Cannot be shown in transparent colors when Plotting the gene expression data

When I reproduce according to the code of the official tutorial Technology focus: Visium HD in jupyterlab, I get an incorrect figure: transparent colors RGBA(1.0,1.0,1.0,0.0) is replaced by white RGB(1.0,1.0,1.0).
How to modify the code?

Associate packages

spatialdata               0.1.2                    pypi_0    pypi
spatialdata-io            0.1.2                    pypi_0    pypi
spatialdata-plot          0.2.1                    pypi_0    pypi
napari-spatialdata        0.4.1                    pypi_0    pypi
matplotlib                3.8.4                    pypi_0    pypi
matplotlib-inline         0.1.7                    pypi_0    pypi
matplotlib-scalebar       0.8.1                    pypi_0    pypi
napari-matplotlib         2.0.1                    pypi_0    pypi
ipympl                    0.9.4                    pypi_0    pypi
numpy                     1.23.4                   pypi_0    pypi

The Plot Code:

import matplotlib.cm as cm
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import LinearSegmentedColormap

# modify the viridis colormap, so that the top color is a green (better visible on the H&E pink), and such that
# the value 0 leads to a transparent color
viridis = cm.get_cmap("viridis", 256)
# using 0.8 instead of 1.0 truncates the colormap
colors = viridis(np.linspace(0, 0.8, 256))
# set the color of zero to be transparent
colors[0, :] = [1.0, 1.0, 1.0, 0.0]

new_cmap = LinearSegmentedColormap.from_list("truncated_viridis", colors)

/tmp/ipykernel_2057419/2380312474.py:8:MatplotlibDeprecationWarning:The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ''matplotlib.colormaps[name]'' or ''matplotlib.colormaps.get_cmap(obj)'' instead.
viridis= cm.get cmap("viridis",256)

gene_name = "AA986860"

# 结合H&E切片绘制16um,8um级
for bin_size in [16, 8]:
    sdata_small.pl.render_images("Visium_HD_Mouse_Small_Intestine_full_image").pl.render_shapes(
        f"Visium_HD_Mouse_Small_Intestine_square_{bin_size:03}um", color=gene_name, cmap=new_cmap
    ).pl.show(coordinate_systems="global", title=f"bin_size={bin_size}µm", figsize=(10, 10))

clipping input data to the valid range for imshow with RGB data （[0..1] for floats or [0..255]for integers)
clipping input data to the valid range for imshow with RGB data （[0..1] for floats or [0..255]for integers)

The plot output:

The data from:

wget -c https://s3.embl.de/spatialdata/spatialdata-sandbox/visium_hd_3.0.0_io.zip &

To make it easier for others to reproduce this issue, I've simplified the code. (circle not square )

import spatialdata as sd
visium_hd_zarr_path = "/Path/to/visium_hd_Mu_intestin/data.zarr"

# input data
sdata = sd.read_zarr(visium_hd_zarr_path)
sdata

# make the var names unique
for table in sdata.tables.values():
    table.var_names_make_unique()

import matplotlib.pyplot as plt
import spatialdata_plot
from spatialdata import get_extent
from spatialdata import bounding_box_query

data_extent = get_extent(sdata["Visium_HD_Mouse_Small_Intestine_full_image"], coordinate_system="global")
data_extent

queried_cytassist = bounding_box_query(
    sdata["Visium_HD_Mouse_Small_Intestine_cytassist_image"],
    min_coordinate=[data_extent["x"][0], data_extent["y"][0]],
    max_coordinate=[data_extent["x"][1], data_extent["y"][1]],
    axes=("x", "y"),
    target_coordinate_system="global",
)
sdata["queried_cytassist"] = queried_cytassist

sdata_small = sdata.query.bounding_box(
    min_coordinate=[7000, 11000], max_coordinate=[10000, 14000], axes=("x", "y"), target_coordinate_system="global"
)

import matplotlib as mpl
#import matplotlib.cm as cm
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import LinearSegmentedColormap

# modify the viridis colormap, so that the top color is a green (better visible on the H&E pink), and such that
# the value 0 leads to a transparent color
viridis = mpl.colormaps.get_cmap("viridis")
# using 0.8 instead of 1.0 truncates the colormap
colors = viridis(np.linspace(0, 0.8, 256))
# set the color of zero to be transparent
colors[0, :] = [1.0, 1.0, 1.0, 0.0]
new_cmap = LinearSegmentedColormap.from_list("truncated_viridis", colors)
plt.get_cmap(new_cmap)

gene_name = "AA986860"
for bin_size in [16, 8, 2]:
    sdata_small.pl.render_images("Visium_HD_Mouse_Small_Intestine_full_image").pl.render_shapes(
        f"Visium_HD_Mouse_Small_Intestine_square_{bin_size:03}um", color=gene_name, cmap=new_cmap
    ).pl.show(coordinate_systems="global", title=f"bin_size={bin_size}µm", figsize=(10, 10))

The output is likely.
I thought of two possible reasons: 1) The matplotlib section sets the color bar code incorrectly; 2) Errors in gene expression value conversion.

The author has already answered in spatialdata-plot issues 242 and release spatialdata-plot 0.2.2, spatialdata-plot pull 239.
The transparent code1.0 from spatialdata-plot issues 242.
after upgrade spatialdata-plot 0.2.2, the figure is ok!

pip install --upgrade spatialdata-plot==0.2.2

the get_cmap() question is new issue spatialdata-plot issues 255

Yeh

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Cannot be shown in transparent colors when Plotting the gene expression data #554

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Cannot be shown in transparent colors when Plotting the gene expression data #554

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The Plot Code:

The plot output:

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Bioin-Mixologist commented on Apr 30, 2024

Bioin-Mixologist commented on May 2, 2024

Bioin-Mixologist commented on May 2, 2024

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