Add example figures

scripts/benchmark/result.py

@@ -99,8 +99,15 @@ def plot_ax(ax, data, xlabel):
     palette = ["#5847AD", "#F7B449"]
     kws = dict(orient="v", errorbar="sd", palette=palette, width=0.6)
     sns.barplot(data=data, ax=ax, **kws)
-    data.melt().pipe((sns.scatterplot, "data"), y="value", x="variable", ax=ax,
-                     zorder=100, color="grey", alpha=0.5)
+    data.melt().pipe(
+        (sns.scatterplot, "data"),
+        y="value",
+        x="variable",
+        ax=ax,
+        zorder=100,
+        color="grey",
+        alpha=0.5,
+    )
     ax.get_xticklabels()[0].set_fontweight("bold")
     ax.tick_params(axis="x", rotation=45)
     ax.tick_params(bottom=False)
@@ -140,7 +147,9 @@ def plot_ax(ax, data, xlabel):
 
     fk.install("Lato", verbose=False)
     data = pd.DataFrame({"Marsilea": marsilea_tokens, "Matplotlib": matplotlib_tokens})
-    _, (ax1, ax2, ax3) = plt.subplots(ncols=3, figsize=(6, 5), gridspec_kw={"wspace": 0.5})
+    _, (ax1, ax2, ax3) = plt.subplots(
+        ncols=3, figsize=(6, 5), gridspec_kw={"wspace": 0.5}
+    )
     # sns.set(style="whitegrid")
     plot_ax(ax1, data, "Tokens")
     # Plot the number of lines of code using seaborn
@@ -156,4 +165,4 @@ def plot_ax(ax, data, xlabel):
     root = Path(__file__).parent
     plt.savefig(root / "marsilea_vs_matplotlib.png", dpi=300, bbox_inches="tight")
     plt.savefig(root / "marsilea_vs_matplotlib.svg", bbox_inches="tight")
-    plt.show()
+    plt.show()

scripts/example_figures/arc-diagrams.py

@@ -0,0 +1,164 @@
+"""
+Les Miserables Arc Diagram
+===============================
+
+This example shows how to create an arc diagram from a network.
+
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import networkx as nx
+
+import marsilea as ma
+import marsilea.plotter as mp
+
+# sphinx_gallery_start_ignore
+import mpl_fontkit as fk
+
+fk.install("Lato", verbose=False)
+# sphinx_gallery_end_ignore
+
+# %%
+# Create Arc Diagram
+# ------------------
+
+data = pd.read_csv("data/PPI.csv")
+G = nx.from_pandas_edgelist(data, "source", "target", create_using=nx.DiGraph)
+
+protein_classification = {
+    "MAP Kinases": [
+        "MAP2K7",
+        "RPS6KA3",
+        "MAPK7",
+        "RPS6KB1",
+        "MAPKAPK5",
+        "RPS6KB2",
+        "JUN",
+        "RPS6KA1",
+        "MAPK10",
+        "MAPK1",
+        "MAPK14",
+        "MAPK3",
+        "MAP2K1",
+        "MAPK12",
+        "MAP2K4",
+        "MAPK9",
+        "MAPK8",
+    ],
+    "Kinases": [
+        "CSNK2A1",
+        "GSK3B",
+        "CSNK1A1",
+        "PRKACA",
+        "SYK",
+        "JAK2",
+        "GSK3A",
+        "PRKCA",
+        "CDK1",
+        "ITK",
+        "ELK1",
+        "LYN",
+        "PRKCD",
+        "CDK4",
+        "PLK1",
+        "PAK1",
+        "CDK7",
+        "LCK",
+        "CDK5",
+        "MAPK8",
+        "GRK2",
+        "AURKB",
+        "PRKCQ",
+        "CDC25C",
+        "CHEK2",
+        "CDK7",
+        "TP53",
+    ],
+    "Transcription Factors": ["NFKBIA", "MEF2A", "ESR1", "CREB1", "NFATC4"],
+    "Tyrosine Kinases": [
+        "HCLS1",
+        "FCGR2A",
+        "BTK",
+        "SYK",
+        "HCK",
+        "PTK2B",
+        "LCK",
+        "FYN",
+        "ZAP70",
+    ],
+    "Adaptor Proteins": ["IRS1", "CBL", "SHC1", "GRB2"],
+    "Ubiquitin Ligases": ["MDM2", "CBL"],
+    "Cell Structure/Signaling": [
+        "STK11",
+        "FGFR1",
+        "CSK",
+        "ILK",
+        "CTTN",
+        "SNCA",
+        "KRT8",
+    ],
+    "Other": ["HNRNPK"],
+}
+
+
+colormap = {
+    "MAP Kinases": "#E2DFD0",
+    "Kinases": "#CA8787",
+    "Transcription Factors": "#E65C19",
+    "Tyrosine Kinases": "#F8D082",
+    "Adaptor Proteins": "#0A6847",
+    "Ubiquitin Ligases": "#7ABA78",
+    "Cell Structure/Signaling": "#03AED2",
+    "Other": ".7",
+}
+
+# Reverse mapping to get labels for each protein
+protein_labels = {}
+for label, proteins in protein_classification.items():
+    for protein in proteins:
+        protein_labels[protein] = label
+
+nodes = list(G.nodes)
+nodes = pd.DataFrame(
+    {"nodes": nodes, "type": [protein_labels[n] for n in nodes]}
+).sort_values("type")["nodes"]
+
+degs = nx.degree(G)
+degree_arr = np.array([[degs[n] for n in nodes]])
+color_arr = np.array([[protein_labels[n] for n in nodes]])
+
+edges = list(G.edges)
+edges_colors = [colormap[protein_labels[a]] for a, _ in edges]
+
+sources = set([a for a, _ in edges])
+is_sources = np.array([["*" if n in sources else "" for n in nodes]])
+
+wb = ma.SizedHeatmap(
+    degree_arr,
+    color_arr,
+    palette=colormap,
+    sizes=(10, 300),
+    frameon=False,
+    width=10.5,
+    height=0.3,
+    size_legend_kws={"func": lambda x: [int(i) for i in x], "title": "Count"},
+    color_legend_kws={"title": "Protein Type"},
+)
+wb.add_bottom(mp.Labels(nodes, align="bottom"))
+wb.add_bottom(mp.Labels(is_sources, fontsize=16))
+arc = mp.Arc(nodes, edges, colors=edges_colors, lw=1, alpha=0.5)
+wb.add_top(arc, size=2)
+wb.add_legends(stack_size=1)
+wb.render()
+
+# sphinx_gallery_start_ignore
+if "__file__" in globals():
+    from pathlib import Path
+
+    save_path = Path(__file__).parent / "figures"
+    wb.save(save_path / "arc_diagram.svg")
+else:
+    plt.show()
+# sphinx_gallery_end_ignore

