معرفی شرکت ها
autoballs-0.0.2
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مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
Python package for segmentation of axons and morphological analysis.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | autoballs-0.0.2 |
| نام | autoballs |
| نسخه کتابخانه | 0.0.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Ryan Greenhalgh |
| ایمیل نویسنده | ryan.greenhalgh@hotmail.co.uk, |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/autoballs/ |
| مجوز | - |
[](https://travis-ci.com/rg314/autoballs)
[](https://codecov.io/gh/rg314/autoballs)
[](https://badge.fury.io/py/autoballs)
<p align="center">
<img src="https://user-images.githubusercontent.com/35999546/109693171-44312080-7b81-11eb-812a-2659d07cd632.png" alt="Autoballs logo" width="400"/>
</p>
Pipeline for analysis of ex vivo explants
## Installation
While writing this package I tried my best not to reinvent the wheel for that reason there are a number of dependencies on [Fiji](https://imagej.net/Fiji). When installing the package please ensure that you create a conda env with relatvent [openjdk8](https://openjdk.java.net/install/) and [Maven](https://maven.apache.org/guides/getting-started/maven-in-five-minutes.html). Furthermore, due to constant updates of Fuji please use ```get_fiji_version.sh``` script to download the working version.
```git clone git@github.com:rg314/autoballs.git```
Go into the autoballs firectory get the working Fiji version
```
source scripts/get_fiji_version.sh
```
Create conda env
```
conda create -n autoballs python=3.8
```
Install autoballs in editable mode
```
pip install -e .
```
For training you might need to install pytorch CUDA otherwise for general useage segmentation will be fine on CPU. However, for CUDA run
```
conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch
```
or for the latest version check out PyTorch [PyTorch](https://pytorch.org/get-started/locally/)
## File structure
Please note you will need to follow the following file conventions for this to work properly. I’ve not yet included the AFM section but I’m planning on adding this part in as well so there will be a later dependency.
```
├── 20210226 Cam Franze
│ ├── afm
│ │ ├── data
│ │ │ ├── e01_nt_fg1_1force-save-2021.02.27-11.46.03.003.jpk-force
│ │ │ ├── ...
│ │ │ └── ve1_fg1_1force-save-2021.02.27-11.34.31.418.jpk-force
│ │ ├── datatxt
│ │ │ ├── e01_nt_fg1_1force-save-2021.02.27-11.46.03.003.txt
│ │ │ ├── ...
│ │ │ └── ve1_fg1_1force-save-2021.02.27-11.34.31.418.txt
│ │ └── run.py
│ ├── e01_nt_fg1_1
│ │ └── series.nd2
│ ├── e01_nt_fg2_1
│ │ └── series.nd2
│ └── metadata.txt
```
## Metadata
You will need to have a metadata file that store info about the frogs and gels for example
```
{
'gel':
{
'e01':'Elastic 100 Pa',
'e1':'Elastic 1 kPa',
've1':'Viscoelastic 1 kPa',
'glass':'Glass',
},
'frog':
{
'fg1':'Upstairs outside box 176b 13:30 17/20',
'fg2':'Downstairs inside box 176b 13:30 17/20',
'fg3':'Downstairs outside box 182b 13:30 17/20',
'fg4':'Upstairs inside box 176b 13:30 17/20',
}
}
```
Note the subfolders need to have the same naming convention i.e. if ```e01_nt_fg1_1``` is processed the frog and gel metadata will be identified as
1) Upstairs outside box 176b 13:30 17/20
2) Elastic 100 Pa
## Usage
Check that the config is set-up correctly in ```scripts/pipeline_cnn.py```
You'll need to ensure that the path is pointing to the correct folder where each subdir has the file structure outlined above.
```
def config():
configs = dict()
if sys.platform == "linux" or sys.platform == "linux2":
path = '/media/ryan/9684408684406AB7/Users/ryan/Google Drive/TFM Cambridge/2021/Frogs'
elif sys.platform == "win32":
path = 'C:\\Users\\ryan\\Google Drive\\TFM Cambridge\\2021\\Frogs'
elif sys.platform == 'darwin':
path = '/Users/ryan/Google Drive/TFM Cambridge/2021/Frogs'
configs['path'] = path
configs['sample'] = '20210305 Cam Franze' #'20210226 Cam Franze'
configs['metadata_file'] = f"{configs['path']}/{configs['sample']}{os.sep}metadata.txt"
configs['metadata'] = biometa(configs['metadata_file'])
configs['frog_metadata'] = configs['metadata']['frog']
configs['gel_metadata'] = configs['metadata']['gel']
configs['sholl'] = True
configs['create_results'] = True
configs['results_path'] = 'results' + os.sep + configs['sample'] + '_results'
configs['seg'] = True
configs['headless'] = True
configs['step_size'] = 5
configs['device'] = 'cuda'
configs['best_model'] = './best_model_1.pth'
```
If you have all path envs and file structures set up correctly you can run the pipeline as
```
python scripts/pipeline_cnn.py
```
A resuls folder will created and stats will be performed on the sample i.e.
