Example Stitching Notebook

Contents

Example Stitching Notebook#

[1]:

import os

os.environ["JAVA_HOME"] = "/Users/sophia/mambaforge/envs/scPortrait/lib/jvm"
import matplotlib.pyplot as plt

from scportrait.data._datasets import dataset_stitching_example
from scportrait.tools.stitch import ParallelStitcher, Stitcher

/Users/sophia/mambaforge/envs/scPortrait/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

Single-threaded Stitching#

initializing the stitcher object#

[3]:

input_dir = dataset_stitching_example()
slidename = "stitching_test"
outdir = os.path.join(input_dir.replace("stitching_example", "example_projects/stitching"), slidename)

row = str(2).zfill(2)  # specify the row of the well you want to stitch
well = str(4).zfill(2)  # specifc the well number you wish to stitch
zstack_value = str(1).zfill(
    3
)  # specify the zstack you want to stitch. for multiple zstacks please make a loop and iterate through each of them.
timepoint = str(1).zfill(3)  # specifz the timepoint you wish to stitch

pattern = f"Timepoint{timepoint}_Row{row}_Well{well}_{{channel}}_zstack{zstack_value}_r{{row:03}}_c{{col:03}}.tif"

# initialize stitcher
stitcher = Stitcher(
    os.path.abspath(input_dir),
    slidename,
    outdir,
    pattern=pattern,
    stitching_channel="Alexa488",
    overlap=0.1,
    max_shift=30,
    filter_sigma=0,
    rescale_range={"Alexa488": (1, 99), "DAPI": (1, 99), "mCherry": (1, 99)},
    overwrite=True,
)

Output directory created at:  /Users/sophia/Documents/GitHub/scPortrait/scportrait_data/example_projects/stitching/stitching_test

Generating thumbnails#

[4]:

stitcher.generate_thumbnail()

    assembling thumbnail 9/9

[5]:

# thumbnail is saved in the stitcher object and can be accessed via stitcher.thumbnail
plt.imshow(stitcher.thumbnail)

[5]:

<matplotlib.image.AxesImage at 0xb0335b520>

../../../_images/pages_tools_stitching_example_stitching_notebook_7_1.png

[6]:

# alterantively it can be saved to a tif file
stitcher.write_thumbnail()

[7]:

stitcher.initialize_reader()

Generating full-scale stitched image#

[8]:

stitcher.stitch()

performing stitching on channel Alexa488 with id number 0
    quantifying alignment error 1000/1000
    aligning edge 12/12

WARNING:root:Folder /var/folders/35/p4c58_4n3bb0bxnzgns1t7kh0000gn/T/temp_mmap_w8wfx989 with temp mmap arrays is being deleted. All existing temp mmapp arrays will be unusable!
WARNING:root:New temp folder location. Temp mmap arrays are written to /Users/sophia/Documents/GitHub/scPortrait/scportrait_data/example_projects/stitching/stitching_test/temp_mmap_s16co0a1. Cleanup of this folder is OS dependant, and might need to be triggered manually!

Alignment complete.
assembling mosaic with shape (3, 3040, 3038)
created tempmmap array for assembled mosaic at /Users/sophia/Documents/GitHub/scPortrait/scportrait_data/example_projects/stitching/stitching_test/temp_mmap_s16co0a1/temp_mmap_4464623890994857332.hdf

  0%|          | 0/3 [00:00<?, ?it/s]

        merging tile 9/9

 33%|███▎      | 1/3 [00:00<00:01,  1.01it/s]


        merging tile 9/9

 67%|██████▋   | 2/3 [00:02<00:01,  1.01s/it]


        merging tile 9/9

100%|██████████| 3/3 [00:03<00:00,  1.02s/it]

../../../_images/pages_tools_stitching_example_stitching_notebook_11_12.png

../../../_images/pages_tools_stitching_example_stitching_notebook_11_13.png

[9]:

# the stitched image is saved in the stitcher object and can be accessed via stitcher.assembled_mosaic
stitcher.assembled_mosaic

[9]:

	Array	Chunk
Bytes	52.85 MiB	52.85 MiB
Shape	(3, 3040, 3038)	(3, 3040, 3038)
Dask graph	1 chunks in 2 graph layers
Data type	uint16 numpy.ndarray

[10]:

# the stitched image can then be written to a variety of output formats

stitcher.write_tif(export_xml=True)
stitcher.write_ome_zarr()

[11]:

import matplotlib.pyplot as plt
from tifffile import imread

fig, axs = plt.subplots(1, 3, figsize=(30, 10))
axs[0].imshow(imread(f"{outdir}/stitching_test_Alexa488.tif"))
axs[0].axis("off")

axs[1].imshow(imread(f"{outdir}/stitching_test_DAPI.tif"))
axs[1].axis("off")

axs[2].imshow(imread(f"{outdir}/stitching_test_mCherry.tif"))
axs[2].axis("off")

[11]:

(-0.5, 3037.5, 3039.5, -0.5)

../../../_images/pages_tools_stitching_example_stitching_notebook_14_1.png

[12]:

del stitcher

Multi-threaded Stitching#

Using the ParallelStitcher class stitching can be speed up by using multiple threads. The code to perform stitching remains more or less the same.

initializing the stitcher object#

[14]:

input_dir = dataset_stitching_example()
slidename = "stitching_test_parallel"
outdir_parallel = os.path.join(input_dir.replace("stitching_example", "example_projects/stitching"), slidename)


row = str(2).zfill(2)  # specify the row of the well you want to stitch
well = str(4).zfill(2)  # specifc the well number you wish to stitch
zstack_value = str(1).zfill(
    3
)  # specify the zstack you want to stitch. for multiple zstacks please make a loop and iterate through each of them.
timepoint = str(1).zfill(3)  # specifz the timepoint you wish to stitch

pattern = f"Timepoint{timepoint}_Row{row}_Well{well}_{{channel}}_zstack{zstack_value}_r{{row:03}}_c{{col:03}}.tif"

