def get_halomodel(required_attrs, get_label=True, kls=HaloModel,
fast_kwargs={"transfer_fit":"BBKS",
"lnk_min":-4,
"lnk_max":2,
"dlnk":1,
"Mmin":13,
"dlog10m":0.5,
"rmin":10,
"rmax":20,
"rnum":4,
"halo_exclusion":"None",
"nonlinear":False,
"scale_dependent_bias":False,
"hod_model":"Zehavi05"},
**kwargs):
return get_hmf(required_attrs, get_label, kls, fast_kwargs, **kwargs)
def test_order():
order = [
"sigma.8: 0.7, ST, z: 0", "sigma.8: 0.8, ST, z: 0",
"sigma.8: 0.7, ST, z: 1", "sigma.8: 0.8, ST, z: 1",
"sigma.8: 0.7, ST, z: 2", "sigma.8: 0.8, ST, z: 2",
"sigma.8: 0.7, PS, z: 0", "sigma.8: 0.8, PS, z: 0",
"sigma.8: 0.7, PS, z: 1", "sigma.8: 0.8, PS, z: 1",
"sigma.8: 0.7, PS, z: 2", "sigma.8: 0.8, PS, z: 2"
]
for i, (quants, mf, label) in enumerate(
tf.get_hmf(['dndm', 'ngtm'],
z=range(3),
hmf_model=["ST", "PS"],
sigma_8=[0.7, 0.8])):
assert label == order[i]
assert isinstance(mf, MassFunction)
assert np.allclose(quants[0], mf.dndm)
assert np.allclose(quants[1], mf.ngtm)
def test_order():
order = [
"sigma.8: 0.7, ST, z: 0",
"sigma.8: 0.8, ST, z: 0",
"sigma.8: 0.7, ST, z: 1",
"sigma.8: 0.8, ST, z: 1",
"sigma.8: 0.7, ST, z: 2",
"sigma.8: 0.8, ST, z: 2",
"sigma.8: 0.7, PS, z: 0",
"sigma.8: 0.8, PS, z: 0",
"sigma.8: 0.7, PS, z: 1",
"sigma.8: 0.8, PS, z: 1",
"sigma.8: 0.7, PS, z: 2",
"sigma.8: 0.8, PS, z: 2",
]
for i, (quants, mf, label) in enumerate(
tf.get_hmf(["dndm", "ngtm"], z=range(3), hmf_model=["ST", "PS"], sigma_8=[0.7, 0.8])
):
assert label == order[i]
assert isinstance(mf, MassFunction)
assert np.allclose(quants[0], mf.dndm)
assert np.allclose(quants[1], mf.ngtm)
def hmf_driver(label, transfer_fit, transfer_options,
**kwargs): # A dictionary of bools for optional extra plots.
# Change directory to this file (for picking up transfer files if pre-made)
os.chdir(os.path.dirname(__file__))
# Set up a logger
hmflog = logging.getLogger("hmf")
stream = io.StringIO()
ch = logging.StreamHandler(stream)
hmflog.addHandler(ch)
# Appropriately handle WDM
if len(kwargs["wdm_mass"]) == 0:
kwargs["wdm_mass"] = [None]
objects = []
labels = []
for res in get_hmf('dndm',
get_label=True,
transfer_fit=transfer_fit,
transfer_options=transfer_options,
**kwargs):
objects += [copy.deepcopy(res[1])]
labels += [label + " " + res[2]]
# Loop through all the different cosmologies
# for cosmo_i, cosmo_dict in enumerate(cosmology_list):
# growths.append([])
# # Save the cosmo_label to the column label
# if len(cosmology_list) > 1 or max(len(cosmology_list), len(k_bounds), len(z_list), len(approaches), len(overdensities)) == 1:
# labels['cosmo'] = cosmo_labels[cosmo_i]
# labels["cosmo_fallback"] = cosmo_labels[cosmo_i]
#
# # Loop through all WDM models (CDM first)
# for k_bound in k_bounds:
# # Save the k_bounds to the label
# lnk = np.linspace(np.log(k_bound[0]), np.log(k_bound[1]), 250)
# if len(k_bounds) > 1:
# labels['k'] = 