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plot_allZ2.py
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import numpy as np
import bottleneck as bn
import GradPak_plot as GPP
import scipy.ndimage as spnd
import scipy.interpolate as spi
#import model_A as mA
import matplotlib.pyplot as plt
import pyfits
#import pywcs
import time
from matplotlib.backends.backend_pdf import PdfPages as PDF
from matplotlib import rc
from matplotlib import colors as mplcolors
from matplotlib.ticker import ScalarFormatter
import glob
import time
import os
glob = glob.glob
rc('text', usetex=False)
rc('font', family='serif')
rc('font', size=10.0)
rc('axes', linewidth=1.0)
rc('lines', linewidth=0.4)
rc('patch', linewidth=0.1)
rc('ps', usedistiller='Xpdf')
rc('xtick', labelsize=10.0)
rc('ytick', labelsize=10.0)
exclude = [[5, 34], [1, 2, 35], [59], [2, 8], [1, 2, 3, 27, 28, 29,5], [35, 36, 38]]
e2 = [[5, 7, 9, 30, 34], [1, 2, 3, 11, 32, 33, 34, 35, 37],
[1, 3, 4, 50, 53, 54, 55, 59], [1, 2, 15], [1, 2, 3, 5, 24, 27, 28, 29,5], [35, 36, 37, 38]]
def all_heights(output, inputprefix='NGC_891', err=True, binned=True):
datname = output.split('.pdf')[0]+'.dat'
f = open(datname,'w')
f.write('#{:5}{:>13}{:>13}{:>13}{:>13}{:>13}{:>13}{:>13}{:>13}{:>13}{:>13}\n#\n'.\
format('','height [kpc]','age [Gyr]',
'age eom','age stderr',
'AV','AV eom','AV stderr',
'Z/Z_sol','Z eom', 'Z stderr'))
pp = PDF(output)
symblist = ["o","^","v","s","*","x"]
colorlist = ['b','c','g','y','m','r']
kpc_scale = 0.0485
plist = [6,3,4,2,1,5]
zcorrlist = [-0.01, -0.113, -0.225, -0.07, -0.142, -0.004]
rlist = [-10.0, -6.4, -2.3, 0.8, 4.4, 8.1]
ageax = None
AVax = None
Zax = None
for i in range(6):
inputfile = glob('{}*P{}*allz2*.dat'.format(inputprefix,plist[i]))[0]
print inputfile
if binned:
fitsname = glob('{}*P{}*.ms*fits'.format(inputprefix,plist[i]))[0]
print fitsname
binhead = pyfits.open(fitsname)[0].header
else:
binhead = False
MLWA, MLWZ, TAUV, SNR = np.loadtxt(inputfile, usecols=(62,64,66,67),
unpack=True)
MLWZ = np.log10(MLWZ)
ageax, tmpz, tmpage, tmperr, tmpstd = GPP.plot_rows(MLWA,
binheader=binhead,
weights=SNR,
ax=ageax,
label='{}'.\
format(rlist[i]),
fullout=True,
kpc_scale=kpc_scale,
zcorr=zcorrlist[i],
err=err,
marker=symblist[i],
linestyle='',
color=colorlist[i])
AVax, _, tmpAV, tmpAVerr, tmpAVstd = GPP.plot_rows(TAUV*1.086,
binheader = binhead,
weights=SNR,
ax=AVax,
label='{}'.\
format(rlist[i]),
fullout=True,
kpc_scale=kpc_scale,
zcorr=zcorrlist[i],
err=err,
marker=symblist[i],
linestyle='',
color=colorlist[i])
Zax, _, tmpZ, tmpZerr, tmpZstd = GPP.plot_rows(MLWZ,
binheader = binhead,
weights=SNR,
ax=Zax,
label='{}'.\
format(rlist[i]),
fullout=True,
kpc_scale=kpc_scale,
zcorr=zcorrlist[i],
err=err,
marker=symblist[i],
linestyle='',
color=colorlist[i])
f.write('\n# P{} '.format(plist[i])+92*'#'+'\n# r ~ {} kpc\n'.\
format(rlist[i]))
for i in range(tmpz.size):
f.write('{:6}{:13.3f}{:13.3f}{:13.3f}{:13.3f}{:13.3f}{:13.3f}{:13.3f}{:13.3f}{:13.3f}{:13.3f}\n'.\
format('',tmpz[i],tmpage[i],tmperr[i],tmpstd[i],
tmpAV[i],tmpAVerr[i],tmpAVstd[i],
tmpZ[i],tmpZerr[i],tmpZstd[i]))
try:
z = np.vstack((z,tmpz))
age = np.vstack((age,tmpage))
err = np.vstack((err,tmperr))
