def load_flat(self):
"""Loading Dark Frames"""
# Load individual bias frames.
self.flat = ImageStack()
self.load_type("Flat",self.flat)
# Set Header Values for each image.
for frame in self.flat.values():
frame.header.update('IMAGETYP','flat')
self.log.debug("Set IMAGETYP=flat for frame %s" % frame)
self.log.debug("Set Header IMAGETYP=flat for frames %r" % self.dark.list())
def load_dark(self):
"""Loading Dark Frames"""
# Load individual bias frames.
self.dark = ImageStack()
self.load_type("Dark",self.dark)
# Set Header Values for each image.
for frame in self.dark.values():
frame.header.update('IMAGETYP','dark')
self.log.debug("Set IMAGETYP=dark for frame %s" % frame)
self.log.debug("Set Header IMAGETYP=dark for frames %r" % self.dark.list())
def load_bias(self):
"""Loading Raw Bias Frames"""
# Load individual bias frames.
self.bias = ImageStack()
self.load_type("Bias",self.bias)
# Set Header Values for each image.
for frame in self.bias.values():
frame.header.update('IMAGETYP','zero')
self.log.debug("Set IMAGETYP=zero for frame %s" % frame)
self.log.debug("Set Header IMAGETYP=zero for frames %r" % self.bias.list())
# Python Imports
import shutil
import os
import collections
# Numpy Imports
import numpy as np
# Package Resources Imports
from pkg_resources import resource_filename
from AstroObject.AstroImage import ImageStack
from AstroObject.AstroObjectLogging import logging
exptime = 1200
images = ImageStack()
images["BiasA"] = np.random.poisson(10,(1000,1000)).astype(np.uint16)
images["BiasB"] = np.random.poisson(10,(1000,1000)).astype(np.uint16)
images["BiasC"] = np.random.poisson(10,(1000,1000)).astype(np.uint16)
images["DarkA"] = np.random.poisson(10,(1000,1000)).astype(np.uint16)
images["DarkB"] = np.random.poisson(10,(1000,1000)).astype(np.uint16)
images["DarkC"] = np.random.poisson(10,(1000,1000)).astype(np.uint16)
for image in ["DarkA","DarkB","DarkC"]:
images[image].header.update('exptime',exptime)
images["FlatA"] = np.ones((1000,1000)).astype(np.uint16)
images["FlatB"] = np.ones((1000,1000)).astype(np.uint16)
images["FlatC"] = np.ones((1000,1000)).astype(np.uint16)
images["DataA"] = np.ones((1000,1000)).astype(np.uint16)
images["DataB"] = np.ones((1000,1000)).astype(np.uint16)
images["DataC"] = np.ones((1000,1000)).astype(np.uint16)
for image in ["DataA","DataB","DataC"]:
class Pipeline(Simulator):
"""A task manager for the RC Pipeline"""
def __init__(self):
super(Pipeline, self).__init__(commandLine=True,name="Example Pipeline",version="1.0")
self.config.load(resource_filename(__name__,"Defaults.yaml"))
self.config.setFile("Main")
self.config.load()
self.collect()
@ignore #Don't load this method as a stage... it is a helper method used to implement other stages.
def load_type(self,key,stack):
"""Load a specific type of files using a generalized loading procedure"""
if isinstance(self.config[key]["Files"],collections.Sequence):
ReadStates = []
for filename in self.config[key]["Files"]:
ReadStates += stack.read(filename)
self.log.debug("Loaded %s: %s" % (key,filename))
return ReadStates
else:
self.log.error("No %s files are given." % key)
raise IOError("No %s files are given." % key)
def load_bias(self):
"""Loading Raw Bias Frames"""
# Load individual bias frames.
self.bias = ImageStack()
self.load_type("Bias",self.bias)
# Set Header Values for each image.
for frame in self.bias.values():
frame.header.update('IMAGETYP','zero')
self.log.debug("Set IMAGETYP=zero for frame %s" % frame)
self.log.debug("Set Header IMAGETYP=zero for frames %r" % self.bias.list())
def load_dark(self):
"""Loading Dark Frames"""
# Load individual bias frames.
self.dark = ImageStack()
self.load_type("Dark",self.dark)
# Set Header Values for each image.
for frame in self.dark.values():
frame.header.update('IMAGETYP','dark')
self.log.debug("Set IMAGETYP=dark for frame %s" % frame)
self.log.debug("Set Header IMAGETYP=dark for frames %r" % self.dark.list())
def load_flat(self):
"""Loading Dark Frames"""
# Load individual bias frames.
self.flat = ImageStack()
self.load_type("Flat",self.flat)
# Set Header Values for each image.
for frame in self.flat.values():
frame.header.update('IMAGETYP','flat')
self.log.debug("Set IMAGETYP=flat for frame %s" % frame)
self.log.debug("Set Header IMAGETYP=flat for frames %r" % self.dark.list())
@help("Create bias frames from the configured bias list.")
@depends("load-bias") # Declare a dependency on another stage: Method ``load_bias()``.
def create_bias(self):
"""Creating Combined Bias Frame"""
self.log.debug("Running iraf.zerocombine on image list...")
iraf.unlearn(iraf.zerocombine)
iraf.zerocombine(self.bias.iinat(),
output=self.bias.iout("Bias"),
combine=self.config["Bias.Combine"],
ccdtype="zero",
reject=self.config["Bias.Reject"],
scale="none", nlow=0, nhigh=1, nkeep=1, mclip="yes", lsigma=3.0, hsigma=3.0, rdnoise="0.", gain ="1."
