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()
# We just give here few examples
# %% [markdown] slideshow={"slide_type": "subslide"}
# #### Smoothing
# %% slideshow={"slide_type": "fragment"}
smoothed = region.smooth(window_length=51, window='hanning')
_ = smoothed.plot(colormap='magma')
# %% [markdown] slideshow={"slide_type": "subslide"}
# #### Baseline correction
# %% slideshow={"slide_type": "fragment"}
region = ds[:, 4000.0:2000.0]
smoothed = region.smooth(window_length=51, window='hanning')
blc = scp.BaselineCorrection(smoothed)
basc = blc.compute([2000., 2300.], [3800., 3900.], method='multivariate', interpolation='pchip', npc=5)
_ = basc.plot()
# %% [markdown] slideshow={"slide_type": "subslide"}
# ## IRIS processing
# %% slideshow={"slide_type": "subslide"}
ds = scp.read('irdata/CO@Mo_Al2O3.SPG')[:, 2250.:1950.]
pressure = [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]
ds.y = scp.Coord(pressure, title='Pressure', units='torr')
_ = ds.plot(colormap='magma')
# %% slideshow={"slide_type": "subslide"}
# %% [markdown]
# We set some plotting preferences and then plot the raw data
# %%
prefs = X.preferences
prefs.figure.figsize = (6, 3)
prefs.colormap = 'Dark2'
prefs.colorbar = True
X.plot()
# %% [markdown]
# Now we can perform some baseline correction
# %%
blc = scp.BaselineCorrection(X)
regions = ([1740., 1800.0], [1550., 1570.], [1250., 1300.]
) # define 3 regions where we want the baseline to reach zero.
Xcorr = blc.compute(*regions) # compute the corrected NDDataset
Xcorr.plot()
# %% [markdown]
# To integrate each row on the full range, we can use the sum or trapz method of a NDDataset.
# %%
inttrapz = Xcorr.trapz(dim='x')
intsimps = Xcorr.simps(dim='x')
# %% [markdown]
# As you can see both method give almost the same results in this case
###############################################################################
# Load data:
nd = scp.NDDataset.read_omnic('irdata/nh4y-activation.spg')
###############################################################################
# Do some slicing to keep only the interesting region:
ndp = (nd - nd[-1])[:, 1291.0:5999.0]
# Important: notice that we use floating point number
# integer would mean points, not wavenumbers!
###############################################################################
# Define the BaselineCorrection object:
ibc = scp.BaselineCorrection(ndp)
###############################################################################
# Launch the interactive view, using the `BaselineCorrection.run` method:
ranges = [[1556.30, 1568.26], [1795.00, 1956.75], [3766.03, 3915.81],
[4574.26, 4616.04], [4980.10, 4998.01],
[5437.52, 5994.70]] # predefined ranges
span = ibc.run(*ranges,
method='multivariate',
interpolation='pchip',
npc=5,
zoompreview=3)
###############################################################################
# Print the corrected dataset: