def test_get_with_default_value(self):
ptree = PropertyTree()
# double
self.assertEqual(
ptree.get_double_with_default_value('missing_double', 3.14), 3.14)
ptree.put_double('present_double', 1.41)
self.assertEqual(
ptree.get_double_with_default_value('present_double', 3.14), 1.41)
# string
self.assertEqual(
ptree.get_string_with_default_value('missing_string', 'missing'),
'missing')
ptree.put_string('present_string', 'present')
self.assertEqual(
ptree.get_string_with_default_value('present_string', 'missing'),
'present')
# int
self.assertEqual(ptree.get_int_with_default_value('missing_int', 255),
255)
ptree.put_int('present_int', 0)
self.assertEqual(ptree.get_int_with_default_value('present_int', 255),
0)
# bool
self.assertEqual(
ptree.get_bool_with_default_value('missing_bool', True), True)
ptree.put_bool('present_bool', False)
self.assertEqual(
ptree.get_bool_with_default_value('present_bool', True), False)
def test_property_tree(self):
# ptree as container to store int, double, string, and bool
ptree = PropertyTree()
ptree.put_int('dim', 3)
self.assertEqual(ptree.get_int('dim'), 3)
ptree.put_double('path.to.pi', 3.14)
self.assertEqual(ptree.get_double('path.to.pi'), 3.14)
ptree.put_string('good.news', 'it works')
self.assertEqual(ptree.get_string('good.news'), 'it works')
ptree.put_bool('is.that.a.good.idea', False)
self.assertEqual(ptree.get_bool('is.that.a.good.idea'), False)
def test_consistency_pycap_simulation(self):
#
# weak run test; simply ensures that Dualfoil object
# can be run with pycap.Charge
#
df1 = Dualfoil(path=path) # will use pycap
df2 = Dualfoil(path=path) # manual runs
im = df_manip.InputManager(path=path)
# testing a charge-to-hold-const-voltage
# manual
# use InputManager to set the input file
c = -12.0 # constant current
im.add_new_leg(c, 5.0, 1)
df1.run()
df1.outbot.update_output()
v = 4.54 # constant voltage
im.add_new_leg(v, 5.0, 0)
df1.run()
df1.outbot.update_output()
# pycap simulation
# build a ptree input
ptree = PropertyTree()
ptree.put_double('time_step', 300.0) # 5 minutes
ptree.put_string('charge_mode', 'constant_current')
ptree.put_double('charge_current', 12.0)
ptree.put_string('charge_stop_at_1', 'voltage_greater_than')
ptree.put_double('charge_voltage_limit', 4.54)
ptree.put_bool('charge_voltage_finish', True)
# hold end voltage after either 5 minutes have passed
# OR current falls under 1 ampere
ptree.put_double('charge_voltage_finish_max_time', 300.0)
ptree.put_double('charge_voltage_finish_current_limit', 1.0)
const_current_const_voltage = Charge(ptree)
const_current_const_voltage.run(df2)
# check the output lists of both devices
o1 = df1.outbot.output
o2 = df2.outbot.output
self.assertEqual(len(o1['time']), len(o2['time']))
for i in range(len(o1['time'])):
self.assertAlmostEqual(o1['time'][i], o2['time'][i])
# BELOW: relaxed delta for voltage
# REASON: dualfoil cuts off its voltages at 5
# decimal places, meaning that this end-digit
# is subject to roundoff errors
error = 1e-5
self.assertAlmostEqual(o1['voltage'][i],
o2['voltage'][i],
delta=error)
self.assertAlmostEqual(o1['current'][i], o2['current'][i])
def test_consistency_pycap_simulation(self):
#
# weak run test; simply ensures that Dualfoil object
# can be run with pycap.Charge
#
df1 = Dualfoil(path=path) # will use pycap
df2 = Dualfoil(path=path) # manual runs
im = df_manip.InputManager(path=path)
# testing a charge-to-hold-const-voltage
# manual
# use InputManager to set the input file
c = -12.0 # constant current
im.add_new_leg(c, 5.0, 1)
df1.run()
df1.outbot.update_output()
v = 4.54 # constant voltage
im.add_new_leg(v, 5.0, 0)
df1.run()
df1.outbot.update_output()
# pycap simulation
# build a ptree input
ptree = PropertyTree()
ptree.put_double('time_step', 300.0) # 5 minutes
ptree.put_string('charge_mode', 'constant_current')
ptree.put_double('charge_current', 12.0)
ptree.put_string('charge_stop_at_1', 'voltage_greater_than')
ptree.put_double('charge_voltage_limit', 4.54)