std = np.vstack((std,tmpstd))
AV = np.vstack((AV,tmpAV))
AVstd = np.vstack((AVstd,tmpAVstd))
AVerr = np.vstack((AVerr,tmpAVerr))
Z = np.vstack((Z,tmpZ))
Zerr = np.vstack((Zerr,tmpZerr))
Zstd = np.vstack((Zstd,tmpZstd))
except UnboundLocalError:
z = tmpz
age = tmpage
err = tmperr
std = tmpstd
AV = tmpAV
AVstd = tmpAVstd
AVerr = tmpAVerr
Z = tmpZ
Zerr = tmpZerr
Zstd = tmpZstd
bigz = np.nanmean(z,axis=0)
bigage = np.nanmean(age,axis=0)
bigerr = np.sqrt(
np.nansum(err*(age - bigage)**2,axis=0)/
((err.shape[0] - 1.)/(err.shape[0]) * np.nansum(err,axis=0)))
bigAV = np.nanmean(AV,axis=0)
bigAVerr = np.sqrt(
np.nansum(AVerr*(AV - bigAV)**2,axis=0)/
((AVerr.shape[0] - 1.)/(AVerr.shape[0]) * np.nansum(AVerr,axis=0)))
bigZ = np.nanmean(Z,axis=0)
bigZerr = np.sqrt(
np.nansum(Zerr*(Z - bigZ)**2,axis=0)/
((Zerr.shape[0] - 1.)/(Zerr.shape[0]) * np.nansum(Zerr,axis=0)))
with open('{}_means.dat'.format(datname.split('.dat')[0]),'w') as fm:
fm.write(str('#{:>9}'+'{:>10}'*6+'\n').format('height',
'MLWA',
'MLWA err',
'Av',
'Av err',
'Z',
'Z err'))
for i in range(bigz.size):
fm.write('{:10.4f}{:10.4f}{:10.4f}{:10.4f}{:10.4f}{:10.4f}{:10.4f}\n'.\
format(bigz[i],bigage[i],bigerr[i],bigAV[i],bigAVerr[i],bigZ[i],bigZerr[i]))
ageax.plot(bigz, bigage)
ageax.fill_between(bigz,bigage-bigerr,bigage+bigerr,alpha=0.1)
ageax.legend(loc=1,title='radius [kpc]',scatterpoints=1,numpoints=1,frameon=False)
ageax.set_xlim(-0.6,2.6)
ageax.set_ylim(0,13)
ageax.set_ylabel('Light-weighted age [Gyr]')
ageax.set_title('Generated on {}'.format(time.asctime()))
AVax.plot(bigz,bigAV)
AVax.fill_between(bigz,bigAV-bigAVerr,bigAV+bigAVerr,alpha=0.1)
AVax.legend(loc=1,title='radius [kpc]',scatterpoints=1,numpoints=1,frameon=False)
AVax.set_xlim(-0.6,2.6)
AVax.set_ylim(0,6)
AVax.set_ylabel(r'$A_V$')
Zax.plot(bigz,bigZ)
Zax.fill_between(bigz, bigZ-bigZerr, bigZ+bigZerr,alpha=0.1)
Zax.legend(loc=1,title='radius [kpc]',scatterpoints=1,
numpoints=1,frameon=False)
Zax.set_xlim(-0.6,2.6)
# Zax.set_ylim(-1.5,3.0)
Zax.set_ylabel(r'Log( MLWZ [$Z_{\odot}$] )')
pp.savefig(ageax.figure)
pp.savefig(AVax.figure)
pp.savefig(Zax.figure)
plt.close(ageax.figure)
plt.close(AVax.figure)
plt.close(Zax.figure)
pp.close()
f.close()
return
def simple_plot(inputsuffix='allz2.dat', label='Mean Light Weighted Age [Gyr]',
col=62, order=5, ylims=None, labelr=False, bigpoints=False,
exclude=[[],[],[],[],[],[]]):
zz = np.array([])
dd = np.array([])
axlist = []
bigax = plt.figure().add_subplot(111)
bigax.set_xlabel('|Height [kpc]|')
bigax.set_ylabel(label)
plist = [6,3,4,2,1,5]
#color_list = ['blue','turquoise','chartreuse','yellow','tomato','red']
color_list = ['blue','seagreen','sienna','sienna','seagreen','blue']
style_list = ['-','-','-','--','--','--']
for i in range(6):
pointing = plist[i]
color = color_list[i]
style = style_list[i]
dat = glob('*P{}*{}'.format(pointing, inputsuffix))[0]
print dat
loc = glob('*P{}*locations.dat'.format(pointing))[0]
print loc
print 'Excluding: ', exclude[pointing-1]
td = np.loadtxt(dat, usecols=(col,), unpack=True)
r, tz = np.loadtxt(loc, usecols=(4,5), unpack=True)
avgr = np.mean(r)
ax = plt.figure().add_subplot(111)
ax.set_xlabel('|Height [kpc]|')
ax.set_ylabel(label)
