semvec/pypi-packages huggingface dataset:
gpscalc-mjeffryes 0.0.5
Calculates Gait Profile Score

# Gait Profile Score Calculator

## Calculate

from gpscalc.calculator import GPSData

subpath = "path to subject kinematics json"
refdir = "path to directory of referecne kinematics jsons"

GPS = GPSData(subpath, refdir)

# Print GPS data for subject relative to the reference group
print(GPS.GPS_SCORE)

# Plot and save the Movement Analysis Profile
subjname = "name or reference for the subject"
outputdir = "path to directory where the plot is to be saved"

GPS.plot_data(subjname, outputdir)


## Check required json structure for kinematics

from gpscalc.calculator import create_example_kinematicsJSON

# Creates a json file in the correct format locally
create_example_kinematicsJSON()

## Check the kinematics variable labels used
from gpscalc.calculator import check_variable_names

# Prints the kinematic variable labeles used and returns a list of them
check_variable_names()
py4tfidf 0.0.4
Term Frequency – Inverse Document Frequency (TF-IDF) Python Library
Requires Python: >=3.7

# py4tfidf

Term Frequency â€“ Inverse Document Frequency (TF-IDF) Python Library

## Getting Started

This project is simply an implementation of TF-IDF algorithm in python programming language.

### Prerequisites

Numpy


### Installing

The easiest way to install py4tfidf is by using pip

```
pip install py4tfidf
```

### Usage
There are 2 public methods of ```Tfidf``` class. It is ```vectorize_train``` and ```vectorize_test```. ```vectorize_train``` used to build the corpus, calculate idf based on training text, and transform it into a usable vector by multiplying its tf and its idf, while ```vectorize_test``` is just simply transforming the test text into a usable vector by multiplying its tf with previously obtained if. Both ```vectorize_train``` and ```vectorize_test``` take 1 argument namely x_train and x_text respectively. Because tokenizing is usually done in the text preprocessing phase, we assume you tokenize your text on your own, so the argument for ```vectorize_train``` and ```vectorize_test``` should be a list of tokenized text.
```
from py4tfidf.vectorizer import Tfidf
vec = Tfidf()
x_train = [['i','love', 'python'],['natrual','language','processing','is','fun'],['python','is','fun']]
x_test = [['python','language','is','fun'],['im','learning','natrual','language','processing']]
x_train = vec.vectorize_train(x_train)
x_test = vec.vectorize_test(x_test)
```
pynse 0.1.4
Library to extract realtime and historical data from NSE website
Keywords: nse,nsepy,stock markets,national stock exchange

# pynse

Library to extract realtime and historical data from NSE website.EOD data like bhavcopy and option chain are also saved to directory. First run will create directories for storing the data and will download the index symbols.

## Installation

This module is installed via pip:

```
pip install pynse
```

### Prerequisites

Python 3.7


## Using the API

### Overview
There is only one class in the whole library `Nse`. 

### Logging
The whole library is equipped with python's `logging` moduele for debugging. If more debug information is needed, enable logging using the following code.

```python
import logging
logging.basicConfig(level=logging.DEBUG)
```

```python
from pynse import *


nse=Nse()
```

### Get Market Status

```python
nse.market_status()
```

### Get Symbol Information
```python
nse.info('SBIN')
```

### Get Quote
Get realtime quote for EQ, FUT and OPT. If no expiry date is provided for derivatives, returns date for nearest expiry.

for cash
```python
nse.get_quote('RELIANCE')
```
for futures
```python
nse.get_quote('TCS', segment=Segment.FUT, expiry=dt.date( 2020, 6, 30 ))
```
for options
```python
nse.get_quote('HDFC', segment=Segment.OPT, optionType=OptionType.PE, strike=1800.)
```

### Bhavcopy for Cash
download bhavcopy from nse
or
read bhavcopy if already downloaded

```python
nse.bhavcopy()
``` 
or
```python
nse.bhavcopy(dt.date(2020,6,17))
``` 

### Bhavcopy for F&O
download bhavcopy from nse
or
read bhavcopy if already downloaded
```python
nse.bhavcopy_fno()
```
or 
```python
nse.bhavcopy_fno(dt.date(2020,6,17))
```

### Pre Open data
get pre open data from nse
```python
nse.pre_open()
```

### Option Chain

Downloads the option chain or reads if already downloaded.
if req_date is not specified returns latest available option chain from nse website

This returns dictonary containing
timestamp as str
option chain as pd.Dataframe
expiry_list as list
```python
nse.option_chain('INFY')
```
or
```python
nse.option_chain('INFY',expiry=dt.date(2020,6,30))
```

### FII and DII Data
get FII and DII data from nse
```python
nse.fii_dii()
```

### Historical Data
get historical data from nse
symbol index or symbol
```python
nse.get_hist('SBIN')
```
or
```python
nse.get_hist('NIFTY 50', from_date=dt.date(2020,1,1),to_date=dt.date(2020,6,26))
```

### Realtime Index
Get realtime index value
```python
nse.get_indices(IndexSymbol.NiftyInfra)
```
or
```python
nse.get_indices(IndexSymbol.Nifty50)
```

### Top Gainers and Losers
presently works only for SECURITIES IN F&O

```python
nse.top_gainers(index=IndexSymbol.FnO, length=10)
```
or
```python
nse.top_losers(index=IndexSymbol.FnO, length=10)
```
### Update Symbol Lists
Update list of symbols.No need to run frequently, its only required when constituent of an index is changed or list of securities in fno are updates

```python
nse.update_symbol_list()
```

## License

Â© 2020 Anoop Jangra

This repository is licensed under MIT license.
See LICENSE for details.