semvec/pypi-packages huggingface dataset:
nwbn-conversion-tools 0.3.3
Convert data to nwb
Keywords: nwb

# nwbn-conversion-tools
Shared tools for converting data from various formats to NWB:N 2.0

[![PyPI version](https://badge.fury.io/py/nwbn-conversion-tools.svg)](https://badge.fury.io/py/nwbn-conversion-tools)

## Installation
To install **nwbn_conversion_tools** directly in an existing environment:
```
$ pip install nwbn-conversion-tools
```

Alternatively, to clone the repository and set up a conda environment, do:
```
$ git clone https://github.com/NeurodataWithoutBorders/nwbn-conversion-tools
$ conda env create -f nwbn-conversion-tools/make_env.yml
$ source activate nwbn_conversion
```

## GUI
After activating the correct environment, nwbn_conversion_tools GUI can be imported and run from the python interpreter.
```python
from nwbn_conversion_tools.gui.nwbn_conversion_gui import nwbn_conversion_gui

# YAML metafile
metafile = 'metafile.yml'

# Conversion module
conversion_module = 'conversion_module.py'

# Source files path
source_paths = {}
source_paths['source_file_1'] = {'type': 'file', 'path': ''}
source_paths['source_file_2'] = {'type': 'file', 'path': ''}

# Other options
kwargs = {'option_1': True, 'option_2': False}

nwbn_conversion_gui(
    metafile=metafile,
    conversion_module=conversion_module,
    source_paths=source_paths,
    kwargs_fields=kwargs,
)
```
[Here](https://github.com/NeurodataWithoutBorders/nwbn-conversion-tools/tree/master/nwbn_conversion_tools/gui) you can find templates for `metafile.yml` and `conversion_module.py`.


## optical physiology
### processing
* [CELLMax](https://github.com/NeurodataWithoutBorders/nwbn-conversion-tools/blob/master/nwbn_conversion_tools/ophys/processing/CELLMax/README.md)
emdpy-z 0.0.3
Empirical Mode Decomposition, EMD
Requires Python: >=3.6

# Empirical Mode Decomposition, EMD
## Installation
```python
    pip install emdpy-z
```
## Quick Start
```python
    # Import 
    from emdpy.emd import *
    import numpy as np

    # Input
    t = np.linspace(0, 1, 1000)
    modes = np.sin(2 * np.pi * 5 * t) + np.sin(2 * np.pi * 10 * t)
    y = modes + t
    x, y = t, y

    # EMD
    imfs, res, (mean, std) = emd(x, y, mode=0, ps=len(x), max_iter_num = 20)

    # Plot the figs
    plot_imfs(imfs, res, col=1)
```
autoth 0.0.1
Automatic threshold optimization
Requires Python: >=3.6

# Autoth: Automatic optimzie hyper parameters

Autoth is a Python toolbox to automatically optimize hyper parameters to maximize scores. For example, Autoth can optimize hyper parameters to maximize F1 score in a classification task. In practice, Autoth numerically calculate gradients of scores over hyper parameters. Then, the hyper parameters are updated according to the gradients iteratively. Please see [1] for details.

## Install
```
pip install autoth
```

## Example
```
python3 example.py
```

## Results
<pre>
------ Manually selected hyper parameters ------
Hyper parameters: [0.3, 0.3, 0.3]
Score: 0.5556

------ Automatic optimized hyper parameters ------
Optimizing hyper parameters ...
learning rate: 0.010, total epochs: 10
    Hyper parameters: [0.3, 0.31, 0.29], score: 0.5556
    Epoch: 0, Time: 0.0181 s
    Hyper parameters: [0.3, 0.3197, 0.2801], score: 0.5556
    Epoch: 1, Time: 0.0178 s
    Hyper parameters: [0.3, 0.3237, 0.2702], score: 0.5714
    Epoch: 2, Time: 0.0212 s
    Hyper parameters: [0.3, 0.3245, 0.263], score: 0.6099
    Epoch: 3, Time: 0.0144 s
    Hyper parameters: [0.3, 0.3232, 0.2548], score: 0.6099
    Epoch: 4, Time: 0.0142 s
    Hyper parameters: [0.3, 0.3204, 0.2464], score: 0.6099
    Epoch: 5, Time: 0.0151 s
    Hyper parameters: [0.3, 0.3164, 0.2382], score: 0.6099
    Epoch: 6, Time: 0.0159 s
    Hyper parameters: [0.3, 0.316, 0.2302], score: 0.5940
    Epoch: 7, Time: 0.0143 s
    Hyper parameters: [0.3, 0.318, 0.2226], score: 0.5940
    Epoch: 8, Time: 0.0150 s
    Hyper parameters: [0.3, 0.3186, 0.2152], score: 0.6099
    Epoch: 9, Time: 0.0177 s

Optimized hyper parameters: [0.3, 0.3186, 0.2152]
Score: 0.6099
</pre>

## Cite
[1] Kong, Qiuqiang, Yong Xu, Wenwu Wang, and Mark D. Plumbley. "Sound Event Detection of Weakly Labelled Data with CNN-Transformer and Automatic Threshold Optimization." arXiv preprint arXiv:1912.04761 (2019).
nvision 1.5.0
Nipa Cloud Platform: Nvision Python SDK
Requires Python: >=3.5
Keywords: Machine Learning,Deep Learning,Computer Vision,Nipa Cloud Service

# Nvision Python SDK

Nipa Cloud Visual-based Machine Learning Services Client Library

Nvision enables machine learning services to extract insightful information from images with simple and easy to use APIs. Provide real-time video analysis with stream processing service. Get the most out of your visual information with the help of Nipa cloud experts. Offer fully customized algorithms to your specifications.