semvec/pypi-packages huggingface dataset:
methylsuite 1.4.0
Python-based Illumina methylation array processing and analysis software composite package
License: MIT
Keywords: methylation dna data processing epigenetics illumina

## Overview

This meta package will install the python packages that together make up the `methylsuite`:

- `methylprep` is a python package for processing DNA methylation data from Illumina arrays or downloading GEO datasets from NIH. It provides:
    - Support for Illumina arrays (27k, 450k, EPIC, mouse).
    - Support for analyzing public data sets from GEO (the NIH Gene Expression Omnibus is a database).
    - Support for managing data in `Pandas` DataFrames.
    - data cleaning functions during processing, including:
        - infer type-I channel switch
        - NOOB
        - poobah (p-value with out-of-band array hybridization, for filtering lose signal-to-noise probes)
        - qualityMask (to exclude historically less reliable probes)
        - nonlinear dye bias correction (AKA signal quantile normalization between red/green channels across a sample)
        - calculate beta-value, m-value, or copy-number matrix
        - large batch memory management, by splitting it up into smaller batches during processing
- `methylcheck` includes:
   - quality control (QC) functions for filtering out unreliable probes, based on the published literature,  sample outlier detection, plots similar to Genome Studio functions, sex prediction, and
   and interactive method for assigning samples to groups, based on array data, in a Jupyter notebook.
- `methylize` provides these analysis functions:
   - differentially methylated probe statistics (between treatment and control samples).
   - volcano plots (which probes are the most different).
   - manhattan plot (where in genome are the differences).

### Parts of `methylsuite`
![](https://github.com/FOXOBioScience/methylprep/blob/master/docs/methyl-suite.png?raw=true)

### Data Processing
![processing pipeline](https://github.com/FOXOBioScience/methylprep/blob/master/docs/methylprep-processing-pipeline.png?raw=true)

### Quality Control
![methylcheck pipeline](https://raw.githubusercontent.com/FOXOBioScience/methylcheck/master/docs/methylcheck_functions.png)

## Installation

This all-in-one command will install all three packages and their required dependencies.

```python
pip3 install methylsuite
```

This command was tested in a naive minimal conda environment, created thusly:

```python
conda create -n testsuite
```

Afterwards, I had to install `statsmodels` using conda apart from the other dependencies, like so:

```python
conda install statsmodels
pip3 install methylsuite
```

(Statsmodels uses additional C-compilers during installation)
AlgoAshutosh 0.0.1
Option Strategy Backtesting Library
License: MIT
Keywords: backtesting

This is a basic trading strategy backtesting library.
It can be used to backtest option strategies.

Three files are needed to backtest the option strategies.
One file with the futures data.
Another file with the call option data.
Third file with the put option data.

Ensure the below format for the files and the files should be of the csv format.

Futures file : Order of Columns : yyyy-mm-dd,	date,	month,	year,	expiry_date(of the format dd-(first three characters of month)-yy example : 26-May-22),	open,	high,	low,	close
Call Option File : Order of Columns : datetime	year	month	Date	expiry_date	Time	strike_price	open	high	low	close (example : 2022-01-03 9:15:00	2022	1	3	27-Jan-22	9:15:00	15500	1961.55	2010	1961.55	2004.45)
Put Option File : Order of Columns : datetime	year	month	date	expiry_date	time	strike_price	open	high	low	close (example : 2022-04-01 9:15:00	2022	4	1	28-Apr-22	9:15:00	15500	25.1	25.55	19.95	20.1)

Argument list for ironCondorStrategy :
self, putFile, callFile, spotFile, sellPut, sellCall, buyCall, buyPut, entry, exit, lotSize

example of code

from AlgoAshutosh import *

object = ironCondor()
object.ironCondorStrategy("/Users/username/Desktop/putOptionsFile.csv","/Users/username/Desktop/callOptionsFile.csv","/Users/username/Desktop/futuresFile.csv",100,100,200,200,1,10,50)
This means if spot price is 15000 then call sold at strike price 15000+100, call bought at 15000+200, put sold at strike price 15000-100, put bought at 15000-200, would be backtested.
The entry would be at the first day of the month and exit at expiry date - 10 days before.
So for example to exit position at expiry date pass that argument as 0.
50 represents the lot size.

Argument list for shortStraddleStrategy :
self, putFile, callFile, spotFile, entry, exit, lotSize

Argument list for shortStrangleStrategy :
self, putFile, callFile, spotFile, sellPut, sellCall, entry,exit, lotSize


CHANGELOG
=========

0.0.1 (09/07/2022)
------------------
-First Release
ontorunner 0.1.3
This is a wrapper project around various entity recognition (NER) tools.
License: MIT
Requires Python: >=3.8,<4.0

# ontoRunNER

This is a wrapper project around the following named entity recognition (NER) tools:
 - [OGER](https://github.com/OntoGene/OGER).
 - [spaCy](https://spacy.io)
   - using [sciSpaCy](https://scispacy.apps.allenai.org) pipeline 
   namely the CRAFT corpus (`en_ner_craft_md`) used by default. Others can be used as listed [here](https://github.com/allenai/scispacy#available-models)

## Documentation

[ontoRunNER using Sphinx](https://monarch-initiative.github.io/ontorunner/index.html)