def test_basecor_multivariate(IR_dataset_2D):
dataset = IR_dataset_2D[5]
basc = BaselineCorrection(dataset)
s = basc([6000.0, 3500.0], [1800.0, 1500.0],
method="multivariate",
interpolation="pchip")
s.plot()
s = basc(
[6000.0, 3500.0],
[1800.0, 1500.0],
method="multivariate",
interpolation="polynomial",
)
s.plot(clear=False, color="red")
dataset = IR_dataset_2D[:15]
basc = BaselineCorrection(dataset)
s = basc([6000.0, 3500.0], [1800.0, 1500.0],
method="multivariate",
interpolation="pchip")
s.plot()
s = basc(
[6000.0, 3500.0],
[1800.0, 1500.0],
method="multivariate",
interpolation="polynomial",
)
s.plot(cmap="copper")
show()
def test_MCRALS():
data = NDDataset.read_matlab(os.path.join('matlabdata', 'als2004dataset.MAT'), transposed=True)
X = data[-1]
assert X.name == 'm1'
X.set_coordset(y=np.arange(51), x=np.arange(96))
X.title = 'concentration'
X.coordset.set_titles(y='spec coord.', x='elution time')
X.plot(title='M1')
guess = data[3]
assert guess.name == 'spure' # spure
guess.set_coordset(y=np.arange(4), x=np.arange(96))
guess.title = 'concentration'
guess.coordset.set_titles(y='#components', x='elution time')
guess.plot(title='spure')
mcr = MCRALS(X, guess, verbose=True)
mcr.C.T.plot(title='Concentration')
mcr.St.plot(title='spectra')
mcr.plotmerit()
guess = EFA(X).get_conc(4)
guess.plot(title='EFA guess')
param = {'normSpec': 'euclid', 'maxit': 100}
mcr2 = MCRALS(X, guess, param=param, verbose=False)
mcr.plotmerit()
assert 'converged !' in mcr2.logs[-15:]
show()
def test_nmr_manual_1D_phasing(NMR_dataset_1D):
dataset1D = NMR_dataset_1D.copy()
dataset1D /= dataset1D.real.data.max() # normalize
dataset1D.em(10. * ur.Hz) # inplace broadening
transf = dataset1D.fft(tdeff=8192, size=2**15) # fft
transf.plot() # plot)
# manual phasing
transfph = transf.pk(verbose=True) # by default pivot = 'auto'
transfph.plot(xlim=(20, -20), clear=False, color='r')
assert_array_equal(transfph.data,
transf.data) # because phc0 already applied
transfph3 = transf.pk(pivot=50, verbose=True)
transfph3.plot(clear=False, color='r')
not assert_array_equal(transfph3.data,
transfph.data) # because phc0 already applied
#
transfph4 = transf.pk(pivot=100, phc0=40., verbose=True)
transfph4.plot(xlim=(20, -20), clear=False, color='g')
assert transfph4 != transfph
transfph4 = transf.pk(pivot=100, verbose=True, inplace=True)
(transfph4 - 10).plot(xlim=(20, -20), clear=False, color='r')
show()
def test_nmr_auto_1D_phasing():
path = os.path.join(prefs.datadir, "nmrdata", "bruker", "tests", "nmr",
"topspin_1d")
ndd = NDDataset.read_topspin(path, expno=1, remove_digital_filter=True)
ndd /= ndd.real.data.max() # normalize
ndd.em(10.0 * ur.Hz, inplace=True)
transf = ndd.fft(tdeff=8192, size=2**15)
transf.plot(xlim=(20, -20), ls=":", color="k")
transfph2 = transf.pk(verbose=True)
transfph2.plot(xlim=(20, -20), clear=False, color="r")
# automatic phasing
transfph3 = transf.apk(verbose=True)
(transfph3 - 1).plot(xlim=(20, -20), clear=False, color="b")
transfph4 = transf.apk(algorithm="acme", verbose=True)
(transfph4 - 2).plot(xlim=(20, -20), clear=False, color="g")
transfph5 = transf.apk(algorithm="neg_peak", verbose=True)
(transfph5 - 3).plot(xlim=(20, -20), clear=False, ls="-", color="r")
transfph6 = transf.apk(algorithm="neg_area", verbose=True)
(transfph6 - 4).plot(xlim=(20, -20), clear=False, ls="-.", color="m")
