Started by upstream project "CE_gpkit_PR_unit_tests" build number 3210
originally caused by:
GitHub pull request #1567 of commit 1d39b41888648b7241714ae51d717e83e48ee516, no merge conflicts.
Running as SYSTEM
[EnvInject] - Loading node environment variables.
Building remotely on windows10x64 in workspace C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt
The recommended git tool is: NONE
using credential 3614a4cf-01de-4393-97de-73734b7dd5a2
Wiping out workspace first.
Cloning the remote Git repository
Cloning repository https://github.com/convexengineering/gpkit
> git init C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt # timeout=10
Fetching upstream changes from https://github.com/convexengineering/gpkit
> git --version # timeout=10
> git --version # 'git version 2.33.0.windows.2'
using GIT_SSH to set credentials
> git fetch --tags --force --progress -- https://github.com/convexengineering/gpkit +refs/heads/*:refs/remotes/origin/* # timeout=10
> git config remote.origin.url https://github.com/convexengineering/gpkit # timeout=10
> git config --add remote.origin.fetch +refs/heads/*:refs/remotes/origin/* # timeout=10
> git config remote.origin.url https://github.com/convexengineering/gpkit # timeout=10
Fetching upstream changes from https://github.com/convexengineering/gpkit
using GIT_SSH to set credentials
> git fetch --tags --force --progress -- https://github.com/convexengineering/gpkit +refs/pull/*:refs/remotes/origin/pr/* +refs/heads/master:refs/remotes/origin/master # timeout=10
Merging Revision e9c3db5c64654accdb05e41eeff2e4e63ad830d7 (refs/remotes/origin/pr/1567/merge) to origin/master, UserMergeOptions{mergeRemote='origin', mergeTarget='master', mergeStrategy='DEFAULT', fastForwardMode='FF'}
> git rev-parse "origin/master^{commit}" # timeout=10
> git config core.sparsecheckout # timeout=10
> git checkout -f origin/master # timeout=3
> git remote # timeout=10
> git config --get remote.origin.url # timeout=10
using GIT_SSH to set credentials
> git merge --ff e9c3db5c64654accdb05e41eeff2e4e63ad830d7 # timeout=10
> git rev-parse "HEAD^{commit}" # timeout=10
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> git show-ref --tags -d # timeout=10
JENKINS-19022: warning: possible memory leak due to Git plugin usage; see: https://plugins.jenkins.io/git/#remove-git-plugin-buildsbybranch-builddata-script
Checking out Revision e9c3db5c64654accdb05e41eeff2e4e63ad830d7 (origin/pr/1567/merge, origin/master)
> git config core.sparsecheckout # timeout=10
> git checkout -f e9c3db5c64654accdb05e41eeff2e4e63ad830d7 # timeout=3
Commit message: "Merge 1d39b41888648b7241714ae51d717e83e48ee516 into 1ac62d2ddd18e93ad499ae3bce248e40cdf382a5"
Using 'Changelog to branch' strategy.
The recommended git tool is: NONE
using credential 3614a4cf-01de-4393-97de-73734b7dd5a2
> git rev-parse "e9c3db5c64654accdb05e41eeff2e4e63ad830d7^{commit}" # timeout=10
The recommended git tool is: NONE
using credential 3614a4cf-01de-4393-97de-73734b7dd5a2
[GitCheckoutListener] Recording commits of 'git https://github.com/convexengineering/gpkit'
[GitCheckoutListener] Found previous build 'CE_gpkit_PR_unit_tests/buildnode=windows10x64,optimizer=cvxopt #3208' that contains recorded Git commits
[GitCheckoutListener] -> Starting recording of new commits since '213abc7'
[GitCheckoutListener] -> Using head commit 'e9c3db5' as starting point
[GitCheckoutListener] -> Git commit decorator successfully obtained 'hudson.plugins.git.browser.GithubWeb@469824b8' to render commit links
[GitCheckoutListener] -> Recorded 200 new commits
Run condition [Execution node ] enabling prebuild for step [Execute shell]
Run condition [Execution node ] enabling prebuild for step [Execute Windows batch command]
[description-setter] Description set: <a title="better sweep printing, clean sgp cost, lint" href="https://github.com/convexengineering/gpkit/pull/1567">PR 1567</a>: better sweep printing, clean sgp cost, lint
[Execution node] check if [windows10x64] is in [[macys, macys_VM, reynolds, reynolds-ubuntu16]]
Run condition [Execution node ] preventing perform for step [Execute shell]
[Execution node] check if [windows10x64] is in [[windows10x64]]
Run condition [Execution node ] enabling perform for step [Execute Windows batch command]
[cvxopt] $ cmd /c call C:\Users\jenkins\AppData\Local\Temp\jenkins9337271063052450063.bat
C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt>REM download test scripts
C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt>git clone ssh://acdl.mit.edu/home/svnroot/JenkinsGPkit
Cloning into 'JenkinsGPkit'...
C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt>REM run tests
C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt>call C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt\JenkinsGPkit\gpkit_unit_tests.bat
C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt>REM turn on the anaconda console
C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt>call JenkinsGPKit/conda_activate.bat
C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt>REM turn on the anaconda console
C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt>call C:\Miniconda3\Scripts\activate.bat
(base) C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt>if 0 NEQ 0 goto activate_conda
(base) C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt>echo on
(base) C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt>virtualenv --system-site-packages C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt\venv_jenkins
created virtual environment CPython3.9.7.final.0-64 in 3812ms
creator CPython3Windows(dest=C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt\venv_jenkins, clear=False, no_vcs_ignore=False, global=True)
seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=C:\Users\jenkins\AppData\Local\pypa\virtualenv)
added seed packages: pip==21.3.1, setuptools==60.5.0, wheel==0.37.1
activators BashActivator,BatchActivator,FishActivator,PowerShellActivator,PythonActivator,XonshActivator
(base) C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt>call C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt\venv_jenkins\Scripts\activate.bat
gcc (x86_64-posix-seh-rev0, Built by MinGW-W64 project) 6.4.0
Copyright (C) 2017 Free Software Foundation, Inc.
