During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/units.py", line 55, in of_division
self.division_cache[key] = float(conversion)
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py", line 788, in __float__
raise DimensionalityError(self._units, "dimensionless")
pint.errors.DimensionalityError: Cannot convert from '1 / foot' to 'dimensionless'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/tests/t_constraints.py", line 155, in test_init
self.assertRaises(ValueError, MonomialEquality, x, y)
File "/usr/local/opt/python/Frameworks/Python.framework/Versions/3.7/lib/python3.7/unittest/case.py", line 756, in assertRaises
return context.handle('assertRaises', args, kwargs)
File "/usr/local/opt/python/Frameworks/Python.framework/Versions/3.7/lib/python3.7/unittest/case.py", line 178, in handle
callable_obj(*args, **kwargs)
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/nomials/math.py", line 515, in __init__
self.unsubbed = self._gen_unsubbed(self.left, self.right)
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/nomials/math.py", line 535, in _gen_unsubbed
l_over_r = unsubbed(self, left, right)
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/nomials/math.py", line 465, in _gen_unsubbed
m_c *= units.of_division(m_gt, p_lt)
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/units.py", line 57, in of_division
raise DimensionalityError(numerator, denominator)
pint.errors.DimensionalityError: Cannot convert from 'y' to 'x [ft]'
======================================================================
ERROR: test_water_tank_mosek_cli (gpkit.tests.t_examples.TestExamples)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/units.py", line 48, in of_division
return self.division_cache[key]
KeyError: (4690775120, 4668367504)
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/units.py", line 55, in of_division
self.division_cache[key] = float(conversion)
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py", line 788, in __float__
raise DimensionalityError(self._units, "dimensionless")
pint.errors.DimensionalityError: Cannot convert from 'meter ** 3 / kilogram' to 'dimensionless'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/tests/helpers.py", line 59, in test
testfn(name, import_dict, path)(self)
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/tests/helpers.py", line 94, in test
imported[name] = importlib.import_module(name)
File "/usr/local/opt/python/Frameworks/Python.framework/Versions/3.7/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1006, in _gcd_import
File "<frozen importlib._bootstrap>", line 983, in _find_and_load
File "<frozen importlib._bootstrap>", line 967, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 677, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 728, in exec_module
File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/docs/source/examples/water_tank.py", line 11, in <module>
bad_monomial_equality = (M == V)
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/nomials/math.py", line 342, in __eq__
return MonomialEquality(self, other)
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/nomials/math.py", line 515, in __init__
self.unsubbed = self._gen_unsubbed(self.left, self.right)
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/nomials/math.py", line 535, in _gen_unsubbed
l_over_r = unsubbed(self, left, right)
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/nomials/math.py", line 465, in _gen_unsubbed
m_c *= units.of_division(m_gt, p_lt)
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/units.py", line 57, in of_division
raise DimensionalityError(numerator, denominator)
pint.errors.DimensionalityError: Cannot convert from 'V [m³]' to 'M [kg]'
======================================================================
ERROR: test_water_tank_mosek_conif (gpkit.tests.t_examples.TestExamples)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/units.py", line 48, in of_division
return self.division_cache[key]
KeyError: (4690775120, 4668367504)
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/units.py", line 55, in of_division
self.division_cache[key] = float(conversion)
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py", line 788, in __float__
raise DimensionalityError(self._units, "dimensionless")
pint.errors.DimensionalityError: Cannot convert from 'meter ** 3 / kilogram' to 'dimensionless'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/tests/helpers.py", line 59, in test
testfn(name, import_dict, path)(self)
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/tests/helpers.py", line 94, in test
imported[name] = importlib.import_module(name)
File "/usr/local/opt/python/Frameworks/Python.framework/Versions/3.7/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1006, in _gcd_import
File "<frozen importlib._bootstrap>", line 983, in _find_and_load
File "<frozen importlib._bootstrap>", line 967, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 677, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 728, in exec_module
File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/docs/source/examples/water_tank.py", line 11, in <module>
bad_monomial_equality = (M == V)
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/nomials/math.py", line 342, in __eq__
return MonomialEquality(self, other)
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/nomials/math.py", line 515, in __init__
self.unsubbed = self._gen_unsubbed(self.left, self.right)
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/nomials/math.py", line 535, in _gen_unsubbed
l_over_r = unsubbed(self, left, right)
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/nomials/math.py", line 465, in _gen_unsubbed
m_c *= units.of_division(m_gt, p_lt)
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/units.py", line 57, in of_division
raise DimensionalityError(numerator, denominator)
pint.errors.DimensionalityError: Cannot convert from 'V [m³]' to 'M [kg]'
======================================================================
FAIL: test_units_sub (gpkit.tests.t_solution_array.TestSolutionArray)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/tests/t_solution_array.py", line 76, in test_units_sub
self.assertEqual(sol(Tmin), tminsub)
AssertionError: <Quantity(4448.22162, 'newton')> != <Quantity(1000, 'force_pound')>
----------------------------------------------------------------------
Ran 239 tests in 18.634s
FAILED (failures=1, errors=4)
Found no installed solvers, beginning a build.
# Building GPkit version 1.0.0pre
# 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
##
# Looks like `mskexpopt` was not found in the default PATH,
# so let's try locating that binary ourselves.
# Adding /Users/jenkins/mosek/8/tools/platform/osx64x86/bin to the PATH
# Calling 'mskexpopt'
##
### CALL BEGINS
### CALL ENDS
##
Found mosek_cli in /Users/jenkins/mosek/8/tools/platform/osx64x86/bin
# Looking for `mosek_conif`
# Trying to import mosek...
Found mosek_conif in the default PYTHONPATH
# Looking for `cvxopt`
# Trying to import cvxopt...
# Did not find
# cvxopt
Replaced found solvers (['mosek_cli', 'mosek_conif']) with environment var GPKITSOLVERS (mosek_cli, mosek_conif)
Found the following solvers: mosek_cli, mosek_conif
# Replacing directory env
GPkit is now installed with solver(s) ['mosek_cli', 'mosek_conif']
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': ['mosek_cli', 'mosek_conif'], 'mosek_bin_dir': '/Users/jenkins/mosek/8/tools/platform/osx64x86/bin', 'default_solver': 'mosek_cli', 'just built!': True}
++ python /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/bin/pip install --no-cache-dir -e /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/
Obtaining file:///Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek
Requirement already satisfied: numpy>=1.16.4 in ./venv2_gpkit/lib/python3.7/site-packages (from gpkit==1.0.0) (1.19.0)
Collecting pint<0.10,>=0.8.1
Downloading Pint-0.9-py2.py3-none-any.whl (138 kB)
Requirement already satisfied: scipy in ./venv2_gpkit/lib/python3.7/site-packages (from gpkit==1.0.0) (1.5.1)
Requirement already satisfied: ad in ./venv2_gpkit/lib/python3.7/site-packages (from gpkit==1.0.0) (1.3.2)
Collecting cvxopt>=1.1.8
Downloading cvxopt-1.2.5-cp37-cp37m-macosx_10_9_x86_64.whl (3.1 MB)
Installing collected packages: pint, cvxopt, gpkit
Attempting uninstall: pint
Found existing installation: Pint 0.14
Uninstalling Pint-0.14:
Successfully uninstalled Pint-0.14
Running setup.py develop for gpkit
Successfully installed cvxopt-1.2.5 gpkit pint-0.9
++ export MSK_IPAR_NUM_THREADS=2
++ MSK_IPAR_NUM_THREADS=2
++ export MKL_NUM_THREADS=2
++ MKL_NUM_THREADS=2
++ export NUMEXPR_NUM_THREADS=2
++ NUMEXPR_NUM_THREADS=2
++ export OPENBLAS_NUM_THREADS=2
++ OPENBLAS_NUM_THREADS=2
++ export OMP_NUM_THREADS=2
++ OMP_NUM_THREADS=2
++ python -c 'from gpkit.tests.test_repo import test_repos; test_repos(xmloutput=True)'
/bin/sh: mskexpopt: command not found
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.]
........................../Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/nomials/substitution.py:40: 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
sub = np.array(sub) if not hasattr(sub, "shape") else sub
.....................................................................................................................................................................................................................Cloning into 'gplibrary'...
Obtaining file:///Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gplibrary
Installing collected packages: gpkitmodels
Running setup.py develop for gpkitmodels
Successfully installed gpkitmodels
Cloning into 'SPaircraft'...
Collecting git+https://github.com/hoburg/turbofan.git
Cloning https://github.com/hoburg/turbofan.git to /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-req-build-yc2utmi2
Running command git clone -q https://github.com/hoburg/turbofan.git /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-req-build-yc2utmi2
Requirement already satisfied: numpy in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from turbofan==0.0.0.0) (1.19.0)
Requirement already satisfied: scipy in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from turbofan==0.0.0.0) (1.5.1)
Requirement already satisfied: pint in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from turbofan==0.0.0.0) (0.9)
Processing /Users/jenkins/Library/Caches/pip/wheels/56/b0/fe/4410d17b32f1f0c3cf54cdfb2bc04d7b4b8f4ae377e2229ba0/future-0.18.2-py3-none-any.whl
Building wheels for collected packages: turbofan
Building wheel for turbofan (setup.py): started
Building wheel for turbofan (setup.py): finished with status 'done'
Created wheel for turbofan: filename=turbofan-0.0.0.0-py3-none-any.whl size=43842 sha256=63c3fa9927815cc078a19a24b3970003dd91c3fb33aafa84ae30c7d90141e4e7
Stored in directory: /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-ephem-wheel-cache-j7jiq3uz/wheels/5e/94/24/992cb23a6749c3bad249589806341b6bef6786d3794831a01c
Successfully built turbofan
Installing collected packages: future, turbofan
Successfully installed future-0.18.2 turbofan-0.0.0.0
Processing /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/SPaircraft
Requirement already satisfied: turbofan in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from SPaircraft==0.0.0) (0.0.0.0)
Requirement already satisfied: gpkit in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek (from SPaircraft==0.0.0) (1.0.0)
Requirement already satisfied: future in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from SPaircraft==0.0.0) (0.18.2)
Requirement already satisfied: numpy in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from turbofan->SPaircraft==0.0.0) (1.19.0)
Requirement already satisfied: pint in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from turbofan->SPaircraft==0.0.0) (0.9)
Requirement already satisfied: scipy in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from turbofan->SPaircraft==0.0.0) (1.5.1)
Requirement already satisfied: ad in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpkit->SPaircraft==0.0.0) (1.3.2)
Requirement already satisfied: cvxopt>=1.1.8 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpkit->SPaircraft==0.0.0) (1.2.5)
Building wheels for collected packages: SPaircraft
Building wheel for SPaircraft (setup.py): started
Building wheel for SPaircraft (setup.py): finished with status 'done'
Created wheel for SPaircraft: filename=SPaircraft-0.0.0-py3-none-any.whl size=1837 sha256=2ae369d0f7a71d8917acbf6a41e213893b227bd37cd7070a7467607e36c58cc2
Stored in directory: /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-ephem-wheel-cache-1mq00crp/wheels/ad/52/68/f211eb375e991b907fddc35682e08b1fb4e4fe5d5d3a393d7b
Successfully built SPaircraft
Installing collected packages: SPaircraft
Successfully installed SPaircraft-0.0.0
Running tests...
