Use implicit string concatenation instead of explicit

pyproject.toml

@@ -125,8 +125,8 @@ line-length = 88
 exclude = ["doc/", "pytensor/_version.py"]
 
 [tool.ruff.lint]
-select = ["C", "E", "F", "I", "UP", "W", "RUF", "PERF", "PTH"]
-ignore = ["C408", "C901", "E501", "E741", "RUF012", "PERF203"]
+select = ["C", "E", "F", "I", "UP", "W", "RUF", "PERF", "PTH", "ISC"]
+ignore = ["C408", "C901", "E501", "E741", "RUF012", "PERF203", "ISC001"]
 
 
 [tool.ruff.lint.isort]

pytensor/bin/pytensor_cache.py

@@ -30,9 +30,7 @@ def print_help(exit_status):
     print('Type "pytensor-cache clear" to erase the cache')
     print('Type "pytensor-cache list" to print the cache content')
     print('Type "pytensor-cache unlock" to unlock the cache directory')
-    print(
-        'Type "pytensor-cache cleanup" to delete keys in the old ' "format/code version"
-    )
+    print('Type "pytensor-cache cleanup" to delete keys in the old format/code version')
     print('Type "pytensor-cache purge" to force deletion of the cache directory')
     print(
         'Type "pytensor-cache basecompiledir" '

pytensor/compile/debugmode.py

@@ -1614,14 +1614,10 @@ class _Linker(LocalLinker):
                             opt = str(reason[0][0])
                             msg = (
                                 f"An optimization (probably {opt}) inserted an "
-                                "apply node that raise an error."
-                                + "\nThe information we have about this "
-                                "optimizations is:"
-                                + str(reason[0][1])
-                                + "\n"
-                                + reason[0][2]
-                                + "\n\nThe original exception: \n"
-                                + str(e)
+                                "apply node that raise an error.\n"
+                                "The information we have about this optimization is:"
+                                f"{reason[0][1]}\n{reason[0][2]}\n"
+                                f"\nThe original exception: \n{e}"
                             )
                             new_e = e.__class__(msg)
                             exc_type, exc_value, exc_trace = sys.exc_info()
@@ -1725,15 +1721,11 @@ class _Linker(LocalLinker):
                                 raise
                             opt = str(reason[0][0])
                             msg = (
-                                f"An optimization (probably {opt}) inserted "
-                                "an apply node that raise an error."
-                                + "\nThe information we have about this "
-                                "optimizations is:"
-                                + str(reason[0][1])
-                                + "\n"
-                                + reason[0][2]
-                                + "\n\nThe original exception: \n"
-                                + str(e)
+                                f"An optimization (probably {opt}) inserted an "
+                                "apply node that raise an error.\n"
+                                "The information we have about this optimization is:"
+                                f"{reason[0][1]}\n{reason[0][2]}\n"
+                                f"\nThe original exception: \n{e}"
                             )
                             new_e = e.__class__(msg)
                             exc_type, exc_value, exc_trace = sys.exc_info()

pytensor/gradient.py

@@ -802,20 +802,20 @@ def _node_to_pattern(node):
         if not isinstance(connection_pattern, list):
             raise TypeError(
                 "Op.connection_pattern should return "
-                + f"list of list of bool, but for Op={node.op}"
-                + f"got {connection_pattern} with type {type(connection_pattern)}."
+                f"list of list of bool, but for Op={node.op}"
+                f"got {connection_pattern} with type {type(connection_pattern)}."
             )
         if len(connection_pattern) != len(node.inputs):
             raise ValueError(
                 f"{node.op}.connection_pattern should have {len(node.inputs)}"
-                + f" rows but has {len(connection_pattern)}."
+                f" rows but has {len(connection_pattern)}."
             )
         for ii, output_pattern in enumerate(connection_pattern):
             if not isinstance(output_pattern, list):
                 raise TypeError(
                     f"{node.op}.connection_pattern should return"
-                    + f" a list of lists, but element {int(ii)}"
-                    + f"is {output_pattern} of type {type(output_pattern)}."
+                    f" a list of lists, but element {int(ii)}"
+                    f"is {output_pattern} of type {type(output_pattern)}."
                 )
     else:
         connection_pattern = [[True for output in node.outputs] for ipt in node.inputs]

pytensor/graph/rewriting/db.py

@@ -229,10 +229,10 @@ class RewriteDatabaseQuery:
     def __str__(self):
         return (
             "RewriteDatabaseQuery("
-            + f"inc={self.include},ex={self.exclude},"
-            + f"require={self.require},subquery={self.subquery},"
-            + f"position_cutoff={self.position_cutoff},"
-            + f"extra_rewrites={self.extra_rewrites})"
+            f"inc={self.include},ex={self.exclude},"
+            f"require={self.require},subquery={self.subquery},"
+            f"position_cutoff={self.position_cutoff},"
+            f"extra_rewrites={self.extra_rewrites})"
         )
 
