def linename_to_restwl(linelistfile=tablepath + 'linelist.txt',
outfile=tablepath + 'newlinelist.txt'):
lines = readcol(linelistfile, fsep='|', twod=False, comment="#")
outf = open(outfile, 'w')
for line in transpose(lines):
name = line[0]
jre = re.compile('\(([0-9]*)\)').search(name)
if jre == None:
print >> outf, "%10s|%10s" % (line[0], line[1])
continue
else:
jl = int(jre.groups()[0])
if name[4] == 'S':
ju = jl + 2
elif name[4] == 'Q':
ju = jl
elif name[4] == 'O':
ju = jl - 2
else:
print >> outf, "%10s|%10s" % (line[0], line[1])
continue
vu = int(name[0])
vl = int(name[2])
rwl = restwl(vu, vl, ju, jl)
if rwl == 0:
rwl = float(line[1])
print >> outf, "%10s|%10.8f" % (name, rwl)
def linename_to_restwl(linelistfile = tablepath+'linelist.txt',outfile=tablepath+'newlinelist.txt'):
lines = readcol(linelistfile,fsep='|',twod=False,comment="#")
outf = open(outfile,'w')
for line in transpose(lines):
name = line[0]
jre = re.compile('\(([0-9]*)\)').search(name)
if jre == None:
print >>outf, "%10s|%10s" % (line[0],line[1])
continue
else:
jl = int( jre.groups()[0] )
if name[4] == 'S':
ju = jl + 2
elif name[4] == 'Q':
ju = jl
elif name[4] == 'O':
ju = jl - 2
else:
print >>outf, "%10s|%10s" % (line[0],line[1])
continue
vu = int( name[0] )
vl = int( name[2] )
rwl = restwl(vu,vl,ju,jl)
if rwl == 0:
rwl = float(line[1])
print >>outf,"%10s|%10.8f" % (name,rwl)
def readspec(image,noiseimage,
linelistfile = '/Users/adam/work/IRAS05358/code/linelist.txt',
path_obs='', #'/Users/adam/observations/IRAS05358/UT090108/',
noiseaperture=[0,10],
aperture=[],
nameregex='2-1 S\(1\)|1-0 S\([1379028]\)|1-0 Q\([1234]\)|3-2 S\([35]\)|4-3 S\(5\)',
apname='',
vlsrcorr=0):
regex = re.compile(nameregex)
im = pyfits.open(path_obs+image)
noiseim = pyfits.open(path_obs+noiseimage)
wlA = im[0].header['CRVAL1'] + im[0].header['CD1_1'] * ( arange(im[0].data.shape[1]) - im[0].header['CRPIX1'] + 1)
data = im[0].data
atmospec = median(noiseim[0].data[noiseaperture[0]:noiseaperture[1]],axis=0)
countsperflux = 2.25e18
stdatmo = noiseim[0].data[noiseaperture[0]:noiseaperture[1]].std(axis=0) # std. dev. of non-backsubtracted data
poisserr = sqrt(abs(noiseim[0].data).mean(axis=0) * countsperflux) / countsperflux # Poisson noise (very approximate correction)
errspec = sqrt( stdatmo**2 + 2*poisserr**2 ) # poisson statistics - once for estimation of the noise, once for the subtraction
errspec /= atmotrans_vect(wlA)**2 # Weight by inverse of atmospheric transmission^2
lines = readcol(linelistfile,fsep='|',twod=False)
lines[1] = asarray(lines[1],dtype='float')*1e4 # convert microns to angstroms
specsegments=[]
for line in transpose(lines):
wl = float(line[1])
name = line[0]
if wl > wlA.min() and wl < wlA.max() and regex.search(name) != None:
closest = argmin(abs(wlA-wl))
minind = closest-7
maxind = closest+7
if len(aperture) != 0:
savedata = data[aperture[0]:aperture[1],minind:maxind].sum(axis=0)
else:
savedata = data[:,minind:maxind]
specsegments.append({
'name':name,
'apname':apname,
'linewl':wl,
'index':closest,
'minind':minind,
'maxind':maxind,
'vlsrcorr':vlsrcorr,
'wavelength':wlA[minind:maxind],
'data':savedata,
'noback':atmospec[minind:maxind],
'err':errspec[minind:maxind],
'model':data[0,minind:maxind]*0
# 'smoothdata':convolve( data[:,minind:maxind] , hanning(3) , 'same')
})
print "Done finding lines"
return specsegments
def planetflux(planet, date):
if planet == 'mars':
mars = readcol('/Users/adam/work/bgps_pipeline/calibration/MARS.DAT',
asStruct=True)
closest = numpy.argmin(numpy.abs(date - mars.MJD))
return mars.fluxbeam[closest]
elif planet == 'uranus':
uranus = readcol(
'/Users/adam/work/bgps_pipeline/calibration/URANUS.DAT',
asStruct=True)
closest = numpy.argmin(numpy.abs(date - uranus.MJD))
return uranus.fluxbeam[closest]
elif planet == 'neptune':
neptune = readcol(
'/Users/adam/work/bgps_pipeline/calibration/NEPTUNE.DAT',
asStruct=True)
closest = numpy.argmin(numpy.abs(date - neptune.MJD))
return neptune.fluxbeam[closest]
elif planet == 'sgrb2':
return 100.0
def readspexspec(
image,
linelistfile='/Users/adam/work/IRAS05358/code/linelist.txt',
path_obs='', #'/Users/adam/observations/IRAS05358/UT090108/',
nameregex='2-1 S\(1\)|1-0 S\([1379028]\)|1-0 Q\([1234]\)|3-2 S\([35]\)|4-3 S\(5\)',
vlsrcorr=0,
backsub=False,
**kwargs):
regex = re.compile(nameregex)
im = pyfits.open(path_obs + image)
wlA = im[0].data[0, :] * 1e4
data = im[0].data[1, :]
countsperflux = 2.25e18
errspec = im[0].data[2, :]
lines = readcol(linelistfile, fsep='|', twod=False)
lines[1] = asarray(lines[1],
dtype='float') * 1e4 # convert microns to angstroms
specsegments = []
for line in transpose(lines):
wl = float(line[1])
name = line[0]
if wl > wlA.min() and wl < wlA.max() and regex.search(name) != None:
closest = argmin(abs(wlA - wl))
minind = closest - 7
maxind = closest + 7
savedata = data[minind:maxind]
if backsub:
savedata -= median(savedata[savedata < median(savedata)])
specsegments.append({
'name': name,
'linewl': wl,
'index': closest,
'minind': minind,
'maxind': maxind,
'vlsrcorr': vlsrcorr,
'wavelength': wlA[minind:maxind],
'data': savedata,
'noback': data[minind:maxind] * 0,
'err': errspec[minind:maxind],
'model': data[minind:maxind] * 0
})
print "Done finding lines"
return specsegments
def cenfile_overlays(cenfile):
cen = readcol(cenfile, comment="#", asStruct=True)
for name, ra, dec, obra, obdec in zip(cen.filename, cen.radeg, cen.decdeg,
cen.obj_ra, cen.obj_dec):
print name
pos = coords.Position((ra, dec))
xc, yc = pos.galactic()
objpos = coords.Position((obra, obdec))
xtr, ytr = objpos.galactic()
make_overlay(name.tolist(), xc, yc, xtr, ytr)
pylab.figure(0)
pylab.clf()
def readspexspec(image,
linelistfile = '/Users/adam/work/IRAS05358/code/linelist.txt',
path_obs='', #'/Users/adam/observations/IRAS05358/UT090108/',
nameregex='2-1 S\(1\)|1-0 S\([1379028]\)|1-0 Q\([1234]\)|3-2 S\([35]\)|4-3 S\(5\)',
vlsrcorr=0,
backsub=False,
**kwargs):
regex = re.compile(nameregex)
im = pyfits.open(path_obs+image)
wlA = im[0].data[0,:] * 1e4
data = im[0].data[1,:]
countsperflux = 2.25e18
errspec = im[0].data[2,:]
lines = readcol(linelistfile,fsep='|',twod=False)
lines[1] = asarray(lines[1],dtype='float')*1e4 # convert microns to angstroms
specsegments=[]
for line in transpose(lines):
wl = float(line[1])
name = line[0]
if wl > wlA.min() and wl < wlA.max() and regex.search(name) != None:
closest = argmin(abs(wlA-wl))
minind = closest-7
maxind = closest+7
savedata = data[minind:maxind]
if backsub:
savedata -= median(savedata[savedata<median(savedata)])
specsegments.append({
'name':name,
'linewl':wl,
'index':closest,
'minind':minind,
'maxind':maxind,
'vlsrcorr':vlsrcorr,
'wavelength':wlA[minind:maxind],
'data':savedata,
'noback':data[minind:maxind]*0,
'err':errspec[minind:maxind],
'model':data[minind:maxind]*0
})
print "Done finding lines"
return specsegments
http://webbook.nist.gov/cgi/cbook.cgi?ID=C1333740&Units=SI&Mask=1000#Diatomic
(see the bottom of the table)
"""
We=4401.21
Be=60.853
WeXe=121.33
