NumPy, short for Numerical Python, is an open source package for scientific computing and data analysis. NumPy serves as the foundation of Python’s scientific computing stack. Among the features of NumPy is ndarray, or N-dimensional array object, which is a flexible storage for data sets in Python. It also contains tools for integrating C/C++ and has capabilities for random number generation and Fourier transform.


NumPy is often preferred over regular Python lists for a variety of reasons. It is seen to be convenient, owing to its many free vector and matrix operations. Data structures in NumPy also takes up less space and performance in terms of speed is better, as well.

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    I'm looking for a personal trainer who can help me learn python from scratch in a practical approach with real-time examples. The pay will be hourly or fixed based on estimated duration and curriculum. will need 2 demo sessions. The goal of me learning python is to automate few redundant daily tasks on Excel, Browser and a few web apps. Eventually to automate simple and small trading strategies. We can talk more if you are interested. All communication will be via Freelance & Zoom only.

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    From the given raw weighted image you need to measure cortical thickness map. It is the thickness of the gray matter of the brain at every point and it is the distance between the white matter and the pial surface. The output should be a python script with a 3D volume where every pixel not in gray matter is assigned a value ZERO and for every pixel in the gray matter is assigned the thickness value of the cortex at that point. Input data sets will be given. you need to use nibabel libraries to load image. Note: The entire project must be done in python using numpy libraries.

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    Outline: In this challenge you will develop an algorithm to estimate cortical thickness map from a raw T1-weighted image. Cortical thickness map is the thickness of the gray matter of the brain at every point. It is defined as the distance between the white matter surface and the pial surface Tips: • Use nibabel libraries to load nifti image into python • You will probably want to segment the white matter first. This will give you the starting point for your thickness estimation algorithm. • Once you have the white matter segmentation, decide how you will go about estimating thickness. Something like the following: o Find a vector orthogonal to the white matter surface o Cast the vector outwards o Use some heuristic to decide where the vector meets the pial surface (cortica...

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