"""
Export selection as ang file
"""
import matplotlib.pyplot as plt
import pyebsd
from builtins import input
import os
angfile = os.path.join('..', 'data', 'ADI_bcc_fcc.ang')
scan = pyebsd.load_scandata(angfile)
# enable matplotlib interactive mode
plt.ion()
# plot IPF
ipf = scan.plot_IPF(gray=scan.IQ, sel=scan.ph == 2)
# enable lasso selector
ipf.lasso_selector()
key = input('Press ENTER to process selected data ')
# disable interactive mode
plt.ioff()
# plot ipf of the selection
ipfsel = scan.plot_IPF(sel=ipf.sel, gray=scan.IQ)
# save selection as selection.ang
scan.save_ang_file('selection.ang', ipf.sel)
# This also works:
# pyebsd.save_ang_file('selection.ang', scan, ipf.sel)
from builtins import input
import pyebsd
if 'fname_local' not in locals() or 'fname_local' not in globals():
fname_local = ''
# fname = os.path.join('..', 'data', 'ADI_bcc_fcc.ang')
fname = os.path.join('..', 'data', 'QP_bcc_fcc_single_austenite_grain.ang')
basename = os.path.basename(fname)
basename = os.path.splitext(basename)[0]
if fname != fname_local:
fname_local = fname
scan = pyebsd.load_scandata(fname_local)
plt.ion()
ipf0 = scan.plot_IPF(gray=scan.IQ, d=[0, 0, 1])
# ipf0.fig.savefig(basename + '_IPF.pdf', bbox_inches='tight', pad_inches=0)
plt.close(ipf0.fig)
phmap = scan.plot_phase(gray=scan.IQ)
# phmap.fig.savefig(basename + '_ph.pdf', bbox_inches='tight', pad_inches=0)
plt.close(phmap.fig)
ipf = scan.plot_IPF(sel=(scan.ph == 2) & (scan.CI > .2),
gray=scan.IQ,
d=[0, 0, 1])
fig = ipf.fig
Lasso selector to select areas
"""
import matplotlib.pyplot as plt
import pyebsd
from builtins import input
import os
# .ang file available in the data folder
# Replace angfile variable with the path to your .ang file
angfile = os.path.join('..', 'data', 'ADI_bcc_fcc.ang')
# load ebsd scandata
# So far, pyebsd only supports loading .ang files generated
# by the TSL OIM software
scan = pyebsd.load_scandata(angfile)
# Enable matplotlib.pyplot interactive node, which means
# that the plots open automatically and do not interrupt
# the python interpreter.
# If plt.ion() is not called, you must to run plt.show()
# after calling pyebsd plot functions.
plt.ion()
# Plot IPF map for only the fcc phase (ph == 2)
ipf = scan.plot_IPF(sel=(scan.ph == 2), gray=scan.IQ)
# Enable lasso_selector. Click with the mouse in the open IPF
# plot and select an area.
# Use the left button to create a new vertex and the right button
data.drop(columns='level_0', inplace=True)
except:
pass
try:
data.drop(columns='index', inplace=True)
except:
pass
return nrows - 1
if __name__ == '__main__':
fname = os.path.join('..', 'data', 'ADI_bcc_fcc_cropped.ang')
kwargs = dict(tiling='hex')
# Test 1: ncols_odd > ncols_even and odd number of rows
scan1 = pyebsd.load_scandata(fname)
plot_tests(scan1, **kwargs)
# Test 2: ncols_odd > ncols_even and even number of rows
data = scan1.data.copy()
nrows, ncols_odd, ncols_even = scan1.nrows, scan1.ncols_odd, scan1.ncols_even
nrows = drop_last_row(data, nrows, ncols_odd, ncols_even)
scan2 = pyebsd.ScanData(data, 'HexGrid', scan1.dx, scan1.dy, ncols_odd,
ncols_even, nrows, scan1.header)
plot_tests(scan2, **kwargs)
# Test 3: ncols_odd < ncols_even and odd number of rows
data = scan1.data.copy()
nrows, ncols_odd, ncols_even = scan1.nrows, scan1.ncols_odd, scan1.ncols_even
# drops last column in a odd row twice
ncols_odd, ncols_even = drop_first_and_last_columns(
# -*- coding: utf-8 -*-
"""
[1] C. Moussa, M. Bernacki, R. Besnard, N. Bozzolo, Ultramicroscopy 179 (2017) 63–72.
"""
import os
import numpy as np
import matplotlib.pyplot as plt
import pyebsd
if __name__ == '__main__':
scan = pyebsd.load_scandata(
os.path.join('..', 'data', 'ADI_bcc_fcc_cropped.ang'))
selection = (scan.ph == 1) # Selects phase indexed as 1
indices, = np.where(selection)
distance = [] # real distance in (normally um) to n-th nearest neighbor
kam = [] # list with KAM values for every pixel for each distance
convention, maxmis, nneighbors = 'OIM', 5., 5
# convention, maxmis, nneighbors = 'fixed', 5., 15
for d in range(1, nneighbors + 1):
distance.append(
scan.get_distance_neighbors(distance=d,
distance_convention=convention))
kam.append(
scan.get_KAM(distance=d,
distance_convention=convention,
sel=selection,
maxmis=maxmis))