This is free software; see the source for copying conditions. There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
Requirement already satisfied: pip in c:\users\jenkins\workspace\ce_gpkit_pr_unit_tests\cvxopt\venv_jenkins\lib\site-packages (21.3.1)
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Installing collected packages: pip
Attempting uninstall: pip
Found existing installation: pip 21.3.1
Uninstalling pip-21.3.1:
Successfully uninstalled pip-21.3.1
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Successfully installed ad-1.3.2
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Successfully installed lxml-4.7.1 unittest-xml-reporting-3.2.0
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Installing collected packages: adce
Successfully installed adce-1.3.3.2
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Installing collected packages: pytz, python-dateutil, numpy, pandas
Successfully installed numpy-1.22.2 pandas-1.4.0 python-dateutil-2.8.2 pytz-2021.3
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Collecting pillow>=6.2.0
Using cached Pillow-9.0.1-cp39-cp39-win_amd64.whl (3.2 MB)
Requirement already satisfied: six>=1.5 in c:\miniconda3\lib\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)
Installing collected packages: pyparsing, pillow, kiwisolver, fonttools, cycler, packaging, matplotlib
Successfully installed cycler-0.11.0 fonttools-4.29.1 kiwisolver-1.3.2 matplotlib-3.5.1 packaging-21.3 pillow-9.0.1 pyparsing-3.0.7
Collecting coverage
Using cached coverage-6.3.1-cp39-cp39-win_amd64.whl (186 kB)
Installing collected packages: coverage
Successfully installed coverage-6.3.1
Requirement already satisfied: numpy in c:\users\jenkins\workspace\ce_gpkit_pr_unit_tests\cvxopt\venv_jenkins\lib\site-packages (1.22.2)
Collecting scipy
Using cached scipy-1.8.0-cp39-cp39-win_amd64.whl (36.9 MB)
Requirement already satisfied: numpy<1.25.0,>=1.17.3 in c:\users\jenkins\workspace\ce_gpkit_pr_unit_tests\cvxopt\venv_jenkins\lib\site-packages (from scipy) (1.22.2)
Installing collected packages: scipy
Successfully installed scipy-1.8.0
Collecting pint
Using cached Pint-0.18-py2.py3-none-any.whl (209 kB)
Requirement already satisfied: packaging in c:\users\jenkins\workspace\ce_gpkit_pr_unit_tests\cvxopt\venv_jenkins\lib\site-packages (from pint) (21.3)
Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in c:\users\jenkins\workspace\ce_gpkit_pr_unit_tests\cvxopt\venv_jenkins\lib\site-packages (from packaging->pint) (3.0.7)
Installing collected packages: pint
Successfully installed pint-0.18
Collecting ipysankeywidget
Using cached ipysankeywidget-0.4.1-py2.py3-none-any.whl (1.2 MB)
Collecting ipywidgets>=7.0.0
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Using cached ipython_genutils-0.2.0-py2.py3-none-any.whl (26 kB)
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Using cached decorator-5.1.1-py3-none-any.whl (9.1 kB)
Requirement already satisfied: setuptools>=18.5 in c:\users\jenkins\workspace\ce_gpkit_pr_unit_tests\cvxopt\venv_jenkins\lib\site-packages (from ipython>=4.0.0->ipywidgets>=7.0.0->ipysankeywidget) (60.5.0)
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Collecting pywinpty>=1.1.0
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Using cached argon2_cffi_bindings-21.2.0-cp36-abi3-win_amd64.whl (30 kB)
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Collecting webencodings
Using cached webencodings-0.5.1-py2.py3-none-any.whl (11 kB)
Requirement already satisfied: packaging in c:\users\jenkins\workspace\ce_gpkit_pr_unit_tests\cvxopt\venv_jenkins\lib\site-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->ipysankeywidget) (21.3)
Requirement already satisfied: pycparser in c:\miniconda3\lib\site-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->ipysankeywidget) (2.21)
Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in c:\users\jenkins\workspace\ce_gpkit_pr_unit_tests\cvxopt\venv_jenkins\lib\site-packages (from packaging->bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->ipysankeywidget) (3.0.7)
Installing collected packages: webencodings, wcwidth, Send2Trash, pure-eval, pickleshare, mypy-extensions, mistune, ipython-genutils, executing, backcall, typing-extensions, traitlets, tornado, tomli, testpath, pyzmq, pywinpty, pyrsistent, pygments, prompt-toolkit, prometheus-client, platformdirs, pathspec, parso, pandocfilters, nest-asyncio, MarkupSafe, jupyterlab-widgets, entrypoints, defusedxml, decorator, debugpy, colorama, attrs, asttokens, terminado, stack-data, matplotlib-inline, jupyterlab-pygments, jupyter-core, jsonschema, jinja2, jedi, click, bleach, argon2-cffi-bindings, nbformat, jupyter-client, black, argon2-cffi, nbclient, ipython, nbconvert, ipykernel, notebook, widgetsnbextension, ipywidgets, ipysankeywidget
Successfully installed MarkupSafe-2.0.1 Send2Trash-1.8.0 argon2-cffi-21.3.0 argon2-cffi-bindings-21.2.0 asttokens-2.0.5 attrs-21.4.0 backcall-0.2.0 black-22.1.0 bleach-4.1.0 click-8.0.3 colorama-0.4.4 debugpy-1.5.1 decorator-5.1.1 defusedxml-0.7.1 entrypoints-0.4 executing-0.8.2 ipykernel-6.9.0 ipysankeywidget-0.4.1 ipython-8.0.1 ipython-genutils-0.2.0 ipywidgets-7.6.5 jedi-0.18.1 jinja2-3.0.3 jsonschema-4.4.0 jupyter-client-7.1.2 jupyter-core-4.9.1 jupyterlab-pygments-0.1.2 jupyterlab-widgets-1.0.2 matplotlib-inline-0.1.3 mistune-0.8.4 mypy-extensions-0.4.3 nbclient-0.5.10 nbconvert-6.4.2 nbformat-5.1.3 nest-asyncio-1.5.4 notebook-6.4.8 pandocfilters-1.5.0 parso-0.8.3 pathspec-0.9.0 pickleshare-0.7.5 platformdirs-2.5.0 prometheus-client-0.13.1 prompt-toolkit-3.0.28 pure-eval-0.2.2 pygments-2.11.2 pyrsistent-0.18.1 pywinpty-2.0.2 pyzmq-22.3.0 stack-data-0.1.4 terminado-0.13.1 testpath-0.5.0 tomli-2.0.1 tornado-6.1 traitlets-5.1.1 typing-extensions-4.0.1 wcwidth-0.2.5 webencodings-0.5.1 widgetsnbextension-3.5.2
Collecting plotly
Using cached plotly-5.6.0-py2.py3-none-any.whl (27.7 MB)
Collecting tenacity>=6.2.0
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Requirement already satisfied: six in c:\miniconda3\lib\site-packages (from plotly) (1.16.0)
Installing collected packages: tenacity, plotly
Successfully installed plotly-5.6.0 tenacity-8.0.1
Collecting cvxopt
Using cached cvxopt-1.2.7-cp39-cp39-win_amd64.whl (9.5 MB)
Installing collected packages: cvxopt
Successfully installed cvxopt-1.2.7
1.2.7
1.8.0
1.22.2
0.18
No filename given.
