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| Original file line number | Diff line number | Diff line change |
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| [runtime] | ||
| sampler = test | ||
| root = ${PWD} | ||
| resume = T | ||
| verbosity = noisy | ||
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| [test] | ||
| save_dir = output/spt3g_2018_lensing | ||
| fatal_errors = T | ||
|
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| [nautilus] | ||
| n_live = 1500 | ||
| verbose = T | ||
|
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| [emcee] | ||
| walkers = 64 | ||
| samples = 1000 | ||
| nsteps = 5 | ||
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| [pipeline] | ||
| ; these names refer to sections later in the file: | ||
| modules = consistency camb spt3g_2018_lensing | ||
| values = examples/candl_test_values.ini | ||
| priors = examples/candl_test_priors.ini | ||
| debug = F | ||
| timing = F | ||
| extra_output = cosmological_parameters/sigma_8 cosmological_parameters/omega_m | ||
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| [spt3g_2018_lensing] | ||
| file = ./likelihood/candl/candl_cosmosis_interface.py ; Location of interface code - change depending on the location of your .ini file | ||
| data_set = 'candl.data.SPT3G_2018_Lens' ; Data set or path to .yaml file | ||
| variant = 'use_CMB' ; Select a variant of the data set if pointing to an index file | ||
| lensing = T ; Switch on for lensing likelihoods | ||
| feedback = T ; Switch on to request feedback from candl initialisation | ||
| data_selection = "..." ; Select a subset of the data set | ||
| clear_1d_internal_priors = F ; Switch off to use candl internal 1d priors | ||
| clear_nd_internal_priors = F ; Switch on to ignore candl internal higher dimensional priors. Careful: higher-dimensional priors are not implemented in CosmoSIS itself. | ||
| force_ignore_transformations = '' ; Backdoor if you want to ignore certain transformations in the data model. | ||
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| [output] | ||
| filename = output/spt3g-18-lens.txt | ||
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| ; The consistency module translates between our chosen parameterization | ||
| ; and any other that modules in the pipeline may want (e.g. camb) | ||
| [consistency] | ||
| file = ./utility/consistency/consistency_interface.py | ||
| cosmomc_theta = T | ||
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| [camb] | ||
| file = boltzmann/camb/camb_interface.py | ||
| feedback = 0 ; verbosity of output | ||
| mode = cmb ; mode to run camb. For CMB lensing only, cmb is sufficient | ||
| lmax = 4000 ; max ell to use for cmb calculation | ||
| lens_margin = 1250 ; Lmax | ||
| AccuracyBoost = 1.0 ; CAMB accuracy boost parameter | ||
| lSampleBoost = 1.0 ; CAMB lsample boost parameter | ||
| lAccuracyBoost = 1.0 ; CAMB lAccuracy boost parameter | ||
| lens_potential_accuracy = 4 ; CAMB lens_potential accuracy paramater | ||
| do_tensors = T ;include tensor modes | ||
| do_lensing = T ;lensing is required w/ Planck data | ||
| NonLinear = lens ; Non-linear calculation | ||
| theta_H0_range = "20 100" ; Set bounds in H0 | ||
| halofit_version = takahashi ; Halofit version |
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| [cosmological_parameters] | ||
| ombh2 = normal 0.02233 0.00036 | ||
| n_s = normal 0.96 0.02 |
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| @@ -0,0 +1,16 @@ | ||
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| [cosmological_parameters] | ||
| omch2 = 0.08 0.12 0.16 | ||
| ombh2 = 0.01 0.02233 0.03 | ||
| log1e10As = 2.9 3.0448 3.2 | ||
| n_s = 0.86 0.96 1.06 | ||
| cosmomc_theta = 0.9 1.04 1.20 | ||
| omega_k = 0.0 | ||
| mnu = 0.06 | ||
| nnu = 3.046 | ||
| yhe = 0.245341 | ||
| tau = 0.0543 | ||
| num_massive_neutrinos = 3 | ||
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| [nuisance_parameters] | ||
| A_fg = 0.0 1.0 2.0 |
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| # This reproduces the chains that make up figure 2 of the DESI paper | ||
| # 2404.03002 | ||
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| output_dir: "./output/desi-campaign" | ||
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| runs: | ||
| - name: base | ||
| base: examples/desi.ini | ||
| params: | ||
| - sampler = metropolis | ||
| - metropolis.samples = 20000 | ||
| - metropolis.use_cobaya = T | ||
| - metropolis.tuning_frequency = 100 | ||
| - metropolis.tuning_grace = 500 | ||
| - metropolis.tuning_end = 2000 | ||
| - emcee.walkers = 32 | ||
| - emcee.samples = 1000 | ||
| - emcee.nsteps = 20 | ||
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| - name: BGS | ||
| parent: base | ||
| params: | ||
| - desi.desi_data_sets = BGS | ||
| - metropolis.samples = 30000 | ||
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| - name: LRG1 | ||
| parent: base | ||
| params: | ||
| - desi.desi_data_sets = LRG1 | ||
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| - name: LRG2 | ||
| parent: base | ||
| params: | ||
| - desi.desi_data_sets = LRG2 | ||
