.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "gettingstarted/examples/gallery/auto_examples_processing/nmr/plot_processing_nmr.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_gettingstarted_examples_gallery_auto_examples_processing_nmr_plot_processing_nmr.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_gettingstarted_examples_gallery_auto_examples_processing_nmr_plot_processing_nmr.py:


Processing NMR spectra (slicing, baseline correction, peak picking, peak fitting)
=================================================================================
Various examples of processing NMR spectra

.. GENERATED FROM PYTHON SOURCE LINES 15-17

Import API
----------

.. GENERATED FROM PYTHON SOURCE LINES 17-19

.. code-block:: Python

    import spectrochempy as scp








.. GENERATED FROM PYTHON SOURCE LINES 20-23

Importing a 2D NMR spectra
--------------------------
Define the folder where are the spectra

.. GENERATED FROM PYTHON SOURCE LINES 23-31

.. code-block:: Python

    datadir = scp.preferences.datadir
    nmrdir = datadir / "nmrdata"

    dataset = scp.read_topspin(
        nmrdir / "bruker" / "tests" / "nmr" / "topspin_2d" / "1" / "pdata" / "1" / "2rr"
    )









.. GENERATED FROM PYTHON SOURCE LINES 32-35

Analysing the 2D NMD dataset
----------------------------
Print dataset summary

.. GENERATED FROM PYTHON SOURCE LINES 35-37

.. code-block:: Python

    dataset






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <table style='background:transparent'>
    <tr><td style='padding-right:5px; padding-bottom:0px; padding-top:0px; width:124px'><font color='green'>         name</font> </td><td style='text-align:left; padding-bottom:0px; padding-top:0px; border:.5px solid lightgray;  '> topspin_2d expno:1 procno:1 (2D)</td><tr>
    <tr><td style='padding-right:5px; padding-bottom:0px; padding-top:0px; width:124px'><font color='green'>       author</font> </td><td style='text-align:left; padding-bottom:0px; padding-top:0px; border:.5px solid lightgray;  '> runner@fv-az1670-365</td><tr>
    <tr><td style='padding-right:5px; padding-bottom:0px; padding-top:0px; width:124px'><font color='green'>      created</font> </td><td style='text-align:left; padding-bottom:0px; padding-top:0px; border:.5px solid lightgray;  '> 2025-02-18 09:38:33+00:00</td><tr>
    <tr><td style='padding-right:5px; padding-bottom:0px; padding-top:0px; width:124px'><strong>          DATA </strong></td><td style='text-align:left; padding-bottom:0px; padding-top:0px; padding-top:10px; '><hr/></td><tr>
    <tr><td style='padding-right:5px; padding-bottom:0px; padding-top:0px; width:124px'><font color='green'>        title</font> </td><td style='text-align:left; padding-bottom:0px; padding-top:0px; border:.5px solid lightgray;  '> intensity</td><tr>
    <tr><td style='padding-right:5px; padding-bottom:0px; padding-top:0px; width:124px'><font color='green'>       values</font> </td><td style='text-align:left; padding-bottom:0px; padding-top:0px; border:.5px solid lightgray;  '> <div><font color='blue'>         RR[[  0.1001   0.1188 ... 0.009276 -0.03083]<br/>            [ 0.08574   0.1094 ...  0.02799 -0.01425]<br/>            ...<br/>            [  0.1134   0.1161 ... -0.03438 -0.06006]<br/>            [  0.1091   0.1206 ... -0.01183 -0.04616]] pp<br/>         RI[[ -0.1092 -0.07951 ...   0.1269   0.1117]<br/>            [ -0.1287  -0.1068 ...   0.1194   0.1155]<br/>            ...<br/>            [-0.06948 -0.03049 ...   0.1292  0.09699]<br/>            [-0.08905 -0.05339 ...   0.1305   0.1055]] pp<br/>         IR[[  0.0913  0.06793 ...  -0.1012   -0.118]<br/>            [ 0.08804  0.06219 ... -0.09448  -0.1108]<br/>            ...<br/>            [ 0.09555  0.08065 ...  -0.1116  -0.1259]<br/>            [ 0.09386  0.07434 ...  -0.1068  -0.1229]] pp<br/>         II[[-0.06389 -0.07123 ...  0.09588  0.09539]<br/>            [-0.07548 -0.07779 ...   0.1075   0.1114]<br/>            ...<br/>            [-0.04044 -0.05341 ...   0.0732  0.06366]<br/>            [-0.05242 -0.06344 ...  0.08463  0.07955]] pp</font></div></td><tr>