Usage:
mskexpopt FILENAME [-primal] [-dual] [-p parameterfile]
Return code: 1052
Description: MSK_RES_ERR_FILE_OPEN [An error occurred while opening a file.]
C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt\gpkit\small_scripts.py:71: SyntaxWarning: "is" with a literal. Did you mean "=="?
if sweep is "sweep" and (isinstance(value, Iterable) or # pylint: disable=literal-comparison
C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt\gpkit\nomials\math.py:586: SyntaxWarning: "is" with a literal. Did you mean "=="?
if posy is 0: # pylint: disable=literal-comparison
C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt\gpkit\nomials\math.py:590: SyntaxWarning: "is" with a literal. Did you mean "=="?
if negy is 0: # pylint: disable=literal-comparison
C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt\gpkit\breakdowns.py:390: SyntaxWarning: "is" with a literal. Did you mean "=="?
subhmap.units = None if units is 1 else units
..........................................................................................................................................................................E...............C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt\gpkit\small_classes.py:158: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
v = np.array(v)
........C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt\gpkit\small_classes.py:158: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
v = np.array(v)
...............
======================================================================
ERROR: test_breakdowns_cvxopt (gpkit.tests.t_examples.TestExamples)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt\gpkit\tests\helpers.py", line 55, in test
testfn(name, import_dict, path)(self)
File "C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt\gpkit\tests\helpers.py", line 90, in test
imported[name] = importlib.import_module(name)
File "C:\Miniconda3\lib\importlib\__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1030, in _gcd_import
File "<frozen importlib._bootstrap>", line 1007, in _find_and_load
File "<frozen importlib._bootstrap>", line 986, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 680, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 850, in exec_module
File "<frozen importlib._bootstrap>", line 228, in _call_with_frames_removed
File "C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt\docs\source\examples\breakdowns.py", line 11, in <module>
sol = pickle.load(open(filepath, "rb"))
AttributeError: Can't get attribute '_build_quantity' on <module 'pint' from 'C:\\Users\\jenkins\\workspace\\CE_gpkit_PR_unit_tests\\cvxopt\\venv_jenkins\\lib\\site-packages\\pint\\__init__.py'>
----------------------------------------------------------------------
Ran 209 tests in 11.541s
FAILED (errors=1)
Found no installed solvers, beginning a build.
# Building GPkit version 1.1
# Moving to the directory from which GPkit was imported.
Attempting to find and build solvers:
# Looking for `mosek_cli`
# (A "success" is if mskexpopt complains that
# we haven't specified a file for it to open.)
# Calling 'mskexpopt'
##
### CALL BEGINS
### CALL ENDS
##
Found mosek_cli in the default PATH
# Looking for `mosek_conif`
# Trying to import mosek...
# Did not find
# mosek_conif
# Looking for `cvxopt`
# Trying to import cvxopt...
Found cvxopt in the default PYTHONPATH
Replaced found solvers (['mosek_cli', 'cvxopt']) with environment var GPKITSOLVERS (cvxopt)
Found the following solvers: cvxopt
# Replacing directory env
GPkit is now installed with solver(s) ['cvxopt']
To incorporate new solvers at a later date, run `gpkit.build()`.
If any tests didn't pass, please post the output above
(starting from "Found no installed solvers, beginning a build.")
to gpkit@mit.edu or https://github.com/convexengineering/gpkit/issues/new
so we can prevent others from having these errors.
The same goes for any other bugs you encounter with GPkit:
send 'em our way, along with any interesting models, speculative features,
comments, discussions, or clarifications you feel like sharing.
Finally, we hope you find our documentation (https://gpkit.readthedocs.io/)
and engineering-design models (https://github.com/convexengineering/gplibrary/)
to be useful resources for your own applications.
Enjoy!
{'installed_solvers': ['cvxopt'], 'default_solver': 'cvxopt', 'just built!': True}
Running tests...
----------------------------------------------------------------------
..........................................................................................................................................................................E...............C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt\gpkit\small_classes.py:158: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
v = np.array(v)
........C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt\gpkit\small_classes.py:158: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
v = np.array(v)
...............
======================================================================
ERROR [0.021s]: test_breakdowns_cvxopt (gpkit.tests.t_examples.TestExamples)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt\gpkit\tests\helpers.py", line 55, in test
testfn(name, import_dict, path)(self)
File "C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt\gpkit\tests\helpers.py", line 90, in test
imported[name] = importlib.import_module(name)
File "C:\Miniconda3\lib\importlib\__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1030, in _gcd_import
File "<frozen importlib._bootstrap>", line 1007, in _find_and_load
File "<frozen importlib._bootstrap>", line 986, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 680, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 850, in exec_module
File "<frozen importlib._bootstrap>", line 228, in _call_with_frames_removed
File "C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt\docs\source\examples\breakdowns.py", line 11, in <module>
sol = pickle.load(open(filepath, "rb"))
AttributeError: Can't get attribute '_build_quantity' on <module 'pint' from 'C:\\Users\\jenkins\\workspace\\CE_gpkit_PR_unit_tests\\cvxopt\\venv_jenkins\\lib\\site-packages\\pint\\__init__.py'>
----------------------------------------------------------------------
Ran 209 tests in 11.835s
FAILED (errors=1)
Generating XML reports...
C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt\venv_jenkins\lib\site-packages\coverage\control.py:788: CoverageWarning: No data was collected. (no-data-collected)
self._warn("No data was collected.", slug="no-data-collected")
hello world
C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt\gpkit\breakdowns.py:390: SyntaxWarning: "is" with a literal. Did you mean "=="?
subhmap.units = None if units is 1 else units
C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt\gpkit\nomials\math.py:586: SyntaxWarning: "is" with a literal. Did you mean "=="?
if posy is 0: # pylint: disable=literal-comparison
C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt\gpkit\nomials\math.py:590: SyntaxWarning: "is" with a literal. Did you mean "=="?
if negy is 0: # pylint: disable=literal-comparison
C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt\gpkit\small_scripts.py:71: SyntaxWarning: "is" with a literal. Did you mean "=="?
if sweep is "sweep" and (isinstance(value, Iterable) or # pylint: disable=literal-comparison
Wrote HTML report to htmlcov\index.html
Obtaining file:///C:/Users/jenkins/workspace/CE_gpkit_PR_unit_tests/cvxopt
Preparing metadata (setup.py): started
Preparing metadata (setup.py): finished with status 'done'
Installing collected packages: gpkit
Running setup.py develop for gpkit
Successfully installed gpkit
No filename given.