De=.0471
Ae=3.062
re=.74144
return h * c * (We*(V+0.5) + Be*(J*(J+1)) - WeXe*(V+.5)**2 - De*J**2*(J+1)**2 - Ae*(V+.5)*(J+1)*J)
try:
# read in rest energies before calling function
resten = readcol(tablepath+'dalgarno1984_table5.txt',verbose=0)
def restwl(vu,vl,ju,jl,calc=False):
""" Uses energy levels measured by Dabrowski & Herzberg, Can J. Physics, 62,1639,1984
vu,vl - upper and lower vibrational states
ju,jl - upper and lower rotational states
returns wavelength in microns
online versions of this table:
http://www.astronomy.ohio-state.edu/~depoy/research/observing/molhyd.htm
http://www.jach.hawaii.edu/UKIRT/astronomy/calib/spec_cal/h2_s.html
"""
if calc:
return 1e4*h*c / (h2level_energy(vu,ju) - h2level_energy(vl,jl))
else:
if ju >= resten.shape[0] or vu >= resten.shape[1]:
return 0
"""
Find the closest magpis GPS point
"""
import numpy as np
def nearest_source(x1,y1,x2,y2):
d = np.sqrt((x1-x2)**2 + (y1-y2)**2)
return d.argmin(),d.min()
import atpy
from agpy import readcol
bgps = atpy.Table('bgps_iras_langston_scuba_match.tbl',type='ascii')
magpis = readcol('/Users/adam/work/catalogs/merge6_20.cat',fixedformat=[ 7, 7, 13, 13, 6, 9, 10, 8, 7, 7, 6, 5, 9, 10, 8, 9, 2],asStruct=True,skipline=10,nullval=' ')
dbest,bestmatch = np.zeros(len(bgps)),np.zeros(len(bgps))
for ii in xrange(len(bgps)):
bestmatch[ii],dbest[ii] = nearest_source(bgps.glon_peak[ii],bgps.glat_peak[ii],magpis.Long,magpis.Lat)
bgps.add_column('Fint6',magpis.Fint6[bestmatch.astype('int')],unit="Jy/beam")
bgps.add_column('Fpeak6',magpis.Fpeak6[bestmatch.astype('int')],unit="Jy")
bgps.add_column('Fint20',magpis.Fint20[bestmatch.astype('int')],unit="Jy/beam")
bgps.add_column('Fpeak20',magpis.Fpeak20[bestmatch.astype('int')],unit="Jy")
bgps.add_column('BGPS-magpisdist',dbest,unit='degrees')
bgps.write("/Users/adam/work/catalogs/bgps_iras_langston_scuba_magpis_match.tbl",overwrite=True)
# from astropy.io import fits
import os
import re
from agpy import readcol
from astropy import log
from astropy import units as u
from sdpy import makecube
bsgs = readcol('arecibo_bsg_freqref.txt',skipafter=1,asRecArray=True)
for line in bsgs:
bsg = line['bsg']
linefreq = line['restfreq']*u.MHz
linename_ha = linename = line['linename']
if linefreq == 0:
continue
# 7/29/2014: remove non-superresolution maps
# 1/10/2014: add "superresolution" maps
vmin = 30
vmax = 90
velocityrange = [-50,150]
#makecube.generate_header(49.523158,-0.34987466,naxis1=96,naxis2=96,pixsize=20,naxis3=1600,cd3=0.5,clobber=True,restfreq=4.8296594e9)
#makecube.generate_header(49.353568,-0.2982199,naxis1=144,naxis2=96,pixsize=20,naxis3=1600,cd3=0.5,clobber=True,restfreq=4.8296594e9)
# reduced to CD3 = 1.0, naxis3 = 350 because of size and because the arecibo spectra looked artificially smoothed
cd3 = 1.0
crval3 = 50.0
naxis3 = int((velocityrange[1]-velocityrange[0]) / cd3)
makecube.generate_header(49.209553,-0.277137,naxis1=308,naxis2=205,pixsize=15,naxis3=int(naxis3),cd3=cd3,crval3=crval3,clobber=True,
def plot_radex(filename,
ngridpts=100,
ncontours=50,
plottype='ratio',
transition="noname",
thirdvarname="Temperature",
cutnumber=None,
cutvalue=10,
vmin=None,
vmax=None,
logscale=False,
save=True,
**kwargs):
"""
Create contour plots in density/column, density/temperature, or column/temperature
filename - Name of the .dat file generated by radex_grid.py
ngridpts - number of points in grid to interpolate onto
ncontours - number of contours / colors
plottype - can be 'ratio','tau1','tau2','tex1','tex2'
transition - The name of the transition, e.g. "1-1_2-2". Only used for saving
thirdvarname - Third variable, i.e. the one that will be cut through. If you want
a density/column plot, choose temperature
cutnumber - Cut on the cutnumber value of thirdvar. e.g., if there are 5 temperatures
[10,20,30,40,50] and you set cutnumber=3, a temperature of 40K will be used
cutvalue - Cut on this value; procedure will fail if there are no columns with this value
vmin - Can force vmin/vmax in plotting procedures
vmax - Can force vmin/vmax in plotting procedures
logscale - takes log10 of plotted value before contouring
save - save the figure as a png?
"""
names, props = readcol(filename, twod=False, names=True)
temperature, density, column, tex1, tex2, tau1, tau2, tline1, tline2, flux1, flux2 = props
ratio = flux1 / flux2
if thirdvarname == "Temperature":
firstvar = density
secondvar = column
thirdvar = temperature
if cutnumber is not None:
cutvalue = unique(thirdvar)[int(cutnumber)]
firstlabel = "log$(n_{H_2}) ($cm$^{-3})$"
secondlabel = "log$(N_{H_2CO}) ($cm$^{-2})$"
savetype = "DenCol_T=%iK" % cutvalue
graphtitle = "T = %g K" % cutvalue
firstvar = temperature
secondvar = column
thirdvar = density
if cutnumber is not None:
cutvalue = unique(thirdvar)[int(cutnumber)]
firstlabel = "Temperature (K)"
secondlabel = "log$(N_{H_2CO}) ($cm$^{-2})$"
savetype = "TemCol_n=1e%gpercc" % cutvalue
graphtitle = "n = %g cm$^{-3}$" % (10**cutvalue)
elif thirdvarname == "Column":
secondvar = density
firstvar = temperature
thirdvar = column
if cutnumber is not None:
cutvalue = unique(thirdvar)[int(cutnumber)]
secondlabel = "log$(n_{H_2}) ($cm$^{-3})$"
firstlabel = "Temperature (K)"
savetype = "TemDen_N=1e%gpersc" % cutvalue
graphtitle = "N = %g cm$^{-2}$" % (10**cutvalue)
if plottype == 'ratio':
cblabel = "$F_{1-1} / F_{2-2}$"
elif plottype == 'tau1':
cblabel = "$\\tau_{1-1}$"
elif plottype == 'tau2':
cblabel = "$\\tau_{2-2}$"
elif plottype == 'tex1':
cblabel = "$\\T_{ex}(1-1)$"
elif plottype == 'tex2':
cblabel = "$\\T_{ex}(2-2)$"
varfilter = (thirdvar == cutvalue)
if varfilter.sum() == 0:
raise ValueError("Cut value %g does not match any of %s values" %
(cutvalue, thirdvarname))
nx = len(unique(firstvar))
ny = len(unique(secondvar))
if firstvar is temperature:
firstarr = linspace((firstvar.min()), (firstvar.max()), nx)
else:
firstarr = linspace(firstvar.min(), firstvar.max(), nx)
secondarr = linspace(secondvar.min(), secondvar.max(), ny)
exec('plotdata = %s' % plottype)
plot_grid = griddata(firstvar[varfilter], secondvar[varfilter],
plotdata[varfilter], firstarr, secondarr)
if vmax:
plot_grid[plot_grid > vmax] = vmax
if vmin:
plot_grid[plot_grid > vmin] = vmin
if logscale:
plot_grid = log10(plot_grid)
figure(1)
clf()
conlevs = logspace(-3, 1, ncontours)
contourf(firstarr,
secondarr,
plot_grid,
conlevs,
norm=matplotlib.colors.LogNorm()
) #,**kwargs) #,norm=asinh_norm.AsinhNorm(**kwargs),**kwargs)
xlabel(firstlabel)
ylabel(secondlabel)
title(graphtitle)
cb = colorbar()
cb.set_label(cblabel)
cb.set_ticks([1e-3, 1e-2, 1e-1, 1, 1e1])
cb.set_ticklabels([1e-3, 1e-2, 1e-1, 1, 1e1])
if save: savefig("%s_%s_%s.png" % (savetype, plottype, transition))
from agpy import plfit
from agpy import readcol
if not globals().has_key('dotests'): dotests = raw_input('Do tests? ') not in ['','n','N']
rcParams['font.size'] = 36
bcnames,bgps = readcol('/Users/adam/work/catalogs/bolocam_gps_v1_0.tbl',names=True,skipline=45)
