def read_xfaster_newdata_output( file, pol=False, win=1, old=False, otit="XF T-P spectra", res=False, xpol=False, lmax=2000, chkbb=False, verbose=False ):
code = ' > read_xfaster_newdata_output: '
xfdir = '/global/scratch2/sd/dpietrob/Software/XFaster/'
if verbose:
print code+'XFaster folder = '+xfdir
tcl = hp.fitsfunc.read_cl(xfdir+'data/planck_lcdm_cl_uK_xf1.e-3.fits')
tcl[4] = 0.
tcl[5] = 0.
nl = len( tcl[0] )
l = np.arange( len( tcl[0] ) )
ll = l * (l+1)/2./np.pi
### NB consistent with my definition of .newdat
if old:
nbtt,nbee,nbbb,nbtb,nbte,nbeb = readcol(file, format=np.arange(6)+1, skipline=1, numline=1, verbose=verbose)
else:
nbtt,nbee,nbbb,nbte,nbtb,nbeb = readcol(file, format=np.arange(6)+1, skipline=1, numline=1, verbose=verbose)
print ' TT # bin = ', nbtt
print ' EE # bin = ', nbee
print ' TE # bin = ', nbte
print ' BB # bin = ', nbbb
print ' TB # bin = ', nbtb
print ' EB # bin = ', nbeb
#if verbose:
#print code+'binning theoretical power spectrum...'
btcltt = bp_binning( tcl[0]*ll, xfdir+'data/bins/ctp/CTP_bin_TT', verbose=verbose )
def bp_binning( cls, bin_file, verbose=False):
code = ' > bp_binning: '
if verbose:
print code+'Assuming l=0, len(cl)-1; lmax=len(cl)-1'
print code+'Reading bin_file %s' %bin_file
nl = len(cls)
fl,ll = readcol(bin_file, verbose=verbose)
nb = len(ll)
if verbose:
print nb, ll[nb-1], ll[nb]
lmax = np.min( [nl-1,ll[nb]] )
if verbose:
print 'lmax = %s' %lmax
#bok = (ll - lmax) > 0
#bmax = nb-1
#if bok[0] > 0:
# bmax = bok[0]-1
bin_cls = np.zeros( nb )
# for ib=0,bmax do bin_cls[ib] = total( cls[fl[ib]:ll[ib]] ) / (ll[ib]-fl[ib]+1)
for ib in np.arange( nb ):
#bin_cls[ib] = np.sum( cls[fl[ib]:ll[ib]] ) / (ll[ib]-fl[ib]+1)
bnl = np.sum( np.nonzero( cls[fl[ib]:ll[ib]] ) )
bin_cls[ib] = np.sum( cls[fl[ib]:ll[ib]] ) / bnl #(ll[ib]-fl[ib]+1)
if verbose:
print ib, fl[ib], ll[ib], bin_cls[ib]
tnl = np.sum( np.nonzero( bin_cls ) )
return bin_cls[0:tnl]
def items():
csv_path = "csvs"+os.sep
pickle_path = "pickles"+os.sep
files = glob.glob(csv_path+"*.csv")
for f in files:
dict = readcol(f,asdict=True,fsep="`")
dict.pop("\n", None) #for some reason readcol grabs the newline character
for k in dict.keys():
if "\n" in k:
dict[k.rstrip()] = dict.pop(k)
unique_dict = {}
key_list = ["name","Sells For","Purchase Price","reorderable","cataloged"] #keys that aren't useful to sort by
for k,v in dict.iteritems():
if k not in key_list: #keys that I want to sort by
unique_dict[k] = list(collections.OrderedDict.fromkeys(v)) #list removes non-unique values
if unique_dict[k] == ['-']: #if the only value is -, then that is not something useful to sort by
unique_dict.pop(k, None)
with open(pickle_path+f[len(csv_path):-3]+"pickle", 'wb') as handle:
pickle.dump((collections.OrderedDict(sorted(dict.items())), collections.OrderedDict(sorted(unique_dict.items()))), handle) #depositing dict by pickle, lazy and this is fast