Usage:
mskexpopt FILENAME [-primal] [-dual] [-p parameterfile]
Return code: 1052
Description: MSK_RES_ERR_FILE_OPEN [An error occurred while opening a file.]
..........................................................................................................................................................................E...............c:\users\jenkins\workspace\ce_gpkit_pr_unit_tests\cvxopt\gpkit\small_classes.py:158: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
v = np.array(v)
........c:\users\jenkins\workspace\ce_gpkit_pr_unit_tests\cvxopt\gpkit\small_classes.py:158: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
v = np.array(v)
...............
======================================================================
ERROR: test_breakdowns_cvxopt (gpkit.tests.t_examples.TestExamples)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\users\jenkins\workspace\ce_gpkit_pr_unit_tests\cvxopt\gpkit\tests\helpers.py", line 55, in test
testfn(name, import_dict, path)(self)
File "c:\users\jenkins\workspace\ce_gpkit_pr_unit_tests\cvxopt\gpkit\tests\helpers.py", line 90, in test
imported[name] = importlib.import_module(name)
File "C:\Miniconda3\lib\importlib\__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1030, in _gcd_import
File "<frozen importlib._bootstrap>", line 1007, in _find_and_load
File "<frozen importlib._bootstrap>", line 986, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 680, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 850, in exec_module
File "<frozen importlib._bootstrap>", line 228, in _call_with_frames_removed
File "C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt\docs\source\examples\breakdowns.py", line 11, in <module>
sol = pickle.load(open(filepath, "rb"))
AttributeError: Can't get attribute '_build_quantity' on <module 'pint' from 'C:\\Users\\jenkins\\workspace\\CE_gpkit_PR_unit_tests\\cvxopt\\venv_jenkins\\lib\\site-packages\\pint\\__init__.py'>
----------------------------------------------------------------------
Ran 209 tests in 11.125s
FAILED (errors=1)
Found no installed solvers, beginning a build.
# Building GPkit version 1.1
# Moving to the directory from which GPkit was imported.
Attempting to find and build solvers:
# Looking for `mosek_cli`
# (A "success" is if mskexpopt complains that
# we haven't specified a file for it to open.)
# Calling 'mskexpopt'
##
### CALL BEGINS
### CALL ENDS
##
Found mosek_cli in the default PATH
# Looking for `mosek_conif`
# Trying to import mosek...
# Did not find
# mosek_conif
# Looking for `cvxopt`
# Trying to import cvxopt...
Found cvxopt in the default PYTHONPATH
Replaced found solvers (['mosek_cli', 'cvxopt']) with environment var GPKITSOLVERS (cvxopt)
Found the following solvers: cvxopt
# Replacing directory env
GPkit is now installed with solver(s) ['cvxopt']
To incorporate new solvers at a later date, run `gpkit.build()`.
If any tests didn't pass, please post the output above
(starting from "Found no installed solvers, beginning a build.")
to gpkit@mit.edu or https://github.com/convexengineering/gpkit/issues/new
so we can prevent others from having these errors.
The same goes for any other bugs you encounter with GPkit:
send 'em our way, along with any interesting models, speculative features,
comments, discussions, or clarifications you feel like sharing.
Finally, we hope you find our documentation (https://gpkit.readthedocs.io/)
and engineering-design models (https://github.com/convexengineering/gplibrary/)
to be useful resources for your own applications.
Enjoy!
Solved after 33 passes, cost logtol +/-0.000992
values of l: [ 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.]
values of A: [ 2.0 5.0 10.0 17.0 26.0 37.0 50.0 65.0 82.0 101.0] meter ** 2
cost lower bound:
[3.99999897e+00 2.49990635e+01 9.99519417e+01 2.88964405e+02
6.75761038e+02 1.36887689e+03 2.49888336e+03 4.22418997e+03
6.72085595e+03 1.02009910e+04]
cost estimate:
[3.99999897e+00 2.50021684e+01 1.00001162e+02 2.89043164e+02
6.76096986e+02 1.36923920e+03 2.50043987e+03 4.22599006e+03
6.72550897e+03 1.02009910e+04]
cost upper bound:
[3.99999897e+00 2.50052737e+01 1.00050406e+02 2.89121944e+02
6.76433102e+02 1.36960161e+03 2.50199736e+03 4.22779092e+03
6.73016521e+03 1.02009910e+04]
Solved after 3 passes, cost logtol +/-0
Table of solutions used in the autosweep:
Optimal Cost
------------
[ 0.333 1 123 ]
Free Variables
--------------
A : [ 0.577 1 11.1 ] [m**2]
Fixed Variables
---------------
l : [ 1 3 10 ] [m]
Variable Sensitivities
----------------------
l : [ +1 +2.5 +4 ]
Most Sensitive Constraints (in last sweep)
------------------------------------------
+2 : A >= (l/3)^2
Optimal Cost
------------
1.621
Free Variables
--------------
dx : 1.2 [m] Length of an element
M : [ 1.98e+03 1.27e+03 713 317 79.2 0.0002 ] [N*m] Internal moment
V : [ 660 528 396 264 132 0.0002 ] [N] Internal shear
th : [ 0.0002 0.177 0.285 0.341 0.363 0.367 ] Slope
w : [ 0.0002 0.107 0.384 0.76 1.18 1.62 ] [m] Displacement
BoundsChecking
==============
Cost Function
-------------
F
Constraints
-----------
F >= D + T
D = rf*V^2*Ap
Ap = nu
T = mf*V
mf >= mi + mb
mf = rf*V
Fs <= mi
Traceback (most recent call last):
File "C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt\docs\source\examples\breakdowns.py", line 11, in <module>
sol = pickle.load(open(filepath, "rb"))
AttributeError: Can't get attribute '_build_quantity' on <module 'pint' from 'C:\\Users\\jenkins\\workspace\\CE_gpkit_PR_unit_tests\\cvxopt\\venv_jenkins\\lib\\site-packages\\pint\\__init__.py'>
<DEBUG> Model is feasible with these modifications:
Arbitrarily Bounded Variables
-----------------------------
value near upper bound of 1e+30: y
sensitive to upper bound of 1e+30: y
Relaxed Constants
-----------------
x_min [ft]: relaxed from 2 to 1
# Now let's try a model unsolvable with relaxed constants
<DEBUG> Model is not feasible with relaxed constants and bounded variables.
