Unit test marking

pytest.ini

@@ -0,0 +1,7 @@
+[pytest]
+testpaths = tests
+adopts = -m "not slow"
+markers = 
+    precision_sensitive: Tests that are sensitive to the numerical precision used in calculations.  
+    integration: Tests to determine if independent units of code work together.  Typically carried out after significant changes. 
+    slow: Tests that take longer than 1 second to run.

tests/test_covariance_builder.py

@@ -7,14 +7,63 @@
 import pickle
 import pyccl as ccl
 import pymaster as nmt
-
+from unittest.mock import MagicMock
 from tjpcov.covariance_builder import CovarianceBuilder
 from scipy.linalg import block_diag
 
 INPUT_YML = "./tests/data/conf_covariance_builder_minimal.yaml"
 OUTDIR = "tests/tmp/"
 
 
+def test_no_config_throws():
+    mock_builder = MagicMock(CovarianceBuilder)
+
+    with pytest.raises(TypeError):
+        CovarianceBuilder.__init__(mock_builder, None)
+
+    with pytest.raises(TypeError):
+        CovarianceBuilder.__init__(mock_builder)
+
+
+def test_invalid_config_throws():
+    mock_builder = MagicMock(CovarianceBuilder)
+
+    with pytest.raises(FileNotFoundError):
+        CovarianceBuilder.__init__(mock_builder, "")
+
+    with pytest.raises(ValueError):
+        CovarianceBuilder.__init__(mock_builder, {})
+
+
+def test_config_values_read():
+    mock_builder = MagicMock(CovarianceBuilder)
+    config = {
+        "tjpcov": {
+            "IA": "IA_Value",
+            "mask_file": "mask_file_value",
+            "mask_names": "mask_names_value",
+            "nside": "nside_value",
+        }
+    }
+
+    CovarianceBuilder.__init__(mock_builder, config)
+
+    assert mock_builder.IA == "IA_Value"
+    assert mock_builder.mask_files == "mask_file_value"
+    assert mock_builder.mask_names == "mask_names_value"
+    assert mock_builder.nside == "nside_value"
+    assert mock_builder.config == config
+    assert mock_builder.cov is None
+    assert mock_builder.cosmo is None
+    assert mock_builder.nbpw is None
+    assert mock_builder.Ngal == {}
+    assert mock_builder.sigma_e == {}
+    # Need to implement __eq__ and __hash__ and a bit of refactoring in
+    # CovarianceIO to make this work
+    # assert mock_builder.io == CovarianceIO(config)
+    assert mock_builder.io.config == config
+
+
 def setup_module():
     os.makedirs(OUTDIR, exist_ok=True)
 

tests/test_covariance_calculator.py

@@ -57,6 +57,7 @@ def test_get_covariance_classes(mock_cov_calc):
         cc.get_covariance_classes()
 
 
+@pytest.mark.slow
 def test_get_covariance(mock_cov_calc):
     cov = mock_cov_calc.get_covariance() + 1e-100
 
@@ -67,6 +68,7 @@ def test_get_covariance(mock_cov_calc):
     assert np.max(np.abs(cov / cov2 - 1) < 1e-10)
 
 
+@pytest.mark.slow
 def test_get_covariance_terms(mock_cov_calc):
     cov_terms = mock_cov_calc.get_covariance_terms()
 
@@ -77,6 +79,8 @@ def test_get_covariance_terms(mock_cov_calc):
     assert np.all(cov_terms["SSC"] == cov_ssc)
 
 
+@pytest.mark.slow
+@pytest.mark.precision_sensitive
 def test_create_sacc_cov(mock_cov_calc):
     cov = mock_cov_calc.get_covariance() + 1e-100
 

tests/test_covariance_clusters.py

@@ -154,6 +154,7 @@ def test_load_from_cosmology(mock_covariance_gauss: CovarianceClusterCounts):
     assert mock_covariance_gauss.cosmo == cosmo
 
 
+@pytest.mark.precision_sensitive
 @pytest.mark.parametrize(
     "z, ref_val",
     [
@@ -168,6 +169,7 @@ def test_integral_mass_no_bias(
     assert test == pytest.approx(ref_val, rel=1e-3)
 