ptree.put_bool('charge_voltage_finish', True)
# hold end voltage after either 5 minutes have passed
# OR current falls under 1 ampere
ptree.put_double('charge_voltage_finish_max_time', 300.0)
ptree.put_double('charge_voltage_finish_current_limit', 1.0)
const_current_const_voltage = Charge(ptree)
const_current_const_voltage.run(df2)
# check the output lists of both devices
o1 = df1.outbot.output
o2 = df2.outbot.output
self.assertEqual(len(o1['time']), len(o2['time']))
for i in range(len(o1['time'])):
self.assertAlmostEqual(o1['time'][i], o2['time'][i])
# BELOW: relaxed delta for voltage
# REASON: dualfoil cuts off its voltages at 5
# decimal places, meaning that this end-digit
# is subject to roundoff errors
error = 1e-5
self.assertAlmostEqual(o1['voltage'][i], o2['voltage'][i],
delta=error)
self.assertAlmostEqual(o1['current'][i], o2['current'][i])
def run_discharge(device, ptree):
data = initialize_data()
# (re)charge the device
initial_voltage = ptree.get_double('initial_voltage')
charge_database = PropertyTree()
charge_database.put_string('charge_mode', 'constant_current')
charge_database.put_double('charge_current', 10.0)
charge_database.put_string('charge_stop_at_1', 'voltage_greater_than')
charge_database.put_double('charge_voltage_limit', initial_voltage)
charge_database.put_bool('charge_voltage_finish', True)
charge_database.put_double('charge_voltage_finish_current_limit', 1e-2)
charge_database.put_double('charge_voltage_finish_max_time', 600)
charge_database.put_double('charge_rest_time', 0)
charge_database.put_double('time_step', 10.0)
charge = Charge(charge_database)
start = time()
charge.run(device, data)
end = time()
# used for tracking time of this substep
print('Charge: %s min' % ((end-start) / 60))
data['time'] -= data['time'][-1]
# discharge at constant power
discharge_power = ptree.get_double('discharge_power')
final_voltage = ptree.get_double('final_voltage')
time_step = ptree.get_double('time_step')
discharge_database = PropertyTree()
discharge_database.put_string('discharge_mode', 'constant_power')
discharge_database.put_double('discharge_power', discharge_power)
discharge_database.put_string('discharge_stop_at_1', 'voltage_less_than')
discharge_database.put_double('discharge_voltage_limit', final_voltage)
discharge_database.put_double('discharge_rest_time', 10 * time_step)
discharge_database.put_double('time_step', time_step)
discharge = Discharge(discharge_database)
start = time()
discharge.run(device, data)
end = time()
# used for tracking time of this substep
print('Discharge: %s min' % ((end-start) / 60))
return data
def run_discharge(device, ptree):
data = initialize_data()
# (re)charge the device
initial_voltage = ptree.get_double('initial_voltage')
charge_database = PropertyTree()
charge_database.put_string('charge_mode', 'constant_current')
charge_database.put_double('charge_current', 10.0)
charge_database.put_string('charge_stop_at_1', 'voltage_greater_than')
charge_database.put_double('charge_voltage_limit', initial_voltage)
charge_database.put_bool('charge_voltage_finish', True)
charge_database.put_double('charge_voltage_finish_current_limit', 1e-2)
charge_database.put_double('charge_voltage_finish_max_time', 600)
charge_database.put_double('charge_rest_time', 0)
charge_database.put_double('time_step', 10.0)
charge = Charge(charge_database)
start = time()
charge.run(device, data)
end = time()
# used for tracking time of this substep
print('Charge: %s min' % ((end - start) / 60))
data['time'] -= data['time'][-1]
# discharge at constant power
discharge_power = ptree.get_double('discharge_power')
final_voltage = ptree.get_double('final_voltage')
time_step = ptree.get_double('time_step')
discharge_database = PropertyTree()
discharge_database.put_string('discharge_mode', 'constant_power')
discharge_database.put_double('discharge_power', discharge_power)
discharge_database.put_string('discharge_stop_at_1', 'voltage_less_than')
discharge_database.put_double('discharge_voltage_limit', final_voltage)
