semvec/pypi-packages huggingface dataset:
mergecat 0.0.7
Automatic voice line clip extractor
Requires Python: >=3.6

# mergecat

Semi-automatic voice line clip extractor

## How to use

1. Install the package via `pip install mergecat` and make sure you have `ffmpeg`
2. Create a timing file in a format like this (using seconds)  
   Example:
   ```
   2 24
   60 75
   92 103
   128 135
   ```
3. Run mergecat via `mergecat video_file timing_file`  
   Example: `mergecat something.mp4 timing.txt`

## Goals

- Automate the process instead of requiring timing files
- Allow batch processing
- Handle temporary file cleanup better
morningstar-stmt 0.0.2
Morningstar Financials Statement Downloader
Requires Python: >=3.6

morningstar-stmt
===============

#### Morningstar Financial Statement Downloader

morningstar-stmt is a simple Python module for downloading financial statement from [www.morningstar.com](http://www.morningstar.com/).

- Current Version: v0.0.2
- Version Released: 02/23/2020

Overview
--------
**morningstar-stmt** depends on [Selenium](https://selenium-python.readthedocs.io/installation.html) to simulate browser operations by following steps: _(goto) ticker's page_ -> _(click) statment tab_ -> _(click) export excel_ to download financial statments.

Features:

 - Support account login.
 - Full stock symbol list.
 - Download financial statmenst in xls format.
 - Only support Chrome webdriver.

Installation
-------------
- morningstar-stmt runs on Python >= 3.6.
- The package depends on [Selenium](https://selenium-python.readthedocs.io/installation.html) to work.

##### 1. Install morningstar-stmt using pip:

    $ pip install morningstar-stmt



##### 2. Install [Chrome webdriver](https://sites.google.com/a/chromium.org/chromedriver/downloads) and place it in **/usr/bin** or **/usr/local/bin**.


Usage Examples
--------------
### 1. Download financial statement for specific ticker

    import morningstar_stmt as ms
    browser = ms.MorningStarStmtBrowser()
    browser.download_stmt('xnas', 'AAPL')
    
##### Login Morningstar account before download

    browser.login('xxiiaaon', 'password')
    browser.download_stmt('xnas', 'AAPL')
    # market code xnas for NASDAQ and xnys for NYSE

##### Specify download directory and log level

    browser = ms.MorningStarStmtBrowser(download_dir='/User/xxiiaaon/stmt', log_level=logging.Info)
    
    
### 2. Download financial statements with ticker list

    import morningstar_stmt as ms
    ms.download_stmt(['xnas,AAPL', 'xnax,AAPL')])
    
##### Use predefined list

    import morningstar_stmt as ms
    from morningstar_stmt import tickerlist as tl
    ms.download_stmt(tl.all)
    # other list like tl.xnas for NASDAQ and tl.xnys for NYSE
    
##### Login when download with ticker list

    ms.download_all_stmt(ms.MorningstarAccount('xxiiaaon', 'password'), tickerlist.all)
pytorch-lr-tuner 0.0.2
Optimum learning rate finder for PyTorch Models
Requires Python: >=3.6

# PyTorch Learning Rate Tuner

Python package to plot loss against varied learning rate for PyTorch neural network models and finding optimal learning rate for specific optimizer.

### Installation:

    pip install pytorch-lr-tuner

### Dependency:

* Python 3.6
* Numpy
* Pandas
* Matplotlib
* PyTorch

### Example:

The package includes `LearningRateFinder` class which can be instantiated with pytorch model reference, optimizer, criterion and training set. The `fit()` method searches for optimal learning rate with multiplicative increment and smoothing with exponential weighted average and bias correction and the visualization of this log can be obtained through calling `plot()` method. 

    from pytorch_lr_tuner import LearningRateFinder

    ESTIMATOR_CONFIG = {'input_shape': 21, 'output_shape': 1, 'hidden_units': [32, 64, 16]}

    binary_crossentropy = nn.BCELoss()

    lr_finder = LearningRateFinder(estimator=VanillaNet, config=ESTIMATOR_CONFIG, optimizer='sgd', criterion=binary_crossentropy, train_set=train_set, val_set=val_set)

    lr_finder.fit()
    lr_finder.plot()

### Output:

<img src='loss_vs_lr.png'> <br>

Here, the learning rate with steepest gradient in loss can be inferred as an optimal one for this specific architecture.