diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml index ab7ac7d13..410aecbb4 100644 --- a/.github/workflows/benchmark.yml +++ b/.github/workflows/benchmark.yml @@ -15,18 +15,15 @@ jobs: python-version: ['3.10'] steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v2 + uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install dependencies run: | python -m pip install --upgrade pip - python -m pip install pytest - if [ -f requirements.txt ]; then pip install -r requirements.txt; fi - python setup.py develop - pip install pytest-benchmark + python -m pip install .[tests] - name: Benchmark with pytest run: | pytest tests/test_db_benchmark.py --benchmark-json output.json diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml index 01ef0cc5e..e6f393ac6 100644 --- a/.github/workflows/codeql.yml +++ b/.github/workflows/codeql.yml @@ -24,18 +24,18 @@ jobs: steps: - name: Checkout - uses: actions/checkout@v3 + uses: actions/checkout@v4 - name: Initialize CodeQL - uses: github/codeql-action/init@v2 + uses: github/codeql-action/init@v3 with: languages: ${{ matrix.language }} queries: +security-and-quality - name: Autobuild - uses: github/codeql-action/autobuild@v2 + uses: github/codeql-action/autobuild@v3 - name: Perform CodeQL Analysis - uses: github/codeql-action/analyze@v2 + uses: github/codeql-action/analyze@v3 with: category: "/language:${{ matrix.language }}" diff --git a/.github/workflows/coveralls.yml b/.github/workflows/coveralls.yml index 5050dff31..2093e968c 100644 --- a/.github/workflows/coveralls.yml +++ b/.github/workflows/coveralls.yml @@ -18,17 +18,16 @@ jobs: python-version: ['3.10'] steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v2 + uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install dependencies run: | python -m pip install --upgrade pip - python -m pip install flake8 pytest - if [ -f requirements.txt ]; then pip install -r requirements.txt; fi - python -m pip install --editable . + python -m pip install flake8 + python -m pip install --editable .[tests] cd tests/cannonsim/src; make; cd ../../.. pip install pytest-cov pip install coveralls diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 705d7f6e5..13a511e91 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -6,7 +6,7 @@ jobs: build: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v4 - name: Copy Dockerfile run: | cp tutorials/kubernetes/Dockerfile . diff --git a/.github/workflows/pdocs.yml b/.github/workflows/pdocs.yml index 7ef191721..9022b37c6 100644 --- a/.github/workflows/pdocs.yml +++ b/.github/workflows/pdocs.yml @@ -13,20 +13,20 @@ jobs: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v4 - name: Set up Python - uses: actions/setup-python@v2 + uses: actions/setup-python@v5 with: python-version: '3.x' - name: Install dependencies run: | python -m pip install --upgrade pip - if [ -f requirements.txt ]; then pip install -r requirements.txt; fi - python setup.py develop - python setup.py build_cannonsim + python -m pip install setuptools + python -m pip install --editable .[docs] + cd tests/cannonsim/src; make; cd ../../.. pip install pdoc - name: Checkout - uses: actions/checkout@v2.3.1 + uses: actions/checkout@v3 - name: Build run: | export PDOC_ALLOW_EXEC=1 diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index 331adc678..d230f244a 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -18,17 +18,16 @@ jobs: python-version: ['3.8', '3.9', '3.10', '3.11'] steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v2 + uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install dependencies run: | python -m pip install --upgrade pip - python -m pip install flake8 pytest - if [ -f requirements.txt ]; then pip install -r requirements.txt; fi - python -m pip install --editable . + python -m pip install flake8 + python -m pip install --editable .[tests] cd tests/cannonsim/src; make; cd ../../.. pip install pytest-cov pip install coveralls diff --git a/.github/workflows/pythonpublish.yml b/.github/workflows/pythonpublish.yml index 4e1ef42d2..349b2e2f6 100644 --- a/.github/workflows/pythonpublish.yml +++ b/.github/workflows/pythonpublish.yml @@ -13,19 +13,22 @@ jobs: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v4 - name: Set up Python - uses: actions/setup-python@v2 + uses: actions/setup-python@v5 with: python-version: '3.x' - name: Install dependencies run: | python -m pip install --upgrade pip - pip install setuptools wheel twine + pip install build twine + - name: Build package + run: python -m build --sdist --wheel + - name: Check package + run: python -m twine check dist/* - name: Build and publish env: TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }} TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }} run: | - python setup.py sdist bdist_wheel twine upload dist/* diff --git a/.gitignore b/.gitignore index 6f81966f1..e341b1a0d 100644 --- a/.gitignore +++ b/.gitignore @@ -21,3 +21,4 @@ pep.sh *.csv env/ .ipynb_checkpoints/ +easyvvuq/_version.py diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 5fc242ca8..1befe0387 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -27,9 +27,9 @@ sphinx: # - pdf # - epub -# Optional but recommended, declare the Python requirements required -# to build your documentation -# See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html -# python: -# install: -# - requirements: docs/requirements.txt \ No newline at end of file +python: + install: + - method: pip + path: . + extra_requirements: + - docs diff --git a/docs/source/old/basic_tutorial.rst b/docs/attic/doc/source/old/basic_tutorial.rst similarity index 100% rename from docs/source/old/basic_tutorial.rst rename to docs/attic/doc/source/old/basic_tutorial.rst diff --git a/docs/source/old/cooling_coffee_cup.rst b/docs/attic/doc/source/old/cooling_coffee_cup.rst similarity index 100% rename from docs/source/old/cooling_coffee_cup.rst rename to docs/attic/doc/source/old/cooling_coffee_cup.rst diff --git a/docs/source/old/custom_encoder.rst b/docs/attic/doc/source/old/custom_encoder.rst similarity index 100% rename from docs/source/old/custom_encoder.rst rename to docs/attic/doc/source/old/custom_encoder.rst diff --git a/docs/source/old/dask_tutorial.rst b/docs/attic/doc/source/old/dask_tutorial.rst similarity index 100% rename from docs/source/old/dask_tutorial.rst rename to docs/attic/doc/source/old/dask_tutorial.rst diff --git a/docs/source/old/fusion_tutorial.rst b/docs/attic/doc/source/old/fusion_tutorial.rst similarity index 100% rename from docs/source/old/fusion_tutorial.rst rename to docs/attic/doc/source/old/fusion_tutorial.rst diff --git a/docs/source/old/hier_sparse_grid_tutorial.rst b/docs/attic/doc/source/old/hier_sparse_grid_tutorial.rst similarity index 100% rename from docs/source/old/hier_sparse_grid_tutorial.rst rename to docs/attic/doc/source/old/hier_sparse_grid_tutorial.rst diff --git a/docs/source/old/mcmc.rst b/docs/attic/doc/source/old/mcmc.rst similarity index 100% rename from docs/source/old/mcmc.rst rename to docs/attic/doc/source/old/mcmc.rst diff --git a/docs/source/old/multiencoder_tutorial.rst b/docs/attic/doc/source/old/multiencoder_tutorial.rst similarity index 100% rename from docs/source/old/multiencoder_tutorial.rst rename to docs/attic/doc/source/old/multiencoder_tutorial.rst diff --git a/docs/source/old/multisampler_tutorial.rst b/docs/attic/doc/source/old/multisampler_tutorial.rst similarity index 100% rename from docs/source/old/multisampler_tutorial.rst rename to docs/attic/doc/source/old/multisampler_tutorial.rst diff --git a/docs/source/old/relocate.rst b/docs/attic/doc/source/old/relocate.rst similarity index 100% rename from docs/source/old/relocate.rst rename to docs/attic/doc/source/old/relocate.rst diff --git a/docs/source/old/tutorials.rst b/docs/attic/doc/source/old/tutorials.rst similarity index 100% rename from docs/source/old/tutorials.rst rename to docs/attic/doc/source/old/tutorials.rst diff --git a/docs/source/old/validate_similarities.rst b/docs/attic/doc/source/old/validate_similarities.rst similarity index 100% rename from docs/source/old/validate_similarities.rst rename to docs/attic/doc/source/old/validate_similarities.rst diff --git a/docs/source/old/workflow_changes.rst b/docs/attic/doc/source/old/workflow_changes.rst similarity index 100% rename from docs/source/old/workflow_changes.rst rename to docs/attic/doc/source/old/workflow_changes.rst diff --git a/docs/source/concepts.rst b/docs/source/concepts.rst index b23facfba..1afffa248 100644 --- a/docs/source/concepts.rst +++ b/docs/source/concepts.rst @@ -62,18 +62,18 @@ central location where all information about your campaign is kept. The `Campaign` handles all validation and is transfers information between each stage of the workflow. -The `Basic Tutorial `_ +The `Basic Tutorial `_ (link to repository) or `Basic Tutorial Binder `_ (link to Binder to directly run the notebook) gives a good hands-on introduction to defining parameters and creating a campaign. + Elements -------- Within VECMA software components that can be reused in a wide range of application scenarios are known as ``Elements``. Within EasyVVUQ we provide five classes of ``Elements`` (:ref:`samplers`, -:ref:`decoders`, :ref:`encoders`, and those providing :ref:`collation`, -for the aggregation step, and :ref:`analysis` functionality) which we +:ref:`decoders`, :ref:`encoders`, and :ref:`analysis` functionality) which we describe below. .. _samplers: @@ -89,7 +89,7 @@ They deal with generic information in the sense that all parameters use the nomenclature and units provided by the user rather than anything specific to any application or workflow. -Detailed information on the Sampler modules is available `here `_. +Detailed information on the Sampler modules is available :doc:`here <_autodoc/easyvvuq.sampling>`. .. _encoders: @@ -107,7 +107,7 @@ generic Encoder base class is picked up and may be used. This enables EasyVVUQ to be easily extended for new applications by experienced users. -Detailed information on the Encoder modules is available `here `_. +Detailed information on the Encoder modules is available :doc:`here <_autodoc/easyvvuq.encoders>`. .. _decoders: @@ -121,7 +121,7 @@ facilitate analysis of a wide range of applications. The `Encoder-Decoder tutorial `_ provides a good introduction to using Encoders and Decoders within EasyVVUQ. Detailed information on the Decoder modules -themselves is available `here `_. +themselves is available :doc:`here <_autodoc/easyvvuq.decoders>`. .. _analysis: @@ -133,5 +133,11 @@ on the simulation output across a range of runs. Different types of analysis (for example bootstrapping of multiple runs from varied initial conditions) are, or will be, provided by EasyVVUQ. -Detailed information on the Analysis modules is available `here `_ +Detailed information on the Analysis modules is available :doc:`here <_autodoc/easyvvuq.analysis>`. + +.. _execution: + +Execution +--------- +Some more information on the use of QCG-Pilothob can be found :doc:`here `. diff --git a/docs/source/conf.py b/docs/source/conf.py index b9b5a126c..3cf702c33 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -12,12 +12,12 @@ # import os import sys -sys.path.insert(0, os.path.abspath('../easyvvuq')) +sys.path.insert(0, os.path.abspath('../..')) from sphinx.ext.apidoc import main as apidoc_main apidoc_main(["--force", "-o", "./_autodoc", "../../easyvvuq"]) -autodoc_mock_imports = ['dill'] +autodoc_mock_imports = ['dill', 'SALib', 'cerberus', 'chaospy', 'scipy', 'qcg', 'kubernetes', 'dask', 'sqlalchemy', 'numpoly', 'sklearn'] # -- Project information ----------------------------------------------------- diff --git a/docs/source/index.rst b/docs/source/index.rst index c5ce0b89b..820097b36 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -11,6 +11,8 @@ and uncertainty quantification (VVUQ) for a wide variety of simulations. It was conceived and developed within the EU funded `VECMA `_ (Verified Exascale Computing for Multiscale Applications) project. +A good introduction can be found in the paper by D. Suleimenova *et al.*, “Tutorial applications for Verification, Validation and Uncertainty Quantification using VECMA toolkit”, J. Comput. Sci. 53, 101402 (2021), `DOI:10.1016/j.jocs.2021.101402 `_. + .. _goals: Goals @@ -57,3 +59,4 @@ Indices and tables * :ref:`genindex` * :ref:`modindex` * :ref:`search` +* :doc:`_autodoc/modules` diff --git a/easyvvuq/__init__.py b/easyvvuq/__init__.py index 6ddc68ff9..d84419bf7 100644 --- a/easyvvuq/__init__.py +++ b/easyvvuq/__init__.py @@ -11,9 +11,14 @@ from . import analysis from . import comparison -# First make sure python version is 3.6+ -assert sys.version_info >= (3, 6), (f"Python version must be >= 3.6," - f"found {sys.version_info}") +from importlib.metadata import version, PackageNotFoundError + +try: + __version__ = version("easyvvuq") +except PackageNotFoundError: + from setuptools_scm import get_version # type: ignore[import] + + __version__ = get_version(root="..", relative_to=__file__) __copyright__ = """ @@ -36,6 +41,3 @@ """ __license__ = "LGPL" - -from . import _version -__version__ = _version.get_versions()['version'] diff --git a/easyvvuq/_version.py b/easyvvuq/_version.py deleted file mode 100644 index a89cb9e83..000000000 --- a/easyvvuq/_version.py +++ /dev/null @@ -1,683 +0,0 @@ - -# This file helps to compute a version number in source trees obtained from -# git-archive tarball (such as those provided by githubs download-from-tag -# feature). Distribution tarballs (built by setup.py sdist) and build -# directories (produced by setup.py build) will contain a much shorter file -# that just contains the computed version number. - -# This file is released into the public domain. -# Generated by versioneer-0.29 -# https://github.com/python-versioneer/python-versioneer - -"""Git implementation of _version.py.""" - -import errno -import os -import re -import subprocess -import sys -from typing import Any, Callable, Dict, List, Optional, Tuple -import functools - - -def get_keywords() -> Dict[str, str]: - """Get the keywords needed to look up the version information.""" - # these strings will be replaced by git during git-archive. - # setup.py/versioneer.py will grep for the variable names, so they must - # each be defined on a line of their own. _version.py will just call - # get_keywords(). - git_refnames = "$Format:%d$" - git_full = "$Format:%H$" - git_date = "$Format:%ci$" - keywords = {"refnames": git_refnames, "full": git_full, "date": git_date} - return keywords - - -class VersioneerConfig: - """Container for Versioneer configuration parameters.""" - - VCS: str - style: str - tag_prefix: str - parentdir_prefix: str - versionfile_source: str - verbose: bool - - -def get_config() -> VersioneerConfig: - """Create, populate and return the VersioneerConfig() object.""" - # these strings are filled in when 'setup.py versioneer' creates - # _version.py - cfg = VersioneerConfig() - cfg.VCS = "git" - cfg.style = "pep440" - cfg.tag_prefix = "" - cfg.parentdir_prefix = "None" - cfg.versionfile_source = "easyvvuq/_version.py" - cfg.verbose = False - return cfg - - -class NotThisMethod(Exception): - """Exception raised if a method is not valid for the current scenario.""" - - -LONG_VERSION_PY: Dict[str, str] = {} -HANDLERS: Dict[str, Dict[str, Callable]] = {} - - -def register_vcs_handler(vcs: str, method: str) -> Callable: # decorator - """Create decorator to mark a method as the handler of a VCS.""" - def decorate(f: Callable) -> Callable: - """Store f in HANDLERS[vcs][method].""" - if vcs not in HANDLERS: - HANDLERS[vcs] = {} - HANDLERS[vcs][method] = f - return f - return decorate - - -def run_command( - commands: List[str], - args: List[str], - cwd: Optional[str] = None, - verbose: bool = False, - hide_stderr: bool = False, - env: Optional[Dict[str, str]] = None, -) -> Tuple[Optional[str], Optional[int]]: - """Call the given command(s).""" - assert isinstance(commands, list) - process = None - - popen_kwargs: Dict[str, Any] = {} - if sys.platform == "win32": - # This hides the console window if pythonw.exe is used - startupinfo = subprocess.STARTUPINFO() - startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW - popen_kwargs["startupinfo"] = startupinfo - - for command in commands: - try: - dispcmd = str([command] + args) - # remember shell=False, so use git.cmd on windows, not just git - process = subprocess.Popen([command] + args, cwd=cwd, env=env, - stdout=subprocess.PIPE, - stderr=(subprocess.PIPE if hide_stderr - else None), **popen_kwargs) - break - except OSError as e: - if e.errno == errno.ENOENT: - continue - if verbose: - print("unable to run %s" % dispcmd) - print(e) - return None, None - else: - if verbose: - print("unable to find command, tried %s" % (commands,)) - return None, None - stdout = process.communicate()[0].strip().decode() - if process.returncode != 0: - if verbose: - print("unable to run %s (error)" % dispcmd) - print("stdout was %s" % stdout) - return None, process.returncode - return stdout, process.returncode - - -def versions_from_parentdir( - parentdir_prefix: str, - root: str, - verbose: bool, -) -> Dict[str, Any]: - """Try to determine the version from the parent directory name. - - Source tarballs conventionally unpack into a directory that includes both - the project name and a version string. We will also support searching up - two directory levels for an appropriately named parent directory - """ - rootdirs = [] - - for _ in range(3): - dirname = os.path.basename(root) - if dirname.startswith(parentdir_prefix): - return {"version": dirname[len(parentdir_prefix):], - "full-revisionid": None, - "dirty": False, "error": None, "date": None} - rootdirs.append(root) - root = os.path.dirname(root) # up a level - - if verbose: - print("Tried directories %s but none started with prefix %s" % - (str(rootdirs), parentdir_prefix)) - raise NotThisMethod("rootdir doesn't start with parentdir_prefix") - - -@register_vcs_handler("git", "get_keywords") -def git_get_keywords(versionfile_abs: str) -> Dict[str, str]: - """Extract version information from the given file.""" - # the code embedded in _version.py can just fetch the value of these - # keywords. When used from setup.py, we don't want to import _version.py, - # so we do it with a regexp instead. This function is not used from - # _version.py. - keywords: Dict[str, str] = {} - try: - with open(versionfile_abs, "r") as fobj: - for line in fobj: - if line.strip().startswith("git_refnames ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["refnames"] = mo.group(1) - if line.strip().startswith("git_full ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["full"] = mo.group(1) - if line.strip().startswith("git_date ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["date"] = mo.group(1) - except OSError: - pass - return keywords - - -@register_vcs_handler("git", "keywords") -def git_versions_from_keywords( - keywords: Dict[str, str], - tag_prefix: str, - verbose: bool, -) -> Dict[str, Any]: - """Get version information from git keywords.""" - if "refnames" not in keywords: - raise NotThisMethod("Short version file found") - date = keywords.get("date") - if date is not None: - # Use only the last line. Previous lines may contain GPG signature - # information. - date = date.splitlines()[-1] - - # git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant - # datestamp. However we prefer "%ci" (which expands to an "ISO-8601 - # -like" string, which we must then edit to make compliant), because - # it's been around since git-1.5.3, and it's too difficult to - # discover which version we're using, or to work around using an - # older one. - date = date.strip().replace(" ", "T", 1).replace(" ", "", 1) - refnames = keywords["refnames"].strip() - if refnames.startswith("$Format"): - if verbose: - print("keywords are unexpanded, not using") - raise NotThisMethod("unexpanded keywords, not a git-archive tarball") - refs = {r.strip() for r in refnames.strip("()").split(",")} - # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of - # just "foo-1.0". If we see a "tag: " prefix, prefer those. - TAG = "tag: " - tags = {r[len(TAG):] for r in refs if r.startswith(TAG)} - if not tags: - # Either we're using git < 1.8.3, or there really are no tags. We use - # a heuristic: assume all version tags have a digit. The old git %d - # expansion behaves like git log --decorate=short and strips out the - # refs/heads/ and refs/tags/ prefixes that would let us distinguish - # between branches and tags. By ignoring refnames without digits, we - # filter out many common branch names like "release" and - # "stabilization", as well as "HEAD" and "master". - tags = {r for r in refs if re.search(r'\d', r)} - if verbose: - print("discarding '%s', no digits" % ",".join(refs - tags)) - if verbose: - print("likely tags: %s" % ",".join(sorted(tags))) - for ref in sorted(tags): - # sorting will prefer e.g. "2.0" over "2.0rc1" - if ref.startswith(tag_prefix): - r = ref[len(tag_prefix):] - # Filter out refs that exactly match prefix or that don't start - # with a number once the prefix is stripped (mostly a concern - # when prefix is '') - if not re.match(r'\d', r): - continue - if verbose: - print("picking %s" % r) - return {"version": r, - "full-revisionid": keywords["full"].strip(), - "dirty": False, "error": None, - "date": date} - # no suitable tags, so version is "0+unknown", but full hex is still there - if verbose: - print("no suitable tags, using unknown + full revision id") - return {"version": "0+unknown", - "full-revisionid": keywords["full"].strip(), - "dirty": False, "error": "no suitable tags", "date": None} - - -@register_vcs_handler("git", "pieces_from_vcs") -def git_pieces_from_vcs( - tag_prefix: str, - root: str, - verbose: bool, - runner: Callable = run_command -) -> Dict[str, Any]: - """Get version from 'git describe' in the root of the source tree. - - This only gets called if the git-archive 'subst' keywords were *not* - expanded, and _version.py hasn't already