def test_get_value_time2(self):
"""
Tests get_value on the variable 'Time'
The testes file is the result of a simulation over 10000s with fixed
amount of intervals (50)
"""
sim = Simulation('Array')
time = np.array(np.arange(0., 10200., 200.))
Time = sim.get_value('Time')
self.assertTrue((time == Time).all(),
'Time should contain exactly 51 values, \
from 0 to 10000 (incl.) in steps of 200')
def test_get_value_time2(self):
"""
Tests get_value on the variable 'Time'
The testes file is the result of a simulation over 10000s with fixed
amount of intervals (50)
"""
sim = Simulation('Array')
time = np.array(np.arange(0., 10200., 200.))
Time = sim.get_value('Time')
self.assertTrue((time == Time).all(),
'Time should contain exactly 51 values, \
from 0 to 10000 (incl.) in steps of 200')
def test_get_value_time1(self):
"""
Tests get_value on the variable 'Time'
The testes file is the result of a simulation over 10000s with fixed
interval of 200s
"""
sim = Simulation('LinkedCapacities')
time = np.array(np.arange(0., 10200., 200.))
sim_time = sim.get_value('Time')
self.assertTrue((time == sim_time).all(),
'Time should contain exactly 51 values, \
from 0 to 10000 (incl.) in steps of 200')
def test_get_value_time1(self):
"""
Tests get_value on the variable 'Time'
The testes file is the result of a simulation over 10000s with fixed
interval of 200s
"""
sim = Simulation('LinkedCapacities')
time = np.array(np.arange(0., 10200., 200.))
sim_time = sim.get_value('Time')
self.assertTrue((time == sim_time).all(),
'Time should contain exactly 51 values, \
from 0 to 10000 (incl.) in steps of 200')
def test_get_shortnames(self):
"""Simdex.get() should return correctly for shortnames too"""
self.simdex.h5.close()
vardic = {'T2': 'c2.T'}
pardic = {'parc': 'c1.C'}
process = Process(parameters=pardic, variables=vardic)
self.simdex = Simdex(folder=getcwd(), process=process)
result = self.simdex.get('T2')
expectedsids = self.simdex.get_SID('linkedcapacities')
self.assertEqual(sorted(result.keys()), sorted(expectedsids))
sim = Simulation('LinkedCapacities_B.mat')
sid = self.simdex.get_SID('LinkedCapacities_B')[0]
self.assertTrue(np.all(result[sid] == sim.get_value('c2.T')))
c1_C = self.simdex.get('parc').values()
c1_C.sort()
exp_result_sorted = [None, 600., 600., 800., 800., 800., 800., 1000.]
self.assertTrue(exp_result_sorted == c1_C)
self.simdex.h5.close()
def test_get_shortnames(self):
"""Simdex.get() should return correctly for shortnames too"""
self.simdex.h5.close()
vardic = {'T2': 'c2.T'}
pardic = {'parc': 'c1.C'}
process=Process(parameters=pardic, variables=vardic)
self.simdex = Simdex(folder=getcwd(), process=process)
result = self.simdex.get('T2')
expectedsids = self.simdex.get_SID('linkedcapacities')
self.assertEqual(sorted(result.keys()), sorted(expectedsids))
sim = Simulation('LinkedCapacities_B.mat')
sid = self.simdex.get_SID('LinkedCapacities_B')[0]
self.assertTrue(np.all(result[sid]==sim.get_value('c2.T')))
c1_C = self.simdex.get('parc').values()
c1_C.sort()
exp_result_sorted = [None, 600., 600., 800.,
800., 800., 800., 1000.]