if labelr:
ax.set_title('{:4.0f} kpc'.format(avgr))
linelabel = '{:4.0f} kpc'.format(avgr)
else:
ax.set_title('{}\nP{}'.format(time.asctime(),pointing))
linelabel = 'P{}'.format(pointing)
exarr = np.array(exclude[pointing-1])-1 #becuase aps are 1-indexed
td = np.delete(td,exarr)
t = np.delete(r,exarr)
tz = np.delete(tz,exarr)
z = np.array([])
d = np.array([])
e = np.array([])
while tz.size > 0:
zi = tz[0]
idx = np.where(np.abs(tz - zi) < 0.05)
d = np.r_[d,np.mean(td[idx])]
e = np.r_[e,np.std(td[idx])]
z = np.r_[z,np.abs(zi)]
tz = np.delete(tz, idx)
td = np.delete(td, idx)
gidx = d == d
d = d[gidx]
z = z[gidx]
e = e[gidx]
sidx = np.argsort(z)
dp = np.r_[d[sidx][order::-1],d[sidx]]
zp = np.r_[z[sidx][order::-1],z[sidx]]
mean = bn.move_mean(dp,order)[order+1:]
std = bn.move_std(dp,order)[order+1:]
spl = spi.UnivariateSpline(z[sidx],d[sidx])
mean = spl(z[sidx])
# mean = np.convolve(d[sidx],np.ones(order)/order,'same')
# std = np.sqrt(np.convolve((d - mean)**2,np.ones(order)/order,'same'))
bigax.plot(z[sidx],mean, label=linelabel, color=color, ls=style)
bigax.fill_between(z[sidx],mean-std,mean+std, alpha=0.1, color=color)
if bigpoints:
bigax.errorbar(z, d, yerr=e, fmt='.', color=color, alpha=0.6, capsize=0)
ax.plot(z[sidx],mean,color=color, ls=style)
ax.fill_between(z[sidx],mean-std,mean+std, alpha=0.1, color=color)
ax.errorbar(z, d, yerr=e, fmt='.', color=color)
ax.set_xlim(-0.1,2.6)
if ylims is not None:
ax.set_ylim(*ylims)
axlist.append(ax)
bigax.legend(loc=0, numpoints=1, scatterpoints=1)
bigax.set_title(time.asctime())
bigax.set_xlim(-0.1,2.6)
if ylims is not None:
bigax.set_ylim(*ylims)
axlist = [bigax] + axlist
return axlist
def plot_heights_with_err(inputsuffix,label=r'$\tau_{\mathrm{V,Balm}}$',basedir='.',
col=1, errcol=2, lowhigh=False, order=5, bigorder=60,
s=None, ylims=None, labelr=False, bigpoints=False,
plotfit=True, exclude=exclude, printdate=True, printfit=True):
zz = np.array([])
dd = np.array([])
if lowhigh:
ee = np.array([[],[]])
else:
ee = np.array([])
axlist = []
bigax = plt.figure().add_subplot(111)
bigax.set_xlabel(r'$|z| \mathrm{\ [kpc]}$')
bigax.set_ylabel(label)
plist = [6,3,4,2,1,5]
color_list = ['blue','seagreen','sienna','orange','yellowgreen','darkturquoise']
style_list = ['-','-','-','--','--','--']
for i in range(6):
pointing = plist[i]
color = color_list[i]
style = style_list[i]
dat = glob('{}/*P{}*{}'.format(basedir, pointing, inputsuffix))[0]
print dat
loc = glob('{}/*P{}*locations.dat'.format(basedir, pointing))[0]
print loc
print 'Excluding: ', exclude[pointing-1]
if errcol is not None:
if lowhigh:
data, Lerr, Herr = np.loadtxt(dat, usecols=(col,errcol,errcol+1), unpack=True)
err = np.vstack((Lerr,Herr))
else:
data, err = np.loadtxt(dat, usecols=(col,errcol), unpack=True)
else:
data = np.loadtxt(dat, usecols=(col,), unpack=True)
err = np.ones(data.size)*0.01
r, z = np.loadtxt(loc, usecols=(4,5), unpack=True)
avgr = np.mean(r)
ax = plt.figure().add_subplot(111)
ax.set_xlabel('|Height [kpc]|')
ax.set_ylabel(label)
if labelr:
ax.set_title('{:4.0f} kpc'.format(avgr))
linelabel = '{:4.0f} kpc'.format(avgr)
else:
ax.set_title('{}\nP{}'.format(time.asctime(),pointing))
linelabel = 'P{}'.format(pointing)
