Add 3x2pt example and update examples README

examples/README.md

@@ -17,7 +17,7 @@ The data-vector, `cosmicshear_sacc.fits`, was computed using the `generate_cosmi
 
 You can run this example from `cosmic_shear/` directory using the following command:
 ```bash
-python -m blinding blind_cosmic_shear_example.yaml
+smokescreen datavector --config blind_cosmic_shear_example.yaml
 ```
 
 You can find a notebook example on how to run this from the command line at `notebooks/test_cosmicshear_example.ipynb`.
@@ -29,6 +29,49 @@ The datavector was also computed from the firecrown example.
 
 You can run this example from the `supernovae/` directory using the following command:
 ```bash
-python -m blinding blind_sn_example.yaml
+smokescreen datavector --config blind_sn_example.yaml
 ```
-You can find a notebook example on how to run this from the command line at `notebooks/test_supernova_example.ipynb`.
+You can find a notebook example on how to run this from the command line at `notebooks/test_supernova_example.ipynb`.
+
+## LSST Y1 3x2pt Example
+> __:warning: Important: this folder contains the example used in the Smokescreen JOSS paper__
+>
+> :pushpin: The 3x2pt LSST Y1 SACC file here (`sacc_forecasting_y1_3x2pt.sacc`) was produced by Pedro H. Ribeiro ( @Arara09 ) as a part of DESC Project 314, if you intend to use this for another project, please give credit to him. 
+
+This folder contains the deterministic concealment on $(A_s, w)$-space of a LSST Y1 3x2pt sacc forecasted data vector. The shifts are determinist as this folder is a proof of concept used in the Smokescreen JOSS Paper.
+
+The fiducial cosmology used to generate this sacc is:
+```yaml
+Omega_c = 0.2906682
+Omega_b = 0.04575
+A_s_1e9 = 1.9019
+n_s = 0.9493
+h0 = 0.6714
+w = -3.0 -1.0 0.0
+wa = 0.0
+mnu = 0.1
+nnu = 3.044
+TCMB = 2.7255
+Omega_k = 0.0
+num_massive_neutrinos = 3
+tau = 0.0544
+```
+
+There are two concealment smokescreen files here:
+* **Blind A**: Shifts $A_s \rightarrow 2.00\times 10^{-09}$ and $w\rightarrow -1.1$
+* **Blind B**: Shifts $A_s \rightarrow 1.80\times 10^{-09}$ and $w\rightarrow -0.9$
+
+You can run these examples with:
+```bash
+smokescreen datavector --config conceal_lsst_y1_3x2pt_blind_[A/B].yaml
+```
+
+### Cosmosis files
+The folder `lsst_3x2pt/cosmosis_ini_files` also contains the cosmosis files to reproduce the posteriors shown in the Smokescreen JOSS paper. Cosmosis will vary only these three cosmological paramters $(A_s, \Omega_{\rm cdm}, w)$ for the example.
+
+The `.ini` files are:
+* `cosmosis_lsst_3x2pt.ini`: the run without concealment.
+* `cosmosis_lsst_3x2pt_blind_A.ini`: the blind A case.
+* `cosmosis_lsst_3x2pt_blind_B.ini`: the blind B case.
+
+**Please note: you may need to change the `project_dir` variable in the `.ini` files to the folder where your copy of smokescreen is located.**