bgpsd = readcol('/Users/adam/work/catalogs/bolocam_gps_v1_0.tbl',names=True,skipline=45,asdict=True)
bgflux = bgps[:,18].astype('float')
bgflux40 = bgps[:,12].astype('float')
bgrad = bgps[:,11]
bgrad = bgrad[bgrad!='null'].astype('float')
print "plBGFLUX"
plBGFLUX = plfit(bgflux,nosmall=True)
if dotests: plBGFLUXp,plBGFLUXksarr = plBGFLUX.test_pl()
# xmin: 4.877 n(>xmin): 369 alpha: 2.60503 +/- 0.0835548 Likelihood: -1009 ks: 0.943031
# p(2500) = .52
figure(1); clf()
xlabel('Flux Density (Jy)'); ylabel('N(S)')
plBGFLUX.plotpdf(nbins=40,dolog=True)
savefig('/Users/adam/work/massfunc/BGPS_bolocat_flux_Nhist.png',papertype='b3')
figure(2); clf();
xlabel('Flux Density (Jy)'); ylabel('$\Delta$N/$\Delta$S')
plBGFLUX.plotpdf(nbins=40,dolog=True,dnds=True)
savefig('/Users/adam/work/massfunc/BGPS_bolocat_flux_dndshist.png',papertype='b3')
clf(); plBGFLUX.alphavsks()
savefig('/Users/adam/work/massfunc/BGPS_bolocat_flux_alphaks.png',papertype='b3')
#import cplfit
import plfit
import time
from numpy.random import rand,seed
from numpy import unique,sort,array,asarray,log,sum,min,max,argmin,argmax,arange
import sys
import powerlaw
from agpy import readcol
try:
ne = int(sys.argv[1])
seed(1)
X=plfit.plexp_inv(rand(ne),1,2.5)
X[:100] = X[100:200]
except ValueError:
X = readcol(sys.argv[1])
if len(sys.argv)>2:
discrete = bool(sys.argv[2])
else:
discrete=None
print "Cython"
t1=time.time(); p3=plfit.plfit(X,discrete=discrete,usefortran=False,usecy=True); print time.time()-t1
print "Fortran"
t1=time.time(); p1=plfit.plfit(X,discrete=discrete,usefortran=True); print time.time()-t1
print "Numpy"
t1=time.time(); p3=plfit.plfit(X,discrete=discrete,usefortran=False); print time.time()-t1
print "Jeff Alcott's Powerlaw"
t5=time.time(); p5=powerlaw.Fit(X,discrete=discrete); print time.time()-t5
We = 4401.21
Be = 60.853
WeXe = 121.33
De = .0471
Ae = 3.062
re = .74144
return h * c * (We * (V + 0.5) + Be * (J * (J + 1)) - WeXe *
(V + .5)**2 - De * J**2 * (J + 1)**2 - Ae * (V + .5) *
(J + 1) * J)
try:
# read in rest energies before calling function
resten = readcol(tablepath + 'dalgarno1984_table5.txt', verbose=0)
def restwl(vu, vl, ju, jl, calc=False):
""" Uses energy levels measured by Dabrowski & Herzberg, Can J. Physics, 62,1639,1984
vu,vl - upper and lower vibrational states
ju,jl - upper and lower rotational states
returns wavelength in microns
online versions of this table:
http://www.astronomy.ohio-state.edu/~depoy/research/observing/molhyd.htm
http://www.jach.hawaii.edu/UKIRT/astronomy/calib/spec_cal/h2_s.html
"""
if calc:
return 1e4 * h * c / (h2level_energy(vu, ju) -
h2level_energy(vl, jl))
else:
if ju >= resten.shape[0] or vu >= resten.shape[1]:
def gridcube(filename,outfilename,var1="density",var2="column",var3="temperature",var4=None,plotvar="tau1",
zerobads=True):
"""
Reads in a radex_grid.py generated .dat file and turns it into a .fits data cube.
filename - input .dat filename
outfilename - output data cube name
var1/var2/var3 - which variable will be used along the x/y/z axis?
plotvar - which variable will be the value in the data cube?
zerobads - set inf/nan values in plotvar to be zero
"""
names,props = readcol(filename,twod=False,names=True)
if var4 is None:
temperature,density,column,tex1,tex2,tau1,tau2,tline1,tline2,flux1,flux2 = props
else:
temperature,density,column,opr,tex1,tex2,tau1,tau2,tline1,tline2,flux1,flux2 = props
opr = numpy.floor(opr*100)/100.
ratio = tau1 / tau2
vardict = {
"temperature":temperature,
"density":density,
"column":column,
"tex1":tex1,
"tex2":tex2,
"tau1":tau1,
"tau2":tau2,
"tline1":tline1,
"tline2":tline2,
"flux1":flux1,
"flux2":flux2,
"ratio":ratio,
"opr":opr,
}
xarr = (unique(vardict[var1])) #linspace(vardict[var1].min(),vardict[var1].max(),nx)
yarr = (unique(vardict[var2])) #linspace(vardict[var2].min(),vardict[var2].max(),ny)
zarr = (unique(vardict[var3])) #linspace(vardict[var2].min(),vardict[var2].max(),ny)
nx = len(xarr)
ny = len(yarr)
nz = len(zarr)
if var4 is not None:
warr = (unique(vardict[var4])) #linspace(vardict[var2].min(),vardict[var2].max(),ny)
nw = len(warr)
newarr = zeros([nw,nz,ny,nx])
if nw != 11:
import pdb; pdb.set_trace()
else:
newarr = zeros([nz,ny,nx])
print "Cube shape will be ",newarr.shape
if zerobads:
pv = vardict[plotvar]
pv[pv!=pv] = 0.0
pv[isinf(pv)] = 0.0
if var4 is None:
for ival,val in enumerate(unique(vardict[var3])):
varfilter = vardict[var3]==val
newarr[ival,:,:] = griddata((vardict[var1][varfilter]),(vardict[var2][varfilter]),vardict[plotvar][varfilter],xarr,yarr)
else:
for ival4,val4 in enumerate(warr):
for ival3,val3 in enumerate(zarr):
varfilter = (vardict[var3]==val3) * (vardict[var4]==val4)
newarr[ival4,ival3,:,:] = griddata((vardict[var1][varfilter]),(vardict[var2][varfilter]),vardict[plotvar][varfilter],xarr,yarr)
newfile = pyfits.PrimaryHDU(newarr)
if var4 is not None:
newfile.header.update('CRVAL4' , (min(warr)) )
newfile.header.update('CRPIX4' , 1 )
newfile.header.update('CTYPE4' , 'LOG--OPR' )
newfile.header.update('CDELT4' , ((warr)[1]) - ((warr)[0]) )
newfile.header.update('BTYPE' , plotvar )
newfile.header.update('CRVAL3' , (min(zarr)) )
newfile.header.update('CRPIX3' , 1 )
newfile.header.update('CTYPE3' , 'LIN-TEMP' )
newfile.header.update('CDELT3' , ((zarr)[1]) - ((zarr)[0]) )
newfile.header.update('CRVAL1' , min(xarr) )
newfile.header.update('CRPIX1' , 1 )
newfile.header.update('CD1_1' , xarr[1]-xarr[0] )
newfile.header.update('CTYPE1' , 'LOG-DENS' )
newfile.header.update('CRVAL2' , min(yarr) )
newfile.header.update('CRPIX2' , 1 )
newfile.header.update('CD2_2' , yarr[1]-yarr[0] )
newfile.header.update('CTYPE2' , 'LOG-COLU' )
newfile.writeto(outfilename,clobber=True)
# removed 'uranus_091210_ob5-6_mask_v2.0_0pca_coalign_map10.fits',
# removed 'uranus_091210_ob7-8_mask_v2.0_0pca_coalign_map10.fits',
'uranus_091216_ob8-9_mask_v2.0_0pca_coalign_map10.fits',
'uranus_091219_o15-6_mask_v2.0_0pca_coalign_map10.fits',
'uranus_091220_ob1-2_mask_v2.0_0pca_coalign_map10.fits',
'uranus_091224_o10-9_mask_v2.0_0pca_coalign_map10.fits',
]
if sample == 'notmars':
#filelist = [ a for a in filelist if a.find('mars')==-1 ]
#filelist = filelist[:4]
imstack = pylab.zeros([300, 300, len(filelist)])
xx, yy = pylab.indices([300, 300])
elif sample == 'dec2011':
filelist = [
a for b in readcol(
'/Users/adam/work/bgps_pipeline/plotting/lists/pointsource_ds2_13pca_default.list'
) for a in b
]
elif sample == 'dec2011notmars':
filelist = [
a for b in readcol(
'/Users/adam/work/bgps_pipeline/plotting/lists/pointsource_ds2_13pca_default.list'
) for a in b if 'mars' not in a
]
marslist = [
a for b in readcol(
'/Users/adam/work/bgps_pipeline/plotting/lists/pointsource_ds2_13pca_default.list'
) for a in b if 'mars' in a
]
imstack = pylab.zeros([300, 300, len(filelist)])
marsstack = pylab.zeros([300, 300, len(marslist)])
def gridcube(filename,
outfilename,
var1="density",
var2="column",
var3="temperature",
plotvar="tau1",
zerobads=True):
"""
Reads in a radex_grid.py generated .dat file and turns it into a .fits data cube.
