thomasaarholt

def bilinear_interpolate(img, points, clipped_nan=True):
    """Bilinearly interpolate points from an image using NumPy or CuPy

    Args:
        img: Image of shape (Y, X) to interpolate from.
        points: array of shape (2, N) of (y, x) coordinates
        clipped_nan: If True, the value of coordinates outside the image shape 
            are set to nan. Otherwise they are clipped to the image edge.

    Returns:

thomasaarholt

def bilinear_binning(points, intensities, subpixel=1, gaussian_blur=False):
    """Bilinear weighting of points onto a grid.
    Extent of grid given by min and max of points in each dimension
    points should be an array of shape (N, 2)
    intensity should be an array of shape (N,)
    subpixel will increase the gridsize by its factor
    gaussian_blur: blur the binned intensity and weighting images before they are divided, avoiding divide-by-zero warnings
    
    TODO: Give a known grid as input
    """

thomasaarholt

def bilinear_bincount_cupy(points, intensities, subpixel=1):
    """Bilinear weighting of points onto a grid.
    Extent of grid given by min and max of points in each dimension
    points should be a cupy array of shape (N, 2)
    intensity should be a cupy array of shape (N,)
    """

    points = subpixel * points
    floor = cp.floor(points)
    ceil = floor + 1

thomasaarholt

def bilinear_bincount_numpy(points, intensities):
    """Bilinear weighting of points onto a grid.
    Extent of grid given by min and max of points in each dimension
    points should have shape (N, 2)
    intensity should have shape (N,)
    """
    floor = np.floor(points)
    ceil = floor + 1
    floored_indices = np.array(floor, dtype=int)
    low0, low1 = floored_indices.min(0)

thomasaarholt

(gpus) [thomasaar@ml2 KDEpy]$ pip install -e .
Obtaining file:///itf-fi-ml/home/thomasaar/github/KDEpy
Requirement already satisfied: numpy>=1.14.2 in /itf-fi-ml/home/thomasaar/.conda/envs/gpus/lib/python3.9/site-packages (from KDEpy==1.1.0) (1.21.0)
Requirement already satisfied: scipy>=1.0.1 in /itf-fi-ml/home/thomasaar/.conda/envs/gpus/lib/python3.9/site-packages (from KDEpy==1.1.0) (1.6.3)
Requirement already satisfied: matplotlib>=2.2.0 in /itf-fi-ml/home/thomasaar/.conda/envs/gpus/lib/python3.9/site-packages (from KDEpy==1.1.0) (3.4.2)
Requirement already satisfied: pillow>=6.2.0 in /itf-fi-ml/home/thomasaar/.conda/envs/gpus/lib/python3.9/site-packages (from matplotlib>=2.2.0->KDEpy==1.1.0) (8.2.0)
Requirement already satisfied: cycler>=0.10 in /itf-fi-ml/home/thomasaar/.conda/envs/gpus/lib/python3.9/site-packages (from matplotlib>=2.2.0->KDEpy==1.1.0) (0.10.0)
Requirement already satisfied: python-dateutil>=2.7 in /itf-fi-ml/home/thomasaar/.conda/envs/gpus/lib/python3.9/site-packages (from matplotlib>=

thomasaarholt

import sympy as sp
import numpy as np

def levenberg(sympy_func, xi, target, sympy_param, guess, weight=None, module='numpy'):
    '''
    Computes the minimum `guess` so that `sympy_func(guess) - target` is minimized
    
    Arguments:
    sympy_func  : Should be a sympy function to be minimized
    xi          : numpy array, the x-axis of `target`

thomasaarholt

(base) ~: mamba create --name torchtest pytorch torchvision torchaudio cpuonly -c pytorch

                  __    __    __    __
                 /  \  /  \  /  \  /  \
                /    \/    \/    \/    \
███████████████/  /██/  /██/  /██/  /████████████████████████
              /  / \   / \   / \   / \  \____
             /  /   \_/   \_/   \_/   \    o \__,
            / _/                       \_____/  `
            |/

thomasaarholt

(base) ~: conda create --name torch pytorch torchvision torchaudio cpuonly -c pytorch
Collecting package metadata (current_repodata.json): done
Solving environment: done

## Package Plan ##

  environment location: /home/thomasaar/mambaforge/envs/torch

  added / updated specs:
    - cpuonly

thomasaarholt

# Let's minimize the function f(x,y) = (x-50)**2 + (y-100)**2
# We can tell from looking at the equation that the minimum should be at (50, 100).

def func(params):
    x, y = params
    return (x-50)**2 + (y-100)**2


# Optionally, view what it looks like

thomasaarholt

fortune = 500_000_000
total_americans = 327_000_000
americans_lives_changed = 0

for american in range(total_americans):
    if fortune - 1_000_000 >= 0:
        fortune -= 1_000_000
        americans_lives_changed += 1
    else:
        raise ValueError(f"Uh oh! He ran out of money! Lives changed: {americans_lives_changed}")