 
+@pytest.mark.precision_sensitive
 def test_double_bessel_integral(
     mock_covariance_gauss: CovarianceClusterCounts,
 ):
@@ -176,12 +178,15 @@ def test_double_bessel_integral(
     assert test == pytest.approx(ref, rel=1e-3)
 
 
+@pytest.mark.slow
+@pytest.mark.precision_sensitive
 def test_shot_noise(mock_covariance_gauss: ClusterCountsGaussian):
     ref = 63973.635143644424
     test = mock_covariance_gauss.shot_noise(0, 0)
     assert test == pytest.approx(ref, rel=1e-3)
 
 
+@pytest.mark.precision_sensitive
 @pytest.mark.parametrize(
     "z, reference_val",
     [
@@ -196,6 +201,7 @@ def test_integral_mass(
     assert test == pytest.approx(reference_val, rel=1e-3)
 
 
+@pytest.mark.precision_sensitive
 @pytest.mark.parametrize(
     "z, reference_val",
     [
@@ -211,6 +217,7 @@ def test_integral_mass_no_mproxy(
     assert test == pytest.approx(reference_val, rel=1e-1)
 
 
+@pytest.mark.precision_sensitive
 def test_mass_richness(mock_covariance_gauss: CovarianceClusterCounts):
     reference_min = 0.0009528852621284171
 
@@ -221,6 +228,7 @@ def test_mass_richness(mock_covariance_gauss: CovarianceClusterCounts):
     assert np.sum(test_min) == pytest.approx(reference_min)
 
 
+@pytest.mark.precision_sensitive
 @pytest.mark.parametrize(
     "z_i, reference_val",
     [
@@ -265,6 +273,8 @@ def test_cov_gaussian_zero_offdiagonal(
     assert cov_10_gauss == 0
 
 
+@pytest.mark.slow
+@pytest.mark.precision_sensitive
 def test_cov_nxn(
     mock_covariance_gauss: ClusterCountsGaussian,
     mock_covariance_ssc: ClusterCountsSSC,

tests/test_covariance_fourier_base.py

@@ -66,6 +66,7 @@ def get_nmt_bin(lmax=95):
     return nmt.NmtBin.from_edges(bpw_edges[:-1], bpw_edges[1:])
 
 
+@pytest.mark.precision_sensitive
 def test_build_matrix_from_blocks():
     class CFT(CovarianceFourierTester):
         def get_covariance_block(self, trs1, trs2):
@@ -85,6 +86,7 @@ def get_covariance_block(self, trs1, trs2):
     assert np.max(np.abs(cov / cov2 - 1)) < 1e-10
 
 
+@pytest.mark.precision_sensitive
 def test__get_covariance_block_for_sacc():
     # Test with matrices ordered as in C
     class CFT(CovarianceFourierTester):
@@ -208,6 +210,7 @@ def test_get_tracer_comb_ncell(mock_cov_fourier):
     )
 
 
+@pytest.mark.precision_sensitive
 def test_get_tracer_info(mock_cov_fourier):
     ccl_tracers1, tracer_noise1 = mock_cov_fourier.get_tracer_info()
     (

tests/test_covariance_fourier_gaussian_nmt.py

@@ -49,7 +49,7 @@ def cov_fg_nmt():
 
 # Useful functions
 def get_config(fname):
-    return CovarianceIO._parse(fname)
+    return CovarianceIO.get_dict_from_yaml(fname)
 
 
 def assert_chi2(s, tracer_comb1, tracer_comb2, cov, cov_bm, threshold):
@@ -343,6 +343,7 @@ def _get_covariance_block_for_sacc(
         )
 
 
+@pytest.mark.precision_sensitive
 def test_get_cl_for_cov(cov_fg_nmt):
     # We just need to test for one case as the function will complain if the
     # Cell inputted has the wrong shape
@@ -402,6 +403,8 @@ def test_get_cl_for_cov(cov_fg_nmt):
         )
 
 
+@pytest.mark.slow
+@pytest.mark.precision_sensitive
 @pytest.mark.parametrize(
     "tracer_comb1,tracer_comb2",
     [
@@ -565,6 +568,7 @@ def test_get_covariance_block(tracer_comb1, tracer_comb2):
         )
 