filename - input .dat filename
outfilename - output data cube name
var1/var2/var3 - which variable will be used along the x/y/z axis?
plotvar - which variable will be the value in the data cube?
zerobads - set inf/nan values in plotvar to be zero
"""
names, props = readcol(filename, twod=False, names=True)
temperature, density, column, tex1, tex2, tau1, tau2, tline1, tline2, flux1, flux2 = props
ratio = tau1 / tau2
vardict = {
"temperature": temperature,
"density": density,
"column": column,
"tex1": tex1,
"tex2": tex2,
"tau1": tau1,
"tau2": tau2,
"tline1": tline1,
"tline2": tline2,
"flux1": flux1,
"flux2": flux2,
"ratio": ratio,
}
nx = len(unique(vardict[var1]))
ny = len(unique(vardict[var2]))
nz = len(unique(vardict[var3]))
xarr = (unique(vardict[var1])
) #linspace(vardict[var1].min(),vardict[var1].max(),nx)
yarr = (unique(vardict[var2])
) #linspace(vardict[var2].min(),vardict[var2].max(),ny)
newarr = zeros([nz, ny, nx])
if zerobads:
pv = vardict[plotvar]
pv[pv != pv] = 0.0
pv[isinf(pv)] = 0.0
for ival, val in enumerate(unique(vardict[var3])):
varfilter = vardict[var3] == val
newarr[ival, :, :] = griddata((vardict[var1][varfilter]),
(vardict[var2][varfilter]),
vardict[plotvar][varfilter], xarr, yarr)
newfile = pyfits.PrimaryHDU(newarr)
newfile.header.update('BTYPE', plotvar)
newfile.header.update('CRVAL3', (min(temperature)))
newfile.header.update('CRPIX3', 1)
newfile.header.update('CTYPE3', 'LIN-TEMP')
newfile.header.update('CD3_3',
(unique(temperature)[1]) - (unique(temperature)[0]))
newfile.header.update('CRVAL1', min(xarr))
newfile.header.update('CRPIX1', 1)
newfile.header.update('CD1_1', xarr[1] - xarr[0])
newfile.header.update('CTYPE1', 'LOG-DENS')
newfile.header.update('CRVAL2', min(yarr))
newfile.header.update('CRPIX2', 1)
newfile.header.update('CD2_2', yarr[1] - yarr[0])
newfile.header.update('CTYPE2', 'LOG-COLU')
newfile.writeto(outfilename, clobber=True)
# coding: utf-8
from agpy import readcol
import plfit
from pylab import *
blackouts = readcol('blackouts.txt')
cities = readcol('cities.txt')
earthquakes = readcol('earthquakes.txt')
melville = readcol('melville.txt')
solarflares = readcol('solarflares.txt')
terrorism = readcol('terrorism.txt')
#print "quakes 0.00 -7.14 0.00 11.6 0.00 -7.09 0.00 -24.4 0.00 with cut-off"
#earthquakeP = plfit.plfit(earthquakes)
pl = plfit.plfit(cities.ravel() / 1e3, usefortran=True, verbose=True)
print "Cities (me) : n:%10i mean,std,max: %8.2f,%8.2f,%8.2f xmin: %8.2f alpha: %8.2f (%8.2f) ntail: %10i p: %5.2f" % (pl.data.shape[0], pl.data.mean(), pl.data.std(), pl.data.max(), pl._xmin, pl._alpha, pl._alphaerr, pl._ngtx, pl._ks_prob)
print "Cities (Clauset): n:%10i mean,std,max: %8.2f,%8.2f,%8.2f xmin: %8.2f alpha: %8.2f (%8.2f) ntail: %10i p: %5.2f" % (19447,9.00,77.83,8009,52.46,2.37,0.08,580,0.76)
figure(1)
clf()
title("Cities")
subplot(131)
pl.plotpdf()
subplot(132)
title("Cities")
pl.xminvsks()
subplot(133)
pl.alphavsks()
savefig("figures/cities_kstests.png")
def gridcube(filename,outfilename,var1="density",var2="column",var3="temperature",plotvar="tau1",
zerobads=True):
"""
Reads in a radex_grid.py generated .dat file and turns it into a .fits data cube.
filename - input .dat filename
outfilename - output data cube name
var1/var2/var3 - which variable will be used along the x/y/z axis?
plotvar - which variable will be the value in the data cube?
zerobads - set inf/nan values in plotvar to be zero
"""
names,props = readcol(filename,twod=False,names=True)
temperature,density,column,tex1,tex2,tau1,tau2,tline1,tline2,flux1,flux2 = props
ratio = tau1 / tau2
vardict = {
"temperature":temperature,
"density":density,
"column":column,
"tex1":tex1,
"tex2":tex2,
"tau1":tau1,
"tau2":tau2,
"tline1":tline1,
"tline2":tline2,
"flux1":flux1,
"flux2":flux2,
"ratio":ratio,
}
nx = len(unique(vardict[var1]))
ny = len(unique(vardict[var2]))
nz = len(unique(vardict[var3]))
xarr = (unique(vardict[var1])) #linspace(vardict[var1].min(),vardict[var1].max(),nx)
yarr = (unique(vardict[var2])) #linspace(vardict[var2].min(),vardict[var2].max(),ny)
newarr = zeros([nz,ny,nx])
if zerobads:
pv = vardict[plotvar]
pv[pv!=pv] = 0.0
pv[isinf(pv)] = 0.0
for ival,val in enumerate(unique(vardict[var3])):
varfilter = vardict[var3]==val
newarr[ival,:,:] = griddata((vardict[var1][varfilter]),(vardict[var2][varfilter]),vardict[plotvar][varfilter],xarr,yarr)
newfile = pyfits.PrimaryHDU(newarr)
newfile.header.update('BTYPE' , plotvar )
newfile.header.update('CRVAL3' , (min(temperature)) )
newfile.header.update('CRPIX3' , 1 )
newfile.header.update('CTYPE3' , 'LIN-TEMP' )
newfile.header.update('CD3_3' , (unique(temperature)[1]) - (unique(temperature)[0]) )
newfile.header.update('CRVAL1' , min(xarr) )
newfile.header.update('CRPIX1' , 1 )
newfile.header.update('CD1_1' , xarr[1]-xarr[0] )
newfile.header.update('CTYPE1' , 'LOG-DENS' )
newfile.header.update('CRVAL2' , min(yarr) )
newfile.header.update('CRPIX2' , 1 )
newfile.header.update('CD2_2' , yarr[1]-yarr[0] )
newfile.header.update('CTYPE2' , 'LOG-COLU' )
newfile.writeto(outfilename,clobber=True)
from agpy import plfit
from agpy import readcol
if not globals().has_key('dotests'):
dotests = raw_input('Do tests? ') not in ['', 'n', 'N']
rcParams['font.size'] = 36
bcnames, bgps = readcol('/Users/adam/work/catalogs/bolocam_gps_v1_0.tbl',
names=True,
skipline=45)
bgpsd = readcol('/Users/adam/work/catalogs/bolocam_gps_v1_0.tbl',
names=True,
skipline=45,
asdict=True)
bgflux = bgps[:, 18].astype('float')
bgflux40 = bgps[:, 12].astype('float')
bgrad = bgps[:, 11]
bgrad = bgrad[bgrad != 'null'].astype('float')
print "plBGFLUX"
plBGFLUX = plfit(bgflux, nosmall=True)
if dotests: plBGFLUXp, plBGFLUXksarr = plBGFLUX.test_pl()
# xmin: 4.877 n(>xmin): 369 alpha: 2.60503 +/- 0.0835548 Likelihood: -1009 ks: 0.943031
# p(2500) = .52
figure(1)
clf()
xlabel('Flux Density (Jy)')
ylabel('N(S)')
plBGFLUX.plotpdf(nbins=40, dolog=True)
#!/usr/bin/env python
# data files come from ftp://cdsarc.u-strasbg.fr/pub/cats/J/A+A/493/687/
from agpy import readcol
import numpy
h2co_oo = readcol('h2co_oo.dat', asStruct=True, comment='%', skipline=3)
h2co_op = readcol('h2co_op.dat', asStruct=True, comment='%', skipline=3)
levels = readcol('o-h2co_levels.dat', asStruct=True)
nlevelso = len(h2co_oo.Jl)
h2co_oo.__dict__['llevelnum'] = numpy.zeros(nlevelso)
h2co_oo.__dict__['ulevelnum'] = numpy.zeros(nlevelso)
nlevelsp = len(h2co_op.Jl)
h2co_op.__dict__['llevelnum'] = numpy.zeros(nlevelsp)
h2co_op.__dict__['ulevelnum'] = numpy.zeros(nlevelsp)
temperatures = numpy.arange(5, 105, 5)
def R(a0, a1, a2, a3, a4, T):
"""
Troscompt et al (2009) coefficients using Faure et al (2004) equation:
log10(R) = sum(a_n T^{-n/6})
where n=0..4, R is presumably cm^3 s^-1
"""
return a0 + a1 * T**(-1. / 6.) + a2 * T**(-2. / 6.) + a3 * T**(
-3. / 6.) + a4 * T**(-4. / 6.)
