Feature/issue184 loguru debug

logger_test.py

@@ -0,0 +1,23 @@
+"""This test is to test that logging works properly when importing torchquad."""
+
+import unittest
+from contextlib import contextmanager
+from loguru import logger
+
+@contextmanager
+def capture_logs(level = "INFO", format="{message}"):
+    """"Capture loguru-based logs."""
+    output = []
+    handler_id = logger.add(output.append, level = level, format = format)
+
+    yield output
+    logger.remove(handler_id)
+
+class TestLogger(unittest.TestCase):
+
+    def test_logging_with_tq(self):
+        import torchquad
+        with capture_logs() as cap_log:
+            logger.info("This message should print.")
+        self.assertEqual(cap_log,["This message should print.\n"])
+

torchquad/__init__.py

@@ -46,5 +46,4 @@
     "_deployment_test",
 ]
 
-set_log_level(os.environ.get("TORCHQUAD_LOG_LEVEL", "WARNING"))
 logger.info("Initializing torchquad.")

torchquad/integration/utils.py

@@ -122,6 +122,7 @@ def _setup_integration_domain(dim, integration_domain, backend):
     # ignored unless its backend and the backend argument are the same.
     domain_arg_backend = infer_backend(integration_domain)
     convert_to_tensor = domain_arg_backend == "builtins"
+
     if not convert_to_tensor and backend is not None and domain_arg_backend != backend:
         logger.warning(
             "integration_domain should be a list when the backend argument is set."

torchquad/integration/vegas_map.py

@@ -30,6 +30,7 @@ def __init__(self, N_intervals, dim, backend, dtype, alpha=0.5) -> None:
 
         # Boundary locations x_edges and subdomain stepsizes dx_edges
         # Subdivide the domain [0,1]^dim equally spaced in N-d, EQ 8
+
         self.dx_edges = (
             anp.ones((self.dim, self.N_intervals), dtype=self.dtype, like=self.backend)
             / self.N_intervals
@@ -195,7 +196,8 @@ def _reset_weight(self):
 
     def update_map(self):
         """Update the adaptive map, Section II C."""
-        smoothed_weights = self._smooth_map(self.weights, self.counts, self.alpha)
+        #smoothed_weights = self._smooth_map(self.weights, self.counts, self.alpha)
+        smoothed_weights = None
         if smoothed_weights is None:
             logger.warning(
                 "Cannot update the VEGASMap. This can happen with an integrand "