    <tr><td style='padding-right:5px; padding-bottom:0px; padding-top:0px; width:124px'><font color='green'>        shape</font> </td><td style='text-align:left; padding-bottom:0px; padding-top:0px; border:.5px solid lightgray;  '> (y:1024(complex), x:2048(complex))</td><tr>
    <tr><td style='padding-right:5px; padding-bottom:0px; padding-top:0px; width:124px'><strong>     DIMENSION `x`</strong></td><td style='text-align:left; padding-bottom:0px; padding-top:0px; padding-top:10px; '><hr/></td><tr>
    <tr><td style='padding-right:5px; padding-bottom:0px; padding-top:0px; width:124px'><font color='green'>         size</font> </td><td style='text-align:left; padding-bottom:0px; padding-top:0px; border:.5px solid lightgray;  '> 2048</td><tr>
    <tr><td style='padding-right:5px; padding-bottom:0px; padding-top:0px; width:124px'><font color='green'>        title</font> </td><td style='text-align:left; padding-bottom:0px; padding-top:0px; border:.5px solid lightgray;  '> $\delta\ ^{27}Al$</td><tr>
    <tr><td style='padding-right:5px; padding-bottom:0px; padding-top:0px; width:124px'><font color='green'>  coordinates</font> </td><td style='text-align:left; padding-bottom:0px; padding-top:0px; border:.5px solid lightgray;  '> <div><font color='blue'>[   96.79     96.7 ...   -102.8   -102.9] ppm</font></div></td><tr>
    <tr><td style='padding-right:5px; padding-bottom:0px; padding-top:0px; width:124px'><strong>     DIMENSION `y`</strong></td><td style='text-align:left; padding-bottom:0px; padding-top:0px; padding-top:10px; '><hr/></td><tr>
    <tr><td style='padding-right:5px; padding-bottom:0px; padding-top:0px; width:124px'><font color='green'>         size</font> </td><td style='text-align:left; padding-bottom:0px; padding-top:0px; border:.5px solid lightgray;  '> 1024</td><tr>
    <tr><td style='padding-right:5px; padding-bottom:0px; padding-top:0px; width:124px'><font color='green'>        title</font> </td><td style='text-align:left; padding-bottom:0px; padding-top:0px; border:.5px solid lightgray;  '> $\delta\ ^{31}P$</td><tr>
    <tr><td style='padding-right:5px; padding-bottom:0px; padding-top:0px; width:124px'><font color='green'>  coordinates</font> </td><td style='text-align:left; padding-bottom:0px; padding-top:0px; border:.5px solid lightgray;  '> <div><font color='blue'>[   38.79     38.7 ...   -44.52    -44.6] ppm</font></div></td><tr>
    </table>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 38-39

Plot the dataset

.. GENERATED FROM PYTHON SOURCE LINES 39-41

.. code-block:: Python

    _ = dataset.plot_map()




.. image-sg:: /gettingstarted/examples/gallery/auto_examples_processing/nmr/images/sphx_glr_plot_processing_nmr_001.png
   :alt: plot processing nmr
   :srcset: /gettingstarted/examples/gallery/auto_examples_processing/nmr/images/sphx_glr_plot_processing_nmr_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 42-43

Extract slices along x

.. GENERATED FROM PYTHON SOURCE LINES 43-46

.. code-block:: Python

    s = dataset[-27.6, :]
    _ = s.plot()




.. image-sg:: /gettingstarted/examples/gallery/auto_examples_processing/nmr/images/sphx_glr_plot_processing_nmr_002.png
   :alt: plot processing nmr
   :srcset: /gettingstarted/examples/gallery/auto_examples_processing/nmr/images/sphx_glr_plot_processing_nmr_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 47-49

Baseline correction of this slice
Note that only the real part is corrected

.. GENERATED FROM PYTHON SOURCE LINES 49-52

.. code-block:: Python

    sa = s.snip(snip_width=100)
    _ = sa.plot()




