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}")

thomasaarholt

import numpy as np
import random

number_of_hens = 100
repeats = 1000 # repeat many times so that we get some decent statistics

sums = []
for _ in range(repeats): 
    hens = np.zeros(number_of_hens, dtype=bool) # reset an array of False
    

thomasaarholt

x_ = np.linspace(0, 12, 1000)
die_y = die_fit_func(x_, *guess)

fig, ax = plt.subplots()
ax.plot(x_, die_y.real)
ax2 = ax.twinx()
ax2.plot(x_, die_y.imag, color='red')

ax.set(xlabel='Energy', ylabel='Real')