'k{' + str(k_bound[0]) + ',' + str(k_bound[1]) + '}'
# for wdm in [None] + WDM_list:
# # Add WDM label
# if len(WDM_list) > 0:
# if wdm is None:
# labels['wdm'] = 'CDM'
# else:
# labels['wdm'] = 'WDM=' + str(wdm)
# # Loop over all redshifts
# for z in z_list:
# # Define a column-name extension for the table
# if len(z_list) > 1 or z > 0:
# labels['z'] = 'z=' + str(z)
#
# # Update pert object optimally with new variables
# pert.update(lnk=lnk, wdm_mass=wdm, z=z, **cosmo_dict)
#
# growths[cosmo_i].append(pert.transfer.growth)
# # Save k-based data
# excludes = ['deltahalo', 'fsig', "cosmo_fallback"]
# k_data["ln(k)_" + (getname(labels, excl=excludes)or getname(labels, excl=excludes[:-1]))] = pert.transfer.lnk
# k_data["ln(P(k))_" + (getname(labels, excl=excludes)or getname(labels, excl=excludes[:-1]))] = pert.transfer.power
#
# # Save Mass-Based data
# mass_data["sigma_" + (getname(labels, excl=excludes) or getname(labels, excl=excludes[:-1]))] = pert.sigma
# mass_data["lnsigma_" + (getname(labels, excl=excludes)or getname(labels, excl=excludes[:-1]))] = pert.lnsigma
# mass_data["neff_" + (getname(labels, excl=excludes)or getname(labels, excl=excludes[:-1]))] = pert.n_eff
#
# # Loop over fitting functions
# for approach in approaches:
# if len(approaches) > 1:
# labels['fsig'] = approach
# for overdensity in overdensities:
# if len(overdensities) > 1:
# labels['deltahalo'] = 'Delta_h=' + str(overdensity)
#
# # Save the data
# pert.update(mf_fit=approach, delta_h=overdensity, delta_c=cosmo_dict['delta_c'])
# mass_data["f(sig)_" + getname(labels, excl="cosmo_fallback")] = pert.fsigma
#
# # ----- Mass Functions -----
# if hmf_form == 'dndlnm':
# mass_data["dndlnm_" + getname(labels, excl=['cosmo_fallback'])] = pert.dndlnm
# mass_data["M*dndlnm_" + getname(labels, excl="cosmo_fallback")] = pert.dndlnm * pert.M
# elif hmf_form == 'dndlog10m':
# mass_data["dndlog10m_" + getname(labels, excl=['cosmo_fallback'])] = pert.dndlog10m
# mass_data["M*dndlog10m_" + getname(labels, excl="cosmo_fallback")] = pert.dndlog10m * pert.M
# elif hmf_form == 'dndm':
# mass_data["dndm_" + getname(labels, excl=['cosmo_fallback'])] = pert.dndm
# mass_data["M*dndm_" + getname(labels, excl="cosmo_fallback")] = pert.dndm * pert.M
#
# # Extra Plots: Easily add more when you need to
# if 'get_ngtm' in extra_plots:
# mass_data["ngtm_" + getname(labels, excl="cosmo_fallback")] = pert.ngtm
# if 'get_mgtm' in extra_plots:
# mass_data["mgtm_" + getname(labels, excl="cosmo_fallback")] = pert.mgtm
# if 'get_nltm' in extra_plots:
# mass_data["nltm_" + getname(labels, excl="cosmo_fallback")] = pert.nltm
# if 'get_mltm' in extra_plots:
# mass_data["mltm_" + getname(labels, excl="cosmo_fallback")] = pert.mltm
# if 'get_L' in extra_plots:
# mass_data["L(N=1)_" + getname(labels, excl="cosmo_fallback")] = pert.how_big
# print stream.getvalue()
warnings = stream.buflist
warnings = list(set(warnings))
