def test_read_dnd(self):
"""Test function for read_dnd.
"""
with pkg_resources.path(testdata, self.path_dict['fname_dnd']) as path:
fpath = path
# extracting scans from original file
raw = loadtxt(fpath, usecols=(0, 16, 17, 18))
index = index_dups(raw[:, 0], 1e-4)
raw = delete(raw, index, 0)
muref = raw[:, 3]
mu = raw[:, 2]
fluo = raw[:, 1]
for scan in ['mu', 'fluo', None]:
# testing with request for mu_ref
group = read_dnd(fpath, scan=scan)
self.assertIsInstance(group, Group) # group class is returned
self.assertIsInstance(group.energy,
ndarray) # ndarray type is returned
self.assertIsInstance(group.mu_ref,
ndarray) # ndarray type is returned
self.assertTrue(allclose(group.mu_ref,
muref)) # mu_ref is properly retrieved
if scan == 'mu':
self.assertIsInstance(getattr(group, scan), ndarray)
self.assertFalse(hasattr(
group, 'fluo')) # instance fluo is not returned
self.assertTrue(allclose(group.mu,
mu)) # mu_ref is properly retrieved
elif scan == 'fluo':
self.assertIsInstance(getattr(group, scan), ndarray)
self.assertFalse(hasattr(group,
'mu')) # instance mu is not returned
self.assertTrue(allclose(group.fluo,
fluo)) # fluo is properly retrieved
for scan in ['mu', 'fluo']:
# testing with no request for mu_ref
group = read_dnd(fpath, scan=scan, ref=False)
self.assertIsInstance(group, Group) # group class is returned
self.assertIsInstance(group.energy,
ndarray) # ndarray type is returned
self.assertFalse(hasattr(
group, 'mu_ref')) # instance mu_ref is not returned
if scan == 'mu':
self.assertFalse(hasattr(
group, 'fluo')) # instance fluo is not returned
self.assertTrue(allclose(group.mu,
mu)) # mu_ref is properly retrieved
elif scan == 'fluo':
self.assertFalse(hasattr(group,
'mu')) # instance mu is not returned
self.assertTrue(allclose(group.fluo,
fluo)) # fluo is properly retrieved
import matplotlib.pyplot as plt
from araucaria.testdata import get_testpath
from araucaria.io import read_dnd
from araucaria.xas import pre_edge, autobk
from araucaria.plot import fig_autobk
fpath = get_testpath('dnd_testfile1.dat')
group = read_dnd(fpath, scan='mu')
pre = pre_edge(group, update=True)
bkg = autobk(group, update=True)
fig, ax = fig_autobk(group, show_window=False)
plt.show(block=False)
from araucaria.testdata import get_testpath
from araucaria import Group
from araucaria.io import read_dnd
from araucaria.xas import pre_edge, autobk, xftf, xftr
from araucaria.utils import check_objattrs
kw = 2
k_range = [2, 10]
r_range = [0.5, 2]
fpath = get_testpath('dnd_testfile1.dat')
group = read_dnd(fpath, scan='mu') # extracting mu and mu_ref scans
pre = pre_edge(group, update=True)
autbk = autobk(group, update=True)
fft = xftf(group, k_range=k_range, kweight=kw, update=True)
rft = xftr(group, r_range=r_range, update=True)
attrs = ['rwin', 'q', 'chiq', 'chiq_mag', 'chiq_re', 'chiq_im']
check_objattrs(group, Group, attrs)
# [True, True, True, True, True, True]
# plotting forward FFT signal
import matplotlib.pyplot as plt
from araucaria.plot import fig_xas_template
fig, ax = fig_xas_template(panels='rq', fig_pars={'kweight': kw})
line = ax[0].plot(group.r, group.chir_mag)
line = ax[0].plot(group.r, group.rwin, color='firebrick')
xlim = ax[0].set_xlim(0, 6)
line = ax[1].plot(group.k, group.k**kw * group.chi)
line = ax[1].plot(group.q, group.chiq_re)
xlim = ax[1].set_xlim(0, 12)
fig.tight_layout()
plt.show(block=False)
from numpy import allclose
from araucaria.testdata import get_testpath
from araucaria import Group
from araucaria.io import read_dnd
from araucaria.xas import deglitch, pre_edge, autobk
from araucaria.utils import check_objattrs
fpath = get_testpath('dnd_glitchfile.dat')
group = read_dnd(fpath, scan='fluo') # extracting fluo and mu_ref scans
cgroup = group.copy()
degli = deglitch(cgroup, update=True)
attrs = ['index_glitches', 'energy_glitches', 'deglitch_pars']
check_objattrs(cgroup, Group, attrs)
# [True, True, True]
allclose(cgroup.energy_glitches, group.energy[cgroup.index_glitches])
# True
print(cgroup.energy_glitches)
# [7552.2789 7548.1747 7390.512 7387.2613]
# plotting original and deglitched spectrum
from araucaria.plot import fig_xas_template
import matplotlib.pyplot as plt
for g in [group, cgroup]:
pre = pre_edge(g, update=True)
autbk = autobk(g, update=True)
fig, ax = fig_xas_template(panels='xe')
line = ax[0].plot(group.energy, group.norm, label='original', color='tab:red')
line = ax[0].plot(cgroup.energy, cgroup.norm, label='degliched', color='k')
line = ax[1].plot(group.k, group.k**2 * group.chi, color='tab:red')
line = ax[1].plot(cgroup.k, cgroup.k**2 * cgroup.chi, color='k')
leg = ax[0].legend()
fig.tight_layout()