----------------------------------------------------------------------
./Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/constraints/gp.py:407: RuntimeWarning: divide by zero encountered in log
for i, mi in enumerate(self.m_idxs) if la[i])
.
----------------------------------------------------------------------
Ran 2 tests in 15.078s
OK
Generating XML reports...
adding test for 'SPaircraft.py'
Starting a sequence of GP solves
for 317 free variables
in 136 locally-GP constraints
and for 1198 free variables
in 4705 posynomial inequalities.
GP Solve 1
Using solver 'mosek_cli'
for 1198 free variables
in 4705 posynomial inequalities.
Solving took 0.507 seconds.
Solved cost was 3.965e+40.
GP Solve 2
Using solver 'mosek_cli'
for 1198 free variables
in 4705 posynomial inequalities.
Solving took 0.307 seconds.
Solved cost was 4.013e+10.
GP Solve 3
Using solver 'mosek_cli'
for 1198 free variables
in 4705 posynomial inequalities.
Solving took 0.329 seconds.
Solved cost was 3.195e+05.
GP Solve 4
Using solver 'mosek_cli'
for 1198 free variables
in 4705 posynomial inequalities.
Solving took 0.318 seconds.
Solved cost was 2.931e+04.
GP Solve 5
Using solver 'mosek_cli'
for 1198 free variables
in 4705 posynomial inequalities.
Solving took 0.278 seconds.
Solved cost was 2.498e+04.
GP Solve 6
Using solver 'mosek_cli'
for 1198 free variables
in 4705 posynomial inequalities.
Solving took 0.271 seconds.
Solved cost was 2.381e+04.
GP Solve 7
Using solver 'mosek_cli'
for 1198 free variables
in 4705 posynomial inequalities.
Solving took 0.358 seconds.
Solved cost was 2.328e+04.
GP Solve 8
Using solver 'mosek_cli'
for 1198 free variables
in 4705 posynomial inequalities.
Solving took 0.316 seconds.
Solved cost was 2.303e+04.
Solving took 3.84 seconds and 8 GP solves.
Solves with these variables bounded:
sensitive to lower bound: Mission.Aircraft.HorizontalTail.WingBox.I_{cap}
value near lower bound: Mission.Aircraft.HorizontalTail.WingBox.M_r, Mission.Aircraft.Wing.WingNoStruct.\bar{A}_{fuel, max}, Mission.Aircraft.LandingGear.h_{nacelle}, Mission.Aircraft.HorizontalTail.WingBox.I_{cap}
WEIGHT DIFFERENCES
Total Fuel Weight Percent Diff: [-14.58138901]
Total Aircraft Weight Percent Diff: [-0.74792307]
Engine Weight Percent Diff: -31.67894313302555
Fuselage Weight Percent Diff: 2.6526976812611798
Payload Weight Percent Diff: [-0.04052335]
VT Weight Percent Diff: 36.22045684746843
HT Weight Percent Diff: -97.00046733347595
Wing Weight Percent Diff: 6.301579926786856
WING DIFFERENCES
Wing Span Percent Diff: -4.037425783973113
Wing Area Percent Diff: 20.489118201482633
HORIZONTAL TAIL DIFFERENCES
HT Area Percent Diff: 31.218905332316265
VERTICAL TAIL DIFFERENCES
VT Span Percent Diff: 0.9750022574577574
VT Area Percent Diff: -48.98415962010767
CRUISE SEGMENT 1 DRAG DIFFERENCES
Overall Cd Percent Diff: [-29.23174136]
L/D Percent Diff: [16.07581914]
Nacelle Cd Percent Diff: [-2.28201711]
HT Cd Percent Diff: [-97.20244273] dimensionless
Fuselage Cd Percent Diff: [108.78744865]
VT Cd Percent Diff: [-35.6733968] dimensionless
Wing Cd percent Diff: [-29.62862624]
Induced Drag Cd Percent Diff: [-33.69778717]
CRUISE SEGMENT 1 TSFC DIFFERENCES
Initial Cruise TSFC Percent Diff: [-5.542846]
FUSELAGE DIFFERENCES
Weight of HB material: -30.558002534225658
Weight of VB material: 115.86605373880947
Fan Propulsive Efficiency in Cruise Segment 1
---------------------
[0.80046647]
Starting a sequence of GP solves
for 317 free variables
in 136 locally-GP constraints
and for 1198 free variables
in 4705 posynomial inequalities.
GP Solve 1
Using solver 'mosek_cli'
for 1198 free variables
in 4705 posynomial inequalities.
Solving took 0.313 seconds.
Solved cost was 3.965e+40.
GP Solve 2
Using solver 'mosek_cli'
for 1198 free variables
in 4705 posynomial inequalities.
Solving took 0.264 seconds.
Solved cost was 4.006e+10.
GP Solve 3
Using solver 'mosek_cli'
for 1198 free variables
in 4705 posynomial inequalities.
Solving took 0.311 seconds.
Solved cost was 3.199e+05.
GP Solve 4
Using solver 'mosek_cli'
for 1198 free variables
in 4705 posynomial inequalities.
Solving took 0.315 seconds.
Solved cost was 2.932e+04.
GP Solve 5
Using solver 'mosek_cli'
for 1198 free variables
in 4705 posynomial inequalities.
Solving took 0.307 seconds.
Solved cost was 2.498e+04.
GP Solve 6
Using solver 'mosek_cli'
for 1198 free variables
in 4705 posynomial inequalities.
Solving took 0.318 seconds.
Solved cost was 2.381e+04.
GP Solve 7
Using solver 'mosek_cli'
for 1198 free variables
in 4705 posynomial inequalities.
Solving took 0.292 seconds.
Solved cost was 2.328e+04.
GP Solve 8
Using solver 'mosek_cli'
for 1198 free variables
in 4705 posynomial inequalities.
Solving took 0.324 seconds.
Solved cost was 2.303e+04.
Solving took 3.63 seconds and 8 GP solves.
Solves with these variables bounded:
sensitive to lower bound: Mission1.Aircraft.HorizontalTail.WingBox.I_{cap}
value near lower bound: Mission1.Aircraft.Wing.WingNoStruct.\bar{A}_{fuel, max}, Mission1.Aircraft.LandingGear.h_{nacelle}, Mission1.Aircraft.HorizontalTail.WingBox.I_{cap}, Mission1.Aircraft.HorizontalTail.WingBox.M_r
WEIGHT DIFFERENCES
Total Fuel Weight Percent Diff: [-14.58138901]
Total Aircraft Weight Percent Diff: [-0.74792307]
Engine Weight Percent Diff: -31.676961793647028
Fuselage Weight Percent Diff: 2.651671159417021
Payload Weight Percent Diff: [-0.04052335]
VT Weight Percent Diff: 36.22154661548231
HT Weight Percent Diff: -97.00046433394179
Wing Weight Percent Diff: 6.301579926786856
WING DIFFERENCES
Wing Span Percent Diff: -4.037713671263935
Wing Area Percent Diff: 20.48863624597367
HORIZONTAL TAIL DIFFERENCES
HT Area Percent Diff: 31.218774113476563
VERTICAL TAIL DIFFERENCES
VT Span Percent Diff: 0.9751032325104769
VT Area Percent Diff: -48.98405758832487
CRUISE SEGMENT 1 DRAG DIFFERENCES
Overall Cd Percent Diff: [-29.23159982]
L/D Percent Diff: [16.07535484]
Nacelle Cd Percent Diff: [-2.27879237]
HT Cd Percent Diff: [-97.20243154] dimensionless
Fuselage Cd Percent Diff: [108.78744865]
VT Cd Percent Diff: [-35.67301084] dimensionless
Wing Cd percent Diff: [-29.62876698]
Induced Drag Cd Percent Diff: [-33.69791978]
CRUISE SEGMENT 1 TSFC DIFFERENCES
Initial Cruise TSFC Percent Diff: [-5.54332773]
FUSELAGE DIFFERENCES
Weight of HB material: -30.629629709630734
Weight of VB material: 115.86756480647438
Fan Propulsive Efficiency in Cruise Segment 1
---------------------
[0.80047367]
Starting a sequence of GP solves
for 317 free variables
in 136 locally-GP constraints
and for 1198 free variables
in 4705 posynomial inequalities.
GP Solve 1
Using solver 'mosek_conif'
for 1198 free variables
in 4705 posynomial inequalities.
Solving took 0.186 seconds.
Solved cost was 3.965e+40.
GP Solve 2
Using solver 'mosek_conif'
for 1198 free variables
in 4705 posynomial inequalities.
Solving took 0.182 seconds.
Solved cost was 2.974e+10.
GP Solve 3
Using solver 'mosek_conif'
for 1198 free variables
in 4705 posynomial inequalities.
Solving took 0.199 seconds.
Solved cost was 4.875e+05.
GP Solve 4
Using solver 'mosek_conif'
for 1198 free variables
in 4705 posynomial inequalities.
Solving took 0.182 seconds.
Solved cost was 3.074e+04.
GP Solve 5
Using solver 'mosek_conif'
for 1198 free variables
in 4705 posynomial inequalities.
Solving took 0.176 seconds.
Solved cost was 2.521e+04.
GP Solve 6
Using solver 'mosek_conif'
for 1198 free variables
in 4705 posynomial inequalities.
Solving took 0.176 seconds.
Solved cost was 2.393e+04.
GP Solve 7
Using solver 'mosek_conif'
for 1198 free variables
in 4705 posynomial inequalities.
Solving took 0.189 seconds.
Solved cost was 2.334e+04.
GP Solve 8
Using solver 'mosek_conif'
for 1198 free variables
in 4705 posynomial inequalities.
Solving took 0.221 seconds.
Solved cost was 2.306e+04.
Solution check warning: Dual cost nan does not match primal cost 23059.901735138388
Solving took 2.56 seconds and 8 GP solves.
Solves with these variables bounded:
sensitive to lower bound: Mission.Aircraft.HorizontalTail.WingBox.I_{cap}
value near lower bound: Mission.Aircraft.Wing.WingNoStruct.\bar{A}_{fuel, max}, Mission.Aircraft.LandingGear.h_{nacelle}, Mission.Aircraft.HorizontalTail.WingBox.I_{cap}
WEIGHT DIFFERENCES
Total Fuel Weight Percent Diff: [-14.46340999]
Total Aircraft Weight Percent Diff: [-0.49077696]
Engine Weight Percent Diff: -30.930575694277376
Fuselage Weight Percent Diff: 3.038718990567549
Payload Weight Percent Diff: [-0.04003537]
VT Weight Percent Diff: 37.10450220588945
HT Weight Percent Diff: -96.9930433484508
Wing Weight Percent Diff: 6.437549119562438
WING DIFFERENCES
Wing Span Percent Diff: -4.0706572005878074
Wing Area Percent Diff: 20.57355476302984
HORIZONTAL TAIL DIFFERENCES
HT Area Percent Diff: 31.53660803257784
VERTICAL TAIL DIFFERENCES
VT Span Percent Diff: 1.1420221585382453
VT Area Percent Diff: -48.8152195803751
CRUISE SEGMENT 1 DRAG DIFFERENCES
Overall Cd Percent Diff: [-29.23327655]
L/D Percent Diff: [15.98551233]
Nacelle Cd Percent Diff: [-1.3892322]
HT Cd Percent Diff: [-97.19585372] dimensionless
Fuselage Cd Percent Diff: [108.78752887]
VT Cd Percent Diff: [-35.47662149] dimensionless
Wing Cd percent Diff: [-29.70517128]
Induced Drag Cd Percent Diff: [-33.71252269]
CRUISE SEGMENT 1 TSFC DIFFERENCES
Initial Cruise TSFC Percent Diff: [-5.6306391]
FUSELAGE DIFFERENCES
Weight of HB material: -48.58184496110234
Weight of VB material: 117.19512321871129
Fan Propulsive Efficiency in Cruise Segment 1
---------------------
[0.80256952]
Cloning into 'robust'...