transfph4 = transfph6.apk(algorithm="acme", verbose=True)
(transfph4 - 6).plot(xlim=(20, -20), clear=False, color="b")
show()
def test_nmr_auto_1D_phasing():
path = os.path.join(prefs.datadir, 'nmrdata', 'bruker', 'tests', 'nmr',
'topspin_1d')
ndd = NDDataset.read_topspin(path, expno=1, remove_digital_filter=True)
ndd /= ndd.real.data.max() # normalize
ndd.em(10. * ur.Hz, inplace=True)
transf = ndd.fft(tdeff=8192, size=2**15)
transf.plot(xlim=(20, -20), ls=':', color='k')
transfph2 = transf.pk(verbose=True)
transfph2.plot(xlim=(20, -20), clear=False, color='r')
# automatic phasing
transfph3 = transf.apk(verbose=True)
(transfph3 - 1).plot(xlim=(20, -20), clear=False, color='b')
transfph4 = transf.apk(algorithm='acme', verbose=True)
(transfph4 - 2).plot(xlim=(20, -20), clear=False, color='g')
transfph5 = transf.apk(algorithm='neg_peak', verbose=True)
(transfph5 - 3).plot(xlim=(20, -20), clear=False, ls='-', color='r')
transfph6 = transf.apk(algorithm='neg_area', verbose=True)
(transfph6 - 4).plot(xlim=(20, -20), clear=False, ls='-.', color='m')
transfph4 = transfph6.apk(algorithm='acme', verbose=True)
(transfph4 - 6).plot(xlim=(20, -20), clear=False, color='b')
show()
def test_nmr_1D_show(NMR_dataset_1D):
# test simple plot
dataset = NMR_dataset_1D.copy()
dataset.plot()
show()
def test_IRIS():
X = NDDataset.read_omnic(os.path.join('irdata', 'CO@Mo_Al2O3.SPG'))
p = [
0.00300, 0.00400, 0.00900, 0.01400, 0.02100, 0.02600, 0.03600, 0.05100,
0.09300, 0.15000, 0.20300, 0.30000, 0.40400, 0.50300, 0.60200, 0.70200,
0.80100, 0.90500, 1.00400
]
X.coordset.update(y=Coord(p, title='pressure', units='torr'))
# Using the `update` method is mandatory because it will preserve the name.
# Indeed, setting using X.coordset[0] = Coord(...) fails unless name is specified: Coord(..., name='y')
# set the optimization parameters, perform the analysis
# and plot the results
param = {
'epsRange': [-8, -1, 20],
'lambdaRange': [-7, -5, 3],
'kernel': 'langmuir'
}
X_ = X[:, 2250.:1950.]
X_.plot()
iris = IRIS(X_, param, verbose=True)
f = iris.f
X_hat = iris.reconstruct()
iris.plotlcurve(scale='ln')
f[0].plot(method='map', plottitle=True)
X_hat[0].plot(plottitle=True)
show()
def test_nmr_2D_imag_compare(NMR_dataset_2D):
# plt.ion()
dataset = NMR_dataset_2D.copy()
dataset.plot()
dataset.plot(imag=True, cmap='jet', data_only=True, alpha=.3, clear=False)
# better not to replot a second colorbar
show()
pass
def test_nmr_fft_1D_our_Hz(NMR_dataset_1D):
dataset1D = NMR_dataset_1D.copy()
dataset1D /= dataset1D.real.data.max() # normalize
LB = 10. * ur.Hz
GB = 50. * ur.Hz
dataset1D.gm(gb=GB, lb=LB)
new = dataset1D.fft(size=32000, ppm=False)
new.plot(xlim=[5000, -5000])
show()
def test_smooth(NMR_dataset_1D):
dataset = NMR_dataset_1D.copy()
dataset /= dataset.real.data.max() # normalize
dataset = dataset.fft(tdeff=8192, size=2**15) + np.random.random(2**15) * 5.0
dataset.plot()
s = dataset.smooth()
s.plot(clear=False, color="r", xlim=[20, -20])
show()
def test_nmr_fft_1D(NMR_dataset_1D):
dataset1D = NMR_dataset_1D.copy()
dataset1D /= dataset1D.real.data.max() # normalize
dataset1D.x.ito('s')
new = dataset1D.fft(tdeff=8192, size=2**15)
new.plot()
new2 = new.ifft()
dataset1D.plot()
(new2 - 1.).plot(color='r', clear=False)
show()
def test_nmr_1D_show_complex(NMR_dataset_1D):
dataset = NMR_dataset_1D.copy()
dataset.plot(xlim=(0., 25000.))
