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eclipse_analysis.py
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eclipse_analysis.py
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import pandas as pd
import numpy as np
import json
import zipfile
import matplotlib.pyplot as plt
from refraction_correction import _find_rotation_matrix
from transforms import to_polar
import scipy
from pathlib import Path
import datetime
from MEE2024util import output_path, _version
import astropy
from astropy.coordinates import EarthLocation,SkyCoord, Distance, get_body, AltAz
from astropy.time import Time
from astropy import units as u
def as_unit_vector(dec, ra):
return np.array([np.cos(dec) * np.cos(ra), np.cos(dec) * np.sin(ra), np.sin(dec)]).T
def eclipse_analysis(path_data, options):
starttime = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
print(path_data)
archive = zipfile.ZipFile(path_data, 'r')
data = json.load(archive.open('distortion_results.txt'))
#image_size = data['img_shape']
df = pd.read_csv(archive.open('CATALOGUE_MATCHED_ERRORS.csv'))
print(df)
df = df.astype({'px':float, 'py':float, 'RA(catalog)':float, 'RA(obs)':float, 'DEC(catalog)':float, 'DEC(obs)':float, 'magV':float}) # fix datatypes
df = df.loc[df['magV'] <= options['eclipse_limiting_mag']]
print(df)
if options['remove_double_stars_eclipse']:
df = df.loc[~df['flag_is_double']]
#print(data)
#print(df)
if data['gravitational correction enabled?']:
print('WARNING: gravity was enabled in the input data')
#raise Exception("expected 'gravitational correction enabled?':False")
observing_location = EarthLocation(lat=data['observation_lat (degrees)'], lon=data['observation_long (degrees)'], height=data['observation_height (m)']*u.m)
observing_time = Time(data['observation_date'] + ' ' + data['observation_time (UTC)'])
sun = get_body("sun", observing_time, observing_location)
moon = get_body("moon", observing_time, observing_location)
print(sun)
print(moon)
sun_apparent_angular_radius = np.degrees((astropy.constants.R_sun / sun.distance).to(u.dimensionless_unscaled).value)
moon_apparent_angular_radius = np.degrees((1737.4*u.km / moon.distance).to(u.dimensionless_unscaled).value)
print(sun_apparent_angular_radius)
print(moon_apparent_angular_radius)
aa = AltAz(location=observing_location, obstime=observing_time)
local_sun = sun.transform_to(aa)
local_moon = moon.transform_to(aa)
print(local_sun)
print(local_moon)
#print(local_sun.ra, local_sun.dec)
#print(local_moon.ra, local_moon.dec)
field_describe = f"Eclipse Field with {df.shape[0]} chosen stars with magnitudes {np.min(df['magV']):.1f} to {np.max(df['magV']):.1f}"
if options['flag_display3']:
fig, ax = plt.subplots()
ax.scatter(df['RA(catalog)'], df['DEC(catalog)'], color='blue', label = 'catalog')
ax.scatter(df['RA(obs)'], df['DEC(obs)'], marker='+', color='orange', label = 'observation')
ax.set_title(field_describe)
ax.set_xlabel("RA (degrees)")
ax.set_ylabel("DEC (degrees)")
sun_circle = plt.Circle((sun.ra.degree, sun.dec.degree), sun_apparent_angular_radius, color='yellow') # NOTE: sun and moon are not actually circles in RA/DEC space!
moon_circle = plt.Circle((moon.ra.degree, moon.dec.degree), moon_apparent_angular_radius, color='black')
if not options['object_centre_moon']:
ax.add_patch(sun_circle)
ax.add_patch(moon_circle)
ax.legend()
ax.set_aspect('equal')
for id_i, ra_i, dec_i, mag_i in zip(df['ID'], df['RA(catalog)'], df['DEC(catalog)'], df['magV']):
ax.annotate(f' {id_i[5:]} mag={mag_i:.1f}', (ra_i, dec_i), fontsize=6)
plt.show()
sun_v = as_unit_vector(sun.dec.radian, sun.ra.radian)
moon_v = as_unit_vector(moon.dec.radian, moon.ra.radian)
reference_v = moon_v if options['object_centre_moon'] else sun_v
stars_obs_v = as_unit_vector(np.radians(df['DEC(obs)']), np.radians(df['RA(obs)']))
stars_cata_v = as_unit_vector(np.radians(df['DEC(catalog)']), np.radians(df['RA(catalog)']))
radial_distances_catalog = np.arcsin(np.linalg.norm(stars_cata_v - reference_v, axis=1) / 2) * 2
rad_dist = np.degrees(radial_distances_catalog) / sun_apparent_angular_radius
delta_vectors = stars_cata_v - reference_v
print("radial distances (in solar radii)", rad_dist)
# deflection: d = A / r
def error_function1(deflection_const, return_rotation=False):
deflection = np.radians(deflection_const / rad_dist / 3600)
delta_vectors_unit = delta_vectors / np.linalg.norm(delta_vectors, axis = 1).reshape(delta_vectors.shape[0], 1)
