Merge pull request #1217 from LSSTDESC/master

Merge branch 'master' into SSC

benchmarks/test_ptpk.py

@@ -3,7 +3,6 @@
 import pyccl as ccl
 import pyccl.nl_pt as pt
 import pytest
-from contextlib import nullcontext
 
 # Set cosmology
 COSMO = ccl.Cosmology(Omega_c=0.25, Omega_b=0.05,
@@ -50,8 +49,7 @@ def test_pt_pk(comb):
     ptt1 = ptt[t1]
     ptt2 = ptt[t2]
 
-    with pytest.warns(ccl.CCLWarning) if comb[1] == "gi" else nullcontext():
-        pk = ptc.get_biased_pk2d(ptt1, tracer2=ptt2, return_ia_bb=return_bb)
+    pk = ptc.get_biased_pk2d(ptt1, tracer2=ptt2, return_ia_bb=return_bb)
 
     for iz, z in enumerate(zs):
         a = 1./(1+z)

pyccl/_nonlimber_FKEM.py

@@ -6,14 +6,13 @@
  (https://jila.colorado.edu/~ajsh/FFTLog/fftlog.pdf)
  to compute integrals over spherical bessel functions
 """
-import warnings
 import numpy as np
 from . import lib, check
 from .pyutils import integ_types
 from scipy.interpolate import interp1d
 from pyccl.pyutils import _fftlog_transform_general
 import pyccl as ccl
-from . import CCLWarning
+from . import CCLWarning, warnings
 
 
 def _get_general_params(b):
@@ -66,7 +65,8 @@ def _nonlimber_FKEM(
         warnings.warn(
             "p_of_k_a and p_of_k_a_lin must be of the same "
             "type: a str in cosmo or a Pk2D object. "
-            "Defaulting to Limber calculation. ", CCLWarning)
+            "Defaulting to Limber calculation. ",
+            category=CCLWarning, importance='high')
         return -1, np.array([]), status
 
     psp_lin = cosmo.parse_pk2d(p_of_k_a_lin, is_linear=True)

pyccl/baryons/baccoemu_baryons.py

@@ -2,9 +2,8 @@
 
 import numpy as np
 from copy import deepcopy
-from warnings import warn
 
-from .. import Pk2D
+from .. import Pk2D, CCLDeprecationWarning
 from . import Baryons
 
 
@@ -216,8 +215,9 @@ class BaccoemuBaryons(BaryonsBaccoemu):
 
     def __init__(self, *args, **kwargs):
         """This throws a deprecation warning on initialization."""
-        warn(f"Class {self.__class__.__name__} will be deprecated. " +
-             f"Please use {BaryonsBaccoemu.__name__} instead.",
-             DeprecationWarning, stacklevel=2)
+        from .. import warnings
+        warnings.warn(f"Class {self.__class__.__name__} will be deprecated. " +
+                      f"Please use {BaryonsBaccoemu.__name__} instead.",
+                      CCLDeprecationWarning, stacklevel=2,
+                      importance='low')
         super().__init__(*args, **kwargs)
-    pass

pyccl/ccl.i

@@ -21,7 +21,7 @@ from .errors import CCLError
 %init %{
     import_array();
     // Tell CCL to not print to stdout/stderr for debugging.
-    ccl_set_debug_policy(CCL_DEBUG_MODE_ON);
+    ccl_set_debug_policy(CCL_DEBUG_MODE_OFF);
 %}
 
 // Automatically document arguments and output types of all functions

pyccl/cells.py

@@ -1,10 +1,8 @@
 __all__ = ("angular_cl",)
 
-import warnings
-
 import numpy as np
 
-from . import DEFAULT_POWER_SPECTRUM, CCLWarning, check, lib
+from . import DEFAULT_POWER_SPECTRUM, CCLWarning, check, lib, warnings
 from .pyutils import integ_types
 from ._nonlimber_FKEM import _nonlimber_FKEM
 
@@ -56,15 +54,15 @@ def angular_cl(
             the kernels are defined will be used (capped to 1E-6 Mpc if this
             value is zero). Users are encouraged to experiment with this parameter
             and ``fkem_Nchi`` to ensure the robustness of the output
-            :math:`C_\\ell`s.
+            :math:`C_\\ell` s.
         fkem_Nchi: Number of values of the comoving distance over which `FKEM`
             will interpolate the radial kernels. If ``None`` the smallest number
             over which the kernels are currently sampled will be used. Note that
             `FKEM` will use a logarithmic sampling for distances between
             ``fkem_chi_min`` and the maximum distance over which the tracers
             are defined.  Users are encouraged to experiment with this parameter
             and ``fkem_chi_min`` to ensure the robustness of the output
-            :math:`C_\\ell`s.
+            :math:`C_\\ell` s.
         p_of_k_a_lin (:class:`~pyccl.pk2d.Pk2D`, :obj:`str` or :obj:`None`): 
             3D linear Power spectrum to project, for special use in
             PT calculations using the FKEM non-limber integration technique.
@@ -84,8 +82,7 @@ def angular_cl(
         warnings.warn(
             "CCL does not properly use the hyperspherical Bessel functions "
             "when computing angular power spectra in non-flat cosmologies!",
-            category=CCLWarning,
-        )
+            category=CCLWarning, importance='low')
 
     if limber_integration_method not in integ_types:
         raise ValueError(

pyccl/errors.py

@@ -1,6 +1,50 @@
-__all__ = ("CCLError", "CCLWarning", "CCLDeprecationWarning",)
+__all__ = ("CCLError", "CCLWarning", "CCLDeprecationWarning",
+           "warnings", "update_warning_verbosity")
 
-import warnings
+import warnings as warnings_builtin
+
+
+_warning_importance = {'high': 10, 'low': 1}
+_verbosity_thresholds = {'none': 100, 'low': 10, 'high': 1}
+
+
+class warnings:
+    _CCL_WARN_THRESHOLD = 10  # Equivalent to "low" verbosity
+
+    def warn(*args, **kwargs):
+        category = kwargs.get('category')
+        importance = _warning_importance[kwargs.pop('importance', 'low')]
+
+        if ((category in (CCLWarning, CCLDeprecationWarning)) and
+                (importance < warnings._CCL_WARN_THRESHOLD)):
+            return
+
+        warnings_builtin.warn(*args, **kwargs)
+
+
+def update_warning_verbosity(verbosity):
+    """ Update the level of verbosity of the CCL warnings. Available
+    levels are "none", "low", and "high". More warning messages will
+    be output for higher verbosity levels. If "none", no CCL-level
+    warnings will be shown. The default verbosity is "low". Note that
+    unless the verbosity level is "high", all C-level warnings will
+    be omitted.
+
+    Args:
+        verbosity (str): one of ``'none'``, ``'low'`` or ``'high'``.
+    """
+
+    if not (verbosity in ['none', 'low', 'high']):
+        raise KeyError("`verbosity` must be one of {'none', 'low', 'high'}")
+    warnings._CCL_WARN_THRESHOLD = _verbosity_thresholds[verbosity]
+
+    # Remove C-level warnings
+    from . import debug_mode
+
+    if verbosity == 'high':
+        debug_mode(True)
+    else:
+        debug_mode(False)
 
 
 class CCLError(RuntimeError):
@@ -40,8 +84,8 @@ def __hash__(self):
 
     @classmethod
     def enable(cls):
-        warnings.simplefilter("always")
+        warnings_builtin.simplefilter("always")
 
     @classmethod
     def disable(cls):
-        warnings.filterwarnings(action="ignore", category=cls)
+        warnings_builtin.filterwarnings(action="ignore", category=cls)