def process_mcmc(datrnk, sample, cap, cosmology, cutoffval):
mcmcfns = glob("{0}/out/sim/{1}/{2}/{3}/mcmc/new/*.mock".
format(datrnk, sample, cap, cosmology))
sim_nz = nzobj(datrnk, sample, cap, cosmology, cutoffval)
sim_nz.read_json()
downfns = glob("{0}/out/sim/{1}/{2}/{3}/mcmc/new/*.down".
format(datrnk, sample, cap, cosmology))
for mock in mcmcfns:
simdown = mock[:-4] + "down"
print simdown
if not simdown in downfns:
try:
sim_nz.mock = np.genfromtxt(mock)
sim_nz.fibre_collisions()
sim_nz.sim_down(simdown)
except:
continue
def process_mocks(dattop, sample, cap, cosmology, cutoffval):
# First get the polynomial fit to the average of all the mock number densities
# mock_avg_poly()
# initialise an nzobj
mock_nz = nzobj(dattop, sample, cap, cosmology, cutoffval)
# load the json data into the nzobj
mock_nz.read_json()
# give the nzobj the polynomial coefficients to use for downsampling
mock_nz.popt = np.loadtxt(dattop + "/out/{0}/{1}/{2}/mocks/avg_poly_coeffs.dat".format(sample, cap, cosmology))
# glob all the mocks
mockfns = glob("{0}/in/boss/{1}/{2}/mocks/*.rdz".
format(dattop, sample, cap))
# now process all those bad boys
for mockfn in mockfns:
mockN = mockfn.split("/")[-1].split("_")[1].split(".")[0]
print "\n", "mock\t", mockN, "\n"
mock_nz.radecz = np.loadtxt(mockfn)
mock_nz.rdz_down("/mocks/rdz/{0}.dat".format(mockN))
def plot_nz(fn):
werds = fn.split("/")
outi = werds.index("out")
dattop = "/".join(werds[:outi])
sample = werds[outi + 1]
cap = werds[outi + 2]
cosmo = werds[outi + 3]
nob = nzobj(dattop, sample, cap, cosmo, 0.65)
nob.read_json()
nob.radecz = np.loadtxt(fn)
nob._compute_z_nbar()
fig = plt.figure()
ax = fig.add_subplot(111)
l, = ax.plot(nob.z_cen, nob.nbar)
ax.set_xlabel(r"$z$")
ax.set_ylabel(r"$ n(z)$")
picname = ''.join(os.path.basename(fn).split(".")[:-1]) + ".pdf"
plotaddress = os.path.dirname(fn) + "/plots/" + picname
fig.savefig(plotaddress)
os.system("scp {0} broiler:~/public_html/tinker/".format(plotaddress))
os.system("ssh broiler chmod 644 /home/kilian/public_html/tinker/{0}"
.format(picname))
def process_mocks(dattop, sample, cap, cosmology, cutoffval):
# First get the polynomial fit to the average of all the mock number densities
# mock_avg_poly()
# initialise an nzobj
mock_nz = nzobj(dattop, sample, cap, cosmology, cutoffval)
# load the json data into the nzobj
mock_nz.read_json()
# give the nzobj the polynomial coefficients to use for downsampling
mock_nz.popt = np.loadtxt(dattop +
"/out/{0}/{1}/{2}/mocks/avg_poly_coeffs.dat".
