Merge pull request #687 from mrava87/feat-logging

feat: improved used of logging library

Author: mrava87

pylops/__init__.py

@@ -45,6 +45,8 @@
 
 """
 
+import logging
+
 from .config import *
 from .linearoperator import *
 from .torchoperator import *
@@ -69,6 +71,9 @@
 from .utils.utils import *
 from .utils.wavelets import *
 
+# Prevent no handler message if an application using PyLops does not configure logging
+logging.getLogger(__name__).addHandler(logging.NullHandler())
+
 try:
     from .version import version as __version__
 except ImportError:

pylops/_torchoperator.py

@@ -1,4 +1,4 @@
-import logging
+import warnings
 
 from pylops.utils import deps
 
@@ -22,8 +22,8 @@ def forward(ctx, x, forw, adj, device, devicetorch):
 
         # check if data is moved to cpu and warn user
         if ctx.device == "cpu" and ctx.devicetorch != "cpu":
-            logging.warning(
-                "pylops operator will be applied on the cpu "
+            warnings.warn(
+                "PyLops operator will be applied on the cpu "
                 "whilst the input torch vector is on "
                 "%s, this may lead to poor performance" % ctx.devicetorch
             )

pylops/avo/avo.py

@@ -9,7 +9,6 @@
     "AVOLinearModelling",
 ]
 
-import logging
 from typing import List, Optional, Tuple, Union
 
 import numpy as np
@@ -22,8 +21,6 @@
 from pylops.utils.decorators import reshaped
 from pylops.utils.typing import DTypeLike, NDArray
 
-logging.basicConfig(format="%(levelname)s: %(message)s", level=logging.WARNING)
-
 
 def zoeppritz_scattering(
     vp1: float,
@@ -659,7 +656,6 @@ def __init__(
         elif linearization == "ps":
             Gs = ps(theta, vsvp, n=self.nt0)
         else:
-            logging.error("%s not an available " "linearization...", linearization)
             raise NotImplementedError(
                 "%s not an available linearization..." % linearization
             )

pylops/avo/poststack.py

@@ -3,7 +3,6 @@
     "PoststackInversion",
 ]
 
-import logging
 from typing import Optional, Tuple, Union
 
 import numpy as np
@@ -32,8 +31,6 @@
 from pylops.utils.signalprocessing import convmtx, nonstationary_convmtx
 from pylops.utils.typing import NDArray, ShapeLike
 
-logging.basicConfig(format="%(levelname)s: %(message)s", level=logging.WARNING)
-
 
 def _PoststackLinearModelling(
     wav,

pylops/avo/prestack.py

@@ -4,7 +4,6 @@
     "PrestackInversion",
 ]
 
-import logging
 from typing import Optional, Tuple, Union
 
 import numpy as np
@@ -36,8 +35,6 @@
 from pylops.utils.signalprocessing import convmtx
 from pylops.utils.typing import NDArray, ShapeLike
 
-logging.basicConfig(format="%(levelname)s: %(message)s", level=logging.WARNING)
-
 _linearizations = {"akirich": 3, "fatti": 3, "ps": 3}
 
 
@@ -167,7 +164,6 @@ def PrestackLinearModelling(
         elif callable(linearization):
             G = linearization(theta, vsvp, n=nt0)
         else:
-            logging.error("%s not an available linearization...", linearization)
             raise NotImplementedError(
                 "%s not an available linearization..." % linearization
             )
@@ -326,7 +322,6 @@ def PrestackWaveletModelling(
     elif callable(linearization):
         G = linearization(theta, vsvp, n=nt0)
     else:
-        logging.error("%s not an available linearization...", linearization)
         raise NotImplementedError(
             "%s not an available linearization..." % linearization
         )

pylops/basicoperators/linearregression.py

@@ -1,14 +1,10 @@
 __all__ = ["LinearRegression"]
 
-import logging
-
 import numpy.typing as npt
 
 from pylops.basicoperators import Regression
 from pylops.utils.typing import DTypeLike
 
-logging.basicConfig(format="%(levelname)s: %(message)s", level=logging.WARNING)
-
 
 class LinearRegression(Regression):
     r"""Linear regression.
@@ -76,5 +72,7 @@ class LinearRegression(Regression):
     ``order=1``.
     """
 
