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biopsykit-0.9.0
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مشاهده بیشترتوضیحات
A Python package for the analysis of biopsychological data.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | biopsykit-0.9.0 |
| نام | biopsykit |
| نسخه کتابخانه | 0.9.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Robert Richer |
| ایمیل نویسنده | robert.richer@fau.de |
| آدرس صفحه اصلی | https://github.com/mad-lab-fau/biopsykit |
| آدرس اینترنتی | https://pypi.org/project/biopsykit/ |
| مجوز | MIT |
<img src="./docs/_static/logo/biopsykit_logo.png" height="200">
# BioPsyKit
[](https://pypi.org/project/biopsykit/)
[](https://joss.theoj.org/papers/4769dbce3a25db943d7e3a23578becd1)
[](https://biopsykit.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/mad-lab-fau/BioPsyKit/actions/workflows/test-and-lint.yml)
[](https://codecov.io/gh/mad-lab-fau/BioPsyKit)
[](https://github.com/psf/black)
A Python package for the analysis of biopsychological data.
With this package you have everything you need for analyzing biopsychological data, including:
* Data processing pipelines for various physiological signals (ECG, EEG, Respiration, Motion, ...).
* Algorithms and data processing pipelines for sleep/wake prediction and computation of sleep endpoints
based on activity or IMU data.
* Functions to import and process data from sleep trackers (e.g., Withings Sleep Analyzer)
* Functions for processing and analysis of salivary biomarker data (cortisol, amylase).
* Implementation of various psychological and HCI-related questionnaires.
* Implementation of classes representing different psychological protocols
(e.g., TSST, MIST, Cortisol Awakening Response Assessment, etc.)
* Functions for easily setting up statistical analysis pipelines.
* Functions for setting up and evaluating machine learning pipelines.
* Plotting wrappers optimized for displaying biopsychological data.
## Details
### Analysis of Physiological Signals
#### ECG Processing
`BioPsyKit` provides a whole ECG data processing pipeline, consisting of:
* Loading ECG data from:
* Generic `.csv` files
* NilsPod binary (`.bin`) files (requires [`NilsPodLib`](https://github.com/mad-lab-fau/NilsPodLib))
* Other sensor types (_coming soon_)
* Splitting data into single study parts (based on time intervals) that will be analyzed separately
* Performing ECG processing, including:
* R peak detection (using [`Neurokit`](https://github.com/neuropsychology/NeuroKit))
* R peak outlier removal and interpolation
* HRV feature computation
* ECG-derived respiration (EDR) estimation for respiration rate and respiratory sinus arrhythmia (RSA)
(_experimental_)
* Instantaneous heart rate resampling
* Computing aggregated results (e.g., mean and standard error) per study part
* Creating plots for visualizing processing results
#### Quick Example
```python
from biopsykit.signals.ecg import EcgProcessor
from biopsykit.example_data import get_ecg_example
ecg_data, sampling_rate = get_ecg_example()
ep = EcgProcessor(ecg_data, sampling_rate)
ep.ecg_process()
print(ep.ecg_result)
```
... more biosignals coming soon!
### Sleep/Wake Prediction
`BioPsyKit` allows to process sleep data collected from IMU or activity sensors (e.g., Actigraphs). This includes:
* Detection of wear periods
* Detection of time spent in bed
* Detection of sleep and wake phases
* Computation of sleep endpoints (e.g., sleep and wake onset, net sleep duration wake after sleep onset, etc.)
#### Quick Example
```python
import biopsykit as bp
from biopsykit.example_data import get_sleep_imu_example
imu_data, sampling_rate = get_sleep_imu_example()
sleep_results = bp.sleep.sleep_processing_pipeline.predict_pipeline_acceleration(imu_data, sampling_rate)
sleep_endpoints = sleep_results["sleep_endpoints"]
print(sleep_endpoints)
```
### Salivary Biomarker Analysis
`BioPsyKit` provides several methods for the analysis of salivary biomarkers (e.g. cortisol and amylase), such as:
* Import data from Excel and csv files into a standardized format
* Compute standard features (maximum increase, slope, area-under-the-curve, mean, standard deviation, ...)
#### Quick Example
```python
import biopsykit as bp
from biopsykit.example_data import get_saliva_example
saliva_data = get_saliva_example(sample_times=[-20, 0, 10, 20, 30, 40, 50])
max_inc = bp.saliva.max_increase(saliva_data)
# remove the first saliva sample (t=-20) from computing the AUC
auc = bp.saliva.auc(saliva_data, remove_s0=True)
print(max_inc)
print(auc)
```
### Questionnaires
`BioPsyKit` implements various established psychological (state and trait) questionnaires, such as:
* Perceived Stress Scale (PSS)
* Positive and Negative Affect Schedule (PANAS)
* Self-Compassion Scale (SCS)
* Big Five Inventory (BFI)
* State Trait Depression and Anxiety Questionnaire (STADI)
* Trier Inventory for Chronic Stress (TICS)
* Primary Appraisal Secondary Appraisal Scale (PASA)
* ...