#!bin/python
from readcol import *
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.cm as cm
from matplotlib.colors import LogNorm
num=1
LUNIT=1.49597871e13
rad0=readcol("rad_chart.txt")
size0=readcol("size_chart.txt");
n_rad=len(rad0)
n_size=len(size0)
print n_rad,n_size
dens_grid=np.ndarray(shape=(n_size,n_rad),dtype=float)
dr=rad0[1]-rad0[0]
dens=np.ndarray(shape=(n_size,n_rad),dtype=float)
print dens
#print np.shape(dens)
#print np.shape(dens[0])
rad,size=np.meshgrid(rad0,size0)
X = 10*np.random.rand(5,3)
dens=np.random.rand(n_size,n_rad)
print dens
fig, ax = plt.subplots()
ax.imshow(X, cmap=cm.jet)#, interpolation='nearest')
print X
plt.show()
#!bin/python
from readcol import *
import matplotlib
#matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.cm as cm
from matplotlib.colors import LogNorm
import sys
num=1
LUNIT=1.49597871e13
rad0=readcol("rad_chart.txt")
size0=readcol("size_chart.txt");
n_rad=len(rad0)
n_size=len(size0)
print n_rad,n_size
dens_grid=np.ndarray(shape=(n_size,n_rad),dtype=float)
dr=rad0[1]-rad0[0]
v_rel=np.ndarray(shape=(n_size,n_size),dtype=float)
print v_rel
#print np.shape(dens)
#print np.shape(dens[0])
rad,size=np.meshgrid(size0,size0)
lev=np.linspace(0,0,256)
lev2=np.linspace(0,10000,256)
plt.rc('text',usetex=True)
for i in range(0,256):
lev[i]=0.00001*np.exp(i*1.0/256*np.log(0.1/0.00001))
for i in range(0000,0001,50):
b[nvis:, 0] = np.rad2deg(np.arctan(np.tan(np.deg2rad(b[nvis:, 0]))))
f1 = functions.norm_l1(lambda_=l)
f2 = functions.norm_l2(y=b, A=A)
step = 0.5 / np.linalg.norm(A, ord=2)**2
solver = solvers.forward_backward(step=step)
ret = solvers.solve([f1, f2], x, solver, rtol=1e-6, maxit=maxit)
return ret
oi_data = 'CALIB_SIM_DATA_uncalib_t_disk_small2_0__PSF_MASK_NRM_F430M_x11_0.82_ref__00.oifits'
#input_path = ['/Users/donut/Library/Mobile Documents/com~apple~CloudDocs/JWST_CS/dataset/disks_clean/']
input_path = [
'/Users/donut/Library/Mobile Documents/com~apple~CloudDocs/JWST_CS/dataset/forms/'
]
input_files = readcol(input_path[0] + 'data_cgauss.txt', twod=True)
scale = 10.0 ## mas
hyperparameter = 0.05
maxit = 10000
for i in range(len(input_files)):
temp = input_path[0] + input_files[i][0]
im_atom = pyfits.getdata(temp)
if i == 0:
v_model, phi_model, t3phi_model = oitools.compute_obs(
oi_data, im_atom, scale)
else:
v_model_temp, phi_model_temp, t3phi_model_temp = oitools.compute_obs(
oi_data, im_atom, scale)
v_model = np.dstack((v_model, v_model_temp))
phi_model = np.dstack((phi_model, phi_model_temp))
# if ERROR: PLIO: reference out of bounds on mask error happen then use image[*,*,1] in input image as ur image may be more then 2 dim or [*,*,1]
# if ERROR: segmentation violation in xgterm use terminal or use linux machine
os.system("sed s/F.fits/F.fits[*,*,1]/g rm_cosmic.in> rm_cosmic_ind.in")
crmasks = '0, 0, 0, 0, 0' # total 5 frames: 2 AGN frame and 3 standard frames.