exarr = np.array(exclude[pointing-1])-1 #becuase aps are 1-indexed
data = np.delete(data,exarr)
r = np.delete(r,exarr)
z = np.delete(z,exarr)
gidx = data == data
data = data[gidx]
z = z[gidx]
if lowhigh:
err = np.delete(err,exarr,axis=1)
err = err[:,gidx]
ee = np.hstack((ee,err))
else:
err = np.delete(err,exarr)
err = err[gidx]
ee = np.r_[ee,err]
zz = np.r_[zz,z]
dd = np.r_[dd,data]
sidx = np.argsort(z)
data_pad = np.r_[data[sidx][order::-1],data[sidx]]
z_pad = np.r_[z[sidx][order::-1],z[sidx]]
# mean = bn.move_mean(data_pad,order)[order+1:]
std = bn.move_std(data_pad,order)[order+1:]
spl = spi.UnivariateSpline(z[sidx],data[sidx])
mean = spl(z[sidx])
# mean = np.convolve(d[sidx],np.ones(order)/order,'same')
# std = np.sqrt(np.convolve((d - mean)**2,np.ones(order)/order,'same'))
bigax.errorbar(z, data, yerr=err, fmt='.', label=linelabel, color=color, capsize=0)
# ax.plot(z[sidx],mean,color=color, ls=style)
# ax.fill_between(z[sidx],mean-std,mean+std, alpha=0.1, color=color)
ax.errorbar(z, data, yerr=err, fmt='.', color=color, capsize=0)
ax.set_xlim(-0.1,2.6)
if ylims is not None:
ax.set_ylim(*ylims)
axlist.append(ax)
if printdate:
plot_title = time.asctime()
else:
plot_title = ''
if plotfit:
sidx = np.argsort(zz)
big_data_pad = np.r_[dd[sidx][bigorder::-1],dd[sidx]]
big_z_pad = np.r_[zz[sidx][bigorder::-1],zz[sidx]]
big_e_pad = np.r_[ee[sidx][bigorder::-1],ee[sidx]]
big_sum = bn.move_sum(big_data_pad/big_e_pad,bigorder)[bigorder+1:]
big_weight = bn.move_sum(1./big_e_pad,bigorder)[bigorder+1:]
big_mean = big_sum/big_weight
# std = bn.move_std(data_pad,order)[order+1:]
# big_spl = spi.UnivariateSpline(zz[sidx],dd[sidx],w = 1./ee[sidx]**2, k=k, s=s)
# big_mean = big_spl(zz[sidx])
# big_pc = np.polyfit(zz[sidx], dd[sidx], polydeg, w=1./ee[sidx]**2)
# big_poly = np.poly1d(big_pc)
# big_mean = big_poly(zz[sidx])
p = np.poly1d(np.polyfit(zz[sidx],big_mean,1))
print p.coeffs
# bigax.plot(zz[sidx],big_mean,'-k',lw=2)
bigax.plot(zz[sidx],p(zz[sidx]),'--k',lw=2)
if printdate:
plot_title += '\n'
if printfit:
plot_title += label+'$={:4.2f}z{:+4.2f}$'.format(p.coeffs[0],p.coeffs[1])
bigax.set_title(plot_title)
bigax.legend(loc=0, numpoints=1, scatterpoints=1)
bigax.set_xlim(-0.1,2.6)
print zz.size
if ylims is not None:
bigax.set_ylim(*ylims)
axlist = [bigax] + axlist
return axlist
def height_plot_across_folders(folder_list, inputsuffix='allz2.dat',
label='Mean Light Weighted Age [Gyr]',
col=6, errcol=None, lowhigh=False,
order=5, ylims=None, bigpoints=False,
binz=True, combine_all=False, plot_std=False,
exclude=[[],[],[],[],[],[]]):
axlist = []
plist = [6,3,4,2,1,5]
#color_list = ['blue','turquoise','chartreuse','yellow','tomato','red']
color_list = ['blue','seagreen','darkorange','crimson','dimgray','mediumorchid','lightblue']
style_list = ['-','-','-','-','-','-','-']
if not isinstance(col,list):
col = [col] * len(folder_list)
for i in range(6):
pointing = plist[i]
ax = plt.figure().add_subplot(111)
ax.set_xlabel('|Height [kpc]|')
ax.set_ylabel(label)
ax.set_title('{}\nP{}'.format(time.asctime(),pointing))
for f, folder in enumerate(folder_list):
color = color_list[f]
style = style_list[f]
dat = glob('{}/*P{}*{}'.format(folder, pointing, inputsuffix))[0]
