def test_all_coordinates_from_map(sub_smap):
coordinates = all_coordinates_from_map(sub_smap)
shape = sub_smap.data.shape
assert coordinates.shape == (shape[0], shape[1])
assert isinstance(coordinates, SkyCoord)
assert isinstance(coordinates.frame, BaseCoordinateFrame)
assert coordinates.frame.name == sub_smap.coordinate_frame.name
def test_all_coordinates_from_map(sub_smap):
coordinates = all_coordinates_from_map(sub_smap)
shape = sub_smap.data.shape
assert coordinates.shape == (shape[0], shape[1])
assert isinstance(coordinates, SkyCoord)
assert isinstance(coordinates.frame, BaseCoordinateFrame)
assert coordinates.frame.name == sub_smap.coordinate_frame.name
def process_kcor_map(m, rsun=2.7, gamma=0.7):
import skimage.exposure
from sunpy.map.maputils import all_coordinates_from_map
hpc_coords = all_coordinates_from_map(m)
r = np.sqrt(hpc_coords.Tx ** 2 + hpc_coords.Ty ** 2) / m.rsun_obs
mask = r > rsun
# remove negative values for gamma correction
rescaled_data = m.data
# don't scale if NRGF
if "PRODUCT" in m.fits_header:
display_min = m.fits_header["DISPMIN"]
display_max = m.fits_header["DISPMAX"]
processed_data = np.clip(rescaled_data, display_min, display_max) - display_min
processed_data /= display_max - display_min
processed_map = Map(processed_data, m.meta, mask=mask)
processed_map.plot_settings["cmap"] = kcor_nrgf_cmap()
processed_map.plot_settings["norm"] = matplotlib.colors.NoNorm()
return(processed_map)
rescaled_data[rescaled_data < 0.0] = 0.0
adjusted_data = skimage.exposure.adjust_gamma(rescaled_data, gamma=gamma)
processed_map = Map(adjusted_data, m.meta, mask=mask)
return(processed_map)
def process_aia_map(m, rsun=1.2, threshold=35):
from sunpy.map.maputils import all_coordinates_from_map
hpc_coords = all_coordinates_from_map(m)
r = np.sqrt(hpc_coords.Tx ** 2 + hpc_coords.Ty ** 2) / m.rsun_obs
mask = ma.logical_and(r > rsun, m.data < threshold)
processed_map = Map(m.data, m.meta, mask=mask)
return(processed_map)
def mask_outside_disk(inst_map):
# Find coordinates and radius
hpc_coords = all_coordinates_from_map(inst_map)
r = np.sqrt(hpc_coords.Tx**2 + hpc_coords.Ty**2) / inst_map.rsun_obs
# Mask everything outside of the solar disk
mask = ma.masked_greater_equal(r, 1)
ma.set_fill_value(mask, np.nan)
where_disk = np.where(mask.mask == 1)
return where_disk
def test_coordinate_is_on_solar_disk(aia171_test_map, all_off_disk_map,
all_on_disk_map, straddles_limb_map):
off_disk = aia171_test_map.bottom_left_coord
on_disk = aia171_test_map.center
# Check for individual coordinates
assert coordinate_is_on_solar_disk(on_disk)
assert ~coordinate_is_on_solar_disk(off_disk)
# Raise the error
with pytest.raises(ValueError):
coordinate_is_on_solar_disk(
on_disk.transform_to(HeliographicStonyhurst))
# Check for sets of coordinates
assert np.any(
coordinate_is_on_solar_disk(all_coordinates_from_map(aia171_test_map)))
assert np.any(~coordinate_is_on_solar_disk(
all_coordinates_from_map(aia171_test_map)))
assert np.all(~coordinate_is_on_solar_disk(
all_coordinates_from_map(all_off_disk_map)))
assert np.all(
coordinate_is_on_solar_disk(all_coordinates_from_map(all_on_disk_map)))
assert np.any(
coordinate_is_on_solar_disk(
all_coordinates_from_map(straddles_limb_map)))
assert np.any(~coordinate_is_on_solar_disk(
all_coordinates_from_map(straddles_limb_map)))
def test_all_coordinates_from_map(sub_smap):
coordinates = all_coordinates_from_map(sub_smap)
shape = sub_smap.data.shape
assert coordinates.shape == (shape[0], shape[1])
assert isinstance(coordinates, SkyCoord)
assert isinstance(coordinates.frame, BaseCoordinateFrame)
assert coordinates.frame.name == sub_smap.coordinate_frame.name
xpix, ypix = sub_smap.world_to_pixel(coordinates[0, 0])
assert_quantity_allclose(xpix, 0 * u.pix, atol=1e-7 * u.pix)
assert_quantity_allclose(ypix, 0 * u.pix, atol=1e-7 * u.pix)
xpix, ypix = sub_smap.world_to_pixel(coordinates[-1, -1])
assert_quantity_allclose(xpix, sub_smap.dimensions[0] - 1 * u.pix)
assert_quantity_allclose(ypix, sub_smap.dimensions[1] - 1 * u.pix)
def test_coordinate_is_on_solar_disk(aia171_test_map, all_off_disk_map, all_on_disk_map, straddles_limb_map):
off_disk = aia171_test_map.bottom_left_coord
on_disk = aia171_test_map.center
# Check for individual coordinates
assert coordinate_is_on_solar_disk(on_disk)
assert ~coordinate_is_on_solar_disk(off_disk)
# Raise the error
with pytest.raises(ValueError):
coordinate_is_on_solar_disk(on_disk.transform_to(HeliographicStonyhurst))
# Check for sets of coordinates
assert np.any(coordinate_is_on_solar_disk(all_coordinates_from_map(aia171_test_map)))
assert np.any(~coordinate_is_on_solar_disk(all_coordinates_from_map(aia171_test_map)))
assert np.all(~coordinate_is_on_solar_disk(all_coordinates_from_map(all_off_disk_map)))
assert np.all(coordinate_is_on_solar_disk(all_coordinates_from_map(all_on_disk_map)))
assert np.any(coordinate_is_on_solar_disk(all_coordinates_from_map(straddles_limb_map)))
assert np.any(~coordinate_is_on_solar_disk(all_coordinates_from_map(straddles_limb_map)))