return objects, labels, warnings
def hmf_driver(label, transfer_fit,
transfer_options,
**kwargs): # A dictionary of bools for optional extra plots.
# Change directory to this file (for picking up transfer files if pre-made)
os.chdir(os.path.dirname(__file__))
# Set up a logger
hmflog = logging.getLogger("hmf")
stream = StringIO.StringIO()
ch = logging.StreamHandler(stream)
hmflog.addHandler(ch)
# Appropriately handle WDM
if len(kwargs["wdm_mass"]) == 0:
kwargs["wdm_mass"] = [None]
objects = []
labels = []
for res in get_hmf('dndm', get_label=True, transfer_fit=transfer_fit,
transfer_options=transfer_options, **kwargs):
objects += [copy.deepcopy(res[1])]
labels += [label + " " + res[2]]
# Loop through all the different cosmologies
# for cosmo_i, cosmo_dict in enumerate(cosmology_list):
# growths.append([])
# # Save the cosmo_label to the column label
# if len(cosmology_list) > 1 or max(len(cosmology_list), len(k_bounds), len(z_list), len(approaches), len(overdensities)) == 1:
# labels['cosmo'] = cosmo_labels[cosmo_i]
# labels["cosmo_fallback"] = cosmo_labels[cosmo_i]
#
# # Loop through all WDM models (CDM first)
# for k_bound in k_bounds:
# # Save the k_bounds to the label
# lnk = np.linspace(np.log(k_bound[0]), np.log(k_bound[1]), 250)
# if len(k_bounds) > 1:
# labels['k'] = 'k{' + str(k_bound[0]) + ',' + str(k_bound[1]) + '}'
# for wdm in [None] + WDM_list:
# # Add WDM label
# if len(WDM_list) > 0:
# if wdm is None:
# labels['wdm'] = 'CDM'
# else:
# labels['wdm'] = 'WDM=' + str(wdm)
# # Loop over all redshifts
# for z in z_list:
# # Define a column-name extension for the table
# if len(z_list) > 1 or z > 0:
# labels['z'] = 'z=' + str(z)
#
# # Update pert object optimally with new variables
# pert.update(lnk=lnk, wdm_mass=wdm, z=z, **cosmo_dict)
#
# growths[cosmo_i].append(pert.transfer.growth)
# # Save k-based data
# excludes = ['deltahalo', 'fsig', "cosmo_fallback"]
# k_data["ln(k)_" + (getname(labels, excl=excludes)or getname(labels, excl=excludes[:-1]))] = pert.transfer.lnk
# k_data["ln(P(k))_" + (getname(labels, excl=excludes)or getname(labels, excl=excludes[:-1]))] = pert.transfer.power
#
# # Save Mass-Based data
# mass_data["sigma_" + (getname(labels, excl=excludes) or getname(labels, excl=excludes[:-1]))] = pert.sigma
# mass_data["lnsigma_" + (getname(labels, excl=excludes)or getname(labels, excl=excludes[:-1]))] = pert.lnsigma
# mass_data["neff_" + (getname(labels, excl=excludes)or getname(labels, excl=excludes[:-1]))] = pert.n_eff
#
# # Loop over fitting functions
# for approach in approaches:
# if len(approaches) > 1:
# labels['fsig'] = approach
# for overdensity in overdensities:
# if len(overdensities) > 1:
# labels['deltahalo'] = 'Delta_h=' + str(overdensity)
#
# # Save the data
# pert.update(mf_fit=approach, delta_h=overdensity, delta_c=cosmo_dict['delta_c'])
# mass_data["f(sig)_" + getname(labels, excl="cosmo_fallback")] = pert.fsigma
#
# # ----- Mass Functions -----
# if hmf_form == 'dndlnm':
# mass_data["dndlnm_" + getname(labels, excl=['cosmo_fallback'])] = pert.dndlnm
# mass_data["M*dndlnm_" + getname(labels, excl="cosmo_fallback")] = pert.dndlnm * pert.M
# elif hmf_form == 'dndlog10m':
# mass_data["dndlog10m_" + getname(labels, excl=['cosmo_fallback'])] = pert.dndlog10m
# mass_data["M*dndlog10m_" + getname(labels, excl="cosmo_fallback")] = pert.dndlog10m * pert.M
# elif hmf_form == 'dndm':
# mass_data["dndm_" + getname(labels, excl=['cosmo_fallback'])] = pert.dndm
# mass_data["M*dndm_" + getname(labels, excl="cosmo_fallback")] = pert.dndm * pert.M
#
# # Extra Plots: Easily add more when you need to
# if 'get_ngtm' in extra_plots:
# mass_data["ngtm_" + getname(labels, excl="cosmo_fallback")] = pert.ngtm
# if 'get_mgtm' in extra_plots:
# mass_data["mgtm_" + getname(labels, excl="cosmo_fallback")] = pert.mgtm
# if 'get_nltm' in extra_plots:
# mass_data["nltm_" + getname(labels, excl="cosmo_fallback")] = pert.nltm
# if 'get_mltm' in extra_plots:
# mass_data["mltm_" + getname(labels, excl="cosmo_fallback")] = pert.mltm
# if 'get_L' in extra_plots:
# mass_data["L(N=1)_" + getname(labels, excl="cosmo_fallback")] = pert.how_big
# print stream.getvalue()
warnings = stream.buflist
warnings = list(set(warnings))
return objects, labels, warnings