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import io

import numpy as np

import pandas as pd

import pytest

from anndata import AnnData

lets_plot = pytest.importorskip("lets_plot")

matplotlib = pytest.importorskip("matplotlib")

from matplotlib.figure import Figure

from actionet.visualization import plot_feature_expression_raster, plot_umap, plot_umap_raster

def _shared_keys(plot) -> set[str]:

if hasattr(plot, "get_plot_shared_data"):

shared = plot.get_plot_shared_data()

if shared is None:

return set()

if isinstance(shared, pd.DataFrame):

return set(shared.columns)

return set(shared.keys())

return set(plot.as_dict().get("data", {}).keys())

def _make_adata() -> AnnData:

x = np.array(

[

[1.0, 0.5, 0.0],

[0.2, 1.1, 0.3],

[0.7, 0.0, 1.4],

[0.9, 0.4, 0.1],

[1.3, 0.8, 0.2],

[0.4, 1.0, 1.1],

dtype=float,

)

adata = AnnData(x)

adata.var_names = pd.Index(["GeneA", "GeneB", "GeneC"])

adata.obs["cluster"] = pd.Categorical(["a", "a", "b", "b", None, "c"])

adata.obs["score"] = np.array([0.1, 0.8, np.nan, 0.3, 0.9, 0.5], dtype=float)

adata.obs["annot_conf"] = np.array([0.2, 0.5, 0.7, 0.1, 0.8, 0.4], dtype=float)

adata.obsm["umap_2d_actionet"] = np.array(

[

[0.0, 0.0],

[1.0, 0.5],

[1.5, 1.0],

[0.5, 1.5],

[1.8, 0.2],

[0.2, 1.8],

dtype=float,

)

adata.obsm["colors_actionet"] = np.array(

[

[0.9, 0.1, 0.1],

[0.1, 0.7, 0.3],

[0.2, 0.4, 0.9],

[0.8, 0.8, 0.1],

[0.6, 0.3, 0.8],

[0.2, 0.8, 0.8],

dtype=float,

)

adata.layers["logcounts"] = x.copy()

return adata

def test_plot_umap_default_spec_omits_redundant_columns() -> None:

plot = plot_umap(_make_adata(), color=None, color_source=None, color_slot=None, legend=False)

spec = plot.as_dict()

assert _shared_keys(plot) == set()

assert set(spec["layers"][0]["data"].keys()) == {"x", "y"}

assert spec["layers"][0]["sampling"] == "none"

assert spec["layers"][0]["tooltips"] == "none"

assert "alpha" not in spec["mapping"]

def test_plot_umap_vector_alpha_keeps_alpha_column() -> None:

alpha = np.linspace(0.2, 1.0, _make_adata().n_obs)

plot = plot_umap(_make_adata(), color="cluster", alpha=alpha)

assert "alpha" in _shared_keys(plot)

def test_plot_umap_random_sampling_validation() -> None:

adata = _make_adata()

with pytest.raises(ValueError, match="sample_n must be provided"):

plot_umap(adata, color="cluster", sampling="random")

with pytest.raises(ValueError, match="positive integer"):

plot_umap(adata, color="cluster", sampling="random", sample_n=0)

def test_plot_umap_random_sampling_serializes_sampling_spec() -> None:

plot = plot_umap(

_make_adata(),

color="cluster",

sampling="random",

sample_n=3,

tooltips="default",

)

layer = plot.as_dict()["layers"][0]

assert layer["sampling"] != "none"

assert "tooltips" not in layer or layer["tooltips"] != "none"

def test_plot_umap_continuous_na_layer_has_minimal_data() -> None:

plot = plot_umap(_make_adata(), color="score", color_source="obs", legend=False)

spec = plot.as_dict()

assert len(spec["layers"]) == 2

assert set(spec["layers"][1]["data"].keys()) == {"x", "y"}

def test_plot_umap_to_png_smoke() -> None:

lets_plot.LetsPlot.setup_html(no_js=True)

plot = plot_umap(_make_adata(), color="cluster")

buf = io.BytesIO()

plot.to_png(buf)

assert buf.getbuffer().nbytes > 0

def test_plot_umap_raster_savefig_smoke() -> None:

fig = plot_umap_raster(_make_adata(), color="score", color_source="obs")

buf = io.BytesIO()

fig.savefig(buf, format="png")

assert isinstance(fig, Figure)

assert buf.getbuffer().nbytes > 0

def test_plot_feature_expression_raster_returns_figure_for_single_feature() -> None:

fig = plot_feature_expression_raster(

_make_adata(),

features="GeneA",

alpha=0,

layer="logcounts",

)

assert isinstance(fig, Figure)

buf = io.BytesIO()

fig.savefig(buf, format="png")

assert buf.getbuffer().nbytes > 0

def test_plot_feature_expression_raster_single_plot_grid() -> None:

fig = plot_feature_expression_raster(

_make_adata(),

features=["GeneA", "GeneB"],

alpha=0,

layer="logcounts",

single_plot=True,

)

assert isinstance(fig, Figure)

assert len(fig.axes) >= 2

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