# To find the brightest pixel, we find the maximum in the AIA image data
# then transform that pixel coordinate to a map coordinate.
pixel_pos = np.argwhere(aia.data == aia.data.max()) * u.pixel
hpc_max = aia.pixel_to_world(pixel_pos[:, 1], pixel_pos[:, 0])
###############################################################################
# Let's plot the results.
fig = plt.figure()
ax = plt.subplot(projection=aia)
aia.plot()
ax.plot_coord(hpc_max, 'bx', color='white', marker='x', markersize=15)
plt.show()
###############################################################################
# A utility function gives us access to the helioprojective coordinate of each
# pixels. We create a new array which contains the normalized radial position
# for each pixel adjusted for the position of the brightest pixel
# (using ``hpc_max``) and then create a new map.
hpc_coords = all_coordinates_from_map(aia)
r_mask = np.sqrt((hpc_coords.Tx - hpc_max.Tx)**2 +
(hpc_coords.Ty - hpc_max.Ty)**2) / aia.rsun_obs
mask = ma.masked_less_equal(r_mask, 0.1)
scaled_map = sunpy.map.Map(aia.data, aia.meta, mask=mask.mask)
###############################################################################
# Let's plot the results.
fig = plt.figure()
ax = plt.subplot(projection=scaled_map)
scaled_map.plot()
plt.show()
def match_disk(fits_file, ref_fits):
sun_map = sunpy.map.Map(fits_file)
hpc_coords = all_coordinates_from_map(sun_map)
r = np.sqrt(hpc_coords.Tx ** 2 + hpc_coords.Ty ** 2) / sun_map.rsun_obs
mask = ma.masked_less_equal(r, 1)
sun_map.data[:] -= sun_map.data.min()
sun_map.data[:] += 10e-6
if sun_map.detector == 'EUVI':
sq_img = sun_map.data * sun_map.data
sun_map.data[:] = sq_img[:]
if sun_map.detector == 'EIT':
sq_img = np.sqrt(sun_map.data)
sun_map.data[:] = sq_img[:]
masked = sun_map.data * mask.mask
masked[masked == 0] = np.nan
# force scale of 1 arcsec per pix
scale = 1 / sun_map.scale[1].value
masked = rescale(masked, sun_map.scale[1].value, anti_aliasing=False)
crpix = masked.shape[0]/2
crop = masked[int(crpix - 600):int(crpix + 600), int(crpix - 600):int(crpix + 600)]
# ------------------------------------
sun_map = sunpy.map.Map(ref_fits)
hpc_coords = all_coordinates_from_map(sun_map)
r = np.sqrt(hpc_coords.Tx ** 2 + hpc_coords.Ty ** 2) / sun_map.rsun_obs
mask = ma.masked_less_equal(r, 1)
sun_map.data[:] -= sun_map.data.min()
sun_map.data[:] += 10e-6
if sun_map.detector == 'EUVI':
sq_img = sun_map.data * sun_map.data
sun_map.data[:] = sq_img[:]
if sun_map.detector == 'EIT':
sq_img = np.sqrt(sun_map.data)
sun_map.data[:] = sq_img[:]
masked = sun_map.data * mask.mask
masked[masked == 0] = np.nan
# force scale of 1 arcsec per pix
scale = 1 / sun_map.scale[1].value
masked = rescale(masked, sun_map.scale[1].value, anti_aliasing=False)
crpix = masked.shape[0]/2
crop_ref = masked[int(crpix - 600):int(crpix + 600), int(crpix - 600):int(crpix + 600)]
matched = match_histograms(crop, crop_ref)
fig = plt.figure()
ax = plt.subplot("131")
im = ax.imshow(crop)
ax.set_title('EUVI squared')
fig.colorbar(im, ax=ax)
ax = plt.subplot("132")
im = ax.imshow(matched)
ax.set_title('EUVI matched')
fig.colorbar(im, ax=ax)
ax = plt.subplot("133")
im = ax.imshow(crop_ref)
ax.set_title('EIT Square Root')
fig.colorbar(im, ax=ax)
return matched