def test_read_labspec():
ramandir = Path('ramandata')
scp.info_(ramandir)
A = scp.read_labspec('Activation.txt', directory=ramandir)
A.plot()
A = scp.read_labspec('532nm-191216-Si 200mu.txt', directory=ramandir)
A.plot()
A = scp.read_labspec('serie190214-1.txt', directory=ramandir)
A.plot(colorbar=True)
A.plot_map(colorbar=True)
A = scp.read_labspec('SMC1-Initial RT.txt', directory=ramandir)
A.plot()
B = scp.read(protocol='labspec', directory=ramandir)
# this pack all spectra of the subdir directory
B = scp.read_dir(directory=ramandir / 'subdir')
B.plot()
scp.show()
def test_basecor_multivariate(IR_dataset_2D):
dataset = IR_dataset_2D[5]
basc = BaselineCorrection(dataset)
s = basc([6000., 3500.], [1800., 1500.],
method='multivariate',
interpolation='pchip')
s.plot()
s = basc([6000., 3500.], [1800., 1500.],
method='multivariate',
interpolation='polynomial')
s.plot(clear=False, color='red')
dataset = IR_dataset_2D[:15]
basc = BaselineCorrection(dataset)
s = basc([6000., 3500.], [1800., 1500.],
method='multivariate',
interpolation='pchip')
s.plot()
s = basc([6000., 3500.], [1800., 1500.],
method='multivariate',
interpolation='polynomial')
s.plot(cmap='copper')
show()
def test_read_topspin():
# Open a dialog for selecting a Topspin directory
A = scp.read_topspin(directory=nmrdir)
assert A.name == 'topspin_2d expno:1 procno:1 (SER)'
# A.plot()
A = scp.read_topspin(nmrdir / 'exam2d_HC/3/pdata/1/2rr')
A.plot_map()
# Select a TOPSPIN spectra using the full name
B = scp.read_topspin(nmrdir / 'topspin_1d/1/fid')
assert str(B) == 'NDDataset: [complex128] unitless (size: 12411)'
C = scp.read_topspin(nmrdir / 'topspin_1d/1/pdata/1/1r')
assert str(C) == 'NDDataset: [complex128] unitless (size: 16384)'
C.plot_map()
# Select a TOPSPIN spectra using the full name
B = scp.read_topspin(nmrdir / 'topspin_2d/1/ser')
assert str(B) == 'NDDataset: [quaternion] unitless (shape: (y:96, x:948))'
B.plot_surface()
C = scp.read_topspin(nmrdir / 'topspin_2d/1/pdata/1/2rr')
assert str(
C) == 'NDDataset: [quaternion] unitless (shape: (y:1024, x:2048))'
C.plot_image()
# alternative syntax
D = scp.read_topspin(nmrdir / 'topspin_2d', expno=1, procno=1)
assert D == C
scp.show()
def test_autosub(IR_dataset_2D):
dataset = IR_dataset_2D
ranges = [5000., 5999.], [1940., 1820.]
