def simulate(self):
for tsname, simconf in self.simulations.items():
ts = self.timestreams[tsname]
if os.path.exists(ts._ffile(0)):
print("Looks like timestream already exists. Skipping....")
else:
m = manager.ProductManager.from_config(simconf["product_directory"])
timestream.simulate(m, ts.directory, **simconf)
def simulate(self):
for tsname, simconf in self.simulations.items():
ts = self.timestreams[tsname]
if os.path.exists(ts._ffile(0)):
print "Looks like timestream already exists. Skipping...."
else:
m = manager.ProductManager.from_config(simconf['product_directory'])
timestream.simulate(m, ts.directory, **simconf)
from drift.pipeline import timestream
from simplearray import DishArray
### Make the analysis products for the telescope. This examples focuses only
### on the m-mode products for mapmaking
# Create telescope object and set zenith
tel = DishArray(latitude=30.0, longitude=0.0)
# Create Beam Transfer manager, and generate products
bt = beamtransfer.BeamTransfer("pydriver/btdir/", telescope=tel)
bt.generate()
### Simulate and make a map froma timestream
# Create an empty ProductManager
m = manager.ProductManager()
# Set the Beam Transfers
m.beamtransfer = bt
# Create a timestream with no noise (ndays=0) from a given map (could be a list of maps)
ts = timestream.simulate(m, "pydriver/ts1/", ["simulated_map.hdf5"], ndays=0)
# Make m-mode from the timestream
ts.generate_mmodes()
# Make a Healpix map from the m-modes (with NSIDE=256)
ts.mapmake_full(256, "observed_map.hdf5")
### Make the analysis products for the telescope. This examples focuses only
### on the m-mode products for mapmaking
# Create telescope object and set zenith
tel = DishArray()
tel.zenith = np.radians(np.array([30.0, 0.0])) # Must be in radians
# Create Beam Transfer manager, and generate products
bt = beamtransfer.BeamTransfer('pydriver/btdir/', telescope=tel)
bt.generate()
### Simulate and make a map froma timestream
# Create an empty ProductManager
m = manager.ProductManager()
# Set the Beam Transfers
m.beamtransfer = bt
# Create a timestream with no noise (ndays=0) from a given map (could be a list of maps)
ts = timestream.simulate(m, 'pydriver/ts1/', ['simulated_map.hdf5'], ndays=0)
# Make m-mode from the timestream
ts.generate_mmodes()
# Make a Healpix map from the m-modes (with NSIDE=256)
ts.mapmake_full(256, 'observed_map.hdf5')
import argparse
from drift.core import manager
from drift.pipeline import timestream
## Read arguments in.
parser = argparse.ArgumentParser(description="Create the visibility timeseries corresponding to a map.")
parser.add_argument("teldir", help="The telescope directory to use.")
parser.add_argument("outdir", help="Output directory for timeseries.")
parser.add_argument("--map", help="Each map argument is a map which contributes to the timeseries.", action='append')
parser.add_argument("--noise", help="Number of days of co-added data (affects noise amplitude).", metavar='NDAYS', default=None, type=int)
parser.add_argument("--resolution", help="Approximate time resolution in seconds.", metavar='NSEC', default=0, type=float)
args = parser.parse_args()
m = manager.ProductManager.from_config(args.teldir)
timestream.simulate(m, args.outdir, args.map, ndays=args.noise, resolution=args.resolution)
from simplearray import DishArray
### Make the analysis products for the telescope. This examples focuses only
### on the m-mode products for mapmaking
# Create telescope object and set zenith
tel = DishArray()
tel.zenith = np.radians(np.array([30.0, 0.0])) # Must be in radians
# Create Beam Transfer manager, and generate products
bt = beamtransfer.BeamTransfer('pydriver/btdir/', telescope=tel)
bt.generate()
### Simulate and make a map froma timestream
# Create an empty ProductManager
m = manager.ProductManager()
# Set the Beam Transfers
m.beamtransfer = bt
# Create a timestream with no noise (ndays=0) from a given map (could be a list of maps)
ts = timestream.simulate(m, 'pydriver/ts1/', ['simulated_map.hdf5'], ndays=0)
# Make m-mode from the timestream
ts.generate_mmodes()
# Make a Healpix map from the m-modes (with NSIDE=256)
ts.mapmake_full(256, 'observed_map.hdf5')