iraf.crutil.cosmicrays("@rm_cosmic_ind.in",
"@rm_cosmic.out",
interactive='no',
crmasks=crmasks)
#os.system("sed s/bfc.fits/bfc.fits[10:507,10:507]/g rm_cosmic.out> trim.in")
# Note: Check the limits. This is for 512 times 512 image size
#os.system("sed s/bfc.fits/bfct.fits/g rm_cosmic.out > trim.out")
filenames = readcol('rm_cosmic.out')
photometry(filenames)
os.chdir('..')
'''
dirlist=glob.glob('*2015*')
file1=open('mag.out', 'w')
day=np.array([1, 2, 3, 4, 5, 6, 8, 9, 10, 14])
for i in range(len(dirlist)):
Bmagfile=glob.glob(strcompress(dirlist[i]+'/'+'mcg*_b1*phot*'))
print("Bmagfile:", Bmagfile[0])
ap=readcol(Bmagfile[0], skipline=107)
Bmag = float(ap[0, 4])
Bemag = float(ap[0, 5])
Vmagfile=glob.glob(strcompress(dirlist[i]+'/'+'mcg*_v1*phot*'))
qualflag)
else:
return np.array(line_pos_fitted), np.array(
line_sigma_fitted), np.array(line_amp_fitted)
else:
if return_qualflag:
return np.array(line_pos_fitted), np.array(qualflag)
else:
return np.array(line_pos_fitted)
###########################################
###########################################
thar_refwlord01, thar_relintord01, flag = readcol(
'/Users/christoph/UNSW/linelists/test_thar_list_order_01.dat',
fsep=';',
twod=False)
thar_refwlord01 *= 1e3
refdata = {}
refdata['order_01'] = {}
refdata['order_01']['wl'] = thar_refwlord01[np.argwhere(
flag ==
' resolved')][::-1] #note the array is turned around to match other arrays
refdata['order_01']['relint'] = thar_relintord01[np.argwhere(
flag ==
' resolved')][::-1] #note the array is turned around to match other arrays
###########################################
def get_dispsol_from_thar(thardata,
refdata,
#line = str(separations[j])+ " "+str(e_separations[j])+ " "+str(separations[j]*dist) + " "+str(e_separations[j]*dist) +"\n"
# outputfileseps.writelines(line)
cl.close()
# outputfilefits.close()
# outputfileseps.close()
print 'end of extract'
dist=230.0
#sources=['Per2','Per11','Per12','Per17','Per18','Per22','Per26','Per27','Per33','Per35','Per36','Per40','Per44','Per48','Per49','Per55','Per106','Per107','Per120','Per122']
#comps=[2,3,2,2,3,2,2,2,4,2,2,2,5,2,2,2,2,2,2,2]
inp(taskname='imfit')
cl.close()
imagelisttemp=readcol("images.txt")
#imagelist=imagelisttemp.array().tolist()
imagelist=[]
for image in imagelisttemp:
imagelist.append(image[:][0])
# print image
#print imagelist
sourcestemp=[line.replace('.B.Ka.cont.image.tt0','') for line in imagelist]
sources=[source.replace('../','') for source in sourcestemp]
#print imagelist
#print sources
sources.append('Per110')
sources.append('Per21')
t0 = t
t = (1. + sqrt(1. + 4. * t**2)) / 2.