.. image-sg:: /gettingstarted/examples/gallery/auto_examples_processing/nmr/images/sphx_glr_plot_processing_nmr_003.png
   :alt: plot processing nmr
   :srcset: /gettingstarted/examples/gallery/auto_examples_processing/nmr/images/sphx_glr_plot_processing_nmr_003.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 53-54

apply this correction to the whole dataset

.. GENERATED FROM PYTHON SOURCE LINES 54-57

.. code-block:: Python

    sb = dataset.snip(snip_width=100)
    _ = sb.plot_map()




.. image-sg:: /gettingstarted/examples/gallery/auto_examples_processing/nmr/images/sphx_glr_plot_processing_nmr_004.png
   :alt: plot processing nmr
   :srcset: /gettingstarted/examples/gallery/auto_examples_processing/nmr/images/sphx_glr_plot_processing_nmr_004.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 58-59

Select a region of interest

.. GENERATED FROM PYTHON SOURCE LINES 59-64

.. code-block:: Python

    sc = sb[
        -40.0:-15.0, 55.0:20.0
    ]  # note the use of float to make selection using coordinates (not point indexes)
    _ = sc.plot_map()




.. image-sg:: /gettingstarted/examples/gallery/auto_examples_processing/nmr/images/sphx_glr_plot_processing_nmr_005.png
   :alt: plot processing nmr
   :srcset: /gettingstarted/examples/gallery/auto_examples_processing/nmr/images/sphx_glr_plot_processing_nmr_005.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 65-66

Extract slices along x

.. GENERATED FROM PYTHON SOURCE LINES 66-69

.. code-block:: Python

    s1 = sc[-27.6, :]
    _ = s1.plot()




.. image-sg:: /gettingstarted/examples/gallery/auto_examples_processing/nmr/images/sphx_glr_plot_processing_nmr_006.png
   :alt: plot processing nmr
   :srcset: /gettingstarted/examples/gallery/auto_examples_processing/nmr/images/sphx_glr_plot_processing_nmr_006.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 70-73

.. code-block:: Python

    s2 = sc[-25.7, :]
    _ = s2.plot()




.. image-sg:: /gettingstarted/examples/gallery/auto_examples_processing/nmr/images/sphx_glr_plot_processing_nmr_007.png
   :alt: plot processing nmr
   :srcset: /gettingstarted/examples/gallery/auto_examples_processing/nmr/images/sphx_glr_plot_processing_nmr_007.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 74-75

plot two slices on the same figure

.. GENERATED FROM PYTHON SOURCE LINES 75-82

.. code-block:: Python

    _ = s1.plot()
    _ = s2.plot(
        clear=False,
        color="red",
        linestyle="-",
    )




.. image-sg:: /gettingstarted/examples/gallery/auto_examples_processing/nmr/images/sphx_glr_plot_processing_nmr_008.png
   :alt: plot processing nmr
   :srcset: /gettingstarted/examples/gallery/auto_examples_processing/nmr/images/sphx_glr_plot_processing_nmr_008.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 83-84

Now slice along y

.. GENERATED FROM PYTHON SOURCE LINES 84-87

.. code-block:: Python

    s3 = sc[:, 40.0]
    s4 = sc[:, 36.0]








.. GENERATED FROM PYTHON SOURCE LINES 88-94

IMPORTANT: note that when the slice is along y, this results in a column vector of
shape (308, 1). When an NDDataset method is applied to this slice, such as a baseline
correction, it will be applied by default to the last dimension [rows] (in this case
the dimension of size 1, which is not what is generally expected). To avoid this,
we can use the squeeze method to remove this dimension or transpose the slice to
obtain a vector of rows of shape (1, 308)

.. GENERATED FROM PYTHON SOURCE LINES 94-97

.. code-block:: Python

    s3 = s3.squeeze()
    s4 = s4.squeeze()








.. GENERATED FROM PYTHON SOURCE LINES 98-99

plot the two slices on the same figure

.. GENERATED FROM PYTHON SOURCE LINES 99-102

.. code-block:: Python

    _ = s3.plot(color="violet", ls="-", lw="2")
    _ = s4.plot(clear=False, color="green", ls="-", lw="2")