been rewritten with a short - version string, meaning we're inside a checked out source tree. - """ - GITS = ["git"] - if sys.platform == "win32": - GITS = ["git.cmd", "git.exe"] - - # GIT_DIR can interfere with correct operation of Versioneer. - # It may be intended to be passed to the Versioneer-versioned project, - # but that should not change where we get our version from. - env = os.environ.copy() - env.pop("GIT_DIR", None) - runner = functools.partial(runner, env=env) - - _, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root, - hide_stderr=not verbose) - if rc != 0: - if verbose: - print("Directory %s not under git control" % root) - raise NotThisMethod("'git rev-parse --git-dir' returned error") - - # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty] - # if there isn't one, this yields HEX[-dirty] (no NUM) - describe_out, rc = runner(GITS, [ - "describe", "--tags", "--dirty", "--always", "--long", - "--match", f"{tag_prefix}[[:digit:]]*" - ], cwd=root) - # --long was added in git-1.5.5 - if describe_out is None: - raise NotThisMethod("'git describe' failed") - describe_out = describe_out.strip() - full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root) - if full_out is None: - raise NotThisMethod("'git rev-parse' failed") - full_out = full_out.strip() - - pieces: Dict[str, Any] = {} - pieces["long"] = full_out - pieces["short"] = full_out[:7] # maybe improved later - pieces["error"] = None - - branch_name, rc = runner(GITS, ["rev-parse", "--abbrev-ref", "HEAD"], - cwd=root) - # --abbrev-ref was added in git-1.6.3 - if rc != 0 or branch_name is None: - raise NotThisMethod("'git rev-parse --abbrev-ref' returned error") - branch_name = branch_name.strip() - - if branch_name == "HEAD": - # If we aren't exactly on a branch, pick a branch which represents - # the current commit. If all else fails, we are on a branchless - # commit. - branches, rc = runner(GITS, ["branch", "--contains"], cwd=root) - # --contains was added in git-1.5.4 - if rc != 0 or branches is None: - raise NotThisMethod("'git branch --contains' returned error") - branches = branches.split("\n") - - # Remove the first line if we're running detached - if "(" in branches[0]: - branches.pop(0) - - # Strip off the leading "* " from the list of branches. - branches = [branch[2:] for branch in branches] - if "master" in branches: - branch_name = "master" - elif not branches: - branch_name = None - else: - # Pick the first branch that is returned. Good or bad. - branch_name = branches[0] - - pieces["branch"] = branch_name - - # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty] - # TAG might have hyphens. - git_describe = describe_out - - # look for -dirty suffix - dirty = git_describe.endswith("-dirty") - pieces["dirty"] = dirty - if dirty: - git_describe = git_describe[:git_describe.rindex("-dirty")] - - # now we have TAG-NUM-gHEX or HEX - - if "-" in git_describe: - # TAG-NUM-gHEX - mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe) - if not mo: - # unparsable. Maybe git-describe is misbehaving? - pieces["error"] = ("unable to parse git-describe output: '%s'" - % describe_out) - return pieces - - # tag - full_tag = mo.group(1) - if not full_tag.startswith(tag_prefix): - if verbose: - fmt = "tag '%s' doesn't start with prefix '%s'" - print(fmt % (full_tag, tag_prefix)) - pieces["error"] = ("tag '%s' doesn't start with prefix '%s'" - % (full_tag, tag_prefix)) - return pieces - pieces["closest-tag"] = full_tag[len(tag_prefix):] - - # distance: number of commits since tag - pieces["distance"] = int(mo.group(2)) - - # commit: short hex revision ID - pieces["short"] = mo.group(3) - - else: - # HEX: no tags - pieces["closest-tag"] = None - out, rc = runner(GITS, ["rev-list", "HEAD", "--left-right"], cwd=root) - pieces["distance"] = len(out.split()) # total number of commits - - # commit date: see ISO-8601 comment in git_versions_from_keywords() - date = runner(GITS, ["show", "-s", "--format=%ci", "HEAD"], cwd=root)[0].strip() - # Use only the last line. Previous lines may contain GPG signature - # information. - date = date.splitlines()[-1] - pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1) - - return pieces - - -def plus_or_dot(pieces: Dict[str, Any]) -> str: - """Return a + if we don't already have one, else return a .""" - if "+" in pieces.get("closest-tag", ""): - return "." - return "+" - - -def render_pep440(pieces: Dict[str, Any]) -> str: - """Build up version string, with post-release "local version identifier". - - Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you - get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty - - Exceptions: - 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += plus_or_dot(pieces) - rendered += "%d.g%s" % (pieces["distance"], pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - else: - # exception #1 - rendered = "0+untagged.%d.g%s" % (pieces["distance"], - pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - return rendered - - -def render_pep440_branch(pieces: Dict[str, Any]) -> str: - """TAG[[.dev0]+DISTANCE.gHEX[.dirty]] . - - The ".dev0" means not master branch. Note that .dev0 sorts backwards - (a feature branch will appear "older" than the master branch). - - Exceptions: - 1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += plus_or_dot(pieces) - rendered += "%d.g%s" % (pieces["distance"], pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - else: - # exception #1 - rendered = "0" - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += "+untagged.%d.g%s" % (pieces["distance"], - pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - return rendered - - -def pep440_split_post(ver: str) -> Tuple[str, Optional[int]]: - """Split pep440 version string at the post-release segment. - - Returns the release segments before the post-release and the - post-release version number (or -1 if no post-release segment is present). - """ - vc = str.split(ver, ".post") - return vc[0], int(vc[1] or 0) if len(vc) == 2 else None - - -def render_pep440_pre(pieces: Dict[str, Any]) -> str: - """TAG[.postN.devDISTANCE] -- No -dirty. - - Exceptions: - 1: no tags. 0.post0.devDISTANCE - """ - if pieces["closest-tag"]: - if pieces["distance"]: - # update the post release segment - tag_version, post_version = pep440_split_post(pieces["closest-tag"]) - rendered = tag_version - if post_version is not None: - rendered += ".post%d.dev%d" % (post_version + 1, pieces["distance"]) - else: - rendered += ".post0.dev%d" % (pieces["distance"]) - else: - # no commits, use the tag as the version - rendered = pieces["closest-tag"] - else: - # exception #1 - rendered = "0.post0.dev%d" % pieces["distance"] - return rendered - - -def render_pep440_post(pieces: Dict[str, Any]) -> str: - """TAG[.postDISTANCE[.dev0]+gHEX] . - - The ".dev0" means dirty. Note that .dev0 sorts backwards - (a dirty tree will appear "older" than the corresponding clean one), - but you shouldn't be releasing software with -dirty anyways. - - Exceptions: - 1: no tags. 0.postDISTANCE[.dev0] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += ".post%d" % pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - rendered += plus_or_dot(pieces) - rendered += "g%s" % pieces["short"] - else: - # exception #1 - rendered = "0.post%d" % pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - rendered += "+g%s" % pieces["short"] - return rendered - - -def render_pep440_post_branch(pieces: Dict[str, Any]) -> str: - """TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] . - - The ".dev0" means not master branch. - - Exceptions: - 1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += ".post%d" % pieces["distance"] - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += plus_or_dot(pieces) - rendered += "g%s" % pieces["short"] - if pieces["dirty"]: - rendered += ".dirty" - else: - # exception #1 - rendered = "0.post%d" % pieces["distance"] - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += "+g%s" % pieces["short"] - if pieces["dirty"]: - rendered += ".dirty" - return rendered - - -def render_pep440_old(pieces: Dict[str, Any]) -> str: - """TAG[.postDISTANCE[.dev0]] . - - The ".dev0" means dirty. - - Exceptions: - 1: no tags. 0.postDISTANCE[.dev0] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += ".post%d" % pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - else: - # exception #1 - rendered = "0.post%d" % pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - return rendered - - -def render_git_describe(pieces: Dict[str, Any]) -> str: - """TAG[-DISTANCE-gHEX][-dirty]. - - Like 'git describe --tags --dirty --always'. - - Exceptions: - 1: no tags. HEX[-dirty] (note: no 'g' prefix) - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"]: - rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) - else: - # exception #1 - rendered = pieces["short"] - if pieces["dirty"]: - rendered += "-dirty" - return rendered - - -def render_git_describe_long(pieces: Dict[str, Any]) -> str: - """TAG-DISTANCE-gHEX[-dirty]. - - Like 'git describe --tags --dirty --always -long'. - The distance/hash is unconditional. - - Exceptions: - 1: no tags. HEX[-dirty] (note: no 'g' prefix) - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) - else: - # exception #1 - rendered = pieces["short"] - if pieces["dirty"]: - rendered += "-dirty" - return rendered - - -def render(pieces: Dict[str, Any], style: str) -> Dict[str, Any]: - """Render the given version pieces into the requested style.""" - if pieces["error"]: - return {"version": "unknown", - "full-revisionid": pieces.get("long"), - "dirty": None, - "error": pieces["error"], - "date": None} - - if not style or style == "default": - style = "pep440" # the default - - if style == "pep440": - rendered = render_pep440(pieces) - elif style == "pep440-branch": - rendered = render_pep440_branch(pieces) - elif style == "pep440-pre": - rendered = render_pep440_pre(pieces) - elif style == "pep440-post": - rendered = render_pep440_post(pieces) - elif style == "pep440-post-branch": - rendered = render_pep440_post_branch(pieces) - elif style == "pep440-old": - rendered = render_pep440_old(pieces) - elif style == "git-describe": - rendered = render_git_describe(pieces) - elif style == "git-describe-long": - rendered = render_git_describe_long(pieces) - else: - raise ValueError("unknown style '%s'" % style) - - return {"version": rendered, "full-revisionid": pieces["long"], - "dirty": pieces["dirty"], "error": None, - "date": pieces.get("date")} - - -def get_versions() -> Dict[str, Any]: - """Get version information or return default if unable to do so.""" - # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have - # __file__, we can work backwards from there to the root. Some - # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which - # case we can only use expanded keywords. - - cfg = get_config() - verbose = cfg.verbose - - try: - return git_versions_from_keywords(get_keywords(), cfg.tag_prefix, - verbose) - except NotThisMethod: - pass - - try: - root = os.path.realpath(__file__) - # versionfile_source is the relative path from the top of the source - # tree (where the .git directory might live) to this file. Invert - # this to find the root from __file__. - for _ in cfg.versionfile_source.split('/'): - root = os.path.dirname(root) - except NameError: - return {"version": "0+unknown", "full-revisionid": None, - "dirty": None, - "error": "unable to find root of source tree", - "date": None} - - try: - pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose) - return render(pieces, cfg.style) - except NotThisMethod: - pass - - try: - if cfg.parentdir_prefix: - return versions_from_parentdir(cfg.parentdir_prefix, root, verbose) - except NotThisMethod: - pass - - return {"version": "0+unknown", "full-revisionid": None, - "dirty": None, - "error": "unable to compute version", "date": None} diff --git a/easyvvuq/actions/__init__.py b/easyvvuq/actions/__init__.py index d98792834..a076e0176 100644 --- a/easyvvuq/actions/__init__.py +++ b/easyvvuq/actions/__init__.py @@ -8,7 +8,7 @@ from .execute_local import ExecuteLocal, ExecutePython, CreateRunDirectory, Encode, Decode, local_execute from .execute_local import CleanUp, Actions -from .execute_qcgpj import QCGPJPool, EasyVVUQBasicTemplate, ExecuteQCGPJ +from .execute_qcgpj import QCGPJPool, EasyVVUQBasicTemplate, EasyVVUQParallelTemplate from .execute_kubernetes import ExecuteKubernetes from .execute_slurm import ExecuteSLURM from .action_statuses import ActionPool diff --git a/easyvvuq/actions/execute_qcgpj.py b/easyvvuq/actions/execute_qcgpj.py index 03db3ae4d..467bdd205 100644 --- a/easyvvuq/actions/execute_qcgpj.py +++ b/easyvvuq/actions/execute_qcgpj.py @@ -37,6 +37,7 @@ def template() -> Tuple[str, Dict[str, Any]]: 'stdout': '${stdout}', 'stderr': '${stderr}', 'venv': '${venv}', + 'modules': ${modules}, 'model': '${model}', 'model_opts': ${model_opts} } @@ -48,6 +49,7 @@ def template() -> Tuple[str, Dict[str, Any]]: 'stdout': 'stdout', 'stderr': 'stderr', 'venv': '', + 'modules': [], 'model': 'default', 'model_opts': {} } @@ -74,6 +76,7 @@ def template() -> Tuple[str, Dict[str, Any]]: 'stdout': '${stdout}', 'stderr': '${stderr}', 'venv': '${venv}', + 'modules': ${modules}, 'model': '${model}', 'model_opts': ${model_opts} }, @@ -93,6 +96,7 @@ def template() -> Tuple[str, Dict[str, Any]]: 'stdout': 'stdout', 'stderr': 'stderr', 'venv': '', + 'modules': [], 'model': 'default', 'model_opts': {}, 'numCores': 1, diff --git a/easyvvuq/analysis/__init__.py b/easyvvuq/analysis/__init__.py index 8694a0bf1..181949133 100644 --- a/easyvvuq/analysis/__init__.py +++ b/easyvvuq/analysis/__init__.py @@ -1,5 +1,5 @@ from .basic_stats import BasicStats -from .ensemble_boot import EnsembleBoot +from .ensemble_boot import EnsembleBoot, EnsembleBootMultiple from .sc_analysis import SCAnalysis from .ssc_analysis import SSCAnalysis from .pce_analysis import PCEAnalysis diff --git a/easyvvuq/analysis/ensemble_boot.py b/easyvvuq/analysis/ensemble_boot.py index 35ba01160..202932086 100644 --- a/easyvvuq/analysis/ensemble_boot.py +++ b/easyvvuq/analysis/ensemble_boot.py @@ -289,3 +289,96 @@ def analyse(self, data_frame=None): stat_name=self.stat_name) return results + +class EnsembleBootMultiple(BaseAnalysisElement): + + def __init__(self, groupby=[], qoi_cols=[], + stat_func=[np.mean], alpha=0.05, + sample_size=None, n_boot_samples=1000, + pivotal=False, stat_name=None): + """ + Element to perform bootstrapping on collated simulation output. + + Parameters + ---------- + groupby : list or None + Columns to use to group the data in `analyse` method before + calculating stats. + qoi_cols : list or None + Columns of quantities of interest (for which stats will be + calculated). + stat_func : list[function] + List of statistical functions to be applied to data for bootstrapping. + alpha : float, default=0.05 + Produce estimate of 100.0*(1-`alpha`) confidence interval. + sample_size : int + Size of the sample to be drawn from the input data. + n_boot_samples : int, default=1000 + Number of times samples are to be drawn from the input data. + pivotal : bool, default=False + Use the pivotal method? Default to percentile method. + stat_name : str, default=None + Name to use to describe columns containing output statistic (for example + 'mean'). If not provided, then attr '__name__' from each func is used. + """ + + if not stat_func or stat_func is None: + raise ValueError('stat_func cannot be empty or None') + + self.groupby = groupby + self.qoi_cols = qoi_cols + + self.stat_func = stat_func + self.alpha = alpha + self.sample_size = sample_size + self.n_boot_samples = n_boot_samples + self.pivotal = pivotal + self.stat_name = stat_name if stat_name is not None else [func.__name__ for func in stat_func] + + self.output_type = OutputType.SUMMARY + + def element_name(self): + """Name for this element for logging purposes""" + return "ensemble_boot_multiple" + + def element_version(self): + """Version of this element for logging purposes""" + return "0.1" + + def analyse(self, data_frame=None): + """Perform bootstrapping analysis on the input `data_frame`. + + The data_frame is grouped according to `self.groupby` if specified and + analysis is performed on the columns selected in `self.qoi_cols` if set. + + Parameters + ---------- + data_frame : :obj:`pandas.DataFrame` + Summary data produced through collation of simulation output. + + Returns + ------- + :obj:`pandas.DataFrame` + Basic statistic for selected columns and groupings of data. + """ + + if data_frame is None: + raise RuntimeError( + "This VVUQ element needs a data frame to analyse") + elif data_frame.empty: + raise RuntimeError( + "No data in data frame passed to analyse element") + frames = [] + for stat_func, stat_name in zip(self.stat_func, self.stat_name): + results = ensemble_bootstrap( + data_frame, + groupby=self.groupby, + qoi_cols=self.qoi_cols, + stat_func=stat_func, + alpha=self.alpha, + sample_size=self.sample_size, + n_samples=self.n_boot_samples, + pivotal=self.pivotal, + stat_name=stat_name) + frames.append(results) + return pd.concat(frames, axis=1, keys=self.stat_name).swaplevel(0, 1, axis=1) diff --git a/easyvvuq/analysis/pce_analysis.py b/easyvvuq/analysis/pce_analysis.py index 8a20c0cfe..e4171c650 100644 --- a/easyvvuq/analysis/pce_analysis.py +++ b/easyvvuq/analysis/pce_analysis.py @@ -129,15 +129,30 @@ def _describe(self, qoi, statistic): return np.array([v.upper[0] for _, v in enumerate( self.raw_data['output_distributions'][qoi])]) elif statistic == '1%': - return self.raw_data['percentiles'][qoi]['p01'] + if isinstance(self.raw_data['percentiles'][qoi]['p01'], np.ndarray): + return self.raw_data['percentiles'][qoi]['p01'] + else: + return np.array([self.raw_data['percentiles'][qoi]['p01']]) elif statistic == '10%': - return self.raw_data['percentiles'][qoi]['p10'] + if isinstance(self.raw_data['percentiles'][qoi]['p10'], np.ndarray): + return self.raw_data['percentiles'][qoi]['p10'] + else: + return np.array([self.raw_data['percentiles'][qoi]['p10']]) elif statistic == '90%': - return self.raw_data['percentiles'][qoi]['p90'] + if isinstance(self.raw_data['percentiles'][qoi]['p90'], np.ndarray): + return self.raw_data['percentiles'][qoi]['p90'] + else: + return np.array([self.raw_data['percentiles'][qoi]['p90']]) elif statistic == '99%': - return self.raw_data['percentiles'][qoi]['p99'] + if isinstance(self.raw_data['percentiles'][qoi]['p99'], np.ndarray): + return self.raw_data['percentiles'][qoi]['p99'] + else: + return np.array([self.raw_data['percentiles'][qoi]['p99']]) elif statistic == 'median': - return self.raw_data['percentiles'][qoi]['p50'] + if isinstance(self.raw_data['percentiles'][qoi]['p50'], np.ndarray): + return self.raw_data['percentiles'][qoi]['p50'] + else: + return np.array([self.raw_data['percentiles'][qoi]['p50']]) else: try: return self.raw_data['statistical_moments'][qoi][statistic] diff --git a/easyvvuq/analysis/qmc_analysis.py b/easyvvuq/analysis/qmc_analysis.py index 328b07eea..45e2ff2d7 100644 --- a/easyvvuq/analysis/qmc_analysis.py +++ b/easyvvuq/analysis/qmc_analysis.py @@ -51,17 +51,21 @@ def supported_stats(self): ------- list of str """ - return ['mean', 'var', 'std', 'percentiles', '10%', '50%', '90%'] + return ['mean', 'var', 'std', 'min', 'max', 'median', 'percentiles', '1%', '10%', '50%', '90%', '99%'] def _describe(self, qoi, statistic): if statistic not in self.supported_stats(): raise NotImplementedError + if statistic == '1%': + return self.raw_data['percentiles'][qoi]['p1'] if statistic == '10%': return self.raw_data['percentiles'][qoi]['p10'] elif statistic == '50%': return self.raw_data['percentiles'][qoi]['p50'] elif statistic == '90%': return self.raw_data['percentiles'][qoi]['p90'] + elif statistic == '99%': + return self.raw_data['percentiles'][qoi]['p99'] else: return self.raw_data['statistical_moments'][qoi][statistic][0] @@ -206,10 +210,16 @@ def analyse(self, data_frame): results['statistical_moments'][k] = {'mean': np.mean(masked_samples, axis=0), 'var': np.var(masked_samples, axis=0), - 'std': np.std(masked_samples, axis=0)} - results['percentiles'][k] = {'p10': np.percentile(masked_samples, 10, 0)[0], + 'std': np.std(masked_samples, axis=0), + 'min': np.min(masked_samples, axis=0), + 'max': np.max(masked_samples, axis=0), + 'median': np.median(masked_samples, axis=0), + } + results['percentiles'][k] = {'p1': np.percentile(masked_samples, 1, 0)[0], + 'p10': np.percentile(masked_samples, 10, 0)[0], 'p50': np.percentile(masked_samples, 50, 0)[0], - 'p90': np.percentile(masked_samples, 90, 0)[0]} + 'p90': np.percentile(masked_samples, 90, 0)[0], + 'p99': np.percentile(masked_samples, 99, 0)[0]} # Replace Nan values by the mean before proceeding with the SA indices = np.where(mask == 0)[0] # samples[~mask] = results[k].mean diff --git a/easyvvuq/analysis/results.py b/easyvvuq/analysis/results.py index dc1ccc0f5..febaf9dfe 100644 --- a/easyvvuq/analysis/results.py +++ b/easyvvuq/analysis/results.py @@ -596,8 +596,9 @@ def plot_moments( self.describe(qoi, 'std'), self.describe(qoi, 'mean') + self.describe(qoi, 'std'), label='std', alpha=alpha) ax.plot(xvalues, self.describe(qoi, 'mean'), label='mean') - ax.plot(xvalues, self.describe(qoi, '1%'), '--', label='1%', color='black') - ax.plot(xvalues, self.describe(qoi, '99%'), '--', label='99%', color='black') + if all(v in self.supported_stats() for v in ['1%', '99%']): + ax.plot(xvalues, self.describe(qoi, '1%'), '--', label='1%', color='black') + ax.plot(xvalues, self.describe(qoi, '99%'), '--', label='99%', color='black') ax.grid(True) if ylabel is None: ax.set_ylabel(qoi) diff --git a/install_EasyVVUQ.sh b/install_EasyVVUQ.sh deleted file mode 100644 index b4cf56f9d..000000000 --- a/install_EasyVVUQ.sh +++ /dev/null @@ -1,13 +0,0 @@ -#1) Install Requirements -echo 'Install Requirements' -pip install -r requirements.txt - -#2) Install EasyVVUQ -echo 'Installing EasyVVUQ' -python -m pip install . - -#3) Build cannonsim test -echo 'Building cannonsim' -cd tests/cannonsim/src -make -cd ../../.. diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 000000000..75a3aa1cb --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,74 @@ +[build-system] +requires = ["setuptools>=64", "setuptools_scm>=8"] +build-backend = "setuptools.build_meta" + +[project] +name = "easyvvuq" +description = "Library to facilitate simple Verification, Validation and Uncertainty Quantification of simulation codes" +readme = "README.md" +authors = [{name = "CCS"}] +license = {file = "LICENSE"} +dynamic = ["version"] +requires-python = ">=3.8" +dependencies = [ + "numpy<2", + "pandas", + "scipy", + "wheel", + "chaospy==4.3.2", + "numpoly==1.1.3", + "SALib", + "SQLAlchemy", + "cerberus", + "dask[complete]", + "dask_jobqueue", + "cloudpickle", + "scikit-learn", + "jinja2", + "kubernetes", + "squarify", + "dill", + "tqdm", + "qcg-pilotjob~=0.13.0", + "qcg-pilotjob-executor-api~=0.13.0", + "h5py", + "tomli", + "fipy", +] + +[project.optional-dependencies] +docs = [ + "sphinx >= 5.3", +] +tests = [ + "pytest >= 3.3.0", + "pytest-pep8", + "pytest-benchmark", + "pytest-dependency", +] +lint = [ + "black", + "ruff", +] + +[project.urls] +Source = "https://github.com/UCL-CCS/EasyVVUQ" +Tracker = "https://github.com/UCL-CCS/EasyVVUQ/issues" +Documentation = "https://easyvvuq.readthedocs.io/en/dev/" + +[tool.setuptools.packages.find] +where = ["easyvvuq"] + +[tool.setuptools_scm] +write_to = "easyvvuq/_version.py" + +[tool.black] +exclude = "easyvvuq/_version.py" + +[tool.pytest.ini_options] +pep8ignore = [ + "*.py E265", + "__init__.py E402", + "tests/*.py E128", +] +pep8maxlinelength = 100 diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index e77a75ee2..000000000 --- a/requirements.txt +++ /dev/null @@ -1,29 +0,0 @@ -numpy -pandas -scipy -wheel -chaospy==4.3.2 -numpoly==1.1.3 -SALib -pytest -pytest-pep8 -pytest-benchmark -pytest-dependency -SQLAlchemy -cerberus -dask[complete] -dask_jobqueue -cloudpickle -scikit-learn -jinja2 -kubernetes -autopep8 -squarify -dill -tqdm -qcg-pilotjob~=0.13.0 -qcg-pilotjob-executor-api~=0.13.0 -h5py -tomli -fipy -setuptools diff --git a/setup.cfg b/setup.cfg deleted file mode 100644 index 7bab74efc..000000000 --- a/setup.cfg +++ /dev/null @@ -1,14 +0,0 @@ -[tool:pytest] -pep8ignore = - *.py E265 - __init__.py E402 - tests/*.py E128 -pep8maxlinelength=100 - -[versioneer] -# Automatic version numbering scheme -VCS = git -style = pep440 -versionfile_source = easyvvuq/_version.py -versionfile_build = easyvvuq/_version.py -tag_prefix = '' diff --git a/setup.py b/setup.py deleted file mode 100644 index 256de2933..000000000 --- a/setup.py +++ /dev/null @@ -1,41 +0,0 @@ -from os import path -import setuptools.command.build_py -from setuptools import setup, find_packages -import distutils -import versioneer -import subprocess - -class BuildPyCommand(setuptools.command.build_py.build_py): - def run(self): - setuptools.command.build_py.build_py.run(self) - -# read the contents of README file -this_directory = path.abspath(path.dirname(__file__)) -with open(path.join(this_directory, 'README.md'), encoding='utf-8') as f: - long_description = f.read() - -cmdclass = versioneer.get_cmdclass() -cmdclass['build_py'] = BuildPyCommand - -setup( - name='easyvvuq', - - version=versioneer.get_version(), - cmdclass=cmdclass, - - description=('Library to facilitate simple Verification, Validation and ' - 'Uncertainty Quantification of simulation codes'), - - long_description=long_description, - long_description_content_type='text/markdown', - - url='https://readthedocs.org/projects/easyvvuq/', - - author='CCS', - - install_requires=open("requirements.txt", "r").readlines(), - - packages=find_packages(), - - include_package_data=True, -) diff --git a/tests/test_ensemble_boot.py b/tests/test_ensemble_boot.py index b1592eb94..b656420a9 100644 --- a/tests/test_ensemble_boot.py +++ b/tests/test_ensemble_boot.py @@ -1,5 +1,5 @@ from easyvvuq.analysis.ensemble_boot import confidence_interval, bootstrap -from easyvvuq.analysis.ensemble_boot import ensemble_bootstrap, EnsembleBoot +from easyvvuq.analysis.ensemble_boot import ensemble_bootstrap, EnsembleBoot, EnsembleBootMultiple import os import numpy as np import pandas as pd @@ -82,3 +82,19 @@ def test_ensemble_boot(): 'b': ['group1'] * VALUES.shape[0] + ['group2'] * VALUES.shape[0]}) results = analysis.analyse(df) assert (not results.empty) + +def test_ensemble_boot_multiple(): + analysis = EnsembleBootMultiple() + assert (analysis.element_name() == 'ensemble_boot_multiple') + assert (analysis.element_version() == '0.1') + with pytest.raises(RuntimeError): + analysis.analyse() + with pytest.raises(RuntimeError): + analysis.analyse(pd.DataFrame({})) + analysis = EnsembleBootMultiple(groupby=['b'], qoi_cols=['a'], stat_func=[np.mean, np.var, np.median]) + df = pd.DataFrame({ + 'a': np.concatenate((VALUES, VALUES)), + 'b': ['group1'] * VALUES.shape[0] + ['group2'] * VALUES.shape[0]}) + results = analysis.analyse(df) + assert (not results.empty) + assert (results.values.shape == (2, 9)) diff --git a/tests/test_mc_analysis_results.py b/tests/test_mc_analysis_results.py index 09766bbf6..8c052d24f 100644 --- a/tests/test_mc_analysis_results.py +++ b/tests/test_mc_analysis_results.py @@ -119,18 +119,28 @@ def test_describe(results_vectors): results_vectors.describe()[ ('g', 1)].to_dict() == { + '1%': 0.007945053328978277, '10%': 0.08156520178597204, - '90%': 0.8729378821725343, + '90%': 0.8729378821725343, + '99%': 0.9950881374303691, + 'max': 0.9983981908285983, 'mean': 0.4691844466934421, - 'var': 0.08534945020531205, - 'std': 0.29214628220347433}) + 'median': 0.4495551186167759, + 'min': 0.0038532031638378594, + 'std': 0.29214628220347433, + 'var': 0.08534945020531205}) assert ( results_vectors.describe('h')[ ('h', 1)].to_dict() == { + '1%': 0.039851582820686876, '10%': 0.21724677702965456, '90%': 0.9764815719704141, + '99%': 0.9969701901368494, + 'max': 0.9993165378092128, 'mean': 0.6873389710989142, - 'var': 0.07501266456861228, - 'std': 0.27388440000958847}) + 'median': 0.7696583654805108, + 'min': 0.01590577381510083, + 'std': 0.27388440000958847, + 'var': 0.07501266456861228}) assert (isinstance(results_vectors.describe('h', 'std'), np.ndarray)) diff --git a/tutorials/fusion-aleatoric-sobol.ipynb b/tutorials/fusion-aleatoric-sobol.ipynb new file mode 100644 index 000000000..1d35a1f63 --- /dev/null +++ b/tutorials/fusion-aleatoric-sobol.ipynb @@ -0,0 +1,8560 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a62ca0c6-c640-417c-a6d4-43dd9e492679", + "metadata": {}, + "source": [ + "# Exploration of the effect of aleatoric uncertainties on Sobol Coefficients\n", + "\n", + "In this notebook we will explore the role od aleatoric uncertainties on the calculation od statistical quantities such as Sobol Coefficients.\n", + "\n", + "To do this we take the results of an existing run using the fusion tutorial model and then perturb the \"measured\" values at the various positions determined by the PCE campaign, and recalculate the Sobol indices. We do this for a number of cases and explore the impact of the chosen noise level on the calculated Sobol indices." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9484a8d4-83cd-4c07-9d15-be451e44d2dd", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-24T09:30:30.043116Z", + "iopub.status.busy": "2024-06-24T09:30:30.042807Z", + "iopub.status.idle": "2024-06-24T09:30:36.881343Z", + "shell.execute_reply": "2024-06-24T09:30:36.880536Z", + "shell.execute_reply.started": "2024-06-24T09:30:30.043086Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pickle\n", + "import easyvvuq as uq\n", + "import os\n", + "import ast\n", + "import time\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "efcea9c5-6757-42cf-b908-56579c8525c8", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-24T09:30:36.883495Z", + "iopub.status.busy": "2024-06-24T09:30:36.882710Z", + "iopub.status.idle": "2024-06-24T09:30:37.536816Z", + "shell.execute_reply": "2024-06-24T09:30:37.536194Z", + "shell.execute_reply.started": "2024-06-24T09:30:36.883472Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# This tutorial builds on the fusion example\n", + "import fusion\n", + "Te, ne, rho, rho_norm = fusion.solve_Te()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "be2393fe-2fea-4cd6-b401-9694b5cd9672", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-24T09:30:37.539550Z", + "iopub.status.busy": "2024-06-24T09:30:37.539361Z", + "iopub.status.idle": "2024-06-24T09:30:37.542314Z", + "shell.execute_reply": "2024-06-24T09:30:37.541702Z", + "shell.execute_reply.started": "2024-06-24T09:30:37.539536Z" + } + }, + "outputs": [], + "source": [ + "# initialize the random number generator\n", + "rng = np.random.default_rng()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "40086617-90ca-441a-8423-3fc9d9e24715", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-24T09:30:37.543460Z", + "iopub.status.busy": "2024-06-24T09:30:37.543313Z", + "iopub.status.idle": "2024-06-24T09:30:37.583273Z", + "shell.execute_reply": "2024-06-24T09:30:37.582846Z", + "shell.execute_reply.started": "2024-06-24T09:30:37.543449Z" + } + }, + "outputs": [], + "source": [ + "# function that will be used to add correlated noise\n", + "def randomize(x, ng=11, lcorr=0.2):\n", + " y = 0.0\n", + " for x0, r in zip(np.linspace(0,1,ng), rng.standard_normal(ng)):\n", + " y += np.exp(-((x-x0)/lcorr)**2)*r\n", + " return y" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7818f638-e2de-40d6-bea7-e3527379549c", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-24T09:30:37.584504Z", + "iopub.status.busy": "2024-06-24T09:30:37.583998Z", + "iopub.status.idle": "2024-06-24T09:30:38.119818Z", + "shell.execute_reply": "2024-06-24T09:30:38.119418Z", + "shell.execute_reply.started": "2024-06-24T09:30:37.584489Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Based on 1000 samples')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Demonstrate how the randomize function works\n", + "Randomize = np.array([randomize(rho_norm) for i in range(1000)])\n", + "plt.clf()\n", + "plt.plot(rho_norm, Randomize.mean(axis=0), lw=5)\n", + "plt.fill_between(rho_norm, Randomize.mean(axis=0)-Randomize.std(axis=0), Randomize.mean(axis=0)+Randomize.std(axis=0), alpha=0.5)\n", + "plt.plot(rho_norm, Randomize.T, alpha=0.05)\n", + "plt.xlabel('rho_tor_norm')\n", + "plt.ylabel('Random number')\n", + "plt.title('Based on 1000 samples')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "53288bbd-9458-4b71-a80d-ac7600ca23dc", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-24T09:30:38.120686Z", + "iopub.status.busy": "2024-06-24T09:30:38.120532Z", + "iopub.status.idle": "2024-06-24T09:30:38.547155Z", + "shell.execute_reply": "2024-06-24T09:30:38.545804Z", + "shell.execute_reply.started": "2024-06-24T09:30:38.120674Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Show what 10% noise applied to the Te profile looks like\n", + "Te_scan = np.array([Te+Te/10*randomize(rho_norm) for i in range(1000)])\n", + "Te_scan = Te*(1+0.1*Randomize)\n", + "\n", + "plt.clf()\n", + "plt.plot(rho_norm, Te_scan.T, alpha=0.01)\n", + "plt.plot(rho_norm, Te_scan.mean(axis=0), lw=5)\n", + "plt.fill_between(rho_norm, Te_scan.mean(axis=0)-Te_scan.std(axis=0), Te_scan.mean(axis=0)+Te_scan.std(axis=0), alpha=0.5)\n", + "plt.plot(rho_norm, Te, '--')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ba2f2caa-0f83-4834-8270-8c5ced53d54b", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-24T09:30:38.552048Z", + "iopub.status.busy": "2024-06-24T09:30:38.551885Z", + "iopub.status.idle": "2024-06-24T09:31:35.307495Z", + "shell.execute_reply": "2024-06-24T09:31:35.306069Z", + "shell.execute_reply.started": "2024-06-24T09:30:38.552037Z" + }, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running locally\n", + "\n", + "Time for phase 1 = 9.115\n", + "Number of samples = 243\n", + "Time for phase 2 = 0.337\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 1.845e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.000e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Volume = 88.8264396098042 m^3\n", + "Heating power = 2.155e+06 W\n", + "Time for phase 3 = 38.900\n", + "Running locally\n", + "Running locally\n", + "Running locally\n", + "Running locally\n", + "Running locally\n", + "Running locally\n", + "Running locally\n", + "Running locally\n", + "C\n", + "Time for phase 4 = 1.025\n", + "Time for phase 5 = 0.050\n", + "Time for phase 6 = 6.191\n", + "Time for phase 7 = 0.015\n", + "Time for phase 8 = 0.057\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Now run an EasyVVUQ campaign\n", + "%run ./easyvvuq_fusion_dask_tutorial.py -l" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "9b24042f-9d6a-4581-a741-b953ef0c1f43", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-24T09:31:35.315238Z", + "iopub.status.busy": "2024-06-24T09:31:35.314700Z", + "iopub.status.idle": "2024-06-24T09:31:35.337307Z", + "shell.execute_reply": "2024-06-24T09:31:35.336781Z", + "shell.execute_reply.started": "2024-06-24T09:31:35.315220Z" + } + }, + "outputs": [], + "source": [ + "# We grab the saved results\n", + "results = pickle.load(open('easyvvuq_fusion_dask_tutorial/fusion_results.pickle', 'rb'))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "75e85025-6477-4c45-b940-519666eaf3b4", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-24T09:31:35.338181Z", + "iopub.status.busy": "2024-06-24T09:31:35.338062Z", + "iopub.status.idle": "2024-06-24T09:31:35.489765Z", + "shell.execute_reply": "2024-06-24T09:31:35.489129Z", + "shell.execute_reply.started": "2024-06-24T09:31:35.338170Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# And plot some results\n", + "rho_norm = results.describe(\"rho_norm\", \"mean\")\n", + "te_mean = results.describe(\"te\", \"mean\")\n", + "te_std = results.describe(\"te\", \"std\")\n", + "te_10_pct = results.describe(\"te\", \"10%\")\n", + "te_90_pct = results.describe(\"te\", \"90%\")\n", + "te_min = results.describe(\"te\", \"min\")\n", + "te_max = results.describe(\"te\", \"max\")\n", + "\n", + "plt.figure()\n", + "plt.plot(rho, te_mean, \"b-\", label=\"Mean\")\n", + "plt.plot(rho, te_mean - te_std, \"b--\", label=\"+1 std deviation\")\n", + "plt.plot(rho, te_mean + te_std, \"b--\")\n", + "plt.fill_between(rho, te_mean - te_std, te_mean + te_std,\n", + " color=\"b\", alpha=0.2)\n", + "plt.plot(rho, te_10_pct, \"b:\", label=\"10 and 90 percentiles\")\n", + "plt.plot(rho, te_90_pct, \"b:\")\n", + "plt.fill_between(rho, te_10_pct, te_90_pct, color=\"b\", alpha=0.1)\n", + "plt.fill_between(rho, te_min, te_max, color=\"b\", alpha=0.05)\n", + "\n", + "plt.legend(loc=0)\n", + "plt.xlabel(\"rho [m]\")\n", + "plt.ylabel(\"Te [eV]\");" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b743bf04-2c4c-4c29-bca6-5e8d22b728f7", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-24T09:31:35.491523Z", + "iopub.status.busy": "2024-06-24T09:31:35.491395Z", + "iopub.status.idle": "2024-06-24T09:31:35.620890Z", + "shell.execute_reply": "2024-06-24T09:31:35.620469Z", + "shell.execute_reply.started": "2024-06-24T09:31:35.491511Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.clf()\n", + "plt.contourf(rho_norm, rho_norm, results.raw_data['correlation_matrices']['te'])\n", + "plt.colorbar()\n", + "plt.xlabel('rho_tor_norm')\n", + "plt.ylabel('rho_tor_norm')\n", + "plt.title('Te correlation matrix')\n", + "plt.contour(rho_norm, rho_norm, results.raw_data['correlation_matrices']['te'], levels=[0.9, 0.99, 0.999, 0.9999, 0.99999, 0.999999], colors='k');" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "6ce05321-8b6e-4f83-a95e-55aec953ccfe", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-24T09:31:35.622419Z", + "iopub.status.busy": "2024-06-24T09:31:35.622044Z", + "iopub.status.idle": "2024-06-24T09:31:35.716935Z", + "shell.execute_reply": "2024-06-24T09:31:35.716605Z", + "shell.execute_reply.started": "2024-06-24T09:31:35.622404Z" + } + }, + "outputs": [], + "source": [ + "# We recover the old campaign\n", + "DIR = 'easyvvuq_fusion_dask_tutorial'\n", + "old_campaign = uq.Campaign(name=\"fusion_pce.\", db_location= f'sqlite:///{os.path.abspath(os.curdir)}/{DIR}/campaign.db')\n", + "old_runs = old_campaign.list_runs()\n", + "\n", + "results_df = old_campaign.get_collation_result()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c5eadff9-7f5e-4e7d-bf80-58604900f5e0", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-24T09:31:35.720810Z", + "iopub.status.busy": "2024-06-24T09:31:35.720632Z", + "iopub.status.idle": "2024-06-24T09:31:35.726007Z", + "shell.execute_reply": "2024-06-24T09:31:35.725345Z", + "shell.execute_reply.started": "2024-06-24T09:31:35.720798Z" + } + }, + "outputs": [], + "source": [ + "# Define a function for perturbing an old result\n", + "def get_case(old_runs, noise=0.1):\n", + "\n", + " new_runs = []\n", + " for r in old_runs:\n", + " d = r[1].copy()\n", + " d['run_id'] = r[0]\n", + " res = ast.literal_eval(d['result'])\n", + " res['te'] = list(np.array(res['te']) * (1+noise*randomize(np.array(res['rho_norm']))))\n", + " d['result'] = res\n", + " new_runs.append(d)\n", + "\n", + " df_runs=[]\n", + " for d in new_runs:\n", + " D = {**d['params'], **d['result']}\n", + " D['run_name'] = d['run_name']\n", + " D['run_dir'] = d['run_dir']\n", + " D['run_id'] = d['run_id']\n", + " pd_result={}\n", + " for key in D.keys():\n", + " if not isinstance(D[key], list):\n", + " try:\n", + " pd_result[(key, 0)].append(D[key])\n", + " except KeyError:\n", + " pd_result[(key, 0)] = D[key]\n", + " else:\n", + " for i, elt in enumerate(D[key]):\n", + " try:\n", + " pd_result[(key, i)].append(D[key][i])[0]\n", + " except KeyError:\n", + " pd_result[(key, i)] = [D[key][i]][0]\n", + " df_runs.append(pd_result)\n", + "\n", + " df = pd.DataFrame(df_runs)\n", + " df.columns = pd.MultiIndex.from_tuples(df.columns)\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ec239ea9-a0d0-4b5b-b5db-22e82796ea5a", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-24T09:31:35.727000Z", + "iopub.status.busy": "2024-06-24T09:31:35.726756Z", + "iopub.status.idle": "2024-06-24T09:31:41.954089Z", + "shell.execute_reply": "2024-06-24T09:31:41.949432Z", + "shell.execute_reply.started": "2024-06-24T09:31:35.726976Z" + } + }, + "outputs": [], + "source": [ + "# See if we recover the old results with zero noise\n", + "df = get_case(old_runs, noise=0.0)\n", + "# Here we create an analysis instance based on the sampler from the old campaign\n", + "analysis = uq.analysis.PCEAnalysis(sampler=old_campaign.get_active_sampler(), qoi_cols=results.qois)\n", + "# And use this to perform the PCE analysis based on the just created dataframe \n", + "R = analysis.analyse(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d914290f-6886-4da4-a504-7146ee98fbbf", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-24T09:31:41.968243Z", + "iopub.status.busy": "2024-06-24T09:31:41.956053Z", + "iopub.status.idle": "2024-06-24T09:31:42.085316Z", + "shell.execute_reply": "2024-06-24T09:31:42.082128Z", + "shell.execute_reply.started": "2024-06-24T09:31:41.968216Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Compare the mean of te\n", + "results.describe('te', 'mean') - R.describe('te', 'mean')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "e590af81-2ceb-4913-8ce7-9ec542c00cd1", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-24T09:31:42.090482Z", + "iopub.status.busy": "2024-06-24T09:31:42.087174Z", + "iopub.status.idle": "2024-06-24T09:31:42.096220Z", + "shell.execute_reply": "2024-06-24T09:31:42.095387Z", + "shell.execute_reply.started": "2024-06-24T09:31:42.090466Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Compare the te ifirst sobol for Qe_tot\n", + "results.raw_data['sobols_first']['te']['Qe_tot'] - R.raw_data['sobols_first']['te']['Qe_tot']" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "e6cc3e74-be46-4cd5-aa0f-e04ce3bf044d", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-24T09:31:42.101695Z", + "iopub.status.busy": "2024-06-24T09:31:42.097196Z", + "iopub.status.idle": "2024-06-24T09:31:42.227549Z", + "shell.execute_reply": "2024-06-24T09:31:42.227062Z", + "shell.execute_reply.started": "2024-06-24T09:31:42.101676Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compare (graphically) the Sobol first results: stabndard and with zero noise\n", + "rho_norm = results.describe('rho_norm', 'mean')\n", + "\n", + "plt.clf()\n", + "for s in results.sobols_first('te').keys():\n", + " plt.plot(rho_norm, results.sobols_first('te')[s], '-', label=f'{s} REF')\n", + "\n", + "for s in R.sobols_first('te').keys():\n", + " plt.plot(rho_norm, R.sobols_first('te')[s], '--', label=s)\n", + "\n", + "plt.xlabel('rho_norm')\n", + "plt.ylabel('sobols first')\n", + "plt.legend(loc=0, ncol=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "1ffc0608-a5bd-4d77-ad80-10d2327cab1d", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-24T09:31:42.228727Z", + "iopub.status.busy": "2024-06-24T09:31:42.228458Z", + "iopub.status.idle": "2024-06-24T09:40:44.325122Z", + "shell.execute_reply": "2024-06-24T09:40:44.324707Z", + "shell.execute_reply.started": "2024-06-24T09:31:42.228711Z" + }, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "noise = 0.01\n", + "i = 0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 523.804\n", + "i = 1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 512.761\n", + "i = 2\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 517.725\n", + "i = 3\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 525.853\n", + "i = 4\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 521.626\n", + "i = 5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 537.664\n", + "i = 6\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 541.288\n", + "i = 7\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 542.135\n", + "i = 8\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 537.483\n", + "i = 9\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 535.795\n", + "i = 10\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 