semvec/pypi-packages huggingface dataset:
py-pod 2.0.3
Implementation of a Proper Orthogonal Decomposition (POD) method
Requires Python: >=3.10,<4.0

# Proper Orthogonal Decomposition

## Principle
The package contains a processing algorithm for decomposing an input using
a limited number of predefined signals from a bigger set.

Decomposition is performed by iterating projections onto the straight lines
generated by the reference signals.

The proposed algorithm takes a vector space approach. A signal, or more 
precisely its sequence of _N_ temporal samples, is mapped to a point *P* 
in a vector space of dimension _N_. A value taken by a signal *P* at 
sample time _t<sub>i</sub>_ becomes the coordinate of *P* along
the axis _t<sub>i</sub>_.

The set of reference signals represents a library the algorithm micks from
in order to synthetize or approximate any input. The reference points form a 
cloud in the space described above. A linear combination of appropriately 
selected reference points will approximate the target signal *S*.

## Use case: asset replication

In order to illustrate how the algorithm works, let's assume we
replicate a _fund_ using two assets _stock 1_ and _stock 2_, with
only 3 data points for past performances.

|             | Day 1 | Day 2 | Day 3 |
|-------------|-------|-------|-------|
| Stock 1     | 0.8   | 2     | 0     |
| Stock 2     | 1.2   | -0.5  | 0     |
| Fund        | 1     | -1    | 3     |
| Replication | 1     | -1    | 0     |

The chart below shows the first and final projections in the case of a 3D space:
3 daily performances for a 2 stocks-universe, plus a target fund to replicate.

The first projection is performed on the second stock (yellow), since it better
matches the fund performances than those of the first stock.

![Example showing the first and final projections in the case of a 3D space](docs/first_and_final_projections.png?raw=true "Example")

The interactive version of the chart above is available 
at [math3d.org](https://www.math3d.org/oXFLiVH54).

From a time perspective:
![Example showing the time series](docs/time-view.png?raw=true "Example")


Typical sizes for asset replication: 1000 dimensions (~5 years of daily data), 200 candidate assets
for use in replication, target portfolio with 5 assets.

## Documentation
Generated with PyDoctor:

`pydoctor --make-html --html-output=docs/api pod`


## Publication
`poetry build`

`poetry publish`
algpred2 1.4
A tool to predict allergenic proteins and mapping of IgE epitopes
Requires Python: >=3.6

# AlgPred2.0
# Introduction
AlgPred2.0 is developed for predicting, mapping and scanning allergen peptides. More information on AlgPred2.0 is available from its web server http://webs.iiitd.edu.in/raghava/. This page provides information about standalone version of AlgPred2.0. 

Minimum USAGE: Minimum ussage is "algpred2 -i peptide.fa" where peptide.fa is an input fasta file. This will predict Allergenic peptides in fasta format. It will use other parameters by default. It will save output in "outfile.csv" in CSV (comma seperated variables).

Full Usage: Following is complete list of all options, you may get these options by "algpred2 -h" 
```
algpred2 [-h] -i INPUT [-o OUTPUT] [-t THRESHOLD] [-m {1,2}] [-d {1,2}]
```

Please provide following arguments

optional arguments:
```
  -h, --help            show this help message and exit
  -i INPUT, --input INPUT
                        Input: protein or peptide sequence in FASTA format or
                        single sequence per line in single letter code
  -o OUTPUT, --output OUTPUT
                        Output: File for saving results by default outfile.csv
  -t THRESHOLD, --threshold THRESHOLD
                        Threshold: Value between 0 to 1 by default 0.3
  -m {1,2}, -- model Model
                        Model: 1: AAC based RF, 2: Hybrid, by default 1
  -d {1,2}, --display {1,2}
                        Display: 1:Allergen peptide, 2: All peptides, by
                        default 1
```

Input File: It allows users to provide input in two formats; i) FASTA format (standard) (e.g. peptide.fa) and ii) Simple Format. In case of simple format, file should have one one peptide sequence in a single line in single letter code (eg. peptide.seq). 

Output File: Program will save result in CSV format, in case user do not provide output file name, it will be stored in outfile.csv.

Threshold: User should provide threshold between 0 and 1, please note score is proportional to allergenic potential of peptide.

Models: In this program, two models have beeen incorporated;  i) Model1 for predicting given input peptide/protein sequence as Allergenic and Non-allergenic peptide/proteins using Random Forest based on amino-acid composition of the peptide/proteins; 

ii) Model2 for predicting given input peptide/protein sequence as Allergenic and Non-allergenic peptide/proteins using Hybrid approach, which is the ensemble of Random Forest+ BLAST+ MERCI. It combines the scores generated from machine learning (RF), MERCI, and BLAST as Hybrid Score, and the prediction is based on Hybrid Score.
streamlit-login-page 0.1.9
Streamlit component that allows you to do X
Requires Python: >=3.7

# streamlit-login-page

Streamlit component that allows you to do X

## Installation instructions 

```sh
pip install streamlit-login-page
```

## Usage instructions

```python
import streamlit as st

from streamlit_login_page import streamlit_login_page

value = streamlit_login_page()

st.write(value)