fixing mflike test and fixing np.float error

requirements.txt

@@ -14,4 +14,5 @@ getdist
 cobaya
 pyccl
 sacc
-fgspectra
+fgspectra>=1.1.0
+syslibrary

setup.cfg

@@ -23,8 +23,8 @@ install_requires =
     cobaya
     pyccl
     sacc
-    fgspectra
-    mflike @ git+https://github.com/simonsobs/lat_mflike@master
+    fgspectra>=1.1.0
+    syslibrary
 
 [options.package_data]
 soliket = *.yaml,*.bibtex,clusters/data/*,clusters/data/selFn_equD56/*,lensing/data/*.txt,lensing/data/*.fits,mflike/*.yaml,tests/*.yaml,data/xcorr_simulated/*.txt,data/CosmoPower/CP_paper/CMB/*.pkl

soliket/bandpass.py

@@ -123,7 +123,7 @@ def _bandpass_construction(self, **params):
 
         if not hasattr(self.bandint_width, "__len__"):
             self.bandint_width = np.full_like(self.freqs, self.bandint_width,
-                                              dtype=np.float)
+                                              dtype=float)
         if np.any(np.array(self.bandint_width) > 0):
             assert self.bandint_nsteps > 1, 'bandint_width and bandint_nsteps not \
                                              coherent'

soliket/mflike/__init__.py

@@ -1,2 +1,2 @@
-from .mflike import MFLike
+from .mflike import MFLike, TestMFLike
 from .theoryforge_MFLike import TheoryForge_MFLike

soliket/mflike/mflike.py

@@ -367,3 +367,10 @@ def _get_power_spectra(self, cmbfg):
             ps_vec[i] = clt
 
         return ps_vec
+
+
+class TestMFLike(MFLike):
+
+    _url = "https://portal.nersc.gov/cfs/sobs/users/MFLike_data"
+    filename = "v0.1_test"
+    install_options = {"download_url": f"{_url}/{filename}.tar.gz"}

soliket/tests/test_mflike.py

@@ -8,8 +8,8 @@
 from packaging.version import Version
 
 import camb
-import mflike  # noqa
 import soliket  # noqa
+from soliket.mflike import TestMFLike
 
 import numpy as np
 
@@ -58,44 +58,31 @@
 }
 
 
-if Version(camb.__version__) < Version('1.3'):
-    chi2s = {"tt": 1384.5669,
-             "te": 1400.2760,
-             "ee": 1428.7597,
-             "tt-te-et-ee": 2412.9275}
+if Version(camb.__version__) >= Version('1.4'):
+    chi2s = {"tt": 545.1257,
+             "te": 137.4146,
+             "ee": 167.9850,
+             "tt-te-et-ee": 790.5121}
 else:
-    chi2s = {"tt": 737.8571537677649,
-             "te-et": 998.2730263280033,
-             "ee": 716.4015196388742,
-             "tt-te-et-ee": 2459.7250}
+    chi2s = {"tt": 544.9745,
+             "te-et": 152.6807,
+             "ee": 168.0953,
+             "tt-te-et-ee": 790.4124}
 
-pre = "data_sacc_"
+pre = "test_data_sacc_"
 
 
 class MFLikeTest(unittest.TestCase):
-    orig = False
 
     def setUp(self):
         from cobaya.install import install
 
-        install({"likelihood": {"mflike.MFLike": None}},
+        install({"likelihood": {"soliket.mflike.TestMFLike": None}},
                 path=packages_path, skip_global=False, force=True, debug=True)
-        install({"likelihood": {"soliket.MFLike": None}},
-                path=packages_path, skip_global=False, force=True, debug=True)
-
-    def get_mflike_type(self, as_string=False):
-        if self.orig:
-            t = "mflike.MFLike"
-        else:
-            t = "soliket.MFLike"
 
-        if as_string:
-            return t
-        else:
-            return eval(t)
 
-    @pytest.mark.skip(reason="don't want to install 300Mb of data!")
     def test_mflike(self):
+
         # As of now, there is not a mechanism
         # in soliket to ensure there is .loglike that can be called like this
         # w/out cobaya
@@ -109,69 +96,60 @@ def test_mflike(self):
         cl_dict = {k: powers["total"][:, v] for
                    k, v in {"tt": 0, "ee": 1, "te": 3}.items()}
 
-        if not self.orig:
 
-            BP = soliket.BandPass()
-            FG = soliket.Foreground()
-            TF = soliket.TheoryForge_MFLike()
+        BP = soliket.BandPass()
+        FG = soliket.Foreground()
+        TF = soliket.TheoryForge_MFLike()
 
-            ell = np.arange(lmax + 1)
-            freqs = TF.freqs
-            requested_cls = TF.requested_cls
-            BP.freqs = freqs
+        ell = np.arange(lmax + 1)
+        freqs = TF.freqs
+        requested_cls = TF.requested_cls
+        BP.freqs = freqs
 