530.801\n", + "i = 11\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 530.119\n", + "i = 12\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 528.910\n", + "i = 13\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 526.084\n", + "i = 14\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 522.177\n", + "i = 15\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 518.935\n", + "i = 16\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 516.479\n", + "i = 17\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 518.418\n", + "i = 18\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 516.187\n", + "i = 19\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 515.655\n", + "i = 20\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 513.973\n", + "i = 21\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 512.072\n", + "i = 22\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 509.565\n", + "i = 23\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 507.948\n", + "i = 24\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 518.050\n", + "i = 25\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 517.703\n", + "i = 26\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 516.303\n", + "i = 27\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 514.141\n", + "i = 28\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 516.056\n", + "i = 29\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 514.429\n", + "i = 30\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 514.206\n", + "i = 31\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 511.338\n", + "i = 32\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 508.486\n", + "i = 33\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 505.979\n", + "i = 34\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 503.444\n", + "i = 35\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 503.981\n", + "i = 36\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 502.220\n", + "i = 37\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 503.446\n", + "i = 38\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 501.199\n", + "i = 39\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 498.976\n", + "i = 40\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 497.503\n", + "i = 41\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 495.600\n", + "i = 42\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 493.719\n", + "i = 43\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 494.126\n", + "i = 44\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 492.947\n", + "i = 45\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 491.403\n", + "i = 46\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 490.314\n", + "i = 47\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 488.461\n", + "i = 48\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 487.030\n", + "i = 49\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 485.400\n", + "i = 50\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 488.884\n", + "i = 51\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 488.238\n", + "i = 52\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 486.171\n", + "i = 53\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 486.474\n", + "i = 54\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 485.311\n", + "i = 55\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 484.098\n", + "i = 56\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 484.017\n", + "i = 57\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 481.985\n", + "i = 58\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 479.749\n", + "i = 59\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 477.598\n", + "i = 60\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 475.672\n", + "i = 61\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 473.684\n", + "i = 62\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 471.804\n", + "i = 63\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 470.220\n", + "i = 64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 468.290\n", + "i = 65\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 466.319\n", + "i = 66\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 464.172\n", + "i = 67\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 462.460\n", + "i = 68\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 460.442\n", + "i = 69\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 459.440\n", + "i = 70\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 457.682\n", + "i = 71\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 455.672\n", + "i = 72\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 454.150\n", + "i = 73\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 452.138\n", + "i = 74\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 450.138\n", + "i = 75\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 448.204\n", + "i = 76\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 446.257\n", + "i = 77\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 444.305\n", + "i = 78\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 442.499\n", + "i = 79\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 440.591\n", + "i = 80\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 438.694\n", + "i = 81\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 436.872\n", + "i = 82\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 435.094\n", + "i = 83\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 434.080\n", + "i = 84\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 432.379\n", + "i = 85\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 431.057\n", + "i = 86\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 429.468\n", + "i = 87\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 428.022\n", + "i = 88\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 426.410\n", + "i = 89\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 424.612\n", + "i = 90\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 423.113\n", + "i = 91\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 421.608\n", + "i = 92\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 419.941\n", + "i = 93\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 420.044\n", + "i = 94\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 419.000\n", + "i = 95\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 417.818\n", + "i = 96\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 416.700\n", + "i = 97\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 415.903\n", + "i = 98\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 414.773\n", + "i = 99\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 413.458\n", + "noise = 0.02\n", + "i = 0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 411.809\n", + "i = 1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 410.050\n", + "i = 2\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 408.388\n", + "i = 3\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 406.731\n", + "i = 4\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 405.297\n", + "i = 5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 403.870\n", + "i = 6\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 402.448\n", + "i = 7\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 400.997\n", + "i = 8\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 399.466\n", + "i = 9\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 397.974\n", + "i = 10\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 396.361\n", + "i = 11\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 395.223\n", + "i = 12\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 393.474\n", + "i = 13\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 391.797\n", + "i = 14\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 390.114\n", + "i = 15\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 388.435\n", + "i = 16\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 386.817\n", + "i = 17\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 385.344\n", + "i = 18\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 383.668\n", + "i = 19\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 382.077\n", + "i = 20\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 380.440\n", + "i = 21\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 378.788\n", + "i = 22\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 377.373\n", + "i = 23\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 375.837\n", + "i = 24\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 374.740\n", + "i = 25\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 373.144\n", + "i = 26\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 371.548\n", + "i = 27\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 370.051\n", + "i = 28\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 368.607\n", + "i = 29\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 367.262\n", + "i = 30\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 365.812\n", + "i = 31\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 364.449\n", + "i = 32\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 363.438\n", + "i = 33\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 362.485\n", + "i = 34\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 361.305\n", + "i = 35\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 360.494\n", + "i = 36\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 359.579\n", + "i = 37\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 358.290\n", + "i = 38\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 356.806\n", + "i = 39\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 355.705\n", + "i = 40\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 354.314\n", + "i = 41\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 352.993\n", + "i = 42\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 351.634\n", + "i = 43\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 350.159\n", + "i = 44\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 348.649\n", + "i = 45\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 347.192\n", + "i = 46\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 345.677\n", + "i = 47\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 344.104\n", + "i = 48\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 342.572\n", + "i = 49\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 341.106\n", + "i = 50\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 339.582\n", + "i = 51\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 338.021\n", + "i = 52\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 336.482\n", + "i = 53\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 335.322\n", + "i = 54\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 333.797\n", + "i = 55\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 332.262\n", + "i = 56\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 331.284\n", + "i = 57\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 329.847\n", + "i = 58\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 328.284\n", + "i = 59\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 326.747\n", + "i = 60\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 325.209\n", + "i = 61\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 323.692\n", + "i = 62\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 322.173\n", + "i = 63\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 320.672\n", + "i = 64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 319.172\n", + "i = 65\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 317.700\n", + "i = 66\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 316.265\n", + "i = 67\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 315.242\n", + "i = 68\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 313.806\n", + "i = 69\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 312.358\n", + "i = 70\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 310.890\n", + "i = 71\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 309.383\n", + "i = 72\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 307.886\n", + "i = 73\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 306.975\n", + "i = 74\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 305.547\n", + "i = 75\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 304.044\n", + "i = 76\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 302.660\n", + "i = 77\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 301.420\n", + "i = 78\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 300.362\n", + "i = 79\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 298.929\n", + "i = 80\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 298.079\n", + "i = 81\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 296.676\n", + "i = 82\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 296.352\n", + "i = 83\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 295.036\n", + "i = 84\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 294.178\n", + "i = 85\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 292.886\n", + "i = 86\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 291.489\n", + "i = 87\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 290.037\n", + "i = 88\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 288.575\n", + "i = 89\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 287.233\n", + "i = 90\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 285.753\n", + "i = 91\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 284.334\n", + "i = 92\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 282.948\n", + "i = 93\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 281.506\n", + "i = 94\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 280.067\n", + "i = 95\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 278.603\n", + "i = 96\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 277.478\n", + "i = 97\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 276.136\n", + "i = 98\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 274.712\n", + "i = 99\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 273.311\n", + "noise = 0.05\n", + "i = 0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 271.882\n", + "i = 1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 270.495\n", + "i = 2\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 269.050\n", + "i = 3\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 267.657\n", + "i = 4\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 266.217\n", + "i = 5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 264.801\n", + "i = 6\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 263.410\n", + "i = 7\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 261.954\n", + "i = 8\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 260.563\n", + "i = 9\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 259.143\n", + "i = 10\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 257.744\n", + "i = 11\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 256.340\n", + "i = 12\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 255.119\n", + "i = 13\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 253.672\n", + "i = 14\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 252.263\n", + "i = 15\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 250.839\n", + "i = 16\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 249.386\n", + "i = 17\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 247.938\n", + "i = 18\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 246.494\n", + "i = 19\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 245.059\n", + "i = 20\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 243.632\n", + "i = 21\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 242.203\n", + "i = 22\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 240.776\n", + "i = 23\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 239.334\n", + "i = 24\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 237.980\n", + "i = 25\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 236.882\n", + "i = 26\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 235.531\n", + "i = 27\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 234.299\n", + "i = 28\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 232.878\n", + "i = 29\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 231.456\n", + "i = 30\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 230.074\n", + "i = 31\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 228.632\n", + "i = 32\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 227.217\n", + "i = 33\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 225.811\n", + "i = 34\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 224.387\n", + "i = 35\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 222.944\n", + "i = 36\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 221.531\n", + "i = 37\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 220.090\n", + "i = 38\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 218.655\n", + "i = 39\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 217.226\n", + "i = 40\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 215.792\n", + "i = 41\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 214.358\n", + "i = 42\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 213.106\n", + "i = 43\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 211.681\n", + "i = 44\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 210.258\n", + "i = 45\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 208.836\n", + "i = 46\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 207.434\n", + "i = 47\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 206.030\n", + "i = 48\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 204.667\n", + "i = 49\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 203.261\n", + "i = 50\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 201.871\n", + "i = 51\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 200.755\n", + "i = 52\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 199.377\n", + "i = 53\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 198.142\n", + "i = 54\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 196.776\n", + "i = 55\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 195.397\n", + "i = 56\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 194.087\n", + "i = 57\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 192.810\n", + "i = 58\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 191.384\n", + "i = 59\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 189.962\n", + "i = 60\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 188.556\n", + "i = 61\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 187.147\n", + "i = 62\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 185.747\n", + "i = 63\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 184.335\n", + "i = 64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 182.925\n", + "i = 65\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 181.509\n", + "i = 66\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 180.125\n", + "i = 67\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 178.756\n", + "i = 68\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 177.352\n", + "i = 69\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 175.966\n", + "i = 70\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 174.855\n", + "i = 71\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 173.506\n", + "i = 72\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 172.446\n", + "i = 73\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 171.242\n", + "i = 74\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 169.884\n", + "i = 75\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 168.555\n", + "i = 76\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 167.162\n", + "i = 77\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 165.759\n", + "i = 78\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 164.378\n", + "i = 79\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 162.981\n", + "i = 80\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 161.582\n", + "i = 81\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 160.339\n", + "i = 82\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 159.024\n", + "i = 83\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 157.938\n", + "i = 84\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 156.575\n", + "i = 85\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 155.191\n", + "i = 86\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 153.876\n", + "i = 87\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 152.516\n", + "i = 88\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 151.213\n", + "i = 89\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 149.838\n", + "i = 90\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 148.431\n", + "i = 91\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 147.031\n", + "i = 92\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 145.618\n", + "i = 93\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 144.221\n", + "i = 94\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 142.818\n", + "i = 95\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 141.410\n", + "i = 96\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 140.004\n", + "i = 97\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 138.605\n", + "i = 98\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 137.208\n", + "i = 99\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 135.818\n", + "noise = 0.1\n", + "i = 