def lum_func(self, Np, method="vmax", min_acceptable=1, eps=0.01,
max_iter=10, new=True):
"""
Calculates the luminosity function of galaxies in Np bins.
Can use either the EEP method or the Vmax method currently.
INPUT PARAMETERS
Np -- The number of bins to use
method ["vmax"] -- A string indicating which method to use (EEP or vmax)
min_acceptable [1] -- Minimum acceptable galaxies in a bin
eps [0.01] -- for EEP, target convergence
max_iter [10] -- for EEP, maximum iterations
new [True] -- whether to force re-calculation even if previous results are available
OUTPUT
M -- Array of absolute magnitude bin centres
phi -- Array of luminosity function values
"""
#We first look to find a pre-calculated function if wanted
if not new:
try:
lfunc = np.genfromtxt(self._dirs["LuminosityFunction"] + method + ".dat")
M = lfunc[:, 0]
phi = lfunc[:, 1]
return M, phi
except IOError:
pass
if method == "EEP":
phi, iteration = lf.EEP(absmag=self.survey_data['absmag'],
apmag=self.survey_data['mag_r'],
Np=Np,
min_acceptable=min_acceptable,
eps=eps,
max_iter=max_iter,
verbose=self.verbose)
#Plot all iterations
phi.plot(logy=True, marker='o')
plt.show()
plt.savefig(self._dirs["LuminosityFunction"] + "allEEP." + self.plottype)
M = np.array(phi.index)
phi = np.array(phi[iteration])
#Plot the final answer
plt.clf()
plt.plot(M, phi)
plt.xlabel("Absolute Magnitude")
plt.ylabel("Unnormalised Luminosity Function")
plt.yscale('log')
plt.savefig(self._dirs["LuminosityFunction"] + "finalEEP." + self.plottype)
elif method == "vmax":
phi, M = lf.vmax_method(absmag=self.survey_data['absmag'],
appmag=self.survey_data['mag_r'],
c_dist=self.survey_data["c_dist"],
redshift=self.survey_data['redshift'],
bins=Np)
#Plot it up
plt.clf()
plt.plot(M, phi)
plt.yscale('log')
plt.savefig(self._dirs["LuminosityFunction"] + "vmax." + self.plottype)
#Save results to file
output = np.vstack((M, phi)).T
np.savetxt(self._dirs["LuminosityFunction"] + method + ".dat", output)
return M, phi
oldpara[i][2] = -1.35
if z[it[i]] < 1.3:
oldpara[i][1] = 5.1e41 * np.power((1 + z[it[i]]), 3.1)
if z[it[i]] >= 1.3:
oldpara[i][1] = 6.8e42
oldLF[i] = LF.LuminosityFunction(oldpara[i], lumHa[i])
grid = np.array([[0.0 for i in range(interpolatingSize)] for i in range(2)])
mpgrid = mpmath.arange(
oldLF[oldLF < -5].max(), oldLF.max(), (oldLF.max() - oldLF[oldLF < -5].max()) / interpolatingSize
)
grid[1] = np.ogrid[0 : 6 : interpolatingSize * 1j]
newLF = np.array([[0.0 for i in range(interpolatingSize)] for i in range(interpolatingSize)])
for i in range(interpolatingSize):
for j in range(interpolatingSize):
newLF[i][j] = optimize.brentq(
LF.rootfinding, 1e30, 1e50, args=(LF.luminosityParameterHa(grid[1][i]), mpgrid[j])
)
z = (z + 1) * (5007.0 / 6563.0) - 1
newLum = interpolate.interpn([grid[1], mpgrid], newLF, np.array([z[it], oldLF]).T, bounds_error=False, fill_value=0.0)
for i in np.where(oldLF < -5)[0]:
newLum[i] = optimize.brentq(LF.rootfinding, 1e30, 1e50, args=(LF.luminosityParameterHa(z[it][i]), oldLF[i]))
z = (z + 1) * (6563.0 / 5007.0) - 1
Ha[it] = newLum / (4 * np.pi * (dl ** 2))
print "Calibrating OIIIb line"
newLumO3b = [0.0 for x in range((it.shape[0]))]
grid = np.array([[0.0 for i in range(interpolatingSize)] for i in range(2)])
mpgrid = mpmath.linspace(oldLF[oldLF < -2.7].max(), oldLF.max(), interpolatingSize)
grid[1] = np.ogrid[0 : 6 : interpolatingSize * 1j]
newLF = np.array([[0.0 for i in range(interpolatingSize)] for i in range(interpolatingSize)])
def __init__(self,
filename, #Filename of survey to be read in (absolute path)
radians=True, #Whether the data is in radians to begin with.
cosmology=fidcosmo, #A dictionary of cosmological parameters. Default is cosmolopy's default.
sample_name=None, #Which sample to use (can always be 'all', otherwise will make one)
sample_cuts=None, #Cuts to apply to the sample
clobber=None, #Whether to automatically overwrite previous project folders
verbose=True):
'''
Imports the survey and creates a project directory and internal structure.
