semvec/pypi-packages huggingface dataset:
seq-align-two-vars 0.1.4
seq_align_two_vars
Requires Python: >=3.7

# seq_align_two_vars

## General info
This project is created by Jeffrey Cai under supervised by Nathan Ryan, Professor in Mathematics department of Bucknell University.  
It is used to align sequences with non-uniform varaibales like time or distance. It is based on the idea of Alexandar Murph(https://github.com/cran/sawnuti). We implemented his method in this python package and not only to analysis non-uniform time intervals, but including analysis of more than one varaibles like both time and dis.

## Technologies
Project is created with:
* numpy version: 1.22.4
* pandas version: 1.5.0
* matplotlib version: 3.6.0
* networkx version: 3.0
* scipy version: 1.10.0
* sklearn version: 1.2.1

## Data import:
1. use make_sample
2. read in datafile
3. input networks, generate random walk data

## Setup
To run this project, install it locally using pip install:

```
$ pip install seq-align-two-vars
```

## Reference guide
See https://caidog1129.github.io/seq_align_two_vars/seq_align_two_vars.html
consortia-commons 1.0.7
The common code supporting the web services in the consortia.
Requires Python: >=3.6

# commons

This repository contains the code supporting several restful microservices used by the consortia.

### Getting Started

The consortia commons library is available through PyPi via the command:

```bash
pip install consortia-commons
```

The consortia-commons requirements can be found [here](requirements.txt)


### Contents

The code includes:
- UBKG: For querying Unified Biomedical Knowledge Graph (UBKG) application interface
- REST: For making standardised rest responses


### Coding Conventions
- Please use:
  - `snake_case` to name methods and variables. 
  - `PascalCase` for class names.
- Do follow any additional code formatting and styles as seen in the project
WaterMap 1.0.1
Water Mapping from Remote Sensing Images

# WaterMap Library

### Watnet and DeepWaterMap Method for Water Detection in Satellite Imagery.

This library contains Watnet and DeepWaterMap methods for the detction of surface water in satellite imagery.

## Input Image:

Input image should be a six band tif image containing the following bands: red, green, blue, near infrared, shortwave infrared 1 and shortwave infrared 2 (the band number of 2, 3, 4, 8, 11, and 12 of sentinel-2 image).

## How to use this library:

### --Step 1

Enter the following commands for installing the library, and then configure the python and deep learning environment.
The deep learning software used in this repo is Tensorflow 2.
  ~~~console
  pip install WaterMap
  ~~~

### -- Step 2

- Output in array form:
In the pytest.py file provide the input image path and run the file to get ouptut in array form array
- Output in png form:
In the pytest.py file provide input to the "out_png_path" to store the output path to a particular path.
- Output in tif form:
In the pytest.py file provide input to the "out_tif_path" to store the output path to a particular path.
- All the output can also be achieved simultaneously by providing both the png and tif output path.
- Example Code for watnet method:
~~~console
  wn.predict(img_path, out_png_path='sample_images/dwm_coastal1.png')
~~~
- Example Code for deepwatermap method:
~~~console
 dwm.predict(img_path, out_png_path='sample_images/dwm_coastal1.png')
~~~

## Thresholding Option:

There is also a option to provide the threshold to both th methods as an input:

- Example Code for deepwatermap method:
- default threshold for watnet is 0.5 and for deepwatermap is 0.35
~~~console
 dwm.predict(img_path, out_png_path='sample_images/dwm_coastal1.png',threshold='0.5')
~~~

### Downloading the Image from Google Earth Engine on Google Drive
- On google earth engine open code editor.
- Select the area of interest from the map and run the following code:
~~~console
var data = ee.ImageCollection("COPERNICUS/S2_SR")
var image= data.filterDate('2022-07-22','2022-07-23')
var dataset = image.filterBounds(aoi).select(['B2', 'B3', 'B4','B8', 'B11', 'B12']);
print(dataset);
Map.addLayer(dataset);
Export.image.toDrive({
  image:dataset.mosaic(),
  description:'sentinel',
  scale:10,
  region:aoi,
})
~~~
- Run the above code and select the task option on the left hand side.
- There a run option will popup. Click on it.
- An pop window will be open afterwards asking the folder details to save the image and the file format.
- Select the file format as tif
- A six band tif image will now be downloaded in google drive.
yolo-v24 0.0.1
To the moon!
Requires Python: >=3.6

# Official YOLOv24

Implementation of paper - [YOLOv24](https://www.youtube.com/watch?v=dQw4w9WgXcQ)

We achieved `skynet` on COCO dataset

<img width=500px src="docs/moon.png"/>

## Installation

```
pip install yolo-v24
```
yolo-v23 0.0.1
To the moon!
Requires Python: >=3.6

# Official YOLOv23

Implementation of paper - [YOLOv23](https://www.youtube.com/watch?v=dQw4w9WgXcQ)

We achieved `skynet` on COCO dataset

<img width=500px src="docs/moon.png"/>

## Installation

```
pip install yolo-v23
```
yolo-v22 0.0.1
To the moon!
Requires Python: >=3.6

# Official YOLOv22

Implementation of paper - [YOLOv22](https://www.youtube.com/watch?v=dQw4w9WgXcQ)

