"""
=====================================
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 <https://www.lmsal.com/hek/hcr?cmd=view-event&event-id=ivo%3A%2F%2Fsot.lmsal.com%2FVOEvent%23VOEvent_IRIS_20140919_051712_3860608353_2014-09-19T05%3A17%3A122014-09-19T05%3A17%3A12.xml>`__.
#
# 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()