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Incorporating CMB Lensing Sources and Statistics into Firecrown for 5x2pt (#123)
* read CMBK * adding and testing CCL NK and GK * adding NK & GK * Update sources.py * add documentation * fix lint errors * few fixes * Update sources.py * Update sources.py * Update sources.py * Update sources.py * Add CMBL test * Update test_cmbl_source.py * Update test_cmbl_source.py
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‎examples/NK&GK/GK.sacc‎

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‎examples/NK&GK/GK.yaml‎

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parameters:
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Omega_k: 0.0
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Omega_g: 0.0
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w0: -1.0
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wa: 0.0
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T_CMB: 2.7
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Neff: 0
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m_nu: 0.0
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transfer_function: 'bbks'
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mass_function: 'tinker'
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Omega_b: 0.045
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Omega_c: 0.21
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h: 0.71
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sigma8: 0.80
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n_s: 0.964
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bias_lens: 1.0
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cosmosis:
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sampler: emcee
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debug: False
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quiet: True
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mpi: True
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test:
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fatal_erros: True
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emcee:
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walkers: 32
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samples: 20
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parameters:
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Omega_c: [0.1, 0.21, 0.9]
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sigma8: [0.4, 0.8, 1.2]
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n_s: [0.87, 0.964, 1.07]
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h: [0.55, 0.71, 0.91]
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Omega_b: [0.03, 0.045, 0.07]
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priors:
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module: firecrown.priors
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Omega_c:
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kind: uniform
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loc: 0.1
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scale: 0.8
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n_s:
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kind: uniform
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loc: 0.87
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scale: 0.2
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sigma8:
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kind: uniform
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loc: 0.40
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scale: 0.80
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h:
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kind: uniform
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loc: 0.55
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scale: 0.36
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Omega_b:
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kind: uniform
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loc: 0.03
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scale: 0.04
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two_point:
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module: firecrown.ccl.two_point
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sacc_data: GK.sacc
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sources:
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{% for i in range(5) %}
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src{{i}}:
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kind: CMBLSource
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sacc_tracer: ck{{i}}
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{% endfor %}
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{% for i in range(5) %}
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lens{{i}}:
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kind: WLSource
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sacc_tracer: wl{{i}}
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{% endfor %}
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statistics:
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{% for i in range(5) %}
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GK{{i}}:
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sources: ['lens{{i}}','src{{i}}']
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sacc_data_type: 'cmbGalaxy_convergenceShear_xi_t'
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{% endfor %}
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likelihood:
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kind: ConstGaussianLogLike
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data_vector:
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{% for i in range(5) %}
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- GK{{i}}
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{% endfor %}
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‎examples/NK&GK/NK.sacc‎

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‎examples/NK&GK/NK.yaml‎

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parameters:
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Omega_k: 0.0
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Omega_g: 0.0
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w0: -1.0
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wa: 0.0
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T_CMB: 2.7
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Neff: 0
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m_nu: 0.0
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transfer_function: 'bbks'
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mass_function: 'tinker'
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sigma8: 0.80
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Omega_b: 0.045
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h: 0.71
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Omega_c: 0.21
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n_s: 0.964
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{% for i in range(5) %}
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bias_lens{{i}}: 1.0
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{% endfor %}
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cosmosis:
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sampler: emcee
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debug: True
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quiet: False
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mpi: False
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test:
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fatal_erros: True
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emcee:
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walkers: 32
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samples: 60
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parameters:
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Omega_c: [0.1, 0.21, 0.9]
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n_s: [0.87, 0.964, 1.07]
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h: [0.55, 0.71, 0.91]
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Omega_b: [0.03, 0.045, 0.07]
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{% for i in range(5) %}
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bias_lens{{i}}: [0.2, 1.0, 4.0]
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{% endfor %}
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priors:
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module: firecrown.priors
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Omega_c:
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kind: uniform
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loc: 0.1
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scale: 0.8
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n_s:
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kind: uniform
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loc: 0.87
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scale: 0.2
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h:
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kind: uniform
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loc: 0.55
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scale: 0.36
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Omega_b:
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kind: uniform
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loc: 0.03
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scale: 0.04
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{% for i in range(5) %}
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bias_lens{{i}}:
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kind: uniform
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loc: 0.2
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scale: 3.8
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{% endfor %}
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two_point:
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module: firecrown.ccl.two_point
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sacc_data: NK.sacc
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sources:
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{% for i in range(5) %}
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src{{i}}:
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kind: CMBLSource
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sacc_tracer: ck{{i}}
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{% endfor %}
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{% for i in range(5) %}
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lens{{i}}:
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kind: NumberCountsSource
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sacc_tracer: gc{{i}}
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bias: bias_lens{{i}}
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{% endfor %}
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statistics:
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{% for i in range(5) %}
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NK{{i}}:
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sources: ['lens{{i}}','src{{i}}']
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sacc_data_type: 'cmbGalaxy_convergenceDensity_xi'
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{% endfor %}
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likelihood:
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kind: ConstGaussianLogLike
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data_vector:
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{% for i in range(5) %}
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- NK{{i}}
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{% endfor %}
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‎examples/NK&GK/README.md‎

