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breathpy-0.9.6
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Breath analysis in python
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | breathpy-0.9.6 |
| نام | breathpy |
| نسخه کتابخانه | 0.9.6 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Philipp Weber |
| ایمیل نویسنده | philmaweb@gmail.com |
| آدرس صفحه اصلی | https://github.com/philmaweb/breathpy |
| آدرس اینترنتی | https://pypi.org/project/breathpy/ |
| مجوز | GPLv3 |
[](https://zenodo.org/badge/latestdoi/267952107)
# BreathPy
## A python library for breath gas biomarker profiling
## Installation
`BreathPy` depends on `python >=3.6`, but does **not yet** support `python==3.9`, as several dependencies are not yet available for python 3.9. It is available through `pip`. Make sure to activate your local virtual environment or use anaconda. To render decision trees we depend on the `graphviz` executable. Either install into your current environment using `pip install breathpy` or create, activate a new anaconda environment "breath" and install `breathpy` and `graphviz`:
```bash
conda create --name breath python=3.8 pip graphviz -y
conda activate breath
pip install breathpy
```
If you want to use the tutorial jupyter notebooks - you also need to install jupyter `conda install jupyter`.
## Usage MCC-IMS
First prepare the example dataset by creating a subdirectory `data` and then linking the example files there.
```python
from pathlib import Path
from urllib.request import urlretrieve
from zipfile import ZipFile
# download example zip-archive
url = 'https://github.com/philmaweb/BreathAnalysis.github.io/raw/master/data/small_candy_anon.zip'
zip_dst = Path("data/small_candy_anon.zip")
dst_dir = Path("data/small_candy_anon/")
dst_dir.mkdir(parents=True, exist_ok=True)
urlretrieve(url, zip_dst)
# unzip archive into data subdirectory
with ZipFile(zip_dst, "r") as archive_handle:
archive_handle.extractall(Path(dst_dir))
```
Then run the example analysis like so:
```python
# import required functions
from breathpy.model.BreathCore import construct_default_parameters, construct_default_processing_evaluation_steps
from breathpy.model.CoreTest import run_start_to_end_pipeline
# define file prefix and default parameters
file_prefix = folder_name = 'small_candy_anon'
# assuming the data directory is in the current directory
plot_parameters, file_parameters = construct_default_parameters(file_prefix, folder_name, make_plots=True)
# create default parameters for preprocessing and evaluation
preprocessing_steps, evaluation_params_dict = construct_default_processing_evaluation_steps()
# call start
run_start_to_end_pipeline(plot_parameters, file_parameters, preprocessing_steps, evaluation_params_dict)
```
For more complete examples see `https://github.com/philmaweb/breathpy/blob/master/breathpy/tutorial/binary_candy.ipynb`, `https://github.com/philmaweb/breathpy/blob/master/breathpy/tutorial/multiclass_mouthwash.ipynb' or 'CoreTest.run_start_to_end_pipeline` and `CoreTest.run_resume_analysis`.
Example data is available at https://github.com/philmaweb/BreathAnalysis.github.io/tree/master/data.
## Usage GC-MS
### Now with experimental support for GC/MS + LC/MS data through pyOpenMS
Download and extract the example datasets into the current data subdirectory:
```python
# handle imports
from urllib.request import urlretrieve
from pathlib import Path
from zipfile import ZipFile
# download and extract data into data/algae directory
url = 'https://github.com/philmaweb/BreathAnalysis.github.io/raw/master/data/algae.zip'
zip_dst = Path("data/algae.zip")
dst_dir = Path("data/algae/")
dst_dir.mkdir(parents=True, exist_ok=True)
urlretrieve(url, zip_dst)
# unzip archive into data subdirectory
with ZipFile(zip_dst, "r") as archive_handle:
archive_handle.extractall(Path(dst_dir))
```
```python
import os
from pathlib import Path
from breathpy.model.BreathCore import construct_default_parameters,construct_default_processing_evaluation_steps
from breathpy.model.ProcessingMethods import GCMSPeakDetectionMethod, PerformanceMeasure
from breathpy.model.GCMSTest import run_gcms_platform_multicore
from breathpy.generate_sample_data import generate_train_test_set_helper
"""
Runs analysis of the algae sample set (Sun M, Yang Z and Wawrik B (2018) Metabolomic Fingerprints
of Individual Algal Cells Using the Single-Probe Mass Spectrometry Technique.
Front. Plant Sci. 9:571. doi: 10.3389/fpls.2018.00571)
19 samples from four conditions - light, dark, nitrogen-limited and replete (post nitrogen-limited)
Samples originated from single-probe mass spectrometry files - we import created featureXML files.
:param cross_val_num:
:return:
"""
cross_val_num=3
# or use your local path to a dataset here
source_dir = Path("data/algae")
target_dir = Path("data")
# will delete previous split and rewrite data
train_df, test_df = generate_train_test_set_helper(source_dir, target_dir, cross_val_num=cross_val_num)
train_dir = Path(target_dir)/"train_algae"
# prepare analysis
set_name = "train_algae"
make_plots = True
# generate parameters
plot_parameters, file_parameters = construct_default_parameters(set_name, set_name, make_plots=make_plots)
preprocessing_params_dict = {GCMSPeakDetectionMethod.ISOTOPEWAVELET: {"hr_data": True}}
_, evaluation_params_dict = construct_default_processing_evaluation_steps(cross_val_num)
# running the full analysis takes less than 30 minutes of computation time using 6 cores - in this example most if not all computations are single core though
run_gcms_platform_multicore(
sample_dir=train_dir,
preprocessing_params=preprocessing_params_dict,
evaluation_parms=evaluation_params_dict, num_cores=6)
```
Also see `model/GCMSTest.py` for reference.
### License
`BreathPy` is licensed under GPLv3, but contains binaries for PEAX, which is a free software for academic use only.
See
> [A modular computational framework for automated peak extraction from ion mobility spectra, 2014, D’Addario *et. al*](https://doi.org/10.1186/1471-2105-15-25)
## Contact
If you run into difficulties using `BreathPy`, please open an issue at our [GitHub](https://github.com/philmaweb/BreathPy) repository. Alternatively you can write an email to [Philipp Weber](mailto:pweber@imada.sdu.dk?subject=[BreathPy]%20BreathPy).
نیازمندی
| مقدار | نام |
|---|---|
| >=0.13.2 | graphviz |
| - | ipdb |
| >=3.2.1 | matplotlib |
| >=0.11.5 | matplotlib-venn |
| >=1.18.1 | numpy |
| >=1.0.3 | pandas |
| >=3.4.2 | psutil |
| ==2.5.0 | pyopenms |
| >=1.1.1 | pywavelets |
| >=0.16.2 | scikit-image |
| <0.24.0,>=0.22.0 | scikit-learn |
| >=1.4.1 | scipy |
| >=0.10.0 | seaborn |
| >=0.2.3 | statannot |
| >=0.11.1 | statsmodels |
| >=1.2.0 | xlrd |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl breathpy-0.9.6:
pip install breathpy-0.9.6.whl
نصب پکیج tar.gz breathpy-0.9.6:
pip install breathpy-0.9.6.tar.gz