examples/lsst_3x2pt/3x2pt_likelihood.py

@@ -0,0 +1,95 @@
+import pathlib
+from firecrown.data_functions import (
+    extract_all_harmonic_data,
+    check_two_point_consistence_harmonic,
+)
+from firecrown.likelihood.factories import load_sacc_data
+from firecrown.likelihood import TwoPoint, TwoPointFactory
+from firecrown.modeling_tools import ModelingTools, CCLCreationMode, CCLPureModeTransferFunction
+from firecrown.modeling_tools import CCLFactory, PoweSpecAmplitudeParameter
+from pyccl.neutrinos import NeutrinoMassSplits
+from firecrown.metadata_types import TwoPointCorrelationSpace
+
+import firecrown.likelihood.weak_lensing as wl
+import firecrown.likelihood.number_counts as nc
+from firecrown.likelihood.weak_lensing import PhotoZShiftFactory
+
+from firecrown.likelihood import (
+    ConstGaussian,
+    Likelihood,
+    NamedParameters,
+)
+
+
+def build_likelihood(
+        build_parameters: NamedParameters,
+) -> tuple[Likelihood, ModelingTools]:
+    """
+    Docstring for build_likelihood
+
+    :param build_parameters: you should pass the following
+        build_parameters:
+        - sacc_data: path to a sacc file
+        - factory_yaml: a yaml file with the firecrown systematics.
+    """
+
+    # load sacc data from build parameters
+    try:
+        sacc_data = build_parameters['sacc_data']
+    except TypeError:
+        sacc_data = build_parameters.data['sacc_data']
+
+    # This is a trick TXPipe uses to pass a sacc file or object
+    # to the likelihood. We need to check if the input is a string
+    if isinstance(sacc_data, (str, pathlib.Path)):
+        sacc_data = load_sacc_data(sacc_data)
+    else:
+        sacc_data = sacc_data.copy()
+
+    # extract the relevant metadata and check things:
+    two_point_cls = extract_all_harmonic_data(sacc_data)
+    check_two_point_consistence_harmonic(two_point_cls)
+
+    # WeakLensing systematics
+    ia_systematic = wl.LinearAlignmentSystematicFactory()
+    wl_photoz = PhotoZShiftFactory()
+    wl_mult_bias = wl.MultiplicativeShearBiasFactory()
+
+    wlf = wl.WeakLensingFactory(
+        per_bin_systematics=[wl_mult_bias, wl_photoz],
+        global_systematics=[ia_systematic],
+    )
+
+    # NumberCounts systematics
+    nc_photoz = PhotoZShiftFactory()
+    ncf = nc.NumberCountsFactory(
+        per_bin_systematics=[nc_photoz],
+        global_systematics=[],
+    )
+
+    all_two_point_functions = TwoPoint.from_measurement(
+        two_point_cls,
+        tp_factory=TwoPointFactory(
+            correlation_space=TwoPointCorrelationSpace.HARMONIC,
+            weak_lensing_factories=[wlf],
+            number_counts_factories=[ncf],
+        ),
+    )
+
+    likelihood_ready = ConstGaussian.create_ready(all_two_point_functions,
+                                                  sacc_data.covariance.dense)
+
+    tools = ModelingTools(ccl_factory=CCLFactory(
+        require_nonlinear_pk=True,
+        amplitude_parameter=PoweSpecAmplitudeParameter.AS,
+        num_neutrino_masses=None,
+        mass_split=NeutrinoMassSplits.EQUAL,
+        creation_mode=CCLCreationMode.DEFAULT,
+        pure_ccl_transfer_function=CCLPureModeTransferFunction.BOLTZMANN_CAMB,
+        use_camb_hm_sampling=False,
+        allow_multiple_camb_instances=False,
+        camb_extra_params=None,
+        )
+        )
+
+    return likelihood_ready, tools

examples/lsst_3x2pt/conceal_lsst_y1_3x2pt_blind_A.yaml

@@ -0,0 +1,38 @@
+path_to_sacc: "./sacc_forecasting_y1_3x2pt.sacc"
+likelihood_path: "./3x2pt_likelihood.py"
+output_suffix: "blind_A"
+shifts_dict:
+    A_s: 2.00e-09
+    w0: -1.1
+seed: 2112
+shift_distribution: "flat"
+systematics:
+    lens0_delta_z: 0.0
+    lens1_delta_z: 0.0
+    lens2_delta_z: 0.0
+    lens3_delta_z: 0.0
+    lens4_delta_z: 0.0
+    src0_delta_z: 0.0
+    src1_delta_z: 0.0
+    src2_delta_z: 0.0
+    src3_delta_z: 0.0
+    src4_delta_z: 0.0
+    src0_mult_bias: 0.0
+    src1_mult_bias: 0.0
+    src2_mult_bias: 0.0
+    src3_mult_bias: 0.0
+    src4_mult_bias: 0.0
+    lens0_bias: 1.2497
+    lens1_bias: 1.3809
+    lens2_bias: 1.5231
+    lens3_bias: 1.6716
+    lens4_bias: 1.8245
+    ia_bias: 1.0
+    alphaz: 0.0
+    z_piv: 0.62
+reference_cosmology:
+    A_s: 1.9019e-09
+    Omega_c: 0.2906
+# only needed if you want a different reference cosmology
+# than ccl.VanillaLCDM
+keep_original_sacc: true

examples/lsst_3x2pt/conceal_lsst_y1_3x2pt_blind_B.yaml

@@ -0,0 +1,36 @@
+path_to_sacc: "./sacc_forecasting_y1_3x2pt.sacc"
+likelihood_path: "./3x2pt_likelihood.py"
+output_suffix: "blind_B"
+shifts_dict:
+    A_s: 1.80e-09
+    w0: -0.9
+seed: 4224
+shift_distribution: "flat"
+systematics:
+    lens0_delta_z: 0.0
+    lens1_delta_z: 0.0
+    lens2_delta_z: 0.0
+    lens3_delta_z: 0.0
+    lens4_delta_z: 0.0
+    src0_delta_z: 0.0
+    src1_delta_z: 0.0
+    src2_delta_z: 0.0
+    src3_delta_z: 0.0
+    src4_delta_z: 0.0
+    src0_mult_bias: 0.0
+    src1_mult_bias: 0.0
+    src2_mult_bias: 0.0
+    src3_mult_bias: 0.0
+    src4_mult_bias: 0.0
+    lens0_bias: 1.2497
+    lens1_bias: 1.3809
+    lens2_bias: 1.5231
+    lens3_bias: 1.6716
+    lens4_bias: 1.8245
+    ia_bias: 1.0
+    alphaz: 0.0
+    z_piv: 0.62
+reference_cosmology:
+    A_s: 1.9019e-09
+    Omega_c: 0.2906
+keep_original_sacc: true

