Added the example to remove cosmic ray hits (#35)

* added reduce cr example

* fixed tox

* Fixed deps

* renamed example file

* ran linters

* Apply suggestions from code review

Co-Authored-By: Nabil Freij <[email protected]>

* added few links

* fixed docs build

* Apply suggestions from code review

Co-Authored-By: Nabil Freij <[email protected]>

* Apply suggestions from code review

Co-Authored-By: Nabil Freij <[email protected]>

* Added note for jp2

* removed jp2

* changed parameters for better results

* added parameter docs

* deleted few lines

* Apply suggestions from code review

Co-Authored-By: Nabil Freij <[email protected]>

* Update examples/remove_CR_hits.py

Co-Authored-By: Nabil Freij <[email protected]>

.circleci/config.yml

@@ -15,7 +15,7 @@ apt-run: &apt-install
   name: Install apt packages
   command: |
     sudo apt update
-    sudo apt install -y graphviz build-essential
+    sudo apt install -y graphviz
 
 permission-run: &permission-run
   name: Fix permssions for installing

changelog/35.doc.rst

@@ -0,0 +1 @@
+Added an example on how to use `astroscrappy.detect_cosmics <https://astroscrappy.readthedocs.io/en/latest/api/astroscrappy.detect_cosmics.html>`__ to eliminate cosmic ray hits in solar images.

examples/remove_CR_hits.py

@@ -0,0 +1,74 @@
+"""
+========================
+Removing Cosmic Ray Hits
+========================
+
+This example illustrates how to remove cosmic ray hits from a LASCO C2 FITS file.
+using `astroscrappy.detect_cosmics <https://astroscrappy.readthedocs.io/en/latest/api/astroscrappy.detect_cosmics.html>`__.
+Astroscrappy is a separate Python package and can be installed separately using ``pip`` or ``conda``.
+"""
+
+import matplotlib.pyplot as plt
+
+from sunpy.map import Map
+
+import astroscrappy
+
+###############################################################################
+# For more details on how to download and plot LASCO FITS file see
+# SunPy's example `Downloading and plotting LASCO C3 data <https://docs.sunpy.org/en/stable/generated/gallery/acquiring_data/skip_downloading_lascoC3.html>`__.
+# To make this example work you need to have SunPy with all the "net" dependencies installed.
+
+from sunpy.net import Fido, attrs as a
+from sunpy.io.file_tools import read_file
+
+###############################################################################
+# In order to download the required FITS file, we use
+# `Fido <sunpy.net.fido_factory.UnifiedDownloaderFactory>`, SunPy's downloader client.
+# We need to define two search variables:  a time range and the instrument.
+
+time_range = a.Time("2000/11/09 00:06", "2000/11/09 00:07")
+instrument = a.Instrument("LASCO")
+detector = a.Detector("C2")
+result = Fido.search(time_range, instrument)
+
+downloaded_files = Fido.fetch(result[0])
+data, header = read_file(downloaded_files[0])[0]
+
+# Add the missing meta information to the header
+header["CUNIT1"] = "arcsec"
+header["CUNIT2"] = "arcsec"
+
+###############################################################################
+# With this fix we can load it into a map and plot the results.
+
+lasco_map = Map(data, header)
+fig1 = plt.figure()
+lasco_map.plot()
+
+###############################################################################
+# Now we will call the `astroscrappy.detect_cosmics <https://astroscrappy.readthedocs.io/en/latest/api/astroscrappy.detect_cosmics.html>`__
+# to remove the cosmic ray hits.
+# This algorithm can perform well with both high and low noise levels in the original data.
+# The function takes a `~numpy.ndarray` as input so we only pass the map data.
+# This particular image has lots of high intensity cosmic ray hits which
+# cannot be effectively removed by using the default set of parameters.
+# So we reduce ``sigclip``, the Laplacian to noise ratio from 4.5 to 2 to mark more hits.
+# We also reduce ``objlim``, the contrast between the Laplacian image and the fine structured image
+# to clean the high intensity bright cosmic ray hits.
+# We also modify the ``readnoise`` parameter to obtain better results.
+
+mask, clean_data = astroscrappy.detect_cosmics(lasco_map.data, sigclip=2, objlim=2, readnoise=4, verbose=True)
+# This returns two variables - mask is a boolean array depicting whether there is
+# a cosmic ray hit at that pixel, clean_data is the cleaned image after removing those
+# hits.
+
+###############################################################################
+# We can now plot the cleaned image.
+
+clean_map1 = Map(clean_data, lasco_map.meta)
+
+fig2 = plt.figure()
+clean_map1.plot()
+
+plt.show()

setup.cfg

@@ -15,7 +15,7 @@ packages = find:
 include_package_data = True
 setup_requires = setuptools_scm
 install_requires =
-  sunpy==1.0.0rc2
+  sunpy>=1.0.0
   scikit-image
 
 [options.extras_require]

sunkit_image/utils/tests/test_utils.py

@@ -2,7 +2,6 @@
 import pytest
 
 import astropy.units as u
-
 from astropy.tests.helper import assert_quantity_allclose
 
 from sunkit_image.utils.utils import (

sunkit_image/utils/utils.py

@@ -4,11 +4,14 @@
 import numpy as np
 
 import astropy.units as u
-from sunpy.coordinates import frames
 from sunpy.map.maputils import all_coordinates_from_map
 
-__all__ = ["equally_spaced_bins", "bin_edge_summary", "find_pixel_radii",
-           "get_radial_intensity_summary"]
+__all__ = [
+    "equally_spaced_bins",
+    "bin_edge_summary",
+    "find_pixel_radii",
+    "get_radial_intensity_summary",
+]
 
 
 def equally_spaced_bins(inner_value=1, outer_value=2, nbins=100):

tox.ini

@@ -21,6 +21,13 @@ commands =
 [testenv:build_docs]
 basepython = python3.7
 extras = all,docs
+deps =
+    astroscrappy
+    beautifulsoup4
+    drms
+    python-dateutil
+    zeep
+    tqdm
 commands = sphinx-build docs docs/_build/html -W -b html
 
 # This env requires tox-conda.