We achieved `skynet` on COCO dataset

<img width=500px src="docs/moon.png"/>

## Installation

```
pip install yolo-v22
```
yolo-v21 0.0.1
To the moon!
Requires Python: >=3.6

# Official YOLOv21

Implementation of paper - [YOLOv21](https://www.youtube.com/watch?v=dQw4w9WgXcQ)

We achieved `skynet` on COCO dataset

<img width=500px src="docs/moon.png"/>

## Installation

```
pip install yolo-v21
```
yolo-v20 0.0.1
To the moon!
Requires Python: >=3.6

# Official YOLOv20

Implementation of paper - [YOLOv20](https://www.youtube.com/watch?v=dQw4w9WgXcQ)

We achieved `skynet` on COCO dataset

<img width=500px src="docs/moon.png"/>

## Installation

```
pip install yolo-v20
```
yolo-v19 0.0.1
To the moon!
Requires Python: >=3.6

# Official YOLOv19

Implementation of paper - [YOLOv19](https://www.youtube.com/watch?v=dQw4w9WgXcQ)

We achieved `skynet` on COCO dataset

<img width=500px src="docs/moon.png"/>

## Installation

```
pip install yolo-v19
```
yolo-v16 0.0.1
To the moon!
Requires Python: >=3.6

# Official YOLOv16

Implementation of paper - [YOLOv16](https://www.youtube.com/watch?v=dQw4w9WgXcQ)

We achieved `skynet` on COCO dataset

<img width=500px src="docs/moon.png"/>

## Installation

```
pip install yolo-v16
```
yolo-v15 0.0.1
To the moon!
Requires Python: >=3.6

# Official YOLOv15

Implementation of paper - [YOLOv15](https://www.youtube.com/watch?v=dQw4w9WgXcQ)

We achieved `skynet` on COCO dataset

<img width=500px src="docs/moon.png"/>

## Installation

```
pip install yolo-v15
```
yolo-v12 0.0.1
To the moon!
Requires Python: >=3.6

# Official YOLOv12

Implementation of paper - [YOLOv12](https://www.youtube.com/watch?v=dQw4w9WgXcQ)

We achieved `skynet` on COCO dataset

<img width=500px src="docs/moon.png"/>

## Installation

```
pip install yolo-v12
```
yolo-v11 0.0.1
To the moon!
Requires Python: >=3.6

# Official YOLOv11

Implementation of paper - [YOLOv11](https://www.youtube.com/watch?v=dQw4w9WgXcQ)

We achieved `skynet` on COCO dataset

<img width=500px src="docs/moon.png"/>

## Installation

```
pip install yolo-v11
```
yolov-18 0.0.1
To the moon!
Requires Python: >=3.6

# Official YOLOv18

Implementation of paper - [YOLOv18](https://www.youtube.com/watch?v=dQw4w9WgXcQ)

We achieved `skynet` on COCO dataset

<img width=500px src="docs/moon.png"/>

## Installation

```
pip install yolov-18
```
yolov-17 0.0.1
To the moon!
Requires Python: >=3.6

# Official YOLOv17

Implementation of paper - [YOLOv17](https://www.youtube.com/watch?v=dQw4w9WgXcQ)

We achieved `skynet` on COCO dataset

<img width=500px src="docs/moon.png"/>

## Installation

```
pip install yolov-17
```
yolov-14 0.0.1
To the moon!
Requires Python: >=3.6

# Official YOLOv14

Implementation of paper - [YOLOv14](https://www.youtube.com/watch?v=dQw4w9WgXcQ)

We achieved `skynet` on COCO dataset

<img width=500px src="docs/moon.png"/>

## Installation

```
pip install yolov-14
```
yolov-13 0.0.1
To the moon!
Requires Python: >=3.6

# Official YOLOv13

Implementation of paper - [YOLOv13](https://www.youtube.com/watch?v=dQw4w9WgXcQ)

We achieved `skynet` on COCO dataset

<img width=500px src="docs/moon.png"/>

## Installation

```
pip install yolov-13
```
roboflow100 0.0.1
To the moon!
Requires Python: >=3.6

# Official YOLOv{{ cookiecutter.yolo_version }}

Implementation of paper - [YOLOv{{ cookiecutter.yolo_version }}](https://www.youtube.com/watch?v=dQw4w9WgXcQ)

We achieved `skynet` on COCO dataset

<img width=500px src="docs/moon.png"/>

## Installation

```
pip install {{ cookiecutter.project_name }}
```
rf100 0.0.2
To the moon!
Requires Python: >=3.6

# Official YOLOv{{ cookiecutter.yolo_version }}

Implementation of paper - [YOLOv{{ cookiecutter.yolo_version }}](https://www.youtube.com/watch?v=dQw4w9WgXcQ)

We achieved `skynet` on COCO dataset

<img width=500px src="docs/moon.png"/>

## Installation

```
pip install {{ cookiecutter.project_name }}
```