"""
import argparse
import numpy as np
import pylab as plt
from astropy.io import fits
from trm import doppler
import copy
parser = argparse.ArgumentParser(description=usage)
# positional
parser.add_argument('map', help='name of the input map')
parser.add_argument('dout', help='default output file')
# OK, done with arguments.
args = parser.parse_args()
# load map
dmap = doppler.Map.rfits(doppler.afits(args.map))
# copy the map to compute the entropy
mcopy = copy.deepcopy(dmap)
# compute default
doppler.comdef(dmap)
# write the result to a FITS file
dmap.wfits(doppler.afits(args.dout))
parser.add_argument('map', help='name of the input map template')
parser.add_argument('data', help='data template file data template')
# optional
parser.add_argument('-n', dest='ntrial', type=int, default=1,
help='number of tests to carry out.')
# OK, done with arguments.
args = parser.parse_args()
if args.ntrial < 1:
print('You must carry out at least one trial.')
exit(1)
# load map and data
dmap = doppler.Map.rfits(doppler.afits(args.map))
ddat = doppler.Data.rfits(doppler.afits(args.data))
# make copies
cmap = copy.deepcopy(dmap)
cdat = copy.deepcopy(ddat)
for i in xrange(args.ntrial):
# fill map with random noise
for image in dmap.data:
image.data = np.asarray(
np.random.normal(size=image.data.shape),dtype=np.float32)
# enact opus
doppler.comdat(dmap, cdat)
parser = argparse.ArgumentParser(description=usage)
# positional
parser.add_argument('map', help='name of the input map')
parser.add_argument('vmax', type=float, help='maximum velocity (km/s)')
# optional
parser.add_argument('-n', dest='nimage', type=int, default=1,
help='number of the image to plot')
parser.add_argument('-s', dest='step', type=int, default=1,
help='pixel step to use. Make > 1 if the rendering seems slow and clunky')
# OK, done with arguments.
args = parser.parse_args()
dmap = doppler.Map.rfits(doppler.afits(args.map))
img = dmap.data[args.nimage-1]
# have to copy the array [perhaps to allow modification?]
data = img.data.T[::args.step,::args.step,::args.step].copy()
nx, ny, nz = data.shape
# transform array
tr = np.empty((12))
tr[1], tr[6], tr[11] = args.step*img.vxy, args.step*img.vxy, args.step*img.vz
tr[2] = tr[3] = tr[5] = tr[7] = tr[9] = tr[10] = 0
tr[0] = -tr[1]*(nx-1)/2
tr[4] = -tr[6]*(ny-1)/2
tr[8] = -tr[11]*(nz-1)/2
parser = argparse.ArgumentParser(description=usage)
# positional
parser.add_argument('map', help='name of the input map')
parser.add_argument('dtemp', help='data template file')
parser.add_argument('dout', help='data output file')
# optional
parser.add_argument('-n', dest='noise', action='store_true',
help='add noise according to uncertainty array in template')
# OK, done with arguments.
args = parser.parse_args()
# load map and data
dmap = doppler.Map.rfits(doppler.afits(args.map))
dtemp = doppler.Data.rfits(doppler.afits(args.dtemp))
flux = dtemp.data[0].flux
ferr = dtemp.data[0].ferr
# compute data
dcopy = copy.deepcopy(dtemp)
doppler.comdat(dmap, dtemp)
# optionally add noise
if args.noise:
for spectra in dtemp.data:
spectra.flux = np.random.normal(spectra.flux, np.abs(spectra.ferr))
else:
chisq = 0.
vy = -50.
vz = 0.
fwhmxy = 200.
fwhmz = 50.
vpeak = 550.
vout1 = 2200.
vout2 = 2400.
ipeak = 0.5
eout = -2.5
ein = +3.0
"""
with open(doppler.acfg(args.config),'w') as fout:
fout.write(config.format(doppler.VERSION))
else:
if not args.clobber and os.path.exists(doppler.afits(args.map)):
print('\nERROR: ',doppler.afits(args.map),
'already exists and will not be overwritten.')
