from astropy.convolution import Gaussian2DKernel
from astropy.modeling import models, fitting
from astropy.stats import gaussian_fwhm_to_sigma, sigma_clipped_stats
from photutils import (make_source_mask,
MedianBackground, SigmaClip, Background2D)
# Import plotting utilities
from matplotlib import pyplot as plt
# Add the AstroImage class
import astroimage as ai
# Add the header handler to the BaseImage class
from Mimir_header_handler import Mimir_header_handler
ai.reduced.ReducedScience.set_header_handler(Mimir_header_handler)
ai.set_instrument('mimir')
# Grad the assigned keyword for the airmass value
airmassKeyword = ai.reduced.ReducedScience.headerKeywordDict['AIRMASS']
#==============================================================================
# *********************** CUSTOM USER CODE ************************************
# this is where the user specifies where the raw data is stored
# and some of the subdirectory structure to find the actual .FITS images
#==============================================================================
# This is where the associated PPOL directory is located
PPOL_dir = 'C:\\Users\\Jordan\\FITS_data\\Mimir_data\\PPOL_Reduced\\201611'
# The user must specify the *name* of the PPOL meta-groups associated with each
# target-filter combination. This will allow the script to locate the computed
import glob
import numpy as np
from skimage import measure, morphology
from astropy.table import Table, Column
from astropy.stats import gaussian_fwhm_to_sigma, sigma_clipped_stats
from astropy.convolution import convolve, convolve_fft, Gaussian2DKernel
# For debugging
import matplotlib.pyplot as plt
# Add the AstroImage class
import astroimage as ai
# Add the header handler to the BaseImage class
from Mimir_header_handler import Mimir_header_handler
ai.set_instrument('2mass')
# This script will read in the background level estimated for each on-target
# image in the previous step. The background level in dimmest parts of the
# on-target image will be directly computed, and the residual between the direct
# estimate and the interpolation will be stored. The distribution of these
# residual will be used to estimate which interpolated background levels can be
# trusted.
#==============================================================================
# *********************** CUSTOM USER CODE ************************************
# this is where the user specifies where the raw data is stored
# and some of the subdirectory structure to find the actual .FITS images
#==============================================================================
# This is the location of all PPOL reduction directory
# hack script to set the bad pixels to the right value for PPOL
import os
import glob
import numpy as np
import astroimage as ai
ai.set_instrument('Mimir')
bkgFreeDir = 'C:\\Users\\Jordan\\FITS_data\\Mimir_data\\pyPol_Reduced\\201611\\bkgFreeHWPimages'
fileList = glob.glob(os.path.join(bkgFreeDir, '*.fits'))
for file1 in fileList:
print('processing {}'.format(os.path.basename(file1)))
img = ai.reduced.ReducedScience.read(file1)
# Capture NaNs and bad values and set them to -1e6 so that PPOL will
# know what to do with those values.
tmpData = img.data
badPix = np.logical_not(np.isfinite(tmpData))
tmpData[np.where(badPix)] = -1e6
badPix = np.abs(tmpData) > 1e5
tmpData[np.where(badPix)] = -1e6
# Store the data in the image object
img.data = tmpData
# Write back to disk
img.write(file1, dtype=np.float32, clobber=True)
print('Done!')
import sys
import time
import numpy as np
import matplotlib.pyplot as plt
from astropy.table import Table, Column
from astropy.coordinates import SkyCoord
import astropy.units as u
from scipy import stats
# Add the AstroImage class
import astroimage as ai
# Add the header handler to the BaseImage class
from Mimir_header_handler import Mimir_header_handler
ai.reduced.ReducedScience.set_header_handler(Mimir_header_handler)
ai.set_instrument('mimir')
#==============================================================================
# *********************** CUSTOM USER CODE ************************************
# this is where the user specifies where the raw data is stored
# and some of the subdirectory structure to find the actual .FITS images
#==============================================================================
# Define the location of the PPOL reduced data to be read and worked on
PPOL_data = 'C:\\Users\\Jordan\\FITS_data\\Mimir_data\\PPOL_Reduced\\201611\\'
S3_dir = os.path.join(PPOL_data, 'S3_Astrometry')
# This is the location where all pyPol data will be saved
pyPol_data = 'C:\\Users\\Jordan\\FITS_data\\Mimir_data\\pyPol_Reduced\\201611'
