def main(source_d, destination_d, thumbnail_size=150):
"""
Create a directory of jpegs from a directory of FITS files, optionally
creating a subdirectory of jpeg thumbnails.
Parameters
----------
source_d : str
Path to the directory of FITS files.
destination_d : str
Path to the directory in which JPEGs will be placed.
thumbnail_size: float
Dimension of thumbnail image. Set to zero to not produce thumbnails.
"""
thumbnail_dir = 'thumbnail'
ic = ImageFileCollection(source_d, keywords='*')
mkdir_even_if_it_exists(destination_d)
if thumbnail_size:
mkdir_even_if_it_exists(os.path.join(destination_d, thumbnail_dir))
for data, fname in ic.data(return_fname=True):
scaled_data = scale_and_downsample(data)
base, _ = os.path.splitext(os.path.basename(fname))
dest_path = os.path.join(destination_d, base + '.jpg')
mimg.imsave(dest_path, scaled_data, cmap="gray")
if thumbnail_size:
tiny = np.array(data.shape) // thumbnail_size
thumb = block_reduce(scaled_data, block_size=tuple(tiny))
thumb_path = os.path.join(destination_d, thumbnail_dir,
base + '.jpg')
mimg.imsave(thumb_path, thumb, cmap='gray')
def main():
configfile = 'processing.cfg'
parser = argparse.ArgumentParser(
description='Process raw RGB FITS files for specific star')
parser.add_argument('--target',
dest='target',
default='ALPLYR',
help='ALPLYR|GAMCYG')
parser.add_argument('--configfile',
dest='configfile',
default=configfile,
help='name for config file')
parser.add_argument('--dispersion',
dest='dispersion',
action='store_true',
default=False,
help='find dispersion relation by marking lines')
args = parser.parse_args()
print(args.dispersion)
plt.style.use(astropy_mpl_style)
config = RawConfigParser()
config.read(configfile)
section = args.target
fitsdirs = glob(config.get(section, 'datapath'))
master = config.get(section, 'master')
numbers = eval(config.get(section, 'numbers'))
rawfile = config.get(section, 'raw_rgb_file')
mincol = int(config.get('MAIN', 'ccdcols_min'))
maxcol = int(config.get('MAIN', 'ccdcols_max'))
minrow = int(config.get('MAIN', 'ccdrows_min'))
maxrow = int(config.get('MAIN', 'ccdrows_max'))
filenames = []
for number in numbers:
filenames.append("%s%03d%s" % (master, number, '.fit'))
ic = ImageFileCollection(fitsdirs[0], keywords='*', filenames=filenames)
rawfile = config.get(section, 'raw_rgb_file')
if os.path.exists(rawfile):
with open(rawfile, 'rb') as f:
data = pickle.load(f)
raw_r = data['raw_r']
raw_g = data['raw_g']
raw_b = data['raw_b']
else:
raw_r = np.zeros(maxcol)
raw_g = np.zeros(maxcol)
raw_b = np.zeros(maxcol)
for data, fname in ic.data(return_fname=True):
minrow = int(config.get('MAIN', 'ccdrows_min'))
maxrow = int(config.get('MAIN', 'ccdrows_max'))
rgb = demosaicing_CFA_Bayer_bilinear(data)
trace = rgb[minrow:maxrow, mincol:maxcol, 1].sum(axis=1)
max_i = max(trace)
ix_line = np.where(trace > (max_i * 0.1))
minrow = np.min(ix_line)
maxrow = np.max(ix_line)
print(fname, minrow, maxrow, maxrow - minrow, max_i)
raw_r = raw_r + rgb[minrow:maxrow, mincol:maxcol, 0].sum(axis=0)
raw_g = raw_g + rgb[minrow:maxrow, mincol:maxcol, 1].sum(axis=0)
raw_b = raw_b + rgb[minrow:maxrow, mincol:maxcol, 2].sum(axis=0)
data = {
'raw_r': raw_r,
'raw_g': raw_g,
'raw_b': raw_b,
}
with open(rawfile, 'wb') as f:
pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)
if args.dispersion == False and config.has_option(section, 'coeff'):
print("Using existing dispersion relation")
coeff = eval(config.get(section, 'coeff'))
einsen = np.linspace(1, len(raw_r), num=len(raw_r))
wave = einsen * einsen * coeff[0] + einsen * coeff[1] + coeff[2]
wave = wave[0:len(einsen)]
fig = plt.figure(figsize=(14, 6))
plt.plot(wave, raw_r[::-1], color='r')
plt.plot(wave, raw_g[::-1], color='g')
plt.plot(wave, raw_b[::-1], color='b')
for wave0 in eval(config.get(section, 'lines')):
plt.plot([wave0, wave0], [0.1e7, 1.0e7])
plt.text(wave0,
1.0e7,
str(wave0),
rotation=90,
rotation_mode='anchor',
fontsize=10)
#plt.Text(wave0,1.0e7,text=str(wave0))
plt.xlim(3500, 8000)
plt.show()
fig = plt.figure(figsize=(14, 6))
plt.plot(wave, raw_r[::-1] + 2 * raw_g[::-1] + raw_b[::-1])
plt.show()
else:
print("Mark spectrum lines for dispersion solution")
print("Hint: typical lines:")
print(