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| - name: LRG3+ELG1 | ||
| parent: base | ||
| params: | ||
| - desi.desi_data_sets = LRG3+ELG1 | ||
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| - name: ELG2 | ||
| parent: base | ||
| params: | ||
| - desi.desi_data_sets = ELG2 | ||
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| - name: QSO | ||
| parent: base | ||
| params: | ||
| - desi.desi_data_sets = QSO | ||
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| - name: Lya QSO | ||
| parent: base | ||
| params: | ||
| - desi.desi_data_sets = Lya QSO | ||
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| - name: All | ||
| parent: base | ||
| params: | ||
| # - pipeline.timing=T | ||
| - desi.desi_data_sets = BGS,LRG1,LRG2,LRG3+ELG1,ELG2,QSO,Lya QSO |
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| import cosmosis.campaign | ||
| from cosmosis.postprocessing.v2 import Chain | ||
| import getdist.plots | ||
| import matplotlib.pyplot as plt | ||
| plt.rcParams['text.usetex'] = True | ||
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| # The campaign file defines all the chains that we want to generate | ||
| campaign, _ = cosmosis.campaign.parse_yaml_run_file("./examples/desi-figure-2-campaign.yaml") | ||
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| # First make all the chains, by looping through the campaign. | ||
| # The next major version of CosmoSIS will do this for you more easily. | ||
| for name, run in campaign.items(): | ||
| if name != "base": | ||
| cosmosis.campaign.launch_run(run) | ||
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| # These colors match those used in the DESI paper as | ||
| # best I could | ||
| colors = { | ||
| 'All': [(0.0, 0.0, 0.0), (0.0, 0.0, 0.0)], | ||
| 'BGS': [(0.729, 0.811, 0.552), (0.517, 0.745, 0.012)], | ||
| 'ELG2': [(0.584, 0.725, 0.772), (0.219, 0.447, 0.6)], | ||
| 'LRG1': [(0.996, 0.854, 0.596), (0.956, 0.627, 0.086)], | ||
| 'LRG2': [(0.960, 0.678, 0.580), (0.929, 0.262, 0.094)], | ||
| 'LRG3+ELG1': [(0.733, 0.603, 0.741), (0.341, 0.211, 0.701)], | ||
| 'Lya QSO': [(0.733, 0.556, 0.749), (0.439, 0.0, 0.474)], | ||
| 'QSO': [(0.662, 0.815, 0.725), (0.247, 0.552, 0.360)] | ||
| } | ||
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| # This is the order we want the contours to be plotted in. It's not the same | ||
| # as the legend order. This is because we don't want the big contours to | ||
| # cover up the small ones | ||
| order = ["BGS", "LRG1", "QSO", "ELG2", "Lya QSO", "LRG3+ELG1", "LRG2", "All"] | ||
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| # The chains are complete now so load them all in | ||
| chain_data = {} | ||
| for i, (name, run) in enumerate(campaign.items()): | ||
| # The base parent run it not used, it's just a template. | ||
| # We will soon be able to specify this in the yaml file | ||
| if name == "base" : | ||
| continue | ||
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| # Load the chain using a chain object. This is in the upcoming | ||
| # v2 api for cosmosis postprocessing, which will soon replace the | ||
| # main version. It uses GetDist to do everything. | ||
| chain_file = campaign[name]['params']['output', 'filename'] | ||
| chain = Chain.load(chain_file, name=name, burn=0.2) | ||
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| # Store the GetDist MCSamples object in a dictionary | ||
| chain_data[name] = chain.mcsamples | ||
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| # split the chains into two groups, the single-data set chains | ||
| # and the combined one. This is because the contours are all filled | ||
| # for the single data sets, but not for the combined one. | ||
| combined_sample = chain_data["All"] | ||
| single_data_samples = {name: chain_data[name] for name in order if name != "All"} | ||
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| # This is the order we want the labels to appear in. | ||
| new_order = [order.index(name) for name in colors.keys()] | ||
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| # The rest of this is standard GetDist calls. | ||
| plotter = getdist.plots.get_single_plotter() | ||
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| plotter.plot_2d(list(single_data_samples.values()), | ||
| "DISTANCES--H0RD", | ||
| "cosmological_parameters--omega_m", | ||
| filled=True, | ||
| colors=[colors[name] for name in single_data_samples.keys()], | ||
| add_legend_proxy=True, | ||
| lims=[70, 130, 0.1, 0.7],) | ||
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| plotter.plot_2d(combined_sample, | ||
| "DISTANCES--H0RD", | ||
| "cosmological_parameters--omega_m", | ||
| filled=False, | ||
| colors=["k", "k"], | ||
| lims=[70, 130, 0.1, 0.7], | ||
| add_legend_proxy=True) | ||
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| ax=plotter.get_axes() | ||
| plotter.add_legend(list(single_data_samples.keys()) + ["All"], label_order=new_order, legend_loc="upper right") | ||
| ax.set_xlabel(r"$\mathrm{H}_0 r_d \, [100 \mathrm{km}\, \mathrm{s}^{-1}]$") | ||
| ax.set_ylabel(r"$\Omega_m$") | ||
| plotter.export("output/desi.pdf") |
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