dataset.plot(imag=True, color='r', data_only=True, clear=False)
# display the real and complex at the same time
dataset.plot(show_complex=True,
color='green',
xlim=(0., 30000.),
zlim=(-200., 200.))
show()
def test_nmr_1D_apodization(NMR_dataset_1D):
dataset = NMR_dataset_1D.copy()
dataset /= dataset.real.data.max() # normalize
lb = 0
arr, apod = dataset.em(lb=lb, retapod=True)
# arr and dataset should be equal as no em was applied
assert_equal(dataset.data, arr.data)
lb = 0.0
gb = 0.0
arr, apod = dataset.gm(lb=lb, gb=gb, retapod=True)
# arr and dataset should be equal as no em was applied
assert_equal(dataset.data, arr.data)
lb = 100
arr, apod = dataset.em(lb=lb, retapod=True)
# arr and dataset should not be equal as inplace=False
assert not np.array_equal(dataset.data, arr.data)
assert_array_almost_equal(apod[1], 0.9987, decimal=4)
# inplace=True
dataset.plot(xlim=(0.0, 6000.0))
dataset.em(lb=100.0 * ur.Hz, inplace=True)
dataset.plot(c="r", data_only=True, clear=False)
# successive call
dataset.em(lb=200.0 * ur.Hz, inplace=True)
dataset.plot(c="g", data_only=True, clear=False)
dataset = NMR_dataset_1D.copy()
dataset.plot()
dataset.em(100.0 * ur.Hz, inplace=True)
dataset.plot(c="r", data_only=True, clear=False)
# first test gm
dataset = NMR_dataset_1D.copy()
dataset /= dataset.real.data.max() # normalize
dataset.plot(xlim=(0.0, 6000.0))
dataset, apod = dataset.gm(lb=-100.0 * ur.Hz,
gb=100.0 * ur.Hz,
retapod=True)
dataset.plot(c="b", data_only=True, clear=False)
apod.plot(c="r", data_only=True, clear=False)
show()
def test_smooth_2D(IR_dataset_2D):
dataset = IR_dataset_2D.copy()
dataset /= dataset.real.data.max() # nromalize
dataset += np.random.random(dataset.shape[-1]) * 0.02
s = dataset.smooth(length=21)
(dataset + 0.25).plot(xlim=[4000, 3000])
s.plot(cmap="copper", clear=False, xlim=[4000, 3000])
s2 = s.smooth(length=21, dim="y")
(s2 - 0.25).plot(cmap="jet", clear=False, xlim=[4000, 3000])
show()
def test_basecor_sequential(IR_dataset_2D):
dataset = IR_dataset_2D[5]
basc = BaselineCorrection(dataset)
s = basc([6000.0, 3500.0], [2200.0, 1500.0],
method="sequential",
interpolation="pchip")
s.plot()
s1 = basc(
[6000.0, 3500.0],
[2200.0, 1500.0],
method="sequential",
interpolation="polynomial",
)
s1.plot(clear=False, color="red")
dataset = IR_dataset_2D[5] # with LinearCoord
basc = BaselineCorrection(dataset)
s2 = basc([6000.0, 3500.0], [2200.0, 1500.0],
method="sequential",
interpolation="pchip")
assert_dataset_almost_equal(s, s2, decimal=5)
s2.plot(clear=False, color="green")
s3 = basc(
[6000.0, 3500.0],
[2200.0, 1500.0],
method="sequential",
interpolation="polynomial",
)
assert_dataset_almost_equal(s1, s3, decimal=5)
s3.plot(clear=False, color="cyan")
dataset = IR_dataset_2D[:15]
basc = BaselineCorrection(dataset)
s = basc([6000.0, 3500.0], [2200.0, 1500.0],
method="sequential",
interpolation="pchip")
s.plot()
s = basc(
[6000.0, 3500.0],
[2200.0, 1500.0],
method="sequential",
interpolation="polynomial",
)
s.plot(cmap="copper")
show()
def _blcorrect_and_plot(self):
slice_x = _str_to_slice(self._x_limits_control.value, self._X, "x")
slice_y = _str_to_slice(self._y_limits_control.value, self._X, "y")
self.original = self._X[slice_y, slice_x]
ranges = _str_to_ranges(self._ranges_control.value)
if self.original is not None: # slicing was OK
# check that no range is outside coordinates
new_ranges, changed = _update_ranges(