# initialize stitcher
stitcher = ParallelStitcher(
    input_dir,
    slidename,
    outdir_parallel,
    pattern=pattern,
    stitching_channel="Alexa488",
    overlap=0.1,
    max_shift=30,
    filter_sigma=0,
    rescale_range={"Alexa488": (1, 99), "DAPI": (1, 99), "mCherry": (1, 99)},
    overwrite=True,
    threads=12,
)

Output directory created at:  /Users/sophia/Documents/GitHub/scPortrait/scportrait_data/example_projects/stitching/stitching_test_parallel

Generating thumbnails#

[15]:

stitcher.generate_thumbnail()

    assembling thumbnail 9/9

[16]:

# thumbnail is saved in the stitcher object and can be accessed via stitcher.thumbnail
plt.imshow(stitcher.thumbnail)

[16]:

<matplotlib.image.AxesImage at 0xb6a597130>

../../../_images/pages_tools_stitching_example_stitching_notebook_21_1.png

[17]:

# alterantively it can be saved to a tif file
stitcher.write_thumbnail()

Generating full-scale stitched image#

[18]:

stitcher.stitch()

performing stitching on channel Alexa488 with id number 0
graph-tool not available, using networkx as default.
 For stitching large datasets, graph-tool is recommended as it provides better performance.
    quantifying alignment error: 100%|██████████| 1000/1000 [00:01<00:00, 907.13it/s]
                  aligning edge: 100%|██████████| 12/12 [00:00<00:00, 21.84it/s]

WARNING:root:Folder /Users/sophia/Documents/GitHub/scPortrait/scportrait_data/example_projects/stitching/stitching_test/temp_mmap_s16co0a1 with temp mmap arrays is being deleted. All existing temp mmapp arrays will be unusable!
WARNING:root:New temp folder location. Temp mmap arrays are written to /Users/sophia/Documents/GitHub/scPortrait/scportrait_data/example_projects/stitching/stitching_test_parallel/temp_mmap_hiwtpfwn. Cleanup of this folder is OS dependant, and might need to be triggered manually!

Alignment complete.
assembling mosaic with shape (3, 3040, 3038)
created tempmmap array for assembled mosaic at /Users/sophia/Documents/GitHub/scPortrait/scportrait_data/example_projects/stitching/stitching_test_parallel/temp_mmap_hiwtpfwn/temp_mmap_2484028152428819120.hdf
assembling channels with 3 workers
assembling mosaic:   0%|          | 0/3 [00:00<?, ?it/s]


























assembling channel 0: 100%|██████████| 9/9 [00:01<00:00,  8.19it/s]
assembling mosaic:  33%|███▎      | 1/3 [00:01<00:02,  1.11s/it]


assembling channel 1: 100%|██████████| 9/9 [00:01<00:00,  8.06it/s]
assembling channel 2: 100%|██████████| 9/9 [00:01<00:00,  8.05it/s]
assembling mosaic: 100%|██████████| 3/3 [00:01<00:00,  2.65it/s]

../../../_images/pages_tools_stitching_example_stitching_notebook_24_3.png

../../../_images/pages_tools_stitching_example_stitching_notebook_24_4.png

[19]:

# the stitched image is saved in the stitcher object and can be accessed via stitcher.assembled_mosaic
stitcher.assembled_mosaic

[19]:

	Array	Chunk
Bytes	52.85 MiB	52.85 MiB
Shape	(3, 3040, 3038)	(3, 3040, 3038)
Dask graph	1 chunks in 2 graph layers
Data type	uint16 numpy.ndarray

[20]:

# the stitched image can then be written to a variety of output formats

stitcher.write_tif(export_xml=True)

# alternatively the stitched images can also be written out to tifs in a multi-threaded format
# speedups here are limited by write speed to disk, the number of threads is limited by the number of channels available
stitcher.write_tif_parallel(export_xml=True)

stitcher.write_ome_zarr()

writing tif files: 100%|██████████| 3/3 [00:00<00:00, 204.13it/s]

Visualize Stitching Output#

[21]:

import matplotlib.pyplot as plt
from tifffile import imread

fig, axs = plt.subplots(1, 3, figsize=(30, 10))
axs[0].imshow(imread(f"{outdir_parallel}/stitching_test_parallel_Alexa488.tif"))
axs[0].axis("off")

axs[1].imshow(imread(f"{outdir_parallel}/stitching_test_parallel_DAPI.tif"))
axs[1].axis("off")

axs[2].imshow(imread(f"{outdir_parallel}/stitching_test_parallel_mCherry.tif"))
axs[2].axis("off")

[21]:

(-0.5, 3037.5, 3039.5, -0.5)

../../../_images/pages_tools_stitching_example_stitching_notebook_28_1.png

[22]:

# compare parallel and non-parallel stitching

imread(f"{outdir_parallel}/stitching_test_parallel_Alexa488.tif") == imread(f"{outdir}/stitching_test_Alexa488.tif")

[22]:

array([[ True,  True,  True, ...,  True,  True,  True],
       [ True,  True,  True, ...,  True,  True,  True],
       [ True,  True,  True, ...,  True,  True,  True],
       ...,
       [ True,  True,  True, ...,  True,  True,  True],
       [ True,  True,  True, ...,  True,  True,  True],
       [ True,  True,  True, ...,  True,  True,  True]])

[23]:

del stitcher