discharge_database.put_double('discharge_rest_time', 10 * time_step)
discharge_database.put_double('time_step', time_step)
discharge = Discharge(discharge_database)
start = time()
discharge.run(device, data)
end = time()
# used for tracking time of this substep
print('Discharge: %s min' % ((end - start) / 60))
return data
def test_accuracy_pycap_simulation(self):
#
# tests the accuracy of a pycap simulation against a
# straight run dualfoil sim with different timesteps
#
df1 = Dualfoil(path=path) # manual runs
df2 = Dualfoil(path=path) # pycap simulation
im = df_manip.InputManager(path=path)
# testing a charge-to-hold-const-voltage
# manual
# use InputManager to set the input file
c = -10.0 # constant charge current
# charge for 5 minutes straight
im.add_new_leg(c, 5, 1)
df1.run()
df1.outbot.update_output()
v = 4.539 # expected voltage after 5 minutes
# hold constant voltage for 3 minutes straight
im.add_new_leg(v, 3.0, 0)
df1.run()
df1.outbot.update_output()
# pycap simulation
# build a ptree input
ptree = PropertyTree()
ptree.put_double('time_step', 30.0) # 30 second time step
ptree.put_string('charge_mode', 'constant_current')
ptree.put_double('charge_current', 10.0)
ptree.put_string('charge_stop_at_1', 'voltage_greater_than')
ptree.put_double('charge_voltage_limit', v)
ptree.put_bool('charge_voltage_finish', True)
# hold end voltage after either 3 minutes have passed
# OR current falls under 1 ampere
ptree.put_double('charge_voltage_finish_max_time', 180.0)
ptree.put_double('charge_voltage_finish_current_limit', 1.0)
const_current_const_voltage = Charge(ptree)
const_current_const_voltage.run(df2)
o1 = df1.outbot.output # contains sim1 output
o2 = df2.outbot.output # contains sim2 output
# affirm we make it this far and have usable data
self.assertTrue(len(o1['time']) > 0)
self.assertTrue(len(o2['time']) > 0)
# lengths of data should be different
self.assertFalse(len(o1['time']) == len(o2['time']))
# TEST LOGIC:
# -Merge the two outputs into one, sorted by
# increasing time stamps.
# -Compare the consistency of the two simulations
# by checking for smooth changes within the curves
# of the combined output lists
o1['time'].extend(o2['time'])
time = ar(o1['time']) # nparray
o1['voltage'].extend(o2['voltage'])
voltage = ar(o1['voltage']) # nparray
o1['current'].extend(o2['current'])
current = ar(o1['current']) # np array
# create a dictionary with the combined output lists
output = {'time': time, 'voltage': voltage, 'current': current}
# sort based on time, keeping the three types aligned
key = argsort(output['time'])
# for using the key to sort the list
tmp = {'time': [], 'voltage': [], 'current': []}
for i in key:
tmp['time'].append(output['time'][i])
tmp['voltage'].append(output['voltage'][i])
tmp['current'].append(output['current'][i])
# reassign ordered set to `output` as nparrays
output['time'] = ar(tmp['time'])
output['voltage'] = ar(tmp['voltage'])
output['current'] = ar(tmp['current'])
# BELOW: first 20 seconds are identical time stamps;
# skip these to avoid errors from incorrect sorting
# REASON FOR ERROR: Dualfoil only prints time data as
# precice as minutes to three decimal places. So when
# the following is generated....
# Manual Run | Pycap Simulation
# (min) (V) (amp) | (min) (V) (amp)
# .001 4.52345 10.0 | .001 4.52345 10.0
# .001 4.52349 10.0 | .001 4.52349 10.0
# ... ...
# ...python's `sorted()` function has no way of
# distinguishing entries; it instead returns this:
# [
# (.001, 4.52345, 10.0),
# (.001, 4.52349, 10.0), <- these two should
# (.001, 4.52345, 10.0), <- be switched
# (.001, 4.52349, 10.0)
# ]
# SOLUTION: consistency test affirms that the exact same
# time step will produce same current and voltage, so
# skip ahead to first instance where time stamps will
# be out of alignment
i = 0
while output['time'][i] <= 0.4: # 24 seconds
i = i + 1
index_limit = len(output['time']) - 1
# go through and affirm smoothness of curve
while i < index_limit:
