"""
Breast cancer mutation with Oncoprinter
=======================================

The Dataset is collected from cBioportal:
Breast Invasive Carcinoma (TCGA, PanCancer Atlas)

"""
# sphinx_gallery_thumbnail_number = -1
import matplotlib.pyplot as plt

import marsilea as ma
import marsilea.plotter as mp
from oncoprinter import OncoPrint

# sphinx_gallery_start_ignore
import mpl_fontkit as fk

fk.install("Lato", verbose=False)
# sphinx_gallery_end_ignore

# %%
# Load data


onco_data = ma.load_data("oncoprint")
cna = onco_data["cna"]
mrna_exp = onco_data["mrna_exp"]
methyl_exp = onco_data["methyl_exp"]
clinical = onco_data["clinical"]

# %%
# Make mRNA expression
# --------------------


h = ma.Heatmap(
    mrna_exp,
    cmap="gist_heat_r",
    height=0.9,
    width=5,
    cbar_kws=dict(orientation="horizontal", title="mRNA Expression"),
)
h.add_title(top="mRNA expression Z-SCORE", align="left", fontsize=10)
h.add_left(mp.Labels(mrna_exp.index), pad=0.1)
h.render()

# %%
# Make Methylation expression
# ---------------------------


m = ma.Heatmap(
    methyl_exp.astype(float),
    height=0.6,
    width=5,
    cmap="summer_r",
    cbar_kws=dict(orientation="horizontal", title="Methylation"),
)
m.add_title(top="Methylation", align="left", fontsize=10)
m.add_left(mp.Labels(methyl_exp.index), pad=0.1)
m.render()

# %%
# Create Oncoprint
# ----------------


op = OncoPrint(cna, name="main")
op.render()

# %%
# Make clinical information
# -------------------------


clinical = clinical[op.samples_order]
tumor_type = clinical.loc["Cancer Type Detailed"]
tumor_colors = mp.Colors(tumor_type, label="Tumor Type", label_loc="left")

mut_count = clinical.loc["Mutation Count"]
mut_number = mp.Numbers(mut_count, show_value=False, color="orange")

# %%
# Add clinical to the oncoprint


op.add_bottom(tumor_colors, size=0.2, pad=0.1)
op.add_bottom(mut_number, size=0.2, name="mutation_count", pad=0.1, legend=False)
op.render()

# %%
# Append expression to the oncoprint

op /= h
op /= m

# %%
# Render

op.set_margin(0.2)
op.add_legends(box_padding=2, stack_size=4)
op.render()
mut_ax = op.get_ax("main", "mutation_count")
mut_ax.set_axis_off()
mut_ax.text(
    0,
    0.5,
    "Mutation Count",
    rotation=0,
    ha="right",
    va="center",
    transform=mut_ax.transAxes,
)

# 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 / "oncoprint.svg", bbox_inches="tight")
# sphinx_gallery_end_ignore