def load_data_from_fits(files, keys):
"""Load data from fits files and return a dataFrame"""
times = []
filenames = []
calcsunx = []
calcsuny = []
i = 0
for file in files:
print("Opening file " + file + " (" + str(i) + "/" + str(len(files)) +
")")
filenames.append(file)
times.append(get_image_time(file))
p = pyas(file)
calcsun = p.find_sun_center()
calcsunx.append(calcsun[0])
calcsuny.append(calcsun[1])
if i == 0:
values = np.array([i] + [p.header.get(key) for key in keys])
else:
values = np.vstack(
[values, [i] + [p.header.get(key) for key in keys]])
i += 1
lc = pandas.DataFrame(values, index=times, columns=['index'] + keys)
lc['filename'] = pandas.Series(files)
lc['filename'] = files
lc['FSUN-CENTER1'] = pandas.Series(calcsunx)
lc['FSUN-CENTER2'] = pandas.Series(calcsuny)
return lc
def load_data_from_fits(files, keys):
"""Load data from fits files and return a dataFrame"""
times = []
filenames = []
calcsunx = []
calcsuny = []
i = 0
for file in files:
print("Opening file " + file + " (" + str(i) + "/" + str(len(files)) + ")")
filenames.append(file)
times.append(get_image_time(file))
p = pyas(file)
calcsun = p.find_sun_center()
calcsunx.append(calcsun[0])
calcsuny.append(calcsun[1])
if i == 0:
values = np.array([i] + [p.header.get(key) for key in keys])
else:
values = np.vstack([values, [i] + [p.header.get(key) for key in keys]])
i += 1
lc = pandas.DataFrame(values, index = times, columns = ['index'] + keys)
lc['filename'] = pandas.Series(files)
lc['filename'] = files
lc['FSUN-CENTER1'] = pandas.Series(calcsunx)
lc['FSUN-CENTER2'] = pandas.Series(calcsuny)
return lc
def get_sun_center_lc(files):
i = 0
times = []
for file in files:
print("Opening file " + file + " (" + str(i) + "/" + str(len(files)) + ")")
times.append(get_image_time(file))
if i == 0:
found_sun_center = check_sun_center(file)
else:
found_sun_center = np.vstack([check_sun_center(file), found_sun_center])
i += 1
lc = pandas.DataFrame(found_sun_center * pixel_size_arcsec, index = times, columns = ["diffX", "diffY"])
return lc
def get_lc(files, key, limit = False):
times = []
values = []
i = 0
for file in files:
print("Opening file " + file + " (" + str(i) + "/" + str(len(files)) + ")")
header = pyfits.getheader(file)
date = datetime.datetime.utcfromtimestamp(header.get('RT_SEC') + header.get('RT_NSEC')/1e9)
times.append(date)
values.append(header.get(key))
i += 1
if type(limit) == type(1):
if i >= limit: break
lc = pandas.Series(values, times)
return lc
def get_sun_center_lc(files):
i = 0
times = []
for file in files:
print("Opening file " + file + " (" + str(i) + "/" + str(len(files)) +
")")
times.append(get_image_time(file))
if i == 0:
found_sun_center = check_sun_center(file)
else:
found_sun_center = np.vstack(
[check_sun_center(file), found_sun_center])
i += 1
lc = pandas.DataFrame(found_sun_center * pixel_size_arcsec,
index=times,
columns=["diffX", "diffY"])
return lc
def get_lc(files, key, limit=False):
times = []
values = []
i = 0
for file in files:
print("Opening file " + file + " (" + str(i) + "/" + str(len(files)) +
")")
header = pyfits.getheader(file)
date = datetime.datetime.utcfromtimestamp(
header.get('RT_SEC') + header.get('RT_NSEC') / 1e9)
times.append(date)
values.append(header.get(key))
i += 1
if type(limit) == type(1):
if i >= limit: break
lc = pandas.Series(values, times)
return lc
def get_sun_lightcurve(files):
"""Load data from fits files and lightcurve"""
times = []
image_max = []
sun_center_flux = []
i = 0
for file in files:
print("Opening file " + file + " (" + str(i) + "/" + str(len(files)) + ")")
p = pyas(file)
image_max.append(p.data.max())
times.append(p.date)
sun_center_flux.append(p.data[np.floor(p.sun_center[1]),np.floor(p.sun_center[0])])
i += 0
lc1 = pandas.Series(image_max, times)
lc2 = pandas.Series(sun_center_flux, times)
lc = pandas.DataFrame({"image max":lc1, "sun center flux":lc2})
return lc
def get_sun_lightcurve(files):
"""Load data from fits files and lightcurve"""
times = []
image_max = []
sun_center_flux = []
i = 0
for file in files:
print("Opening file " + file + " (" + str(i) + "/" + str(len(files)) +
")")
p = pyas(file)
image_max.append(p.data.max())
times.append(p.date)
sun_center_flux.append(p.data[np.floor(p.sun_center[1]),
np.floor(p.sun_center[0])])
i += 0
lc1 = pandas.Series(image_max, times)
lc2 = pandas.Series(sun_center_flux, times)
lc = pandas.DataFrame({"image max": lc1, "sun center flux": lc2})
return lc