examples/lsst_3x2pt/cosmosis_ini_files/cosmosis_lsst_3x2pt.ini

@@ -0,0 +1,60 @@
+[runtime]
+verbosity = standard
+root = ${PWD}
+sampler = nautilus
+
+[DEFAULT]
+# You may need to change this your full path
+project_dir = ${PWD}/../../
+
+[output]
+format = text
+verbosity = 1
+filename = output/lsst_y1_3x2pt.txt
+
+[test]
+save_dir=output/lsst_y1_3x2pt
+fatal_errors=T
+
+[nautilus]
+n_live = 150
+n_networks = 6
+n_batch = 75
+verbose = True
+f_live = 0.02
+n_eff = 1000
+discard_exploration = True
+resume = True
+
+[consistency]
+file = ${COSMOSIS_STD_LIB_DIR}/utility/consistency/consistency_interface.py
+
+[pipeline]
+modules = consistency camb firecrown_likelihood
+likelihoods = firecrown
+timing = F
+debug = F
+values = cosmosis_values.ini
+priors = cosmosis_priors.ini
+extra_output=cosmological_parameters/omega_m cosmological_parameters/S8 cosmological_parameters/sigma_8
+
+[camb]
+file = ${COSMOSIS_STD_LIB_DIR}/boltzmann/camb/camb_interface.py
+mode = all
+accurate_massive_neutrino_transfers = True
+use_ppf_w = True
+nonlinear = 'pk'
+halofit_version = mead2020_feedback
+zmin = 0.0
+zmax = 3.5
+nz = 256
+kmax = 5.0
+nk = 300
+neutrino_hierarchy = degenerate
+
+[firecrown_likelihood]
+file = ${FIRECROWN_DIR}/firecrown/connector/cosmosis/likelihood.py
+require_nonlinear_pk = True
+sampling_parameters_sections = firecrown_two_point
+likelihood_source = %(project_dir)s/examples/lsst_y1_3x2pt/3x2pt_likelihood.py
+sacc_data = %(project_dir)s/examples/lsst_y1_3x2pt/sacc_forecasting_y1_3x2pt.sacc

examples/lsst_3x2pt/cosmosis_ini_files/cosmosis_lsst_3x2pt_blindA.ini

@@ -0,0 +1,59 @@
+[runtime]
+verbosity = standard
+root = ${PWD}
+sampler = nautilus
+
+[DEFAULT]
+project_dir = ${PWD}/../../
+
+[output]
+format = text
+verbosity = 1
+filename = output_blind_A/lsst_y1_3x2pt.txt
+
+[test]
+save_dir=output_blindA/lsst_y1_3x2pt
+fatal_errors=T
+
+[nautilus]
+n_live = 150
+n_networks = 6
+n_batch = 75
+verbose = True
+f_live = 0.02
+n_eff = 1000
+discard_exploration = True
+resume = True
+
+[consistency]
+file = ${COSMOSIS_STD_LIB_DIR}/utility/consistency/consistency_interface.py
+
+[pipeline]
+modules = consistency camb firecrown_likelihood
+likelihoods = firecrown
+timing = F
+debug = F
+values = cosmosis_values.ini
+priors = cosmosis_priors.ini
+extra_output=cosmological_parameters/omega_m cosmological_parameters/S8 cosmological_parameters/sigma_8
+
+[camb]
+file = ${COSMOSIS_STD_LIB_DIR}/boltzmann/camb/camb_interface.py
+mode = all
+accurate_massive_neutrino_transfers = True
+use_ppf_w = True
+nonlinear = 'pk'
+halofit_version = mead2020_feedback
+zmin = 0.0
+zmax = 3.5
+nz = 256
+kmax = 5.0
+nk = 300
+neutrino_hierarchy = degenerate
+
+[firecrown_likelihood]
+file = ${FIRECROWN_DIR}/firecrown/connector/cosmosis/likelihood.py
+require_nonlinear_pk = True
+sampling_parameters_sections = firecrown_two_point
+likelihood_source = %(project_dir)s/examples/lsst_y1_3x2pt/3x2pt_likelihood.py
+sacc_data = %(project_dir)s/examples/lsst_y1_3x2pt/sacc_forecasting_y1_3x2pt_blind_A.fits