scripts/example_figures/cooking_oils.py

@@ -0,0 +1,122 @@
+"""
+Fat content in cooking oils
+===========================
+
+This example shows how to apply x-layout on statistical plots.
+
+"""
+
+import marsilea as ma
+import marsilea.plotter as mp
+
+import mpl_fontkit as fk
+
+fk.install_fontawesome(verbose=False)
+fk.install("Lato", verbose=False)
+
+# sphinx_gallery_start_ignore
+import matplotlib as mpl
+
+mpl.rcParams["font.size"] = 12
+# sphinx_gallery_end_ignore
+
+
+# %%
+# Load data
+# ---------
+oils = ma.load_data("cooking_oils")
+
+red = "#cd442a"
+yellow = "#f0bd00"
+green = "#7e9437"
+gray = "#eee"
+
+mapper = {0: "\uf58a", 1: "\uf11a", 2: "\uf567"}
+cmapper = {0: "#609966", 1: "#DC8449", 2: "#F16767"}
+flavour = [mapper[i] for i in oils["flavour"].values]
+flavour_colors = [cmapper[i] for i in oils["flavour"].values]
+fat_content = oils[
+    ["saturated", "polyunsaturated (omega 3 & 6)", "monounsaturated", "other fat"]
+]
+
+# %%
+# Visualize the oil contents
+# --------------------------
+
+fat_stack_bar = mp.StackBar(
+    fat_content.T * 100,
+    colors=[red, yellow, green, gray],
+    width=0.8,
+    orient="h",
+    label="Fat Content (%)",
+    legend_kws={"ncol": 2, "fontsize": 10},
+)
+fmt = lambda x: f"{x:.1f}" if x > 0 else ""
+trans_fat_bar = mp.Numbers(
+    oils["trans fat"] * 100,
+    fmt=fmt,
+    color="#3A98B9",
+    label="Trans Fat (%)",
+)
+
+flavour_emoji = mp.Labels(
+    flavour, fontfamily="Font Awesome 6 Free", text_props={"color": flavour_colors}
+)
+
+oil_names = mp.Labels(oils.index.str.capitalize())
+
+fmt = lambda x: f"{int(x)}" if x > 0 else ""
+
+omege_bar = ma.plotter.CenterBar(
+    (oils[["omega 3", "omega 6"]] * 100).astype(int),
+    names=["Omega 3 (%)", "Omega 6 (%)"],
+    colors=["#7DB9B6", "#F5E9CF"],
+    fmt=fmt,
+    show_value=True,
+)
+conditions_text = [
+    "Control",
+    ">230 °C\nDeep-frying",
+    "200-229 °C\nStir-frying",
+    "150-199 °C\nLight saute",
+    "<150 °C\nDressings",
+]
+colors = ["#e5e7eb", "#c2410c", "#fb923c", "#fca5a5", "#fecaca"]
+conditions = ma.plotter.Chunk(conditions_text, colors, rotation=0, padding=10)
+
+cb = ma.ClusterBoard(fat_content.to_numpy(), height=10)
+cb.add_layer(fat_stack_bar)
+cb.add_left(trans_fat_bar, pad=0.2, name="trans fat")
+cb.add_right(flavour_emoji)
+cb.add_right(oil_names, pad=0.1)
+cb.add_right(omege_bar, size=2, pad=0.2)
+
+order = [
+    "Control",
+    ">230 °C (Deep-frying)",
+    "200-229 °C (Stir-frying)",
+    "150-199 °C (Light saute)",
+    "<150 °C (Dressings)",
+]
+cb.hsplit(labels=oils["cooking conditions"], order=order)
+cb.add_left(conditions, pad=0.1)
+cb.add_dendrogram(
+    "left", add_meta=False, colors=colors, linewidth=1.5, size=0.5, pad=0.02
+)
+cb.add_title(top="Fat in Cooking Oils", fontsize=16)
+cb.add_legends("bottom", pad=0.3)
+cb.render()
+
+axes = cb.get_ax("trans fat")
+for ax in axes:
+    ax.set_xlim(4.2, 0)
+
+# sphinx_gallery_start_ignore
+if "__file__" in globals():
+    from pathlib import Path
+    import matplotlib.pyplot as plt
+
+    plt.rcParams["svg.fonttype"] = "none"
+    save_path = Path(__file__).parent / "imgs"
+    plt.savefig(save_path / "oil_well.svg", bbox_inches="tight")
+# sphinx_gallery_end_ignore