s1 = dataset.copy()
ref = s1[-1].squeeze()
dataset.plot_stack()
ref.plot(clear=False, linewidth=2., 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_plot2D_as_3D():
data = NDDataset.read_matlab(
os.path.join('matlabdata', 'als2004dataset.MAT'))
X = data[0]
# X.plot_3D()
X.plot_surface()
X.set_coordset(y=Coord(title='elution time'), x=Coord(title='wavenumbers'))
X.title = 'intensity'
X.plot_surface()
X.plot_surface(colorbar=True)
show()
pass
def test_basecor_sequential(IR_dataset_2D):
dataset = IR_dataset_2D[5]
basc = BaselineCorrection(dataset)
s = basc([6000., 3500.], [2200., 1500.],
method='sequential',
interpolation='pchip')
s.plot()
s1 = basc([6000., 3500.], [2200., 1500.],
method='sequential',
interpolation='polynomial')
s1.plot(clear=False, color='red')
dataset = IR_dataset_2D[5] # with LinearCoord
basc = BaselineCorrection(dataset)
s2 = basc([6000., 3500.], [2200., 1500.],
method='sequential',
interpolation='pchip')
assert_dataset_almost_equal(s, s2, decimal=5)
s2.plot(clear=False, color='green')
s3 = basc([6000., 3500.], [2200., 1500.],
method='sequential',
interpolation='polynomial')
assert_dataset_almost_equal(s1, s3, decimal=5)
s3.plot(clear=False, color='cyan')
show()
dataset = IR_dataset_2D[:15]
basc = BaselineCorrection(dataset)
s = basc([6000., 3500.], [2200., 1500.],
method='sequential',
interpolation='pchip')
s.plot()
s = basc([6000., 3500.], [2200., 1500.],
method='sequential',
interpolation='polynomial')
s.plot(cmap='copper')
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., 3500.], [1800., 1500.],
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., 3500.], [1800., 1500.],
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., 2.e3], [3.0e4, 3.3e4]],
dim='y',
interpolation='polynomial',
order=1,
method='sequential')
blc.corrected.plot()
scp.show()
# 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.:20., :])
blc.compute(*[[10., 11.], [19., 20.]],
dim='y',
interpolation='polynomial',
order=1,
method='sequential')
blc.corrected.T.plot()
scp.show()
def test_plot2D():
A = NDDataset.read_omnic('irdata/nh4y-activation.spg')
A.y -= A.y[0]
A.y.to('hour', inplace=True)
A.y.title = u'Aquisition time'
A.copy().plot_stack()
A.copy().plot_stack(data_transposed=True)
A.copy().plot_image(style=['sans', 'paper'], fontsize=9)
# use preferences
prefs = A.preferences
prefs.reset()
prefs.image.cmap = 'magma'
prefs.font.size = 10
prefs.font.weight = 'bold'
prefs.axes.grid = True
A.plot()
A.plot(style=['sans', 'paper', 'grayscale'], colorbar=False)
show()
pass
def test_plot2D():
A = NDDataset.read_omnic("irdata/nh4y-activation.spg")
A.y -= A.y[0]
A.y.to("hour", inplace=True)
A.y.title = "Acquisition time"
A.copy().plot_stack()
A.copy().plot_stack(data_transposed=True)
A.copy().plot_image(style=["sans", "paper"], fontsize=9)
# use preferences
prefs = A.preferences
prefs.reset()
prefs.image.cmap = "magma"
prefs.font.size = 10
prefs.font.weight = "bold"
prefs.axes.grid = True
A.plot()
A.plot(style=["sans", "paper", "grayscale"], colorbar=False)
show()
def test_plot2D_as_3D():
data = NDDataset.read_matlab(
os.path.join("matlabdata", "als2004dataset.MAT"))
X = data[0]
X.plot_surface()
X.set_coordset(
y=Coord(title="elution time", units="s"),
x=Coord(title="wavenumbers", units="cm^-1"),
)
X.title = "intensity"
X.plot_surface()
X.plot_surface(colorbar=True)
show()
pass
def test_ab_nmr(NMR_dataset_1D):
dataset = NMR_dataset_1D.copy()
dataset /= dataset.real.data.max() # nromalize
dataset.em(10. * ur.Hz, inplace=True)
dataset = dataset.fft(tdeff=8192, size=2**15)
dataset = dataset[150.0:-150.] + 1.
dataset.plot()
transf = dataset.copy()
transfab = transf.ab(window=.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 test_fit_single_dataset(IR_dataset_2D, script):
dataset = IR_dataset_2D[54, 3700.:3400.]