exit(1)
config = ConfigParser.RawConfigParser()
config.read(doppler.acfg(args.config))
tver = config.getint('main', 'version')
if tver != doppler.VERSION:
print('Version number in config file =',tver,
'conflicts with version of script =',doppler.VERSION)
print('Will continue but there may be problems')
target = config.get('main', 'target')
if target != 'maps':
parser = argparse.ArgumentParser(description=usage)
# positional
parser.add_argument('map', help='name of the input map')
parser.add_argument('data', help='data file')
parser.add_argument('scaled', help='scaled output map')
# optional
parser.add_argument('-i', dest='iscale', action='store_true',
help='individual scaling (else a single global scale)')
# OK, done with arguments.
args = parser.parse_args()
# load map and data
dmap = doppler.Map.rfits(doppler.afits(args.map))
data = doppler.Data.rfits(doppler.afits(args.data))
nscale = 0
for image in dmap.data:
nscale += len(image.wave)
if args.iscale and nscale > 1:
# in this option we try to individually scale the images
mtemp = copy.deepcopy(dmap)
flux, ferr = retarr(data)
wgt = np.empty_like(ferr)
ok = ferr > 0
wgt[ok] = 1./ferr[ok]**2
# create indices to access the scale factors
from astropy.io import fits
from trm import doppler
parser = argparse.ArgumentParser(description=usage)
# positional
parser.add_argument('imap', help='name of the input map')
parser.add_argument('data', help='data file')
parser.add_argument('niter', type=int, help='number of iterations')
parser.add_argument('caim', type=float, help='reduced chi**2 to aim for')
parser.add_argument('omap', help='name of the output map')
# optional
parser.add_argument('-r', dest='rmax', type=float,
default=0.2, help='maximum change')
parser.add_argument('-t', dest='tlim', type=float,
default=1.e-4, help='test limit for stopping iterations')
# OK, done with arguments.
args = parser.parse_args()
# load map and data
dmap = doppler.Map.rfits(doppler.afits(args.imap))
data = doppler.Data.rfits(doppler.afits(args.data))
# mem iterations
doppler.memit(dmap, data, args.niter, args.caim, args.tlim, args.rmax)
# write to fits file
dmap.wfits(doppler.afits(args.omap))
scale1 = 1.0
wave2 = 434.0
gamma2 = 120.
scale2 = 0.6
# keywords / values for the FITS header. Optional
[fitshead]
ORIGIN = makegrid.py
OBJECT = SS433
"""
with open(doppler.acfg(args.config),'w') as fout:
fout.write(config.format(doppler.VERSION))
else:
if not args.clobber and os.path.exists(doppler.afits(args.grid)):
print('\nERROR: ',doppler.afits(args.grid),
'already exists and will not be overwritten.')
exit(1)
config = ConfigParser.RawConfigParser()
config.read(doppler.acfg(args.config))
tver = config.getint('main', 'version')
if tver != doppler.VERSION:
print('Version number in config file =',tver,
'conflicts with version of script =',doppler.VERSION)
print('Will continue but there may be problems')
target = config.get('main', 'target')
if target != 'grids':
wave1 = 480.
wave2 = 500.
nwave = 100
wrms = 0.01
time1 = 50000.0
time2 = 50000.1
nspec = 20
error = 0.1
fwhm = 100.
nsub = 3
"""
with open(doppler.acfg(args.config),'w') as fout:
fout.write(config.format(doppler.VERSION))
else:
if not args.clobber and os.path.exists(doppler.afits(args.data)):
print('\nERROR: ',doppler.afits(args.data),
'already exists and will not be overwritten.')
exit(1)
config = ConfigParser.RawConfigParser()
config.read(doppler.acfg(args.config))
tver = config.getint('main', 'version')
if tver != doppler.VERSION:
print('Version number in config file =',tver,
'conflicts with version of script =',version)
print('Will continue but there may be problems')
target = config.get('main', 'target')
if target != 'data':
parser.add_argument('flow', type=float, help='lowest frequency to search')
parser.add_argument('fhigh', type=float, help='highest frequency to search')
parser.add_argument('delta', type=float, help='number of cycles change over time base per frequency step')
parser.add_argument('vgrid', type=float, help='velocity spacing of grid')
parser.add_argument('ngrid', type=int, help='number of gaussians along side of grid')
parser.add_argument('fratio', type=float, help='ratio FWHM/VGRID')
parser.add_argument('ofile', help='ASCII file for output')
parser.add_argument('cond', type=float, nargs='+', help='SVD condition parameter(s)')
# optional
parser.add_argument('-n', dest='ntdiv', type=int,
default=11, help='spectrum sub-division factor')
args = parser.parse_args()
model = doppler.Map.rfits(doppler.afits(args.model))
if len(model.data) != 1:
print 'precover requires just one image in the model file'
exit(1)
data = doppler.Data.rfits(doppler.afits(args.data))
nmonte = args.nmonte
flow = args.flow
fhigh = args.fhigh
delta = args.delta
vgrid = args.vgrid
ngrid = args.ngrid
fratio = args.fratio
ntdiv = args.ntdiv
ofile = args.ofile
cond = args.cond
import argparse
import numpy as np
from trm import doppler
parser = argparse.ArgumentParser(description=usage)
# positional
parser.add_argument('grid', help='name of the grid')
parser.add_argument('tmap', help='template map file')
parser.add_argument('map', help='output map')