-            bandpass = BP._bandpass_construction(**nuisance_params)
+        bandpass = BP._bandpass_construction(**nuisance_params)
 
-            fg_dict = FG._get_foreground_model(requested_cls=requested_cls,
+        fg_dict = FG._get_foreground_model(requested_cls=requested_cls,
                                                     ell=ell,
                                                     freqs=freqs,
                                                     bandint_freqs=bandpass,
                                                     **nuisance_params)
 
-            dlobs_dict = TF.get_modified_theory(cl_dict, fg_dict, **nuisance_params)
+        dlobs_dict = TF.get_modified_theory(cl_dict, fg_dict, **nuisance_params)
 
         for select, chi2 in chi2s.items():
-            MFLike = self.get_mflike_type()
 
-            my_mflike = MFLike(
+            my_mflike = TestMFLike(
                 {
+                    "external": TestMFLike,
                     "packages_path": packages_path,
-                    "data_folder": "MFLike/v0.6",
+                    "data_folder": "TestMFLike",
                     "input_file": pre + "00000.fits",
-                    "cov_Bbl_file": pre + "w_covar_and_Bbl.fits",
                     "defaults": {
                         "polarizations": select.upper().split("-"),
                         "scales": {
-                            "TT": [2, 5000],
-                            "TE": [2, 5000],
-                            "ET": [2, 5000],
-                            "EE": [2, 5000],
+                            "TT": [2, 179],
+                            "TE": [2, 179],
+                            "ET": [2, 179],
+                            "EE": [2, 179],
                         },
                         "symmetrize": False,
                     },
                 }
             )
 
-            if not self.orig:
-                loglike = my_mflike.loglike(dlobs_dict)
-            else:
-                loglike = my_mflike.loglike(cl_dict, **nuisance_params)
+            loglike = my_mflike.loglike(dlobs_dict)
 
             self.assertAlmostEqual(-2 * (loglike - my_mflike.logp_const), chi2, 2)
 
-    @pytest.mark.skip(reason="don't want to install 300Mb of data!")
+    #@pytest.mark.skip(reason="don't want to install 300Mb of data!")
     def test_cobaya(self):
-        mflike_type = self.get_mflike_type(as_string=True)
-
-        # params = dict(cosmo_params)
-        # params['a_tSZ'] = 3.3
 
         info = {
             "likelihood": {
-                mflike_type: {
-                    "data_folder": "MFLike/v0.6",
+                "soliket.mflike.TestMFLike": {
+                    "datapath": os.path.join(packages_path, "data/TestMFLike"),
+                    "data_folder": "TestMFLike",
                     "input_file": pre + "00000.fits",
-                    "cov_Bbl_file": pre + "w_covar_and_Bbl.fits",
                 }
             },
             "theory": {"camb": {"extra_args": {"lens_potential_accuracy": 1},
@@ -180,17 +158,13 @@ def test_cobaya(self):
             "modules": packages_path,
             "debug": True,
         }
-        if not self.orig:
-            info["theory"]["soliket.TheoryForge_MFLike"] = {'stop_at_error': True}
-            info["theory"]["soliket.Foreground"] = {'stop_at_error': True}
-            info["theory"]["soliket.BandPass"] = {'stop_at_error': True}
+
+        info["theory"]["soliket.TheoryForge_MFLike"] = {'stop_at_error': True}
+        info["theory"]["soliket.Foreground"] = {'stop_at_error': True}
+        info["theory"]["soliket.BandPass"] = {'stop_at_error': True}
         from cobaya.model import get_model
 
         model = get_model(info)
-        my_mflike = model.likelihood[mflike_type]
+        my_mflike = model.likelihood["soliket.mflike.TestMFLike"]
         chi2 = -2 * (model.loglikes(nuisance_params)[0] - my_mflike.logp_const)
         self.assertAlmostEqual(chi2[0], chi2s["tt-te-et-ee"], 2)
-
-
-class MFLikeTestOrig(MFLikeTest):
-    orig = True

soliket/tests/test_mflike.yaml

@@ -3,7 +3,8 @@
 debug: True
 
 likelihood:
-  soliket.mflike.MFLike:
+  soliket.mflike.TestMFLike:
+    data_folder: TestMFLike
     input_file: data_sacc_00044.fits
     cov_Bbl_file: data_sacc_w_covar_and_Bbl.fits
     defaults:

tox.ini

@@ -46,7 +46,7 @@ extras =
 
 commands =
     pip freeze
-    cobaya-install planck_2018_highl_plik.TTTEEE_lite_native --no-set-global
+    all: cobaya-install planck_2018_highl_plik.TTTEEE_lite_native --no-set-global
     !cov: pytest -v --pyargs soliket {posargs}
     cov: pytest -v --pyargs soliket --cov soliket --cov-report=xml --cov-config={toxinidir}/setup.cfg {posargs}