error-decay-with-uncert-multiple.svg
-[error-decay-with-uncert]: assets/error-decay-with-uncert.svg
-[elements-hist]: assets/elements-hist.svg
-[matbench-phonons-structures-2d]: assets/matbench-phonons-structures-2d.svg
-[normal-prob-plot-multiple]: assets/normal-prob-plot-multiple.svg
-[normal-prob-plot]: assets/normal-prob-plot.svg
-[precision-recall-curve]: assets/precision-recall-curve.svg
-[ptable-heatmap-plotly-log]: assets/ptable-heatmap-plotly-log.svg
-[ptable-heatmap-plotly-more-hover-data]: assets/ptable-heatmap-plotly-more-hover-data.svg
-[ptable-heatmap-ratio]: assets/ptable-heatmap-ratio.svg
-[ptable-heatmap]: assets/ptable-heatmap.svg
-[residual-vs-actual]: assets/residual-vs-actual.svg
-[roc-curve]: assets/roc-curve.svg
-[scatter-with-err-bar]: assets/scatter-with-err-bar.svg
-[spg-num-sunburst]: assets/spg-num-sunburst.svg
-[spg-symbol-sunburst]: assets/spg-symbol-sunburst.svg
-[struct-2d-mp-12712-Hf9Zr9Pd24-disordered]: assets/struct-2d-mp-12712-Hf9Zr9Pd24-disordered.svg
-[struct-2d-mp-19017-Li4Mn0.8Fe1.6P4C1.6O16-disordered]: assets/struct-2d-mp-19017-Li4Mn0.8Fe1.6P4C1.6O16-disordered.svg
+| [`roc_curve(targets, proba_pos)`](assets/scripts/relevance/roc_curve.py) | [`precision_recall_curve(targets, proba_pos)`](assets/scripts/relevance/precision_recall_curve.py) |
+| :----------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------: |
+|                         ![roc-curve][roc-curve]                          |                         ![precision-recall-curve][precision-recall-curve]                          |
+
+[cumulative-error]: assets/svg/cumulative-error.svg
+[cumulative-residual]: assets/svg/cumulative-residual.svg
+[error-decay-with-uncert-multiple]: assets/svg/error-decay-with-uncert-multiple.svg
+[error-decay-with-uncert]: assets/svg/error-decay-with-uncert.svg
+[elements-hist]: assets/svg/elements-hist.svg
+[matbench-phonons-structures-2d]: assets/svg/matbench-phonons-structures-2d.svg
+[normal-prob-plot-multiple]: assets/svg/normal-prob-plot-multiple.svg
+[normal-prob-plot]: assets/svg/normal-prob-plot.svg
+[precision-recall-curve]: assets/svg/precision-recall-curve.svg
+[ptable-heatmap-plotly-log]: assets/svg/ptable-heatmap-plotly-log.svg
+[ptable-heatmap-plotly-more-hover-data]: assets/svg/ptable-heatmap-plotly-more-hover-data.svg
+[ptable-heatmap-ratio]: assets/svg/ptable-heatmap-ratio.svg
+[ptable-heatmap]: assets/svg/ptable-heatmap.svg
+[residual-vs-actual]: assets/svg/residual-vs-actual.svg
+[roc-curve]: assets/svg/roc-curve.svg
+[scatter-with-err-bar]: assets/svg/scatter-with-err-bar.svg
+[spg-num-sunburst]: assets/svg/spg-num-sunburst.svg
+[spg-symbol-sunburst]: assets/svg/spg-symbol-sunburst.svg
+[struct-2d-mp-12712-Hf9Zr9Pd24-disordered]: assets/svg/struct-2d-mp-12712-Hf9Zr9Pd24-disordered.svg
+[struct-2d-mp-19017-Li4Mn0.8Fe1.6P4C1.6O16-disordered]: assets/svg/struct-2d-mp-19017-Li4Mn0.8Fe1.6P4C1.6O16-disordered.svg
 
 ## How to cite `pymatviz`
 


Test patch:
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 0c0b17aa..f853400f 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -10,6 +10,9 @@ on:
   workflow_dispatch:
   workflow_call:
 
