semvec/pypi-packages huggingface dataset:
psearch 0.0.4
PSearch: ligand-based pharmacophore modeling and screening
Requires Python: >=3.6

# PSearch - 3D ligand-based pharmacophore modeling

PSearch is a tool to generate 3D ligand-based pharmacophore models and perform virtual screening with them.

## Installation

```bash
pip install psearch
```

## Dependency

`pmapper >= 0.3.1`

## Example

### Creation of ligand-based pharmacophore models
It is recommended to create an empty dir which would be your `$PROJECT_DIR` and copy an input file to that location.  
There are two steps of pharmacophore model generation.  

1. Dataset preparation. 

```python
prepare_datatset -i $PROJECT_DIR/input.smi -c 4
```
`-i` - path to the input file  
`-c` - number of CPUs to use  
There are some other arguments which one can use. Invoke script with `-h` key to get full information.  

The script takes as input a tab-separated SMILES file containing `SMILES`, `compound id`, `activity` columns without a header. 
The third column should contain a word `active` or `inactive`.
The script splits input compounds on active and inactive subsets, generates stereoisomers and conformers, creates databases of active and inactive compounds with labeled pharmacophore features.  

2. Model building.  

```python
psearch -p $PROJECT_DIR -c 4
```
`-p` - path to the project dir  
`-c`- number of CPUs to use

There are two other arguments which are worth to mention:  
`-t` - threshold for compound clustering to create training sets. Default: 0.4.  
`-ts` - strategies to create training sets. `1` - a single training set will be created from centroids of individual clusters (capturing a common binding mode for all compounds). `2` - multiple training sets will be created, one per cluster (capturing individual binding modes for compound clusters).
By default both strategies are used.  

### Virtual screening with pharmacophore models 

1. Database creation. 

The script takes as input a tab-separated SMILES file containing `SMILES` and `compound id` columns.

```python
prepare_db -i compounds.smi -o compounds.db -c 4 -v
```
`-i` - path to the input file  
`-c` - number of CPUs to use
`-v` - print progress  
There are other arguments available to tweak database generation. To get the full list of arguments invoke `-h` key.

2. Virtual screening.

```python
screen_db -d compounds.db -q $PROJECT_DIR/models/ -o screen_results/ -c 4
```
`-d` - input generated SQLite database  
`-q` - pharmacophore model or models or a directory with models   
If a directory would be specified all pma- and xyz-files will be recognized as pharmacophores and will be used for screening  
`-o` - path to an output directory if multiple models were supplied for screening or a path to a text file    
`-c`- number of CPUs to use

## Documentation

All scripts have `-h` argument to retrieve descriptions of all available options and arguments.

## Authors
Alina Kutlushina, Pavel Polishchuk

## Citation
Ligand-Based Pharmacophore Modeling Using Novel 3D Pharmacophore Signatures  
Alina Kutlushina, Aigul Khakimova, Timur Madzhidov, Pavel Polishchuk  
*Molecules* **2018**, 23(12), 3094  
https://doi.org/10.3390/molecules23123094

## License
BSD-3 clause
vantage 0.3.0a5
Package and utilities for distributed learning
Requires Python: >=3

[![Coverage Status](https://coveralls.io/repos/github/IKNL/vantage/badge.svg?branch=master)](https://coveralls.io/github/IKNL/vantage?branch=master)
[![Codacy Badge](https://api.codacy.com/project/badge/Grade/bcde6ed5c77440c6969462bfead0774c)](https://app.codacy.com/app/frankcorneliusmartin/vantage?utm_source=github.com&utm_medium=referral&utm_content=IKNL/ppDLI&utm_campaign=Badge_Grade_Dashboard)
[![PyPI version](https://badge.fury.io/py/ppDLI.svg)](https://badge.fury.io/py/vantage)
[![Build Status](https://travis-ci.com/IKNL/ppDLI.svg?branch=master)](https://travis-ci.com/IKNL/vantage)
# Introduction
The growing complexity of cancer diagnosis and treatment requires data sets that are larger than currently available in a single hospital or even in cancer registries. However, sharing patient data is difficult due to patient privacy and data protection needs. Privacy preserving distributed learning technology has the potential to overcome these limitations. In this setting organizations can collaborate by exchanging aggregated data and/or statistics while keeping the underlying data safely on site and undisclosed. This repository contains software (and instructions) to setup a distributed learning infrastructure.

For an overview of the architecture and information on how to use the infrastructure, please see [https://distributedlearning.ai](https://distributedlearning.ai). For documentation please see [https://distributedlearning.readme.io](https://distributedlearning.readme.io).

## Hardware and software requirements
### Server
Running the central server requires a (virtual) machine that:
* is accessible from the internet
* has Python 3.6+ and the vantage package installed (`pip install vantage`)

### Node
Running a node/site requires a (virtual) machine that has:
* Python 3.6+ and the vantage package installed (`pip install vantage`)
* Docker CE installed (the user running the node software needs to have the proper permissions to perform docker commands)
* Access to a local data store
* Access to the internet and/or central server

## Installation
See the [readme.io](https://docs.distributedlearning.ai) for detailed instructions on how to install the server and nodes.
outlier-asharma-3027 1.1
A python package for removing outliers from a dataset using InterQuartile Range (IQR)
Requires Python: >=3.6

# Outlier Removal Using InterQuartile Range

**Project 2 : UCS633**


Submitted By: **Abhishek Sharma - 101703027**


## InterQuartile Range (IQR) Description

Any set of data can be described by its five-number summary. These five numbers, which give you the information you need to find patterns and outliers, consist of:

The minimum or lowest value of the dataset.
<br>
The first quartile Q1, which represents a quarter of the way through the list of all data.
<br>
The median of the data set, which represents the midpoint of the whole list of data.
<br>
The third quartile Q3, which represents three-quarters of the way through the list of all data.
<br>
The maximum or highest value of the data set.
<br>
<br>
These five numbers tell a person more about their data than looking at the numbers all at once could, or at least make this much easier.