<DEBUG> Model is feasible with these modifications:
Relaxed Constraints
-------------------
1: 3500% relaxed, from x [ft] >= 1 [yd]
to 36*x [ft] >= 1 [yd]
# And one that's only unbounded
<DEBUG> Model is feasible with these modifications:
Arbitrarily Bounded Variables
-----------------------------
value near upper bound of 1e+30: y
sensitive to upper bound of 1e+30: y
<DEBUG> Model seems feasible without modification, or only needs relaxations of less than 1%. Check the returned solution for details.
from gpkit import Variable, VectorVariable # Demonstration of nomenclature syntax
#
# Lines that end in "Variables" will be parsed as a scalar variable table
# until the next blank line.
#
# Variables
# ---------
A = self.A = Variable('A', 'm^2', 'surface area') # from 'A [m^2] surface area'
V = self.V = Variable('V', 100, 'L', 'minimum volume') # from 'V 100 [L] minimum volume'
#
# Lines that end in "Variables of length $N" will be parsed as vector
# variables of length $N until the next blank line.
#
# Variables of length 3
# ---------------------
s = self.s = VectorVariable(3, 's', 'm', 'side length') # from 's [m] side length'
#
# Let's introduce more variables: (any line ending in "Variables" is parsed)
#
# Zoning Variables
# ----------------
h = self.h = Variable('h', 1, 'm', 'minimum height') # from 'h 1 [m] minimum height'
#
# Upper Unbounded
# ---------------
# A
#
# The ordering of these blocks doesn't affect anything; order them in the
# way that makes the most sense to someone else reading your model.
#
Optimal Cost
------------
1.465
Free Variables
--------------
A : 1.465 [m**2] surface area
s : [ 0.316 0.316 1 ] [m] side length
Fixed Variables
---------------
V : 100 [l] minimum volume
h : 1 [m] minimum height
Variable Sensitivities
----------------------
V : +0.57 minimum volume
h : +0.3 minimum height
Most Sensitive Constraints
--------------------------
+1 : A >= 2*(s[0]*s[1] + s[1]*s[2] + s[2]*s[0])
+0.57 : V <= s[:].prod()
+0.3 : s[2] >= h
Optimal Cost
------------
0.7071
Free Variables
--------------
x : 0.7854
y : 0.7071
Optimal Cost
------------
0.005511
Free Variables
--------------
x : 8.165
y : 4.082
z : 5.443
Most Sensitive Constraints
--------------------------
+1.5 : 2*x*y + 2*x*z + 2*y*z <= 200
+0.17 : x >= 2*y
The optimal value is 0.005511.
Optimal Cost
------------
1
Model Sensitivities
-------------------
+1.0 : System.Fleet2
: System.Fleet2.Vehicle
Free Variables
--------------
| System.Fleet2
z : [ 1 ]
| System.Fleet2.Vehicle
a : [ 1 1 ]
Fixed Variables
---------------
| System.Fleet2
x : [ 4 ]
y : [ 3 1 ]
Variable Sensitivities
----------------------
| System.Fleet2
y : [ - +0.25 ]
Most Sensitive Constraints
--------------------------
| System.Fleet2
+1 : z[0] >= a[0,0]*y[0,0]/x[0] + y[1,0]/x[0]*a[1,0]
| System.Fleet2.Vehicle
+0.75 : a[0,0] >= 1
+0.25 : a[1,0] >= 1
Optimal Cost
------------
3
Model Sensitivities
-------------------
+1.0 : System2.Fleet2
: System2.Fleet2.Vehicle
Free Variables
--------------
| System2.Fleet2
z : [ 1 1 1 ]
| System2.Fleet2.Vehicle
a : [ 1 1 1
1 1 1 ]
Fixed Variables
---------------
| System2.Fleet2
x : [ 4 4 4 ]
y : [ 3 3 3
1 1 1 ]
Variable Sensitivities
----------------------
| System2.Fleet2
y : [ - - -
+0.083 +0.083 +0.083 ]
Most Sensitive Constraints
--------------------------
| System2.Fleet2
+0.33 : z[0] >= a[0,0]*y[0,0]/x[0] + y[1,0]/x[0]*a[1,0]
+0.33 : z[1] >= a[0,1]*y[0,1]/x[1] + y[1,1]/x[1]*a[1,1]
+0.33 : z[2] >= a[0,2]*y[0,2]/x[2] + y[1,2]/x[2]*a[1,2]
| System2.Fleet2.Vehicle
+0.25 : a[0,0] >= 1
+0.25 : a[0,1] >= 1
Optimal Cost
------------
20
Swept Variables
---------------
y : [ 1 2 3 ]
Free Variables
--------------
x : [ 2 6 12 ]
Fixed Variables
---------------
z : [ 1 4 9 ]
Variable Sensitivities
----------------------
y : [ +0.15 +0.5 +1 ]
Most Sensitive Constraints
--------------------------
+0.6 : x[2] >= y[2] + z[2]
+0.3 : x[1] >= y[1] + z[1]
+0.1 : x[0] >= y[0] + z[0]
Optimal Cost
------------
15
Free Variables
--------------
| Yum1.Cake
y : [ 3 3 3 3 3 ]
Fixed Variables
---------------
| Yum1.Cake.Pie
x : [ 2 2 2 2 2
3 3 3 3 3 ]
z : [ 1 1 1 1 1
1 1 1 1 1 ]
Variable Sensitivities
----------------------
| Yum1.Cake.Pie
x : [ +7.1e-07 +7.1e-07 +7.1e-07 +7.1e-07 +7.1e-07
+0.2 +0.2 +0.2 +0.2 +0.2 ]
Most Sensitive Constraints
--------------------------
| Yum1.Cake
+0.2 : y[0] >= x[1,0]
+0.2 : y[1] >= x[1,1]
+0.2 : y[2] >= x[1,2]
+0.2 : y[3] >= x[1,3]
+0.2 : y[4] >= x[1,4]
Optimal Cost
------------
3
Free Variables
--------------
| Yum2.Cake
y : [ 3 ]
Fixed Variables
---------------
| Yum2.Cake.Pie
x : [ 2 3 ]
z : [ 1 1 ]
Variable Sensitivities
----------------------
| Yum2.Cake.Pie
x : [ +8.4e-08 +1 ]
Most Sensitive Constraints
--------------------------
| Yum2.Cake
+1 : y[0] >= x[1,0]
Optimal Cost
------------
[ 1.41 2.14 2.68 3.13 ... ]
~~~~~~~~
WARNINGS
~~~~~~~~
Freed Choice Variables
----------------------
This model has the discretized choice variables [x], but since the 'cvxopt' solver doesn't support discretization they were treated as continuous variables.