Processing /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/robust
ERROR: Command errored out with exit status 1:
command: /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/bin/python -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-req-build-7mjdk83n/setup.py'"'"'; __file__='"'"'/private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-req-build-7mjdk83n/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' egg_info --egg-base /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-eq5rxn5d
cwd: /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-req-build-7mjdk83n/
Complete output (14 lines):
running egg_info
creating /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-eq5rxn5d/robust.egg-info
writing /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-eq5rxn5d/robust.egg-info/PKG-INFO
writing dependency_links to /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-eq5rxn5d/robust.egg-info/dependency_links.txt
writing requirements to /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-eq5rxn5d/robust.egg-info/requires.txt
writing top-level names to /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-eq5rxn5d/robust.egg-info/top_level.txt
writing manifest file '/private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-eq5rxn5d/robust.egg-info/SOURCES.txt'
reading manifest file '/private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-eq5rxn5d/robust.egg-info/SOURCES.txt'
writing manifest file '/private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-eq5rxn5d/robust.egg-info/SOURCES.txt'
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-req-build-7mjdk83n/setup.py", line 41, in <module>
s
NameError: name 's' is not defined
----------------------------------------
ERROR: Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.
Processing /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/robust
ERROR: Command errored out with exit status 1:
command: /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/bin/python -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-req-build-k4cme89g/setup.py'"'"'; __file__='"'"'/private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-req-build-k4cme89g/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' egg_info --egg-base /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-25pycko8
cwd: /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-req-build-k4cme89g/
Complete output (14 lines):
running egg_info
creating /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-25pycko8/robust.egg-info
writing /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-25pycko8/robust.egg-info/PKG-INFO
writing dependency_links to /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-25pycko8/robust.egg-info/dependency_links.txt
writing requirements to /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-25pycko8/robust.egg-info/requires.txt
writing top-level names to /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-25pycko8/robust.egg-info/top_level.txt
writing manifest file '/private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-25pycko8/robust.egg-info/SOURCES.txt'
reading manifest file '/private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-25pycko8/robust.egg-info/SOURCES.txt'
writing manifest file '/private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-25pycko8/robust.egg-info/SOURCES.txt'
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-req-build-k4cme89g/setup.py", line 41, in <module>
s
NameError: name 's' is not defined
----------------------------------------
ERROR: Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.
Processing /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/robust
ERROR: Command errored out with exit status 1:
command: /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/bin/python -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-req-build-c__1glu9/setup.py'"'"'; __file__='"'"'/private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-req-build-c__1glu9/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' egg_info --egg-base /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-n4exmw0d
cwd: /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-req-build-c__1glu9/
Complete output (14 lines):
running egg_info
creating /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-n4exmw0d/robust.egg-info
writing /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-n4exmw0d/robust.egg-info/PKG-INFO
writing dependency_links to /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-n4exmw0d/robust.egg-info/dependency_links.txt
writing requirements to /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-n4exmw0d/robust.egg-info/requires.txt
writing top-level names to /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-n4exmw0d/robust.egg-info/top_level.txt
writing manifest file '/private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-n4exmw0d/robust.egg-info/SOURCES.txt'
reading manifest file '/private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-n4exmw0d/robust.egg-info/SOURCES.txt'
writing manifest file '/private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-n4exmw0d/robust.egg-info/SOURCES.txt'
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-req-build-c__1glu9/setup.py", line 41, in <module>
s
NameError: name 's' is not defined
----------------------------------------
ERROR: Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.
Processing /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/robust
ERROR: Command errored out with exit status 1:
command: /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/bin/python -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-req-build-ciq78tsb/setup.py'"'"'; __file__='"'"'/private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-req-build-ciq78tsb/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' egg_info --egg-base /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-e7i9byix
cwd: /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-req-build-ciq78tsb/
Complete output (14 lines):
running egg_info
creating /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-e7i9byix/robust.egg-info
writing /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-e7i9byix/robust.egg-info/PKG-INFO
writing dependency_links to /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-e7i9byix/robust.egg-info/dependency_links.txt
writing requirements to /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-e7i9byix/robust.egg-info/requires.txt
writing top-level names to /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-e7i9byix/robust.egg-info/top_level.txt
writing manifest file '/private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-e7i9byix/robust.egg-info/SOURCES.txt'
reading manifest file '/private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-e7i9byix/robust.egg-info/SOURCES.txt'
writing manifest file '/private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-e7i9byix/robust.egg-info/SOURCES.txt'
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-req-build-ciq78tsb/setup.py", line 41, in <module>
s
NameError: name 's' is not defined
----------------------------------------
ERROR: Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.
Processing /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/robust
ERROR: Command errored out with exit status 1:
command: /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/bin/python -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-req-build-379ipy3f/setup.py'"'"'; __file__='"'"'/private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-req-build-379ipy3f/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' egg_info --egg-base /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-avcxrzgb
cwd: /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-req-build-379ipy3f/
Complete output (14 lines):
running egg_info
creating /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-avcxrzgb/robust.egg-info
writing /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-avcxrzgb/robust.egg-info/PKG-INFO
writing dependency_links to /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-avcxrzgb/robust.egg-info/dependency_links.txt
writing requirements to /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-avcxrzgb/robust.egg-info/requires.txt
writing top-level names to /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-avcxrzgb/robust.egg-info/top_level.txt
writing manifest file '/private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-avcxrzgb/robust.egg-info/SOURCES.txt'
reading manifest file '/private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-avcxrzgb/robust.egg-info/SOURCES.txt'
writing manifest file '/private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-pip-egg-info-avcxrzgb/robust.egg-info/SOURCES.txt'
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-req-build-379ipy3f/setup.py", line 41, in <module>
s
NameError: name 's' is not defined
----------------------------------------
ERROR: Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.
Running tests...
----------------------------------------------------------------------
Running tests...
----------------------------------------------------------------------
..........................................
----------------------------------------------------------------------
Ran 42 tests in 75.240s
OK
Generating XML reports...
.
Running tests...
----------------------------------------------------------------------
..........................................
----------------------------------------------------------------------
Ran 42 tests in 38.962s
OK
Generating XML reports...
.
----------------------------------------------------------------------
Ran 2 tests in 114.693s
OK
Generating XML reports...
adding test for 'run_tests.py'
SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 2.5% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
Final solution let signomial constraints slacken by 0.056%. Calling .localsolve with a higher `pccp_penalty` (it was 200 this time) will reduce final slack if the model is solvable with less. If you think it might not be, check by solving with `use_pccp=False, x0=(this model's final solution)`.
Final solution let signomial constraints slacken by 0.66%. Calling .localsolve with a higher `pccp_penalty` (it was 200 this time) will reduce final slack if the model is solvable with less. If you think it might not be, check by solving with `use_pccp=False, x0=(this model's final solution)`.
SGP not convergent: Cost rose by 0.045% on GP solve 4. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 0.029% on GP solve 4. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
Final solution let signomial constraints slacken by 0.028%. Calling .localsolve with a higher `pccp_penalty` (it was 200 this time) will reduce final slack if the model is solvable with less. If you think it might not be, check by solving with `use_pccp=False, x0=(this model's final solution)`.
SGP not convergent: Cost rose by 2.2% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
Final solution let signomial constraints slacken by 0.062%. Calling .localsolve with a higher `pccp_penalty` (it was 200 this time) will reduce final slack if the model is solvable with less. If you think it might not be, check by solving with `use_pccp=False, x0=(this model's final solution)`.
SGP not convergent: Cost rose by 1.5% on GP solve 3. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
Final solution let signomial constraints slacken by 0.032%. Calling .localsolve with a higher `pccp_penalty` (it was 200 this time) will reduce final slack if the model is solvable with less. If you think it might not be, check by solving with `use_pccp=False, x0=(this model's final solution)`.
SGP not convergent: Cost rose by 0.028% on GP solve 4. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 0.0012% on GP solve 6. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 0.047% on GP solve 6. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 0.0051% on GP solve 6. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 0.2% on GP solve 3. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 9.4% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 0.33% on GP solve 4. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 11% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 0.0077% on GP solve 4. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
Final solution let signomial constraints slacken by 0.085%. Calling .localsolve with a higher `pccp_penalty` (it was 200 this time) will reduce final slack if the model is solvable with less. If you think it might not be, check by solving with `use_pccp=False, x0=(this model's final solution)`.
SGP not convergent: Cost rose by 0.015% on GP solve 4. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 0.049% on GP solve 6. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 9.4% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 0.33% on GP solve 4. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
Final solution let signomial constraints slacken by 0.077%. Calling .localsolve with a higher `pccp_penalty` (it was 200 this time) will reduce final slack if the model is solvable with less. If you think it might not be, check by solving with `use_pccp=False, x0=(this model's final solution)`.
SGP not convergent: Cost rose by 0.11% on GP solve 3. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 0.075% on GP solve 3. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 0.033% on GP solve 4. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 0.014% on GP solve 4. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 9.4% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 0.33% on GP solve 4. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 0.042% on GP solve 4. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 0.032% on GP solve 4. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 0.13% on GP solve 3. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 0.31% on GP solve 3. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 0.044% on GP solve 3. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 9.4% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 0.33% on GP solve 4. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
Cloning into 'shopping'...
Running tests...
----------------------------------------------------------------------
...
----------------------------------------------------------------------
Ran 3 tests in 0.218s
OK
Generating XML reports...
adding test for 'test.py'
Using solver 'mosek_cli'
for 25 free variables
in 40 posynomial inequalities.
Solving took 0.0382 seconds.
Using solver 'mosek_cli'
for 25 free variables
in 40 posynomial inequalities.
Solving took 0.0374 seconds.
Using solver 'mosek_conif'
for 25 free variables
in 40 posynomial inequalities.
Solving took 0.0171 seconds.
Cloning into 'gassolar'...
