# aicspylibczi
[](https://github.com/AllenCellModeling/aicspylibczi/actions)
[](https://github.com/AllenCellModeling/aicspylibczi/actions)
[](https://codecov.io/gh/AllenCellModeling/aicspylibczi)
[](https://github.com/AllenCellModeling/aicspylibczi/blob/master/LICENSE)
Python module to expose [libCZI](https://github.com/zeiss-microscopy/libCZI) functionality for reading (subset of) Zeiss
CZI files and meta-data. We only support 64bit architectures currently if you desperately need 32 bit support please make an issue or modify the source and build it for your use case.
## Usage
The first example show how to work with a standard CZI file (Single or Multi-Scene). The second example shows how to work with a Mosaic CZI file.
#### Example 1: Read in a czi and select a portion of the image to display
```python
import numpy as np
from aicspylibczi import CziFile
from pathlib import Path
import matplotlib.pyplot as plt
pth = Path('20190610_S02-02.czi')
czi = CziFile(pth)
# Get the shape of the data, the coordinate pairs are (start index, size)
dimensions = czi.get_dims_shape() # [{'X': (0, 1900), 'Y': (0, 1300), 'Z': (0, 60), 'C': (0, 4), 'S': (0, 40), 'B': (0, 1)}]
czi.dims # BSCZYX
czi.size # (1, 40, 4, 60, 1300, 1900)
# Load the image slice I want from the file
img, shp = czi.read_image(S=13, Z=16)
# shp = [('B', 1), ('S', 1), ('C', 4), ('Z', 1), ('Y', 1300), ('X', 1900)] # List[(Dimension, size), ...]
# img.shape = (1, 1, 4, 1, 1300, 1900) # numpy.ndarray
# define helper functions
def norm_by(x, min_, max_):
norms = np.percentile(x, [min_, max_])
i2 = np.clip((x - norms[0]) / (norms[1] - norms[0]), 0, 1)
return i2
def recolor(im): # transform from rgb to cyan-magenta-yellow
im_shape = np.array(im.shape)
color_transform = np.array([[1, 1, 0], [0, 1, 1], [1, 0, 1]]).T
im_reshape = im.reshape([np.prod(im_shape[0:2]), im_shape[2]]).T
im_recolored = np.matmul(color_transform.T, im_reshape).T
im_shape[2] = 3
im = im_recolored.reshape(im_shape)
return im
# normalize, combine into RGB and transform to CMY
c1 = (norm_by(img[0, 0, 0, 0, 0:750, 250:1000], 50, 99.8) * 255).astype(np.uint8)
c2 = (norm_by(img[0, 0, 1, 0, 0:750, 250:1000], 50, 99.8) * 255).astype(np.uint8)
c3 = (norm_by(img[0, 0, 2, 0, 0:750, 250:1000], 0, 100) * 255).astype(np.uint8)
rgb = np.stack((c1, c2, c3), axis=2)
cmy = np.clip(recolor(rgb), 0, 255)
# plot using matplotlib¶
plt.figure(figsize=(10, 10))
plt.imshow(cmy)
plt.axis('off')
```
#### Example 2: Read in a mosaic file
```python
import numpy as np
import aicspylibczi
import pathlib
from PIL import Image
mosaic_file = pathlib.Path('mosaic_test.czi')
czi = aicspylibczi.CziFile(mosaic_file)
# Get the shape of the data
dimensions = czi.dims # 'STCZMYX'
czi.size # (1, 1, 1, 1, 2, 624, 924)
czi.get_dims_shape() # [{'X': (0, 924), 'Y': (0, 624), 'Z': (0, 1), 'C': (0, 1), 'T': (0, 1), 'M': (0, 2), 'S': (0, 1)}]
czi.is_mosaic() # True
# Mosaic files ignore the S dimension and use an internal mIndex to reconstruct, the scale factor allows one to generate a manageable image
mosaic_data = czi.read_mosaic(C=0, scale_factor=1)
mosaic_data.shape # (1, 1, 624, 1756)
# the C channel has been specified S & M are used internally for position so this is (T, Z, Y, X)
normed_mosaic_data = norm_by(mosaic_data[0, 0, :, :], 5, 98) * 255
img = Image.fromarray(normed_mosaic_data.astype(np.uint8))
```
## Installation
The preferred installation method is with `pip install`.
This will install the aicspylibczi python module and extension binaries ([hosted on PyPI](https://pypi.org/project/aicspylibczi/)):
`pip install aicspylibczi`
**If this doesn't work:** Please investigate the following (generally windows issues):
- your OS is 64 bit - we only support 64 bit binaries
- your python is a 64 bit application (not 32 bit)
- are your C++ runtime libraries up to date? [vc_redist.exe](https://aka.ms/vs/16/release/vc_redist.x64.exe)
If you have tried this and are still having trouble please reach out to us and we will endeavor to help.
## Documentation
Documentation is available at
[github.io](https://allencellmodeling.github.io/aicspylibczi).
