semvec/pypi-packages huggingface dataset:
HNSCPred 0.0.8
A computational approach tool to predict Head and Neck Cancer affected patients from their single cell RNA seq data.
Keywords: python,Head and neck cancer,Diagnosis,Single cell,Genomics


# HNSCPred :- A Tool for Identification of HNSCC from Single Cell Genome using Deep Learning

A computational approach tool to predict Head and Neck Cancer affected patients from their single cell RNA seq data.


## Introduction

Head and neck cancer, which encompasses a range of malignancies affecting the respiratory tract and upper digestive tract and also the seventh most common cancer in the world. 

This tool aims to use Artifical Neural Network (Deep Learning) model to classify Normal Control(NC) patients and Head and Neck Cancer (HNSCC) patients from
their single cell RNA seq data. The tool takes 10x single cell genomics data as input and predicts whether the patient is diseased or healthy with the help of highly trained model.

An excellent feature selection method called mRMR (Minimum Redundancy Maximum Relevance) was used to find out top 100 features which act as promising biomarkers
in classification and prediction of Normal and Diseased patients. Also further classified diseased patients into HPV+ and HPV-.

## Installation

Install my-project with pip

```bash
  pip install HNSCPred
```
    
You if previously installed please update the python package to the latest version using the command below

```bash
  pip3 install --upgrade HNSCPred
```
## Usage/Examples

After installation of the HNSCPred package in your python enviornment. Import the library using the below code.


```python
import HNSCPred
```
The HNSCPred comes with 1 inbuilt module. 

- Predict
Please import only 1 module in your python enviornment using the code below.

```python
from HNSCPred import Validation
```

After importing all the important pre requisites. You can follow the demo below for your case.


```python
import pandas as pd
df = pd.read_csv("Your file path here")

Validation.predict(df)

```




## Authors


- Akanksha Jarwal. 
- Aman Srivastava.
- Anjali Dhall.
- Sumeet Patiyal.  
- [Prof. G.P.S. Raghava](https://webs.iiitd.edu.in/raghava/)
AlzScPred 0.0.3
A python based tool for prediction patient diagnosis i.e. (Alzheimer or Healthy) from single cell data.
Keywords: python,Alzheimers,Diagnosis,Single cell,Genomics


# AlzScPred :- A Tool for Identification of Alzheimer's from Single Cell Genome using Deep Learning

A computational approach tool to predict Alzheimer's affected patients from their single cell RNA seq data.


## Introduction

Alzheimer's Disease is progressing as the most common cause of neurological disorder worldwide. 

This tool aims to use Artifical Neural Network (Deep Learning) model to classify Normal Control(NC) patients and Alzheimer’s Disease(AD) patients from
their single cell RNA seq data. The tool takes 10x single cell genomics data as input and predicts whether the patient is diseased or healthy with the help of highly trained model.

An excellent feature selection method called mRMR (Minimum Redundancy Maximum Relevance) was used to find out top 100 features for classification. 
Followed by Incremental Feature Selection (IFS) which led to identification of 35 conserved genes which act as promising biomarkers
in classification and prediction of Normal and Diseased patients.

Please read/cite the content about the AlzScPredict for complete information including algorithm behind the approach.
## Installation

Install my-project with pip

```bash
  pip install AlzScPred
```
    
You if previously installed please update the python package to the latest version using the command below

```bash
  pip3 install --upgrade AlzScPred
```
## Usage/Examples

After installation of the AlzScPredict package in your python enviornment. Import the library using the below code.


```python
import AlzScPred
```
The AlzScPredict comes with 1 inbuilt module . 

- Prediction Module

Please import the modules in your python enviornment before executing the code below.

```python
from AlzScPred import Validation

```

After importing all the important pre requisites. You can follow the demo below for your case.


```python
import pandas as pd
df = pd.read_csv("Your file path here")

# Prediction:- Execute the code below to get the output. It takes 1 argument i.e the dataframe with features in columns and rows as cells.

Validation.predict_patient(df)

```

Note: Please make sure that your single cell data file is prepared in the above example.csv format. And the file should also contain the read count data for the selected 35 genes in the above 35_genes.txt file.

