thomasaarholt

ffmpeg -r 20 -i img_%03d.png -c:v libx264 -pix_fmt yuv420p -vf "pad=ceil(iw/2)*2:ceil(ih/2)*2" -y out.mp4

thomasaarholt

import numpy as np
from scipy.constants import electron_mass, elementary_charge, c, hbar, h

def relativistic_wavelength(kV=300):
    "Returns relativistic wavelength in meters"
    V = 1e3 * kV
    top = h * c
    bottom = np.sqrt(
        elementary_charge * V * (2 * electron_mass * c ** 2 + elementary_charge * V)
    )

thomasaarholt

import numpy as np
import matplotlib.pyplot as plt

N = 600
probe_positions = np.arange(N)
def probe_function(probe_positions):
    return np.sin(2*np.pi*probe_positions / 150)**2

strength = 3
period = 50

thomasaarholt

import hyperspy.api as hs
import numpy as np
import matplotlib.pyplot as plt

def cell2sig(cell, pixel_size = 0.05, sigma=0.25):
    Signal2D = hs.signals.Signal2D
    Gaussian2D = hs.model.components2D.Gaussian2D


    XLEN, YLEN = (cell.cell.diagonal()[:2] // pixel_size).astype(int)

thomasaarholt

import hyperspy.api as hs
expr = "where(Eg < x, A*(x-Eg)*n, 0)"
bandgap = hs.model.components1D.Expression(expr, 'Bandgap', Eg=3.0, n=1, A=1)

thomasaarholt

%matplotlib widget
import hyperspy.api as hs
import matplotlib.pyplot as plt
import numpy as np
ll_sum = hs.load('ll_sum.hspy')
s_sum = hs.load('s_sum.hspy')

s_sum.metadata.Acquisition_instrument.TEM.beam_energy=200
s_sum.metadata.Acquisition_instrument.TEM.convergence_angle=22.5
s_sum.metadata.Acquisition_instrument.TEM.Detector.EELS.collection_angle=37.9

thomasaarholt

def fit_Zr_L(sZr, ll=None):
    "Returns the model only"
    print("Will produce 10 progress bars")
    mZr = sZr.create_model(ll = ll, GOS="Hartree-Slater", auto_add_edges=False)
    mZr.fit_component(mZr["PowerLaw"], bounded=True, signal_range=[2150.,2210.], fit_independent=True, only_current=True)
    mZr.assign_current_values_to_all()
    mZr.fit_component(mZr["PowerLaw"], bounded=True, signal_range=[2150.,2210.], fit_independent=True, only_current=False)
    #mZr["PowerLaw"].set_parameters_not_free()

thomasaarholt

%matplotlib widget
import hyperspy.api as hs
import numpy as np
import matplotlib.pyplot as plt

s = hs.datasets.example_signals.object_hologram()
ax0 = s.axes_manager[0]
ax1 = s.axes_manager[1]
ax0.scale=2
ax1.scale=1

thomasaarholt

import numpy as np
import matplotlib.pyplot as plt
import scipy.interpolate

from concurrent.futures import ThreadPoolExecutor
from os import cpu_count
executor = ThreadPoolExecutor(max_workers=cpu_count())
print(f"Processors available: {cpu_count()}")

# parallel=True uses the following map function

thomasaarholt

"""Another way, note this one will load the whole array into memory ."""
from keras.preprocessing.image import ImageDataGenerator
import h5py
from keras.utils.io_utils import HDF5Matrix
seed=0
batch_size=32

# we create two instances with the same arguments
data_gen_args = dict( 
    rotation_range=90.,