Merge pull request #118 from esa/Release

release -> main for Release 0.2.3

docs/source/conf.py

@@ -21,7 +21,7 @@
 author = "ESA Advanced Concepts Team"
 
 # The full version, including alpha/beta/rc tags
-release = "0.2.1"
+release = "0.2.3"
 
 
 # -- General configuration ---------------------------------------------------

environment.yml

@@ -3,7 +3,7 @@ channels:
 - conda-forge
 - pytorch
 dependencies:
-#- cudatoolkit>=11.1
+- cudatoolkit>=11.1
 - loguru>=0.5.3
 - matplotlib>=3.3.3
 - pytest>=6.2.1
@@ -12,4 +12,4 @@ dependencies:
 - scipy>=1.6.0
 - sphinx>=3.4.3
 - sphinx_rtd_theme>=0.5.1
-- tqdm>=4.56.0
+- tqdm>=4.56.0

setup.py

@@ -9,14 +9,15 @@
 
 setup(
     name="torchquad",
-    version="0.2.1",
+    version="0.2.3",
     description="Package providing torch-based numerical integration methods.",
     long_description=open("README.md").read(),
     long_description_content_type="text/markdown",
     url="https://github.com/esa/torchquad",
     author="ESA Advanced Concepts Team",
     author_email="[email protected]",
     install_requires=[
+        "loguru>=0.5.3",
         "matplotlib>=3.3.3",
         "scipy>=1.6.0",
         "tqdm>=4.56.1",

torchquad/__init__.py

@@ -39,4 +39,3 @@
 
 set_log_level("INFO")
 logger.info("Initializing torchquad.")
-enable_cuda()

torchquad/integration/vegas.py

@@ -179,7 +179,8 @@ def _warmup_grid(self, warmup_N_it=5, N_samples=1000):
             jf = 0  # jacobians * function
             jf2 = 0
 
-            yrnd = torch.rand(size=[N_samples, self._dim])
+            # Multiplying by 0.99999999 as the edge case of y=1 leads to an error
+            yrnd = torch.rand(size=[N_samples, self._dim]) * 0.999999
             x = self.map.get_X(yrnd)
             f_eval = self._eval(x).squeeze()
             jac = self.map.get_Jac(yrnd)

torchquad/integration/vegas_stratification.py

@@ -133,9 +133,9 @@ def get_Y(self, nevals):
         indices = self._get_indices(indices)
 
         # Get random numbers (we get a few more just to vectorize properly)
-        # This might increase the memorz requirements slightly but is probably
-        # worth it
-        random_uni = torch.rand(size=[len(nevals), nevals.max(), self.dim])
+        # This might increase the memory requirements slightly but is probably
+        # worth it.
+        random_uni = torch.rand(size=[len(nevals), nevals.max(), self.dim]) * 0.999999
 
         # Sum the random numbers onto the index locations and scale with dy
         # Note that the resulting tensor is still slightly too large

torchquad/utils/deployment_test.py

@@ -8,6 +8,8 @@
 
 from torchquad import enable_cuda
 from torchquad import set_precision
+from torchquad import set_log_level
+from loguru import logger
 
 
 def _deployment_test():
@@ -17,38 +19,44 @@ def _deployment_test():
     """
     """[summary]
     """
-    print()
-    print()
-    print()
-    print("######## TESTING DEPLOYMENT ########")
-    print()
+    set_log_level("INFO")
+    logger.info("####################################")
+    logger.info("######## TESTING DEPLOYMENT ########")
+    logger.info("####################################")
+    logger.info("")
 
-    print("Testing CUDA init... ", end="")
+    logger.info("Testing CUDA init... ")
     # Test inititialization on GPUs if available
     enable_cuda()
     set_precision("double")
-    print("Done.")
+    logger.info("Done.")
 
-    print("Initializing integrators... ", end="")
+    logger.info("")
+    logger.info("####################################")
+
+    logger.info("Initializing integrators... ")
     tp = Trapezoid()
     sp = Simpson()
     boole = Boole()
     mc = MonteCarlo()
     vegas = VEGAS()
-    print("Done.")
+    logger.info("Done.")
 
     def some_test_function(x):
         return torch.exp(x) * torch.pow(x, 2)
 
-    print("Testing integrate functions... ", end="")
+    logger.info("")
+    logger.info("####################################")
+
+    logger.info("Testing integrate functions... ")
     tp.integrate(some_test_function, dim=1, N=101)
     sp.integrate(some_test_function, dim=1, N=101)
     boole.integrate(some_test_function, dim=1, N=101)
     mc.integrate(some_test_function, dim=1, N=101)
     vegas.integrate(some_test_function, dim=1, N=101)
-    print("Done.")
+    logger.info("Done.")
+    logger.info("")
 
-    print()
-    print()
-    print()
-    print("######## ALL DONE. ########")
+    logger.info("####################################")
+    logger.info("############ ALL DONE. #############")
+    logger.info("####################################")