self.assertTrue(exp_result_sorted == c1_C)
self.simdex.h5.close()
# second, get the used parameters and simulation results
# paramaeteres = contains short and long names of the parameters we need.
# The data will be extracted from the .mat files and put in a dictionary
# The short names will be the keys in that dictionary
parameters = {}
for par in pars:
parameters[par[2]] = par[0]
for r in results:
print ''.join(['\t - ', r['mat_file']])
# r is the dictionary containing all results for this specific run
sim = Simulation(r['mat_file'])
# we add info to the dictionary: key = var, values = value for var
for k,v in parameters.iteritems():
r[k] = sim.get_value(v)
for k,v in variables.iteritems():
r[k] = sim.get_value(v)
# we also get Time
r['Time'] = sim.get_value('Time')
###############################################################################
if analyse_results:
print 'Analyzing results for:'
for r in results:
print ''.join(['\t - ', r['mat_file']])
# DHW analysis
TDHW_too_low = np.nonzero(r['TMixed'] < (273.15+45.))[0]
m_flow_discomfort = r['mDHW'][TDHW_too_low]
m_flow_discomfort_weighted = (r['mDHW'] * (273.15+45.-r['TMixed']))[TDHW_too_low]
DHW_discomfort = sum(m_flow_discomfort)/sum(r['mDHW'])
def test_get_value_par(self):
""" Tests get_value on a parameter """
sim = Simulation('LinkedCapacities')
self.assertEqual(600.0, sim.get_value('c1.C'), 'c1.C should be 600.0')
#runfile(r'C:\Workspace\Python\SimulationManagement\test_simman.py',
# wdir=r'C:\Workspace\Python\SimulationManagement')
# a Simulation is a python object for 1 simulation result file
sim = Simulation('LinkedCapacities_A') #with our without .mat extension
sim.separate() # optional, makes attributes for parameters and variables
sim.parameters
sim.variables
# in big simulation files, it's not always easy to find the right parameter
# or variable
sim.exist('q_flow')
time = sim.get_value('Time')
Q = sim.get_value(u'r.heatPort_a.Q_flow')
Q_sum = np.trapz(Q, time)
print "The total energy that flowed through the resistance is %.1f J" % Q_sum
for p, v in zip(sim.parameters, sim.parametervalues):
print ''.join([p, ' = ', str(v)])
# there are different ways to create a simdex
s1 = Simdex() # an empty simdex object
s1.scan(
) # scan current folder and add all found simulation files to the simdex
s2 = Simdex('SubfolderWithCrappyFiles')
# create a simdex directly from a folder
def test_get_value_par(self):
""" Tests get_value on a parameter """
sim = Simulation('LinkedCapacities')
self.assertEqual(600.0, sim.get_value('c1.C'), 'c1.C should be 600.0')
# second, get the used parameters and simulation results
# paramaeteres = contains short and long names of the parameters we need.
# The data will be extracted from the .mat files and put in a dictionary
# The short names will be the keys in that dictionary
parameters = {}
for par in pars:
parameters[par[2]] = par[0]
for r in results:
print ''.join(['\t - ', r['mat_file']])
# r is the dictionary containing all results for this specific run
sim = Simulation(r['mat_file'])
# we add info to the dictionary: key = var, values = value for var
for k, v in parameters.iteritems():
r[k] = sim.get_value(v)
for k, v in variables.iteritems():
r[k] = sim.get_value(v)
# we also get Time
r['Time'] = sim.get_value('Time')
###############################################################################
if analyse_results:
print 'Analyzing results for:'
for r in results:
print ''.join(['\t - ', r['mat_file']])
# DHW analysis
TDHW_too_low = np.nonzero(r['TMixed'] < (273.15 + 45.))[0]
m_flow_discomfort = r['mDHW'][TDHW_too_low]
m_flow_discomfort_weighted = (
r['mDHW'] * (273.15 + 45. - r['TMixed']))[TDHW_too_low]