0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 134.421\n", + "i = 1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 133.039\n", + "i = 2\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 131.653\n", + "i = 3\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 130.253\n", + "i = 4\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 128.929\n", + "i = 5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 127.533\n", + "i = 6\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 126.154\n", + "i = 7\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 124.762\n", + "i = 8\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 123.375\n", + "i = 9\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 121.987\n", + "i = 10\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 120.662\n", + "i = 11\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 119.336\n", + "i = 12\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 117.993\n", + "i = 13\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 116.684\n", + "i = 14\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 115.330\n", + "i = 15\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 114.059\n", + "i = 16\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 112.760\n", + "i = 17\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 111.406\n", + "i = 18\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 110.132\n", + "i = 19\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 108.832\n", + "i = 20\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 107.461\n", + "i = 21\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 106.113\n", + "i = 22\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 104.761\n", + "i = 23\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 103.395\n", + "i = 24\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 102.019\n", + "i = 25\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 100.652\n", + "i = 26\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 99.280\n", + "i = 27\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 97.965\n", + "i = 28\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 96.626\n", + "i = 29\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 95.248\n", + "i = 30\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 93.874\n", + "i = 31\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 92.494\n", + "i = 32\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 91.116\n", + "i = 33\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 89.745\n", + "i = 34\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 88.369\n", + "i = 35\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 86.994\n", + "i = 36\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 85.663\n", + "i = 37\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 84.289\n", + "i = 38\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 82.919\n", + "i = 39\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 81.547\n", + "i = 40\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 80.174\n", + "i = 41\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 78.803\n", + "i = 42\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 77.434\n", + "i = 43\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 76.061\n", + "i = 44\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 74.761\n", + "i = 45\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 73.396\n", + "i = 46\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 72.026\n", + "i = 47\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 70.658\n", + "i = 48\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 69.283\n", + "i = 49\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 67.923\n", + "i = 50\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 66.551\n", + "i = 51\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 65.176\n", + "i = 52\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 63.870\n", + "i = 53\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 62.505\n", + "i = 54\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 61.130\n", + "i = 55\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 59.763\n", + "i = 56\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 58.430\n", + "i = 57\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 57.079\n", + "i = 58\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 55.729\n", + "i = 59\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 54.371\n", + "i = 60\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 53.010\n", + "i = 61\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 51.642\n", + "i = 62\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 50.276\n", + "i = 63\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 48.907\n", + "i = 64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 47.568\n", + "i = 65\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 46.216\n", + "i = 66\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 44.862\n", + "i = 67\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 43.506\n", + "i = 68\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 42.140\n", + "i = 69\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 40.804\n", + "i = 70\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 39.440\n", + "i = 71\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 38.076\n", + "i = 72\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 36.714\n", + "i = 73\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 35.348\n", + "i = 74\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 33.988\n", + "i = 75\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 32.622\n", + "i = 76\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 31.259\n", + "i = 77\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 29.897\n", + "i = 78\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 28.532\n", + "i = 79\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 27.170\n", + "i = 80\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 25.808\n", + "i = 81\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 24.445\n", + "i = 82\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 23.083\n", + "i = 83\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 21.720\n", + "i = 84\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 20.358\n", + "i = 85\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 19.006\n", + "i = 86\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 17.645\n", + "i = 87\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 16.283\n", + "i = 88\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 14.923\n", + "i = 89\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 13.565\n", + "i = 90\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 12.206\n", + "i = 91\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 10.848\n", + "i = 92\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 9.490\n", + "i = 93\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 8.134\n", + "i = 94\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 6.778\n", + "i = 95\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 5.422\n", + "i = 96\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 4.066\n", + "i = 97\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 2.711\n", + "i = 98\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected remaining time = 1.355\n", + "i = 99\n", + "Expected remaining time = 0.000\n", + "Elapsed time was 542.092 seconds\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:529: RuntimeWarning: Skipping computation of cp.Corr\n", + " warnings.warn(f\"Skipping computation of cp.Corr\", RuntimeWarning)\n", + "/Volumes/UserData/dpc/GIT/EasyVVUQ/env/lib/python3.10/site-packages/easyvvuq/analysis/pce_analysis.py:545: RuntimeWarning: Skipping computation of cp.QoI_Dist\n", + " warnings.warn(f\"Skipping computation of cp.QoI_Dist\", RuntimeWarning)\n" + ] + } + ], + "source": [ + "# Now perform a campaign over four noise levels with 100 samples each\n", + "# We will also restrict the analysis to just 'te' and switch off the calculation/saving of CorrelationMatrices and CorrelationMatrices\n", + "analysis = uq.analysis.PCEAnalysis(sampler=old_campaign.get_active_sampler(), qoi_cols=['te'], CorrelationMatrices=False, OutputDistributions=False)\n", + "N = 4 * 100\n", + "time_start = time.time()\n", + "icnt = 0\n", + "collect_results={}\n", + "for noise in [0.01, 0.02, 0.05, 0.10]:\n", + " print(f'{noise = }')\n", + " collect_results[noise] = []\n", + " for i in range(100):\n", + " print(f'{i = }')\n", + " df = get_case(old_runs, noise=noise)\n", + " R = analysis.analyse(df)\n", + " collect_results[noise].append({'df': df, 'R': R})\n", + " icnt += 1\n", + " print(f'Expected remaining time = {(time.time() - time_start)/icnt*(N-icnt):0.3f}')\n", + "time_end = time.time()\n", + "print(f'Elapsed time was {time_end-time_start:0.3f} seconds')" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "41c9bf3d-a6e6-41bd-b5a6-a1586aa66ee9", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-24T09:40:44.328271Z", + "iopub.status.busy": "2024-06-24T09:40:44.328058Z", + "iopub.status.idle": "2024-06-24T09:40:48.568909Z", + "shell.execute_reply": "2024-06-24T09:40:48.568052Z", + "shell.execute_reply.started": "2024-06-24T09:40:44.328252Z" + } + }, + "outputs": [], + "source": [ + "# Save the results\n", + "with open('collect_results_100.pickle', \"bw\") as f_pickle:\n", + " pickle.dump(collect_results, f_pickle)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "0e0e955b-e433-4d8b-82c2-505c5be9511a", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-24T09:40:48.570214Z", + "iopub.status.busy": "2024-06-24T09:40:48.569992Z", + "iopub.status.idle": "2024-06-24T09:40:51.517977Z", + "shell.execute_reply": "2024-06-24T09:40:51.516976Z", + "shell.execute_reply.started": "2024-06-24T09:40:48.570192Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot all of the samples, together with the reference values\n", + "for noise in collect_results.keys():\n", + " plt.figure()\n", + " for s in results.sobols_first('te').keys():\n", + " plt.plot(rho_norm, results.sobols_first('te')[s], '-', lw=3, label=f'{s} REF')\n", + " for c in collect_results[noise]:\n", + " for s in results.sobols_first('te').keys():\n", + " plt.plot(rho_norm, c['R'].sobols_first('te')[s], alpha=0.5)\n", + " plt.xlabel('rho_norm')\n", + " plt.ylabel('sobols first')\n", + " plt.title(f'{noise = }')\n", + " plt.legend(loc=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "a5236dca-007d-4101-bfad-769ed5eb7bcc", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-24T09:40:51.519971Z", + "iopub.status.busy": "2024-06-24T09:40:51.519693Z", + "iopub.status.idle": "2024-06-24T09:40:56.814543Z", + "shell.execute_reply": "2024-06-24T09:40:56.813758Z", + "shell.execute_reply.started": "2024-06-24T09:40:51.519955Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the mean and standard deviation of the perturbed results, together with the original value\n", + "for noise in collect_results.keys():\n", + " plt.figure()\n", + " for s in results.sobols_first('te').keys():\n", + " plt.plot(rho_norm, results.sobols_first('te')[s], '--', lw=3, label=f'{s} REF')\n", + " for c in collect_results[noise]:\n", + " for s in results.sobols_first('te').keys():\n", + " V = np.array([c['R'].sobols_first('te')[s] for c in collect_results[noise]])\n", + " plt.plot(rho_norm, V.mean(axis=0), '-')\n", + " plt.fill_between(rho_norm, V.mean(axis=0) - V.std(axis=0), V.mean(axis=0) + V.std(axis=0), alpha=0.1)\n", + " plt.xlabel('rho_norm')\n", + " plt.ylabel('sobols first')\n", + " plt.title(f'{noise = }')\n", + " plt.legend(loc=0)" + ] + }, + { + "cell_type": "markdown", + "id": "c9db5b1f-9bdc-4c28-bfa8-0af751ead81c", + "metadata": {}, + "source": [ + "At higher levels of noise we see that the Sobol first values for H0 drop, particularly in the range 0.5 -- 0.9" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/kubernetes/Dockerfile b/tutorials/kubernetes/Dockerfile index 850d784e3..55371951e 100644 --- a/tutorials/kubernetes/Dockerfile +++ b/tutorials/kubernetes/Dockerfile @@ -4,7 +4,7 @@ RUN apt-get update && \ apt-get install -y python3-pip && \ apt-get install -y git && \ apt-get install -y tini && \ - pip3 install easyvvuq && \ + pip3 install easyvvuq --break-system-packages && \ git clone https://github.com/UCL-CCS/EasyVVUQ.git ENTRYPOINT ["tini", "--"] diff --git a/versioneer.py b/versioneer.py deleted file mode 100644 index 1e3753e63..000000000 --- a/versioneer.py +++ /dev/null @@ -1,2277 +0,0 @@ - -# Version: 0.29 - -"""The Versioneer - like a rocketeer, but for versions. - -The Versioneer -============== - -* like a rocketeer, but for versions! -* https://github.com/python-versioneer/python-versioneer -* Brian Warner -* License: Public Domain (Unlicense) -* Compatible with: Python 3.7, 3.8, 3.9, 3.10, 3.11 and pypy3 -* [![Latest Version][pypi-image]][pypi-url] -* [![Build Status][travis-image]][travis-url] - -This is a tool for managing a recorded version number in setuptools-based -python projects. The goal is to remove the tedious and error-prone "update -the embedded version string" step from your release process. Making a new -release should be as easy as recording a new tag in your version-control -system, and maybe making new tarballs. - - -## Quick Install - -Versioneer provides two installation modes. The "classic" vendored mode installs -a copy of versioneer into your repository. The experimental build-time dependency mode -is intended to allow you to skip this step and simplify the process of upgrading. - -### Vendored mode - -* `pip install versioneer` to somewhere in your $PATH - * A [conda-forge recipe](https://github.com/conda-forge/versioneer-feedstock) is - available, so you can also use `conda install -c conda-forge versioneer` -* add a `[tool.versioneer]` section to your `pyproject.toml` or a - `[versioneer]` section to your `setup.cfg` (see [Install](INSTALL.md)) - * Note that you will need to add `tomli; python_version < "3.11"` to your - build-time dependencies if you use `pyproject.toml` -* run `versioneer install --vendor` in your source tree, commit the results -* verify version information with `python setup.py version` - -### Build-time dependency mode - -* `pip install versioneer` to somewhere in your $PATH - * A [conda-forge recipe](https://github.com/conda-forge/versioneer-feedstock) is - available, so you can also use `conda install -c conda-forge versioneer` -* add a `[tool.versioneer]` section to your `pyproject.toml` or a - `[versioneer]` section to your `setup.cfg` (see [Install](INSTALL.md)) -* add `versioneer` (with `[toml]` extra, if configuring in `pyproject.toml`) - to the `requires` key of the `build-system` table in `pyproject.toml`: - ```toml - [build-system] - requires = ["setuptools", "versioneer[toml]"] - build-backend = "setuptools.build_meta" - ``` -* run `versioneer install --no-vendor` in your source tree, commit the results -* verify version information with `python setup.py version` - -## Version Identifiers - -Source trees come from a variety of places: - -* a version-control system checkout (mostly used by developers) -* a nightly tarball, produced by build automation -* a snapshot tarball, produced by a web-based VCS browser, like github's - "tarball from tag" feature -* a release tarball, produced by "setup.py sdist", distributed through PyPI - -Within each source tree, the version identifier (either a string or a number, -this tool is format-agnostic) can come from a variety of places: - -* ask the VCS tool itself, e.g. "git describe" (for checkouts), which knows - about recent "tags" and an absolute revision-id -* the name of the directory into which the tarball was unpacked -* an expanded VCS keyword ($Id$, etc) -* a `_version.py` created by some earlier build step - -For released software, the version identifier is closely related to a VCS -tag. Some projects use tag names that include more than just the version -string (e.g. "myproject-1.2" instead of just "1.2"), in which case the tool -needs to strip the tag prefix to extract the version identifier. For -unreleased software (between tags), the version identifier should provide -enough information to help developers recreate the same tree, while also -giving them an idea of roughly how old the tree is (after version 1.2, before -version 1.3). Many VCS systems can report a description that captures this, -for example `git describe --tags --dirty --always` reports things like -"0.7-1-g574ab98-dirty" to indicate that the checkout is one revision past the -0.7 tag, has a unique revision id of "574ab98", and is "dirty" (it has -uncommitted changes). - -The version identifier is used for multiple purposes: - -* to allow the module to self-identify its version: `myproject.__version__` -* to choose a name and prefix for a 'setup.py sdist' tarball - -## Theory of Operation - -Versioneer works by adding a special `_version.py` file into your source -tree, where your `__init__.py` can import it. This `_version.py` knows how to -dynamically ask the VCS tool for version information at import time. - -`_version.py` also contains `$Revision$` markers, and the installation -process marks `_version.py` to have this marker rewritten with a tag name -during the `git archive` command. As a result, generated tarballs will -contain enough information to get the proper version. - -To allow `setup.py` to compute a version too, a `versioneer.py` is added to -the top level of your source tree, next to `setup.py` and the `setup.cfg` -that configures it. This overrides several distutils/setuptools commands to -compute the version when invoked, and changes `setup.py build` and `setup.py -sdist` to replace `_version.py` with a small static file that contains just -the generated version data. - -## Installation - -See [INSTALL.md](./INSTALL.md) for detailed installation instructions. - -## Version-String Flavors - -Code which uses Versioneer can learn about its version string at runtime by -importing `_version` from your main `__init__.py` file and running the -`get_versions()` function. From the "outside" (e.g. in `setup.py`), you can -import the top-level `versioneer.py` and run `get_versions()`. - -Both functions return a dictionary with different flavors of version -information: - -* `['version']`: A condensed version string, rendered using the selected - style. This is the most commonly used value for the project's version - string. The default "pep440" style yields strings like `0.11`, - `0.11+2.g1076c97`, or `0.11+2.g1076c97.dirty`. See the "Styles" section - below for alternative styles. - -* `['full-revisionid']`: detailed revision identifier. For Git, this is the - full SHA1 commit id, e.g. "1076c978a8d3cfc70f408fe5974aa6c092c949ac". - -* `['date']`: Date and time of the latest `HEAD` commit. For Git, it is the - commit date in ISO 8601 format. This will be None if the date is not - available. - -* `['dirty']`: a boolean, True if the tree has uncommitted changes. Note that - this is only accurate if run in a VCS checkout, otherwise it is likely to - be False or None - -* `['error']`: if the version string could not be computed, this will be set - to a string describing the problem, otherwise it will be None. It may be - useful to throw an exception in setup.py if this is set, to avoid e.g. - creating tarballs with a version string of "unknown". - -Some variants are more useful than others. Including `full-revisionid` in a -bug report should allow developers to reconstruct the exact code being tested -(or indicate the presence of local changes that should be shared with the -developers). `version` is suitable for display in an "about" box or a CLI -`--version` output: it can be easily compared against release notes and lists -of bugs fixed in various releases. - -The installer adds the following text to your `__init__.py` to place a basic -version in `YOURPROJECT.__version__`: - - from ._version import get_versions - __version__ = get_versions()['version'] - del get_versions - -## Styles - -The setup.cfg `style=` configuration controls how the VCS information is -rendered into a version string. - -The default style, "pep440", produces a PEP440-compliant string, equal to the -un-prefixed tag name for actual releases, and containing an additional "local -version" section with more detail for in-between builds. For Git, this is -TAG[+DISTANCE.gHEX[.dirty]] , using information from `git describe --tags ---dirty --always`. For example "0.11+2.g1076c97.dirty" indicates that the -tree is like the "1076c97" commit but has uncommitted changes (".dirty"), and -that this commit is two revisions ("+2") beyond the "0.11" tag. For released -software (exactly equal to a known tag), the identifier will only contain the -stripped tag, e.g. "0.11". - -Other styles are available. See [details.md](details.md) in the Versioneer -source tree for descriptions. - -## Debugging - -Versioneer tries to avoid fatal errors: if something goes wrong, it will tend -to return a version of "0+unknown". To investigate the problem, run `setup.py -version`, which will run the version-lookup code in a verbose mode, and will -display the full contents of `get_versions()` (including the `error` string, -which may help identify what went wrong). - -## Known Limitations - -Some situations are known to cause problems for Versioneer. This details the -most significant ones. More can be found on Github -[issues page](https://github.com/python-versioneer/python-versioneer/issues). - -### Subprojects - -Versioneer has limited support for source trees in which `setup.py` is not in -the root directory (e.g. `setup.py` and `.git/` are *not* siblings). The are -two common reasons why `setup.py` might not be in the root: - -* Source trees which contain multiple subprojects, such as - [Buildbot](https://github.com/buildbot/buildbot), which contains both - "master" and "slave" subprojects, each with their own `setup.py`, - `setup.cfg`, and `tox.ini`. Projects like these produce multiple PyPI - distributions (and upload multiple independently-installable tarballs). -* Source trees whose main purpose is to contain a C library, but which also - provide bindings to Python (and perhaps other languages) in subdirectories. - -Versioneer will look for `.git` in parent directories, and most operations -should get the right version string. However `pip` and `setuptools` have bugs -and implementation details which frequently cause `pip install .