# Set the filename for the reduced data indexFile and read it in
reducedFileIndexFile = os.path.join(pyPol_data, 'reducedFileIndex.csv')
# Short script to read in and display all the images in the diagnostic dir
import glob
import numpy as np
import time
from astropy.stats import sigma_clipped_stats, freedman_bin_width
from scipy import ndimage
import astroimage as ai
ai.set_instrument('Mimir')
fileList = glob.glob('*.fits')
fileList = np.array(fileList)
fileList.sort()
# Define a quick mode function
def mode(array):
"""An estimate of the statistical mode of this image"""
# SUPER fast and sloppy mode estimate:
mean, median, std = sigma_clipped_stats(array)
quickModeEst = 3*median - 2*mean
# Compute an approximately 3-sigma range about this
modeRegion = quickModeEst + std*np.array([-1.5, +1.5])
# Now compute the number of bins to generate in this range
binWidth = freedman_bin_width(array.flatten())
bins = np.arange(modeRegion[0], modeRegion[1], binWidth)
# Loop through larger and larger binning until find unique solution
foundMode = False
while not foundMode:
os.mkdir(bkgFreeImagesDir, 0o755)
# Read in the indexFile data and select the filenames
indexFile = os.path.join(pyPol_data, 'reducedFileIndex.csv')
fileIndex = Table.read(indexFile, format='csv')
# Read in the kokopelli mask
from astropy.io import fits
kokopelliHDUlist = fits.open('kokopelliMask.fits')
kokopelliMask = (kokopelliHDUlist[0].data > 0)
# Read in the 2MASS masks
TMASSdir = ".\\2MASSimages"
# Set the instrument to 2MASS
ai.set_instrument('2MASS')
# Read in all the 2MASS images and store them in a dictionary for quick reference
TMASS_Hfiles = np.array(glob.glob(os.path.join(TMASSdir, '*H_mask.fits')))
TMASS_Kfiles = np.array(glob.glob(os.path.join(TMASSdir, '*Ks_mask.fits')))
# Read in the 2MASS images
TMASS_HmaskList = [ai.reduced.ReducedScience.read(f) for f in TMASS_Hfiles]
TMASS_KmaskList = [ai.reduced.ReducedScience.read(f) for f in TMASS_Kfiles]
# Parse the targets for each file
TMASS_Htargets = [os.path.basename(f).split('_')[0] for f in TMASS_Hfiles]
TMASS_Ktargets = [os.path.basename(f).split('_')[0] for f in TMASS_Kfiles]
# Store these masks in a dictionary
TMASS_HimgDict = dict(zip(TMASS_Htargets, TMASS_HmaskList))
import glob
import numpy as np
from skimage import measure, morphology
from astropy.table import Table, Column
from astropy.stats import gaussian_fwhm_to_sigma, sigma_clipped_stats
from astropy.convolution import convolve, convolve_fft, Gaussian2DKernel
# For debugging
import matplotlib.pyplot as plt
# Add the AstroImage class
import astroimage as ai
# Add the header handler to the BaseImage class
from Mimir_header_handler import Mimir_header_handler
ai.set_instrument('2mass')
# This script will read in the background level estimated for each on-target
# image in the previous step. The background level in dimmest parts of the
# on-target image will be directly computed, and the residual between the direct
# estimate and the interpolation will be stored. The distribution of these
# residual will be used to estimate which interpolated background levels can be
# trusted.
#==============================================================================
# *********************** CUSTOM USER CODE ************************************
# this is where the user specifies where the raw data is stored
# and some of the subdirectory structure to find the actual .FITS images
#==============================================================================
# This is the location of all PPOL reduction directory
os.mkdir(bkgFreeImagesDir, 0o755)
# Read in the indexFile data and select the filenames
indexFile = os.path.join(pyPol_data, 'reducedFileIndex.csv')
fileIndex = Table.read(indexFile, format='csv')
# Read in the kokopelli mask
from astropy.io import fits
kokopelliHDUlist = fits.open('kokopelliMask.fits')
kokopelliMask = (kokopelliHDUlist[0].data > 0)
# Read in the 2MASS masks
TMASSdir = ".\\2MASSimages"
# Set the instrument to 2MASS
ai.set_instrument('2MASS')
# Read in all the 2MASS images and store them in a dictionary for quick reference
TMASS_Hfiles = np.array(glob.glob(os.path.join(TMASSdir, '*H_mask.fits')))
TMASS_Kfiles = np.array(glob.glob(os.path.join(TMASSdir, '*Ks_mask.fits')))
# Read in the 2MASS images
TMASS_HmaskList = [ai.reduced.ReducedScience.read(f) for f in TMASS_Hfiles]
TMASS_KmaskList = [ai.reduced.ReducedScience.read(f) for f in TMASS_Kfiles]
# Parse the targets for each file
TMASS_Htargets = [os.path.basename(f).split('_')[0] for f in TMASS_Hfiles]
TMASS_Ktargets = [os.path.basename(f).split('_')[0] for f in TMASS_Kfiles]
# Store these masks in a dictionary
TMASS_HimgDict = dict(zip(