## Calculation of IQR

IQR = Q3 – Q1
<br>
MIN = Q1 - (1.5*IQR)
<br>
MAX = Q3 + (1.5*IQR)
<br>


## Sample dataset

Marks | Students 
:------------: | :-------------:
3 | Student1
57 | Student2
65 | Student3
98 | Student4
43 | Student5
44 | Student6
54 | Student7
99 | Student8
1 | Student9

<br>


## Output Dataset after Removal

Marks | Students 
:------------: | :-------------:
57 | Student2
65 | Student3
98 | Student4
43 | Student5
44 | Student6
54 | Student7

<br>

It is clearly visible that the rows containing Student1, Student8 and Student9 have been removed due to them being Outliers.


## License
[MIT](https://choosealicense.com/licenses/mit/)
statannot 0.2.3
add statistical annotations on an existing boxplot/barplot generated by seaborn.
Requires Python: >=3.5

## What is it

Python package to optionnally compute statistical test and add statistical annotations on an existing boxplot/barplot generated by seaborn.

## Features

- Single function to add statistical annotations on an existing boxplot/barplot generated by seaborn boxplot.
- Integrated statistical tests (binding to `scipy.stats` methods):
    - Mann-Whitney
    - t-test (independent and paired)
    - Welch's t-test
    - Levene test
    - Wilcoxon test
    - Kruskal-Wallis test
- Smart layout of multiple annotations with correct y offsets.
- Annotations can be located inside or outside the plot.
- Format of the statistical test annotation can be customized: star annotation, simplified p-value, or explicit p-value.
- Optionally, custom p-values can be given as input. In this case, no statistical test is performed.

## Installation

The latest stable release can be installed from PyPI:

```python
pip install statannot
```
You may instead want to use the development version from Github:

```python
pip install git+https://github.com/webermarcolivier/statannot.git
```

## Documentation

See example jupyter notebook `example/example.ipynb`.

## Usage

Here is a minimal example:

```python
import seaborn as sns
from statannot import add_stat_annotation

df = sns.load_dataset("tips")
x = "day"
y = "total_bill"
order = ['Sun', 'Thur', 'Fri', 'Sat']
ax = sns.boxplot(data=df, x=x, y=y, order=order)
test_results = add_stat_annotation(ax, data=df, x=x, y=y, order=order,
                                   box_pairs=[("Thur", "Fri"), ("Thur", "Sat"), ("Fri", "Sun")],
                                   test='Mann-Whitney', text_format='star',
                                   loc='outside', verbose=2)
test_results
```

More examples are available in the jupyter notebook `example/example.ipynb`.


## Examples

![Example 1](/example/example_non-hue_outside.png)

![Example 2](/example/example_hue_layout.png)

## Requirements

+ Python >= 3.5
+ numpy >= 1.12.1
+ seaborn >= 0.8.1
+ matplotlib >= 2.2.2
+ pandas >= 0.23.0
+ scipy >= 1.1.0
missingval-asharma-3027 1.0
A python package to handle Missing Values using SimpleImputer Class
Requires Python: >=3.6

# Handling Missing Values using SimpleImputer Class

**Project 3 : UCS633**


Submitted By: **Abhishek Sharma - 101703027 **


## SimpleImputer Class

SimpleImputer is a scikit-learn class which is helpful in handling the missing data in the predictive model dataset.
It replaces the NaN values with a specified placeholder.
It is implemented by the use of the SimpleImputer() method which takes the following arguments:
<br>
missing_data : The missing_data placeholder which has to be imputed. By default is NaN.
<br>
stategy : The data which will replace the NaN values from the dataset. The strategy argument can take the values – ‘mean'(default), ‘median’, ‘most_frequent’ and ‘constant’.
<br>
fill_value : The constant value to be given to the NaN data using the constant strategy.



## How to use this package:

missingval-asharma-3027 can be run as shown below:


### In Command Prompt
```
>> missingValues dataset.csv
```
<br>


## Sample dataset

a | b | c 
:------------: | :-------------: | :-------------:
NaN | 7 | 0
0 | NaN | 4
2 | NaN | 4
1 | 7 | 0
1 | 3 | 9
7 | 4 | 9
2 | 6 | 9
9 | 6 | 4
3 | 0 | 9
9 | 0 | 1

<br>


## Output Dataset after Handling the Missing Values

a | b | c 
:------------: | :-------------: | :-------------:
3.777778  | 7 | 0
0 | 4.125  | 4
2 |  4.125 | 4
1 | 7 | 0
1 | 3 | 9
7 | 4 | 9
2 | 6 | 9
9 | 6 | 4
3 | 0 | 9
9 | 0 | 1

<br>

It is clearly visible that the rows,columns containing Null Values have been Handled Successfully.


## License
[MIT]