Requirement already satisfied: pandas in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (1.0.5)
Requirement already satisfied: pytz>=2017.2 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from pandas) (2020.1)
Requirement already satisfied: numpy>=1.13.3 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from pandas) (1.19.0)
Requirement already satisfied: python-dateutil>=2.6.1 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from pandas) (2.8.1)
Requirement already satisfied: six>=1.5 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from python-dateutil>=2.6.1->pandas) (1.15.0)
Collecting git+https://github.com/hoburg/gpfit.git
Cloning https://github.com/hoburg/gpfit.git to /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-req-build-psjufa44
Running command git clone -q https://github.com/hoburg/gpfit.git /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-req-build-psjufa44
Requirement already satisfied: numpy in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpfit==0.1) (1.19.0)
Requirement already satisfied: scipy in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpfit==0.1) (1.5.1)
Requirement already satisfied: gpkit in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek (from gpfit==0.1) (1.0.0)
Requirement already satisfied: pint<0.10,>=0.8.1 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpkit->gpfit==0.1) (0.9)
Requirement already satisfied: ad in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpkit->gpfit==0.1) (1.3.2)
Requirement already satisfied: cvxopt>=1.1.8 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpkit->gpfit==0.1) (1.2.5)
Building wheels for collected packages: gpfit
Building wheel for gpfit (setup.py): started
Building wheel for gpfit (setup.py): finished with status 'done'
Created wheel for gpfit: filename=gpfit-0.1-py3-none-any.whl size=25375 sha256=2bf057c4a3e113ff75d813a68b2b1e0f70302ad73a226e5d438b75ce4dfabbcd
Stored in directory: /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-ephem-wheel-cache-riz3l3tx/wheels/57/29/3e/8d7ba8db76ea975ecfe679ec45f25d64a6eaec893d16b3d378
Successfully built gpfit
Installing collected packages: gpfit
Successfully installed gpfit-0.1
Processing /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gassolar
Requirement already satisfied: numpy in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gassolar==0.0.0.0) (1.19.0)
Requirement already satisfied: scipy in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gassolar==0.0.0.0) (1.5.1)
Requirement already satisfied: gpkit in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek (from gassolar==0.0.0.0) (1.0.0)
Requirement already satisfied: pandas in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gassolar==0.0.0.0) (1.0.5)
Requirement already satisfied: gpfit in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gassolar==0.0.0.0) (0.1)
Requirement already satisfied: pint<0.10,>=0.8.1 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpkit->gassolar==0.0.0.0) (0.9)
Requirement already satisfied: ad in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpkit->gassolar==0.0.0.0) (1.3.2)
Requirement already satisfied: cvxopt>=1.1.8 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpkit->gassolar==0.0.0.0) (1.2.5)
Requirement already satisfied: pytz>=2017.2 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from pandas->gassolar==0.0.0.0) (2020.1)
Requirement already satisfied: python-dateutil>=2.6.1 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from pandas->gassolar==0.0.0.0) (2.8.1)
Requirement already satisfied: six>=1.5 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from python-dateutil>=2.6.1->pandas->gassolar==0.0.0.0) (1.15.0)
Building wheels for collected packages: gassolar
Building wheel for gassolar (setup.py): started
Building wheel for gassolar (setup.py): finished with status 'done'
Created wheel for gassolar: filename=gassolar-0.0.0.0-py3-none-any.whl size=4876521 sha256=3628614f35a9721a7e89cee19236b783e167206d573e7ff72ae42e5155cf857f
Stored in directory: /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-ephem-wheel-cache-2qgsuw7u/wheels/75/4b/9c/55e026860e0f74bd7d4510104c5d7ba848b3e850250bb6be0e
Successfully built gassolar
Installing collected packages: gassolar
Successfully installed gassolar-0.0.0.0
Running tests...
----------------------------------------------------------------------
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/gpfit/fit_constraintset.py:46: 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
for k in range(fitdata["K"])]
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/numpy/core/_asarray.py:83: 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
return array(a, dtype, copy=False, order=order)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/gpfit/fit_constraintset.py:46: 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
for k in range(fitdata["K"])]
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/numpy/core/_asarray.py:83: 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
return array(a, dtype, copy=False, order=order)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
./Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/gpfit/fit_constraintset.py:46: 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
for k in range(fitdata["K"])]
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/numpy/core/_asarray.py:83: 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
return array(a, dtype, copy=False, order=order)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/gpfit/fit_constraintset.py:46: 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
for k in range(fitdata["K"])]
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/numpy/core/_asarray.py:83: 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
return array(a, dtype, copy=False, order=order)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
./Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/gassolar/environment/solar_irradiance.py:40: RuntimeWarning: overflow encountered in exp
tau = np.exp(-0.175/costhsun)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/gassolar/environment/solar_irradiance.py:40: RuntimeWarning: overflow encountered in exp
tau = np.exp(-0.175/costhsun)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
./Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/gassolar/environment/solar_irradiance.py:40: RuntimeWarning: overflow encountered in exp
tau = np.exp(-0.175/costhsun)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/gassolar/environment/solar_irradiance.py:40: RuntimeWarning: overflow encountered in exp
tau = np.exp(-0.175/costhsun)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
.
----------------------------------------------------------------------
Ran 4 tests in 13.903s
OK
Generating XML reports...
adding test for 'gassolar/gas/gas.py'
adding test for 'gassolar/solar/solar.py'
Using solver 'mosek_cli'
for 592 free variables
in 918 posynomial inequalities.
Solving took 0.0864 seconds.
Warning: Variable Mission.Climb.FlightSegment.AircraftPerf.EnginePerf.P_{total}[:]/Mission.Climb.FlightSegment.Aircraf could cause inaccurate result because it is above upper bound. Solution is 1.0000 but bound is 0.9685
Starting a sequence of GP solves
for 8 free variables
in 2 locally-GP constraints
and for 600 free variables
in 926 posynomial inequalities.
Solving took 0.405 seconds and 4 GP solves.
Warning: Variable Mission1.Climb.FlightSegment.AircraftPerf.EnginePerf.P_{total}[:]/Mission1.Climb.FlightSegment.Aircr could cause inaccurate result because it is above upper bound. Solution is 1.0000 but bound is 0.9685
Using solver 'mosek_conif'
for 592 free variables
in 918 posynomial inequalities.
Solving took 0.0913 seconds.
Warning: Variable Mission.Climb.FlightSegment.AircraftPerf.EnginePerf.P_{total}[:]/Mission.Climb.FlightSegment.Aircraf could cause inaccurate result because it is above upper bound. Solution is 1.0000 but bound is 0.9685
Starting a sequence of GP solves
for 8 free variables
in 2 locally-GP constraints
and for 600 free variables
in 926 posynomial inequalities.
Solving took 0.464 seconds and 4 GP solves.
Warning: Variable Mission1.Climb.FlightSegment.AircraftPerf.EnginePerf.P_{total}[:]/Mission1.Climb.FlightSegment.Aircr could cause inaccurate result because it is above upper bound. Solution is 1.0000 but bound is 0.9685
Using solver 'mosek_cli'
for 1250 free variables
in 1872 posynomial inequalities.
Solving took 0.28 seconds.
Warning: Variable Mission.FlightSegment.AircraftPerf.WingAero.Re could cause inaccurate result because it is below lower bound. Solution is 102558.6388 but bound is 150000.0000
Warning: Variable Mission.FlightSegment10.AircraftPerf.WingAero.Re could cause inaccurate result because it is below lower bound. Solution is 102558.6388 but bound is 150000.0000
Starting a sequence of GP solves
for 38 free variables
in 12 locally-GP constraints
and for 1298 free variables
in 1930 posynomial inequalities.
Solving took 1.21 seconds and 4 GP solves.
Warning: Variable Mission1.FlightSegment.AircraftPerf.WingAero.Re could cause inaccurate result because it is below lower bound. Solution is 103379.2971 but bound is 150000.0000
Warning: Variable Mission1.FlightSegment10.AircraftPerf.WingAero.Re could cause inaccurate result because it is below lower bound. Solution is 103406.1793 but bound is 150000.0000
Using solver 'mosek_conif'
for 1250 free variables
in 1872 posynomial inequalities.
Solving took 0.317 seconds.
Warning: Variable Mission.FlightSegment.AircraftPerf.WingAero.Re could cause inaccurate result because it is below lower bound. Solution is 102558.2722 but bound is 150000.0000
Warning: Variable Mission.FlightSegment10.AircraftPerf.WingAero.Re could cause inaccurate result because it is below lower bound. Solution is 102558.3771 but bound is 150000.0000
Starting a sequence of GP solves
for 38 free variables
in 12 locally-GP constraints
and for 1298 free variables
in 1930 posynomial inequalities.
Solving took 2.83 seconds and 4 GP solves.
Warning: Variable Mission1.FlightSegment.AircraftPerf.WingAero.Re could cause inaccurate result because it is below lower bound. Solution is 103380.0605 but bound is 150000.0000
Warning: Variable Mission1.FlightSegment10.AircraftPerf.WingAero.Re could cause inaccurate result because it is below lower bound. Solution is 103406.2044 but bound is 150000.0000
Cloning into 'jho'...
Requirement already satisfied: pandas in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (1.0.5)
Requirement already satisfied: numpy>=1.13.3 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from pandas) (1.19.0)
Requirement already satisfied: pytz>=2017.2 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from pandas) (2020.1)
Requirement already satisfied: python-dateutil>=2.6.1 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from pandas) (2.8.1)
Requirement already satisfied: six>=1.5 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from python-dateutil>=2.6.1->pandas) (1.15.0)
Collecting git+https://github.com/hoburg/gpfit.git
Cloning https://github.com/hoburg/gpfit.git to /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-req-build-_vde12cw
Running command git clone -q https://github.com/hoburg/gpfit.git /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-req-build-_vde12cw
Requirement already satisfied (use --upgrade to upgrade): gpfit==0.1 from git+https://github.com/hoburg/gpfit.git in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages
Requirement already satisfied: numpy in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpfit==0.1) (1.19.0)
Requirement already satisfied: scipy in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpfit==0.1) (1.5.1)
Requirement already satisfied: gpkit in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek (from gpfit==0.1) (1.0.0)
Requirement already satisfied: pint<0.10,>=0.8.1 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpkit->gpfit==0.1) (0.9)
Requirement already satisfied: ad in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpkit->gpfit==0.1) (1.3.2)
Requirement already satisfied: cvxopt>=1.1.8 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpkit->gpfit==0.1) (1.2.5)
Building wheels for collected packages: gpfit
Building wheel for gpfit (setup.py): started
Building wheel for gpfit (setup.py): finished with status 'done'
Created wheel for gpfit: filename=gpfit-0.1-py3-none-any.whl size=25375 sha256=d2c68e3f88226ababb1e47e5fd7d9cd20f488ef08a486d13b8073578bb14d466
Stored in directory: /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-ephem-wheel-cache-8lb4_v93/wheels/57/29/3e/8d7ba8db76ea975ecfe679ec45f25d64a6eaec893d16b3d378
Successfully built gpfit
Running tests...
