out,table = p.sfrl1_parse('slug_comp_rere.dat')
#out2,table2 = p.sfrl1_parse('slug_comp_rere.dat')
#get names of tags
#out.dtype.names
sfrs = out.sfr_x
x = out.x
#get the sfr closest to \log sfr = -2
for i in xrange(3):
index = np.argmin(abs(out.sfr_x -sfr[i]))
this_pdf = table[index]
#this_pdf2 = table2[index]
print sfrs[index]
# plt.plot(p.ln2mv(x), p.ln2mvpdf(this_pdf2), lw=3, color=(1.,0.5,0.5), label='Uncorrected')
plt.plot(p.ln2mv(x), p.ln2mvpdf(this_pdf), lw=3, color='blue', label="CLOC")
print "Normalization"
print np.trapz(p.ln2mvpdf(this_pdf),p.ln2mv(x))
plt.xlim([-1,-18])
f=open(files[i], 'r')
slug = np.array(f.read().split("\n"))
slug=slug[0:-1]
slug = slug.astype(float)
bn=20
if i == 1: bn = 30
plt.hist(slug, normed=True, bins=bn, histtype="stepfilled", color='grey',\
edgecolor='none')
plt.xlabel(r"$M_V$")
plt.ylabel(r"$p(M_V)$")
plt.title(r"$\log_{10} \textrm{SFR} = "+repr(sfr[i])+"$")
plt.legend(prop={'size':14}, frameon=False)
out, table = p.sfrl1_parse('slug_comp_cut.dat')
#out2,table2 = p.sfrl1_parse('slug_comp2.dat')
#get names of tags
#out.dtype.names
sfrs = out.sfr_x
x = out.x
#get the sfr closest to \log sfr = -2
for i in xrange(3):
index = np.argmin(abs(out.sfr_x - sfr[i]))
this_pdf = table[index]
#this_pdf2 = table2[index]
print sfrs[index]
plt.plot(p.ln2mv(x),
p.ln2mvpdf(this_pdf),
lw=3,
color='blue',
label='CLOC')
#plt.plot(p.ln2mv(x), p.ln2mvpdf(this_pdf), lw=3, color='blue', label="Corrected")
plt.xlim([-1, -18])
f = open(files[i], 'r')
slug = np.array(f.read().split("\n"))
slug = slug[0:-1]
slug = slug.astype(float)
plt.hist(slug, normed=True, bins=40, histtype="stepfilled", color='grey',\
edgecolor='none')
plt.xlabel(r"$M_V$")
plt.ylabel(r"$p(M_V)$")
plt.title(r"$\log_{10} \textrm{SFR} = " + repr(sfr[i]) + "$")
plt.legend(prop={'size': 14}, frameon=False)
out,table = p.sfrl1_parse('slug_comp_cut.dat')
#out2,table2 = p.sfrl1_parse('slug_comp2.dat')
#get names of tags
#out.dtype.names
sfrs = out.sfr_x
x = out.x
#get the sfr closest to \log sfr = -2
for i in xrange(3):
index = np.argmin(abs(out.sfr_x -sfr[i]))
this_pdf = table[index]
#this_pdf2 = table2[index]
print sfrs[index]
plt.plot(p.ln2mv(x), p.ln2mvpdf(this_pdf), lw=3, color='blue', label='CLOC')
#plt.plot(p.ln2mv(x), p.ln2mvpdf(this_pdf), lw=3, color='blue', label="Corrected")
plt.xlim([-1,-18])
f=open(files[i], 'r')
slug = np.array(f.read().split("\n"))
slug=slug[0:-1]
slug = slug.astype(float)
plt.hist(slug, normed=True, bins=40, histtype="stepfilled", color='grey',\
edgecolor='none')
plt.xlabel(r"$M_V$")
plt.ylabel(r"$p(M_V)$")
plt.title(r"$\log_{10} \textrm{SFR} = "+repr(sfr[i])+"$")
plt.legend(prop={'size':14}, frameon=False)
plt.show()
plt.savefig("slugcomp"+repr(i)+"_cut.eps")
plt.clf()
gamma_min = 2.438e19, sfr_err=0.005, step=0.125/4)
out,table = p.sfrl1_parse('slug_comp.dat')
mcdata = open("/Users/rdasilva/Dropbox/sfr_l1_plots/montecarlo/mctest.txt",'r')
mcdata = mcdata.read().split("\n")[0:-1]
mcdata = np.array(mcdata).astype(float)
mcdata=mcdata[mcdata<-3]
#get names of tags
#out.dtype.names
sfrs = out.sfr_x
x = out.x
#get the sfr closest to \log sfr = -1
index = np.argmin(abs(out.sfr_x -(-1)))
this_pdf = table[index]
plt.xlim([-1,-18])
plt.hist(mcdata, normed=True, bins=80*4, histtype="stepfilled", color='grey',\
edgecolor='none')
plt.plot(p.ln2mv(x), p.ln2mvpdf(this_pdf), lw=3, color='blue')
plt.xlabel(r"$M_V$")
plt.ylabel(r"$p(M_V)$")
plt.show()
plt.savefig("compmc.eps")
plt.clf()
#out2,table2 = p.sfrl1_parse('slug_comp_rere.dat')
#get names of tags
#out.dtype.names
sfrs = out.sfr_x
x = out.x
#get the sfr closest to \log sfr = -2
for i in xrange(3):
index = np.argmin(abs(out.sfr_x - sfr[i]))
this_pdf = table[index]
#this_pdf2 = table2[index]
print sfrs[index]
# plt.plot(p.ln2mv(x), p.ln2mvpdf(this_pdf2), lw=3, color=(1.,0.5,0.5), label='Uncorrected')
plt.plot(p.ln2mv(x),
p.ln2mvpdf(this_pdf),
lw=3,
color='blue',
label="CLOC")
print "Normalization"
print np.trapz(p.ln2mvpdf(this_pdf), p.ln2mv(x))
plt.xlim([-1, -18])
f = open(files[i], 'r')
slug = np.array(f.read().split("\n"))
slug = slug[0:-1]
slug = slug.astype(float)
bn = 20
if i == 1: bn = 30
plt.hist(slug, normed=True, bins=bn, histtype="stepfilled", color='grey',\
edgecolor='none')
plt.xlabel(r"$M_V$")