thomasaarholt

'''
Python script to load any format supported by hyperspy directly into GMS
Must be copied and pasted into Gatan DigitalMicrograph (aka Gatan Microscopy Suite) version 3.4+
Call by `load_img(filepath)` at the bottom of the script. Can not be called outside of GMS.
Does not automatically convert the data type to EELS or EDS
Written by Thomas Aarholt, see https://gist.github.com/thomasaarholt/fccf06d56ff84cf76345b44dae30871e for newer versions
Feedback and forks are very welcome.

If the script runs forever, then this is due to a bug with numpy's FFT algorithm. To fix, in hyperspy\_signals\signal2d.py, move the line `from skimage.feature.register_translation import _upsampled_dft
` into the first line in `estimate_image_shift` in the same file.

thomasaarholt

import keras.backend as K
import tensorflow as tf
from keras.layers import Layer
from keras.layers import Conv2D

"""Not tested, I'll play around with GANs soon with it."""


class CoordConv2D(Layer):
    def __init__(self, channel, kernel, padding='valid', activation=None, **kwargs):

thomasaarholt

(tensor)/shared/users/thomasaar/2020Feb: python
Python 3.7.6 | packaged by conda-forge | (default, Jan  7 2020, 22:33:48)
[GCC 7.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
2020-02-10 16:54:19.147934: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer.so.6'; dlerror: libnvinfer.so.6: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-10.1/lib64:/usr/local/cuda-10.1/lib64:/shared/users/thomasaar/py37/lib:/shared/users/thomasaar/compiled/fftw/lib:/shared/users/thomasaar/compiled/prismatic/lib:
2020-02-10 16:54:19.149366: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer_plugin.so.6'; dlerror: libnvinfer_plugin.so.6: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-10.1/lib64:/usr/local/cuda-10.1/lib64:/shared/users/thomasaar/py37/lib:/shared/users/th

thomasaarholt

# Content of script run in GMS
import sys
sys.path.append(r"C:\Users\thomasaar\Documents\GMS Scripts")
import thomas
from importlib import reload
reload(thomas)

thomas.helloworld()

# Content of thomas.py in GMS Scripts folder

thomasaarholt

# In Velox, choose a dataset and select all elements in the periodic table (This is a little tedious)
# From the Velox Menu, go "EDS" -> "Export Quantification Details..."
# This exports two files, one called "... Lines" and one called "... Composition". We want the former.

import pandas as pd
df = pd.read_csv(r"exported_eds_quant-Lines.csv")
DF = df.iloc[1:] # There was a single blank line in my dataset, so I get rid of it
DF.loc[:,'K-factor'] = DF['K-factor'].astype(float) # String to float on the k-factors
# Two functions that we map across the dataset to split the header into separate elements and line
def splitelement(entry):

thomasaarholt

import numpy as np
import matplotlib.pyplot as plt

fig = plt.figure()
ax1 = fig.add_subplot(111)
ax2 = ax1.twiny()

X = np.linspace(1,1000,1000)
Y = np.cos(X/200)

thomasaarholt

def is_notebook():
    try:
        from IPython import get_ipython

        if "IPKernelApp" not in get_ipython().config:  # pragma: no cover
            raise ImportError("console")
            return False
        if "VSCODE_PID" in os.environ:  # pragma: no cover
            raise ImportError("vscode")
            return False

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,