## Build
Use these steps to build and install aicspylibczi locally:
- Clone the repository including submodules (`--recurse-submodules`).
- Requirements:
- libCZI requires a c++11 compatible compiler. Built & Tested with clang.
- Development requirements are those required for libCZI: **libpng**, **zlib**
- Install the package:
```
pip install .
pip install -e .[dev] # for development (-e means editable so changes take effect when made)
pip install .[all] # for everything including jupyter notebook to work with the Example_Usage above
```
- libCZI is automatically built as a submodule and linked statically into aicspylibczi.
- Note: If you get the message directly below on windows you need to set PYTHONHOME to be the folder the python.exe you are compiling against lives in.
```
EXEC : Fatal Python error : initfsencoding: unable to load the file system codec ...
ModuleNotFoundError: No module named 'encodings'
```
## Known Issues
- with read_mosaic if the scale_factor is not 1.0 Zeiss's libCZI will, on some files, fail to render certain subblocks
within the composite mosaic image. It is not currently known if this is an issue with the file or with libCZI.
## History
aicspylibczi was originally a fork of [pylibczi](https:://github.com/elhuhdron/pylibczi) that was developed by
Paul Watkins and focused on mSEM data. In attempting to extend the work to we transitioned
to pybind11, implemented c++ and python tests, added continuous integration via github actions,
and added the functionality to read individual subblocks and stacks of subblocks as a numpy.ndarray.
Metadata reading, including specific subblock metadata reading has also been added.
We intend for this work to be merged back into the original project once we have the new work integrated with
the original work.
## Licenses & Acknowledgements
This project was created from a fork of pylibczi as explained above in the history section and Paul Watkins
is a developer on our repo as well. Pylibczi, from
the [Center of Advanced European Studies And Research](https://www.caesar.de/en/about-caesar/)
and the core dependency libCZI, are covered by the GPLv3 license.
The [GPLv3 license](https://www.gnu.org/licenses/gpl-3.0.en.html) is a consequence of libCZI which imposes GPLv3. If
you wish to use libCZI or this derivative in a commercial product you may need to talk to
Zeiss and CAESAR. [A discussion about GPLv3](https://choosealicense.com/licenses/gpl-3.0/).
Raw data
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"description": "# aicspylibczi\n\n[](https://github.com/AllenCellModeling/aicspylibczi/actions)\n[](https://github.com/AllenCellModeling/aicspylibczi/actions)\n[](https://codecov.io/gh/AllenCellModeling/aicspylibczi)\n[](https://github.com/AllenCellModeling/aicspylibczi/blob/master/LICENSE)\n\nPython module to expose [libCZI](https://github.com/zeiss-microscopy/libCZI) functionality for reading (subset of) Zeiss\nCZI files and meta-data. We only support 64bit architectures currently if you desperately need 32 bit support please make an issue or modify the source and build it for your use case.\n\n## Usage\n\nThe first example show how to work with a standard CZI file (Single or Multi-Scene). The second example shows how to work with a Mosaic CZI file.\n\n#### Example 1: Read in a czi and select a portion of the image to display\n\n```python\nimport numpy as np\nfrom aicspylibczi import CziFile\nfrom pathlib import Path\nimport matplotlib.pyplot as plt\n\npth = Path('20190610_S02-02.czi')\nczi = CziFile(pth)\n\n# Get the shape of the data, the coordinate pairs are (start index, size)\ndimensions = czi.get_dims_shape() # [{'X': (0, 1900), 'Y': (0, 1300), 'Z': (0, 60), 'C': (0, 4), 'S': (0, 40), 'B': (0, 1)}]\n\nczi.dims # BSCZYX\n\nczi.size # (1, 40, 4, 60, 1300, 1900)\n\n# Load the image slice I want from the file\nimg, shp = czi.read_image(S=13, Z=16)\n\n\n# shp = [('B', 1), ('S', 1), ('C', 4), ('Z', 1), ('Y', 1300), ('X', 1900)] # List[(Dimension, size), ...]