outf = open('o-h2co_troscompt.dat', 'w')
rcParams['font.family'] = 'serif'
rcParams['font.serif'][0] = 'Times New Roman'
rcParams['font.size'] = 16.0
prefix = '/Volumes/disk2/data/bgps/releases/v1.0/v1.0.2/'
#mosaic = prefix+'MOSAIC.fits'
mosaic = '/Volumes/disk2/data/bgps/releases/IPAC/MOSAIC_snmap.fits'
outf = prefix + 'fillfact_v1.0.2.txt'
#fillingfactorplot(mosaic,outf,cutoff=[0.1,0.3,0.5],binsize=[0.1,0.5,1.0],lmin=-10.5,lmax=90.5)
fillingfactorplot(mosaic,
outf,
cutoff=[3, 9, 12],
binsize=[0.1, 0.5, 1.0],
lmin=-10.5,
lmax=90.5)
from agpy import readcol
binpt1 = readcol(prefix + 'fillfact_v1.0.2_binsize0.10.txt', asStruct=True)
binpt5 = readcol(prefix + 'fillfact_v1.0.2_binsize0.50.txt', asStruct=True)
bin1 = readcol(prefix + 'fillfact_v1.0.2_binsize1.00.txt', asStruct=True)
comosaic = '/Volumes/disk2/data/co/Wco_DHT2001.fits'
cooutf = prefix + 'cofillfact_v1.0.2.txt'
fillingfactorplot(comosaic,
cooutf,
cutoff=[50, 100, 150],
binsize=[0.1, 0.5, 1.0],
lmin=-10.5,
lmax=90.5)
cobinpt1 = readcol(prefix + 'cofillfact_v1.0.2_binsize0.10.txt',
asStruct=True)
cobinpt5 = readcol(prefix + 'cofillfact_v1.0.2_binsize0.50.txt',
asStruct=True)
def readspec(
image,
noiseimage,
linelistfile='/Users/adam/work/IRAS05358/code/linelist.txt',
path_obs='', #'/Users/adam/observations/IRAS05358/UT090108/',
noiseaperture=[0, 10],
aperture=[],
nameregex='2-1 S\(1\)|1-0 S\([1379028]\)|1-0 Q\([1234]\)|3-2 S\([35]\)|4-3 S\(5\)',
apname='',
vlsrcorr=0):
regex = re.compile(nameregex)
im = pyfits.open(path_obs + image)
noiseim = pyfits.open(path_obs + noiseimage)
wlA = im[0].header['CRVAL1'] + im[0].header['CD1_1'] * (
arange(im[0].data.shape[1]) - im[0].header['CRPIX1'] + 1)
data = im[0].data
atmospec = median(noiseim[0].data[noiseaperture[0]:noiseaperture[1]],
axis=0)
countsperflux = 2.25e18
stdatmo = noiseim[0].data[noiseaperture[0]:noiseaperture[1]].std(
axis=0) # std. dev. of non-backsubtracted data
poisserr = sqrt(
abs(noiseim[0].data).mean(axis=0) * countsperflux
) / countsperflux # Poisson noise (very approximate correction)
errspec = sqrt(
stdatmo**2 + 2 * poisserr**2
) # poisson statistics - once for estimation of the noise, once for the subtraction
errspec /= atmotrans_vect(
wlA)**2 # Weight by inverse of atmospheric transmission^2
lines = readcol(linelistfile, fsep='|', twod=False)
lines[1] = asarray(lines[1],
dtype='float') * 1e4 # convert microns to angstroms
specsegments = []
for line in transpose(lines):
wl = float(line[1])
name = line[0]
if wl > wlA.min() and wl < wlA.max() and regex.search(name) != None:
closest = argmin(abs(wlA - wl))
minind = closest - 7
maxind = closest + 7
if len(aperture) != 0:
savedata = data[aperture[0]:aperture[1],
minind:maxind].sum(axis=0)
else:
savedata = data[:, minind:maxind]
specsegments.append({
'name': name,
'apname': apname,
'linewl': wl,
'index': closest,
'minind': minind,
'maxind': maxind,
'vlsrcorr': vlsrcorr,
'wavelength': wlA[minind:maxind],
'data': savedata,
'noback': atmospec[minind:maxind],
'err': errspec[minind:maxind],
'model': data[0, minind:maxind] * 0
# 'smoothdata':convolve( data[:,minind:maxind] , hanning(3) , 'same')
})
print "Done finding lines"
return specsegments
"""
Smooth the LVG models with distributions to get tau, then fit it with
optimization procedures.
"""
import numpy as np
import hopkins_pdf
import turbulent_pdfs
from turbulent_pdfs import lognormal_massweighted
try:
from agpy import readcol
radtab = readcol('radex_data/1-1_2-2_XH2CO=1e-9_troscompt.dat',asRecArray=True)
except ImportError:
import astropy.table
radtab = astropy.table.read('radex_data/1-1_2-2_XH2CO=1e-9_troscompt.dat',format='ascii')
# plotting stuff
import pylab as pl
pl.rc('font',size=20)
_datacache = {}
def select_data(abundance=-8.5, opr=1, temperature=20, tolerance=0.1):
key = (abundance, opr, temperature, tolerance)
if key in _datacache:
return _datacache[key]
else:
#tolerance = {-10:0.1, -9.5: 0.3, -9: 0.1, -8:0.1, -8.5: 0.3}[abundance]
OKtem = radtab['Temperature'] == temperature
OKopr = radtab['opr'] == opr
#import cplfit
import plfit
import time
from numpy.random import rand, seed
from numpy import unique, sort, array, asarray, log, sum, min, max, argmin, argmax, arange
import sys
import powerlaw
from agpy import readcol
try:
ne = int(sys.argv[1])
seed(1)
X = plfit.plexp_inv(rand(ne), 1, 2.5)
X[:100] = X[100:200]
except ValueError:
X = readcol(sys.argv[1])
if len(sys.argv) > 2:
discrete = bool(sys.argv[2])
else:
discrete = None
print("Cython")
t1 = time.time()
p3 = plfit.plfit(X, discrete=discrete, usefortran=False, usecy=True)
print(time.time() - t1)
print("Fortran")
t1 = time.time()
p1 = plfit.plfit(X, discrete=discrete, usefortran=True)
print(time.time() - t1)
print("Numpy")
def gridcube(filename,
outfilename,
var1="density",
var2="column",
var3="temperature",
var4=None,
plotvar="tau1",
zerobads=True,
ratio_type='flux',
round=2):
"""
Reads in a radex_grid.py generated .dat file and turns it into a .fits data cube.