cata_vectors_corrected = stars_cata_v + deflection.reshape(delta_vectors.shape[0], 1) * delta_vectors_unit
cata_vectors_corrected = cata_vectors_corrected / np.linalg.norm(cata_vectors_corrected, axis = 1).reshape(delta_vectors.shape[0], 1)
rot = _find_rotation_matrix(stars_obs_v, cata_vectors_corrected)
corrected = (rot.T @ stars_obs_v.T).T
rms = np.degrees(np.sqrt(np.linalg.norm(corrected - cata_vectors_corrected)**2 / stars_cata_v.shape[0]))*3600
if return_rotation:
return rms, rot
return rms
# allow power-law relationship for deflection: d = A / r^B
def error_function2(deflection_const, return_rotation=False):
deflection = np.radians(deflection_const[0] / rad_dist**deflection_const[1] / 3600)
delta_vectors_unit = delta_vectors / np.linalg.norm(delta_vectors, axis = 1).reshape(delta_vectors.shape[0], 1)
cata_vectors_corrected = stars_cata_v + deflection.reshape(delta_vectors.shape[0], 1) * delta_vectors_unit
cata_vectors_corrected = cata_vectors_corrected / np.linalg.norm(cata_vectors_corrected, axis = 1).reshape(delta_vectors.shape[0], 1)
rot = _find_rotation_matrix(stars_obs_v, cata_vectors_corrected)
corrected = (rot.T @ stars_obs_v.T).T
rms = np.degrees(np.sqrt(np.linalg.norm(corrected - cata_vectors_corrected)**2 / stars_cata_v.shape[0]))*3600
if return_rotation:
return rms, rot
return rms
result1 = scipy.optimize.minimize(error_function1, 0, method = 'Nelder-Mead')
print(result1)
result2 = scipy.optimize.minimize(error_function2, (0, 1), method = 'Nelder-Mead')
print(result2)
### rms minimisation curve
if options['flag_display3']:
xxx = np.linspace(-0.25, 3)
yyy = [error_function1(_)for _ in xxx]
plt.plot(xxx, yyy)
plt.xlabel("deflection constant (arcsec)")
plt.ylabel("rms (arcsec)")
rms, rot = error_function1(result1.x[0], return_rotation=True)
naive_error = result1.fun/np.sqrt(df.shape[0])
string = f"deflection constant = {result1.x[0]:.5f}\ndifference vs. accepted value: {100*(result1.x[0]-1.751)/1.751:.3f}%\n\ndeflected star position rms = {result1.fun:.3f} arcsec\nrms / sqrt(nstars) = {naive_error:.5f} arcsec\nnaive uncertainty estimate = {100*naive_error/1.751:.1f}%\n"
obs_rot = (rot.T @ stars_obs_v.T).T
radial_distances_obs = np.arcsin(np.linalg.norm(obs_rot - reference_v, axis=1) / 2) * 2
deflection_obs = np.degrees(radial_distances_obs - radial_distances_catalog)*3600
if data['gravitational correction enabled?']:
deflection_obs += 1.751 / rad_dist
if options['flag_display3']:
plt.annotate(string, xy = (result1.x[0], result1.fun), xytext=(result1.x[0]-0.3, result1.fun+0.2), fontsize=14, arrowprops=dict(facecolor='black', shrink=0.05))
plt.title(f"Least-squares deflection fit for {df.shape[0]} chosen stars with magnitudes {np.min(df['magV']):.1f} to {np.max(df['magV']):.1f}")
plt.show()
### scatter of deflection vs. radius
plt.scatter(rad_dist, deflection_obs)
plt.ylabel("radial deflection (arcsec)")
plt.xlabel("radial position (solar radii)")
plt.annotate(f"L = {result1.x[0]:.5f}", (3, 1.25), fontsize=11)
plt.title(field_describe)
xx = np.linspace(1, np.max(rad_dist))
yy = result1.x[0] / xx
plt.plot(xx, yy, color='black')
plt.savefig(output_path(f'ECLIPSE_DEFLECTIONS{starttime}.png', options), dpi=400)
if options['flag_display3']:
plt.show()
plt.close()
output_name = f'ECLIPSE_OUTPUT{starttime}.txt'
output_file = Path(output_path(output_name, options))
print(output_file)
with open(output_file, 'w') as f:
f.write(f"MEE2024 version: {_version()}\n")
f.write(f"input file: {path_data}\n\n")
f.write(f"limiting magnitude: {options['eclipse_limiting_mag']}\n")
f.write(f"remove double stars: {options['remove_double_stars_eclipse']}\n")
f.write(f"number of stars used: {df.shape[0]}\n")
f.write(string)
f.write(f"\na/R^b fit: a = {result2.x[0]:.3f}, b = {result2.x[1]:.3f}, rms = {result2.fun:.3f} arcsec\n\n")
f.write("radial distances: " + str(rad_dist) + "\n\n")
f.write("deflection (arcsec): " + str(deflection_obs)+"\n")
if __name__ == '__main__':
pass
#eclipse_analysis('D:/eclipsetest/DISTORTION_OUTPUT20240323214311__data (2)20240317002546/distortion.zip', {}) # no cheat
#eclipse_analysis('D:/eclipsetest/DISTORTION_OUTPUT20240323224137__data (2)20240317002546/distortion.zip', {}) # yes cheat
eclipse_analysis('D:/Don 2017 eclipse data/DISTORTION_OUTPUT20240324141609__data_eclipse20240317002546/distortion.zip', {'output_dir':'D:/output4', 'flag_display3':True})