# second, get the used parameters and simulation results
# paramaeteres = contains short and long names of the parameters we need.
# The data will be extracted from the .mat files and put in a dictionary
# The short names will be the keys in that dictionary
parameters = {}
for par in pars:
parameters[par[2]] = par[0]
for r in results:
# r is the dictionary containing all results for this specific run
sim = Simulation(r['mat_file'])
# we add info to the dictionary: key = var, values = value for var
for k,v in parameters.iteritems():
r[k] = sim.get_value(v)
for k,v in variables.iteritems():
r[k] = sim.get_value(v)
# we also get Time
r['Time'] = sim.get_value('Time')
###############################################################################
if analyse_results:
for r in results:
# analyse what you want and add results to the dictionary
TDHW_too_low = np.nonzero(r['TMixed'] < (273.15+45.))[0]
m_flow_discomfort = r['m_flowTotal'][TDHW_too_low]
m_flow_discomfort_weighted = (r['m_flowTotal'] * (273.15+45.-r['TMixed']))[TDHW_too_low]
DHW_discomfort = sum(m_flow_discomfort)/sum(r['m_flowTotal'])
dT_discomfort = sum(m_flow_discomfort_weighted)/sum(m_flow_discomfort)
r['DHW_discomfort'] = DHW_discomfort
#runfile(r'C:\Workspace\Python\SimulationManagement\test_simman.py',
# wdir=r'C:\Workspace\Python\SimulationManagement')
# a Simulation is a python object for 1 simulation result file
sim=Simulation('LinkedCapacities_A') #with our without .mat extension
sim.separate() # optional, makes attributes for parameters and variables
sim.parameters
sim.variables
# in big simulation files, it's not always easy to find the right parameter
# or variable
sim.exist('q_flow')
time = sim.get_value('Time')
Q = sim.get_value(u'r.heatPort_a.Q_flow')
Q_sum = np.trapz(Q, time)
print "The total energy that flowed through the resistance is %.1f J" %Q_sum
for p,v in zip(sim.parameters, sim.parametervalues):
print ''.join([p, ' = ', str(v)])
# there are different ways to create a simdex
s1 = Simdex() # an empty simdex object
s1.scan() # scan current folder and add all found simulation files to the simdex
s2 = Simdex('SubfolderWithCrappyFiles')
# create a simdex directly from a folder
# second, get the used parameters and simulation results
# paramaeteres = contains short and long names of the parameters we need.
# The data will be extracted from the .mat files and put in a dictionary
# The short names will be the keys in that dictionary
parameters = {}
for par in pars:
parameters[par[2]] = par[0]
for r in results:
# r is the dictionary containing all results for this specific run
sim = Simulation(r['mat_file'])
# we add info to the dictionary: key = var, values = value for var
for k, v in parameters.iteritems():
r[k] = sim.get_value(v)
for k, v in variables.iteritems():
r[k] = sim.get_value(v)
# we also get Time
r['Time'] = sim.get_value('Time')
###############################################################################
if analyse_results:
for r in results:
# analyse what you want and add results to the dictionary
TDHW_too_low = np.nonzero(r['TMixed'] < (273.15 + 45.))[0]
m_flow_discomfort = r['m_flowTotal'][TDHW_too_low]
m_flow_discomfort_weighted = (
r['m_flowTotal'] * (273.15 + 45. - r['TMixed']))[TDHW_too_low]
DHW_discomfort = sum(m_flow_discomfort) / sum(r['m_flowTotal'])
dT_discomfort = sum(m_flow_discomfort_weighted) / sum(
# every simulation has a simulation summary which is a dictionary
# ALL information that is linked to a simulation is added to this
# dictionary
# All these dictionaries are grouped in lists. There can be different
# lists, grouping eg the summaries according to successfulness, or sorting
# them according to a certain criteria
results_successful = [s for s in results if s['successful'] == True]
# now create a sim object, and add the info we need from the result file
# to the summary dictionary
vars_to_check.append('Time')
for summary in results_successful:
sim = Simulation(summary['result file'])
for var in vars_to_check:
summary[var] = sim.get_value(var)
res_suc_sortedCPU = sorted(results_successful,
key = lambda x: float(x['CPU_time']))
algos = [s['solver ID'] for s in res_suc_sortedCPU]
CPU_time = [s['CPU_time'] for s in res_suc_sortedCPU]
perc_wrong = [s['perc_wrong'] for s in res_suc_sortedCPU]
alg = [s['algorithm'] for s in res_suc_sortedCPU]
algorithm = [''.join(['ID = ', ID, ' - ', a]) for ID, a in zip(algos, alg)]
# now, make graph of ALL successfull simulations, whether they have many
# rejected timesteps or not
fig = plt.figure()
ax1 = plt.subplot(211)
