Remove Dust from IRIS Slit-Jaw Imager

This example shows how to remove dust from IRIS slit-jaw imager (SJI).

import matplotlib.pyplot as plt
import pooch

from irispy.io import read_files

We start with getting data from the IRIS data archive.

In this case, we will use pooch to keep this example self-contained but you can download the data manually using your browser as well.

You will need to update the path to the data in the next section if you do that.

sji_filename = pooch.retrieve(
    "http://www.lmsal.com/solarsoft/irisa/data/level2_compressed/2014/09/19/20140919_051712_3860608353/iris_l2_20140919_051712_3860608353_SJI_2832_t000.fits.gz",
    known_hash="7ec0f3d63d97bc7620675c78fb6c670ef5b4249d31ef7818435b629c04b72f60",
)

Now we just read it and apply the irispy.utils.dust.remove_dust method to the cube.

sji_2832 = read_files(sji_filename)
# We crop the cube to reduce memory usage for the online documentation build.
sji_subset = sji_2832[44:46]
clean_subset = sji_subset.remove_dust()

Finally, we can compare one frame before and after the dust treatment.

frame_index = 1
original_frame = sji_subset[frame_index]
cleaned_frame = clean_subset[frame_index]

fig = plt.figure(figsize=(10, 4.5))
fig.subplots_adjust(wspace=0.03)

ax0 = fig.add_subplot(121, projection=original_frame.wcs)
original_frame.plot(axes=ax0)
ax0.set_title("Original")

ax1 = fig.add_subplot(122, projection=cleaned_frame.wcs)
cleaned_frame.plot(axes=ax1)
ax1.set_title("Dust Removed")

for ax in (ax0, ax1):
    ax.coords[0].set_ticks_visible(False)
    ax.coords[0].set_ticklabel_visible(False)
    ax.coords[1].set_ticks_visible(False)
    ax.coords[1].set_ticklabel_visible(False)

fig.tight_layout()
plt.show()