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')

thomasaarholt

def P1(P0=(0,0), Pc=(2,2), step=1):
    "Get the point P1, the point to which one moves from P0 with a fixed step in the direction of Pc"
    X0, Y0 = P0
    Xc, Yc = Pc
    
    diffX = Xc - X0
    diffY = Yc - Y0
    
    magnitude = (diffX**2 + diffY**2)**0.5
    

thomasaarholt

from pprint import pprint
import temscript as ts
from math import pi

dwelltime = 1e-5 # seconds

tem = ts.Microscope()
illumination = tem._tem_illumination
instrument = ts.GetInstrument()

thomasaarholt

import hyperspy.api as hs
expr = "where(abs(x - center) < radius, A, 0)"
tophat = hs.model.components1D.Expression(expr, 'Top Hat', position="center", A=1, center=0, radius=1)

thomasaarholt

import hyperspy.api as hs
expr = "where(sqrt((x - xc)**2 + (y - yc)**2) < radius, A, 0)"
tophat = hs.model.components2D.Expression(expr, 'Top Hat', position=("xc", "yc"), A=1, xc=0, yc=0, radius=1)

thomasaarholt

import hyperspy.api as hs
expr = "where(And(left < x, x < right), A, 0)"
tophat = hs.model.components1D.Expression(expr, 'Top Hat', position="left", A=1, left=0, right=1)

thomasaarholt

def loadXRD(filename):
    with open(filename, 'r') as f:
        lines = f.readlines()
    angles = []
    intensity = []
    for line in lines[1:-2]:
        x, y = line.split('\n')[0].split(",")[:-1]
        angles.append(float(x))
        intensity.append(float(y))
    x = np.array(angles)

thomasaarholt

import numpy as np
from scipy.signal import convolve2d, gaussian
from scipy.misc import face
import matplotlib.pyplot as plt

def convolve2d_fft(arr1, arr2):
    s0, s1 = arr1.shape
    
    conv = np.fft.irfft2(
        np.fft.rfft2(arr1) * np.fft.rfft2(arr2),