` from a -subproject directory to fail to find a correct version string (so it usually -defaults to `0+unknown`). - -`pip install --editable .` should work correctly. `setup.py install` might -work too. - -Pip-8.1.1 is known to have this problem, but hopefully it will get fixed in -some later version. - -[Bug #38](https://github.com/python-versioneer/python-versioneer/issues/38) is tracking -this issue. The discussion in -[PR #61](https://github.com/python-versioneer/python-versioneer/pull/61) describes the -issue from the Versioneer side in more detail. -[pip PR#3176](https://github.com/pypa/pip/pull/3176) and -[pip PR#3615](https://github.com/pypa/pip/pull/3615) contain work to improve -pip to let Versioneer work correctly. - -Versioneer-0.16 and earlier only looked for a `.git` directory next to the -`setup.cfg`, so subprojects were completely unsupported with those releases. - -### Editable installs with setuptools <= 18.5 - -`setup.py develop` and `pip install --editable .` allow you to install a -project into a virtualenv once, then continue editing the source code (and -test) without re-installing after every change. - -"Entry-point scripts" (`setup(entry_points={"console_scripts": ..})`) are a -convenient way to specify executable scripts that should be installed along -with the python package. - -These both work as expected when using modern setuptools. When using -setuptools-18.5 or earlier, however, certain operations will cause -`pkg_resources.DistributionNotFound` errors when running the entrypoint -script, which must be resolved by re-installing the package. This happens -when the install happens with one version, then the egg_info data is -regenerated while a different version is checked out. Many setup.py commands -cause egg_info to be rebuilt (including `sdist`, `wheel`, and installing into -a different virtualenv), so this can be surprising. - -[Bug #83](https://github.com/python-versioneer/python-versioneer/issues/83) describes -this one, but upgrading to a newer version of setuptools should probably -resolve it. - - -## Updating Versioneer - -To upgrade your project to a new release of Versioneer, do the following: - -* install the new Versioneer (`pip install -U versioneer` or equivalent) -* edit `setup.cfg` and `pyproject.toml`, if necessary, - to include any new configuration settings indicated by the release notes. - See [UPGRADING](./UPGRADING.md) for details. -* re-run `versioneer install --[no-]vendor` in your source tree, to replace - `SRC/_version.py` -* commit any changed files - -## Future Directions - -This tool is designed to make it easily extended to other version-control -systems: all VCS-specific components are in separate directories like -src/git/ . The top-level `versioneer.py` script is assembled from these -components by running make-versioneer.py . In the future, make-versioneer.py -will take a VCS name as an argument, and will construct a version of -`versioneer.py` that is specific to the given VCS. It might also take the -configuration arguments that are currently provided manually during -installation by editing setup.py . Alternatively, it might go the other -direction and include code from all supported VCS systems, reducing the -number of intermediate scripts. - -## Similar projects - -* [setuptools_scm](https://github.com/pypa/setuptools_scm/) - a non-vendored build-time - dependency -* [minver](https://github.com/jbweston/miniver) - a lightweight reimplementation of - versioneer -* [versioningit](https://github.com/jwodder/versioningit) - a PEP 518-based setuptools - plugin - -## License - -To make Versioneer easier to embed, all its code is dedicated to the public -domain. The `_version.py` that it creates is also in the public domain. -Specifically, both are released under the "Unlicense", as described in -https://unlicense.org/. - -[pypi-image]: https://img.shields.io/pypi/v/versioneer.svg -[pypi-url]: https://pypi.python.org/pypi/versioneer/ -[travis-image]: -https://img.shields.io/travis/com/python-versioneer/python-versioneer.svg -[travis-url]: https://travis-ci.com/github/python-versioneer/python-versioneer - -""" -# pylint:disable=invalid-name,import-outside-toplevel,missing-function-docstring -# pylint:disable=missing-class-docstring,too-many-branches,too-many-statements -# pylint:disable=raise-missing-from,too-many-lines,too-many-locals,import-error -# pylint:disable=too-few-public-methods,redefined-outer-name,consider-using-with -# pylint:disable=attribute-defined-outside-init,too-many-arguments - -import configparser -import errno -import json -import os -import re -import subprocess -import sys -from pathlib import Path -from typing import Any, Callable, cast, Dict, List, Optional, Tuple, Union -from typing import NoReturn -import functools - -have_tomllib = True -if sys.version_info >= (3, 11): - import tomllib -else: - try: - import tomli as tomllib - except ImportError: - have_tomllib = False - - -class VersioneerConfig: - """Container for Versioneer configuration parameters.""" - - VCS: str - style: str - tag_prefix: str - versionfile_source: str - versionfile_build: Optional[str] - parentdir_prefix: Optional[str] - verbose: Optional[bool] - - -def get_root() -> str: - """Get the project root directory. - - We require that all commands are run from the project root, i.e. the - directory that contains setup.py, setup.cfg, and versioneer.py . - """ - root = os.path.realpath(os.path.abspath(os.getcwd())) - setup_py = os.path.join(root, "setup.py") - pyproject_toml = os.path.join(root, "pyproject.toml") - versioneer_py = os.path.join(root, "versioneer.py") - if not ( - os.path.exists(setup_py) - or os.path.exists(pyproject_toml) - or os.path.exists(versioneer_py) - ): - # allow 'python path/to/setup.py COMMAND' - root = os.path.dirname(os.path.realpath(os.path.abspath(sys.argv[0]))) - setup_py = os.path.join(root, "setup.py") - pyproject_toml = os.path.join(root, "pyproject.toml") - versioneer_py = os.path.join(root, "versioneer.py") - if not ( - os.path.exists(setup_py) - or os.path.exists(pyproject_toml) - or os.path.exists(versioneer_py) - ): - err = ("Versioneer was unable to run the project root directory. " - "Versioneer requires setup.py to be executed from " - "its immediate directory (like 'python setup.py COMMAND'), " - "or in a way that lets it use sys.argv[0] to find the root " - "(like 'python path/to/setup.py COMMAND').") - raise VersioneerBadRootError(err) - try: - # Certain runtime workflows (setup.py install/develop in a setuptools - # tree) execute all dependencies in a single python process, so - # "versioneer" may be imported multiple times, and python's shared - # module-import table will cache the first one. So we can't use - # os.path.dirname(__file__), as that will find whichever - # versioneer.py was first imported, even in later projects. - my_path = os.path.realpath(os.path.abspath(__file__)) - me_dir = os.path.normcase(os.path.splitext(my_path)[0]) - vsr_dir = os.path.normcase(os.path.splitext(versioneer_py)[0]) - if me_dir != vsr_dir and "VERSIONEER_PEP518" not in globals(): - print("Warning: build in %s is using versioneer.py from %s" - % (os.path.dirname(my_path), versioneer_py)) - except NameError: - pass - return root - - -def get_config_from_root(root: str) -> VersioneerConfig: - """Read the project setup.cfg file to determine Versioneer config.""" - # This might raise OSError (if setup.cfg is missing), or - # configparser.NoSectionError (if it lacks a [versioneer] section), or - # configparser.NoOptionError (if it lacks "VCS="). See the docstring at - # the top of versioneer.py for instructions on writing your setup.cfg . - root_pth = Path(root) - pyproject_toml = root_pth / "pyproject.toml" - setup_cfg = root_pth / "setup.cfg" - section: Union[Dict[str, Any], configparser.SectionProxy, None] = None - if pyproject_toml.exists() and have_tomllib: - try: - with open(pyproject_toml, 'rb') as fobj: - pp = tomllib.load(fobj) - section = pp['tool']['versioneer'] - except (tomllib.TOMLDecodeError, KeyError) as e: - print(f"Failed to load config from {pyproject_toml}: {e}") - print("Try to load it from setup.cfg") - if not section: - parser = configparser.ConfigParser() - with open(setup_cfg) as cfg_file: - parser.read_file(cfg_file) - parser.get("versioneer", "VCS") # raise error if missing - - section = parser["versioneer"] - - # `cast`` really shouldn't be used, but its simplest for the - # common VersioneerConfig users at the moment. We verify against - # `None` values elsewhere where it matters - - cfg = VersioneerConfig() - cfg.VCS = section['VCS'] - cfg.style = section.get("style", "") - cfg.versionfile_source = cast(str, section.get("versionfile_source")) - cfg.versionfile_build = section.get("versionfile_build") - cfg.tag_prefix = cast(str, section.get("tag_prefix")) - if cfg.tag_prefix in ("''", '""', None): - cfg.tag_prefix = "" - cfg.parentdir_prefix = section.get("parentdir_prefix") - if isinstance(section, configparser.SectionProxy): - # Make sure configparser translates to bool - cfg.verbose = section.getboolean("verbose") - else: - cfg.verbose = section.get("verbose") - - return cfg - - -class NotThisMethod(Exception): - """Exception raised if a method is not valid for the current scenario.""" - - -# these dictionaries contain VCS-specific tools -LONG_VERSION_PY: Dict[str, str] = {} -HANDLERS: Dict[str, Dict[str, Callable]] = {} - - -def register_vcs_handler(vcs: str, method: str) -> Callable: # decorator - """Create decorator to mark a method as the handler of a VCS.""" - def decorate(f: Callable) -> Callable: - """Store f in HANDLERS[vcs][method].""" - HANDLERS.setdefault(vcs, {})[method] = f - return f - return decorate - - -def run_command( - commands: List[str], - args: List[str], - cwd: Optional[str] = None, - verbose: bool = False, - hide_stderr: bool = False, - env: Optional[Dict[str, str]] = None, -) -> Tuple[Optional[str], Optional[int]]: - """Call the given command(s).""" - assert isinstance(commands, list) - process = None - - popen_kwargs: Dict[str, Any] = {} - if sys.platform == "win32": - # This hides the console window if pythonw.exe is used - startupinfo = subprocess.STARTUPINFO() - startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW - popen_kwargs["startupinfo"] = startupinfo - - for command in commands: - try: - dispcmd = str([command] + args) - # remember shell=False, so use git.cmd on windows, not just git - process = subprocess.Popen([command] + args, cwd=cwd, env=env, - stdout=subprocess.PIPE, - stderr=(subprocess.PIPE if hide_stderr - else None), **popen_kwargs) - break - except OSError as e: - if e.errno == errno.ENOENT: - continue - if verbose: - print("unable to run %s" % dispcmd) - print(e) - return None, None - else: - if verbose: - print("unable to find command, tried %s" % (commands,)) - return None, None - stdout = process.communicate()[0].strip().decode() - if process.returncode != 0: - if verbose: - print("unable to run %s (error)" % dispcmd) - print("stdout was %s" % stdout) - return None, process.returncode - return stdout, process.returncode - - -LONG_VERSION_PY['git'] = r''' -# This file helps to compute a version number in source trees obtained from -# git-archive tarball (such as those provided by githubs download-from-tag -# feature). Distribution tarballs (built by setup.py sdist) and build -# directories (produced by setup.py build) will contain a much shorter file -# that just contains the computed version number. - -# This file is released into the public domain. -# Generated by versioneer-0.29 -# https://github.com/python-versioneer/python-versioneer - -"""Git implementation of _version.py.""" - -import errno -import os -import re -import subprocess -import sys -from typing import Any, Callable, Dict, List, Optional, Tuple -import functools - - -def get_keywords() -> Dict[str, str]: - """Get the keywords needed to look up the version information.""" - # these strings will be replaced by git during git-archive. - # setup.py/versioneer.py will grep for the variable names, so they must - # each be defined on a line of their own. _version.py will just call - # get_keywords(). - git_refnames = "%(DOLLAR)sFormat:%%d%(DOLLAR)s" - git_full = "%(DOLLAR)sFormat:%%H%(DOLLAR)s" - git_date = "%(DOLLAR)sFormat:%%ci%(DOLLAR)s" - keywords = {"refnames": git_refnames, "full": git_full, "date": git_date} - return keywords - - -class VersioneerConfig: - """Container for Versioneer configuration parameters.""" - - VCS: str - style: str - tag_prefix: str - parentdir_prefix: str - versionfile_source: str - verbose: bool - - -def get_config() -> VersioneerConfig: - """Create, populate and return the VersioneerConfig() object.""" - # these strings are filled in when 'setup.py versioneer' creates - # _version.py - cfg = VersioneerConfig() - cfg.VCS = "git" - cfg.style = "%(STYLE)s" - cfg.tag_prefix = "%(TAG_PREFIX)s" - cfg.parentdir_prefix = "%(PARENTDIR_PREFIX)s" - cfg.versionfile_source = "%(VERSIONFILE_SOURCE)s" - cfg.verbose = False - return cfg - - -class NotThisMethod(Exception): - """Exception raised if a method is not valid for the current scenario.""" - - -LONG_VERSION_PY: Dict[str, str] = {} -HANDLERS: Dict[str, Dict[str, Callable]] = {} - - -def register_vcs_handler(vcs: str, method: str) -> Callable: # decorator - """Create decorator to mark a method as the handler of a VCS.""" - def decorate(f: Callable) -> Callable: - """Store f in HANDLERS[vcs][method].""" - if vcs not in HANDLERS: - HANDLERS[vcs] = {} - HANDLERS[vcs][method] = f - return f - return decorate - - -def run_command( - commands: List[str], - args: List[str], - cwd: Optional[str] = None, - verbose: bool = False, - hide_stderr: bool = False, - env: Optional[Dict[str, str]] = None, -) -> Tuple[Optional[str], Optional[int]]: - """Call the given command(s).""" - assert isinstance(commands, list) - process = None - - popen_kwargs: Dict[str, Any] = {} - if sys.platform == "win32": - # This hides the console window if pythonw.exe is used - startupinfo = subprocess.STARTUPINFO() - startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW - popen_kwargs["startupinfo"] = startupinfo - - for command in commands: - try: - dispcmd = str([command] + args) - # remember shell=False, so use git.cmd on windows, not just git - process = subprocess.Popen([command] + args, cwd=cwd, env=env, - stdout=subprocess.PIPE, - stderr=(subprocess.PIPE if hide_stderr - else None), **popen_kwargs) - break - except OSError as e: - if e.errno == errno.ENOENT: - continue - if verbose: - print("unable to run %%s" %% dispcmd) - print(e) - return None, None - else: - if verbose: - print("unable to find command, tried %%s" %% (commands,)) - return None, None - stdout = process.communicate()[0].strip().decode() - if process.returncode != 0: - if verbose: - print("unable to run %%s (error)" %% dispcmd) - print("stdout was %%s" %% stdout) - return None, process.returncode - return stdout, process.returncode - - -def versions_from_parentdir( - parentdir_prefix: str, - root: str, - verbose: bool, -) -> Dict[str, Any]: - """Try to determine the version from the parent directory name. - - Source tarballs conventionally unpack into a directory that includes both - the project name and a version string. We will also support searching up - two directory levels for an appropriately named parent directory - """ - rootdirs = [] - - for _ in range(3): - dirname = os.path.basename(root) - if dirname.startswith(parentdir_prefix): - return {"version": dirname[len(parentdir_prefix):], - "full-revisionid": None, - "dirty": False, "error": None, "date": None} - rootdirs.append(root) - root = os.path.dirname(root) # up a level - - if verbose: - print("Tried directories %%s but none started with prefix %%s" %% - (str(rootdirs), parentdir_prefix)) - raise NotThisMethod("rootdir doesn't start with parentdir_prefix") - - -@register_vcs_handler("git", "get_keywords") -def git_get_keywords(versionfile_abs: str) -> Dict[str, str]: - """Extract version information from the given file.""" - # the code embedded in _version.py can just fetch the value of these - # keywords. When used from setup.py, we don't want to import _version.py, - # so we do it with a regexp instead. This function is not used from - # _version.py. - keywords: Dict[str, str] = {} - try: - with open(versionfile_abs, "r") as fobj: - for line in fobj: - if line.strip().startswith("git_refnames ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["refnames"] = mo.group(1) - if line.strip().startswith("git_full ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["full"] = mo.group(1) - if line.strip().startswith("git_date ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["date"] = mo.group(1) - except OSError: - pass - return keywords - - -@register_vcs_handler("git", "keywords") -def git_versions_from_keywords( - keywords: Dict[str, str], - tag_prefix: str, - verbose: bool, -) -> Dict[str, Any]: - """Get version information from git keywords.""" - if "refnames" not in keywords: - raise NotThisMethod("Short version file found") - date = keywords.get("date") - if date is not None: - # Use only the last line. Previous lines may contain GPG signature - # information. - date = date.splitlines()[-1] - - # git-2.2.0 added "%%cI", which expands to an ISO-8601 -compliant - # datestamp. However we prefer "%%ci" (which expands to an "ISO-8601 - # -like" string, which we must then edit to make compliant), because - # it's been around since git-1.5.3, and it's too difficult to - # discover which version we're using, or to work around using an - # older one. - date = date.strip().replace(" ", "T", 1).replace(" ", "", 1) - refnames = keywords["refnames"].strip() - if refnames.startswith("$Format"): - if verbose: - print("keywords are unexpanded, not using") - raise NotThisMethod("unexpanded keywords, not a git-archive tarball") - refs = {r.strip() for r in refnames.strip("()").split(",")} - # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of - # just "foo-1.0". If we see a "tag: " prefix, prefer those. - TAG = "tag: " - tags = {r[len(TAG):] for r in refs if r.startswith(TAG)} - if not tags: - # Either we're using git < 1.8.3, or there really are no tags. We use - # a heuristic: assume all version tags have a digit. The old git %%d - # expansion behaves like git log --decorate=short and strips out the - # refs/heads/ and refs/tags/ prefixes that would let us distinguish - # between branches and tags. By ignoring refnames without digits, we - # filter out many common branch names like "release" and - # "stabilization", as well as "HEAD" and "master". - tags = {r for r in refs if re.search(r'\d', r)} - if verbose: - print("discarding '%%s', no digits" %% ",".join(refs - tags)) - if verbose: - print("likely tags: %%s" %% ",".join(sorted(tags))) - for ref in sorted(tags): - # sorting will prefer e.g. "2.0" over "2.0rc1" - if ref.startswith(tag_prefix): - r = ref[len(tag_prefix):] - # Filter out refs that exactly match prefix or that don't start - # with a number once the prefix is stripped (mostly a concern - # when prefix is '') - if not re.match(r'\d', r): - continue - if verbose: - print("picking %%s" %% r) - return {"version": r, - "full-revisionid": keywords["full"].strip(), - "dirty": False, "error": None, - "date": date} - # no suitable tags, so version is "0+unknown", but full hex is still there - if verbose: - print("no suitable tags, using unknown + full revision id") - return {"version": "0+unknown", - "full-revisionid": keywords["full"].strip(), - "dirty": False, "error": "no suitable tags", "date": None} - - -@register_vcs_handler("git", "pieces_from_vcs") -def git_pieces_from_vcs( - tag_prefix: str, - root: str, - verbose: bool, - runner: Callable = run_command -) -> Dict[str, Any]: - """Get version from 'git describe' in the root of the source tree. - - This only gets called if the git-archive 'subst' keywords were *not* - expanded, and _version.py hasn't already been rewritten with a short - version string, meaning we're inside a checked out source tree. - """ - GITS = ["git"] - if sys.platform == "win32": - GITS = ["git.cmd", "git.exe"] - - # GIT_DIR can interfere with correct operation of Versioneer. - # It may be intended to be passed to the Versioneer-versioned project, - # but that should not change where we get our version from. - env = os.environ.copy() - env.pop("GIT_DIR", None) - runner = functools.partial(runner, env=env) - - _, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root, - hide_stderr=not verbose) - if rc != 0: - if verbose: - print("Directory %%s not under git control" %% root) - raise NotThisMethod("'git rev-parse --git-dir' returned error") - - # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty] - # if there isn't one, this yields HEX[-dirty] (no NUM) - describe_out, rc = runner(GITS, [ - "describe", "--tags", "--dirty", "--always", "--long", - "--match", f"{tag_prefix}[[:digit:]]*" - ], cwd=root) - # --long was added in git-1.5.5 - if describe_out is None: - raise NotThisMethod("'git describe' failed") - describe_out = describe_out.strip() - full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root) - if full_out is None: - raise NotThisMethod("'git rev-parse' failed") - full_out = full_out.strip() - - pieces: Dict[str, Any] = {} - pieces["long"] = full_out - pieces["short"] = full_out[:7] # maybe improved later - pieces["error"] = None - - branch_name, rc = runner(GITS, ["rev-parse", "--abbrev-ref", "HEAD"], - cwd=root) - # --abbrev-ref