# SplashLab
Python packages intended to assist with experimental research in the field of fluid dynamics. The tools are divided into
two categories: computer vision and dimensional analysis. The entire package can be installed with:
```
pip install splashlab
```
There are two main modules: computer vision and dimensional analysis. As their names suggest computer vision contacts
classes and functions to assist with computer vision tasks; it is mostly built on top of OpenCV. Dimensional analysis
has function and classes intended to assist with tasks such as keeping track of units during calculations and finding
nondimensional groups.
## Computer Vision
### Reading and Displaying Images
The computer vision module contains several functions and classes that assist mainly with processing image and video
data. The first example shows how to use `read_image_folder` to convert all images in a directory to a single numpy
array and display the images with `animate_images`:
```
import splashlab.computer_vision as vision
images = vision.read_image_folder(folder_path, file_extension='.tif', start=0, end=None, step=1, read_color=False)
vision.animate_images(images, wait_time=10, wait_key=False, BGR=True, close=True)
```
## Dimensional Analysis
Dimensional analysis, as the name suggests, helps with dimensional analysis, like applying Buckingham Pi Theorem.
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"description": "# SplashLab\r\nPython packages intended to assist with experimental research in the field of fluid dynamics. The tools are divided into\r\ntwo categories: computer vision and dimensional analysis. The entire package can be installed with:\r\n```\r\npip install splashlab\r\n```\r\nThere are two main modules: computer vision and dimensional analysis. As their names suggest computer vision contacts \r\nclasses and functions to assist with computer vision tasks; it is mostly built on top of OpenCV. Dimensional analysis \r\nhas function and classes intended to assist with tasks such as keeping track of units during calculations and finding \r\nnondimensional groups.\r\n\r\n## Computer Vision\r\n### Reading and Displaying Images\r\nThe computer vision module contains several functions and classes that assist mainly with processing image and video \r\ndata. The first example shows how to use `read_image_folder` to convert all images in a directory to a single numpy \r\narray and display the images with `animate_images`:\r\n```\r\nimport splashlab.computer_vision as vision\r\n\r\nimages = vision.read_image_folder(folder_path, file_extension='.tif', start=0, end=None, step=1, read_color=False)\r\nvision.animate_images(images, wait_time=10, wait_key=False, BGR=True, close=True)\r\n```\r\n\r\n## Dimensional Analysis\r\nDimensional analysis, as the name suggests, helps with dimensional analysis, like applying Buckingham Pi Theorem.\r\n",
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