)
self.bias.idone()
@help("Create Dark Frames")
@depends("load-dark")
def create_dark(self):
"""Creating Combined Dark Frame"""
self.log.debug("Running iraf.darkcombine on image list...")
iraf.unlearn(iraf.darkcombine)
iraf.darkcombine(self.dark.iraf.inatfile(),
output=self.dark.iraf.outfile("Dark"),
combine=self.config["Dark.Combine"],
ccdtype="dark",
reject=self.config["Dark.Reject"],
process="no", scale="exposure", nlow=0, nhigh=1, nkeep=1, mclip="yes", lsigma=3.0, hsigma=3.0, rdnoise="0.", gain ="1."
)
self.dark.iraf.done()
@help("Create Flat Frames")
@depends("load-flat")
def create_flat(self):
"""Creating Combined Flat Frame"""
self.log.debug("Runnign iraf.flatcombine on image list...")
iraf.unlearn(iraf.flatcombine)
iraf.flatcombine(self.flat.iraf.inatfile(),
output=self.flat.iraf.outfile("Flat"),
combine=self.config["Flat.Combine"],
ccdtype="flat",
reject=self.config["Flat.Reject"],
scale=self.config["Flat.Scale"],
process="no", subsets="no", nlow=0, nhigh=1, nkeep=1, mclip="yes", lsigma=3.0, hsigma=3.0, rdnoise="0.", gain ="1.")
self.flat.iraf.done()
def load_data(self):
"""Loading Raw Data into the system."""
self.data = ImageStack()
self.load_type("Data",self.data)
@include # Set this stage as something to be run with the *all macro.
@depends("create-bias","load-data")
@help("Subtract Bias Frame")
def subtract_bias(self):
"""Subtracting Bias Frame"""
iraf.unlearn(iraf.ccdproc)
iraf.ccdproc(self.data.iraf.modatfile(),
ccdtype="", fixpix="no", overscan="no", trim ="no", zerocor="yes", darkcor="no", flatcor ="no",
zero=self.bias.iin("Bias"))
self.data.idone()
@include # Set this stage as something to be run with the *all macro.
@depends("create-dark","load-data")
@help("Subtract Dark Frame")
def subtract_dark(self):
"""Subtracting Dark Frame"""
iraf.unlearn(iraf.ccdproc)
iraf.ccdproc(self.data.iraf.modatfile(),
ccdtype="", fixpix="no", overscan="no", trim ="no", zerocor="no", darkcor="yes", flatcor ="no",
dark=self.dark.iin("Dark"))
self.data.idone()
@include # Set this stage as something to be run with the *all macro.
@depends("create-flat","load-data")
@help("Divide out flat frame")
def divide_flat(self):
"""Dividing by Flat Frame"""
iraf.unlearn(iraf.ccdproc)
iraf.ccdproc(self.data.iraf.inatfile(),
output=self.data.iraf.outatfile(append="-Flat"),
flat=self.flat.iin("Flat"),
ccdtype="", fixpix="no", overscan="no", trim ="no", zerocor="no", flatcor="yes", darkcor ="no")
self.data.iraf.done()
# Since the simulator loads and runs stages in order, this stage will always
# be run last.
@include # Set this stage as something to be run with the *all macro.
@depends("load-data")
def save_file(self):
"""Save the new fits file"""
self.data.write("DataFile.fits",frames=[self.data.framename],clobber=True)
@help("Save Partial Images")
@depends("create-flat","create-dark","create-bias")
def save_partials(self):
"""Saving partial images"""
self.bias.write(frames=["Bias"],filename=self.config["Bias.Master"],clobber=True)
self.dark.write(frames=["Dark"],filename=self.config["Dark.Master"],clobber=True)
self.flat.write(frames=["Flat"],filename=self.config["Flat.Master"],clobber=True)
def load_data(self):
"""Loading Raw Data into the system."""
self.data = ImageStack()
self.load_type("Data",self.data)
# -*- coding: utf-8 -*-
# This example assumes that the following files exist:
# - data.fits
# - bias.fits (IMAGTYP=zero)
# - dark.fits (IMAGTYP=dark)
# - flat.fits (IMAGTYP=flat)
from AstroObject.AstroImage import ImageStack
from AstroObject.iraftools import UseIRAFTools
ImageStack = UseIRAFTools(ImageStack) # Enable IRAFTools
from pyraf import iraf
from pyraf.iraf import imred, ccdred
Data = ImageStack.fromFile("data.fits")
Data.read("bias.fits")
Data.read("dark.fits")
Data.read("flat.fits")
Data.select("data")
iraf.ccdproc(
Data.iraf.modatfile("data", append="-reduced"),
flat=Data.iraf.infile("flat"),
dark=Data.iraf.infile("dark"),
zero=Data.iraf.infile("bias"),
ccdtype="",
fixpix="no",
overscan="no",
trim="no",
zerocor="yes",
darkcor="yes",
flatcor="yes",
)