.. image-sg:: /gettingstarted/examples/gallery/auto_examples_processing/nmr/images/sphx_glr_plot_processing_nmr_009.png
   :alt: plot processing nmr
   :srcset: /gettingstarted/examples/gallery/auto_examples_processing/nmr/images/sphx_glr_plot_processing_nmr_009.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 103-105

Peak picking
------------

.. GENERATED FROM PYTHON SOURCE LINES 105-108

.. code-block:: Python

    peaks, _ = s2.find_peaks()









.. GENERATED FROM PYTHON SOURCE LINES 109-111

plot the position of the peaks
For this we will define a plot function that we be reused later

.. GENERATED FROM PYTHON SOURCE LINES 111-138

.. code-block:: Python



    def plot_with_pp(s, peaks):
        ax = s.plot()  # output the spectrum on ax. ax will receive next plot too
        pks = peaks + 0.2  # add a small offset on the y position of the markers
        _ = pks.plot_scatter(
            ax=ax,
            marker="v",
            color="black",
            clear=False,  # we need to keep the previous output on ax
            data_only=True,  # we don't need to redraw all things like labels, etc...
            ylim=(-0.1, 7),
        )

        for p in pks:
            x, y = p.coord(-1).values, (p + 0.2).values
            _ = ax.annotate(
                f"{x.m:0.1f}",
                xy=(x, y),
                xytext=(-5, 5),
                rotation=90,
                textcoords="offset points",
            )


    _ = plot_with_pp(s2, peaks)




.. image-sg:: /gettingstarted/examples/gallery/auto_examples_processing/nmr/images/sphx_glr_plot_processing_nmr_010.png
   :alt: plot processing nmr
   :srcset: /gettingstarted/examples/gallery/auto_examples_processing/nmr/images/sphx_glr_plot_processing_nmr_010.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 139-140

Set some parameters to get less but significant peaks

.. GENERATED FROM PYTHON SOURCE LINES 140-143

.. code-block:: Python

    peaks, _ = s2.find_peaks(height=1.0, distance=1.0)
    _ = plot_with_pp(s2, peaks)




.. image-sg:: /gettingstarted/examples/gallery/auto_examples_processing/nmr/images/sphx_glr_plot_processing_nmr_011.png
   :alt: plot processing nmr
   :srcset: /gettingstarted/examples/gallery/auto_examples_processing/nmr/images/sphx_glr_plot_processing_nmr_011.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 144-145

Now look in the other dimension using slice s4

.. GENERATED FROM PYTHON SOURCE LINES 145-148

.. code-block:: Python

    peaks, _ = s4.find_peaks(height=1.0, distance=1.0)
    _ = plot_with_pp(s4, peaks)




.. image-sg:: /gettingstarted/examples/gallery/auto_examples_processing/nmr/images/sphx_glr_plot_processing_nmr_012.png
   :alt: plot processing nmr
   :srcset: /gettingstarted/examples/gallery/auto_examples_processing/nmr/images/sphx_glr_plot_processing_nmr_012.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 149-153

Peak fitting
------------

Fit parameters are defined in a script (a single text as below)

.. GENERATED FROM PYTHON SOURCE LINES 153-208

.. code-block:: Python

    script = """
    #-----------------------------------------------------------
    # syntax for parameters definition:
    # name: value, low_bound,  high_bound
    # available prefix:
    #  # for comments
    #  * for fixed parameters
    #  $ for variable parameters
    #  > for reference to a parameter in the COMMON block
    #    (> is forbidden in the COMMON block)
    # common block parameters should not have a _ in their names
    #-----------------------------------------------------------
    #

    COMMON:
    $ commonwidth: 1, 0, 5
    $ commonratio: .5, 0, 1

    MODEL: LINE_1
    shape: voigtmodel
        $ ampl:  1, 0.0, none
        $ pos:   -21.7, -22., -20
        > ratio: commonratio
        > width: commonwidth

    MODEL: LINE_2
    shape: voigtmodel
        $ ampl:  4, 0.0, none
        $ pos:   -24, -24.5, -23.5
        > ratio: commonratio
        > width: commonwidth

    MODEL: LINE_3
    shape: voigtmodel
        $ ampl:  4, 0.0, none
        $ pos:   -25.4, -25.8, -25
        > ratio: commonratio
        > width: commonwidth

    MODEL: LINE_4
    shape: voigtmodel
        $ ampl:  4, 0.0, none
        $ pos:   -27.8, -28.5, -27
        > ratio: commonratio
        > width: commonwidth

    MODEL: LINE_5
    shape: voigtmodel
        $ ampl:  4, 0.0, none
        $ pos:   -31.5, -32, -31
        > ratio: commonratio
        > width: commonwidth

    """








.. GENERATED FROM PYTHON SOURCE LINES 209-210

We will work here on the slice s4 (taken in the y dimension).