Instantiating the class requires a filename or directory in which is kept (solely) the survey information.
This step is designed to be as seamless as possible. To this end, note the following:
The default expected input is one ascii file with columns as data fields.
If the survey information is in a completely different format, then the best thing to to do is reformat it
yourself and try again. And email me the problem and I'll try to update the class to deal with it.
Once the information is read in, a project directory is automatically created. This serves to speed up future
calculations and simplify the layout of results from your survey. The project folder is named
<survey_filename>_project/ and included is an empty file "_PyLSSproject_" which serves to tell this class that
the folder is in fact formatted in the default way. Within this folder the directory structure looks like:
<my_survey>.[dat,csv, etc.]
<my_survey>_project/
_PyLSSproject_
<my_survey>.h5 #Correctly formatted (HDF5) survey
All/
_PyLSSsample_
<plot_varieties>/
plot.pdf
another_plot.pdf
...
...
<other_sample_name>/
_PyLSSsample_
<plot_varieties>/
plot.pdf
another_plot.pdf
...
...
A note on cosmologies: changing the fiducial cosmology will potentially change many of the calculations
for a given survey (especially those relying on distances). This is reflected in the "Sample" directory
level. That is, two identical samples of the main survey will be called different samples if their
cosmology is specified differently.
INPUT:
radians [True] bool
#Whether the data is in radians to begin with.
cosmology [fidcosmo] dict
#A dictionary of cosmological parameters. Default is cosmolopy's default.
sample_name [None] string
#Which sample to use (can always be 'all', otherwise will make one)
sample_cuts [None] dict
#Cuts to apply to the sample, takes the form
sample_cuts = {"redshift":[min_z,max_z],"ra":[min_z,max_z]...}
clobber [None] bool
# Whether to overwrite project directory if appropriate
'''
#TODO: doesn't work if you choose NOT to overwrite if inputting .csv (line 640 survey_data doesn't exist!)
#Save the cosmology (default fiducial cosmology of cosmolopy).
self.cosmology = cosmology
self.sample_name = sample_name #Which sample to use (can always be 'all', otherwise will make one)
self.sample_cuts = sample_cuts #Cuts to apply to the sample
self._clobber = clobber #Whether to automatically overwrite previous project folders
self.verbose = verbose
self.plottype = 'png'
#The columns wanted from the original file (some of these are not needed after read-in though)
self._raw_columns = ["redshift", "ra", "dec", "mag_r", "mag_g", "id", 'extinction_r', "extinction_g"]
#Determine whether the given filename is a project file or not - also check whether there is a project file for the survey.
#filename is returned modified if a project file was found for the input filename (which wasn't a project file)
is_project_file, self._project_dir, filename = self._determine_if_project(filename)
#If it is NOT a project file, we need to import the ascii file first and make a project file
if not is_project_file:
# Import given file and do data reduction
self._initial_file_import(filename, radians)
# Create the project directory and write the project file to it.
self._create_project_dir(filename)
os.chdir(self._project_dir)
self._write_project_file(filename)
# Re-set the filename to the created project file
filename = os.path.join(self._project_dir, os.path.split(filename)[1].split('.')[0] + '.h5')
# Now we definitely have a HDF5 project file so we carry on.
# Define the sample: get the sample name, the cuts and turn these into a query object
self.sample_name, self.sample_cuts, query = self._define_sample(filename)
print query
#Create the sample directory (if it doesn't already exist)
self._create_sample_dir(self.sample_name)
#Save the sample directory path to the instance
self._sample_dir = os.path.join(self._project_dir, self.sample_name)
#Create results directories inside the sample
self._make_result_dirs()
#Write the sample cuts and cosmo to file for next time
self._write_sample_cuts_cosmo(filename, self.sample_name, self.sample_cuts)
#Import the project file
self._project_file_import(filename, query)
#Apply sample cuts
self._cut_data(self.sample_cuts)
# Now the base data is all in. We need to calculate extra properties.
#Calculate the comoving distance to each particle.
self.survey_data['c_dist'] = cd.comoving_distance(self.survey_data["redshift"], **self.cosmology)
lum_dist = lf.lum_dist(self.survey_data['redshift'], self.survey_data['c_dist'])
#Find the g-r colour and K-Correction to find Abs_Mag and luminosity.
gr = self.survey_data["mag_g"] - self.survey_data["mag_r"] - self.survey_data["extinction_g"] - self.survey_data["extinction_r"]
self.survey_data['kcorr'] = lf.k_correct(self.survey_data['redshift'], gr)
self.survey_data["absmag"] = lf.absolute_mag(self.survey_data['mag_r'], lum_dist, self.survey_data['kcorr'], self.survey_data["extinction_r"])
self.survey_data['lum'] = lf.absmag_to_lum(self.survey_data['absmag'])
#Delete stupid entries
self.clean()
# Define some overall parameters of the sample.
self.N = self.survey_data.shape[0]
self.survey_name = os.path.split(self._project_dir)[1].replace('_Project', '')
self._define_z_from_dist()