f1 = Fit(dataset, script, silent=True)
f1.run(maxiter=10, every=1)
# assert_approx_equal(dataset.model_A, -116.40475, significant=4)
# assert_approx_equal(f1.fp['width_line_2'], 195.7273, significant=4)
dataset.plot(plot_model=True)
dataset2 = dataset.copy() * 2.34
f2 = Fit(dataset2, script, silent=True)
f2.run(maxiter=10, every=1)
dataset2.plot(plot_model=True)
assert_approx_equal(dataset2.model_A, 272.3309560470805, significant=4)
assert_approx_equal(f2.fp['width_line_2'], 195.7273, significant=4)
f2 = Fit(dataset2, script, silent=False)
f2.run(maxiter=1000, every=1)
dataset2.plot(plot_model=True)
show()
def test_ndmath_and_api_methods(IR_dataset_1D, IR_dataset_2D):
# CREATION _LIKE METHODS
# ----------------------
# from a list
x = [1, 2, 3]
# _like as an API method
ds = NDDataset(x).full_like(2.5, title='empty')
ds = scp.full_like(x, 2)
assert np.all(ds.data == np.full((3, ), 2))
assert ds.implements('NDDataset')
# _like as a classmethod
ds = NDDataset.full_like(x, 2)
assert np.all(ds.data == np.full((3, ), 2))
assert ds.implements('NDDataset')
# _like as an instance method
ds = NDDataset(x).full_like(2)
assert np.all(ds.data == np.full((3, ), 2))
assert ds.implements('NDDataset')
# _like as an instance method
ds = NDDataset(x).empty_like(title='empty')
assert ds.implements('NDDataset')
assert ds.title == 'empty'
# from an array
x = np.array([1, 2, 3])
ds = NDDataset(x).full_like(2)
assert np.all(ds.data == np.full((3, ), 2))
assert ds.implements('NDDataset')
# from a NDArray subclass with units
x = NDDataset([1, 2, 3], units='km')
ds = scp.full_like(x, 2)
assert np.all(ds.data == np.full((3, ), 2))
assert ds.implements('NDDataset')
assert ds.units == ur.km
ds1 = scp.full_like(ds, np.nan, dtype=np.double, units='m')
assert ds1.units == Unit('m')
# change of units is forced
ds2 = scp.full_like(ds, 2, dtype=np.double, units='s')
assert ds2.units == ur.s
# other like creation functions
nd = scp.empty_like(ds, dtype=np.double, units='m')
assert str(nd) == 'NDDataset: [float64] m (size: 3)'
assert nd.dtype == np.dtype(np.double)
nd = scp.zeros_like(ds, dtype=np.double, units='m')
assert str(nd) == 'NDDataset: [float64] m (size: 3)'
assert np.all(nd.data == np.zeros((3, )))
nd = scp.ones_like(ds, dtype=np.double, units='m')
assert str(nd) == 'NDDataset: [float64] m (size: 3)'
assert np.all(nd.data == np.ones((3, )))
# FULL
# ----
ds = NDDataset.full((6, ), 0.1)
assert ds.size == 6
assert str(ds) == 'NDDataset: [float64] unitless (size: 6)'
# ZEROS
# -----
ds = NDDataset.zeros((6, ), units='km')
assert ds.size == 6
assert str(ds) == 'NDDataset: [float64] km (size: 6)'
# ONES
# ----
ds = NDDataset.ones((6, ))
ds = scp.full((6, ), 0.1)
assert ds.size == 6
assert str(ds) == 'NDDataset: [float64] unitless (size: 6)'