z = x + ((t0 - 1.) / t) * (x - xold)
this_pobj = 0.5 * linalg.norm(A.dot(x) - b)**2 + l * linalg.norm(x, 1)
pobj.append((time.time() - time0, this_pobj))
print(this_pobj)
times, pobj = map(np.array, zip(*pobj))
return x, pobj, times
### INPUT PARAMETERS ###
oi_data = 'COMB_JWST_SAM_tot.fits'
dict_filename = 'Dict3.npy'
input_path = ['dataset/']
input_files = readcol(input_path[0] + 'ring_gauss.txt', twod=True)
scale = 10.0 ## mas
hyperparameter = 0.01
maxit = 10000
output_filename = 'recovered_im_lasso.fits' #Output filename
####### AUTOMATIC FROM HERE #################
### Append, normalize and reshape the data :
obs = oitools.extract_data(oi_data)
v2_data = obs['vis2'].reshape(-1)
phi_data = obs['phi'].reshape(-1)
t3phi_data = obs['t3'].reshape(-1)
v2_data = prepro.scale(v2_data)
#!bin/python
import matplotlib.pyplot as plt
from readcol import *
a1, a2 = readcol("vr_estimate.txt", twod=False)
plt.plot(a1, a2, ls="--", label="estimate")
a1, a2 = readcol("drift_velocity.txt", twod=False)
plt.plot(a1, a2, label="calculate")
plt.show()
import numpy as np
import matplotlib.pyplot as plt
import pdb
import astropy.io.fits as pyfits
import oitools
from readcol import *
import matplotlib.cm as cm
import matplotlib.colors as colors
from matplotlib import gridspec
oi_data = 's0_a200_COMB_pa_2018_HD163296.fits'
[ims] = readcol('new_mean_ims_ring_2018.txt', twod=False)
tit = 'Observables from BSMEM Images [Ring Model 2019]'
year = 2019
#wave_range = [2.30e-6, 2.40e-6]
scale = 0.1 ## mas
oidata = pyfits.open(oi_data)
oi_wave = oidata['OI_WAVELENGTH'].data
#oi_vis = oidata['OI_VIS'].data
oi_vis2 = oidata['OI_VIS2'].data
oi_t3 = oidata['OI_T3'].data
waves = oi_wave['EFF_WAVE']
#vis = oi_vis['VISAMP']
#vis_err = oi_vis['VISAMPERR']
#phase = oi_vis['VISPHI']
#phase_err = oi_vis['VISPHIERR']
vis2 = oi_vis2['VIS2DATA']
vis2_err = oi_vis2['VIS2ERR']
import matplotlib.pyplot as plt
from readcol import *
for i in range(0, 40, 1):
in_name = "pebble_num_" + str(i) + ".txt"
rad, yr, a1, a2 = readcol(in_name, twod=False)
plt.plot(rad, yr)
plt.yscale("log")
# plt.xscale("log")
plt.xlabel("r (AU)")
plt.ylabel("time (yr)")
plt.show()
from readcol import *
cd = True
if cd == True:
import oi_merge as oi_merge
else:
import oi_merge2 as oi_merges #
import importlib
importlib.reload(oi_merge)
data = 'combine_data_tot.txt'
[files] = readcol(data, twod=False)
merged = oi_merge.oi_merge(files)
merged.write('COMB_JWST_SAM_tot.fits')
from matplotlib.colors import LogNorm
LUNIT = 1.49597871e13
TUNIT = 3.15569e7
m_earth = 5.97219e27
rho_peb = 1.4
TWO_POP = False
name = sys.argv[1]
num = sys.argv[2]
fig = plt.figure(figsize=(10, 6))
gs = gridspec.GridSpec(2, 2) #,width_ratios=[1,1])
r = readcol(name + "/rad.txt")
sig0 = readcol(name + "/dust_sigma0.txt")
sig = readcol(name + "/dust_sigma" + num + ".txt")
size0 = readcol(name + "/dust_size0.txt")
size = readcol(name + "/dust_size" + num + ".txt")
if TWO_POP:
a_drift = readcol(name + "/dust_drift" + num + ".txt")
a_frag = readcol(name + "/dust_frag" + num + ".txt")
a_df = readcol(name + "/dust_df" + num + ".txt")
a_gr = readcol(name + "/dust_gr" + num + ".txt")
tau_gr = readcol(name + "/tau_gr" + num + ".txt")
rB = np.load('rB.npy')
