import scomv
import stlearn as st
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import cv2
import pandas as pd
import numpy as np
import anndata
import scanpy as sc
import seaborn as sns
from adjustText import adjust_text
from scipy.cluster.hierarchy import linkage, dendrogram, ClusterNode, to_tree

/Users/nomura/miniconda3/envs/sc-test310/lib/python3.10/site-packages/louvain/__init__.py:54: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
  from pkg_resources import get_distribution, DistributionNotFound
/Users/nomura/miniconda3/envs/sc-test310/lib/python3.10/site-packages/numba/core/decorators.py:282: RuntimeWarning: nopython is set for njit and is ignored
  warnings.warn('nopython is set for njit and is ignored', RuntimeWarning)
/Users/nomura/miniconda3/envs/sc-test310/lib/python3.10/site-packages/stlearn/tl/cci/het.py:206: NumbaDeprecationWarning: The keyword argument 'nopython=False' was supplied. From Numba 0.59.0 the default is being changed to True and use of 'nopython=False' will raise a warning as the argument will have no effect. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  @jit(parallel=True, nopython=False)

#!mkdir tutorial_data
#!mkdir tutorial_data/xenium_data
#!wget -P tutorial_data/xenium_data/ https://cf.10xgenomics.com/samples/xenium/preview/Xenium_FFPE_Human_Breast_Cancer_Rep1/Xenium_FFPE_Human_Breast_Cancer_Rep1_cell_feature_matrix.h5
#!wget -P tutorial_data/xenium_data/ https://cf.10xgenomics.com/samples/xenium/preview/Xenium_FFPE_Human_Breast_Cancer_Rep1/Xenium_FFPE_Human_Breast_Cancer_Rep1_cells.csv.gz

# Load Xenium data using stlearn
adata = st.ReadXenium(
    feature_cell_matrix_file="../tutorial_data/xenium_data/cell_feature_matrix.h5",
    cell_summary_file="../tutorial_data/xenium_data/cells.csv.gz",
    library_id="example data",
    image_path=None,
    scale=1,
    spot_diameter_fullres=10
)

Warning: Using default pixel size of 0.2125 microns. Consider providing experiment_xenium_file for accurate pixel size.

# Gridding at 10μm interval using stlearn
N_COL = int((adata.obs.imagecol.max() - adata.obs.imagecol.min()) / 10)
N_ROW = int((adata.obs.imagerow.max() - adata.obs.imagerow.min()) / 10)
grid = st.tl.cci.grid(adata, n_row=N_ROW, n_col=N_COL, n_cpus=10, verbose=False)

from scomv.preparation.skny_calc_distance import calculate_distance

## apply SKNY
grid = calculate_distance(
    grid, pos_marker_ls=['CDH1',"EPCAM"],
)

# Figure 2B
fig, ax = plt.subplots(figsize=(8, 6), dpi=150)
ax.imshow(cv2.cvtColor(grid.uns["marker_median_delineation"], cv2.COLOR_BGR2RGB),
          interpolation="nearest")
ax.axis("off")
plt.show()

#plt.savefig("Figure_2B.png", dpi=300)

import plotly.express as px
import plotly.io as pio
import cv2
import numpy as np

# Convert BGR (OpenCV) image to RGB for Plotly display
img = cv2.cvtColor(grid.uns["marker_median_delineation"], cv2.COLOR_BGR2RGB)

scale = 10  # spatial scaling factor

fig = px.imshow(img)

h, w = img.shape[:2]

# ---- Scale axis tick labels by a factor of 10 ----
step = 200
fig.update_xaxes(
    tickmode="array",
    tickvals=list(range(0, w, step)),
    ticktext=[str(v * scale) for v in range(0, w, step)]
)
fig.update_yaxes(
    tickmode="array",
    tickvals=list(range(0, h, step)),
    ticktext=[str(v * scale) for v in range(0, h, step)]
)

# ---- Create a full-resolution coordinate grid for hover display ----
# xx: scaled x-coordinates for each pixel (shape: h × w)
# yy: scaled y-coordinates for each pixel (shape: h × w)
xx = (np.arange(w)[None, :] * scale).repeat(h, axis=0)
yy = (np.arange(h)[:, None] * scale).repeat(w, axis=1)

# Stack into (h, w, 2) so hover can access scaled x and y
custom = np.dstack([xx, yy])

# ---- Override hover text to show scaled spatial coordinates ----
fig.update_traces(
    customdata=custom,
    hovertemplate="x: %{customdata[0]}<br>y: %{customdata[1]}<extra></extra>"
)

fig.update_layout(
    title="Figure 2B",
    xaxis_title="x",
    yaxis_title="y",
    height=700
)