----------------------------------------------------------------------
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/gpfit/fit_constraintset.py:46: 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
for k in range(fitdata["K"])]
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/numpy/core/_asarray.py:83: 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
return array(a, dtype, copy=False, order=order)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
./Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/gpfit/fit_constraintset.py:46: 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
for k in range(fitdata["K"])]
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/numpy/core/_asarray.py:83: 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
return array(a, dtype, copy=False, order=order)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
./Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/gpfit/fit_constraintset.py:46: 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
for k in range(fitdata["K"])]
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/numpy/core/_asarray.py:83: 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
return array(a, dtype, copy=False, order=order)
EE
======================================================================
ERROR [0.479s]: test_model_print_perf_py_mosek_cli (gpkit.tests.from_paths.TestFiles)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/tests/helpers.py", line 59, in test
testfn(name, import_dict, path)(self)
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/tests/from_paths.py", line 48, in <lambda>
lambda self: getattr(self, name)())) # pylint:disable=undefined-variable
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/tests/from_paths.py", line 37, in test_fn
mod.test()
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/jho/model/print_perf.py", line 195, in test
jho_subs(M)
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/jho/model/print_perf.py", line 39, in jho_subs
del model.substitutions["Mission.Aircraft.Fuselage.m_{fac}"]
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/keydict.py", line 240, in __delitem__
raise ValueError("KeyDict.__delitem__() requires a keyed object"
ValueError: KeyDict.__delitem__() requires a keyed object such as a gpkit.Variable instance.
======================================================================
ERROR [0.457s]: test_model_print_perf_py_mosek_conif (gpkit.tests.from_paths.TestFiles)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/tests/helpers.py", line 59, in test
testfn(name, import_dict, path)(self)
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/tests/from_paths.py", line 48, in <lambda>
lambda self: getattr(self, name)())) # pylint:disable=undefined-variable
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/tests/from_paths.py", line 37, in test_fn
mod.test()
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/jho/model/print_perf.py", line 195, in test
jho_subs(M)
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/jho/model/print_perf.py", line 39, in jho_subs
del model.substitutions["Mission.Aircraft.Fuselage.m_{fac}"]
File "/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gpkit/keydict.py", line 240, in __delitem__
raise ValueError("KeyDict.__delitem__() requires a keyed object"
ValueError: KeyDict.__delitem__() requires a keyed object such as a gpkit.Variable instance.
----------------------------------------------------------------------
Ran 4 tests in 4.871s
FAILED (errors=2)
Generating XML reports...
adding test for 'model/jho.py'
adding test for 'model/print_perf.py'
Starting a sequence of GP solves
for 8 free variables
in 6 locally-GP constraints
and for 847 free variables
in 2010 posynomial inequalities.
Solving took 0.929 seconds and 4 GP solves.
Starting a sequence of GP solves
for 8 free variables
in 6 locally-GP constraints
and for 847 free variables
in 2010 posynomial inequalities.
Solving took 1.13 seconds and 4 GP solves.
Cloning into 'turbofan'...
Processing /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/turbofan
Requirement already satisfied: numpy in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from turbofan==0.0.0.0) (1.19.0)
Requirement already satisfied: scipy in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from turbofan==0.0.0.0) (1.5.1)
Requirement already satisfied: pint in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from turbofan==0.0.0.0) (0.9)
Requirement already satisfied: future in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from turbofan==0.0.0.0) (0.18.2)
Building wheels for collected packages: turbofan
Building wheel for turbofan (setup.py): started
Building wheel for turbofan (setup.py): finished with status 'done'
Created wheel for turbofan: filename=turbofan-0.0.0.0-py3-none-any.whl size=43842 sha256=f55c685d9b051369be16492a85f93834f1f416de57992abe10def3c542e9bc93
Stored in directory: /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-ephem-wheel-cache-5w0hniuq/wheels/4d/03/98/f79de28f80c623b90d210bc9f17a04f6f763e27fc06147f60c
Successfully built turbofan
Installing collected packages: turbofan
Attempting uninstall: turbofan
Found existing installation: turbofan 0.0.0.0
Uninstalling turbofan-0.0.0.0:
Successfully uninstalled turbofan-0.0.0.0
Successfully installed turbofan-0.0.0.0
Running tests...
----------------------------------------------------------------------
.
----------------------------------------------------------------------
Ran 1 test in 0.658s
OK
Generating XML reports...
adding test for 'turbofan/engine_test.py'
Starting a sequence of GP solves
for 43 free variables
in 14 locally-GP constraints
and for 244 free variables
in 492 posynomial inequalities.
GP Solve 1
Using solver 'mosek_conif'
for 244 free variables
in 492 posynomial inequalities.
Solving took 0.0595 seconds.
Solved cost was 7.929e+13.
GP Solve 2
Using solver 'mosek_conif'
for 244 free variables
in 492 posynomial inequalities.
Solving took 0.0191 seconds.
Solved cost was 30.82.
GP Solve 3
Using solver 'mosek_conif'
for 244 free variables
in 492 posynomial inequalities.
Solving took 0.0193 seconds.
Solved cost was 10.14.
GP Solve 4
Using solver 'mosek_conif'
for 244 free variables
in 492 posynomial inequalities.
Solving took 0.02 seconds.
Solved cost was 7.828.
GP Solve 5
Using solver 'mosek_conif'
for 244 free variables
in 492 posynomial inequalities.
Solving took 0.0193 seconds.
Solved cost was 7.282.
GP Solve 6
Using solver 'mosek_conif'
for 244 free variables
in 492 posynomial inequalities.
Solving took 0.0189 seconds.
Solved cost was 7.108.
GP Solve 7
Using solver 'mosek_conif'
for 244 free variables
in 492 posynomial inequalities.
Solving took 0.0182 seconds.
Solved cost was 7.029.
GP Solve 8
Using solver 'mosek_conif'
for 244 free variables
in 492 posynomial inequalities.
Solving took 0.0187 seconds.
Solved cost was 6.992.
GP Solve 9
Using solver 'mosek_conif'
for 244 free variables
in 492 posynomial inequalities.
Solving took 0.0186 seconds.
Solved cost was 6.973.
GP Solve 10
Using solver 'mosek_conif'
for 244 free variables
in 492 posynomial inequalities.
Solving took 0.0188 seconds.
Solved cost was 6.965.
GP Solve 11
Using solver 'mosek_conif'
for 244 free variables
in 492 posynomial inequalities.
Solving took 0.0201 seconds.
Solved cost was 6.96.
GP Solve 12
Using solver 'mosek_conif'
for 244 free variables
in 492 posynomial inequalities.
Solving took 0.0186 seconds.
Solved cost was 6.958.
GP Solve 13
Using solver 'mosek_conif'
for 244 free variables
in 492 posynomial inequalities.
Solving took 0.0188 seconds.
Solved cost was 6.957.
Solving took 0.459 seconds and 13 GP solves.
Cloning into 'solar'...
Requirement already satisfied: pandas in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (1.0.5)
Requirement already satisfied: python-dateutil>=2.6.1 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from pandas) (2.8.1)
Requirement already satisfied: pytz>=2017.2 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from pandas) (2020.1)
Requirement already satisfied: numpy>=1.13.3 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from pandas) (1.19.0)
Requirement already satisfied: six>=1.5 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from python-dateutil>=2.6.1->pandas) (1.15.0)
Collecting git+https://github.com/convexengineering/gpfit.git
Cloning https://github.com/convexengineering/gpfit.git to /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-req-build-e_lwwjer
Running command git clone -q https://github.com/convexengineering/gpfit.git /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-req-build-e_lwwjer
Requirement already satisfied (use --upgrade to upgrade): gpfit==0.1 from git+https://github.com/convexengineering/gpfit.git in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages
Requirement already satisfied: numpy in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpfit==0.1) (1.19.0)
Requirement already satisfied: scipy in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpfit==0.1) (1.5.1)
Requirement already satisfied: gpkit in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek (from gpfit==0.1) (1.0.0)
Requirement already satisfied: pint<0.10,>=0.8.1 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpkit->gpfit==0.1) (0.9)
Requirement already satisfied: ad in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpkit->gpfit==0.1) (1.3.2)
Requirement already satisfied: cvxopt>=1.1.8 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpkit->gpfit==0.1) (1.2.5)
Building wheels for collected packages: gpfit
Building wheel for gpfit (setup.py): started
Building wheel for gpfit (setup.py): finished with status 'done'
Created wheel for gpfit: filename=gpfit-0.1-py3-none-any.whl size=25375 sha256=480a095749cc7c0104ac9bb32d05492a314a427a1705409626106232f7f882c1
Stored in directory: /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-ephem-wheel-cache-5njgjpa7/wheels/68/65/b3/0afb13c0a818424d0e07427d1c5a4312849fc7491f18bd34a0
Successfully built gpfit
Collecting git+https://github.com/convexengineering/gassolar.git
Cloning https://github.com/convexengineering/gassolar.git to /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-req-build-ij0hzxo4
Running command git clone -q https://github.com/convexengineering/gassolar.git /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-req-build-ij0hzxo4
Requirement already satisfied (use --upgrade to upgrade): gassolar==0.0.0.0 from git+https://github.com/convexengineering/gassolar.git in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages
Requirement already satisfied: numpy in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gassolar==0.0.0.0) (1.19.0)
Requirement already satisfied: scipy in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gassolar==0.0.0.0) (1.5.1)
Requirement already satisfied: gpkit in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek (from gassolar==0.0.0.0) (1.0.0)
Requirement already satisfied: pandas in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gassolar==0.0.0.0) (1.0.5)
Requirement already satisfied: gpfit in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gassolar==0.0.0.0) (0.1)
Requirement already satisfied: pint<0.10,>=0.8.1 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpkit->gassolar==0.0.0.0) (0.9)
Requirement already satisfied: ad in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpkit->gassolar==0.0.0.0) (1.3.2)
Requirement already satisfied: cvxopt>=1.1.8 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpkit->gassolar==0.0.0.0) (1.2.5)
Requirement already satisfied: pytz>=2017.2 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from pandas->gassolar==0.0.0.0) (2020.1)
Requirement already satisfied: python-dateutil>=2.6.1 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from pandas->gassolar==0.0.0.0) (2.8.1)
Requirement already satisfied: six>=1.5 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from python-dateutil>=2.6.1->pandas->gassolar==0.0.0.0) (1.15.0)
Building wheels for collected packages: gassolar
Building wheel for gassolar (setup.py): started
Building wheel for gassolar (setup.py): finished with status 'done'
Created wheel for gassolar: filename=gassolar-0.0.0.0-py3-none-any.whl size=4876521 sha256=3bd95502aefe156740b5a7513512776b4ca8c1a40b00474b625d40b72d36d6d6
Stored in directory: /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-ephem-wheel-cache-1obvwpra/wheels/84/16/d2/54d8f9e64f486a627f1c5353e5786ffa7a35c88bec592de580
Successfully built gassolar
Processing /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/solar
Requirement already satisfied: numpy in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from solar==0.0.0.0) (1.19.0)
Requirement already satisfied: scipy in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from solar==0.0.0.0) (1.5.1)
Requirement already satisfied: gpkit in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek (from solar==0.0.0.0) (1.0.0)
Requirement already satisfied: pandas in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from solar==0.0.0.0) (1.0.5)
Requirement already satisfied: gpfit in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from solar==0.0.0.0) (0.1)
Requirement already satisfied: gpkitmodels in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gplibrary (from solar==0.0.0.0) (0.0.0.0)
Requirement already satisfied: pint<0.10,>=0.8.1 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpkit->solar==0.0.0.0) (0.9)
Requirement already satisfied: ad in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpkit->solar==0.0.0.0) (1.3.2)
Requirement already satisfied: cvxopt>=1.1.8 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpkit->solar==0.0.0.0) (1.2.5)
Requirement already satisfied: python-dateutil>=2.6.1 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from pandas->solar==0.0.0.0) (2.8.1)
Requirement already satisfied: pytz>=2017.2 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from pandas->solar==0.0.0.0) (2020.1)
Requirement already satisfied: future in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpkitmodels->solar==0.0.0.0) (0.18.2)
Requirement already satisfied: six>=1.5 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from python-dateutil>=2.6.1->pandas->solar==0.0.0.0) (1.15.0)
Building wheels for collected packages: solar
Building wheel for solar (setup.py): started
Building wheel for solar (setup.py): finished with status 'done'
Created wheel for solar: filename=solar-0.0.0.0-py3-none-any.whl size=14841 sha256=57af95328d0b2f9db4c99c8c1fcc4bdca125cbd07d5badb01f7b13ff22dc6df6
Stored in directory: /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-ephem-wheel-cache-vtodfujh/wheels/24/52/36/dc3de7f2ee9245ba0a91e76d816269ddc6d795d6c5d0d68de1
Successfully built solar
Installing collected packages: solar
Successfully installed solar-0.0.0.0
Running tests...