print dat
loc = glob('{}/*P{}*locations.dat'.format(folder, pointing))[0]
print loc
print 'Excluding: ', exclude[pointing-1]
if errcol == None:
td = np.loadtxt(dat, usecols=(col[f],), unpack=True)
else:
if lowhigh:
td, low, high = np.loadtxt(dat, usecols=(col[f],errcol,errcol+1), unpack=True)
te = np.vstack((low,high))
else:
td, te = np.loadtxt(dat, usecols=(col[f],errcol), unpack=True)
r, tz = np.loadtxt(loc, usecols=(4,5), unpack=True)
exarr = np.array(exclude[pointing-1])-1 #becuase aps are 1-indexed
td = np.delete(td,exarr)
r = np.delete(r,exarr)
tz = np.delete(tz,exarr)
if errcol != None:
if lowhigh:
te = np.delete(te,exarr,axis=1)
else:
te = np.delete(te,exarr)
alpha=1.0
if combine_all and f == 0:
bigD = np.zeros(td.size)
alpha=0.3
if binz:
z = np.array([])
d = np.array([])
e = np.array([])
while tz.size > 0:
zi = tz[0]
idx = np.where(np.abs(tz - zi) < 0.05)
d = np.r_[d,np.mean(td[idx])]
e = np.r_[e,np.std(td[idx])]
z = np.r_[z,np.abs(zi)]
tz = np.delete(tz, idx)
td = np.delete(td, idx)
else:
z = tz
d = td
if errcol == None:
e = np.zeros(tz.size)
else:
e = te
if combine_all:
bigD = np.vstack((bigD,d))
bigz = z
gidx = d == d
d = d[gidx]
z = z[gidx]
if lowhigh:
e = e[:,gidx]
else:
e = e[gidx]
sidx = np.argsort(z)
dp = np.r_[d[sidx][order::-1],d[sidx]]
zp = np.r_[z[sidx][order::-1],z[sidx]]
mean = bn.move_mean(dp,order)[order+1:]
std = bn.move_std(dp,order)[order+1:]
spl = spi.UnivariateSpline(z[sidx],d[sidx])
mean = spl(z[sidx])
# mean = np.convolve(d[sidx],np.ones(order)/order,'same')
# std = np.sqrt(np.convolve((d - mean)**2,np.ones(order)/order,'same'))
# ax.plot(z[sidx],mean,color=color, ls=style, label=folder, alpha=alpha)
# ax.fill_between(z[sidx],mean-std,mean+std, alpha=0.1, color=color)
# print d.shape, np.sum(e,axis=0).shape
# d = d/np.sum(e,axis=0)
# e = np.diff(e,axis=0)[0]
# print e.shape
ax.errorbar(z, d, yerr=e, fmt='.', color=color,alpha=alpha,capsize=0, label=folder)
ax.set_xlim(-0.1,2.6)
if ylims is not None:
ax.set_ylim(*ylims)
ax.legend(loc=0,numpoints=1)
if combine_all:
sidx = np.argsort(bigz)
bigD = bigD[1:]
bigMean = bn.nanmean(bigD,axis=0)
bigStd = bn.nanstd(bigD,axis=0)
bigspl = spi.UnivariateSpline(bigz[sidx],bigMean[sidx])
bigFit = bigspl(bigz[sidx])
ax.plot(bigz[sidx], bigFit, 'k-', lw=2)
ax.errorbar(bigz, bigMean, yerr=bigStd, fmt='.', color='k',capsize=0)
axlist.append(ax)
if combine_all and plot_std:
ax2 = plt.figure().add_subplot(111)
ax2.set_xlabel('|Height [kpc]|')
ax2.set_ylabel('$\delta$'+label)
ax2.set_title(ax.get_title())
ax2.plot(bigz, bigStd, 'k')
axlist.append(ax2)
return axlist
def histogram_across_folders(folder_list, inputsuffix='allz2.dat',
label='Mean Light Weighted Age [Gyr]',
col=62, bins=10, ylims=None, xlims=None,
delta=False,exclude=exclude,alpha=0.3,
label_list = [],
color_list = ['#e41a1c','#377eb8','#4daf4a',
'#984ea3','#ff7f00','#ffff33','#a65628']):
axlist = []
plist = [6,3,4,2,1,5]
#color_list = ['blue','turquoise','chartreuse','yellow','tomato','red']
# color_list = ['blue','turquoise','seagreen','darkorange','crimson','dimgray','mediumorchid','maroon']
style_list = ['-','-','-','-','-','-','-','-']
if not isinstance(col,list):