#### Quick Example
```python
import biopsykit as bp
from biopsykit.example_data import get_questionnaire_example
data = get_questionnaire_example()
pss_data = data.filter(like="PSS")
pss_result = bp.questionnaires.pss(pss_data)
print(pss_result)
```
#### List Supported Questionnaires
```python
import biopsykit as bp
print(bp.questionnaires.utils.get_supported_questionnaires())
```
### Psychological Protocols
`BioPsyKit` implements methods for easy handling and analysis of data recorded with several established psychological
protocols, such as:
* Montreal Imaging Stress Task (MIST)
* Trier Social Stress Test (TSST)
* Cortisol Awakening Response Assessment (CAR)
* ...
#### Quick Example
```python
from biopsykit.protocols import TSST
from biopsykit.example_data import get_saliva_example
from biopsykit.example_data import get_hr_subject_data_dict_example
# specify TSST structure and the durations of the single phases
structure = {
"Pre": None,
"TSST": {
"Preparation": 300,
"Talk": 300,
"Math": 300
},
"Post": None
}
tsst = TSST(name="TSST", structure=structure)
saliva_data = get_saliva_example(sample_times=[-20, 0, 10, 20, 30, 40, 50])
hr_subject_data_dict = get_hr_subject_data_dict_example()
# add saliva data collected during the whole TSST procedure
tsst.add_saliva_data(saliva_data, saliva_type="cortisol")
# add heart rate data collected during the "TSST" study part
tsst.add_hr_data(hr_subject_data_dict, study_part="TSST")
# compute heart rate results: normalize ECG data relative to "Preparation" phase; afterwards, use data from the
# "Talk" and "Math" phases and compute the average heart rate for each subject and study phase, respectively
tsst.compute_hr_results(
result_id="hr_mean",
study_part="TSST",
normalize_to=True,
select_phases=True,
mean_per_subject=True,
params={
"normalize_to": "Preparation",
"select_phases": ["Talk", "Math"]
}
)
```
### Statistical Analysis
`BioPsyKit` implements methods for simplified statistical analysis of biopsychological data by offering an
object-oriented interface for setting up statistical analysis pipelines, displaying the results, and adding
statistical significance brackets to plots.
#### Quick Example
```python
import matplotlib.pyplot as plt
from biopsykit.stats import StatsPipeline
from biopsykit.plotting import multi_feature_boxplot
from biopsykit.example_data import get_stats_example
data = get_stats_example()
# configure statistical analysis pipeline which consists of checking for normal distribution and performing paired
# t-tests (within-variable: time) on each questionnaire subscale separately (grouping data by subscale).
pipeline = StatsPipeline(
steps=[("prep", "normality"), ("test", "pairwise_ttests")],
params={"dv": "PANAS", "groupby": "subscale", "subject": "subject", "within": "time"}
)
# apply statistics pipeline on data
pipeline.apply(data)
# plot data and add statistical significance brackets from statistical analysis pipeline
fig, axs = plt.subplots(ncols=3)
features = ["NegativeAffect", "PositiveAffect", "Total"]
# generate statistical significance brackets
box_pairs, pvalues = pipeline.sig_brackets(
"test", stats_effect_type="within", plot_type="single", x="time", features=features, subplots=True
)
# plot data
multi_feature_boxplot(
data=data, x="time", y="PANAS", features=features, group="subscale", order=["pre", "post"],
stats_kwargs={"box_pairs": box_pairs, "pvalues": pvalues}, ax=axs
)
```
### Machine Learning Analysis
`BioPsyKit` implements methods for simplified and systematic evaluation of different machine learning pipelines.
#### Quick Example
```python
# Utils
from sklearn.datasets import load_breast_cancer
# Preprocessing & Feature Selection
from sklearn.feature_selection import SelectKBest
from sklearn.preprocessing import MinMaxScaler, StandardScaler
# Classification
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
# Cross-Validation
from sklearn.model_selection import KFold
from biopsykit.classification.model_selection import SklearnPipelinePermuter
# load example dataset
breast_cancer = load_breast_cancer()
X = breast_cancer.data
y = breast_cancer.target
# specify estimator combinations
model_dict = {
"scaler": {
"StandardScaler": StandardScaler(),
"MinMaxScaler": MinMaxScaler()
},
"reduce_dim": {
"SelectKBest": SelectKBest(),
},
"clf" : {
"KNeighborsClassifier": KNeighborsClassifier(),
"DecisionTreeClassifier": DecisionTreeClassifier(),
}
}
# specify hyperparameter for grid search
params_dict = {
"StandardScaler": None,
"MinMaxScaler": None,
"SelectKBest": { "k": [2, 4, "all"] },
"KNeighborsClassifier": { "n_neighbors": [2, 4], "weights": ["uniform", "distance"] },
"DecisionTreeClassifier": {"criterion": ['gini', 'entropy'], "max_depth": [2, 4] },
}
pipeline_permuter = SklearnPipelinePermuter(model_dict, params_dict)
pipeline_permuter.fit(X, y, outer_cv=KFold(5), inner_cv=KFold(5))
# print mean performance scores for each pipeline and parameter combinations, averaged over all outer CV folds
print(pipeline_permuter.mean_pipeline_score_results())
# print overall best-performing pipeline and the performances over all outer CV folds
print(pipeline_permuter.best_pipeline())
# print summary of all relevant metrics for the best pipeline for each evaluated pipeline combination
print(pipeline_permuter.metric_summary())
```
## Installation
``BioPsyKit`` requires Python >=3.8. First, install a compatible version of Python. Then install ``BioPsyKit`` via pip.