# OK, done with arguments.
args = parser.parse_args()
# load grid and data
grid = doppler.Grid.rfits(doppler.afits(args.grid))
tmap = doppler.Map.rfits(doppler.afits(args.tmap))
nside = grid.data.shape[0]
for image in tmap.data:
dmap = image.data
if dmap.ndim != 2:
print 'ERROR: Map images must be 2D for this routine.'
exit(1)
nvy, nvx = dmap.shape
vx = np.linspace(-image.vxy*(nvx-1)/2., image.vxy*(nvx-1)/2., nvx)
vy = np.linspace(-image.vxy*(nvy-1)/2., image.vxy*(nvy-1)/2., nvy)
VX, VY = np.meshgrid(vx, vy)
dmap[:] = 0
efac = 1./(grid.vgrid*grid.fratio/doppler.EFAC)**2/2.
user-specified fraction of the maximum.
"""
import argparse
import numpy as np
import pylab as plt
from astropy.io import fits
from trm import doppler
import copy
parser = argparse.ArgumentParser(description=usage)
# positional
parser.add_argument('inmap', help='name of the input map')
parser.add_argument('llim', type=float, help='lower limit as fraction of maximum')
parser.add_argument('outmap', help='name of output map')
# OK, done with arguments.
args = parser.parse_args()
# load map
imap = doppler.Map.rfits(doppler.afits(args.inmap))
for image in imap.data:
fmax = image.data.max()
image.data = np.maximum(args.llim*fmax, image.data)
# write the result to a FITS file
imap.wfits(doppler.afits(args.outmap))
parser = argparse.ArgumentParser(description=usage)
# positional
parser.add_argument('grid', help='name of the input grid')
parser.add_argument('data', help='data file')
parser.add_argument('svd', help='name of the SVD output file')
# optional
parser.add_argument('-n', dest='ntdiv', type=int,
default=11, help='spectrum sub-division factor')
# OK, done with arguments.
args = parser.parse_args()
# load grid and data
grid = doppler.Grid.rfits(doppler.afits(args.grid))
data = doppler.Data.rfits(doppler.afits(args.data))
# generate the matrix
A = doppler.genmat(grid, data, args.ntdiv)
# Carry out full SVD, returning smallest matrices possible
u, s, v = np.linalg.svd(A,full_matrices=False)
ng = grid.data.shape[0]
# Matrix v is the one we want. It should have dimensions (ng*ng) x (ng*ng) where
# N is number along each side of the grids. Need to re-shape
v = np.reshape(v, (ng*ng,ng,ng))
# now write to FITS
help='spectrum sub-division factor for finite exposures')
# OK, done with arguments.
args = parser.parse_args()
# a few checks
if args.flow <= 0 or args.fhigh <= args.flow:
print 'Upper frequency must excedd the lower, and both muts be > 0'
exit(1)
if args.delta <= 0 or args.delta >=1:
print 'Cycle change per frequency step must lie between 0 and 1.'
exit(1)
# load map and data
grid = doppler.Grid.rfits(doppler.afits(args.grid))
data = doppler.Data.rfits(doppler.afits(args.data))
tbase = data.data[0].time.max() - data.data[0].time.min()
nf = int((args.fhigh-args.flow)*tbase/args.delta)
fs = np.linspace(args.flow,args.fhigh,nf)
# a bit of header
print '# Output from the SVD routine, psearch.py'
print '#'
print '# Grid file =',args.grid
print '# Data file =',args.data
print '# Frequency range, number of frequencies =',args.flow,args.fhigh,nf
print '# Cycle change per frequency =',args.delta
print '# Condition values =',args.cond
print '#'