ds = NDDataset.ones((6, ), units='absorbance', dtype='complex128')
assert ds.size == 3
assert str(ds) == 'NDDataset: [complex128] a.u. (size: 3)'
assert ds[0].data == 1. + 1.j
# LINSPACE
# --------
c2 = Coord.linspace(1, 20, 200, units='m', name='mycoord')
assert c2.name == 'mycoord'
assert c2.size == 200
assert c2[-1].data == 20
assert c2[0].values == Quantity(1, 'm')
# ARANGE
# -------
c3 = Coord.arange(1, 20.0001, 1, units='s', name='mycoord')
assert c3.name == 'mycoord'
assert c3.size == 20
assert c3[-1].data == 20
assert c3[0].values == Quantity(1, 's')
# EYE
# ----
ds1 = scp.NDDataset.eye(2, dtype=int)
assert str(ds1) == 'NDDataset: [float64] unitless (shape: (y:2, x:2))'
ds = scp.eye(3, k=1, units='km')
assert (ds.data == np.eye(3, k=1)).all()
assert ds.units == ur.km
# IDENTITY
# --------
ds = scp.identity(3, units='km')
assert (ds.data == np.identity(3, )).all()
assert ds.units == ur.km
# RANDOM
# ------
ds = scp.random((3, 3), units='km')
assert str(ds) == 'NDDataset: [float64] km (shape: (y:3, x:3))'
# adding coordset
c1 = Coord.linspace(1, 20, 200, units='m', name='axe_x')
ds = scp.random((200, ), units='km', coordset=scp.CoordSet(x=c1))
# DIAGONAL
# --------
# extract diagonal
nd = scp.full((2, 2), 0.5, units='s', title='initial')
assert str(nd) == "NDDataset: [float64] s (shape: (y:2, x:2))"
ndd = scp.diagonal(nd, title='diag')
assert str(ndd) == 'NDDataset: [float64] s (size: 2)'
assert ndd.units == Unit('s')
cx = scp.Coord([0, 1])
cy = scp.Coord([2, 5])
nd = NDDataset.full((2, 2),
0.5,
units='s',
coordset=scp.CoordSet(cx, cy),
title='initial')
assert str(nd) == "NDDataset: [float64] s (shape: (y:2, x:2))"
ndd = nd.diagonal(title='diag2')
assert str(ndd) == 'NDDataset: [float64] s (size: 2)'
assert ndd.units == Unit('s')
assert ndd.title == 'diag2'
cx = scp.Coord([0, 1, 2])
cy = scp.Coord([2, 5])
nd = NDDataset.full((2, 3),
0.5,
units='s',
coordset=scp.CoordSet(x=cx, y=cy),
title='initial')
assert str(nd) == "NDDataset: [float64] s (shape: (y:2, x:3))"
ndd = nd.diagonal(title='diag3')
assert str(ndd) == 'NDDataset: [float64] s (size: 2)'
assert ndd.units == Unit('s')
assert ndd.title == 'diag3'
assert_array_equal(nd.x.data[:ndd.x.size], ndd.x.data)
ndd = nd.diagonal(title='diag4', dim='y')
assert str(ndd) == 'NDDataset: [float64] s (size: 2)'
assert ndd.units == Unit('s')
assert ndd.title == 'diag4'
assert_array_equal(nd.y.data[:ndd.y.size], ndd.y.data)
# DIAG
# ----
ref = NDDataset(np.diag((3, 3.4, 2.3)), units='m', title='something')
# Three forms should return the same NDDataset
ds = scp.diag((3, 3.4, 2.3), units='m', title='something')
assert_dataset_equal(ds, ref)
ds = NDDataset.diag((3, 3.4, 2.3), units='m', title='something')
assert_dataset_equal(ds, ref)
ds = NDDataset((3, 3.4, 2.3)).diag(units='m', title='something')