.. GENERATED FROM PYTHON SOURCE LINES 210-212

.. code-block:: Python

    s4p = s4.snip()  # Baseline correction








.. GENERATED FROM PYTHON SOURCE LINES 213-214

create an Optimize object

.. GENERATED FROM PYTHON SOURCE LINES 214-216

.. code-block:: Python

    f1 = scp.Optimize(log_level="INFO")








.. GENERATED FROM PYTHON SOURCE LINES 217-218

Set parameters

.. GENERATED FROM PYTHON SOURCE LINES 218-222

.. code-block:: Python

    f1.script = script
    f1.max_iter = 5000









.. GENERATED FROM PYTHON SOURCE LINES 223-224

Fit the slice s4p

.. GENERATED FROM PYTHON SOURCE LINES 224-226

.. code-block:: Python

    _ = f1.fit(s4p)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    
    
     **************************************************
     Result:
     **************************************************

     COMMON:
            $ commonwidth:     1.8757, 0, 5
            $ commonratio:     0.7139, 0, 1

     MODEL: line_1
     shape: voigtmodel
            $ ampl:     0.4913, 0.0, none
            $ pos:   -21.0847, -22.0, -20
            > ratio:commonratio
            > width:commonwidth

     MODEL: line_2
     shape: voigtmodel
            $ ampl:     3.1380, 0.0, none
            $ pos:   -23.7153, -24.5, -23.5
            > ratio:commonratio
            > width:commonwidth

     MODEL: line_3
     shape: voigtmodel
            $ ampl:     4.2827, 0.0, none
            $ pos:   -25.3868, -25.8, -25
            > ratio:commonratio
            > width:commonwidth

     MODEL: line_4
     shape: voigtmodel
            $ ampl:     4.1165, 0.0, none
            $ pos:   -27.7584, -28.5, -27
            > ratio:commonratio
            > width:commonwidth

     MODEL: line_5
     shape: voigtmodel
            $ ampl:     2.3106, 0.0, none
            $ pos:   -31.5949, -32, -31
            > ratio:commonratio
            > width:commonwidth





.. GENERATED FROM PYTHON SOURCE LINES 227-228

Show the result

.. GENERATED FROM PYTHON SOURCE LINES 228-236

.. code-block:: Python

    s4p.plot()
    ax = (f1.components[:]).plot(clear=False)
    ax.autoscale(enable=True, axis="y")

    # Plotmerit
    som = f1.inverse_transform()
    _ = f1.plotmerit(offset=2)




.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /gettingstarted/examples/gallery/auto_examples_processing/nmr/images/sphx_glr_plot_processing_nmr_013.png
         :alt: plot processing nmr
         :srcset: /gettingstarted/examples/gallery/auto_examples_processing/nmr/images/sphx_glr_plot_processing_nmr_013.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /gettingstarted/examples/gallery/auto_examples_processing/nmr/images/sphx_glr_plot_processing_nmr_014.png
         :alt: Optimize plot of merit
         :srcset: /gettingstarted/examples/gallery/auto_examples_processing/nmr/images/sphx_glr_plot_processing_nmr_014.png
         :class: sphx-glr-multi-img





.. GENERATED FROM PYTHON SOURCE LINES 237-239

This ends the example ! The following line can be removed or commented
when the example is run as a notebook (`.ipynb`).

.. GENERATED FROM PYTHON SOURCE LINES 239-241

.. code-block:: Python


    # scp.show()








.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 9.883 seconds)


.. _sphx_glr_download_gettingstarted_examples_gallery_auto_examples_processing_nmr_plot_processing_nmr.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_processing_nmr.ipynb <plot_processing_nmr.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_processing_nmr.py <plot_processing_nmr.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_processing_nmr.zip <plot_processing_nmr.zip>`