----------------------------------------------------------------------
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/gpfit/fit_constraintset.py:46: 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
for k in range(fitdata["K"])]
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/numpy/core/_asarray.py:83: 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
return array(a, dtype, copy=False, order=order)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/gpfit/fit_constraintset.py:46: 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
for k in range(fitdata["K"])]
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/numpy/core/_asarray.py:83: 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
return array(a, dtype, copy=False, order=order)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/gpfit/fit_constraintset.py:46: 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
for k in range(fitdata["K"])]
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/numpy/core/_asarray.py:83: 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
return array(a, dtype, copy=False, order=order)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/gpfit/fit_constraintset.py:46: 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
for k in range(fitdata["K"])]
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/numpy/core/_asarray.py:83: 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
return array(a, dtype, copy=False, order=order)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/gpfit/fit_constraintset.py:46: 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
for k in range(fitdata["K"])]
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/numpy/core/_asarray.py:83: 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
return array(a, dtype, copy=False, order=order)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/gpfit/fit_constraintset.py:46: 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
for k in range(fitdata["K"])]
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/numpy/core/_asarray.py:83: 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
return array(a, dtype, copy=False, order=order)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
./Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/gpfit/fit_constraintset.py:46: 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
for k in range(fitdata["K"])]
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/numpy/core/_asarray.py:83: 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
return array(a, dtype, copy=False, order=order)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/gpfit/fit_constraintset.py:46: 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
for k in range(fitdata["K"])]
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/numpy/core/_asarray.py:83: 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
return array(a, dtype, copy=False, order=order)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/gpfit/fit_constraintset.py:46: 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
for k in range(fitdata["K"])]
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/numpy/core/_asarray.py:83: 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
return array(a, dtype, copy=False, order=order)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/gpfit/fit_constraintset.py:46: 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
for k in range(fitdata["K"])]
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/numpy/core/_asarray.py:83: 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
return array(a, dtype, copy=False, order=order)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/gpfit/fit_constraintset.py:46: 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
for k in range(fitdata["K"])]
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/numpy/core/_asarray.py:83: 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
return array(a, dtype, copy=False, order=order)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/gpfit/fit_constraintset.py:46: 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
for k in range(fitdata["K"])]
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/numpy/core/_asarray.py:83: 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
return array(a, dtype, copy=False, order=order)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
./Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/gpfit/fit_constraintset.py:46: 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
for k in range(fitdata["K"])]
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/numpy/core/_asarray.py:83: 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
return array(a, dtype, copy=False, order=order)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/gassolar/environment/solar_irradiance.py:40: RuntimeWarning: overflow encountered in exp
tau = np.exp(-0.175/costhsun)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
./Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/gpfit/fit_constraintset.py:46: 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
for k in range(fitdata["K"])]
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/numpy/core/_asarray.py:83: 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
return array(a, dtype, copy=False, order=order)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/gassolar/environment/solar_irradiance.py:40: RuntimeWarning: overflow encountered in exp
tau = np.exp(-0.175/costhsun)
./Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
./Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/gpfit/fit_constraintset.py:46: 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
for k in range(fitdata["K"])]
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/numpy/core/_asarray.py:83: 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
return array(a, dtype, copy=False, order=order)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
.
----------------------------------------------------------------------
Ran 6 tests in 42.086s
OK
Generating XML reports...
adding test for 'solar/sens_chart.py'
adding test for 'solar/season.py'
adding test for 'solar/npod_trade.py'
N=1
Starting a sequence of GP solves
for 85 free variables
in 21 locally-GP constraints
and for 980 free variables
in 1300 posynomial inequalities.
Solving took 0.719 seconds and 4 GP solves.
Warning: Variable Mission.Climb.AircraftDrag.WingAero.CL[:] could cause inaccurate result because it is below lower bound. Solution is 0.8904 but bound is 0.9465
Warning: Variable Mission.Climb.AircraftDrag.WingAero.Re[:] could cause inaccurate result because it is above upper bound. Solution is 1632919.3776 but bound is 600000.0000
Warning: Variable Mission.Climb.AircraftDrag.TailAero1.Re[:] could cause inaccurate result because it is above upper bound. Solution is 1259329.5070 but bound is 1000000.0000
N=3
Starting a sequence of GP solves
for 97 free variables
in 23 locally-GP constraints
and for 1032 free variables
in 1384 posynomial inequalities.
Solving took 0.807 seconds and 4 GP solves.
Warning: Variable Mission1.Climb.AircraftDrag.WingAero.CL[:] could cause inaccurate result because it is below lower bound. Solution is 1.1135 but bound is 0.9465
Warning: Variable Mission1.Climb.AircraftDrag.WingAero.Re[:] could cause inaccurate result because it is above upper bound. Solution is 1036209.6021 but bound is 600000.0000
Warning: Variable Mission1.Climb.AircraftDrag.TailAero1.Re[:] could cause inaccurate result because it is above upper bound. Solution is 1006641.3990 but bound is 1000000.0000
N=5
Starting a sequence of GP solves
for 85 free variables
in 21 locally-GP constraints
and for 1084 free variables
in 1492 posynomial inequalities.
Solving took 0.781 seconds and 4 GP solves.
Warning: Variable Mission2.Climb.AircraftDrag.WingAero.CL[:] could cause inaccurate result because it is below lower bound. Solution is 1.0043 but bound is 0.9465
Warning: Variable Mission2.Climb.AircraftDrag.WingAero.Re[:] could cause inaccurate result because it is above upper bound. Solution is 1329202.9037 but bound is 600000.0000
Warning: Variable Mission2.Climb.AircraftDrag.TailAero1.Re[:] could cause inaccurate result because it is above upper bound. Solution is 1208149.0070 but bound is 1000000.0000
N=7
Starting a sequence of GP solves
for 115 free variables
in 27 locally-GP constraints
and for 1136 free variables
in 1624 posynomial inequalities.
Solving took 0.854 seconds and 4 GP solves.
Warning: Variable Mission3.Climb.AircraftDrag.WingAero.CL[:] could cause inaccurate result because it is below lower bound. Solution is 1.1643 but bound is 0.9465
Warning: Variable Mission3.Climb.AircraftDrag.WingAero.Re[:] could cause inaccurate result because it is above upper bound. Solution is 976138.7332 but bound is 600000.0000
Warning: Variable Mission3.Climb.AircraftDrag.TailAero1.Re[:] could cause inaccurate result because it is above upper bound. Solution is 1042059.9154 but bound is 1000000.0000
N=9
Starting a sequence of GP solves
for 124 free variables
in 29 locally-GP constraints
and for 1188 free variables
in 1780 posynomial inequalities.
Solving took 1.17 seconds and 4 GP solves.
Warning: Variable Mission4.Climb.AircraftDrag.WingAero.CL[:] could cause inaccurate result because it is below lower bound. Solution is 1.1567 but bound is 0.9465
Warning: Variable Mission4.Climb.AircraftDrag.WingAero.Re[:] could cause inaccurate result because it is above upper bound. Solution is 1007396.6632 but bound is 600000.0000
Warning: Variable Mission4.Climb.AircraftDrag.TailAero1.Re[:] could cause inaccurate result because it is above upper bound. Solution is 1092544.5684 but bound is 1000000.0000
N=0
Starting a sequence of GP solves
for 85 free variables
in 21 locally-GP constraints
and for 954 free variables
in 1269 posynomial inequalities.
Solving took 0.755 seconds and 4 GP solves.
Warning: Variable Mission5.Climb.AircraftDrag.WingAero.CL[:] could cause inaccurate result because it is below lower bound. Solution is 1.0536 but bound is 0.9465
Warning: Variable Mission5.Climb.AircraftDrag.WingAero.Re[:] could cause inaccurate result because it is above upper bound. Solution is 1119116.3963 but bound is 600000.0000
N=1
Starting a sequence of GP solves
for 85 free variables
in 21 locally-GP constraints
and for 980 free variables
in 1300 posynomial inequalities.
Solving took 1.1 seconds and 4 GP solves.
Warning: Variable Mission.Climb.AircraftDrag.WingAero.CL[:] could cause inaccurate result because it is below lower bound. Solution is 0.8904 but bound is 0.9465
Warning: Variable Mission.Climb.AircraftDrag.WingAero.Re[:] could cause inaccurate result because it is above upper bound. Solution is 1632892.5627 but bound is 600000.0000
Warning: Variable Mission.Climb.AircraftDrag.TailAero1.Re[:] could cause inaccurate result because it is above upper bound. Solution is 1259311.9673 but bound is 1000000.0000
N=3
Starting a sequence of GP solves
for 97 free variables
in 23 locally-GP constraints
and for 1032 free variables
in 1384 posynomial inequalities.
Solving took 1.34 seconds and 4 GP solves.
Warning: Variable Mission1.Climb.AircraftDrag.WingAero.CL[:] could cause inaccurate result because it is below lower bound. Solution is 1.1135 but bound is 0.9465
Warning: Variable Mission1.Climb.AircraftDrag.WingAero.Re[:] could cause inaccurate result because it is above upper bound. Solution is 1036202.9977 but bound is 600000.0000
Warning: Variable Mission1.Climb.AircraftDrag.TailAero1.Re[:] could cause inaccurate result because it is above upper bound. Solution is 1006643.7437 but bound is 1000000.0000
N=5
Starting a sequence of GP solves
for 85 free variables
in 21 locally-GP constraints
and for 1084 free variables
in 1492 posynomial inequalities.
SGP not convergent: Cost rose by 0.00089% on GP solve 4. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
Solving took 1.85 seconds and 6 GP solves.