Installation from [PyPi](https://pypi.org/):
```bash
pip install biopsykit
```
Installation from [PyPi](https://pypi.org/) with extras
(e.g., `jupyter` to directly install all required dependencies for the use with Jupyter Lab):
```bash
pip install "biopsykit[jupyter]"
```
Installation from local repository copy:
```bash
git clone https://github.com/mad-lab-fau/BioPsyKit.git
cd BioPsyKit
pip install .
```
### For Developer
If you are a developer and want to contribute to ``BioPsyKit`` you can install an editable version of the package from
a local copy of the repository.
BioPsyKit uses [poetry](https://python-poetry.org) to manage dependencies and packaging. Once you installed poetry,
run the following commands to clone the repository, initialize a virtual env and install all development dependencies:
#### Without Extras
```bash
git clone https://github.com/mad-lab-fau/BioPsyKit.git
cd BioPsyKit
poetry install
```
#### With all Extras (e.g., extended functionalities for IPython/Jupyter Notebooks)
```bash
git clone https://github.com/mad-lab-fau/BioPsyKit.git
cd BioPsyKit
poetry install -E mne -E jupyter
```
To run any of the tools required for the development workflow, use the `poe` commands of the
[poethepoet](https://github.com/nat-n/poethepoet) task runner:
```bash
$ poe
docs Build the html docs using Sphinx.
format Reformat all files using black.
format_check Check, but not change, formatting using black.
lint Lint all files with Prospector.
test Run Pytest with coverage.
update_version Bump the version in pyproject.toml and biopsykit.__init__ .
register_ipykernel Register a new IPython kernel named `biopsykit` linked to the virtual environment.
remove_ipykernel Remove the associated IPython kernel.
```
----
#### Some Notes
* The `poe` commands are only available if you are in the virtual environment associated with this project.
You can either activate the virtual environment manually (e.g., `source .venv/bin/activate`) or use the `poetry shell`
command to spawn a new shell with the virtual environment activated.
* In order to use jupyter notebooks with the project you need to register a new IPython
kernel associated with the venv of the project (`poe register_ipykernel` - see below).
When creating a notebook, make to sure to select this kernel (top right corner of the notebook).
* In order to build the documentation, you need to additionally install [pandoc](https://pandoc.org/installing.html).
----
See the [Contributing Guidelines](https://biopsykit.readthedocs.io/en/latest/source/contributing/CONTRIBUTING.html) for further information.
## Examples
See the [Examples Gallery](https://biopsykit.readthedocs.io/en/latest/examples/index.html) for example on how to use BioPsyKit.
## Citing BioPsyKit
If you use `BioPsyKit` in your work, please report the version you used in the text. Additionally, please also cite the corresponding paper:
```
Richer et al., (2021). BioPsyKit: A Python package for the analysis of biopsychological data. Journal of Open Source Software, 6(66), 3702, https://doi.org/10.21105/joss.03702
```
If you use a specific algorithm please also to make sure you cite the original paper of the algorithm! We recommend the following citation style:
```
We used the algorithm proposed by Author et al. [paper-citation], implemented by the BioPsykit package [biopsykit-citation].
```
نیازمندی
| مقدار | نام |
|---|---|
| >=1,<2 | numpy |
| >=1,<2 | pandas |
| >=3.4,<4.0 | matplotlib |
| >=0,<1 | seaborn |
| >=0,<1 | neurokit2 |
| >=0.5.2,<0.6.0 | pingouin |
| >=1,<2 | scipy |
| >=0.2.3,<0.3.0 | statannot |
| >=4,<5 | tqdm |
| >=1,<2 | joblib |
| >=1.0,<2.0 | scikit-learn |
| >=3.4.1 | nilspodlib |
| >=3,<4 | openpyxl |
| >=1,<2 | XlsxWriter |
| >=2,<3 | xlrd |
| >=0.13,<0.14 | statsmodels |
| >=1.1,<2.0 | fau-colors |
| >=3,<4 | bioread |
| >=0.23.0,<0.24.0 | mne |
| >=7,<8 | IPython |
| >=8,<9 | ipywidgets |
| >=0.9,<0.10 | ipympl |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.8,<3.11 | Python |
نحوه نصب
نصب پکیج whl biopsykit-0.9.0:
pip install biopsykit-0.9.0.whl
نصب پکیج tar.gz biopsykit-0.9.0:
pip install biopsykit-0.9.0.tar.gz