filename - input .dat filename
outfilename - output data cube name
var1/var2/var3 - which variable will be used along the x/y/z axis?
plotvar - which variable will be the value in the data cube?
zerobads - set inf/nan values in plotvar to be zero
"""
names, props = readcol(filename, twod=False, names=True)
if round:
for ii, name in enumerate(names):
if name in ('Temperature', 'log10(dens)', 'log10(col)', 'opr'):
props[ii] = np.round(props[ii], round)
if var4 is None:
temperature, density, column, tex1, tex2, tau1, tau2, tline1, tline2, flux1, flux2 = props
else:
temperature, density, column, opr, tex1, tex2, tau1, tau2, tline1, tline2, flux1, flux2 = props
opr = np.floor(opr * 100) / 100.
if ratio_type == 'flux':
ratio = flux1 / flux2
else:
ratio = tau1 / tau2
vardict = {
"temperature": temperature,
"density": density,
"column": column,
"tex1": tex1,
"tex2": tex2,
"tau1": tau1,
"tau2": tau2,
"tline1": tline1,
"tline2": tline2,
"flux1": flux1,
"flux2": flux2,
"ratio": ratio,
}
if var4 is not None:
vardict['opr'] = opr
nx = len(unique(vardict[var1]))
ny = len(unique(vardict[var2]))
nz = len(unique(vardict[var3]))
if var4 is not None:
nw = len(unique(vardict[var4]))
xarr = (unique(vardict[var1])
) #linspace(vardict[var1].min(),vardict[var1].max(),nx)
yarr = (unique(vardict[var2])
) #linspace(vardict[var2].min(),vardict[var2].max(),ny)
zarr = (unique(vardict[var3])
) #linspace(vardict[var2].min(),vardict[var2].max(),ny)
if var4 is not None:
warr = (unique(vardict[var4])
) #linspace(vardict[var2].min(),vardict[var2].max(),ny)
if var4 is None:
newarr = zeros([nz, ny, nx])
else:
newarr = zeros([nw, nz, ny, nx])
print "Cube shape will be ", newarr.shape
if zerobads:
pv = vardict[plotvar]
pv[pv != pv] = 0.0
pv[isinf(pv)] = 0.0
if var4 is None:
for ival, val in enumerate(unique(vardict[var3])):
varfilter = vardict[var3] == val
#newarr[ival,:,:] = griddata((vardict[var1][varfilter]),(vardict[var2][varfilter]),vardict[plotvar][varfilter],xarr,yarr,interp='linear')
newarr[ival, :, :] = interpolate.griddata(
np.array([vardict[var1][varfilter],
vardict[var2][varfilter]]).T,
vardict[plotvar][varfilter], tuple(np.meshgrid(xarr, yarr)))
else:
for ival4, val4 in enumerate(unique(vardict[var4])):
for ival3, val3 in enumerate(unique(vardict[var3])):
varfilter = (vardict[var3] == val3) * (vardict[var4] == val4)
#newarr[ival4,ival3,:,:] = griddata((vardict[var1][varfilter]),(vardict[var2][varfilter]),vardict[plotvar][varfilter],xarr,yarr,interp='linear')
if np.count_nonzero(varfilter) == 0:
warnings.warn(
"ERROR: There are no matches for {var3} == {val3} and {var4} == {val4}"
.format(val3=val3, val4=val4, var3=var3, var4=var4))
continue
newarr[ival4, ival3, :, :] = interpolate.griddata(
np.array([
vardict[var1][varfilter], vardict[var2][varfilter]
]).T, vardict[plotvar][varfilter],
tuple(np.meshgrid(xarr, yarr)))
newfile = fits.PrimaryHDU(newarr)
if var4 is not None:
newfile.header.update('CRVAL4', (min(warr)))
newfile.header.update('CRPIX4', 1)
newfile.header.update('CTYPE4', 'NLIN-OPR')
newfile.header.update('CDELT4', (unique(warr)[1]) - (unique(warr)[0]))
newfile.header.update('BTYPE', plotvar)
newfile.header.update('CRVAL3', (min(zarr)))
newfile.header.update('CRPIX3', 1)
if len(unique(zarr)) == 1:
newfile.header.update('CTYPE3', 'ONE-TEMP')
newfile.header.update('CDELT3', zarr[0])
else:
newfile.header.update('CTYPE3', 'LIN-TEMP')
newfile.header.update('CDELT3', (unique(zarr)[1]) - (unique(zarr)[0]))
newfile.header.update('CRVAL1', min(xarr))
newfile.header.update('CRPIX1', 1)
newfile.header.update('CD1_1', xarr[1] - xarr[0])
newfile.header.update('CTYPE1', 'LOG-DENS')
newfile.header.update('CRVAL2', min(yarr))
newfile.header.update('CRPIX2', 1)
newfile.header.update('CD2_2', yarr[1] - yarr[0])
newfile.header.update('CTYPE2', 'LOG-COLU')
newfile.writeto(outfilename, clobber=True)
########################################################
# Started Logging At: 2012-09-06 10:26:32
########################################################
########################################################
# # Started Logging At: 2012-09-06 10:26:33
########################################################
import matplotlib
import numpy
import numpy as np
errstate = np.seterr(all="ignore")
import agpy
T = agpy.readcol('/Volumes/disk2/data/bgps/releases/v2.0/final/bolocat/bgps_v2.0_culled_ipac.txt',asStruct=True)
print T.glon_max[T.field=='gemob1']
T.field
print T.glon_max[T.field=='gemob1b']
print (T.glon_max[T.field=='gemob1b']-188)*3600
print (T.glon_max[T.field=='gemob1b']-188)*3600/7.2
print (T.glon_max[T.field=='gemob1b']-189)*3600/7.2
print (T.glon_max[T.field=='w5']-136)*3600/7.2
print (T.glon_max[T.field=='sh235']-136)*3600/7.2
print (T.glon_max[T.field=='sh235'])*3600/7.2 % 1
print ((T.glon_max[T.field=='sh235'])*3600/7.2 % 1)*10
print ((T.glon_max[T.field=='orionB'])*3600/7.2 % 1)*10
"""
Smooth the LVG models with distributions to get tau, then fit it with
optimization procedures.
"""
import numpy as np
import hopkins_pdf
import turbulent_pdfs
from turbulent_pdfs import lognormal_massweighted
try:
from agpy import readcol
tablereader = lambda x: readcol(x, asRecArray=True)
except ImportError:
import astropy.table
tablereader = lambda x: astropy.table.read(x, format='ascii')
import os
path = __file__
pwd = os.path.split(path)[0]
# plotting stuff
import pylab as pl
pl.rc('font',size=20)
class SmoothtauModels(object):
def __init__(self, datafile=os.path.join(pwd,'radex_data/1-1_2-2_XH2CO=1e-9_troscompt.dat')):
self.datafile = datafile
def plot_radex(filename,ngridpts=100,ncontours=50,plottype='ratio',
transition="noname",thirdvarname="Temperature",
cutnumber=None,cutvalue=10,vmin=None,vmax=None,logscale=False,
save=True,**kwargs):
"""
Create contour plots in density/column, density/temperature, or column/temperature
filename - Name of the .dat file generated by radex_grid.py
ngridpts - number of points in grid to interpolate onto
ncontours - number of contours / colors
plottype - can be 'ratio','tau1','tau2','tex1','tex2'
transition - The name of the transition, e.g. "1-1_2-2". Only used for saving
thirdvarname - Third variable, i.e. the one that will be cut through. If you want
a density/column plot, choose temperature
cutnumber - Cut on the cutnumber value of thirdvar. e.g., if there are 5 temperatures
[10,20,30,40,50] and you set cutnumber=3, a temperature of 40K will be used
cutvalue - Cut on this value; procedure will fail if there are no columns with this value
vmin - Can force vmin/vmax in plotting procedures
vmax - Can force vmin/vmax in plotting procedures
logscale - takes log10 of plotted value before contouring
save - save the figure as a png?