was added in git-1.6.3 - if rc != 0 or branch_name is None: - raise NotThisMethod("'git rev-parse --abbrev-ref' returned error") - branch_name = branch_name.strip() - - if branch_name == "HEAD": - # If we aren't exactly on a branch, pick a branch which represents - # the current commit. If all else fails, we are on a branchless - # commit. - branches, rc = runner(GITS, ["branch", "--contains"], cwd=root) - # --contains was added in git-1.5.4 - if rc != 0 or branches is None: - raise NotThisMethod("'git branch --contains' returned error") - branches = branches.split("\n") - - # Remove the first line if we're running detached - if "(" in branches[0]: - branches.pop(0) - - # Strip off the leading "* " from the list of branches. - branches = [branch[2:] for branch in branches] - if "master" in branches: - branch_name = "master" - elif not branches: - branch_name = None - else: - # Pick the first branch that is returned. Good or bad. - branch_name = branches[0] - - pieces["branch"] = branch_name - - # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty] - # TAG might have hyphens. - git_describe = describe_out - - # look for -dirty suffix - dirty = git_describe.endswith("-dirty") - pieces["dirty"] = dirty - if dirty: - git_describe = git_describe[:git_describe.rindex("-dirty")] - - # now we have TAG-NUM-gHEX or HEX - - if "-" in git_describe: - # TAG-NUM-gHEX - mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe) - if not mo: - # unparsable. Maybe git-describe is misbehaving? - pieces["error"] = ("unable to parse git-describe output: '%%s'" - %% describe_out) - return pieces - - # tag - full_tag = mo.group(1) - if not full_tag.startswith(tag_prefix): - if verbose: - fmt = "tag '%%s' doesn't start with prefix '%%s'" - print(fmt %% (full_tag, tag_prefix)) - pieces["error"] = ("tag '%%s' doesn't start with prefix '%%s'" - %% (full_tag, tag_prefix)) - return pieces - pieces["closest-tag"] = full_tag[len(tag_prefix):] - - # distance: number of commits since tag - pieces["distance"] = int(mo.group(2)) - - # commit: short hex revision ID - pieces["short"] = mo.group(3) - - else: - # HEX: no tags - pieces["closest-tag"] = None - out, rc = runner(GITS, ["rev-list", "HEAD", "--left-right"], cwd=root) - pieces["distance"] = len(out.split()) # total number of commits - - # commit date: see ISO-8601 comment in git_versions_from_keywords() - date = runner(GITS, ["show", "-s", "--format=%%ci", "HEAD"], cwd=root)[0].strip() - # Use only the last line. Previous lines may contain GPG signature - # information. - date = date.splitlines()[-1] - pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1) - - return pieces - - -def plus_or_dot(pieces: Dict[str, Any]) -> str: - """Return a + if we don't already have one, else return a .""" - if "+" in pieces.get("closest-tag", ""): - return "." - return "+" - - -def render_pep440(pieces: Dict[str, Any]) -> str: - """Build up version string, with post-release "local version identifier". - - Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you - get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty - - Exceptions: - 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += plus_or_dot(pieces) - rendered += "%%d.g%%s" %% (pieces["distance"], pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - else: - # exception #1 - rendered = "0+untagged.%%d.g%%s" %% (pieces["distance"], - pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - return rendered - - -def render_pep440_branch(pieces: Dict[str, Any]) -> str: - """TAG[[.dev0]+DISTANCE.gHEX[.dirty]] . - - The ".dev0" means not master branch. Note that .dev0 sorts backwards - (a feature branch will appear "older" than the master branch). - - Exceptions: - 1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += plus_or_dot(pieces) - rendered += "%%d.g%%s" %% (pieces["distance"], pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - else: - # exception #1 - rendered = "0" - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += "+untagged.%%d.g%%s" %% (pieces["distance"], - pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - return rendered - - -def pep440_split_post(ver: str) -> Tuple[str, Optional[int]]: - """Split pep440 version string at the post-release segment. - - Returns the release segments before the post-release and the - post-release version number (or -1 if no post-release segment is present). - """ - vc = str.split(ver, ".post") - return vc[0], int(vc[1] or 0) if len(vc) == 2 else None - - -def render_pep440_pre(pieces: Dict[str, Any]) -> str: - """TAG[.postN.devDISTANCE] -- No -dirty. - - Exceptions: - 1: no tags. 0.post0.devDISTANCE - """ - if pieces["closest-tag"]: - if pieces["distance"]: - # update the post release segment - tag_version, post_version = pep440_split_post(pieces["closest-tag"]) - rendered = tag_version - if post_version is not None: - rendered += ".post%%d.dev%%d" %% (post_version + 1, pieces["distance"]) - else: - rendered += ".post0.dev%%d" %% (pieces["distance"]) - else: - # no commits, use the tag as the version - rendered = pieces["closest-tag"] - else: - # exception #1 - rendered = "0.post0.dev%%d" %% pieces["distance"] - return rendered - - -def render_pep440_post(pieces: Dict[str, Any]) -> str: - """TAG[.postDISTANCE[.dev0]+gHEX] . - - The ".dev0" means dirty. Note that .dev0 sorts backwards - (a dirty tree will appear "older" than the corresponding clean one), - but you shouldn't be releasing software with -dirty anyways. - - Exceptions: - 1: no tags. 0.postDISTANCE[.dev0] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += ".post%%d" %% pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - rendered += plus_or_dot(pieces) - rendered += "g%%s" %% pieces["short"] - else: - # exception #1 - rendered = "0.post%%d" %% pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - rendered += "+g%%s" %% pieces["short"] - return rendered - - -def render_pep440_post_branch(pieces: Dict[str, Any]) -> str: - """TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] . - - The ".dev0" means not master branch. - - Exceptions: - 1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += ".post%%d" %% pieces["distance"] - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += plus_or_dot(pieces) - rendered += "g%%s" %% pieces["short"] - if pieces["dirty"]: - rendered += ".dirty" - else: - # exception #1 - rendered = "0.post%%d" %% pieces["distance"] - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += "+g%%s" %% pieces["short"] - if pieces["dirty"]: - rendered += ".dirty" - return rendered - - -def render_pep440_old(pieces: Dict[str, Any]) -> str: - """TAG[.postDISTANCE[.dev0]] . - - The ".dev0" means dirty. - - Exceptions: - 1: no tags. 0.postDISTANCE[.dev0] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += ".post%%d" %% pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - else: - # exception #1 - rendered = "0.post%%d" %% pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - return rendered - - -def render_git_describe(pieces: Dict[str, Any]) -> str: - """TAG[-DISTANCE-gHEX][-dirty]. - - Like 'git describe --tags --dirty --always'. - - Exceptions: - 1: no tags. HEX[-dirty] (note: no 'g' prefix) - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"]: - rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"]) - else: - # exception #1 - rendered = pieces["short"] - if pieces["dirty"]: - rendered += "-dirty" - return rendered - - -def render_git_describe_long(pieces: Dict[str, Any]) -> str: - """TAG-DISTANCE-gHEX[-dirty]. - - Like 'git describe --tags --dirty --always -long'. - The distance/hash is unconditional. - - Exceptions: - 1: no tags. HEX[-dirty] (note: no 'g' prefix) - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"]) - else: - # exception #1 - rendered = pieces["short"] - if pieces["dirty"]: - rendered += "-dirty" - return rendered - - -def render(pieces: Dict[str, Any], style: str) -> Dict[str, Any]: - """Render the given version pieces into the requested style.""" - if pieces["error"]: - return {"version": "unknown", - "full-revisionid": pieces.get("long"), - "dirty": None, - "error": pieces["error"], - "date": None} - - if not style or style == "default": - style = "pep440" # the default - - if style == "pep440": - rendered = render_pep440(pieces) - elif style == "pep440-branch": - rendered = render_pep440_branch(pieces) - elif style == "pep440-pre": - rendered = render_pep440_pre(pieces) - elif style == "pep440-post": - rendered = render_pep440_post(pieces) - elif style == "pep440-post-branch": - rendered = render_pep440_post_branch(pieces) - elif style == "pep440-old": - rendered = render_pep440_old(pieces) - elif style == "git-describe": - rendered = render_git_describe(pieces) - elif style == "git-describe-long": - rendered = render_git_describe_long(pieces) - else: - raise ValueError("unknown style '%%s'" %% style) - - return {"version": rendered, "full-revisionid": pieces["long"], - "dirty": pieces["dirty"], "error": None, - "date": pieces.get("date")} - - -def get_versions() -> Dict[str, Any]: - """Get version information or return default if unable to do so.""" - # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have - # __file__, we can work backwards from there to the root. Some - # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which - # case we can only use expanded keywords. - - cfg = get_config() - verbose = cfg.verbose - - try: - return git_versions_from_keywords(get_keywords(), cfg.tag_prefix, - verbose) - except NotThisMethod: - pass - - try: - root = os.path.realpath(__file__) - # versionfile_source is the relative path from the top of the source - # tree (where the .git directory might live) to this file. Invert - # this to find the root from __file__. - for _ in cfg.versionfile_source.split('/'): - root = os.path.dirname(root) - except NameError: - return {"version": "0+unknown", "full-revisionid": None, - "dirty": None, - "error": "unable to find root of source tree", - "date": None} - - try: - pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose) - return render(pieces, cfg.style) - except NotThisMethod: - pass - - try: - if cfg.parentdir_prefix: - return versions_from_parentdir(cfg.parentdir_prefix, root, verbose) - except NotThisMethod: - pass - - return {"version": "0+unknown", "full-revisionid": None, - "dirty": None, - "error": "unable to compute version", "date": None} -''' - - -@register_vcs_handler("git", "get_keywords") -def git_get_keywords(versionfile_abs: str) -> Dict[str, str]: - """Extract version information from the given file.""" - # the code embedded in _version.py can just fetch the value of these - # keywords. When used from setup.py, we don't want to import _version.py, - # so we do it with a regexp instead. This function is not used from - # _version.py. - keywords: Dict[str, str] = {} - try: - with open(versionfile_abs, "r") as fobj: - for line in fobj: - if line.strip().startswith("git_refnames ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["refnames"] = mo.group(1) - if line.strip().startswith("git_full ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["full"] = mo.group(1) - if line.strip().startswith("git_date ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["date"] = mo.group(1) - except OSError: - pass - return keywords - - -@register_vcs_handler("git", "keywords") -def git_versions_from_keywords( - keywords: Dict[str, str], - tag_prefix: str, - verbose: bool, -) -> Dict[str, Any]: - """Get version information from git keywords.""" - if "refnames" not in keywords: - raise NotThisMethod("Short version file found") - date = keywords.get("date") - if date is not None: - # Use only the last line. Previous lines may contain GPG signature - # information. - date = date.splitlines()[-1] - - # git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant - # datestamp. However we prefer "%ci" (which expands to an "ISO-8601 - # -like" string, which we must then edit to make compliant), because - # it's been around since git-1.5.3, and it's too difficult to - # discover which version we're using, or to work around using an - # older one. - date = date.strip().replace(" ", "T", 1).replace(" ", "", 1) - refnames = keywords["refnames"].strip() - if refnames.startswith("$Format"): - if verbose: - print("keywords are unexpanded, not using") - raise NotThisMethod("unexpanded keywords, not a git-archive tarball") - refs = {r.strip() for r in refnames.strip("()").split(",")} - # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of - # just "foo-1.0". If we see a "tag: " prefix, prefer those. - TAG = "tag: " - tags = {r[len(TAG):] for r in refs if r.startswith(TAG)} - if not tags: - # Either we're using git < 1.8.3, or there really are no tags. We use - # a heuristic: assume all version tags have a digit. The old git %d - # expansion behaves like git log --decorate=short and strips out the - # refs/heads/ and refs/tags/ prefixes that would let us distinguish - # between branches and tags. By ignoring refnames without digits, we - # filter out many common branch names like "release" and - # "stabilization", as well as "HEAD" and "master". - tags = {r for r in refs if re.search(r'\d', r)} - if verbose: - print("discarding '%s', no digits" % ",".join(refs - tags)) - if verbose: - print("likely tags: %s" % ",".join(sorted(tags))) - for ref in sorted(tags): - # sorting will prefer e.g. "2.0" over "2.0rc1" - if ref.startswith(tag_prefix): - r = ref[len(tag_prefix):] - # Filter out refs that exactly match prefix or that don't start - # with a number once the prefix is stripped (mostly a concern - # when prefix is '') - if not re.match(r'\d', r): - continue - if verbose: - print("picking %s" % r) - return {"version": r, - "full-revisionid": keywords["full"].strip(), - "dirty": False, "error": None, - "date": date} - # no suitable tags, so version is "0+unknown", but full hex is still there - if verbose: - print("no suitable tags, using unknown + full revision id") - return {"version": "0+unknown", - "full-revisionid": keywords["full"].strip(), - "dirty": False, "error": "no suitable tags", "date": None} - - -@register_vcs_handler("git", "pieces_from_vcs") -def git_pieces_from_vcs( - tag_prefix: str, - root: str, - verbose: bool, - runner: Callable = run_command -) -> Dict[str, Any]: - """Get version from 'git describe' in the root of the source tree. - - This only gets called if the git-archive 'subst' keywords were *not* - expanded, and _version.py hasn't already been rewritten with a short - version string, meaning we're inside a checked out source tree. - """ - GITS = ["git"] - if sys.platform == "win32": - GITS = ["git.cmd", "git.exe"] - - # GIT_DIR can interfere with correct operation of Versioneer. - # It may be intended to be passed to the Versioneer-versioned project, - # but that should not change where we get our version from. - env = os.environ.copy() - env.pop("GIT_DIR", None) - runner = functools.partial(runner, env=env) - - _, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root, - hide_stderr=not verbose) - if rc != 0: - if verbose: - print("Directory %s not under git control" % root) - raise NotThisMethod("'git rev-parse --git-dir' returned error") - - # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty] - # if there isn't one, this yields HEX[-dirty] (no NUM) - describe_out, rc = runner(GITS, [ - "describe", "--tags", "--dirty", "--always", "--long", - "--match", f"{tag_prefix}[[:digit:]]*" - ], cwd=root) - # --long was added in git-1.5.5 - if describe_out is None: - raise NotThisMethod("'git describe' failed") - describe_out = describe_out.strip() - full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root) - if full_out is None: - raise NotThisMethod("'git rev-parse' failed") - full_out = full_out.strip() - - pieces: Dict[str, Any] = {} - pieces["long"] = full_out - pieces["short"] = full_out[:7] # maybe improved later - pieces["error"] = None - - branch_name, rc = runner(GITS, ["rev-parse", "--abbrev-ref", "HEAD"], - cwd=root) - # --abbrev-ref was added in git-1.6.3 - if rc != 0 or branch_name is None: - raise NotThisMethod("'git rev-parse --abbrev-ref' returned error") - branch_name = branch_name.strip() - - if branch_name == "HEAD": - # If we aren't exactly on a branch, pick a branch which represents - # the current commit. If all else fails, we are on a branchless - # commit. - branches, rc = runner(GITS, ["branch", "--contains"], cwd=root) - # --contains was added in git-1.5.4 - if rc != 0 or branches is None: - raise NotThisMethod("'git branch --contains' returned error") - branches = branches.split("\n") - - # Remove the first line if we're running detached - if "(" in branches[0]: - branches.pop(0) - - # Strip off the leading "* " from the list of branches. - branches = [branch[2:] for branch in branches] - if "master" in branches: - branch_name = "master" - elif not branches: - branch_name = None - else: - # Pick the first branch that is returned. Good or bad. - branch_name = branches[0] - - pieces["branch"] = branch_name - - # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty] - # TAG might have hyphens. - git_describe = describe_out - - # look for -dirty suffix - dirty = git_describe.endswith("-dirty") - pieces["dirty"] = dirty - if dirty: - git_describe = git_describe[:git_describe.rindex("-dirty")] - - # now we have TAG-NUM-gHEX or HEX - - if "-" in git_describe: - # TAG-NUM-gHEX - mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe) - if not mo: - # unparsable. Maybe git-describe is misbehaving? - pieces["error"] = ("unable to parse git-describe output: '%s'" - % describe_out) - return pieces - - # tag - full_tag = mo.group(1) - if not full_tag.startswith(tag_prefix): - if verbose: - fmt = "tag '%s' doesn't start with prefix '%s'" - print(fmt % (full_tag, tag_prefix)) - pieces["error"] = ("tag '%s' doesn't start with prefix '%s'" - % (full_tag, tag_prefix)) - return pieces - pieces["closest-tag"] = full_tag[len(tag_prefix):] - - # distance: number of commits since tag - pieces["distance"] = int(mo.group(2)) - - # commit: short hex revision ID - pieces["short"] = mo.group(3) - - else: - # HEX: no tags - pieces["closest-tag"] = None - out, rc = runner(GITS, ["rev-list", "HEAD", "--left-right"], cwd=root) - pieces["distance"] = len(out.split()) # total number of commits - - # commit date: see ISO-8601 comment in git_versions_from_keywords() - date = runner(GITS, ["show", "-s", "--format=%ci", "HEAD"], cwd=root)[0].strip() - # Use only the last line. Previous lines may contain GPG signature - # information. - date = date.splitlines()[-1] - pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1) - - return pieces - - -def do_vcs_install(versionfile_source: str, ipy: Optional[str]) -> None: - """Git-specific installation logic for Versioneer. - - For Git, this means creating/changing .gitattributes to mark _version.py - for export-subst keyword substitution. - """ - GITS = ["git"] - if sys.platform == "win32": - GITS = ["git.cmd", "git.exe"] - files = [versionfile_source] - if ipy: - files.append(ipy) - if "VERSIONEER_PEP518" not in globals(): - try: - my_path = __file__ - if my_path.endswith((".pyc", ".pyo")): - my_path = os.path.splitext(my_path)[0] + ".py" - versioneer_file = os.path.relpath(my_path) - except NameError: - versioneer_file = "versioneer.py" - files.append(versioneer_file) - present = False - try: - with open(".gitattributes", "r") as fobj: - for line in fobj: - if line.strip().startswith(versionfile_source): - if "export-subst" in line.strip().split()[1:]: - present = True - break - except OSError: - pass - if not present: - with open(".gitattributes", "a+") as fobj: - fobj.write(f"{versionfile_source} export-subst\n") - files.append(".gitattributes") - run_command(GITS, ["add", "--"] + files) - - -def versions_from_parentdir( - parentdir_prefix: str, - root: str, - verbose: bool, -) -> Dict[str, Any]: - """Try to determine the version from the parent directory name. - - Source tarballs conventionally unpack into a directory that includes both - the project name and a version string. We will also support searching up - two directory levels for an appropriately named parent directory - """ - rootdirs = [] - - for _ in range(3): - dirname = os.path.basename(root) - if dirname.startswith(parentdir_prefix): - return {"version": dirname[len(parentdir_prefix):], - "full-revisionid": None, - "dirty": False, "error": None, "date": None} - rootdirs.append(root) - root = os.path.dirname(root) # up a level - - if verbose: - print("Tried directories %s but none started with prefix %s" % - (str(rootdirs), parentdir_prefix)) - raise NotThisMethod("rootdir doesn't start with parentdir_prefix") - - -SHORT_VERSION_PY = """ -# This file was generated by 'versioneer.py' (0.29) from -# revision-control system data, or from the parent directory name of an -# unpacked source archive. Distribution tarballs contain a pre-generated copy -# of this file. - -import json - -version_json = ''' -%s -''' # END VERSION_JSON - - -def get_versions(): - return json.loads(version_json) -""" - - -def versions_from_file(filename: str) -> Dict[str, Any]: - """Try to determine the version from _version.py if present.""" - try: - with open(filename) as f: - contents = f.read() - except OSError: - raise NotThisMethod("unable to read _version.py") - mo = re.search(r"version_json = '''\n(.*)''' # END VERSION_JSON", - contents, re.M | re.S) - if not mo: - mo = re.search(r"version_json = '''\r\n(.