_str_to_ranges(self._ranges_control.value),
self.original.x.data)
if changed:
ranges = _round_ranges(new_ranges)
self._ranges_control.value = _ranges_to_str(ranges)
blc = BaselineCorrection(self.original)
self.corrected = blc.compute(
*ranges,
interpolation=self._interpolation_selector.value,
order=self._order_slider.value,
method=self._method_selector.value,
npc=self._npc_slider.value,
)
self.baseline = self.original - self.corrected
self._output.clear_output(True)
with self._output:
axes = multiplot(
[
concatenate(self.original, self.baseline, dims="y"),
self.corrected,
],
labels=["Original", "Corrected"],
sharex=True,
nrow=2,
ncol=1,
fig=self._fig,
figsize=(8, 6),
dpi=96,
left=0.12,
mpl_event=False,
)
axes["axe11"].get_xaxis().set_visible(False)
blc.show_regions(axes["axe21"])
self._fig = axes["axe21"].figure
show()
self._done = True
def test_autosub(IR_dataset_2D):
dataset = IR_dataset_2D
ranges = [5000.0, 5999.0], [1940.0, 1820.0]
s1 = dataset.copy()
ref = s1[-1].squeeze()
dataset.plot_stack()
ref.plot(clear=False, linewidth=2.0, color="r")
s2 = dataset.copy()
s3 = s2.autosub(ref, *ranges, dim=-1, method="vardiff", inplace=False)
s3.plot()
# inplace = False
assert np.round(s2.data[-1, 0], 4) != 0.0000
assert np.round(s3.data[-1, 0], 4) == 0.0000
s3.name = "vardiff"
s3.plot_stack()
s4 = dataset.copy()
s4.autosub(ref, *ranges, method="ssdiff", inplace=True)
s4.name = "ssdiff, inplace"
assert np.round(s4.data[-1, 0], 4) == 0.0000
s4.plot_stack() # true avoid blocking due to graphs
s4 = dataset.copy()
s = autosub(s4, ref, *ranges, method="ssdiff")
assert np.round(s4.data[-1, 0], 4) != 0.0000
assert np.round(s.data[-1, 0], 4) == 0.0000
s.name = "ssdiff direct call"
s.plot_stack()
# s5 = dataset.copy()
# ref2 = s5[:, 0].squeeze()
# ranges2 = [0, 5], [45, 54]
# TODO: not yet implemented
# s6 = s5.autosub(ref2, *ranges2, dim='y', method='varfit', inplace=False)
# s6.plot()
show()
def test_issue_227():
# IR spectrum, we want to make a baseline correction on the absorbance vs. time axis:
ir = scp.read("irdata/nh4y-activation.spg")
# baseline correction along x
blc = scp.BaselineCorrection(ir)
s1 = blc([5999.0, 3500.0], [1800.0, 1500.0],
method="multivariate",
interpolation="pchip")
# baseline correction the transposed data along x (now on axis 0) -> should produce the same results
# baseline correction along axis -1 previuosly
blc = scp.BaselineCorrection(ir.T)
s2 = blc(
[5999.0, 3500.0],
[1800.0, 1500.0],
dim="x",
method="multivariate",
interpolation="pchip",
)
# compare
assert_dataset_equal(s1, s2.T)
ir.y = ir.y - ir[0].y
irs = ir[:, 2000.0:2020.0]
blc = scp.BaselineCorrection(irs)
blc.compute(*[[0.0, 2.0e3], [3.0e4, 3.3e4]],
dim="y",
interpolation="polynomial",
order=1,
method="sequential")
blc.corrected.plot()
# MS profiles, we want to make a baseline correction on the ion current vs. time axis:
ms = scp.read("msdata/ion_currents.asc", timestamp=False)
blc = scp.BaselineCorrection(ms[10.0:20.0, :])
blc.compute(*[[10.0, 11.0], [19.0, 20.0]],
dim="y",
interpolation="polynomial",
order=1,
method="sequential")
blc.corrected.T.plot()
show()
def test_nmr_2D_em_y(NMR_dataset_2D):
dataset = NMR_dataset_2D.copy()
assert dataset.shape == (96, 948)
dataset.plot_map() # plot original
dataset = NMR_dataset_2D.copy()
dataset.plot_map()
dataset.em(lb=50. * ur.Hz, dim=0)
assert dataset.shape == (96, 948)