format(sample, cap, cosmology))
# glob all the mocks
mockfns = glob("{0}/in/boss/{1}/{2}/mocks/*.rdz".format(
dattop, sample, cap))
# now process all those bad boys
for mockfn in mockfns:
mockN = mockfn.split("/")[-1].split("_")[1].split(".")[0]
print "\n", "mock\t", mockN, "\n"
mock_nz.radecz = np.loadtxt(mockfn)
mock_nz.rdz_down("/mocks/rdz/{0}.dat".format(mockN))
def plot_nz(dirnam):
werds = dirnam.split("/")
outi = werds.index("out")
dattop = "/".join(werds[:outi])
sample = werds[outi + 1]
cap = werds[outi + 2]
cosmo = werds[outi + 3]
allmocks = glob(dirnam + "/*.dat")
nob = nzobj(dattop, sample, cap, cosmo, 0.65)
nob.read_json()
fig = plt.figure()
ax = fig.add_subplot(111)
lines = []
i = 0 # just get the first 100
for fn in allmocks:
i += 1
if i > 100:
break
nob.radecz = np.loadtxt(fn)
nob._compute_z_nbar()
lines.append(ax.plot(nob.z_cen, nob.nbar, color='b', alpha=0.05))
datfn = dattop + "/".join([sample, cap, cosmo]) + "/radecz.dat"
nob.radecz = np.loadtxt(datfn)
nob._compute_z_nbar()
lines.append(ax.plot(nob.z_cen, nob.nbar, color='r', alpha=0.5))
ax.set_xlabel(r"$z$")
ax.set_ylabel(r"$ n(z)$")
ax.set_ylim([0.0002, 0.0003])
picname = "mock_dat_nbars.pdf"
plotaddress = dirnam + "/plots/" + picname
fig.savefig(plotaddress)
os.system("scp {0} broiler:~/public_html/tinker/".format(plotaddress))
os.system(
"ssh broiler chmod 644 /home/kilian/public_html/tinker/{0}".format(
picname))
def plot_nz(dirnam):
werds = dirnam.split("/")
outi = werds.index("out")
dattop = "/".join(werds[:outi])
sample = werds[outi + 1]
cap = werds[outi + 2]
cosmo = werds[outi + 3]
allmocks = glob(dirnam + "/*.dat")
nob = nzobj(dattop, sample, cap, cosmo, 0.65)
nob.read_json()
fig = plt.figure()
ax = fig.add_subplot(111)
lines = []
i = 0 # just get the first 100
for fn in allmocks:
i += 1
if i > 100:
break
nob.radecz = np.loadtxt(fn)
nob._compute_z_nbar()
lines.append(ax.plot(nob.z_cen, nob.nbar, color='b', alpha=0.05))
datfn = dattop + "/out/" + "/".join([sample, cap, cosmo]) + "/radecz.dat"
nob.radecz = np.loadtxt(datfn)
nob._compute_z_nbar()
lines.append(ax.plot(nob.z_cen, nob.nbar, color='r', alpha=0.5))
ax.set_xlabel(r"$z$")
ax.set_ylabel(r"$ n(z)$")
ax.set_ylim([0.000224, 0.000226])
picname = "mock_dat_nbars.pdf"
plotaddress = dirnam + "/plots/" + picname
fig.savefig(plotaddress)
os.system("scp {0} broiler:~/public_html/tinker/".format(plotaddress))
os.system("ssh broiler chmod 644 /home/kilian/public_html/tinker/{0}"
.format(picname))
def process_sim(datrnk, simfile, sample, cap, cosmology, cutoffval):
simout = datrnk + "/out/sim/" + sample + "/" + cap + "/" + cosmology +
"/" + simfile
sim_nz = nzobj(datrnk, sample, cap, cosmology, cutoffval)
sim_nz.read_json()
sim_nz.mock = np.genfromtxt(simout)
sim_nz.fibre_collisions()
hodroot = simfile.split(".")[0]
print os.path.dirname(simout)
sim_nz.sim_down(os.path.dirname(simout) + hodroot + ".down")
def process_data(dattop, sample, cap, cosmology, cutoffval):
data_nz = nzobj(dattop, sample, cap, cosmology, cutoffval)
data_nz.get_zrange()
# get the galaxy data to compute number density from
galfits = fits.open(
glob("{0}/in/boss/{1}/{2}/*.fits".format(dattop, sample, cap))[0])
# array of all the RAs Decs and redshifts
radecz = np.dstack((galfits[1].data["RA"], galfits[1].data["DEC"],
galfits[1].data["Z"]))[0]
# give the rdz to the nzobj
data_nz.radecz = radecz
data_nz.fit_polynom()
data_nz.rdz_down("/radecz.dat")
data_nz.write_json()
def process_data(dattop, sample, cap, cosmology, cutoffval):
data_nz = nzobj(dattop, sample, cap, cosmology, cutoffval)
data_nz.get_zrange()
# get the galaxy data to compute number density from
galfits = fits.open(glob("{0}/in/boss/{1}/{2}/*.fits".
