semvec/pypi-packages huggingface dataset:
sclass 1.0.0
Basic class used as a SuperClass for other classes

# sclass

Simple module that allows you to create classes with the concept of Private and Public attributes and methods.

Just create your own class inheriting from `sclass`.
Private methods are recognized by the first character of the attribute being `'_'`

`
from sclass import sclass


class Test(sclass):
    def __init__(self):
        self.name = "lorenzo"
        self._surname = "bartolini"

    def publicmethod(self):
        return 'Public'

    def _privatemethod(self):
        return 'private'

x = Test()

print(x.name)
print(x.publicmethod())
print(x._surname)
print(x._privatemethod())
`

Last two instructions will raise a custom exception: `PrivateException`
gp-maps-python 0.0.3
Gaussian Processes for orientation preference maps
Requires Python: >=3.6

# Gaussian Processes for Orientation Preference Maps

This package is a Python implementation of a Gaussian Process (GP) method for inferring cortical maps ([Macke et al., 2011](https://www.sciencedirect.com/science/article/pii/S1053811910007007)), based on their original [MATLAB-code](https://bitbucket.org/mackelab/gp_maps/). Compared to conventional (vector averaging) approaches, the method computes better maps from little data and can be used to quantify the uncertainty in an estimated orientation preference map (OPM). It also provides a principled way to choose the parameters in the smoothing step and incorporate inhomogeneous noise variances as well as noise correlations.

The basic idea is to specify a prior over OPMs as a GP. This means that a-priori assumptions about correlations of the pixels in a map are specified as a covariance function K. In this case, we use a Difference of Gaussians (DoG) kernel. This covariance function encodes our assumptions that OPMs are smooth and have a semi-periodic structure. This prior is then updated via Bayes' rule with imaging data to obtain a smooth posterior estimate of an OPM. 

My notes on the method (including derivations not in the original paper) can be found [here](https://github.com/mackelab/gp-maps-python/blob/master/SupportingInfo.pdf).

## Installation & Usage

You can clone this package and install all its dependencies using

```
git clone git@github.com:mackelab/gp-maps-python.git
python -m pip install -r requirements.txt
```

Alternatively, this module is now pip-installable via

`python -m pip install gp-maps-python`

A number of Jupyter notebooks illustrate how to use the package:

## [OPM.ipynb](https://github.com/mackelab/gp-maps-python/blob/master/demos/OPM.ipynb)

contains basic functionality for handling OPMs:

- sample from this prior to obtain a ground truth OPM
- visualize OPMs
- simulate neural responses given an OPM and a set of stimuli
- compute maximum likelihood/vector averaging estimate of the OPM
- smooth the OPM with a Gaussian kernel


## [BayesianOPM.ipynb](https://github.com/mackelab/gp-maps-python/blob/master/demos/BayesianOPM.ipynb)

shows details about the GP method:

- covariance function (DoG kernel)
- estimate hyperparameters of the prior covariance function from the empirical map
- learn noise model iteratively from data
- posterior inference

## [FerretData.ipynb](https://github.com/mackelab/gp-maps-python/blob/master/demos/FerretData.ipynb)

serves as an illustration on how to use GPs to infer OPMs from real imaging data (replicating the results of Macke et al., 2011):

- visualize inhomogeneous and correlated noise structure of the data
- obtain smooth estimate based on "ground truth"
- GP posterior inference on real data
covid19-counter-richwellum 0.0.1
Track some basic covid19 statistics
Requires Python: >=3.6

# covid19

Simple but somewhat morbid tool to display information about COVID19.

Runs on a loop checking the number of Confirmed, Recovered and Deaths and also
the percentage died.

All data is pulled from: <https://github.com/CSSEGISandData/COVID-19>

User can specify the interval to check, the type of display (for small
displays), and display the record of any changes. In addition there's a test
option which allows you to enter your own data files, or make modifications.

Data on Deaths, Confirmed cases, Recoveries and Percentage Died are calculated
by pulling the CSV files down, and creating pandas df's, summing the data up
ofthe last known column, which is also the latest.

![Output looks like](./covid_output.png)

To install on a Mac:

```bash
    cd covid19 # cd to the directory
    brew install pipenv
    pipenv install # Seems temperamental - so use manual steps below if needed
```

Otherwise you need python3 and the packages (pip3 install): pandas, colorama,
termcolor, requests:

```bash
    brew install python3  #(or sudo apt install python3 etc)
    pip3 install pandas colorama termcolor requests
```

Example running:

```bash
    RWELLUM-M-C5JH:covid19 rwellum$ ./covid19.py -i 400 -h
    usage: covid19.py [-h] [-i INTERVAL] [-r] [-s] [-f] [-v] [-t]

    Grab and process the latest COVID-19 data

    optional arguments:
    -h, --help            show this help message and exit
    -i INTERVAL, --interval INTERVAL
                            interval in seconds between retrieving the data again,
                            default one hour(3600s)
    -r, --record          view a record of all changes
    -s, --split           split the display to fit smaller terminals
    -f, --force           bypass safety rails - very dangerous
    -v, --verbose         turn on verbose messages, commands and outputs
    -t, --test            run with a test file

    E.g.: ./covid19.py -i 600 -s
```