"""
names,props = readcol(filename,twod=False,names=True)
temperature,density,column,tex1,tex2,tau1,tau2,tline1,tline2,flux1,flux2 = props
ratio = flux1 / flux2
if thirdvarname == "Temperature":
firstvar = density
secondvar = column
thirdvar = temperature
if cutnumber is not None:
cutvalue = unique(thirdvar)[int(cutnumber)]
firstlabel = "log$(n_{H_2}) ($cm$^{-3})$"
secondlabel = "log$(N_{H_2CO}) ($cm$^{-2})$"
savetype = "DenCol_T=%iK" % cutvalue
graphtitle = "T = %g K" % cutvalue
firstvar = temperature
secondvar = column
thirdvar = density
if cutnumber is not None:
cutvalue = unique(thirdvar)[int(cutnumber)]
firstlabel = "Temperature (K)"
secondlabel = "log$(N_{H_2CO}) ($cm$^{-2})$"
savetype = "TemCol_n=1e%gpercc" % cutvalue
graphtitle = "n = %g cm$^{-3}$" % (10**cutvalue)
elif thirdvarname == "Column":
secondvar = density
firstvar = temperature
thirdvar = column
if cutnumber is not None:
cutvalue = unique(thirdvar)[int(cutnumber)]
secondlabel = "log$(n_{H_2}) ($cm$^{-3})$"
firstlabel = "Temperature (K)"
savetype = "TemDen_N=1e%gpersc" % cutvalue
graphtitle = "N = %g cm$^{-2}$" % (10**cutvalue)
if plottype == 'ratio':
cblabel = "$F_{1-1} / F_{2-2}$"
elif plottype == 'tau1':
cblabel = "$\\tau_{1-1}$"
elif plottype == 'tau2':
cblabel = "$\\tau_{2-2}$"
elif plottype == 'tex1':
cblabel = "$\\T_{ex}(1-1)$"
elif plottype == 'tex2':
cblabel = "$\\T_{ex}(2-2)$"
varfilter = (thirdvar==cutvalue)
if varfilter.sum() == 0:
raise ValueError("Cut value %g does not match any of %s values" % (cutvalue, thirdvarname))
nx = len(unique(firstvar))
ny = len(unique(secondvar))
if firstvar is temperature:
firstarr = linspace((firstvar.min()),(firstvar.max()),nx)
else:
firstarr = linspace(firstvar.min(),firstvar.max(),nx)
secondarr = linspace(secondvar.min(),secondvar.max(),ny)
exec('plotdata = %s' % plottype)
plot_grid = griddata(firstvar[varfilter],secondvar[varfilter],plotdata[varfilter],firstarr,secondarr)
if vmax:
plot_grid[plot_grid > vmax] = vmax
if vmin:
plot_grid[plot_grid > vmin] = vmin
if logscale:
plot_grid = log10(plot_grid)
figure(1)
clf()
conlevs = logspace(-3,1,ncontours)
contourf(firstarr,secondarr,plot_grid,conlevs,norm=matplotlib.colors.LogNorm())#,**kwargs) #,norm=asinh_norm.AsinhNorm(**kwargs),**kwargs)
xlabel(firstlabel)
ylabel(secondlabel)
title(graphtitle)
cb = colorbar()
cb.set_label(cblabel)
cb.set_ticks([1e-3,1e-2,1e-1,1,1e1])
cb.set_ticklabels([1e-3,1e-2,1e-1,1,1e1])
if save: savefig("%s_%s_%s.png" % (savetype,plottype,transition))
#!/usr/bin/env python
# data files come from ftp://cdsarc.u-strasbg.fr/pub/cats/J/A+A/493/687/
from agpy import readcol
import numpy
h2co_oo = readcol('h2co_oo.dat',asStruct=True,comment='%',skipline=3)
h2co_op = readcol('h2co_op.dat',asStruct=True,comment='%',skipline=3)
levels = readcol('o-h2co_levels.dat',asStruct=True)
nlevelso = len(h2co_oo.Jl)
h2co_oo.__dict__['llevelnum'] = numpy.zeros(nlevelso)
h2co_oo.__dict__['ulevelnum'] = numpy.zeros(nlevelso)
nlevelsp = len(h2co_op.Jl)
h2co_op.__dict__['llevelnum'] = numpy.zeros(nlevelsp)
h2co_op.__dict__['ulevelnum'] = numpy.zeros(nlevelsp)
temperatures = numpy.arange(5,105,5)
def R(a0,a1,a2,a3,a4,T):
"""
Troscompt et al (2009) coefficients using Faure et al (2004) equation:
log10(R) = sum(a_n T^{-n/6})
where n=0..4, R is presumably cm^3 s^-1
"""
return a0 + a1*T**(-1./6.) + a2*T**(-2./6.) + a3*T**(-3./6.) + a4*T**(-4./6.)
outf = open('o-h2co_troscompt.dat','w')
print >>outf, "!MOLECULE"
print >>outf, "o-H2CO"
print >>outf, "!MOLECULAR WEIGHT"
pylab.plot(xarr,pylab.polyval(pf1,xarr),'b--',label='y=%0.2fx+%0.2f' % (pf1[0],pf1[1]))
pylab.plot(xarr,pylab.polyval(pf2,xarr),'g:', label='y=%0.2fx+%0.2f' % (pf2[0],pf2[1]))
pylab.legend(loc='best')
pylab.savefig(savedir+'BGPS_correction_factors.png',bbox_inches='tight')
pylab.figure(4)
pylab.clf()
pylab.hist(correction_factor_v1,bins=pylab.linspace(0.5,2.0,20),alpha=0.5,color='r')
pylab.hist(correction_factor_v1[goodv1_lt2],bins=pylab.linspace(0.5,2.0,20),alpha=0.5,color='r')
pylab.hist(correction_factor_v2,bins=pylab.linspace(0.5,2.0,20),alpha=0.5,color='b')
pylab.hist(correction_factor_v2[goodv2_lt2],bins=pylab.linspace(0.5,2.0,20),alpha=0.5,color='b')
pylab.xlabel("S(PPS)/S(BGPS) ``Correction Factor''")
pylab.savefig(savedir+'BGPS_correction_factor_histograms.png',bbox_inches='tight')
"""
dat = readcol(savedir + 'compare_plot_data.txt', asStruct=True)
pf1 = pylab.polyfit(dat.PPS_120, dat.PPS_120 / dat.v10_120, 1)
pf2 = pylab.polyfit(dat.PPS_120, dat.PPS_120 / dat.v20_120, 1)
pylab.figure(3)
pylab.clf()
pylab.plot(dat.PPS_120, dat.PPS_120 / dat.v10_120, 'o', label='v1.0')
pylab.plot(dat.PPS_120, dat.PPS_120 / dat.v20_120, 's', label='v2.0')
pylab.xlabel('120" flux density in PPS (Jy)')
pylab.ylabel('S(PPS)/S(BGPS) ``Correction Factor"')
pylab.axis([0, 20, 0, 2])
xarr = pylab.linspace(0, 20, 100)
pylab.plot(xarr,
pylab.polyval(pf1, xarr),
'b--',
def gridcube(filename, outfilename, var1="density", var2="column",
var3="temperature", var4=None, plotvar="tau1", zerobads=True,
ratio_type='flux', round=2):
"""
Reads in a radex_grid.py generated .dat file and turns it into a .fits data cube.
filename - input .dat filename
outfilename - output data cube name
var1/var2/var3 - which variable will be used along the x/y/z axis?
plotvar - which variable will be the value in the data cube?
zerobads - set inf/nan values in plotvar to be zero
"""
names,props = readcol(filename,twod=False,names=True)
if round:
for ii,name in enumerate(names):
if name in ('Temperature','log10(dens)','log10(col)','opr'):
props[ii] = np.round(props[ii],round)
if var4 is None:
temperature,density,column,tex1,tex2,tau1,tau2,tline1,tline2,flux1,flux2 = props
else:
temperature,density,column,opr,tex1,tex2,tau1,tau2,tline1,tline2,flux1,flux2 = props
opr = np.floor(opr*100)/100.