# Use the notebook-connected renderer for interactive display
pio.renderers.default = "notebook_connected"

fig.show()

# ↓↓ Zooming and cropping are supported interactively ↓↓

# annotation each section to obs object
df_shotest = getattr(grid, "shortest")
df_grid = grid.to_df()

# extract grid info
df_grid = pd.merge(
    pd.DataFrame(index=["grid_" + str(i+1) for i in range(N_ROW * N_COL)]),
    df_grid, right_index=True, left_index=True, how="left"
).fillna(np.nan)

# extract section info
df_region = pd.DataFrame(
    np.array(df_shotest["region"]).reshape(N_ROW, N_COL).T.reshape(N_ROW * N_COL),
    index=["grid_" + str(i+1) for i in range(N_ROW * N_COL)], columns=["region"]
)

# marge
df_grid_region = pd.merge(
    df_grid, df_region,
    right_index=True, left_index=True, how="left"
)
df_grid_region = df_grid_region.dropna()

# add to obs
grid.obs = pd.merge(
    grid.obs, df_grid_region[["region"]],
    right_index=True, left_index=True, how="left"
)

# shaping
grid.obs["region_10"] = [str(i*10) for i in grid.obs["region"]]
grid.obs["region_10"] = ["("+str(int(float(i.split(", ")[0][1:])))+", "+str(int(float(i.split(", ")[-1][:-1])))+"]" if i != "nan" else np.nan for i in grid.obs["region_10"]]
# exclude because of small number
grid.obs["region_10"] = grid.obs["region_10"].replace(
    {"(-150, -120]": np.nan}
)

from scomv.preparation.choose_roi import extract_roi, contour_regions

# select ROI
roi = (2400, 3400, 2400, 3800)

subset_grid, filtered_shortest, xy_list = extract_roi(
    grid=grid,
    roi=roi,
    bin_size=10,
    region_col="region_10",
)

g_x_cont, g_y_cont, g_x_inside, g_y_inside = contour_regions(
    filtered_shortest, adata,
    min_x=0, max_x=4000, min_y=0, max_y=4000,
    #out_dir_base="./roi_plots",
    show=True,
)

from scomv.preparation.scomv_calc_vector import compute_min_vectors_polar

outline_points = list(zip(g_x_cont, g_y_cont))
inside_points  = list(zip(g_x_inside, g_y_inside))

min_vector_df = compute_min_vectors_polar(
    xy_list=xy_list,
    outline_points=outline_points,
    inside_points=inside_points,
    invert_y=True,
    make_inside_negative=True,
)

from scomv.gene_view_tool import plot_gene_polar_hist2d, plot_2d_expression

## Draw polar maps
genes = list(subset_grid.var.index)
A_counts, total_n, idx = plot_gene_polar_hist2d(
    subset_grid=subset_grid,
    min_vector_df=min_vector_df,
    gene="CD3E",
)

CD3E: n=3431

## Draw 2D pictures
g = ["CD3E"] # the genes you want to draw
for g in ["CD3E"]:
    plot_2d_expression(g, subset_grid)

Please specify a valid `library_id` or set it permanently in `adata.uns['spatial']`

from scomv.gene_pipeline import SCOMVPipeline

gene_pipe = SCOMVPipeline(adata=adata, grid=grid, bin_size=10)
gene_out = gene_pipe.run(roi=(2400, 3400, 2400, 3800))

/Users/nomura/miniconda3/envs/sc-test310/lib/python3.10/site-packages/scanpy/metrics/_morans_i.py:105: UserWarning:

1 variables were constant, will return nan for these.

/Users/nomura/miniconda3/envs/sc-test310/lib/python3.10/site-packages/scanpy/metrics/_morans_i.py:105: UserWarning:

1 variables were constant, will return nan for these.

/Users/nomura/miniconda3/envs/sc-test310/lib/python3.10/site-packages/louvain/__init__.py:54: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
  from pkg_resources import get_distribution, DistributionNotFound
/Users/nomura/miniconda3/envs/sc-test310/lib/python3.10/site-packages/numba/core/decorators.py:282: RuntimeWarning: nopython is set for njit and is ignored
  warnings.warn('nopython is set for njit and is ignored', RuntimeWarning)

import plotly.io as pio
gene_pipe.plot_pcoa_plotly(gene_pipe.coords, gene_pipe.selected_genes)

pcoa_two_dims = gene_out["coords"][["PC1", "PC2"]]
df = pcoa_two_dims

# --- Gene sets ---
t_cells  = {"CD3D", "CD3E", "CD4", "CD247", "CD8A", "GZMA", "TRAC"}
micro_f  = ["CD68", "CD80", "CD86", "CD163"]
b_cells  = ["CD19", "BANK1", "MS4A1", "TCL1A", "CD79A"]
mono     = ["CD14"]
epi      = ["CDH1", "EPCAM", "KRT8"]
smooth   = ["ACTA2", "MYH11", "ACTG2"]
endo     = ["CD93", "PECAM1", "VWF"]
fib      = ["POSTN", "LUM", "MMP2"]
focus    = ["PLVAP", "RHOA", "SMAD6", "TP53", "VEGFA", "MKI67", "ADGRL4", "RGS5",
            "PI3", "CAV1", "EGFR", "AQP3", "C15orf48"]

