thomasaarholt

import matplotlib.pyplot as plt

def figure(figsize=None):
    'Temporary workaround for traditional figure behaviour with the ipympl widget backend'
    fig = plt.figure()
    if figsize:
        w, h =  figsize
    else:
        w, h = plt.rcParams['figure.figsize']
    fig.canvas.layout.height = str(h) + 'in'

thomasaarholt

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

def Gauss2D(X, Y, A=1, cx=0, cy=0, sx=1, sy=1):
    return A*np.exp(-( ((X - cx)**2)/(2*sx**2) + ((Y - cy)**2)/(2*sy**2)))

def Gauss(x, A=1, c=0, s=1):
    return A*np.exp(-((x - c)**2)/(2*s**2))

thomasaarholt

import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import tensorflow_probability as tfp
from time import time

def toImage(tensor):
    return tensor.reshape(tensor.shape[:2])

def gaussian_kernel(size: int,

thomasaarholt

import numpy as np
from scipy import signal

def gaussian_kernel(n, std, normalised=False):
    '''
    Generates a n x n matrix with a centered gaussian 
    of standard deviation std centered on it. If normalised,
    its volume equals 1.'''
    gaussian1D = signal.gaussian(n, std)
    gaussian2D = np.outer(gaussian1D, gaussian1D)

thomasaarholt

def approx_acquisition_method():
    self.spect.data.synchronizedOn(self.escan.newPosition)
    startt = time.time()

    self.spect.data.subscribe(self.receive_spect_point)

    sed_data = self.sed.data.get()
    # wait the last point is fully acquired
    self.acq_complete.wait()

thomasaarholt

def autoscale(ax=None, axis='y', margin=0.1):
    '''Autoscales the x or y axis of a given matplotlib ax object
    to fit the margins set by manually limits of the other axis,
    with margins in fraction of the width of the plot
    
    Defaults to current axes object if not specified.
    '''
    import matplotlib.pyplot as plt
    import numpy as np
    if ax is None:

thomasaarholt

(/shared/users/thomasaar/py37) /shared/users/thomasaar/downloads/q-e-gpu-qe-gpu-6.4.1a1: ./configure --with-cuda=/opt/pgi/linux86-64/2018/cuda/9.2/bin --with-cuda-runtime=9.2 --with-cuda-cc=7.5 --enable-openmp --with-scalapack=no --prefix=/shared/users/thomasaar/compiled/q-e-gpu
checking build system type... x86_64-pc-linux-gnu
checking ARCH... x86_64
checking setting AR... ... /shared/users/thomasaar/py37/bin/x86_64-conda_cos6-linux-gnu-ar
checking setting ARFLAGS... ... ruv
checking whether the Fortran compiler works... yes
checking for Fortran compiler default output file name... a.out
checking for suffix of executables...
checking whether we are cross compiling... no
checking for suffix of object files... o

thomasaarholt

import numpy as np

x = np.array([500, 500, 500, 1000, 1000, 1000, 1500])
y = np.array([1,2,3,4,5,6,7])

data = np.stack([x,y]).T

means = []
stds = []
for xi in np.unique(data[:,0]):

thomasaarholt

# Script should be applied to a single Velox emd file, where the file contains a single DCFI dataset
# that has been applied to the detector signal that one wishes to apply to the other detector signals

# A good example is using DCFI on a DF image, and then applying this correction to HAADF, BF and ABF.
import hyperspy.api as hs
import json
import h5py
import numpy as np

def get_nonDCFI_signals(list_of_signals):

thomasaarholt

(/shared/users/thomasaar/py37) /shared/users/thomasaar/downloads/q-e-gpu/install: more make.inc.in
# @configure_input@

# compilation rules

.SUFFIXES :
.SUFFIXES : .o .c .f .f90

# most fortran compilers can directly preprocess c-like directives: use
#       $(MPIF90) $(F90FLAGS) -c $<