```
(autoballs) ryan@ryan:~/Documents/GitHub/autoballs$ python scripts/pipeline_cnn.py
Multiple Comparison of Means - Tukey HSD, FWER=0.05
==========================================================================
group1 group2 meandiff p-adj lower upper reject
--------------------------------------------------------------------------
Elastic 1 kPa Elastic 100 Pa -43.0119 0.0031 -74.6687 -11.3551 True
Elastic 1 kPa Glass -15.4829 0.4857 -43.6976 12.7318 False
Elastic 1 kPa Viscoelastic 1 kPa -17.8286 0.7525 -66.5802 30.9231 False
Elastic 100 Pa Glass 27.529 0.0466 0.2912 54.7668 True
Elastic 100 Pa Viscoelastic 1 kPa 25.1833 0.5246 -23.0095 73.3762 False
Glass Viscoelastic 1 kPa -2.3457 0.9 -48.3508 43.6594 False
--------------------------------------------------------------------------
Multiple Comparison of Means - Tukey HSD, FWER=0.05
==========================================================================
group1 group2 meandiff p-adj lower upper reject
--------------------------------------------------------------------------
Elastic 1 kPa Elastic 100 Pa -43.0119 0.0031 -74.6687 -11.3551 True
Elastic 1 kPa Glass -15.4829 0.4857 -43.6976 12.7318 False
Elastic 1 kPa Viscoelastic 1 kPa -17.8286 0.7525 -66.5802 30.9231 False
Elastic 100 Pa Glass 27.529 0.0466 0.2912 54.7668 True
Elastic 100 Pa Viscoelastic 1 kPa 25.1833 0.5246 -23.0095 73.3762 False
Glass Viscoelastic 1 kPa -2.3457 0.9 -48.3508 43.6594 False
--------------------------------------------------------------------------
```
<p align="center">
<img src="https://user-images.githubusercontent.com/35999546/110252098-a328d800-7f7b-11eb-941b-605ae460e7d5.png" alt="drawing" width="400"/>
</p>
## Example
نیازمندی
| مقدار | نام |
|---|---|
| ==2.1.11 | codecov |
| ==6.2.2 | pytest |
| ==0.5.2 | albumentations |
| ==20.3.0 | attrs |
| ==2020.12.5 | certifi |
| ==4.0.0 | chardet |
| ==5.5 | coverage |
| ==0.10.0 | cycler |
| ==4.4.2 | decorator |
| ==0.6.3 | efficientnet-pytorch |
| ==2.10 | idna |
| ==2.9.0 | imageio |
| ==0.4.0 | imgaug |
| ==1.0.0 | imglyb |
| ==1.1.1 | iniconfig |
| ==1.0.0 | jgo |
| ==1.0.1 | joblib |
| ==1.2.1 | JPype1 |
| ==1.3.1 | kiwisolver |
| ==3.3.4 | matplotlib |
| ==2.5.0 | munch |
| ==3.2.3 | nd2reader |
| ==2.5 | networkx |
| ==0.46 | olefile |
| ==4.5.1.48 | opencv-python |
| ==4.5.1.48 | opencv-python-headless |
| ==20.9 | packaging |
| ==1.2.3 | pandas |
| ==0.5.1 | patsy |
| >=6.2.0 | Pillow |
| ==0.5 | PIMS |
| ==0.13.1 | pluggy |
| ==0.7.4 | pretrainedmodels |
| ==5.8.0 | psutil |
| ==1.10.0 | py |
| ==1.0.0 | pyimagej |
| ==2.4.7 | pyparsing |
| ==2.8.1 | python-dateutil |
| ==2021.1 | pytz |
| ==1.1.1 | PyWavelets |
| ==5.4.1 | PyYAML |
| ==2.25.1 | requests |
| ==0.18.1 | scikit-image |
| ==0.24.1 | scikit-learn |
| ==1.6.1 | scipy |
| ==1.1.0 | scyjava |
| ==0.11.1 | seaborn |
| ==0.1.3 | segmentation-models-pytorch |
| ==1.7.1 | Shapely |
| ==0.0 | sklearn |
| ==1.0.0 | slicerator |
| ==0.12.2 | statsmodels |
| ==2.1.0 | threadpoolctl |
| ==2021.2.26 | tifffile |
| ==0.3.2 | timm |
| ==0.10.2 | toml |
| ==1.8.0 | torch |
| ==0.8.0 | torchaudio |
| ==0.9.0 | torchvision |
| ==4.59.0 | tqdm |
| ==1.26.3 | urllib3 |
| ==0.17.0 | xarray |
| ==0.12.0 | xmltodict |
نحوه نصب
نصب پکیج whl autoballs-0.0.2:
pip install autoballs-0.0.2.whl
نصب پکیج tar.gz autoballs-0.0.2:
pip install autoballs-0.0.2.tar.gz