~~~~~~~~
Swept Variables
---------------
numerator : [ 0.5
1.15
1.8
2.45
3.1
3.75
4.4
5.05
5.7
6.35
7 ]
Free Variables
--------------
x : [ 0.707
1.07
1.34
1.57
1.76
1.94
2.1
2.25
2.39
2.52
2.65 ]
Variable Sensitivities
----------------------
numerator : [ +0.5
+0.5
+0.5
+0.5
+0.5
+0.5
+0.5
+0.5
+0.5
+0.5
+0.5 ]
Most Sensitive Constraints (in last sweep)
------------------------------------------
(none)
Getting the only var 'E': PowerSystem.Battery.E [MJ]
The top-level var 'm': PowerSystem.m [lb]
All the variables 'm': [gpkit.Variable(PowerSystem.Battery.m [lb]), gpkit.Variable(PowerSystem.Motor.m [lb]), gpkit.Variable(PowerSystem.m [lb])]
Cost Function
-------------
Wfuel[0]
Constraints
-----------
Mission
"fuel constraints":
Wfuel[:-1] >= Wfuel[1:] + Wburn[:-1]
Wfuel[3] >= Wburn[3]
FlightSegment
AircraftP
Wburn[:] >= 0.1*D[:]
Aircraft.W + Wfuel[:] <= 0.5*Mission.FlightSegment.FlightState.rho[:]*CL[:]*S*V[:]^2
"performance":
WingAero
D[:] >= 0.5*Mission.FlightSegment.FlightState.rho[:]*V[:]^2*CD[:]*S
Re[:] = Mission.FlightSegment.FlightState.rho[:]*V[:]*c/mu[:]
CD[:] >= 0.074/Re[:]^0.2 + CL[:]^2/PI/A/e[:]
FlightState
(no constraints)
Aircraft
Aircraft.W >= Fuselage.W + Wing.W
Fuselage
(no constraints)
Wing
c = (S/A)^0.5
Wing.W >= S*Wing.rho
Optimal Cost
------------
1.091
Model Sensitivities
-------------------
+2.5 : Mission
+2.4 : Mission.FlightSegment.AircraftP
+2.1 : Mission.FlightSegment.AircraftP.WingAero
+1.4 : Aircraft
+0.5 : Aircraft.Wing
Free Variables
--------------
| Aircraft
W : 144.1 [lbf] weight
| Aircraft.Wing
S : 44.14 [ft**2] surface area
W : 44.14 [lbf] weight
c : 1.279 [ft] mean chord
| Mission.FlightSegment.AircraftP
Wburn : [ 0.274 0.273 0.272 0.272 ] [lbf] segment fuel burn
Wfuel : [ 1.09 0.817 0.544 0.272 ] [lbf] fuel weight
| Mission.FlightSegment.AircraftP.WingAero
D : [ 2.74 2.73 2.72 2.72 ] [lbf] drag force
Insensitive Constraints |below +1e-05|
--------------------------------------
(none)
Solution Diff (for selected variables)
======================================
(argument is the baseline solution)
Constraint Differences
**********************
@@ -31,3 +31,4 @@
Wing
c = (S/A)^0.5
Wing.W >= S*Wing.rho
+ Wburn[:] >= 0.2*D[:]
**********************
Relative Differences |above 1%|
-------------------------------
Wburn : [ +102.1% +101.6% +101.1% +100.5% ] segment fuel burn
Wfuel : [ +101.3% +101.1% +100.8% +100.5% ] fuel weight
D : [ +1.1% - - - ] drag force
c:\users\jenkins\workspace\ce_gpkit_pr_unit_tests\cvxopt\gpkit\small_classes.py:158: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
v = np.array(v)
C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt\venv_jenkins\lib\site-packages\matplotlib\cbook\__init__.py:1298: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.
return np.asarray(x, float)
C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt\docs\source\examples\plot_sweep1d.py:20: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
f.show()
C:\Users\jenkins\workspace\CE_gpkit_PR_unit_tests\cvxopt\docs\source\examples\plot_sweep1d.py:28: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
f.show()
Original model
==============
Cost Function
-------------
x
Constraints
-----------
x <= x_max
x >= x_min
With constraints relaxed equally
================================
Cost Function
-------------
C
Constraints
-----------
"minimum relaxation":
C >= 1
"relaxed constraints":
x <= C*x_max
x_min <= C*x
Optimal Cost
------------
1.414
~~~~~~~~
WARNINGS
~~~~~~~~
Relaxed Constraints
-------------------
All constraints relaxed by 42%
~~~~~~~~
Free Variables
--------------
x : 1.414
| Relax
C : 1.414
Fixed Variables
---------------
x_max : 1
x_min : 2
Variable Sensitivities
----------------------
x_max : -0.5
x_min : +0.5
Most Sensitive Constraints
--------------------------
+0.5 : x <= C*x_max
+0.5 : x_min <= C*x
C (1.41)
breaks down into:
C (1.41)
which in: x <= C*x_max (sensitivity +0.5)
{ through a factor of 1/x_max (1, fixed) }
breaks down into:
x (1.41)
which in: x_min <= C*x (sensitivity +0.5)
breaks down into:
{ through a factor of 1/C (0.707) }
x_min (2, fixed)
With constraints relaxed individually
=====================================
Cost Function
-------------
C[:].prod()*x^0.01
Constraints
-----------
"minimum relaxation":
C[:] >= 1
"relaxed constraints":
x <= C[0]*x_max
x_min <= C[1]*x
Optimal Cost
------------
2
~~~~~~~~
WARNINGS
~~~~~~~~
Relaxed Constraints
-------------------
1: 100% relaxed, from x >= x_min
to x_min <= 2*x
~~~~~~~~
Free Variables
--------------
x : 1
| Relax1
C : [ 1 2 ]
Fixed Variables
---------------
x_max : 1
x_min : 2
Variable Sensitivities
----------------------
x_min : +1
x_max : -0.99
Most Sensitive Constraints
--------------------------
+1 : x_min <= C[1]*x
+0.99 : x <= C[0]*x_max
+0.01 : C[0] >= 1
With constants relaxed individually
===================================
Cost Function
-------------
[Relax2.x_max, Relax2.x_min].prod()*x^0.01
Constraints
-----------
Relax2
"original constraints":
x <= x_max
x >= x_min
"relaxation constraints":
"x_max":
Relax2.x_max >= 1
x_max >= OriginalValues.x_max/Relax2.x_max
x_max <= OriginalValues.x_max*Relax2.x_max
"x_min":
Relax2.x_min >= 1
x_min >= OriginalValues.x_min/Relax2.x_min
x_min <= OriginalValues.x_min*Relax2.x_min
Optimal Cost
------------
2
~~~~~~~~
WARNINGS
~~~~~~~~
Relaxed Constants
-----------------
x_min: relaxed from 2 to 1
~~~~~~~~
Free Variables
--------------
x : 1
x_max : 1
x_min : 1
| Relax2
x_max : 1
x_min : 2
Fixed Variables
---------------
| Relax2.OriginalValues
x_max : 1
x_min : 2
Variable Sensitivities
----------------------
x_min : +1
x_max : -0.99
Most Sensitive Constraints
--------------------------
+1 : x >= x_min
+1 : x_min >= OriginalValues.x_min/Relax2.x_min
+0.99 : x <= x_max
+0.99 : x_max <= OriginalValues.x_max*Relax2.x_max
SINGLE
======
Optimal Cost
------------
303.1
Free Variables
--------------
A : 8.46 aspect ratio
C_D : 0.02059 Drag coefficient of wing
C_L : 0.4988 Lift coefficent of wing
C_f : 0.003599 skin friction coefficient
D : 303.1 [N] total drag force
Re : 3.675e+06 Reynold's number
S : 16.44 [m**2] total wing area
V : 38.15 [m/s] cruising speed
W : 7341 [N] total aircraft weight
W_w : 2401 [N] wing weight
Solution Diff
=============
(argument is the baseline solution)
** no constraint differences **
Relative Differences |above 1%|
-------------------------------
The largest is +0%.
