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NDDataset creation and plotting example

In this example, we create a 3D NDDataset from scratch, and then we plot one section (a 2D plane)

Creation

Now we will create a 3D NDDataset from scratch

Data

here we make use of numpy array functions to create the data for coordinates axis and the array of data

import numpy as np

As usual, we start by loading the spectrochempy library

import spectrochempy as scp

We create the data for the coordinates axis and the array of data

c0 = np.linspace(200.0, 300.0, 3)
c1 = np.linspace(0.0, 60.0, 100)
c2 = np.linspace(4000.0, 1000.0, 100)
nd_data = np.array(
    [
        np.array([np.sin(2.0 * np.pi * c2 / 4000.0) * np.exp(-y / 60) for y in c1]) * t
        for t in c0
    ]
)

Coordinates

The Coord object allow making an array of coordinates with additional metadata such as units, labels, title, etc

coord0 = scp.Coord(
    data=c0, labels=["cold", "normal", "hot"], units="K", title="temperature"
)
coord1 = scp.Coord(data=c1, labels=None, units="minutes", title="time-on-stream")
coord2 = scp.Coord(data=c2, labels=None, units="cm^-1", title="wavenumber")

Labels can be useful for instance for indexing

a = coord0["normal"]
print(a)

Coord: [float64] K (size: 1)

nd-Dataset

The NDDataset object allow making the array of data with units, etc…

mydataset = scp.NDDataset(
    nd_data, coordset=[coord0, coord1, coord2], title="Absorbance", units="absorbance"
)
mydataset.description = """Dataset example created for this tutorial.
It's a 3-D dataset (with dimensionless intensity: absorbance )"""
mydataset.name = "An example from scratch"
mydataset.author = "Blake and Mortimer"
print(mydataset)

NDDataset: [float64] a.u. (shape: (z:3, y:100, x:100))

We want to plot a section of this 3D NDDataset:

NDDataset can be sliced like conventional numpy-array…

new = mydataset[..., 0]

or maybe more conveniently in this case, using an axis labels:

new = mydataset["hot"]

To plot a dataset, use the plot command (generic plot). As the section NDDataset is 2D, a stack plot is displayed by default. As you can see, the x-axis is in wavenumber and the ordinate axis is in absorbance units (au). The y dimension of the dataset is the time-on-stream (in minutes). Because the time-on-stream values are floats, this triggers the default sequential colormap (‘viridis’). The corresponding values can be seen if colorbar' is passed as `True:

_ = new.plot(colorbar=True)

It is also possible to display this dataset as an image (actually a filled contour plot). The x is the same as before, but the ordinates are now the time-on-stream values. The color of the pixels is now related to the value of the absorbance. As the dataset contains both negative and positive values, the default colormap is diverging (`RdBu’).

sphinx_gallery_thumbnail_number = 2

_ = new.plot(method="image", colorbar=True)

If a dataset contains only positive values, the default colormap is sequential (`:

_ = np.abs(new).plot(method="image", colorbar=True)

_ = new.plot(method="map")
_ = np.abs(new).plot(method="map")

• 
• 

/home/runner/work/spectrochempy/spectrochempy/src/spectrochempy/plotting/dispatcher.py:65: DeprecationWarning: method="map" is deprecated, use method="contour" instead
  return backend_module.plot_dataset_impl(dataset, method, **kwargs)

Note that the scp allows one to use this syntax too:

_ = scp.plot_map(new)

This ends the example ! The following line can be uncommented if no plot shows when running the .py script with python

# scp.show()