assert_dataset_equal(ds, ref)
# and this too
ds1 = NDDataset((3, 3.4, 2.3), units='s', title='another')
ds = scp.diag(ds1, units='m', title='something')
assert_dataset_equal(ds, ref)
ds = ds1.diag(units='m', title='something')
assert_dataset_equal(ds, ref)
# BOOL : ALL and ANY
# ------------------
ds = NDDataset([[True, False], [True, True]])
b = np.all(ds)
assert not b
b = scp.all(ds)
assert not b
b = ds.all()
assert not b
b = NDDataset.any(ds)
assert b
b = ds.all(dim='y')
assert_array_equal(b, np.array([True, False]))
b = ds.any(dim='y')
assert_array_equal(b, np.array([True, True]))
# ARGMAX, MAX
# -----------
nd1 = IR_dataset_1D.copy()
nd1[1290.:890.] = MASKED
assert nd1.is_masked
assert str(nd1) == 'NDDataset: [float64] a.u. (size: 5549)'
idx = nd1.argmax()
assert idx == 3122
mx = nd1.max()
# alternative
mx = scp.max(nd1)
mx = NDDataset.max(nd1)
assert mx == Quantity(3.8080601692199707, 'absorbance')
mxk = nd1.max(keepdims=True)
assert isinstance(mxk, NDDataset)
assert str(mxk) == 'NDDataset: [float64] a.u. (size: 1)'
assert mxk.values == mx
# test on a 2D NDDataset
nd2 = IR_dataset_2D.copy()
nd2[:, 1290.:890.] = MASKED
mx = nd2.max() # no axis specified
assert mx == Quantity(3.8080601692199707, 'absorbance')
mxk = nd2.max(keepdims=True)
assert str(mxk) == 'NDDataset: [float64] a.u. (shape: (y:1, x:1))'
nd2m = nd2.max('y') # axis selected
ax = nd2m.plot()
nd2[0].plot(ax=ax, clear=False)
scp.show()
nd2m2 = nd2.max('x') # axis selected
nd2m2.plot()
scp.show()
nd2m = nd2.max('y', keepdims=True)
assert nd2m.shape == (1, 5549)
nd2m = nd2.max('x', keepdims=True)
assert nd2m.shape == (55, 1)
mx = nd2.min() # no axis specified
assert mx == Quantity(-0.022955093532800674, 'absorbance')
mxk = nd2.min(keepdims=True)
assert str(mxk) == 'NDDataset: [float64] a.u. (shape: (y:1, x:1))'
nd2m = nd2.min('y') # axis selected
ax = nd2m.plot()
nd2[0].plot(ax=ax, clear=False)
scp.show()
nd2m2 = nd2.min('x') # axis selected
nd2m2.plot()
scp.show()
nd2m = nd2.min('y', keepdims=True)
assert nd2m.shape == (1, 5549)
nd2m = nd2.min('x', keepdims=True)
assert nd2m.shape == (55, 1)
# CLIP
# ----
nd3 = nd2 - 2.
assert nd3.units == nd2.units
nd3c = nd3.clip(-.5, 1.)
assert nd3c.max().m == 1.
assert nd3c.min().m == -.5
# COORDMIN AND COORDMAX
# ---------------------
cm = nd2.coordmin()
assert np.around(cm['x'], 3) == Quantity(1290.165, 'cm^-1')
cm = nd2.coordmin(dim='y')
assert cm.size == 1
cm = nd2.coordmax(dim='y')
assert cm.size == 1
cm = nd2.coordmax(dim='x')
assert cm.size == 1
# ABS
# ----
nd2a = scp.abs(nd2)
mxa = nd2a.min()
assert mxa > 0
nd2a = NDDataset.abs(nd2)
mxa = nd2a.min()
assert mxa > 0
nd2a = np.abs(nd2)
mxa = nd2a.min()
assert mxa > 0
ndd = NDDataset([1., 2. + 1j, 3.])
val = np.abs(ndd)
val = ndd[1] * 1.2 - 10.