SWEEP
=====
Optimal Cost
------------
[ 338 396 294 326 ]
Swept Variables
---------------
V : [ 45 55 45 55 ] [m/s] cruising speed
V_{min} : [ 20 20 25 25 ] [m/s] takeoff speed
Free Variables
--------------
A : [ 6.2 4.77 8.84 7.16 ] aspect ratio
C_D : [ 0.0146 0.0123 0.0196 0.0157 ] Drag coefficient of wing
C_L : [ 0.296 0.198 0.463 0.31 ] Lift coefficent of wing
C_f : [ 0.00333 0.00314 0.00361 0.00342 ] skin friction coefficient
D : [ 338 396 294 326 ] [N] total drag force
Re : [ 5.38e+06 7.24e+06 3.63e+06 4.75e+06 ] Reynold's number
S : [ 18.6 17.3 12.1 11.2 ] [m**2] total wing area
W : [ 6.85e+03 6.4e+03 6.97e+03 6.44e+03 ] [N] total aircraft weight
W_w : [ 1.91e+03 1.46e+03 2.03e+03 1.5e+03 ] [N] wing weight
Solution Diff
=============
(argument is the baseline solution)
** no constraint differences **
Relative Differences |above 1%|
-------------------------------
Re : [ +46.4% +97.1% -1.1% +29.2% ] Reynold's number
C_L : [ -40.6% -60.2% -7.2% -37.9% ] Lift coefficent of wing
V : [ +18.0% +44.2% +18.0% +44.2% ] cruising speed
W_w : [ -20.7% -39.3% -15.6% -37.4% ] wing weight
C_D : [ -29.0% -40.4% -5.0% -23.9% ] Drag coefficient of wing
A : [ -26.7% -43.6% +4.5% -15.3% ] aspect ratio
S : [ +12.8% +5.5% -26.5% -32.0% ] total wing area
D : [ +11.5% +30.7% -2.9% +7.5% ] total drag force
V_{min} : [ -9.1% -9.1% +13.6% +13.6% ] takeoff speed
W : [ -6.8% -12.8% -5.1% -12.2% ] total aircraft weight
C_f : [ -7.3% -12.7% - -5.0% ] skin friction coefficient
Absolute Differences |above 0.1|
--------------------------------
Re : [ +1.7e+06 +3.6e+06 -4.1e+04 +1.1e+06 ] Reynold's number
W : [ -5e+02 -9.4e+02 -3.8e+02 -9e+02 ] [N] total aircraft weight
W_w : [ -5e+02 -9.4e+02 -3.8e+02 -9e+02 ] [N] wing weight
D : [ +35 +93 -8.8 +23 ] [N] total drag force
V : [ +6.8 +17 +6.8 +17 ] [m/s] cruising speed
S : [ +2.1 +0.9 -4.4 -5.3 ] [m**2] total wing area
V_{min} : [ -2 -2 +3 +3 ] [m/s] takeoff speed
A : [ -2.3 -3.7 +0.38 -1.3 ] aspect ratio
C_L : [ -0.2 -0.3 - -0.19 ] Lift coefficent of wing
Sensitivity Differences |above 0.1|
-----------------------------------
V : [ +0.59 +0.97 +0.25 +0.75 ] cruising speed
V_{min} : [ -0.45 -0.67 - -0.34 ] takeoff speed
C_{L,max} : [ -0.23 -0.34 - -0.17 ] max CL with flaps down
e : [ +0.15 +0.25 - +0.19 ] Oswald efficiency factor
W_0 : [ - -0.17 - -0.16 ] aircraft weight excluding wing
\rho : [ - +0.13 - +0.19 ] density of air
(\frac{S}{S_{wet}}) : [ +0.13 +0.20 - +0.11 ] wetted area ratio
k : [ +0.13 +0.20 - +0.11 ] form factor
N_{ult} : [ -0.11 -0.18 - -0.14 ] ultimate load factor
W_{W_{coeff1}} : [ -0.11 -0.18 - -0.14 ] Wing Weight Coefficent 1
\tau : [ +0.11 +0.18 - +0.14 ] airfoil thickness to chord ratio
Optimal Cost
------------
0.003674
Free Variables
--------------
d : 8.17 [m] depth
h : 8.163 [m] height
w : 4.081 [m] width
Fixed Variables
---------------
A_{floor} : 50 [m**2] upper limit, floor area
A_{wall} : 200 [m**2] upper limit, wall area
alpha : 2 lower limit, wall aspect ratio
beta : 10 upper limit, wall aspect ratio
delta : 10 upper limit, floor aspect ratio
gamma : 2 lower limit, floor aspect ratio
Variable Sensitivities
----------------------
A_{wall} : -1.5 upper limit, wall area
alpha : +0.5 lower limit, wall aspect ratio
Most Sensitive Constraints
--------------------------
+1.5 : A_{wall} >= 2*h*w + 2*h*d
+0.5 : alpha <= h/w
Optimal Cost
------------
0.9
Free Variables
--------------
x : 0.9
y : 0.1
x values of each GP solve (note convergence)
2.50000, 0.92548, 0.90003, 0.90000
Optimal Cost
------------
0.7854
Free Variables
--------------
x : 0.7854
y : 0.7854
c:\users\jenkins\workspace\ce_gpkit_pr_unit_tests\cvxopt\gpkit\small_classes.py:158: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
v = np.array(v)
Optimal Cost
------------
[ 4.63e+03 6.23e+03 7.36e+03 ]
~~~~~~~~
WARNINGS
~~~~~~~~
Unexpectedly Loose Constraints in sweep 0
-----------------------------------------
0.5886 >= 0.5775 : V_{f_{avail}} >= V_f
Unexpectedly Loose Constraints in sweep 1
-----------------------------------------
