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

[[language]]
name = "python"
scope = "source.python"
injection-regex = "python"
file-types = ["py","pyi","py3","pyw",".pythonstartup",".pythonrc"]
shebangs = ["python"]
roots = [".", "pyproject.toml", "pyrightconfig.json"]
comment-token = "#"
language-servers = ["pyright", "ruff"]
indent = { tab-width = 4, unit = "    " }

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.
MUST: First import of hyperspy (or scipy) must NOT be run with "Execute on background thread" checked. One
can then swap to background thread and rerun.
v. 0.3: Added delete statements to ensure python objects don't stay in memory.

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

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
Arc = matplotlib.patches.Arc

def halfangle(a, b):
    "Gets the middle angle between a and b, when increasing from a to b"
    if b < a:
        b += 360
    return (a + b)/2 % 360

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

# Use the pymc master branch
from collections.abc import Callable
from typing import Literal

from numpy._typing import ArrayLike
import pymc as pm
from numpy import ndarray
from pymc.distributions.shape_utils import (
    Dims,
    Shape,

thomasaarholt

# print stashes that exist for the currently switched-to branch
GREEN='\033[0;32m'
NC='\033[0m' # No Color

branch=$(git rev-parse --abbrev-ref HEAD)
stashes=`git stash list | grep "WIP on $branch"`
if [ "$stashes" ]
then
  echo "${GREEN}You have the following stashes for this branch:"
  echo "${stashes}${NC}"

thomasaarholt

import sys
from time import time

N_repeats = 100


def func(foo: list[int]):
    return [x for x in foo if x % 2 == 0]

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):