semvec/pypi-packages huggingface dataset:
pytest-rt 1.3.2
pytest data collector plugin for Testgr

# pytest-rt - pytest data collector for Testgr

Plugin sends pytest data to Testgr server.

### Setup

```pip install pytest-rt```

### Launch
```
pytest --rt --rtu="http://your_testgr_url/loader" -> will launch pytest with pytest-rt plugin.
```
Additional parameters: 

* --rte "your_environment" -> Send your environment name as additional data to the Testgr server. 
* --rt-job-report - Enable email sending after job finish.
* --rt-return-job - Write last finished job UUID in testgr_last_job.txt file.
* --rt-custom-data - Send any custom data to the Testgr in dict format. Example:
```
--rt-custom data='{"Git":"dev-branch"}'
```
* --rt-custom-id - requirement for pytest-xdist plugin. Use with random ID. 
```
--rt-custom-id="unique_id"
```

## Authors
[**Andrey Smirnov**](https://github.com/and-sm)

## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details
otpbomber 0.9
Powerful OTP Bomber Tool

<!--
https://pypi.org/project/otpbomber
-->

[![](https://img.shields.io/pypi/pyversions/readme-generator.svg?longCache=True)](https://pypi.org/project/readme-generator/)

#### Installation :
```
pip install otpbomber && otpbomber
```

#### Features :
+   **`otpbomber`**, ** 25+ Working API updated  **


#### How to install :
```
$ pkg install python -y
$ pip install otpbomber
$ otpbomber
```
#### Run :
```
$ otpbomber
```
#### Changelog
```
[✓] Removed Victim Information
[✓] Removed Call APIs
[✓] Updated 25+ APIs
[+] Wait for Next Update ;)
```
#### Disclaimer :
```
We are not responsible for your loss by this tool.
this tool is made for Education/Learning Purpose.
Dont harm anyone using this tool.
```

#### About Us :
```
Created by Parixit Sutariya
Github : github.com/Bhai4You
Blogspot : https://bhai4you.blogspot.com
jrm33 1.0.3
Python implementation of the JRM33 model

# jrm33

JRM33 model (Connerney et al. 2022) implementation using Python.

## Installation

Install using `pip`:

```bash
pip3 install jrm33 --user
```

Or by cloning this repo:

```bash
git clone https://github.com/mattkjames7/jrm33.git
cd jrm09

#EITHER create a wheel and install (replace X.X.X with the version number):
python3 setup.py bdist_wheel
pip3 install dist/jrm33-X.X.X-py3-none-any.whl --user

#OR install directly using setup.py
python3 setup.py install --user
```

## Usage

The model accepts right-handed System III coordinates either in Cartesian form (`jrm33.ModelCart()`) or in spherical polar form (`jrm33.Model()`), e.g.:

```python
import jrm33

#get some Cartesian field vectors (Deg keyword is optional)
Bx,By,Bz = jrm33.ModelCart(x,y,z,Deg=13)

#or spherical polar ones
Br,Bt,Bp = jrm33.Model(r,theta,phi,Deg=13)
```

Please read the docstrings for `jrm33.Model()` and `jrm33.ModelCart()` using `help` or `?` e.g. `help(jrm33.Model)` .

There is also a test function which requires `matplotlib` to be installed:

```python
#evaluate the model at some R
jrm33.Test(R=0.85)
```

which produces this (based on figure 4 of Connerney et al. 2018):

![jrm09test.png](jrm33test.png)

## References

Connerney, J. E. P., Kotsiaros, S., Oliversen, R. J., Espley, J. R.,  Joergensen, J. L., Joergensen, P. S., et al. (2018). A new model of Jupiter's magnetic field from Juno's first nine orbits. Geophysical Research Letters, 45, 2590– 2596. https://doi.org/10.1002/2018GL077312

Connerney, J. E. P., Timmins, S., Oliversen, R. J., Espley, J. R., Joergensen, J. L., Kotsiaros, S., et al. (2022). A new model of Jupiter's magnetic field at the completion of Juno's Prime Mission. *Journal of Geophysical Research: Planets*, 127, e2021JE007055. [A New Model of Jupiter's Magnetic Field at the Completion of Juno's Prime Mission - Connerney - 2022 - Journal of Geophysical Research: Planets - Wiley Online Library](https://doi.org/10.1029/2021JE007055)
stroke-predictions-p0w11 1.0.3
Stroke Predictions packages
Requires Python: >=3.8

# Strokes and Heart Failure Predictions Model

## Users :
* Medical professionals
* Clinics / hospitals
* Medical devices

## Usage Description:

* After providing the necessary information to the health professionals of the user or inputting his or her personal & health information on the medical device.
In a first step our application will predict heart failure the probability of having a heart failure.
Next our model will use these results and the information provided by the user above to predict the probability of having a stroke. 
After it will display a detailed result about the patient status and possible precautions or advices to visit a professional

## Chosen features:
Our application will use both :
* Data ingestion and data ingestion to collect our data based on the user inputs.
* Pipeline retraining,  to retrain our model to make it even more accurate ( we are talking about heart failure and predictions so our model needs highly critical.)


## Dataset: 
* 11 clinical features for predicting stroke events:
https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset
* Heart Failure Prediction:
https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction




![Screenshot 2022-04-27 at 6 56 27 PM](https://user-images.githubusercontent.com/49615833/165579996-2b784dfc-404d-40c8-99ee-c8ec92497faa.png)