 
+@pytest.mark.precision_sensitive
 @pytest.mark.parametrize(
     "tracer_comb1,tracer_comb2",
     [
@@ -660,6 +664,7 @@ def test_get_covariance_block_cache(cov_fg_nmt, tracer_comb1, tracer_comb2):
         assert_chi2(s, tracer_comb1, tracer_comb2, cov, cov_bm, 1e-6)
 
 
+@pytest.mark.precision_sensitive
 @pytest.mark.parametrize(
     "kwargs",
     [{}, {"l_toeplitz": 10, "l_exact": 10, "dl_band": 10, "n_iter": 0}],
@@ -767,6 +772,7 @@ def test_get_covariance_workspace(cov_fg_nmt, kwargs):
     # assert not os.path.isfile(fname)
 
 
+@pytest.mark.precision_sensitive
 @pytest.mark.parametrize("nmt_conf", [{}, {"n_iter": 0}])
 def test_get_fields_dict(cov_fg_nmt, nmt_conf):
     tr = get_tracers_dict_for_cov()
@@ -965,6 +971,7 @@ def get_data_types(self):
     assert nell == cov_fg_nmt.get_nell(nside=NSIDE)
 
 
+@pytest.mark.precision_sensitive
 @pytest.mark.parametrize(
     "kwargs",
     [{}, {"l_toeplitz": 10, "l_exact": 10, "dl_band": 10, "n_iter": 0}],
@@ -1028,6 +1035,7 @@ def test_get_workspace(cov_fg_nmt, kwargs):
     # assert not os.path.isfile(fname)
 
 
+@pytest.mark.precision_sensitive
 @pytest.mark.parametrize(
     "kwargs",
     [{}, {"l_toeplitz": 10, "l_exact": 10, "dl_band": 10, "n_iter": 0}],
@@ -1120,6 +1128,8 @@ def test_get_workspace_dict(cov_fg_nmt, kwargs):
     w2 = cov_fg_nmt.get_workspaces_dict(tracers, None, cache=cache, **kwargs)
 
 
+@pytest.mark.slow
+@pytest.mark.precision_sensitive
 @pytest.mark.flaky(reruns=5, reruns_delay=1)
 def test_full_covariance_benchmark():
     config = get_config(INPUT_YML)
@@ -1200,6 +1210,8 @@ def test_full_covariance_benchmark():
     assert np.max(np.abs(cov / cov2 - 1)) < 1e-10
 
 
+@pytest.mark.slow
+@pytest.mark.precision_sensitive
 def test_txpipe_like_input():
     # We don't need to pass the bins because we have provided the workspaces
     # through the cache in the configuration file

tests/test_covariance_fourier_ssc.py

@@ -94,6 +94,8 @@ def test_smoke():
     FourierSSCHaloModel(INPUT_YML_SSC)
 
 
+@pytest.mark.slow
+@pytest.mark.precision_sensitive
 @pytest.mark.parametrize(
     "tracer_comb1,tracer_comb2",
     [
@@ -243,6 +245,8 @@ def test_get_covariance_block(cov_fssc, tracer_comb1, tracer_comb2):
     assert np.all(covf["cov"] == cov_ssc_zb)
 
 
+@pytest.mark.slow
+@pytest.mark.precision_sensitive
 def test_get_covariance_block_WL_benchmark(cov_fssc):
     # Based on CCL benchmark test in benchmarks/test_covariances.py
     #

tests/test_covariance_gaussian_fsky.py

@@ -73,6 +73,7 @@ def test_Fourier_get_binning_info(cov_fg_fsky):
         cov_fg_fsky.get_binning_info("log")
 
 
+@pytest.mark.slow
 def test_Fourier_get_covariance_block(cov_fg_fsky, mock_cosmo):
     # Test made independent of pickled objects
     tracer_comb1 = ("lens0", "lens0")
@@ -129,6 +130,7 @@ def test_Fourier_get_covariance_block(cov_fg_fsky, mock_cosmo):
     np.testing.assert_allclose(cov2, cov)
 
 
+@pytest.mark.slow
 @pytest.mark.parametrize(
     "tracer_comb1",
     [
@@ -159,6 +161,7 @@ def test_Real_get_fourier_block(
     assert np.all(cov == cov2 / norm)
 
 
+@pytest.mark.slow
 def test_smoke_get_covariance(cov_fg_fsky, cov_rg_fsky):
     # Check that we can get the full covariance
     cov_fg_fsky.get_covariance()