     def __setstate__(self, state):

pytensor/link/c/cmodule.py

@@ -1744,9 +1744,7 @@ def std_lib_dirs_and_libs() -> tuple[list[str], ...] | None:
                 if not os.path.exists(os.path.join(libdir, f)):
                     print(
                         "Your Python version is from Canopy. "
-                        + "You need to install the package '"
-                        + lib
-                        + "' from Canopy package manager."
+                        f"You need to install the package '{lib}' from Canopy package manager."
                     )
             libdirs = [
                 # Used in older Canopy
@@ -1763,9 +1761,7 @@ def std_lib_dirs_and_libs() -> tuple[list[str], ...] | None:
                 ):
                     print(
                         "Your Python version is from Canopy. "
-                        + "You need to install the package '"
-                        + lib
-                        + "' from Canopy package manager."
+                        f"You need to install the package '{lib}' from Canopy package manager."
                     )
             python_lib_dirs.insert(0, libdir)
         std_lib_dirs_and_libs.data = [libname], python_lib_dirs

pytensor/link/utils.py

@@ -351,8 +351,8 @@ def raise_with_op(
         clients = [[c[0] for c in fgraph.clients[var]] for var in node.outputs]
         detailed_err_msg += (
             f"Inputs shapes: {shapes}"
-            + f"\nInputs strides: {strides}"
-            + f"\nInputs values: {scalar_values}"
+            f"\nInputs strides: {strides}"
+            f"\nInputs values: {scalar_values}"
         )
         if verbosity == "high":
             inpts = [

pytensor/scalar/basic.py

@@ -1753,7 +1753,7 @@ class ScalarMaximum(BinaryScalarOp):
         if any(i.type in complex_types for i in node.inputs):
             raise NotImplementedError()
         # Test for both y>x and x>=y to detect NaN
-        return f"{z} = (({y})>({x})? ({y}): " f'(({x})>=({y})? ({x}): nan("")));'
+        return f'{z} = (({y})>({x})? ({y}): (({x})>=({y})? ({x}): nan("")));'
 
     def L_op(self, inputs, outputs, gout):
         (x, y) = inputs
@@ -1795,7 +1795,7 @@ class ScalarMinimum(BinaryScalarOp):
         (z,) = outputs
         if any(i.type in complex_types for i in node.inputs):
             raise NotImplementedError()
-        return f"{z} = (({y})<({x})? ({y}): " f'(({x})<=({y})? ({x}): nan("")));'
+        return f'{z} = (({y})<({x})? ({y}): (({x})<=({y})? ({x}): nan("")));'
 
     def L_op(self, inputs, outputs, gout):
         (x, y) = inputs

pytensor/sparse/basic.py

@@ -1382,8 +1382,8 @@ class GetItem2d(Op):
                 isinstance(ind, Variable) and getattr(ind, "ndim", -1) == 0
             ) or np.isscalar(ind):
                 raise NotImplementedError(
-                    "PyTensor has no sparse vector"
-                    + "Use X[a:b, c:d], X[a:b, c:c+1] or X[a:b] instead."
+                    "PyTensor has no sparse vector. "
+                    "Use X[a:b, c:d], X[a:b, c:c+1] or X[a:b] instead."
                 )
             else:
                 raise ValueError(

pytensor/tensor/conv/abstract_conv.py

@@ -2284,7 +2284,7 @@ class BaseAbstractConv(Op):
         """
         if mode not in ("valid", "full"):
             raise ValueError(
-                f"invalid mode {mode}, which must be either " '"valid" or "full"'
+                f'invalid mode {mode}, which must be either "valid" or "full"'
             )
         if isinstance(dilation, int):
             dilation = (dilation,) * self.convdim

pytensor/tensor/fft.py

@@ -80,7 +80,7 @@ class IRFFTOp(Op):
         if a.ndim < 3:
             raise TypeError(
                 f"{self.__class__.__name__}: input must have dimension >= 3,  with "
-                + "first dimension batches and last real/imag parts"
+                "first dimension batches and last real/imag parts"
             )
 
         if s is None:

tests/tensor/test_blas_c.py

@@ -196,8 +196,8 @@ class TestCGemv(OptimizationTestMixin):
         if check_force_gemv_init():
             warn(
                 "WARNING: The current BLAS requires PyTensor to initialize"
-                + " memory for some GEMV calls which will result in a minor"
-                + " degradation in performance for such calls."
+                " memory for some GEMV calls which will result in a minor"
+                " degradation in performance for such calls."
             )
 
     def t_gemv1(self, m_shp):