Astronomy, 2D{1,1,1} dataset (Creating image composition)¶
More often the image in astronomy is a composition of datasets measured at a different wavelength over an area of the sky. Here, we show an example of an image composition using the data from the Eagle Nebula. Import the csdmpy model and load the dataset.
>>> import csdmpy as cp
>>> import matplotlib.pyplot as plt
>>> filename = 'Test Files/EagleNebula/eagleNebula.csdfe'
>>> eagle_nebula = cp.load(filename)
Let’s get the tuple of dimension and dependent variable objects from
eagle_nebula
instance.
>>> x = eagle_nebula.dimensions
>>> y = eagle_nebula.dependent_variables
Before we create an image composition, let’s take a look at the individual
dependent variables from the dataset. The three dependent variables correspond
to signal acquisition at 502 nm, 656 nm, and 673 nm, respectively. This
information is also listed in the
name
attribute of the
respective dependent variable instances,
>>> y[0].name
'Eagle Nebula acquired @ 502 nm'
>>> y[1].name
'Eagle Nebula acquired @ 656 nm'
>>> y[2].name
'Eagle Nebula acquired @ 673 nm'
Tip
A script to plot an intensity plot.
>>> import matplotlib.pyplot as plt
>>> from mpl_toolkits.axes_grid1 import make_axes_locatable
>>> from matplotlib.colors import LogNorm
>>> def plot_scalar(yx):
... plt.figure(figsize=(6,4.5))
...
... # Set the extents of the image plot.
... extent = [x[0].coordinates[0].value, x[0].coordinates[-1].value,
... x[1].coordinates[0].value, x[1].coordinates[-1].value]
...
... # Add the image plot.
... y0 = yx.components[0]
... y0 = y0/y0.max()
... im = plt.imshow(y0, origin='lower', extent=extent, cmap='bone', vmax=0.1)
...
... # Add a colorbar.
... divider = make_axes_locatable(plt.gca())
... cax = divider.append_axes("right", size="5%", pad=0.05)
... cbar = plt.gca().figure.colorbar(im, cax)
... cbar.ax.set_ylabel(yx.axis_label[0])
...
... # Set up the axes label and figure title.
... plt.xlabel(x[0].axis_label)
... plt.ylabel(x[1].axis_label)
... plt.title(yx.name)
...
... # Set up the grid lines.
... plt.grid(color='k', linestyle='--', linewidth=0.5)
...
... plt.tight_layout(pad=0, w_pad=0, h_pad=0)
... plt.show()
Let’s plot the dependent variables, first dependent variable,
>>> plot_scalar(y[0])

second dependent variable, and
>>> plot_scalar(y[1])

the third dependent variable.
>>> plot_scalar(y[2])

Image composition¶
In our image composition, we will assign the dependent variable at index 0 as the blue channel, at index 1 as the green channel, and index 2 as the red channel of an RGB image. First, create an empty array to hold the RGB dataset.
>>> shape = y[0].components[0].shape + (3,)
>>> image = np.empty(shape, dtype=np.float64)
Here, image
is a variable we use for storing the composition. Let’s add the
respective dependent variables to the designated color channel in the
image
array,
>>> image[...,0] = y[2].components[0]/y[2].components[0].max() # red channel
>>> image[...,1] = y[1].components[0]/y[1].components[0].max() # green channel
>>> image[...,2] = y[0].components[0]/y[0].components[0].max() # blue channel
If you follow the above figures, the component intensity from
y[1]
and, therefore, the green channel dominates the other two. If we
plot the image
data, the image will be saturated with green intensity. To
attain a color-balanced image, we arbitrarily scale the intensities from the
three channels. You may choose any scaling factor. Each scaling factor will
produce a different composition. In this example, we use the following,
>>> image[...,0] = image[...,0]*65.0 # red channel
>>> image[...,1] = image[...,1]*7.5 # green channel
>>> image[...,2] = image[...,2]*75.0 # blue channel
Now to plot this composition.
>>> def image_composition():
... # Set the extents of the image plot.
... extent = [x[0].coordinates[0].value, x[0].coordinates[-1].value,
... x[1].coordinates[0].value, x[1].coordinates[-1].value]
...
... # add figure
... plt.figure(figsize=(5,4.5))
... plt.imshow(image, origin='lower', extent=extent)
...
... plt.xlabel(x[0].axis_label)
... plt.ylabel(x[1].axis_label)
... plt.title('composition')
...
... plt.tight_layout(pad=0, w_pad=0, h_pad=0)
... plt.show()
>>> image_composition()