val = np.abs(val)
# FROMFUNCTION
# ------------
# 1D
def func1(t, v):
d = v * t
return d
time = Coord.linspace(
0,
9,
10,
)
distance = NDDataset.fromfunction(func1, v=134, coordset=CoordSet(t=time))
assert distance.dims == ['t']
assert_array_equal(distance.data, np.fromfunction(func1, (10, ), v=134))
time = Coord.linspace(0, 90, 10, units='min')
distance = NDDataset.fromfunction(func1,
v=Quantity(134, 'km/hour'),
coordset=CoordSet(t=time))
assert distance.dims == ['t']
assert_array_equal(distance.data,
np.fromfunction(func1, (10, ), v=134) * 10 / 60)
# 2D
def func2(x, y):
d = x + 1 / y
return d
c0 = Coord.linspace(0, 9, 3)
c1 = Coord.linspace(10, 20, 2)
# implicit ordering of coords (y,x)
distance = NDDataset.fromfunction(func2, coordset=CoordSet(c1, c0))
assert distance.shape == (2, 3)
assert distance.dims == ['y', 'x']
# or equivalent
distance = NDDataset.fromfunction(func2, coordset=[c1, c0])
assert distance.shape == (2, 3)
assert distance.dims == ['y', 'x']
# explicit ordering of coords (y,x) #
distance = NDDataset.fromfunction(func2, coordset=CoordSet(u=c0, v=c1))
assert distance.shape == (2, 3)
assert distance.dims == ['v', 'u']
assert distance[0, 2].data == distance.u[2].data + 1. / distance.v[0].data
# with units
def func3(x, y):
d = x + y
return d
c0u = Coord.linspace(0, 9, 3, units='km')
c1u = Coord.linspace(10, 20, 2, units='m')
distance = NDDataset.fromfunction(func3, coordset=CoordSet(u=c0u, v=c1u))
assert distance.shape == (2, 3)
assert distance.dims == ['v', 'u']
assert distance[0, 2].values == distance.u[2].values + distance.v[0].values
c0u = Coord.linspace(0, 9, 3, units='km')
c1u = Coord.linspace(10, 20, 2, units='m^-1')
distance = NDDataset.fromfunction(func2, coordset=CoordSet(u=c0u, v=c1u))
assert distance.shape == (2, 3)
assert distance.dims == ['v', 'u']
assert distance[
0, 2].values == distance.u[2].values + 1. / distance.v[0].values
# FROMITER
# --------
iterable = (x * x for x in range(5))
nit = scp.fromiter(iterable, float, units='km')
assert str(nit) == 'NDDataset: [float64] km (size: 5)'
assert_array_equal(nit.data, np.array([0, 1, 4, 9, 16]))
# MEAN, AVERAGE
# -----
nd = IR_dataset_2D.copy()
m = scp.mean(nd)
assert m == Quantity(np.mean(nd.data), "absorbance")
m = scp.average(nd)
assert m == Quantity(np.average(nd.data), "absorbance")
mx = scp.mean(nd, keepdims=True)
assert mx.shape == (1, 1)
mxd = scp.mean(nd, dim='y')
assert str(mxd) == 'NDDataset: [float64] a.u. (size: 5549)'
assert str(mxd.x) == 'LinearCoord: [float64] cm^-1 (size: 5549)'
def test_1D():
dataset = NDDataset.read_omnic(
os.path.join(prefs.datadir, 'irdata', 'nh4y-activation.spg'))
# get first spectrum
nd0 = dataset[0]
# plot generic
nd0.plot()
# nd0.plot(output=os.path.join(figures_dir, 'IR_dataset_1D'),
# savedpi=150)
#
# # plot generic style
# nd0.plot(style='poster',
# output=os.path.join(figures_dir, 'IR_dataset_1D_poster'),
# savedpi=150)
#
# # check that style reinit to default
# nd0.plot(output='IR_dataset_1D', savedpi=150)
# # try:
# # assert same_images('IR_dataset_1D.png',
# # os.path.join(figures_dir, 'IR_dataset_1D.png'))
# # except AssertionError:
# # os.remove('IR_dataset_1D.png')
# # raise AssertionError('comparison fails')
# # os.remove('IR_dataset_1D.png')
#
# # try other type of plots
# nd0.plot_pen()
# nd0[:, ::100].plot_scatter()
# nd0.plot_lines()
# nd0[:, ::100].plot_bar()
#
# show()
#
# # multiple
# d = dataset[:, ::100]
# datasets = [d[0], d[10], d[20], d[50], d[53]]
# labels = ['sample {}'.format(label) for label in
# ["S1", "S10", "S20", "S50", "S53"]]
#
# # plot multiple
# plot_multiple(method='scatter',
# datasets=datasets, labels=labels, legend='best',
# output=os.path.join(figures_dir,
# 'multiple_IR_dataset_1D_scatter'),
# savedpi=150)
#
# # plot mupltiple with style
# plot_multiple(method='scatter', style='sans',
# datasets=datasets, labels=labels, legend='best',
# output=os.path.join(figures_dir,
# 'multiple_IR_dataset_1D_scatter_sans'),
# savedpi=150)
#
# # check that style reinit to default
# plot_multiple(method='scatter',
# datasets=datasets, labels=labels, legend='best',
# output='multiple_IR_dataset_1D_scatter',
# savedpi=150)
# try:
# assert same_images('multiple_IR_dataset_1D_scatter',
# os.path.join(figures_dir,
# 'multiple_IR_dataset_1D_scatter'))
# except AssertionError:
# os.remove('multiple_IR_dataset_1D_scatter.png')
# raise AssertionError('comparison fails')
# os.remove('multiple_IR_dataset_1D_scatter.png')
# plot 1D column
col = dataset[:, 3500.] # note the indexing using wavenumber!