0.7884 >= 0.7769 : V_{f_{avail}} >= V_f
Unexpectedly Loose Constraints in sweep 2
-----------------------------------------
0.9585 >= 0.9187 : V_{f_{avail}} >= V_f
~~~~~~~~
Swept Variables
---------------
V_f_wing : [ 0.1 0.3 0.5 ] [m**3] fuel volume in the wing
Free Variables
--------------
(CDA0) : [ 0.05 0.05 0.05 ] [m**2] fuselage drag area
A : [ 12.4 3.78 2.35 ] aspect ratio
C_D : [ 0.0136 0.011 0.0099 ] drag coefficient
C_L : [ 0.327 0.162 0.121 ] lift coefficient of wing
C_f : [ 0.00343 0.00284 0.00261 ] skin friction coefficient
D : [ 466 774 1e+03 ] [N] total drag force
L/D : [ 24.1 14.8 12.2 ] lift-to-drag ratio
Re : [ 4.64e+06 1.21e+07 1.83e+07 ] Reynold's number
S : [ 22 29.7 35.6 ] [m**2] total wing area
T_{flight} : [ 16.6 13.4 12.3 ] [hr] flight time
V : [ 50.3 62.1 67.9 ] [m/s] cruising speed
V_f : [ 0.577 0.777 0.919 ] [m**3] fuel volume
V_{f_{avail}} : [ 0.589 0.788 0.958 ] [m**3] fuel volume available
W : [ 1.35e+04 1.45e+04 1.59e+04 ] [N] total aircraft weight
W_f : [ 4.63e+03 6.23e+03 7.36e+03 ] [N] fuel weight
W_w : [ 2.65e+03 2.05e+03 2.29e+03 ] [N] wing weight
W_w_strc : [ 1.33e+03 269 151 ] [N] wing structural weight
W_w_surf : [ 1.32e+03 1.78e+03 2.14e+03 ] [N] wing skin weight
Cost Function
-------------
Wfuel[0]
Constraints
-----------
Mission
"fuel constraints":
Wfuel[:-1] >= Wfuel[1:] + Wburn[:-1]
Wfuel[3] >= Wburn[3]
FlightSegment
AircraftP
Wburn[:] >= 0.1*D[:]
Aircraft.W + Wfuel[:] <= 0.5*Mission.FlightSegment.FlightState.rho[:]*CL[:]*S*V[:]^2
"performance":
WingAero
D[:] >= 0.5*Mission.FlightSegment.FlightState.rho[:]*V[:]^2*CD[:]*S
Re[:] = Mission.FlightSegment.FlightState.rho[:]*V[:]*c/mu[:]
CD[:] >= 0.074/Re[:]^0.2 + CL[:]^2/PI/A/e[:]
FlightState
(no constraints)
Aircraft
Aircraft.W >= Fuselage.W + Wing.W
Fuselage
(no constraints)
Wing
c = (S/A)^0.5
Wing.W >= S*Wing.rho
Optimal Cost
------------
1.091
Model Sensitivities
-------------------
+2.5 : Mission
+2.4 : Mission.FlightSegment.AircraftP
+2.1 : Mission.FlightSegment.AircraftP.WingAero
+1.4 : Aircraft
+0.5 : Aircraft.Wing
Free Variables
--------------
| Aircraft
W : 144.1 [lbf] weight
| Aircraft.Wing
S : 44.14 [ft**2] surface area
W : 44.14 [lbf] weight
c : 1.279 [ft] mean chord
| Mission.FlightSegment.AircraftP
Wburn : [ 0.274 0.273 0.272 0.272 ] [lbf] segment fuel burn
Wfuel : [ 1.09 0.817 0.544 0.272 ] [lbf] fuel weight
| Mission.FlightSegment.AircraftP.WingAero
D : [ 2.74 2.73 2.72 2.72 ] [lbf] drag force
Insensitive Constraints |below +1e-05|
--------------------------------------
(none)
Solution Diff (for selected variables)
======================================
(argument is the baseline solution)
Constraint Differences
**********************
@@ -31,3 +31,4 @@
Wing
c = (S/A)^0.5
Wing.W >= S*Wing.rho
+ Wburn[:] >= 0.2*D[:]
**********************
Relative Differences |above 1%|
-------------------------------
Wburn : [ +102.1% +101.6% +101.1% +100.5% ] segment fuel burn
Wfuel : [ +101.3% +101.1% +100.8% +100.5% ] fuel weight
D : [ +1.1% - - - ] drag force
Optimal Cost
------------
1e-30
~~~~~~~~
WARNINGS
~~~~~~~~
Arbitrarily Bounded Variables
-----------------------------
value near upper bound of 1e+30: x
sensitive to upper bound of 1e+30: x
~~~~~~~~
Free Variables
--------------
x : 1e+30
SCALAR
Optimal Cost
------------
1
Free Variables
--------------
x : 1
__________
VECTORIZED
Optimal Cost
------------
2
Free Variables
--------------
x : [ 1 2 1 ]
Infeasible monomial equality: Cannot convert from 'V [m**3]' to 'M [kg]'
Optimal Cost
------------
1.293
Free Variables
--------------
A : 1.293 [m**2] Surface Area of the Tank
V : 0.1 [m**3] Volume of the Tank
d : [ 0.464 0.464 0.464 ] [m] Dimension Vector
Optimal cost: 1
Optimal x val: 1
Build step 'Console output (build log) parsing' changed build result to FAILURE
[Cobertura] Publishing Cobertura coverage report...
Recording test results
[Checks API] No suitable checks publisher found.
[WS-CLEANUP] Deleting project workspace...
[WS-CLEANUP] Deferred wipeout is used...
[WS-CLEANUP] done
Finished: FAILURE