format(dattop, sample, cap))[0])
# array of all the RAs Decs and redshifts
radecz = np.dstack((galfits[1].data["RA"],
galfits[1].data["DEC"],
galfits[1].data["Z"]))[0]
# give the rdz to the nzobj
data_nz.radecz = radecz
data_nz.fit_polynom()
data_nz.rdz_down("/radecz.dat")
data_nz.write_json()
def plot_nz(fn):
werds = fn.split("/")
outi = werds.index("out")
dattop = "/".join(werds[:outi])
sample = werds[outi + 1]
cap = werds[outi + 2]
cosmo = werds[outi + 3]
nob = nzobj(dattop, sample, cap, cosmo, 0.65)
nob.read_json()
nob.radecz = np.loadtxt(fn)
nob._compute_z_nbar()
fig = plt.figure()
ax = fig.add_subplot(111)
l, = ax.plot(nob.z_cen, nob.nbar)
ax.set_xlabel(r"$z$")
ax.set_ylabel(r"$ n(z)$")
picname = ''.join(os.path.basename(fn).split(".")[:-1]) + ".pdf"
plotaddress = os.path.dirname(fn) + "/plots/" + picname
fig.savefig(plotaddress)
os.system("scp {0} broiler:~/public_html/tinker/".format(plotaddress))
os.system(
"ssh broiler chmod 644 /home/kilian/public_html/tinker/{0}".format(
picname))
def plot_nz(datrnk, fndown):
sample = "CMASS"
cap = "NGC"
fig = plt.figure()
ax = fig.add_subplot(111)
# plot corrected
nob = nzobj(datrnk, sample, cap, "WMAP", 0.65)
nob.get_zrange()
ax.scatter(nob.nbar_corr[:, 0], nob.nbar_full,
color='m', label=r"$n_{corrected}$")
# plot raw data
galfits = fits.open(glob("{0}/in/boss/{1}/{2}/*.fits".
format(datrnk, sample, cap))[0])
# array of all the RAs Decs and redshifts
radecz = np.dstack((galfits[1].data["RA"],
galfits[1].data["DEC"],
galfits[1].data["Z"]))[0]
buf = len(nob.zcen)
# give the rdz to the nzobj
nob.radecz = radecz
nob._compute_z_nbar(buf=buf)
ldat, = ax.plot(nob.z_cen, nob.nbar, label=r"$n_{data}$")
# plot downsampled nbar
nob.radecz = np.loadtxt(fndown)
nob._compute_z_nbar(buf=5)
nob.nbar[:5] = 0.
nob.nbar[-5:] = 0.
ldown, = ax.plot(nob.z_cen, nob.nbar,
color='r', label=r"$n_{downsampled}$")
ax.set_xlabel(r"$z$")
ax.set_ylabel(r"$ n(z)$ $(Mpc / h)^{-3}$")
ax.set_title("CMASS NGC Number Densities")
ax.set_xlim([0.3, 0.75])
ax.set_ylim([0., 0.0005])
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles, labels)
picname = "nbar_talk.pdf"
plotaddress = picname
fig.savefig(plotaddress)
os.system("scp {0} broiler:~/public_html/tinker/".format(plotaddress))
os.system("ssh broiler chmod 644 /home/kilian/public_html/tinker/{0}"
.format(picname))