def angle_in_circle(image_filename, position_of_star, rht_data, radius):
""" Gets a star, draws a circle around it and returns the average rht angle and standard deviation in this circle. """
ipoints, jpoints, hthets, naxis1, naxis2, wlen, smr, thresh, thets_deg = rht_data
c = CirclePixelRegion(PixCoord(
np.round(position_of_star)[0],
np.round(position_of_star)[1]),
radius=radius)
# Check which RHT data is in this region, these are the indices:
indices = c.contains(PixCoord(ipoints, jpoints))
# hthets[indices] are all the pixel angle distributions found in the circle
# Loop through pixels:
# We multiply by thets_deg to get angle distribution, then normalise by dividing by the sum of the distribution, and finally take the sum of all values to get the average angle.
average_on_pixel = []
for i, v in enumerate(hthets[indices]):
average_on_pixel.append(
(hthets[indices][i] * thets_deg / hthets[indices][i].sum()).sum())
return np.mean(average_on_pixel), np.std(average_on_pixel)
if(PRINTER.input_text("Automatically find most-intense region? [y/n]") == "y"):
PRINTER.info_text("Identifying region")
px = np.argwhere(MAPCUBE[0].data == MAPCUBE[0].data.max()) * u.pixel
if len(px) > 1:
temp = ndimage.measurements.center_of_mass(np.array(px))
px = [px[int(temp[0] + 0.5)]]
#########################
center = PixCoord(x = 2048, y = 2048)
radius = 1600
region = CirclePixelRegion(center, radius)
point = PixCoord(px[0][1], px[0][0])
if not region.contains(point):
PRINTER.info_text("Identified region is outside the solar disk")
PRINTER.info_text("Defaulting to user selection...")
INIT_COORD = cutout_selection(MAPCUBE)
else:
INIT_COORD = MAPCUBE[0].pixel_to_world(px[0][1], px[0][0])
auto_sel = True
else:
INIT_COORD = cutout_selection(MAPCUBE)
auto_sel = False
#########################
PRINTER.line()