A Python Library for VISoR Image.
Project description
visor-py
A Python Library for VISoR Image.
Usage
Install Module
pip install visor-py
Import Module
import visor.image as vimg
Examples
- Read .vsr
import visor.image as vimg
# Open .vsr file with read-only ('r') mode
# img is a visor.Image object
img = vimg.open_vsr('path/to/VISOR001.vsr', 'r')
print(img.info)
- Read raw image
import visor.image as vimg
img = vimg.open_vsr('path/to/VISOR001.vsr', 'r')
# Read raw image of slice_1_10x from zarr file
# arr is a visor.Array object
arr = img.read(img_type='raw',
zarr_file='slice_1_10x.zarr',
resolution=0)
print(arr.info)
- Read arrays by named stacks or channels
import visor.image as vimg
img = vimg.open_vsr('path/to/VISOR001.vsr', 'r')
arr = img.read(img_type='raw',
zarr_file='slice_1_10x.zarr',
resolution=0)
# Read stack by label
# s1_array is a dask.array, dimensions: c,z,y,x
s1_array = arr.read(stack='stack_1')
print(f's1_array shape: {s1_array.shape}, dimensions: c,z,y,x')
# Read channel by wavelength
# c488_array is a dask.array, dimensions: s,z,y,x
c488_array = arr.read(channel='488')
print(f'c488_array shape: {c488_array.shape}, dimensions: s,z,y,x')
# Read stack and channel
# s1c488_array is a dask.array, dimensions: z,y,x
s1c488_array = arr.read(stack='stack_1', channel='488')
print(f's1c488_array shape: {s1c488_array.shape}, dimensions: z,y,x')
- Read array by index
import visor.image as vimg
img = vimg.open_vsr('path/to/VISOR001.vsr', 'r')
arr = img.read(img_type='raw',
zarr_file='slice_1_10x.zarr',
resolution=0)
# Read by index
# the_array is a dask.array
the_array = arr.array
print(f'the_array shape: {the_array.shape}, dimensions: s,c,z,y,x')
# subarr is a dask.array
subarr = the_array[0,0,:,:,:]
print(f'subarr shape: {subarr.shape}, dimensions: z,y,x')
- Write .vsr
import visor.image as vimg
import numpy as np
import dask.array as da
# Open .vsr file with write ('w') mode
# img is a visor.Image object
img = vimg.open_vsr('path/to/VISOR001.vsr', 'w')
# Generate a random array
# new_arr is a dask array
new_arr = da.random.random(size=(2, 2, 4, 4, 4), chunks=(1, 1, 2, 2, 2))
# Metadata
# follow https://visor-tech.github.io/visor-data-schema/
img_info = {} # info.json
arr_info = {} # .zattrs
selected = {} # selected.json
# Write array to .vsr
img.write(arr=new_arr,
img_type='raw',
file='slice_1_10x',
resolution=0,
img_info=img_info,
arr_info=arr_info,
selected=selected)
References
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