_ = col.plot_scatter()
show()
def test_MCRALS():
"""Test MCRALS with synthetic data"""
def gaussian(x, h, c, w, noise):
return h * (np.exp(-1 / 2 * ((x.data - c) / w) ** 2)) + noise * np.random.randn(
len(x)
) # a gaussian with added noise
def expon(t, c0, l, noise):
return c0 * (np.exp(l * t.data)) + noise * np.random.randn(len(t.data))
def get_C(C):
return C
n_PS = 2 # number of pure species
n_t = 10
n_wl = 10
h = [1, 1]
c = [250, 750]
w = [100, 100]
noise_spec = [0.0, 0.0]
noise_conc = [0.0, 0.0]
c0 = [10, 1]
l = np.array([-2, 2]) * 1e-2
t_c = Coord(np.arange(0, 100, 100 / n_t), title="time", units="s")
wl_c = Coord(np.arange(0, 1000, 1000 / n_wl), title="wavelength", units="nm")
PS_c = Coord(
range(n_PS),
title="species",
units=None,
labels=["PS#" + str(i) for i in range(n_PS)],
)
St = NDDataset.zeros((n_PS, len(wl_c)), coordset=(PS_c, wl_c))
C = NDDataset.zeros((len(t_c), n_PS), coordset=(t_c, PS_c))
St0 = NDDataset.zeros((n_PS, len(wl_c)), coordset=(PS_c, wl_c))
C0 = NDDataset.zeros((len(t_c), n_PS), coordset=(t_c, PS_c))
for i, id in enumerate((0, 1)):
C.data[:, i] = expon(t_c, c0[id], l[id], noise_conc[id])
St.data[i, :] = gaussian(wl_c, h[id], c[id], w[id], noise_spec[id])
C0.data[:, i] = expon(t_c, c0[id], l[id], 0)
St0.data[i, :] = gaussian(wl_c, h[id], c[id], w[id], 0)
D = dot(C, St)
D.title = "intensity"
#############################
# Test normal functioning
# guess = C0
mcr = MCRALS(D, C0, tol=30.0)
assert "converged !" in mcr.log[-15:]
# test attributes
for attr in [
"log",
"logs",
"params",
"Chard",
"Stsoft",
"St",
"C",
"extOutput",
"fixedC",
"X",
]:
assert hasattr(mcr, attr)
# test plot
mcr.plotmerit()
show()
# test diverging
mcr = MCRALS(
D,
C0,
monoIncConc=[0, 1],
monoIncTol=1.0,
unimodSpec=[0, 1],
normSpec="euclid",
closureConc=[0, 1],
closureMethod="constantSum",
maxdiv=1,
)
assert "Stop ALS optimization" in mcr.log[-40:]
# guess = C0, hard modeling
mcr = MCRALS(D, C0, hardConc=[0, 1], getConc=get_C, argsGetConc=(C0,), tol=30.0)
assert "converged !" in mcr.log[-15:]
# guess = C, test with deprecated parameters
# and few other parameters set to non-default values to improve coverage
mcr = MCRALS(
D,
C0,
# deprecated:
unimodMod="strict",
unimodTol=1.1,
verbose=True,
# other parameters set to non-default values
monoIncConc=[0],
monoDecConc=[1],
closureConc=[0, 1],
nonnegConc=None,
unimodConc=None,
unimodSpec="all",
nonnegSpec=None,
normSpec="max",
maxit=1,
)
set_loglevel("WARN")
# guess = C0.data, test with other parameters
mcr = MCRALS(
D,
C0.data,
normSpec="euclid",
closureConc=[0, 1],
closureMethod="constantSum",
maxit=1,
)
assert "Convergence criterion ('tol')" in mcr.log[-100:]