Warning: Variable Mission2.Climb.AircraftDrag.WingAero.CL[:] could cause inaccurate result because it is below lower bound. Solution is 1.0043 but bound is 0.9465
Warning: Variable Mission2.Climb.AircraftDrag.WingAero.Re[:] could cause inaccurate result because it is above upper bound. Solution is 1329205.8839 but bound is 600000.0000
Warning: Variable Mission2.Climb.AircraftDrag.TailAero1.Re[:] could cause inaccurate result because it is above upper bound. Solution is 1208151.8737 but bound is 1000000.0000
N=7
Starting a sequence of GP solves
for 115 free variables
in 27 locally-GP constraints
and for 1136 free variables
in 1624 posynomial inequalities.
Solving took 1.26 seconds and 4 GP solves.
Warning: Variable Mission3.Climb.AircraftDrag.WingAero.CL[:] could cause inaccurate result because it is below lower bound. Solution is 1.1643 but bound is 0.9465
Warning: Variable Mission3.Climb.AircraftDrag.WingAero.Re[:] could cause inaccurate result because it is above upper bound. Solution is 976106.4997 but bound is 600000.0000
Warning: Variable Mission3.Climb.AircraftDrag.TailAero1.Re[:] could cause inaccurate result because it is above upper bound. Solution is 1042012.6660 but bound is 1000000.0000
N=9
Starting a sequence of GP solves
for 124 free variables
in 29 locally-GP constraints
and for 1188 free variables
in 1780 posynomial inequalities.
Solving took 1.26 seconds and 4 GP solves.
Warning: Variable Mission4.Climb.AircraftDrag.WingAero.CL[:] could cause inaccurate result because it is below lower bound. Solution is 1.1567 but bound is 0.9465
Warning: Variable Mission4.Climb.AircraftDrag.WingAero.Re[:] could cause inaccurate result because it is above upper bound. Solution is 1007399.6143 but bound is 600000.0000
Warning: Variable Mission4.Climb.AircraftDrag.TailAero1.Re[:] could cause inaccurate result because it is above upper bound. Solution is 1092544.4471 but bound is 1000000.0000
N=0
Starting a sequence of GP solves
for 85 free variables
in 21 locally-GP constraints
and for 954 free variables
in 1269 posynomial inequalities.
Solving took 1.32 seconds and 4 GP solves.
Warning: Variable Mission5.Climb.AircraftDrag.WingAero.CL[:] could cause inaccurate result because it is below lower bound. Solution is 1.0536 but bound is 0.9465
Warning: Variable Mission5.Climb.AircraftDrag.WingAero.Re[:] could cause inaccurate result because it is above upper bound. Solution is 1119119.2317 but bound is 600000.0000
Using solver 'mosek_cli'
for 7672 free variables
in 10814 posynomial inequalities.
Solving took 1.72 seconds.
Warning: Variable Mission.Climb.AircraftDrag.WingAero.CL[:] could cause inaccurate result because it is below lower bound. Solution is 1.2008 but bound is 0.9465
Warning: Variable Mission.Climb.AircraftDrag.WingAero.Re[:] could cause inaccurate result because it is above upper bound. Solution is 839120.8369 but bound is 600000.0000
Using solver 'mosek_conif'
for 7672 free variables
in 10814 posynomial inequalities.
Solving took 2.37 seconds.
Starting a sequence of GP solves
for 85 free variables
in 21 locally-GP constraints
and for 954 free variables
in 1269 posynomial inequalities.
Solving took 0.762 seconds and 4 GP solves.
Warning: Variable Mission.Climb.AircraftDrag.WingAero.CL[:] could cause inaccurate result because it is below lower bound. Solution is 1.0536 but bound is 0.9465
Warning: Variable Mission.Climb.AircraftDrag.WingAero.Re[:] could cause inaccurate result because it is above upper bound. Solution is 1119116.3963 but bound is 600000.0000
Starting a sequence of GP solves
for 85 free variables
in 21 locally-GP constraints
and for 954 free variables
in 1269 posynomial inequalities.
Solving took 1.07 seconds and 4 GP solves.
Warning: Variable Mission.Climb.AircraftDrag.WingAero.CL[:] could cause inaccurate result because it is below lower bound. Solution is 1.0536 but bound is 0.9465
Warning: Variable Mission.Climb.AircraftDrag.WingAero.Re[:] could cause inaccurate result because it is above upper bound. Solution is 1119114.9897 but bound is 600000.0000
fatal: destination path 'gplibrary' already exists and is not an empty directory.
fatal: destination path 'gplibrary' already exists and is not an empty directory.
fatal: destination path 'gplibrary' already exists and is not an empty directory.
fatal: destination path 'gplibrary' already exists and is not an empty directory.
fatal: destination path 'gplibrary' already exists and is not an empty directory.
Requirement already satisfied: pandas in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (1.0.5)
Requirement already satisfied: python-dateutil>=2.6.1 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from pandas) (2.8.1)
Requirement already satisfied: pytz>=2017.2 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from pandas) (2020.1)
Requirement already satisfied: numpy>=1.13.3 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from pandas) (1.19.0)
Requirement already satisfied: six>=1.5 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from python-dateutil>=2.6.1->pandas) (1.15.0)
Collecting git+https://github.com/hoburg/gpfit.git
Cloning https://github.com/hoburg/gpfit.git to /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-req-build-23mys8ae
Running command git clone -q https://github.com/hoburg/gpfit.git /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-req-build-23mys8ae
Requirement already satisfied (use --upgrade to upgrade): gpfit==0.1 from git+https://github.com/hoburg/gpfit.git in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages
Requirement already satisfied: numpy in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpfit==0.1) (1.19.0)
Requirement already satisfied: scipy in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpfit==0.1) (1.5.1)
Requirement already satisfied: gpkit in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek (from gpfit==0.1) (1.0.0)
Requirement already satisfied: pint<0.10,>=0.8.1 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpkit->gpfit==0.1) (0.9)
Requirement already satisfied: ad in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpkit->gpfit==0.1) (1.3.2)
Requirement already satisfied: cvxopt>=1.1.8 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpkit->gpfit==0.1) (1.2.5)
Building wheels for collected packages: gpfit
Building wheel for gpfit (setup.py): started
Building wheel for gpfit (setup.py): finished with status 'done'
Created wheel for gpfit: filename=gpfit-0.1-py3-none-any.whl size=25375 sha256=b005599511d1c22e81a967d1d70b4f023991d6bc430197eeff8a4b80a18630b2
Stored in directory: /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-ephem-wheel-cache-eo8vr3er/wheels/57/29/3e/8d7ba8db76ea975ecfe679ec45f25d64a6eaec893d16b3d378
Successfully built gpfit
Processing /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/gplibrary
Requirement already satisfied: numpy>=1.12 in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpkitmodels==0.0.0.0) (1.19.0)
Requirement already satisfied: scipy in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpkitmodels==0.0.0.0) (1.5.1)
Requirement already satisfied: pint in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpkitmodels==0.0.0.0) (0.9)
Requirement already satisfied: future in /Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages (from gpkitmodels==0.0.0.0) (0.18.2)
Building wheels for collected packages: gpkitmodels
Building wheel for gpkitmodels (setup.py): started
Building wheel for gpkitmodels (setup.py): finished with status 'done'
Created wheel for gpkitmodels: filename=gpkitmodels-0.0.0.0-py3-none-any.whl size=66127 sha256=bcb073a7df57688d0a0d9187f067b81165358c46444cc2f30293a1fcea4ba2e6
Stored in directory: /private/var/folders/42/s1whb7rd4mddfcnzk96g_d9h0000gp/T/pip-ephem-wheel-cache-r5d7e2_e/wheels/f6/57/0b/dd08708bad2e4d873384a7865dbcc42293a904438906070d02
Successfully built gpkitmodels
Installing collected packages: gpkitmodels
Attempting uninstall: gpkitmodels
Found existing installation: gpkitmodels 0.0.0.0
Uninstalling gpkitmodels-0.0.0.0:
Successfully uninstalled gpkitmodels-0.0.0.0
Successfully installed gpkitmodels-0.0.0.0
Running tests...
----------------------------------------------------------------------
../Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
./Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
./Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
./Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
./Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
./Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
./Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
./Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/lib/python3.7/site-packages/pint/quantity.py:1377: UnitStrippedWarning: The unit of the quantity is stripped.
warnings.warn("The unit of the quantity is stripped.", UnitStrippedWarning)
.......
----------------------------------------------------------------------
Ran 16 tests in 7.484s
OK
Generating XML reports...
adding test for 'gpkitmodels/GP/aircraft/wing/wing_test.py'
adding test for 'gpkitmodels/GP/aircraft/tail/tail_tests.py'
adding test for 'gpkitmodels/GP/aircraft/fuselage/test_fuselage.py'
adding test for 'gpkitmodels/GP/aircraft/prop/prop_test.py'
adding test for 'gpkitmodels/GP/aircraft/motor/motor_test.py'
adding test for 'gpkitmodels/SP/SimPleAC/SimPleAC.py'
adding test for 'gpkitmodels/SP/SimPleAC/SimPleAC_mission.py'
adding test for 'gpkitmodels/SP/SimPleAC/SimPleAC_multimission.py'
Using solver 'mosek_cli'
for 11 free variables
in 14 posynomial inequalities.
Solving took 0.0375 seconds.
Using solver 'mosek_conif'
for 11 free variables
in 14 posynomial inequalities.
Solving took 0.0145 seconds.
Using solver 'mosek_cli'
for 9 free variables
in 15 posynomial inequalities.
Solving took 0.0363 seconds.
Using solver 'mosek_cli'
for 25 free variables
in 35 posynomial inequalities.
Solving took 0.045 seconds.
Using solver 'mosek_cli'
for 25 free variables
in 35 posynomial inequalities.
Solving took 0.0377 seconds.
Starting a sequence of GP solves
for 53 free variables
in 11 locally-GP constraints
and for 129 free variables
in 211 posynomial inequalities.
Solving took 0.99 seconds and 16 GP solves.
Using solver 'mosek_conif'
for 9 free variables
in 15 posynomial inequalities.
Solving took 0.0135 seconds.
Using solver 'mosek_conif'
for 25 free variables
in 35 posynomial inequalities.
Solving took 0.0116 seconds.
Using solver 'mosek_conif'
for 25 free variables
in 35 posynomial inequalities.
Solving took 0.0125 seconds.
Starting a sequence of GP solves
for 53 free variables
in 11 locally-GP constraints
and for 129 free variables
in 211 posynomial inequalities.
Solving took 0.543 seconds and 16 GP solves.
Using solver 'mosek_cli'
for 13 free variables
in 16 posynomial inequalities.
Solving took 0.0365 seconds.
Starting a sequence of GP solves
for 53 free variables
in 11 locally-GP constraints
and for 117 free variables
in 190 posynomial inequalities.
Solving took 0.639 seconds and 11 GP solves.
Warning: Variable BladeElementProp.BladeElementPerf.cl[:] could cause inaccurate result because it is below lower bound. Solution is 0.6000 but bound is 0.4742
Warning: Variable BladeElementProp.BladeElementPerf.Re[:] could cause inaccurate result because it is above upper bound. Solution is 1619891.9379 but bound is 700000.0000
Using solver 'mosek_conif'
for 13 free variables
in 16 posynomial inequalities.