"Balmer Series: H-alpha 6563 H-beta 4861 H-gamma 4340 H-delta 4102 H-epsilon 3970 H-zeta 3889 H-eta 3835 "
)
print("Telluric lines: 6863, 7594")
fig = plt.figure(figsize=(14, 6))
plt.axis([1000, 3500, 0, 2.0e7])
n = len(raw_r[::-1])
pixels = np.linspace(1, n - 1, num=n - 1)
pixels = np.arange(1, n + 1, 1)
line, = plt.plot(pixels, raw_r[::-1], color='r')
dc = DispersionDataCursor(plt.gca())
fig.canvas.mpl_connect('pick_event', dc)
line.set_picker(5) # Tolerance in points
plt.show()
print("detected:")
print(dc.getPositions())
print(dc.getWavelengths())
sortedPositions = np.sort(dc.getPositions())
sortedWavelengths = np.sort(dc.getWavelengths())
print(sortedPositions)
print(sortedWavelengths)
query = input('accept (Y/N)? ')
if query == 'Y':
config.set(section, 'positions', value=str(sortedPositions))
config.set(section, 'wavelengths', value=str(sortedWavelengths))
fig = plt.figure(figsize=(14, 6))
plt.plot(sortedPositions, sortedWavelengths)
plt.show()
with open(configfile, 'w') as cf:
config.write(cf)
from astropy.nddata import CCDData
import astropy.units as u
import ccdproc as ccdp
import os
import pathlib
from ccdproc import ImageFileCollection
from astropy.visualization import hist
import itertools
from astropy.stats import sigma_clip, mad_std
import time
import sys
flatlist = ImageFileCollection('Trimmed_Flat')
print(flatlist.summary)
for file in flatlist.data():
print(file.mean())
a = flatlist.summary['file'][0]
b = flatlist.summary['file'][4]
ccd1 = CCDData.read('Trimmed_Flat/' + a, unit='adu')
ccd2 = CCDData.read('Trimmed_Flat/' + b, unit='adu')
ccd = ccd1.divide(ccd2)
print(ccd.data.mean())
flatt = ccdp.trim_image(ccd, fits_section='[235:1564,1046:2509]')
import itertools
#
# a=[]
#
rawfile = config.get(section,'raw_rgb_file')
if os.path.exists(rawfile):
with open(rawfile, 'rb') as f:
data = pickle.load(f)
raw_r = data['raw_r']
raw_g = data['raw_g']
raw_b = data['raw_b']
else:
raw_r = np.zeros(maxcol)
raw_g = np.zeros(maxcol)
raw_b = np.zeros(maxcol)
for data, fname in ic.data( return_fname=True):
minrow = int(config.get('MAIN','ccdrows_min'))
maxrow = int(config.get('MAIN','ccdrows_max'))
rgb = demosaicing_CFA_Bayer_bilinear(data)
trace = rgb[minrow:maxrow,mincol:maxcol,1].sum(axis=1)
max_i = max(trace)
ix_line = np.where(trace > (max_i*0.1))
minrow = np.min(ix_line)
maxrow = np.max(ix_line)
print (fname, minrow, maxrow, maxrow-minrow, max_i)
raw_r = raw_r + rgb[minrow:maxrow,mincol:maxcol,0].sum(axis=0)
raw_g = raw_g + rgb[minrow:maxrow,mincol:maxcol,1].sum(axis=0)
raw_b = raw_b + rgb[minrow:maxrow,mincol:maxcol,2].sum(axis=0)
def main():
configfile = 'processing.cfg'
parser = argparse.ArgumentParser(
description='Process raw RGB FITS files for specific star')
parser.add_argument('--target',
dest='target',
default='ALPLYR',
help='ALPLYR|GAMCYG')
parser.add_argument('--configfile',
dest='configfile',
default=configfile,
help='name for config file')
parser.add_argument('--dispersion',
dest='dispersion',
action='store_true',
default=False,
help='find dispersion relation by marking lines')
args = parser.parse_args()
print(args.dispersion)
plt.style.use(astropy_mpl_style)
config = RawConfigParser()
config.read(configfile)
section = args.target
fitsdirs = glob(config.get(section, 'datapath'))
master = config.get(section, 'master')
numbers = eval(config.get(section, 'numbers'))
rawfile = config.get(section, 'raw_rgb_file')
mincol = int(config.get('MAIN', 'ccdcols_min'))
maxcol = int(config.get('MAIN', 'ccdcols_max'))
minrow = int(config.get('MAIN', 'ccdrows_min'))
maxrow = int(config.get('MAIN', 'ccdrows_max'))
filenames = []
for number in numbers:
filenames.append("%s%03d%s" % (master, number, '.fit'))
ic = ImageFileCollection(fitsdirs[0], keywords='*', filenames=filenames)
rawfile = config.get(section, 'raw_rgb_file')
raw_r = np.zeros(maxcol)
raw_g = np.zeros(maxcol)
raw_b = np.zeros(maxcol)
for data, fname in ic.data(return_fname=True):
minrow = int(config.get('MAIN', 'ccdrows_min'))
maxrow = int(config.get('MAIN', 'ccdrows_max'))
rgb = demosaicing_CFA_Bayer_bilinear(data)
trace = rgb[minrow:maxrow, mincol:maxcol, 1].sum(axis=1)
max_i = max(trace)
fig = plt.figure(figsize=(14, 6))
#plt.plot(wave_np,flux_r,'r')
plt.plot(trace / max_i)
plt.show()