-    def __init__(self, taxis: npt.ArrayLike, dtype: DTypeLike = "float64", name: str = 'L'):
+    def __init__(
+        self, taxis: npt.ArrayLike, dtype: DTypeLike = "float64", name: str = "L"
+    ):
         super().__init__(taxis=taxis, order=1, dtype=dtype, name=name)

pylops/basicoperators/matrixmult.py

@@ -1,6 +1,7 @@
 __all__ = ["MatrixMult"]
 
 import logging
+import warnings
 from typing import Optional, Union
 
 import numpy as np
@@ -12,7 +13,7 @@
 from pylops.utils.backend import get_array_module
 from pylops.utils.typing import DTypeLike, InputDimsLike, NDArray
 
-logging.basicConfig(format="%(levelname)s: %(message)s", level=logging.WARNING)
+logger = logging.getLogger(__name__)
 
 
 class MatrixMult(LinearOperator):
@@ -92,8 +93,8 @@ def __init__(
 
         # Check if forceflat is needed and set it back to None otherwise
         if otherdims is not None and forceflat is not None:
-            logging.warning(
-                "setting forceflat=None since otherdims!=None. "
+            logger.warning(
+                "Setting forceflat=None since otherdims!=None. "
                 "PyLops will automatically detect whether to return "
                 "a 1d or nd array based on the shape of the input "
                 "array."
@@ -102,7 +103,7 @@ def __init__(
         # Check dtype for correctness (upcast to complex when A is complex)
         if np.iscomplexobj(A) and not np.iscomplexobj(np.ones(1, dtype=dtype)):
             dtype = A.dtype
-            logging.warning("Matrix A is a complex object, dtype cast to %s" % dtype)
+            warnings.warn("Matrix A is a complex object, dtype cast to %s" % dtype)
         super().__init__(
             dtype=np.dtype(dtype),
             dims=dims,

pylops/basicoperators/regression.py

@@ -1,16 +1,12 @@
 __all__ = ["Regression"]
 
-import logging
-
 import numpy as np
 import numpy.typing as npt
 
 from pylops import LinearOperator
 from pylops.utils.backend import get_array_module
 from pylops.utils.typing import DTypeLike, NDArray
 
-logging.basicConfig(format="%(levelname)s: %(message)s", level=logging.WARNING)
-
 
 class Regression(LinearOperator):
     r"""Polynomial regression.
@@ -92,7 +88,6 @@ def __init__(
     ) -> None:
         ncp = get_array_module(taxis)
         if not isinstance(taxis, ncp.ndarray):
-            logging.error("t must be ndarray...")
             raise TypeError("t must be ndarray...")
         else:
             self.taxis = taxis

pylops/basicoperators/restriction.py

@@ -16,7 +16,7 @@
 )
 from pylops.utils.typing import DTypeLike, InputDimsLike, IntNDArray, NDArray
 
-logging.basicConfig(format="%(levelname)s: %(message)s", level=logging.WARNING)
+logger = logging.getLogger(__name__)
 
 
 def _compute_iavamask(dims, axis, iava, ncp):
@@ -123,8 +123,8 @@ def __init__(
         # check if forceflat is needed and set it back to None otherwise
         if len(dims) > 2:
             if forceflat is not None:
-                logging.warning(
-                    f"setting forceflat=None since len(dims)={len(dims)}>2. "
+                logger.warning(
+                    f"Setting forceflat=None since len(dims)={len(dims)}>2. "
                     f"PyLops will automatically detect whether to return "
                     f"a 1d or nd array based on the shape of the input"
                     f"array."

pylops/basicoperators/spread.py

@@ -22,7 +22,7 @@
         _rmatvec_numba_table,
     )
 
-logging.basicConfig(format="%(levelname)s: %(message)s", level=logging.WARNING)
+logger = logging.getLogger(__name__)
 
 
 class Spread(LinearOperator):
@@ -184,7 +184,7 @@ def __init__(
             self.engine = "numba"
         else:
             if engine == "numba" and jit is not None:
-                logging.warning(jit_message)
+                logger.warning(jit_message)
             self.engine = "numpy"
 
         # axes
@@ -222,7 +222,7 @@ def __init__(
                 if len(fh(0, 0)) == 2:
                     self.interp = True
         if interp is not None and self.interp != interp:
-            logging.warning("interp has been overridden to %r.", self.interp)
+            logger.warning("interp has been overridden to %r.", self.interp)
 
     def _matvec_numpy(self, x: NDArray) -> NDArray:
         y = np.zeros(self.dimsd, dtype=self.dtype)