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# NK and GK constraints
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This is part of 5x2pt analysis. `NK.yaml` constrains cosmology based on galaxy number count x CMB lensing correlation (data stored in `NK.sacc`). `GK.yanml` constrains cosmology based on galaxy weak lensing x CMB lensing correlation (data stored in `GK.sacc`).
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Both constraints use `emcee` as the sampler for MCMC chains.
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To run this example, type
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```bash
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$ firecrown run-cosmosis NK.yaml
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```
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or for GK, type
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```bash
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$ firecrown run-cosmosis GK.yaml
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```

‎examples/des_y1_3x2pt/plot_des_chain.ipynb‎

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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"version": "3.7.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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"nbformat_minor": 4
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}

‎examples/des_y1_3x2pt/plot_des_data_model.ipynb‎

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.6"
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"version": "3.7.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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"nbformat_minor": 4
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}

‎firecrown/ccl/sources/sources.py‎

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from ..systematics import IdentityFunctionMOR, TopHatSelectionFunction
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__all__ = ['WLSource', 'NumberCountsSource', 'ClusterSource']
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__all__ = ['WLSource', 'NumberCountsSource', 'ClusterSource', 'CMBLSource']
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class WLSource(Source):
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systematics[systematic].apply(cosmo, params, self)
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self.dndz_interp_ = Akima1DInterpolator(self.z_, self.dndz_)
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class CMBLSource(Source):
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"""A CCL CMB Lensing Source.
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Parameters
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----------
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sacc_tracer : str
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The name of the tracer in the SACC file.
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scale : float, optional
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The default scale for this source. Usually the default of 1.0 is
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correct.
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systematics : list of str, optional
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A list of the source-level systematics to apply to the source. The
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default of `None` implies no systematics.
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Attributes
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----------
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scale_ : float
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The overall scale associated with the source. Set after a call to
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`render`.
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tracer_ : `pyccl.CMBLensingTracer`
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The CCL tracer associated with this source. Set after a call to
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`render`.
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Methods
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-------
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render : apply systematics to this source and build the
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`pyccl.CMBLSource`
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"""
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def __init__(self, *, sacc_tracer, scale=1.0, systematics=None):
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self.sacc_tracer = sacc_tracer
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self.scale = scale
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self.systematics = systematics or []
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def read(self, sacc_data):
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"""
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Read the data for this source from the SACC file.
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Parameters
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----------
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sacc_data : sacc.Sacc
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The data in the sacc format.
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"""
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pass
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def render(self, cosmo, params, systematics=None):
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"""
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Render a source by applying systematics.
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Parameters
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----------
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cosmo : pyccl.Cosmology
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A pyccl.Cosmology object.
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params : dict
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A dictionary mapping parameter names to their current values.
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systematics : dict
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A dictionary mapping systematic names to their objects. The
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default of `None` corresponds to no systematics.
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"""
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systematics = systematics or {}
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self.scale_ = self.scale
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for systematic in self.systematics:
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systematics[systematic].apply(cosmo, params, self)
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tracer = ccl.CMBLensingTracer(cosmo, 1100.)
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self.tracer_ = tracer

‎firecrown/ccl/sources/tests/test_cmbl_source.py‎

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import numpy as np
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import pytest
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import sacc
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import pyccl as ccl
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from ..sources import CMBLSource
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@pytest.fixture
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def cmbl_data():
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sacc_data = sacc.Sacc()
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params = dict(
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Omega_c=0.27,
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Omega_b=0.045,
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Omega_k=0.0,
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w0=-1.0,
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wa=0.0,
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sigma8=0.8,
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n_s=0.96,
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h=0.67)
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cosmo = ccl.Cosmology(**params)
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ell = np.arange(3000)
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sacc_data.add_tracer('Map', 'trc1', quantity='cmb_convergence', spin=0,
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ell=ell, beam=None)
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return {
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'cosmo': cosmo,
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'sacc_tracer': 'trc1',
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'sacc_data': sacc_data,
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'params': params,
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'systematics': [],
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'systematics_dict': {},
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'scale_': 1.05}
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def test_cmbl_source_nosys(cmbl_data):
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src = CMBLSource(
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sacc_tracer=cmbl_data['sacc_tracer'],
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scale=0.5)
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src.read(cmbl_data['sacc_data'])
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src.render(
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cmbl_data['cosmo'],
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cmbl_data['params'],
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cmbl_data['systematics_dict'])
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assert np.allclose(src.scale, 0.5)
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assert np.allclose(src.scale_, 0.5)
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assert src.systematics == []
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assert isinstance(src.tracer_, ccl.CMBLensingTracer)

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