col = [col] * len(folder_list)
bigD = dict(zip(folder_list,[np.array([]) for i in range(len(folder_list))]))
for i in range(6):
pointing = plist[i]
ax = plt.figure().add_subplot(111)
ax.set_xlabel(label)
ax.set_ylabel('$N$')
ax.set_title('{}\nP{}'.format(time.asctime(),pointing))
for f, folder in enumerate(folder_list):
color = color_list[f]
style = style_list[f]
dat = glob('{}/*P{}*{}'.format(folder, pointing, inputsuffix))[0]
print dat
print 'Excluding: ', exclude[pointing-1]
d = np.loadtxt(dat, usecols=(col[f],), unpack=True)
exarr = np.array(exclude[pointing-1])-1 #becuase aps are 1-indexed
d = np.delete(d,exarr)
gidx = d == d
d = d[gidx]
if delta:
d -= np.min(d)
if len(label_list) == len(folder_list):
hlabel = label_list[f]
else:
hlabel = os.path.basename(folder)
ax.hist(d, bins=bins, histtype='stepfilled', color=color, label=hlabel,alpha=alpha)
bigD[folder] = np.r_[bigD[folder], d]
if xlims is not None:
ax.set_xlim(*xlims)
if ylims is not None:
ax.set_ylim(*ylims)
ax.legend(loc=0,numpoints=1)
axlist.append(ax)
bigax = plt.figure().add_subplot(111)
bigax.set_xlabel(label)
bigax.set_ylabel('$N$')
bigax.set_title(time.asctime())
for f, folder in enumerate(folder_list):
color = color_list[f]
style = style_list[f]
if len(label_list) == len(folder_list):
hlabel = label_list[f]
else:
hlabel = os.path.basename(folder)
bigax.hist(bigD[folder], bins=bins, histtype='stepfilled', color=color, label=hlabel,alpha=alpha)
bigax.legend(loc=0,numpoints=1)
if xlims is not None:
bigax.set_xlim(*xlims)
if ylims is not None:
bigax.set_ylim(*ylims)
axlist = [bigax] + axlist
return axlist, bigD
def simple_batch(prefix, order=5, exclude=[[],[],[],[],[],[]]):
clist = [62,61,66,63]
llist = ['Mean Light Weighted Age [Gyr]',
'Mean Mass Weighted Age [Gyr]',
r'$\tau_V$',
'Mean Light Weighted Metallicity [Z$_{\odot}$]']
yllist = [[0,11],[0,11],[-1,6],[0,3]]
sllist = ['MLWA','MMWA','TauV','MLWZ']
for c, l, sl, yl in zip(clist, llist, sllist, yllist):
pp = PDF('{}_{}_heights.pdf'.format(prefix,sl))
for x in simple_plot(col=c,label=l,ylims=yl,exclude=exclude,order=order):
pp.savefig(x.figure)
pp.close()
plt.close('all')
return
def dfk_batch(prefix, order=5, exclude=[[],[],[],[],[],[]],
offset=0, labelr=False, bigpoints=False):
clist = [6,5,10,7,8]
llist = ['Mean Light Weighted Age [Gyr]',
'Mean Mass Weighted Age [Gyr]',
r'$\tau_V$',
'Mean Mass Weighted Metallicity [Z$_{\odot}$]',
'Mean Light Weighted Metallicity [Z$_{\odot}$]']
yllist = [[0,11],[0,11],[-1,6],[0,1.3],[0,1.3]]
sllist = ['MLWA','MMWA','TauV','MMWZ','MLWZ']
for c, l, sl, yl in zip(clist, llist, sllist, yllist):
pp = PDF('{}_{}_heights.pdf'.format(prefix,sl))
for x in simple_plot(col=c+offset,label=l,ylims=yl,
exclude=exclude,order=order,
labelr=labelr,bigpoints=bigpoints):
pp.savefig(x.figure)
pp.close()
plt.close('all')
return
def coef_height_plot(field_name, output, err_name=None, ylim=[0,12],
exclude=exclude, suffix='coef', oplotdir=None,
nodate=False, label=None, basedir='.'):
plist = [6,3,4,2,1,5]
color_list = ['blue','seagreen','sienna','orange','yellowgreen','darkturquoise']
# style_list = ['-','-','-','--','--','--']
style_list = ['.'] * 6
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xlabel(r'$|z| \mathrm{[kpc]}$')