dataset.plot_map(cmap='copper', data_only=True, clear=False) # em on dim=x
dataset = NMR_dataset_2D.copy()
dataset.plot_map()
dataset.em(lb=50. * ur.Hz, dim='y')
assert dataset.shape == (96, 948)
dataset.plot_map(cmap='copper', data_only=True, clear=False) # em on dim=x
show()
def test_nmr_multiple_manual_1D_phasing():
path = os.path.join(prefs.datadir, 'nmrdata', 'bruker', 'tests', 'nmr',
'topspin_1d')
ndd = NDDataset.read_topspin(path, expno=1, remove_digital_filter=True)
ndd /= ndd.real.data.max() # normalize
ndd.em(10. * ur.Hz) # inplace broadening
transf = ndd.fft(tdeff=8192, size=2**15)
transfph1 = transf.pk(verbose=True)
transfph1.plot(xlim=(20, -20), color='k')
transfph2 = transf.pk(verbose=True)
transfph2.plot(xlim=(20, -20), clear=False, color='r')
transfph3 = transf.pk(52.43836, -16.8366, verbose=True)
transfph3.plot(xlim=(20, -20), clear=False, color='b')
show()
def test_issue_375():
# minimal example
n_pc = 3
color1, color2 = "b", "r"
ratio = NDDataset([1, 2, 3])
cum = ratio.cumsum()
ax1 = ratio.plot_bar(color=color1, title="Scree plot")
assert len(ax1.lines) == 0, "no lines"
assert len(ax1.patches) == 3, "bar present"
ax2 = cum.plot_scatter(color=color2, pen=True, markersize=7.0, twinx=ax1)
assert len(ax2.lines) == 1, "1 lines"
assert len(ax2.patches) == 0, "no bar present on the second plot"
# TODO: Don't know yet how to get the marker present.
ax1.set_title("Scree plot")
show()
def test_nmr_2D_em_x(NMR_dataset_2D):
dataset = NMR_dataset_2D.copy()
assert dataset.shape == (96, 948)
dataset.plot_map() # plot original
dataset = NMR_dataset_2D.copy()
dataset.plot_map()
dataset.em(lb=50.0 * ur.Hz, axis=-1)
assert dataset.shape == (96, 948)
dataset.plot_map(cmap="copper", data_only=True, clear=False) # em on dim=x
dataset = NMR_dataset_2D.copy()
dataset.plot_map()
dataset.em(lb=50.0 * ur.Hz, dim="x")
assert dataset.shape == (96, 948)
dataset.plot_map(cmap="copper", data_only=True, clear=False) # em on dim=x
show()
pass
def test_nmr_multiple_auto_1D_phasing():
path = os.path.join(prefs.datadir, "nmrdata", "bruker", "tests", "nmr",
"topspin_1d")
ndd = NDDataset.read_topspin(path, expno=1, remove_digital_filter=True)
ndd /= ndd.real.data.max() # normalize
ndd.em(10.0 * ur.Hz) # inplace broadening
transf = ndd.fft(tdeff=8192, size=2**15)
transf.plot(xlim=(20, -20), ls=":", color="k")
t1 = transf.apk(algorithm="neg_peak", verbose=True)
(t1 - 5.0).plot(xlim=(20, -20), clear=False, color="b")
t2 = t1.apk(algorithm="neg_area", verbose=True)
(t2 - 10).plot(xlim=(20, -20), clear=False, ls="-.", color="m")
t3 = t2.apk(algorithm="acme", verbose=True)
(t3 - 15).plot(xlim=(20, -20), clear=False, color="r")
show()
def test_nmr_multiple_auto_1D_phasing():
path = os.path.join(prefs.datadir, 'nmrdata', 'bruker', 'tests', 'nmr',
'topspin_1d')
ndd = NDDataset.read_topspin(path, expno=1, remove_digital_filter=True)
ndd /= ndd.real.data.max() # normalize
ndd.em(10. * ur.Hz) # inplace broadening
transf = ndd.fft(tdeff=8192, size=2**15)
transf.plot(xlim=(20, -20), ls=':', color='k')
t1 = transf.apk(algorithm='neg_peak', verbose=True)
(t1 - 5.).plot(xlim=(20, -20), clear=False, color='b')
t2 = t1.apk(algorithm='neg_area', verbose=True)
(t2 - 10).plot(xlim=(20, -20), clear=False, ls='-.', color='m')
t3 = t2.apk(algorithm='acme', verbose=True)
(t3 - 15).plot(xlim=(20, -20), clear=False, color='r')
show()
def test_EFA(IR_dataset_2D):