# --- Others (everything except T-cell set) ---
others = sorted(list(set(df.index) - set(t_cells)))

# --- Styling config (keeps your colors/labels/sizes) ---
groups = [
    ("T cell",          t_cells,  "#1f77b4", 200, True),
    ("B cell",          b_cells,  "cyan",    200, True),
    ("epithelial cell", epi,      "#ffbb78", 200, True),
    ("macrophage",      micro_f,  "#2ca02c", 200, True),
    ("monocyte",        mono,     "#ff9896", 200, True),
    ("smooth muscle",   smooth,   "#aec7e8", 200, True),
    ("endotherial",     endo,     "#8B4513", 200, True), 
    ("fibroblast",      fib,      "red",     200, True),
    (None,              focus,    "black",   200, True),  # no legend label
]

# --- Plot ---
plt.figure(figsize=(20, 15))
sns.set_theme(style="white")
texts = []

# Others: gray points (no labels)
for g in others:
    if g in df.index:
        plt.scatter(df.loc[g, "PC1"], df.loc[g, "PC2"], color="gray", alpha=0.3, s=150)

# Highlighted groups
for label, gene_list, color, s, add_text in groups:
    for g in gene_list:
        if g in df.index:
            if label is None:
                plt.scatter(df.loc[g, "PC1"], df.loc[g, "PC2"], color=color, s=s)
            else:
                plt.scatter(df.loc[g, "PC1"], df.loc[g, "PC2"], color=color, label=label, s=s)
            if add_text:
                texts.append(plt.text(df.loc[g, "PC1"], df.loc[g, "PC2"], g, fontsize=25))

plt.xlabel("PCoA1", fontsize=30)
plt.ylabel("PCoA2", fontsize=30)
plt.xticks(fontsize=25)
plt.yticks(fontsize=25)
plt.title("PCoA Plot", fontsize=40)

# Legend (remove duplicates)
handles, labels = plt.gca().get_legend_handles_labels()
by_label = dict(zip(labels, handles))
plt.legend(by_label.values(), by_label.keys(), loc="best", prop={"size": 23}, ncol=1)

adjust_text(
    texts,
    arrowprops=dict(arrowstyle="->", color="gray", lw=0.5),
    only_move={"points": "xy", "text": "xy"},
    force_text=1.5,
    force_points=1.0,
    expand_text=(1.2, 1.2),
    expand_points=(1.2, 1.2),
    lim=200,
    precision=0.01,
    autoalign="y",
)

plt.show()

Looks like you are using a tranform that doesn't support FancyArrowPatch, using ax.annotate instead. The arrows might strike through texts. Increasing shrinkA in arrowprops might help.

from scomv.plot_3d import plot_3d
import plotly.io as pio

fig = plot_3d(
    adata=gene_out["subset_grid"],
    genes=("POSTN", "CD3E"),
    anchor_gene="CDH1",
    bin_size=10,
    x_range=(2400, 3100),
    y_range=(2400, 3400),
    height_scale=3,
    anchor_scale=1,
    threshold=4,
    sigma=1.0,
)
pio.renderers.default = "notebook_connected"
fig.show()

from scomv.dendrogram import dendrogram2newick
z = linkage(gene_out["dist_df"], metric='euclidean', method='ward')
tree= to_tree(z)
newick = dendrogram2newick(tree, tree.dist, gene_out["selected_genes"])

/var/folders/h5/02yhm93d3kn1__fczqjdr9cw0000gp/T/ipykernel_63534/1097620655.py:2: ClusterWarning:

scipy.cluster: The symmetric non-negative hollow observation matrix looks suspiciously like an uncondensed distance matrix

# The Newick output can be used to visualize a dendrogram in iTOL(https://www.google.com/url?sa=t&source=web&rct=j&opi=89978449&url=https://itol.embl.de/&ved=2ahUKEwiY7vnzxoOSAxWRrlYBHUuiHisQFnoECBoQAQ&usg=AOvVaw2ipidUpg8SauabV53lQ6Jh).
#newick