Solving took 0.0119 seconds.
Starting a sequence of GP solves
for 53 free variables
in 11 locally-GP constraints
and for 117 free variables
in 190 posynomial inequalities.
Solving took 0.467 seconds and 11 GP solves.
Warning: Variable BladeElementProp.BladeElementPerf.cl[:] could cause inaccurate result because it is below lower bound. Solution is 0.6000 but bound is 0.4742
Warning: Variable BladeElementProp.BladeElementPerf.Re[:] could cause inaccurate result because it is above upper bound. Solution is 1619844.0595 but bound is 700000.0000
Warning: Variable TailAero.Re could cause inaccurate result because it is above upper bound. Solution is 2220959.7939 but bound is 1000000.0000
Warning: Variable TailAero1.Re could cause inaccurate result because it is above upper bound. Solution is 2499796.7837 but bound is 1000000.0000
Warning: Variable TailAero2.Re could cause inaccurate result because it is above upper bound. Solution is 2223871.1577 but bound is 1000000.0000
Warning: Variable TailAero3.Re could cause inaccurate result because it is above upper bound. Solution is 2223871.1577 but bound is 1000000.0000
Warning: Variable TailAero4.Re could cause inaccurate result because it is above upper bound. Solution is 1853896.1324 but bound is 1000000.0000
Warning: Variable TailAero5.Re could cause inaccurate result because it is above upper bound. Solution is 1853896.1324 but bound is 1000000.0000
Warning: Variable TailAero.Re could cause inaccurate result because it is above upper bound. Solution is 2220953.3820 but bound is 1000000.0000
Warning: Variable TailAero1.Re could cause inaccurate result because it is above upper bound. Solution is 2499787.8944 but bound is 1000000.0000
Warning: Variable TailAero2.Re could cause inaccurate result because it is above upper bound. Solution is 1860717.5304 but bound is 1000000.0000
Warning: Variable TailAero3.Re could cause inaccurate result because it is above upper bound. Solution is 1860716.3861 but bound is 1000000.0000
Warning: Variable TailAero4.Re could cause inaccurate result because it is above upper bound. Solution is 1853897.2888 but bound is 1000000.0000
Warning: Variable TailAero5.Re could cause inaccurate result because it is above upper bound. Solution is 1853896.7178 but bound is 1000000.0000
Warning: Variable WingAero.Re could cause inaccurate result because it is above upper bound. Solution is 1884889.9591 but bound is 700000.0000
Warning: Variable WingAero1.Re could cause inaccurate result because it is above upper bound. Solution is 1910412.9866 but bound is 700000.0000
Warning: Variable WingAero.Re could cause inaccurate result because it is above upper bound. Solution is 1910420.0863 but bound is 700000.0000
Warning: Variable WingAero1.Re could cause inaccurate result because it is above upper bound. Solution is 1910419.4626 but bound is 700000.0000
SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 27% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 0.49% on GP solve 4. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 27% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
SGP not convergent: Cost rose by 0.49% on GP solve 4. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.
Starting a sequence of GP solves
for 4 free variables
in 1 locally-GP constraints
and for 21 free variables
in 22 posynomial inequalities.
GP Solve 1
Using solver 'mosek_cli'
for 21 free variables
in 22 posynomial inequalities.
Solving took 0.0381 seconds.
Solved cost was 5717.
GP Solve 2
Using solver 'mosek_cli'
for 21 free variables
in 22 posynomial inequalities.
Solving took 0.0372 seconds.
Solved cost was 4538.
GP Solve 3
Using solver 'mosek_cli'
for 21 free variables
in 22 posynomial inequalities.
Solving took 0.0379 seconds.
Solved cost was 4536.
GP Solve 4
Using solver 'mosek_cli'
for 21 free variables
in 22 posynomial inequalities.
Solving took 0.0378 seconds.
Solved cost was 4536.
Solving took 0.159 seconds and 4 GP solves.
Starting a sequence of GP solves
for 4 free variables
in 1 locally-GP constraints
and for 21 free variables
in 22 posynomial inequalities.
GP Solve 1
Using solver 'mosek_conif'
for 21 free variables
in 22 posynomial inequalities.
Solving took 0.0136 seconds.
Solved cost was 5717.
GP Solve 2
Using solver 'mosek_conif'
for 21 free variables
in 22 posynomial inequalities.
Solving took 0.0115 seconds.
Solved cost was 4538.
GP Solve 3
Using solver 'mosek_conif'
for 21 free variables
in 22 posynomial inequalities.
Solving took 0.0115 seconds.
Solved cost was 4536.
GP Solve 4
Using solver 'mosek_conif'
for 21 free variables
in 22 posynomial inequalities.
Solving took 0.0127 seconds.
Solved cost was 4536.
Solving took 0.0529 seconds and 4 GP solves.
Cloning into 'eVTOL'...
Running tests...
----------------------------------------------------------------------
..
----------------------------------------------------------------------
Ran 2 tests in 2.999s
OK
Generating XML reports...
adding test for 'models/model_tests.py'
----------------------------------------------------------------------
Ran 239 tests in 16.758s
OK
Found no installed solvers, beginning a build.
# Building GPkit version 1.0.0pre
# 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
##
# Looks like `mskexpopt` was not found in the default PATH,
# so let's try locating that binary ourselves.
# Adding /Users/jenkins/mosek/8/tools/platform/osx64x86/bin to the PATH
# Calling 'mskexpopt'
##
### CALL BEGINS
### CALL ENDS
##
Found mosek_cli in /Users/jenkins/mosek/8/tools/platform/osx64x86/bin
# Looking for `mosek_conif`
# Trying to import mosek...
Found mosek_conif in the default PYTHONPATH
# Looking for `cvxopt`
# Trying to import cvxopt...
Found cvxopt in the default PYTHONPATH
Replaced found solvers (['mosek_cli', 'mosek_conif', 'cvxopt']) with environment var GPKITSOLVERS (mosek_cli, mosek_conif)
Found the following solvers: mosek_cli, mosek_conif
# Replacing directory env
GPkit is now installed with solver(s) ['mosek_cli', 'mosek_conif']
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!
calling ['git', 'clone', '--depth', '1', '-b', 'master', 'https://github.com/convexengineering/gplibrary.git']
attempt 1
calling ['python', '/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/bin/pip', 'install', '--no-cache-dir', '--no-deps', '-e', 'gplibrary']
attempt 1
calling ['git', 'clone', '--depth', '1', '-b', 'master', 'https://github.com/convexengineering/SPaircraft.git']
attempt 1
SETTINGS
defaultdict(<class 'str'>, {'skipsolvers': 'cvxopt', 'pip install': 'git+https://github.com/hoburg/turbofan.git', 'gpkit-models branch': 'master'})
calling ['python', '/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/bin/pip', 'install', 'git+https://github.com/hoburg/turbofan.git']
attempt 1
calling ['python', '/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/bin/pip', 'install', '.']
attempt 1
calling ['git', 'clone', '--depth', '1', '-b', 'master', 'https://github.com/convexengineering/robust.git']
attempt 1
SETTINGS
defaultdict(<class 'str'>, {})
calling ['python', '/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/bin/pip', 'install', '.']
attempt 1
attempt 2
attempt 3
attempt 4
attempt 5
calling ['git', 'clone', '--depth', '1', '-b', 'master', 'https://github.com/convexengineering/shopping.git']
attempt 1
SETTINGS
defaultdict(<class 'str'>, {})
calling ['git', 'clone', '--depth', '1', '-b', 'master', 'https://github.com/convexengineering/gassolar.git']
attempt 1
SETTINGS
defaultdict(<class 'str'>, {'pip install': 'pandas, git+https://github.com/hoburg/gpfit.git', 'gpkit-models branch': 'master', 'skipsolvers': 'cvxopt'})
calling ['python', '/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/bin/pip', 'install', 'pandas']
attempt 1
calling ['python', '/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/bin/pip', 'install', 'git+https://github.com/hoburg/gpfit.git']
attempt 1
calling ['python', '/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/bin/pip', 'install', '.']
attempt 1
calling ['git', 'clone', '--depth', '1', '-b', 'master', 'https://github.com/convexengineering/jho.git']
attempt 1
SETTINGS
defaultdict(<class 'str'>, {'pip install': 'pandas, git+https://github.com/hoburg/gpfit.git', 'gpkit-models branch': 'master', 'skipsolvers': 'cvxopt'})
calling ['python', '/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/bin/pip', 'install', 'pandas']
attempt 1
calling ['python', '/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/bin/pip', 'install', 'git+https://github.com/hoburg/gpfit.git']
attempt 1
calling ['git', 'clone', '--depth', '1', '-b', 'master', 'https://github.com/convexengineering/turbofan.git']
attempt 1
SETTINGS
defaultdict(<class 'str'>, {'skipsolvers': 'cvxopt, mosek_cli', 'gpkit-models branch': 'master'})
calling ['python', '/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/bin/pip', 'install', '.']
attempt 1
calling ['git', 'clone', '--depth', '1', '-b', 'master', 'https://github.com/convexengineering/solar.git']
attempt 1
SETTINGS
defaultdict(<class 'str'>, {'pip install': 'pandas, git+https://github.com/convexengineering/gpfit.git, git+https://github.com/convexengineering/gassolar.git', 'gpkit-models branch': 'master', 'skipsolvers': 'cvxopt'})
calling ['python', '/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/bin/pip', 'install', 'pandas']
attempt 1
calling ['python', '/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/bin/pip', 'install', 'git+https://github.com/convexengineering/gpfit.git']
attempt 1
calling ['python', '/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/bin/pip', 'install', 'git+https://github.com/convexengineering/gassolar.git']
attempt 1
calling ['python', '/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/bin/pip', 'install', '.']
attempt 1
calling ['git', 'clone', '--depth', '1', '-b', 'master', 'https://github.com/convexengineering/gplibrary.git']
attempt 1
attempt 2
attempt 3
attempt 4
attempt 5
SETTINGS
defaultdict(<class 'str'>, {'pip install': 'pandas, git+https://github.com/hoburg/gpfit.git'})
calling ['python', '/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/bin/pip', 'install', 'pandas']
attempt 1
calling ['python', '/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/bin/pip', 'install', 'git+https://github.com/hoburg/gpfit.git']
attempt 1
calling ['python', '/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/mosek/venv2_gpkit/bin/pip', 'install', '.']
attempt 1
calling ['git', 'clone', '--depth', '1', '-b', 'master', 'https://github.com/convexengineering/eVTOL.git']
attempt 1
SETTINGS
defaultdict(<class 'str'>, {'skipsolvers': 'cvxopt'})
[Execution node] check if [macys_VM] is in [[windows10x64, windows7x64]]
Run condition [Execution node ] preventing perform for step [Execute Windows batch command]
Build step 'Console output (build log) parsing' changed build result to FAILURE
Recording test results
[WS-CLEANUP] Deleting project workspace...
[WS-CLEANUP] Deferred wipeout is used...
[WS-CLEANUP] done
Finished: FAILURE
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