*)''' # END VERSION_JSON", - contents, re.M | re.S) - if not mo: - raise NotThisMethod("no version_json in _version.py") - return json.loads(mo.group(1)) - - -def write_to_version_file(filename: str, versions: Dict[str, Any]) -> None: - """Write the given version number to the given _version.py file.""" - contents = json.dumps(versions, sort_keys=True, - indent=1, separators=(",", ": ")) - with open(filename, "w") as f: - f.write(SHORT_VERSION_PY % contents) - - print("set %s to '%s'" % (filename, versions["version"])) - - -def plus_or_dot(pieces: Dict[str, Any]) -> str: - """Return a + if we don't already have one, else return a .""" - if "+" in pieces.get("closest-tag", ""): - return "." - return "+" - - -def render_pep440(pieces: Dict[str, Any]) -> str: - """Build up version string, with post-release "local version identifier". - - Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you - get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty - - Exceptions: - 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += plus_or_dot(pieces) - rendered += "%d.g%s" % (pieces["distance"], pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - else: - # exception #1 - rendered = "0+untagged.%d.g%s" % (pieces["distance"], - pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - return rendered - - -def render_pep440_branch(pieces: Dict[str, Any]) -> str: - """TAG[[.dev0]+DISTANCE.gHEX[.dirty]] . - - The ".dev0" means not master branch. Note that .dev0 sorts backwards - (a feature branch will appear "older" than the master branch). - - Exceptions: - 1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += plus_or_dot(pieces) - rendered += "%d.g%s" % (pieces["distance"], pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - else: - # exception #1 - rendered = "0" - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += "+untagged.%d.g%s" % (pieces["distance"], - pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - return rendered - - -def pep440_split_post(ver: str) -> Tuple[str, Optional[int]]: - """Split pep440 version string at the post-release segment. - - Returns the release segments before the post-release and the - post-release version number (or -1 if no post-release segment is present). - """ - vc = str.split(ver, ".post") - return vc[0], int(vc[1] or 0) if len(vc) == 2 else None - - -def render_pep440_pre(pieces: Dict[str, Any]) -> str: - """TAG[.postN.devDISTANCE] -- No -dirty. - - Exceptions: - 1: no tags. 0.post0.devDISTANCE - """ - if pieces["closest-tag"]: - if pieces["distance"]: - # update the post release segment - tag_version, post_version = pep440_split_post(pieces["closest-tag"]) - rendered = tag_version - if post_version is not None: - rendered += ".post%d.dev%d" % (post_version + 1, pieces["distance"]) - else: - rendered += ".post0.dev%d" % (pieces["distance"]) - else: - # no commits, use the tag as the version - rendered = pieces["closest-tag"] - else: - # exception #1 - rendered = "0.post0.dev%d" % pieces["distance"] - return rendered - - -def render_pep440_post(pieces: Dict[str, Any]) -> str: - """TAG[.postDISTANCE[.dev0]+gHEX] . - - The ".dev0" means dirty. Note that .dev0 sorts backwards - (a dirty tree will appear "older" than the corresponding clean one), - but you shouldn't be releasing software with -dirty anyways. - - Exceptions: - 1: no tags. 0.postDISTANCE[.dev0] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += ".post%d" % pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - rendered += plus_or_dot(pieces) - rendered += "g%s" % pieces["short"] - else: - # exception #1 - rendered = "0.post%d" % pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - rendered += "+g%s" % pieces["short"] - return rendered - - -def render_pep440_post_branch(pieces: Dict[str, Any]) -> str: - """TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] . - - The ".dev0" means not master branch. - - Exceptions: - 1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += ".post%d" % pieces["distance"] - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += plus_or_dot(pieces) - rendered += "g%s" % pieces["short"] - if pieces["dirty"]: - rendered += ".dirty" - else: - # exception #1 - rendered = "0.post%d" % pieces["distance"] - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += "+g%s" % pieces["short"] - if pieces["dirty"]: - rendered += ".dirty" - return rendered - - -def render_pep440_old(pieces: Dict[str, Any]) -> str: - """TAG[.postDISTANCE[.dev0]] . - - The ".dev0" means dirty. - - Exceptions: - 1: no tags. 0.postDISTANCE[.dev0] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += ".post%d" % pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - else: - # exception #1 - rendered = "0.post%d" % pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - return rendered - - -def render_git_describe(pieces: Dict[str, Any]) -> str: - """TAG[-DISTANCE-gHEX][-dirty]. - - Like 'git describe --tags --dirty --always'. - - Exceptions: - 1: no tags. HEX[-dirty] (note: no 'g' prefix) - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"]: - rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) - else: - # exception #1 - rendered = pieces["short"] - if pieces["dirty"]: - rendered += "-dirty" - return rendered - - -def render_git_describe_long(pieces: Dict[str, Any]) -> str: - """TAG-DISTANCE-gHEX[-dirty]. - - Like 'git describe --tags --dirty --always -long'. - The distance/hash is unconditional. - - Exceptions: - 1: no tags. HEX[-dirty] (note: no 'g' prefix) - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) - else: - # exception #1 - rendered = pieces["short"] - if pieces["dirty"]: - rendered += "-dirty" - return rendered - - -def render(pieces: Dict[str, Any], style: str) -> Dict[str, Any]: - """Render the given version pieces into the requested style.""" - if pieces["error"]: - return {"version": "unknown", - "full-revisionid": pieces.get("long"), - "dirty": None, - "error": pieces["error"], - "date": None} - - if not style or style == "default": - style = "pep440" # the default - - if style == "pep440": - rendered = render_pep440(pieces) - elif style == "pep440-branch": - rendered = render_pep440_branch(pieces) - elif style == "pep440-pre": - rendered = render_pep440_pre(pieces) - elif style == "pep440-post": - rendered = render_pep440_post(pieces) - elif style == "pep440-post-branch": - rendered = render_pep440_post_branch(pieces) - elif style == "pep440-old": - rendered = render_pep440_old(pieces) - elif style == "git-describe": - rendered = render_git_describe(pieces) - elif style == "git-describe-long": - rendered = render_git_describe_long(pieces) - else: - raise ValueError("unknown style '%s'" % style) - - return {"version": rendered, "full-revisionid": pieces["long"], - "dirty": pieces["dirty"], "error": None, - "date": pieces.get("date")} - - -class VersioneerBadRootError(Exception): - """The project root directory is unknown or missing key files.""" - - -def get_versions(verbose: bool = False) -> Dict[str, Any]: - """Get the project version from whatever source is available. - - Returns dict with two keys: 'version' and 'full'. - """ - if "versioneer" in sys.modules: - # see the discussion in cmdclass.py:get_cmdclass() - del sys.modules["versioneer"] - - root = get_root() - cfg = get_config_from_root(root) - - assert cfg.VCS is not None, "please set [versioneer]VCS= in setup.cfg" - handlers = HANDLERS.get(cfg.VCS) - assert handlers, "unrecognized VCS '%s'" % cfg.VCS - verbose = verbose or bool(cfg.verbose) # `bool()` used to avoid `None` - assert cfg.versionfile_source is not None, \ - "please set versioneer.versionfile_source" - assert cfg.tag_prefix is not None, "please set versioneer.tag_prefix" - - versionfile_abs = os.path.join(root, cfg.versionfile_source) - - # extract version from first of: _version.py, VCS command (e.g. 'git - # describe'), parentdir. This is meant to work for developers using a - # source checkout, for users of a tarball created by 'setup.py sdist', - # and for users of a tarball/zipball created by 'git archive' or github's - # download-from-tag feature or the equivalent in other VCSes. - - get_keywords_f = handlers.get("get_keywords") - from_keywords_f = handlers.get("keywords") - if get_keywords_f and from_keywords_f: - try: - keywords = get_keywords_f(versionfile_abs) - ver = from_keywords_f(keywords, cfg.tag_prefix, verbose) - if verbose: - print("got version from expanded keyword %s" % ver) - return ver - except NotThisMethod: - pass - - try: - ver = versions_from_file(versionfile_abs) - if verbose: - print("got version from file %s %s" % (versionfile_abs, ver)) - return ver - except NotThisMethod: - pass - - from_vcs_f = handlers.get("pieces_from_vcs") - if from_vcs_f: - try: - pieces = from_vcs_f(cfg.tag_prefix, root, verbose) - ver = render(pieces, cfg.style) - if verbose: - print("got version from VCS %s" % ver) - return ver - except NotThisMethod: - pass - - try: - if cfg.parentdir_prefix: - ver = versions_from_parentdir(cfg.parentdir_prefix, root, verbose) - if verbose: - print("got version from parentdir %s" % ver) - return ver - except NotThisMethod: - pass - - if verbose: - print("unable to compute version") - - return {"version": "0+unknown", "full-revisionid": None, - "dirty": None, "error": "unable to compute version", - "date": None} - - -def get_version() -> str: - """Get the short version string for this project.""" - return get_versions()["version"] - - -def get_cmdclass(cmdclass: Optional[Dict[str, Any]] = None): - """Get the custom setuptools subclasses used by Versioneer. - - If the package uses a different cmdclass (e.g. one from numpy), it - should be provide as an argument. - """ - if "versioneer" in sys.modules: - del sys.modules["versioneer"] - # this fixes the "python setup.py develop" case (also 'install' and - # 'easy_install .'), in which subdependencies of the main project are - # built (using setup.py bdist_egg) in the same python process. Assume - # a main project A and a dependency B, which use different versions - # of Versioneer. A's setup.py imports A's Versioneer, leaving it in - # sys.modules by the time B's setup.py is executed, causing B to run - # with the wrong versioneer. Setuptools wraps the sub-dep builds in a - # sandbox that restores sys.modules to it's pre-build state, so the - # parent is protected against the child's "import versioneer". By - # removing ourselves from sys.modules here, before the child build - # happens, we protect the child from the parent's versioneer too. - # Also see https://github.com/python-versioneer/python-versioneer/issues/52 - - cmds = {} if cmdclass is None else cmdclass.copy() - - # we add "version" to setuptools - from setuptools import Command - - class cmd_version(Command): - description = "report generated version string" - user_options: List[Tuple[str, str, str]] = [] - boolean_options: List[str] = [] - - def initialize_options(self) -> None: - pass - - def finalize_options(self) -> None: - pass - - def run(self) -> None: - vers = get_versions(verbose=True) - print("Version: %s" % vers["version"]) - print(" full-revisionid: %s" % vers.get("full-revisionid")) - print(" dirty: %s" % vers.get("dirty")) - print(" date: %s" % vers.get("date")) - if vers["error"]: - print(" error: %s" % vers["error"]) - cmds["version"] = cmd_version - - # we override "build_py" in setuptools - # - # most invocation pathways end up running build_py: - # distutils/build -> build_py - # distutils/install -> distutils/build ->.. - # setuptools/bdist_wheel -> distutils/install ->.. - # setuptools/bdist_egg -> distutils/install_lib -> build_py - # setuptools/install -> bdist_egg ->.. - # setuptools/develop -> ? - # pip install: - # copies source tree to a tempdir before running egg_info/etc - # if .git isn't copied too, 'git describe' will fail - # then does setup.py bdist_wheel, or sometimes setup.py install - # setup.py egg_info -> ? - - # pip install -e . and setuptool/editable_wheel will invoke build_py - # but the build_py command is not expected to copy any files. - - # we override different "build_py" commands for both environments - if 'build_py' in cmds: - _build_py: Any = cmds['build_py'] - else: - from setuptools.command.build_py import build_py as _build_py - - class cmd_build_py(_build_py): - def run(self) -> None: - root = get_root() - cfg = get_config_from_root(root) - versions = get_versions() - _build_py.run(self) - if getattr(self, "editable_mode", False): - # During editable installs `.py` and data files are - # not copied to build_lib - return - # now locate _version.py in the new build/ directory and replace - # it with an updated value - if cfg.versionfile_build: - target_versionfile = os.path.join(self.build_lib, - cfg.versionfile_build) - print("UPDATING %s" % target_versionfile) - write_to_version_file(target_versionfile, versions) - cmds["build_py"] = cmd_build_py - - if 'build_ext' in cmds: - _build_ext: Any = cmds['build_ext'] - else: - from setuptools.command.build_ext import build_ext as _build_ext - - class cmd_build_ext(_build_ext): - def run(self) -> None: - root = get_root() - cfg = get_config_from_root(root) - versions = get_versions() - _build_ext.run(self) - if self.inplace: - # build_ext --inplace will only build extensions in - # build/lib<..> dir with no _version.py to write to. - # As in place builds will already have a _version.py - # in the module dir, we do not need to write one. - return - # now locate _version.py in the new build/ directory and replace - # it with an updated value - if not cfg.versionfile_build: - return - target_versionfile = os.path.join(self.build_lib, - cfg.versionfile_build) - if not os.path.exists(target_versionfile): - print(f"Warning: {target_versionfile} does not exist, skipping " - "version update. This can happen if you are running build_ext " - "without first running build_py.") - return - print("UPDATING %s" % target_versionfile) - write_to_version_file(target_versionfile, versions) - cmds["build_ext"] = cmd_build_ext - - if "cx_Freeze" in sys.modules: # cx_freeze enabled? - from cx_Freeze.dist import build_exe as _build_exe # type: ignore - # nczeczulin reports that py2exe won't like the pep440-style string - # as FILEVERSION, but it can be used for PRODUCTVERSION, e.g. - # setup(console=[{ - # "version": versioneer.get_version().split("+", 1)[0], # FILEVERSION - # "product_version": versioneer.get_version(), - # ... - - class cmd_build_exe(_build_exe): - def run(self) -> None: - root = get_root() - cfg = get_config_from_root(root) - versions = get_versions() - target_versionfile = cfg.versionfile_source - print("UPDATING %s" % target_versionfile) - write_to_version_file(target_versionfile, versions) - - _build_exe.run(self) - os.unlink(target_versionfile) - with open(cfg.versionfile_source, "w") as f: - LONG = LONG_VERSION_PY[cfg.VCS] - f.write(LONG % - {"DOLLAR": "$", - "STYLE": cfg.style, - "TAG_PREFIX": cfg.tag_prefix, - "PARENTDIR_PREFIX": cfg.parentdir_prefix, - "VERSIONFILE_SOURCE": cfg.versionfile_source, - }) - cmds["build_exe"] = cmd_build_exe - del cmds["build_py"] - - if 'py2exe' in sys.modules: # py2exe enabled? - try: - from py2exe.setuptools_buildexe import py2exe as _py2exe # type: ignore - except ImportError: - from py2exe.distutils_buildexe import py2exe as _py2exe # type: ignore - - class cmd_py2exe(_py2exe): - def run(self) -> None: - root = get_root() - cfg = get_config_from_root(root) - versions = get_versions() - target_versionfile = cfg.versionfile_source - print("UPDATING %s" % target_versionfile) - write_to_version_file(target_versionfile, versions) - - _py2exe.run(self) - os.unlink(target_versionfile) - with open(cfg.versionfile_source, "w") as f: - LONG = LONG_VERSION_PY[cfg.VCS] - f.write(LONG % - {"DOLLAR": "$", - "STYLE": cfg.style, - "TAG_PREFIX": cfg.tag_prefix, - "PARENTDIR_PREFIX": cfg.parentdir_prefix, - "VERSIONFILE_SOURCE": cfg.versionfile_source, - }) - cmds["py2exe"] = cmd_py2exe - - # sdist farms its file list building out to egg_info - if 'egg_info' in cmds: - _egg_info: Any = cmds['egg_info'] - else: - from setuptools.command.egg_info import egg_info as _egg_info - - class cmd_egg_info(_egg_info): - def find_sources(self) -> None: - # egg_info.find_sources builds the manifest list and writes it - # in one shot - super().find_sources() - - # Modify the filelist and normalize it - root = get_root() - cfg = get_config_from_root(root) - self.filelist.append('versioneer.py') - if cfg.versionfile_source: - # There are rare cases where versionfile_source might not be - # included by default, so we must be explicit - self.filelist.append(cfg.versionfile_source) - self.filelist.sort() - self.filelist.remove_duplicates() - - # The write method is hidden in the manifest_maker instance that - # generated the filelist and was thrown away - # We will instead replicate their final normalization (to unicode, - # and POSIX-style paths) - from setuptools import unicode_utils - normalized = [unicode_utils.filesys_decode(f).replace(os.sep, '/') - for f in self.filelist.files] - - manifest_filename = os.path.join(self.egg_info, 'SOURCES.txt') - with open(manifest_filename, 'w') as fobj: - fobj.write('\n'.join(normalized)) - - cmds['egg_info'] = cmd_egg_info - - # we override different "sdist" commands for both environments - if 'sdist' in cmds: - _sdist: Any = cmds['sdist'] - else: - from setuptools.command.sdist import sdist as _sdist - - class cmd_sdist(_sdist): - def run(self) -> None: - versions = get_versions() - self._versioneer_generated_versions = versions - # unless we update this, the command will keep using the old - # version - self.distribution.metadata.version = versions["version"] - return _sdist.run(self) - - def make_release_tree(self, base_dir: str, files: List[str]) -> None: - root = get_root() - cfg = get_config_from_root(root) - _sdist.make_release_tree(self, base_dir, files) - # now locate _version.py in the new base_dir directory - # (remembering that it may be a hardlink) and replace it with an - # updated value - target_versionfile = os.path.join(base_dir, cfg.versionfile_source) - print("UPDATING %s" % target_versionfile) - write_to_version_file(target_versionfile, - self._versioneer_generated_versions) - cmds["sdist"] = cmd_sdist - - return cmds - - -CONFIG_ERROR = """ -setup.cfg is missing the necessary Versioneer configuration. You need -a section like: - - [versioneer] - VCS = git - style = pep440 - versionfile_source = src/myproject/_version.py - versionfile_build = myproject/_version.py - tag_prefix = - parentdir_prefix = myproject- - -You will also need to edit your setup.py to use the results: - - import versioneer - setup(version=versioneer.get_version(), - cmdclass=versioneer.get_cmdclass(), ...) - -Please read the docstring in ./versioneer.py for configuration instructions, -edit setup.cfg, and re-run the installer or 'python versioneer.py setup'. -""" - -SAMPLE_CONFIG = """ -# See the docstring in versioneer.py for instructions. Note that you must -# re-run 'versioneer.py setup' after changing this section, and commit the -# resulting files. - -[versioneer] -#VCS = git -#style = pep440 -#versionfile_source = -#versionfile_build = -#tag_prefix = -#parentdir_prefix = - -""" - -OLD_SNIPPET = """ -from ._version import get_versions -__version__ = get_versions()['version'] -del get_versions -""" - -INIT_PY_SNIPPET = """ -from . import {0} -__version__ = {0}.get_versions()['version'] -""" - - -def do_setup() -> int: - """Do main VCS-independent setup function for installing Versioneer.""" - root = get_root() - try: - cfg = get_config_from_root(root) - except (OSError, configparser.NoSectionError, - configparser.NoOptionError) as e: - if isinstance(e, (OSError, configparser.NoSectionError)): - print("Adding sample versioneer config to setup.cfg", - file=sys.stderr) - with open(os.path.join(root, "setup.cfg"), "a") as f: - f.write(SAMPLE_CONFIG) - print(CONFIG_ERROR, file=sys.stderr) - return 1 - - print(" creating %s" % cfg.versionfile_source) - with open(cfg.versionfile_source, "w") as f: - LONG = LONG_VERSION_PY[cfg.VCS] - f.write(LONG % {"DOLLAR": "$", - "STYLE": cfg.style, - "TAG_PREFIX": cfg.tag_prefix, - "PARENTDIR_PREFIX": cfg.parentdir_prefix, - "VERSIONFILE_SOURCE": cfg.versionfile_source, - }) - - ipy = os.path.join(os.path.dirname(cfg.versionfile_source), - "__init__.py") - maybe_ipy: Optional[str] = ipy - if os.path.exists(ipy): - try: - with open(ipy, "r") as f: - old = f.read() - except OSError: - old = "" - module = os.path.splitext(os.path.basename(cfg.versionfile_source))[0] - snippet = INIT_PY_SNIPPET.format(module) - if OLD_SNIPPET in old: - print(" replacing boilerplate in %s" % ipy) - with open(ipy, "w") as f: - f.write(old.replace(OLD_SNIPPET, snippet)) - elif snippet not in old: - print(" appending to %s" % ipy) - with open(ipy, "a") as f: - f.write(snippet) - else: - print(" %s unmodified" % ipy) - else: - print(" %s doesn't exist, ok" % ipy) - maybe_ipy = None - - # Make VCS-specific changes. For git, this means creating/changing - # .gitattributes to mark _version.py for export-subst keyword - # substitution. - do_vcs_install(cfg.versionfile_source, maybe_ipy) - return 0 - - -def scan_setup_py() -> int: - """Validate the contents of setup.py against Versioneer's expectations.""" - found = set() - setters = False - errors = 0 - with open("setup.py", "r") as f: - for line in f.readlines(): - if "import versioneer" in line: - found.add("import") - if "versioneer.get_cmdclass()" in line: - found.add("cmdclass") - if "versioneer.get_version()" in line: - found.add("get_version") - if "versioneer.VCS" in line: - setters = True - if "versioneer.versionfile_source" in line: - setters = True - if len(found) != 3: - print("") - print("Your setup.py appears to be missing some important items") - print("(but I might be wrong). Please make sure it has something") - print("roughly like the following:") - print("") - print(" import versioneer") - print(" setup( version=versioneer.get_version(),") - print(" cmdclass=versioneer.get_cmdclass(), ...)") - print("") - errors += 1 - if setters: - print("You should remove lines like 'versioneer.VCS = ' and") - print("'versioneer.versionfile_source = ' . This configuration") - print("now lives in setup.cfg, and should be removed from setup.py") - print("") - errors += 1 - return errors - - -def setup_command() -> NoReturn: - """Set up Versioneer and exit with appropriate error code.""" - errors = do_setup() - errors += scan_setup_py() - sys.exit(1 if errors else 0) - - -if __name__ == "__main__": - cmd = sys.argv[1] - if cmd == "setup": - setup_command()