ax.set_xlim(0,2.6)
ax.set_ylim(*ylim)
if label:
ax.set_ylabel(label)
else:
ax.set_ylabel(field_name)
if not nodate:
ax.set_title(time.asctime())
for p, color, ex in zip(plist, color_list, exclude):
loc = '{}/NGC_891_P{}_bin30_locations.dat'.format(basedir,p)
coef = '{}/NGC_891_P{}_bin30_allz2.{}.fits'.format(basedir,p,suffix)
z = np.loadtxt(loc,usecols=(5,),unpack=True)
z = np.abs(z)
data = pyfits.open(coef)[1].data
values = data[field_name]
exarr = np.array(ex) - 1
z = np.delete(z, exarr)
values = np.delete(values, exarr)
if oplotdir is not None:
oplotdata = pyfits.open('{}/{}'.format(oplotdir,coef))[1].data
oplot = oplotdata[field_name]
oplot = np.delete(oplot, exarr)
if err_name is None:
ax.plot(z, values, '.', color=color, label='P{}'.format(p))
else:
err = data[err_name]
err = np.delete(err, exarr)
ax.errorbar(z, values, yerr=err, fmt='.', color=color, capsize=0, label='P{}'.format(p))
if oplotdir is not None:
ax.scatter(z, oplot, marker='s', color=color, alpha=0.4)
ax.legend(loc=0,scatterpoints=1,numpoints=1)
pp = PDF(output)
pp.savefig(fig)
pp.close()
return
def coef_covar(field1, field2, output, plotmono=False, field3=False):
plist = [6,3,4,2,1,5]
color_list = ['blue','seagreen','sienna','orange','yellowgreen','darkturquoise']
style_list = ['.'] * 6
for p, color in zip(plist, color_list):
N = 1
pp = PDF('{}_P{}.pdf'.format(output,p))
if plotmono:
monobase = '/d/monk/eigenbrot/WIYN/14B-0456/anal/model_balmer/bc03/single_Z/no_weight'
monod = {}
for Z in ['0.2Z','0.4Z','1Z','2.5Z']:
monod[Z] = pyfits.open('{}/{}/NGC_891_P{}_bin30_allz2.coef.fits'.\
format(monobase,Z,p))[1].data
while True:
try:
coef = 'MCdir/NGC_891_P{}_bin30_allz2.MC{:03n}.fits'.format(p,N)
data = pyfits.open(coef)[1].data
except IOError:
break
print coef
values1 = data[field1]
values2 = data[field2]
if field3:
c = data[field3]
else:
c = 'b'
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xlabel(field1)
ax.set_ylabel(field2)
ax.set_title('P{}.{}\n{}'.format(p,N,time.asctime()))
ax.scatter(values1, values2, c=c, alpha=1, linewidth=0, cmap=plt.cm.gnuplot)
if plotmono:
for Z in ['0.2Z','0.4Z','1Z','2.5Z']:
ax.scatter(monod[Z][field1][N-1],
monod[Z][field2][N-1], marker='s', color='r',
linewidths=0)
pp.savefig(fig)
plt.close(fig)
N += 1
pp.close()
return
def coef_MCcovar_3panel(field1, field2, field3, output,
label1 = False, label2 = False, label3 = False,
lim1 = [], lim2 = [], lim3 = [], colorbar=True,
basedir='.', zlims=[0,0.4], plist=[1,2,3,4,5,6]):
fig = plt.figure()
ax1 = fig.add_subplot(221)
ax2 = fig.add_subplot(223)
ax3 = fig.add_subplot(224)
for p in plist:
loc = '{}/NGC_891_P{}_bin30_locations.dat'.format(basedir,p)
z = np.loadtxt(loc, usecols=(5,), unpack=True)
z = np.abs(z)
idx = np.where((z >= zlims[0]) & (z < zlims[1]))[0]
N = 1
while True:
try:
coef = '{}/MCdir/NGC_891_P{:}_bin30_allz2.MC{:03n}.fits'.format(basedir,p,N)
c = pyfits.open(coef)[1].data
except IOError:
break
if N-1 in idx:
print coef, z[N-1]
p1 = c[field1] - np.mean(c[field1])
p2 = c[field2] - np.mean(c[field2])
p3 = c[field3] - np.mean(c[field3])
ax1.scatter(p1,p2,alpha=0.4,linewidth=0,c=[z[N-1]]*c[field2].size,vmin=0,vmax=2.6,cmap=plt.cm.gnuplot)
