semvec/pypi-packages huggingface dataset:
stcorr 0.0.0
Spatial and Temporal Correlation Analysis

# SPATIAL AND TEMPORAL CORRELATION ANALYSIS WITH APPLICATION TO FMRI Data 
Correlation analysis between two groups of time series is common in many fields, for example in analysis of functional magnetic resonance imaging (fMRI) data. The most widely used approach in fMRI is probably to compute Pearson's correlation between the group-mean temporal vectors, averaged across the (spatial) variables in each group. This approach does not account for the   continuity of the time series and the inhomogeneity in the variables. In this project, we propose a spatial and temporal correlation analysis (STC) that addresses these two issues simultaneously. It integrates the functional correlation and canonical correlation analysis (CCA) in a unified optimization-based framework. This allows, for example, varying contributions of spatial variables and increased signal strength.  Simulation results show the proposed method outperforms other competing methods. Applying to a fMRI dataset, we identify the connection strength between brain regions and the inhomogeneous functions within regions.


## Getting Started

### Prerequisites

What things you need to install the software and how to install them

```
See setup.py for details of packages requirements. 
```

### Installing from GitHub


Download the packages by using git clone https://github.com/xuefeicao/stc.git

```
python setup.py install
```

If you experience problems related to installing the dependency Matplotlib on OSX, please see https://matplotlib.org/faq/osx_framework.html 



## Running the tests

Go to test folder,  run 

```
python test.py 
```

|## Built With

* Python 2.7

## Compatibility
* Python 2.7 (Guaranteed)


## Authors

* **Jun Ke** 
* **Xuefei Cao** - *Maintainer* - (https://github.com/xuefeicao)
* **Xi Luo** (http://bigcomplexdata.com/)



## License

This project is licensed under the MIT License - see the LICENSE file for details
cachedprop 0.1.0
cached property method decorator
Requires Python: >=3.6,<4.0

# cachedprop
[![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/)
[![Python 3.7](https://img.shields.io/badge/python-3.7-blue.svg)](https://www.python.org/downloads/release/python-370/)
[![Build Status](https://travis-ci.com/Spin14/cachedprop.svg?branch=master)](https://travis-ci.com/Spin14/cachedprop) 

cached property method decorator

## Demo
```python
from cachedprop import cpd


class LabRat:
    def __init__(self) -> None:
        self._hp = None
    
    def expensive_hp_getter(self) -> int:
        ...
       
    @cpd
    def hp(self):
        return self.expensive_hp_getter()
        

rat = LabRat()
print(rat.hp) # expensive_hp_getter is called, value gets "cached", prints value
print(rat.hp) # prints "cached" value only.
```