if ratio_type == 'flux':
ratio = flux1 / flux2
else:
ratio = tau1 / tau2
vardict = {
"temperature":temperature,
"density":density,
"column":column,
"tex1":tex1,
"tex2":tex2,
"tau1":tau1,
"tau2":tau2,
"tline1":tline1,
"tline2":tline2,
"flux1":flux1,
"flux2":flux2,
"ratio":ratio,
}
if var4 is not None:
vardict['opr'] = opr
nx = len(unique(vardict[var1]))
ny = len(unique(vardict[var2]))
nz = len(unique(vardict[var3]))
if var4 is not None:
nw = len(unique(vardict[var4]))
xarr = (unique(vardict[var1])) #linspace(vardict[var1].min(),vardict[var1].max(),nx)
yarr = (unique(vardict[var2])) #linspace(vardict[var2].min(),vardict[var2].max(),ny)
zarr = (unique(vardict[var3])) #linspace(vardict[var2].min(),vardict[var2].max(),ny)
if var4 is not None:
warr = (unique(vardict[var4])) #linspace(vardict[var2].min(),vardict[var2].max(),ny)
if var4 is None:
newarr = zeros([nz,ny,nx])
else:
newarr = zeros([nw,nz,ny,nx])
print "Cube shape will be ",newarr.shape
if zerobads:
pv = vardict[plotvar]
pv[pv!=pv] = 0.0
pv[isinf(pv)] = 0.0
if var4 is None:
for ival,val in enumerate(unique(vardict[var3])):
varfilter = vardict[var3]==val
#newarr[ival,:,:] = griddata((vardict[var1][varfilter]),(vardict[var2][varfilter]),vardict[plotvar][varfilter],xarr,yarr,interp='linear')
newarr[ival,:,:] = interpolate.griddata(np.array([ vardict[var1][varfilter],vardict[var2][varfilter] ]).T,
vardict[plotvar][varfilter],
tuple(np.meshgrid(xarr,yarr)) )
else:
for ival4,val4 in enumerate(unique(vardict[var4])):
for ival3,val3 in enumerate(unique(vardict[var3])):
varfilter = (vardict[var3]==val3) * (vardict[var4]==val4)
#newarr[ival4,ival3,:,:] = griddata((vardict[var1][varfilter]),(vardict[var2][varfilter]),vardict[plotvar][varfilter],xarr,yarr,interp='linear')
if np.count_nonzero(varfilter) == 0:
warnings.warn("ERROR: There are no matches for {var3} == {val3} and {var4} == {val4}".format(val3=val3, val4=val4, var3=var3, var4=var4))
continue
newarr[ival4,ival3,:,:] = interpolate.griddata(np.array([ vardict[var1][varfilter],vardict[var2][varfilter] ]).T,
vardict[plotvar][varfilter],
tuple(np.meshgrid(xarr,yarr)) )
newfile = fits.PrimaryHDU(newarr)
if var4 is not None:
newfile.header.update('CRVAL4' , (min(warr)) )
newfile.header.update('CRPIX4' , 1 )
newfile.header.update('CTYPE4' , 'NLIN-OPR' )
newfile.header.update('CDELT4' , (unique(warr)[1]) - (unique(warr)[0]) )
newfile.header.update('BTYPE' , plotvar )
newfile.header.update('CRVAL3' , (min(zarr)) )
newfile.header.update('CRPIX3' , 1 )
if len(unique(zarr)) == 1:
newfile.header.update('CTYPE3' , 'ONE-TEMP' )
newfile.header.update('CDELT3' , zarr[0])
else:
newfile.header.update('CTYPE3' , 'LIN-TEMP' )
newfile.header.update('CDELT3' , (unique(zarr)[1]) - (unique(zarr)[0]) )
newfile.header.update('CRVAL1' , min(xarr) )
newfile.header.update('CRPIX1' , 1 )
newfile.header.update('CD1_1' , xarr[1]-xarr[0] )
newfile.header.update('CTYPE1' , 'LOG-DENS' )
newfile.header.update('CRVAL2' , min(yarr) )
newfile.header.update('CRPIX2' , 1 )
newfile.header.update('CD2_2' , yarr[1]-yarr[0] )
newfile.header.update('CTYPE2' , 'LOG-COLU' )
newfile.writeto(outfilename,clobber=True)
"""
import numpy as np
def nearest_source(x1, y1, x2, y2):
d = np.sqrt((x1 - x2)**2 + (y1 - y2)**2)
return d.argmin(), d.min()
import atpy
from agpy import readcol
bgps = atpy.Table('bgps_iras_langston_scuba_match.tbl', type='ascii')
magpis = readcol(
'/Users/adam/work/catalogs/merge6_20.cat',
fixedformat=[7, 7, 13, 13, 6, 9, 10, 8, 7, 7, 6, 5, 9, 10, 8, 9, 2],
asStruct=True,
skipline=10,
nullval=' ')
dbest, bestmatch = np.zeros(len(bgps)), np.zeros(len(bgps))
for ii in xrange(len(bgps)):
bestmatch[ii], dbest[ii] = nearest_source(bgps.glon_peak[ii],
bgps.glat_peak[ii], magpis.Long,
magpis.Lat)
bgps.add_column('Fint6', magpis.Fint6[bestmatch.astype('int')], unit="Jy/beam")
bgps.add_column('Fpeak6', magpis.Fpeak6[bestmatch.astype('int')], unit="Jy")
bgps.add_column('Fint20',
magpis.Fint20[bestmatch.astype('int')],
unit="Jy/beam")
bgps.add_column('Fpeak20', magpis.Fpeak20[bestmatch.astype('int')], unit="Jy")
def gridcube(filename,
outfilename,
var1="density",
var2="column",
var3="temperature",
var4=None,
plotvar="tau1",
zerobads=True):
"""
Reads in a radex_grid.py generated .dat file and turns it into a .fits data cube.
filename - input .dat filename
outfilename - output data cube name
var1/var2/var3 - which variable will be used along the x/y/z axis?
plotvar - which variable will be the value in the data cube?
zerobads - set inf/nan values in plotvar to be zero
"""
names, props = readcol(filename, twod=False, names=True)
if var4 is None:
temperature, density, column, tex1, tex2, tau1, tau2, tline1, tline2, flux1, flux2 = props
else:
temperature, density, column, opr, tex1, tex2, tau1, tau2, tline1, tline2, flux1, flux2 = props
opr = numpy.floor(opr * 100) / 100.
ratio = tau1 / tau2
vardict = {
"temperature": temperature,
"density": density,
"column": column,
"tex1": tex1,
"tex2": tex2,
"tau1": tau1,
"tau2": tau2,
"tline1": tline1,
"tline2": tline2,
"flux1": flux1,
"flux2": flux2,
"ratio": ratio,
"opr": opr,
}
xarr = (unique(vardict[var1])
) #linspace(vardict[var1].min(),vardict[var1].max(),nx)
yarr = (unique(vardict[var2])
) #linspace(vardict[var2].min(),vardict[var2].max(),ny)
zarr = (unique(vardict[var3])
) #linspace(vardict[var2].min(),vardict[var2].max(),ny)
nx = len(xarr)
ny = len(yarr)
nz = len(zarr)
if var4 is not None:
warr = (unique(vardict[var4])
) #linspace(vardict[var2].min(),vardict[var2].max(),ny)
nw = len(warr)
newarr = zeros([nw, nz, ny, nx])
if nw != 11:
import pdb
pdb.set_trace()
else:
newarr = zeros([nz, ny, nx])
print "Cube shape will be ", newarr.shape
if zerobads:
pv = vardict[plotvar]
pv[pv != pv] = 0.0
pv[isinf(pv)] = 0.0
if var4 is None:
for ival, val in enumerate(unique(vardict[var3])):
varfilter = vardict[var3] == val
newarr[ival, :, :] = griddata(
(vardict[var1][varfilter]), (vardict[var2][varfilter]),
vardict[plotvar][varfilter], xarr, yarr)
else:
for ival4, val4 in enumerate(warr):
for ival3, val3 in enumerate(zarr):
varfilter = (vardict[var3] == val3) * (vardict[var4] == val4)
newarr[ival4, ival3, :, :] = griddata(
(vardict[var1][varfilter]), (vardict[var2][varfilter]),
vardict[plotvar][varfilter], xarr, yarr)
newfile = pyfits.PrimaryHDU(newarr)
if var4 is not None:
newfile.header.update('CRVAL4', (min(warr)))
newfile.header.update('CRPIX4', 1)
newfile.header.update('CTYPE4', 'LOG--OPR')
newfile.header.update('CDELT4', ((warr)[1]) - ((warr)[0]))
newfile.header.update('BTYPE', plotvar)
newfile.header.update('CRVAL3', (min(zarr)))
newfile.header.update('CRPIX3', 1)
newfile.header.update('CTYPE3', 'LIN-TEMP')
newfile.header.update('CDELT3', ((zarr)[1]) - ((zarr)[0]))
newfile.header.update('CRVAL1', min(xarr))
newfile.header.update('CRPIX1', 1)
newfile.header.update('CD1_1', xarr[1] - xarr[0])
newfile.header.update('CTYPE1', 'LOG-DENS')
newfile.header.update('CRVAL2', min(yarr))
newfile.header.update('CRPIX2', 1)
newfile.header.update('CD2_2', yarr[1] - yarr[0])
newfile.header.update('CTYPE2', 'LOG-COLU')
newfile.writeto(outfilename, clobber=True)