## Output

The output of the code can be viewed in your python output terminal. Example can be seen in the output.png file.
## Authors

- [Aman Srivastava](https://github.com/AmanSriv97).
- Akanksha Jarwal. 
- Anjali Dhall.
- Sumeet Patiyal.
- [Prof. G.P.S. Raghava](https://webs.iiitd.edu.in/raghava/)
seira 1.1.2
SEIRA: DATA PROCESSING FOR SEISMIC REFRACTION AND MASW
Keywords: python,seismic refraction tomography,masw

# SEIRA
A python module for doing routine seismic near surface data processing

# Module Feature:
[1. Multichannel Analysis of Surface Wave]
  * [1.1 Plot data]
  * [1.2 Dispersion Curve]
  * [1.3 Picking Dispersion]
  * [1.4 Inversion]
pavan-housing-project 0.1
'A housing package to predict the price of houses in california'
Requires Python: >=3.7

# Guid for Housing-Project

## Cloning the Repository
To clone the repository, you will need to have git installed on your machine. You can check if git is installed by running the command git --version in your terminal. If git is not installed, you can download it from the official website.

Once git is installed, you can clone the repository by running the following command:

```
git clone https://github.com/pavanbongu7/mle-training-mlflow>.git
```
## Setting up the Environment
To set up the environment, you will need to have conda installed on your machine. You can check if conda is installed by running the command conda --version in your terminal. If conda is not installed, you can download it from the official website.

Once conda is installed, you can create the environment by running the following command:
```
conda env create -f env.yml
```
This command will create an environment based on the specifications in the env.yml file.

To activate the environment, run the following command:
```
conda activate mle-dev
```
## Installing the Package
To install the package, you can use the following command:
```
pip install .
pip install housing_package
```
django-init-admin-command 0.1.0
A Django admin command to create an admin user if no user exists

# django_init_admin
Django admin command to create an admin/admin superuser if no user exists


The main purpose is to init an admin user for the applications you deliver. Your end-user will be able to login with admin (pwd: admin) and manage his users.

# usage 
* Install the command in your python environment:
  ```bash
  pip install django_init_admin_command
  ```
* Include this command in you project settings.py file:
   ```python
   INSTALLED_APPS = [
    # ...
    'init_admin_command',
    # ...
   ```
* Create the admin user in a deploy / install script:
   ```bash
   python manage.py initadmin
   ```
pydt-range 1.1.0
pydt-range is purely python mini library that allows to iterate over datetime objects with a specified step (similar to built-in range())
Requires Python: >=3.7,<4.0

# Python Datetime Range
[![tests](https://github.com/skroll182/pydt-range/actions/workflows/test.yml/badge.svg)](https://github.com/skroll182/datetime-range/actions/workflows/test.yml)

`pydt-range` is purely python mini library that allows to iterate over `datetime` objects with a specified step (similar to built-in `range()`)

## Installation
### Pip
```bash
pip install pydt-range
```
### Poetry
```bash
poetry add pydt-range
```

## Usage
### With default step

```python
from datetime import datetime

from pydt_range import datetime_range

start_dt = datetime(2022, 1, 1)
end_dt = datetime(2022, 1, 10)

for dt in datetime_range(start_dt, end_dt):  # Default step is timedelta(days=1)
    print(dt)
"""
2022-01-01 00:00:00
2022-01-02 00:00:00
2022-01-03 00:00:00
2022-01-04 00:00:00
2022-01-05 00:00:00
2022-01-06 00:00:00
2022-01-07 00:00:00
2022-01-08 00:00:00
2022-01-09 00:00:00
"""
```
### With custom step

```python
from datetime import datetime
from dateutil.relativedelta import relativedelta

from pydt_range import datetime_range

start_dt = datetime(2022, 1, 1)
end_dt = datetime(2022, 1, 10)
step = relativedelta(hours=6)

for dt in datetime_range(start_dt, end_dt, step):
    print(dt)
"""
2022-01-01 00:00:00
2022-01-01 06:00:00
2022-01-01 12:00:00
2022-01-01 18:00:00
2022-01-02 00:00:00
2022-01-02 06:00:00
2022-01-02 12:00:00
2022-01-02 18:00:00
2022-01-03 00:00:00
2022-01-03 06:00:00
2022-01-03 12:00:00
2022-01-03 18:00:00
2022-01-04 00:00:00
2022-01-04 06:00:00
2022-01-04 12:00:00
2022-01-04 18:00:00
2022-01-05 00:00:00
2022-01-05 06:00:00
2022-01-05 12:00:00
2022-01-05 18:00:00
2022-01-06 00:00:00
2022-01-06 06:00:00
2022-01-06 12:00:00
2022-01-06 18:00:00
2022-01-07 00:00:00
2022-01-07 06:00:00
2022-01-07 12:00:00
2022-01-07 18:00:00
2022-01-08 00:00:00
2022-01-08 06:00:00
2022-01-08 12:00:00
2022-01-08 18:00:00
2022-01-09 00:00:00
2022-01-09 06:00:00
2022-01-09 12:00:00
2022-01-09 18:00:00
"""
```