ax2.scatter(p1,p3,alpha=0.4,linewidth=0,c=[z[N-1]]*c[field2].size,vmin=0,vmax=2.6,cmap=plt.cm.gnuplot)
scat = ax3.scatter(p2,p3,alpha=0.4,linewidth=0,c=[z[N-1]]*c[field2].size,vmin=0,vmax=2.6,cmap=plt.cm.gnuplot)
N += 1
ax1.set_xticklabels([])
ax1.set_xlim(*lim1)
ax1.set_ylim(*lim2)
ax1.set_ylabel(label2 or '{0} - <{0}>'.format(field2))
ax2.set_xlim(*ax1.get_xlim())
ax2.set_ylim(*lim3)
ax2.set_xlabel(label1 or '{0} - <{0}>'.format(field1))
ax2.set_ylabel(label3 or '{0} - <{0}>'.format(field3))
ax3.set_ylim(*ax2.get_ylim())
ax3.set_xlim(*lim2)
ax3.set_yticklabels([])
ax3.set_xlabel(label2 or '{0} - <{0}>'.format(field2))
fig.subplots_adjust(hspace=0.001,wspace=0.001)
if colorbar:
cb = fig.colorbar(scat, ax=[ax1,ax2,ax3])
cb.set_alpha(1)
cb.set_label('$|z| \mathrm{[kpc]}$')
cb.draw_all()
pp = PDF(output)
pp.savefig(fig)
pp.close()
plt.close(fig)
return
def coef_MCcovar_contour(field1, field2, field3, output,
label1 = False, label2 = False, label3 = False,
lim1 = [], lim2 = [], lim3 = [], bins=30,
basedir='.', zlims=[0,0.4], plist=[1,2,3,4,5,6]):
fig = plt.figure()
ax1 = fig.add_subplot(221)
ax2 = fig.add_subplot(223)
ax3 = fig.add_subplot(224)
data1_list = []
data2_list = []
data3_list = []
for p in plist:
loc = '{}/NGC_891_P{}_bin30_locations.dat'.format(basedir,p)
z = np.loadtxt(loc, usecols=(5,), unpack=True)
z = np.abs(z)
idx = np.where((z >= zlims[0]) & (z < zlims[1]))[0]
N = 1
while True:
try:
coef = '{}/MCdir/NGC_891_P{:}_bin30_allz2.MC{:03n}.fits'.format(basedir,p,N)
c = pyfits.open(coef)[1].data
except IOError:
break
if N-1 in idx:
print coef, z[N-1]
p1 = c[field1] - np.mean(c[field1])
p2 = c[field2] - np.mean(c[field2])
p3 = c[field3] - np.mean(c[field3])
data1_list.append(p1)
data2_list.append(p2)
data3_list.append(p3)
N += 1
data1 = np.hstack(data1_list)
data2 = np.hstack(data2_list)
data3 = np.hstack(data3_list)
print data1.shape
H1, xe1, ye1 = np.histogram2d(data1, data2, bins=bins)
x1 = 0.5*(xe1[:-1] + xe1[1:])
y1 = 0.5*(ye1[:-1] + ye1[1:])
H2, xe2, ye2 = np.histogram2d(data1, data3, bins=bins)
x2 = 0.5*(xe2[:-1] + xe2[1:])
y2 = 0.5*(ye2[:-1] + ye2[1:])
H3, xe3, ye3 = np.histogram2d(data2, data3, bins=bins)
x3 = 0.5*(xe3[:-1] + xe3[1:])
y3 = 0.5*(ye3[:-1] + ye3[1:])
H1 /= np.max(H1)
H2 /= np.max(H2)
H3 /= np.max(H3)
levels = [0.005,0.1, 0.4, 0.7, 0.9]
ax1.contour(x1,y1,H1.T,levels,colors='k')
ax2.contour(x2,y2,H2.T,levels,colors='k')
ax3.contour(x3,y3,H3.T,levels,colors='k')
ax1.set_xticklabels([])
ax1.set_xlim(*lim1)
ax1.set_ylim(*lim2)
ax1.set_ylabel(label2 or '{0} - <{0}>'.format(field2))
ax2.set_xlim(*ax1.get_xlim())
ax2.set_ylim(*lim3)
ax2.set_xlabel(label1 or '{0} - <{0}>'.format(field1))
ax2.set_ylabel(label3 or '{0} - <{0}>'.format(field3))
ax3.set_ylim(*ax2.get_ylim())
ax3.set_xlim(*lim2)
ax3.set_yticklabels([])
ax3.set_xlabel(label2 or '{0} - <{0}>'.format(field2))
fig.subplots_adjust(hspace=0.001,wspace=0.001)
if not output:
return fig
else:
pp = PDF(output)
pp.savefig(fig)
pp.close()
plt.close(fig)
return H1
def err_histogram(output, basedir='.',bins=10, field='MLWA', err='dMLWA', suffix='coef',
label=r'$\delta\tau_{L,\mathrm{fit}}/\tau_L$',exclude=exclude, ymax=90):
ratio_list = []
for p in range(6):