ds = IR_dataset_2D.copy()
# columns masking
ds[:, 1230.0:920.0] = MASKED # do not forget to use float in slicing
ds[:, 5900.0:5890.0] = MASKED
# difference spectra
ds -= ds[-1]
# column masking for bad columns
ds[10:12] = MASKED
efa = EFA(ds)
n_pc = 4
c = efa.get_conc(n_pc)
c.T.plot()
show()
def test_pca():
dataset = NDDataset.read("irdata/nh4y-activation.spg")
# with masks
dataset[:, 1240.0:920.0] = MASKED # do not forget to use float in slicing
pca = PCA(dataset)
assert str(pca)[:3] == "\nPC"
# test alternative options
pca = PCA(dataset, centered=False, standardized=True, scaled=True)
assert str(pca)[:3] == "\nPC"
_, LT = pca.reduce(n_pc="auto")
assert LT.shape[0] == 15
dataset_hat = pca.reconstruct(n_pc=5)
assert (dataset_hat - dataset).max() < 0.4
# test with observations < variables
pca2 = PCA(dataset[:, 4000.0:3995.0])
_, LT2 = pca2.reduce(n_pc="auto")
assert LT2.shape[0] == 5
# test text and plots outputs
pca.printev(n_pc=5)
pca.screeplot(n_pc=5)
pca.screeplot(n_pc="auto")
pca.scoreplot((1, 2))
pca.scoreplot(1, 2, 3)
pca.plotmerit
show()
def test_pca():
dataset = NDDataset.read('irdata/nh4y-activation.spg')
# with masks
dataset[:, 1240.0:920.0] = MASKED # do not forget to use float in slicing
pca = PCA(dataset)
pca.printev(n_pc=5)
assert str(pca)[:3] == '\nPC'
pca.screeplot(n_pc=0.999)
pca.screeplot(n_pc='auto')
pca.scoreplot((1, 2))
pca.scoreplot(1, 2, 3)
show()
def test_ab_nmr(NMR_dataset_1D):
dataset = NMR_dataset_1D.copy()
dataset /= dataset.real.data.max() # nromalize
dataset.em(10.0 * ur.Hz, inplace=True)
dataset = dataset.fft(tdeff=8192, size=2**15)
dataset = dataset[150.0:-150.0] + 1.0
dataset.plot()
transf = dataset.copy()
transfab = transf.ab(window=0.25)
transfab.plot(clear=False, color="r")
transf = dataset.copy()
base = transf.ab(mode="poly", dryrun=True)
transfab = transf - base
transfab.plot(xlim=(150, -150), clear=False, color="b")
base.plot(xlim=(150, -150), ylim=[-2, 10], clear=False, color="y")
show()
def generate_fake():
from spectrochempy.utils import show
# define properties of the spectra and concentration profiles
# ----------------------------------------------------------------------------------------------------------------------
POS = (6000., 4000., 2000., 2500.)
WIDTH = (6000., 1000., 600., 800.)
AMPL = (100., 100., 20., 50.)
def C1(t):
return t * .05 + .01 # linear evolution of the baseline
def C2(t):
return np.exp(-t / .5) * .3 + .1
def C3(t):
return np.exp(-t / 3.) * .7
def C4(t):
return 1. - C2(t) - C3(t)
spec = _make_spectra_matrix(POS, WIDTH, AMPL)
spec.plot_stack(colorbar=False)
conc = _make_concentrations_matrix(C1, C2, C3, C4)
conc.plot_stack(colorbar=False)
d = _generate_2D_spectra(conc, spec)
# add some noise
d.data = np.random.normal(d.data, .007 * d.data.max())
d.plot_stack()
show()
d.save('test_full2D')
spec.save('test_spectra')
conc.save('test_concentration')
def test_simplisma():
print("")
data = NDDataset.read_matlab(os.path.join("matlabdata",
"als2004dataset.MAT"),
merge=False)
print(
"Dataset (Jaumot et al., Chemometr. Intell. Lab. 76 (2005) 101-110)):")
print("")
for mat in data:
print(" " + mat.name, str(mat.shape))
ds = data[-1]
assert ds.name == "m1"
print("\n test simplisma on {}\n".format(ds.name))
pure = SIMPLISMA(ds, n_pc=20, tol=0.2, noise=3, verbose=True)
pure.C.T.plot()
pure.St.plot()
pure.plotmerit()
assert "3 29 29.0 0.0072 0.9981" in pure.logs
show()