# guess = St as ndarray
mcr = MCRALS(D, St0.data, tol=15.0)
assert "converged !" in mcr.log[-15:]
#########################
# Text exceptions
# guess with wrong number of dimensions
try:
mcr = MCRALS(
D,
np.random.rand(n_t - 1, n_PS),
)
except ValueError as e:
assert e.args[0] == "the dimensions of guess do not match the data"
# guess with wrong nonnegConc parameter
try:
mcr = MCRALS(D, C, nonnegConc=[2])
except ValueError as e:
assert "please check nonnegConc" in e.args[0]
try:
mcr = MCRALS(D, C, nonnegConc=[0, 1, 1])
except ValueError as e:
assert "please check nonnegConc" in e.args[0]
# guess with wrong unimodConc parameter
try:
mcr = MCRALS(D, C, unimodConc=[2])
except ValueError as e:
assert "please check unimodConc" in e.args[0]
try:
mcr = MCRALS(D, C, unimodConc=[0, 1, 1])
except ValueError as e:
assert "please check unimodConc" in e.args[0]
# wrong closureTarget
try:
mcr = MCRALS(D, C, closureTarget=[0, 1, 1])
except ValueError as e:
assert "please check closureTarget" in e.args[0]
# wrong hardC_to_C_idx
try:
mcr = MCRALS(D, C, hardC_to_C_idx=[2])
except ValueError as e:
assert "please check hardC_to_C_idx" in e.args[0]
try:
mcr = MCRALS(D, C, hardC_to_C_idx=[0, 1, 1])
except ValueError as e:
assert "please check hardC_to_C_idx" in e.args[0]
# wrong unimodSpec
try:
mcr = MCRALS(D, C, unimodSpec=[2])
except ValueError as e:
assert "please check unimodSpec" in e.args[0]
try:
mcr = MCRALS(D, C, unimodSpec=[0, 1, 1])
except ValueError as e:
assert "please check unimodSpec" in e.args[0]
# wrong nonnegSpec
try:
mcr = MCRALS(D, C, nonnegSpec=[2])
except ValueError as e:
assert "please check nonnegSpec" in e.args[0]
try:
mcr = MCRALS(D, C, nonnegSpec=[0, 1, 1])
except ValueError as e:
assert "please check nonnegSpec" in e.args[0]
# -*- coding: utf-8 -*-
# flake8: noqa
# ======================================================================================================================
# Copyright (©) 2015-2020 LCS - Laboratoire Catalyse et Spectrochimie, Caen, France. =
# CeCILL-B FREE SOFTWARE LICENSE AGREEMENT - See full LICENSE agreement in the root directory =
# ======================================================================================================================
"""
Loading Bruker OPUS files
============================================
Here we load an experimental Bruker OPUS files and plot it.
"""
import spectrochempy as scp
Z = scp.read_opus(['test.0000', 'test.0001', 'test.0002', 'test.0003'],
directory='irdata/OPUS')
print(Z)
# %%
# plot it
Z.plot()
scp.show(
) # uncomment to show plot if needed (not necessary in jupyter notebook)