+env:
+  MPLBACKEND: Agg # non-interactive backend for matplotlib
+
 jobs:
   tests:
     strategy:
@@ -36,7 +39,7 @@ jobs:
       - name: Find Python scripts
         id: set-matrix
         run: |
-          SCRIPTS=$(find examples/make_assets -name "*.py" | jq -R -s -c 'split("\n")[:-1]')
+          SCRIPTS=$(find assets/scripts -name "*.py" | jq -R -s -c 'split("\n")[:-1]')
           echo "script_list=$SCRIPTS" >> $GITHUB_OUTPUT
 
   test-scripts:
diff --git a/tests/powerups/test_matplotlib_powerups.py b/tests/powerups/test_matplotlib_powerups.py
index 66d09392..7318d986 100644
--- a/tests/powerups/test_matplotlib_powerups.py
+++ b/tests/powerups/test_matplotlib_powerups.py
@@ -82,7 +82,10 @@ def test_with_marginal_hist() -> None:
     assert isinstance(ax_main, plt.Axes)
     assert len(fig.axes) == 4
 
-    gs = fig.add_gridspec(2, 2)
+    gs = fig.add_gridspec(ncols=2, nrows=2)
     ax_main = pmv.powerups.with_marginal_hist([1, 2, 3], [4, 5, 6], cell=gs[1, 0])
     assert isinstance(ax_main, plt.Axes)
-    assert len(fig.axes) == 7
+    assert len(fig.axes) == 4
+    assert fig.get_axes()[0].get_position().get_points().flat == pytest.approx(
+        [0.125, 0.11, 0.9, 0.88]
+    )
diff --git a/tests/test_data.py b/tests/test_data.py
new file mode 100644
index 00000000..c1bd25a8
--- /dev/null
+++ b/tests/test_data.py
@@ -0,0 +1,25 @@
+"""Test regression data generation."""
+
+import pytest
+
+import pymatviz as pmv
+
+
+def test_get_regression_data() -> None:
+    """Test random regression arrays have correct shape and stats."""
+    n_samples = 500
+    y_true, y_pred, y_std = pmv.data.regression(n_samples=n_samples)
+
+    assert y_true.shape == (n_samples,)
+    assert y_pred.shape == (n_samples,)
+    assert y_std.shape == (n_samples,)
+
+    # Test y_true generated from normal(5, 4)
+    assert y_true.mean() == pytest.approx(5.0, abs=0.2)
+    assert y_true.std() == pytest.approx(4.0, abs=0.2)
+    # Test y_pred = 1.2 * y_true with added noise
+    assert y_pred.mean() == pytest.approx(3.801761, abs=0.2)
+
+    # Test y_std is non-zero and finite
+    assert 5 < y_std.max() < 10
+    assert y_std.mean() == pytest.approx(0.0, abs=0.2)
diff --git a/tests/test_utils.py b/tests/test_utils.py
index 815dda31..ff655956 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -725,3 +725,27 @@ def test_normalize_to_dict_mixed_classes(
 
     with pytest.raises(TypeError, match=r"Invalid inputs=\["):
         normalize_to_dict([instance1, instance2], cls=cls1)
+
+
+def test_apply_matplotlib_template() -> None:
+    """Test that apply_matplotlib_template() correctly sets matplotlib parameters."""
+    # Store original values
+    orig_params = plt.rcParams.copy()
+
+    try:
+        # Apply the template
+        pmv.utils.apply_matplotlib_template()
+
+        # Check that parameters were set correctly
+        assert plt.rcParams["font.size"] == 14
+        assert plt.rcParams["savefig.bbox"] == "tight"
+        assert plt.rcParams["savefig.dpi"] == 200
+        assert plt.rcParams["axes.titlesize"] == 16
+        assert plt.rcParams["axes.titleweight"] == "bold"
+        assert plt.rcParams["figure.dpi"] == 200
+        assert plt.rcParams["figure.titlesize"] == 20
+        assert plt.rcParams["figure.titleweight"] == "bold"
+        assert plt.rcParams["figure.constrained_layout.use"] is True
+
+    finally:  # Restore original values
+        plt.rcParams.update(orig_params)