\n# img.shape = (1, 1, 4, 1, 1300, 1900) # numpy.ndarray\n\n# define helper functions\ndef norm_by(x, min_, max_):\n norms = np.percentile(x, [min_, max_])\n i2 = np.clip((x - norms[0]) / (norms[1] - norms[0]), 0, 1)\n return i2\n\n\ndef recolor(im): # transform from rgb to cyan-magenta-yellow\n im_shape = np.array(im.shape)\n color_transform = np.array([[1, 1, 0], [0, 1, 1], [1, 0, 1]]).T\n im_reshape = im.reshape([np.prod(im_shape[0:2]), im_shape[2]]).T\n im_recolored = np.matmul(color_transform.T, im_reshape).T\n im_shape[2] = 3\n im = im_recolored.reshape(im_shape)\n return im\n\n\n# normalize, combine into RGB and transform to CMY\nc1 = (norm_by(img[0, 0, 0, 0, 0:750, 250:1000], 50, 99.8) * 255).astype(np.uint8)\nc2 = (norm_by(img[0, 0, 1, 0, 0:750, 250:1000], 50, 99.8) * 255).astype(np.uint8)\nc3 = (norm_by(img[0, 0, 2, 0, 0:750, 250:1000], 0, 100) * 255).astype(np.uint8)\nrgb = np.stack((c1, c2, c3), axis=2)\ncmy = np.clip(recolor(rgb), 0, 255)\n\n# plot using matplotlib\u00b6\nplt.figure(figsize=(10, 10))\nplt.imshow(cmy)\nplt.axis('off')\n```\n\n\n\n#### Example 2: Read in a mosaic file\n\n```python\nimport numpy as np\nimport aicspylibczi\nimport pathlib\nfrom PIL import Image\n\nmosaic_file = pathlib.Path('mosaic_test.czi')\nczi = aicspylibczi.CziFile(mosaic_file)\n\n# Get the shape of the data\ndimensions = czi.dims # 'STCZMYX'\n\nczi.size # (1, 1, 1, 1, 2, 624, 924)\n\nczi.get_dims_shape() # [{'X': (0, 924), 'Y': (0, 624), 'Z': (0, 1), 'C': (0, 1), 'T': (0, 1), 'M': (0, 2), 'S': (0, 1)}]\n\nczi.is_mosaic() # True\n# Mosaic files ignore the S dimension and use an internal mIndex to reconstruct, the scale factor allows one to generate a manageable image\nmosaic_data = czi.read_mosaic(C=0, scale_factor=1)\n\nmosaic_data.shape # (1, 1, 624, 1756)\n# the C channel has been specified S & M are used internally for position so this is (T, Z, Y, X)\n\nnormed_mosaic_data = norm_by(mosaic_data[0, 0, :, :], 5, 98) * 255\nimg = Image.fromarray(normed_mosaic_data.astype(np.uint8))\n```\n\n\n\n## Installation\n\nThe preferred installation method is with `pip install`.\nThis will install the aicspylibczi python module and extension binaries ([hosted on PyPI](https://pypi.org/project/aicspylibczi/)):\n\n`pip install aicspylibczi`\n\n**If this doesn't work:** Please investigate the following (generally windows issues):\n\n- your OS is 64 bit - we only support 64 bit binaries\n- your python is a 64 bit application (not 32 bit)\n- are your C++ runtime libraries up to date? [vc_redist.exe](https://aka.ms/vs/16/release/vc_redist.x64.exe)\n\nIf you have tried this and are still having trouble please reach out to us and we will endeavor to help.\n\n## Documentation\n\nDocumentation is available at\n[github.io](https://allencellmodeling.github.io/aicspylibczi).\n\n## Build\n\nUse these steps to build and install aicspylibczi locally:\n\n- Clone the repository including submodules (`--recurse-submodules`).\n- Requirements:\n - libCZI requires a c++11 compatible compiler. Built & Tested with clang.\n - Development requirements are those required for libCZI: **libpng**, **zlib**\n - Install the package:\n ```\n pip install .\n pip install -e .[dev] # for development (-e means editable so changes take effect when made)\n pip install .[all] # for everything including jupyter notebook to work with the Example_Usage above\n ```\n - libCZI is automatically built as a submodule and linked statically into aicspylibczi.\n- Note: If you get the message directly below on windows you need to set PYTHONHOME to be the folder the python.exe you are compiling against lives in.\n\n```\nEXEC : Fatal Python error : initfsencoding: unable to load the file system codec ...\nModuleNotFoundError: No module named 'encodings'\n```\n\n## Known Issues\n\n- with read_mosaic if the scale_factor is not 1.0 Zeiss's libCZI will, on some files, fail to render certain subblocks\n within the composite mosaic image. It is not currently known if this is an issue with the file or with libCZI.\n\n## History\n\naicspylibczi was originally a fork of [pylibczi](https:://github.com/elhuhdron/pylibczi) that was developed by\nPaul Watkins and focused on mSEM data. In attempting to extend the work to we transitioned\nto pybind11, implemented c++ and python tests, added continuous integration via github actions,\nand added the functionality to read individual subblocks and stacks of subblocks as a numpy.ndarray.\nMetadata reading, including specific subblock metadata reading has also been added.\n\nWe intend for this work to be merged back into the original project once we have the new work integrated with\nthe original work.\n\n## Licenses & Acknowledgements\n\nThis project was created from a fork of pylibczi as explained above in the history section and Paul Watkins\nis a developer on our repo as well. Pylibczi, from\nthe [Center of Advanced European Studies And Research](https://www.caesar.de/en/about-caesar/)\nand the core dependency libCZI, are covered by the GPLv3 license.\n\nThe [GPLv3 license](https://www.gnu.org/licenses/gpl-3.0.en.html) is a consequence of libCZI which imposes GPLv3. If\nyou wish to use libCZI or this derivative in a commercial product you may need to talk to\nZeiss and CAESAR. [A discussion about GPLv3](https://choosealicense.com/licenses/gpl-3.0/).\n",
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