Merge pull request esa#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("####################################")