# Check if time values are the same to 3 decimal places.
# If so, current and voltage are not guarunteed
# to also be exactly the same, but should be close
if output['time'][i] == output['time'][i - 1]:
# affirm that current is virtually the same
self.assertAlmostEqual(output['current'][i],
output['current'][i - 1])
# BELOW: delta is eased slightly
# REASON: `sorted()` can't tell which entry came
# first from same time-stamp if from different
# simulations; allow for this with error
error = 3e-5
self.assertAlmostEqual(output['voltage'][i],
output['voltage'][i - 1],
delta=error)
else:
# Time values are different
# Check to affirm that the variable NOT being held
# constant is steadily increasing / decreasing
# First part happens in first 4 minutes
if output['time'][i] <= 5.0: # part 1, const currrent
# current should be equal
self.assertEqual(output['current'][i],
output['current'][i - 1])
# voltage should not have decreased
self.assertTrue(output['voltage'][i],
output['voltage'][i - 1])
else: # part 2, const voltage
# current should be getting less positive
self.assertTrue(
output['current'][i] <= output['current'][i - 1],
msg=(output['current'][i - 2:i + 10],
output['time'][i - 2:i + 10]))
# voltage should decrease, then stay at 4.54
if output['voltage'][i - 1] == 4.54:
self.assertEqual(output['voltage'][i],
output['voltage'][i - 1])
else:
self.assertTrue(
output['voltage'][i] <= output['voltage'][i - 1])
# update index
i = i + 1
def test_accuracy_pycap_simulation(self):
#
# tests the accuracy of a pycap simulation against a
# straight run dualfoil sim with different timesteps
#
df1 = Dualfoil(path=path) # manual runs
df2 = Dualfoil(path=path) # pycap simulation
im = df_manip.InputManager(path=path)
# testing a charge-to-hold-const-voltage
# manual
# use InputManager to set the input file
c = -10.0 # constant charge current
# charge for 5 minutes straight
im.add_new_leg(c, 5, 1)
df1.run()
df1.outbot.update_output()
v = 4.539 # expected voltage after 5 minutes
# hold constant voltage for 3 minutes straight
im.add_new_leg(v, 3.0, 0)
df1.run()
df1.outbot.update_output()
# pycap simulation
# build a ptree input
ptree = PropertyTree()
ptree.put_double('time_step', 30.0) # 30 second time step
ptree.put_string('charge_mode', 'constant_current')
ptree.put_double('charge_current', 10.0)
ptree.put_string('charge_stop_at_1', 'voltage_greater_than')
ptree.put_double('charge_voltage_limit', v)
ptree.put_bool('charge_voltage_finish', True)
# hold end voltage after either 3 minutes have passed
# OR current falls under 1 ampere
ptree.put_double('charge_voltage_finish_max_time', 180.0)
ptree.put_double('charge_voltage_finish_current_limit', 1.0)
const_current_const_voltage = Charge(ptree)
const_current_const_voltage.run(df2)
o1 = df1.outbot.output # contains sim1 output
o2 = df2.outbot.output # contains sim2 output
# affirm we make it this far and have usable data
self.assertTrue(len(o1['time']) > 0)
self.assertTrue(len(o2['time']) > 0)
# lengths of data should be different
self.assertFalse(len(o1['time']) == len(o2['time']))
# TEST LOGIC:
# -Merge the two outputs into one, sorted by
# increasing time stamps.
# -Compare the consistency of the two simulations
# by checking for smooth changes within the curves
# of the combined output lists
o1['time'].extend(o2['time'])
time = ar(o1['time']) # nparray
o1['voltage'].extend(o2['voltage'])
voltage = ar(o1['voltage']) # nparray
o1['current'].extend(o2['current'])
current = ar(o1['current']) # np array
# create a dictionary with the combined output lists
output = {'time': time,
'voltage': voltage,
'current': current
}
# sort based on time, keeping the three types aligned
key = argsort(output['time'])
# for using the key to sort the list
tmp = {'time': [], 'voltage': [], 'current': []}
for i in key:
tmp['time'].append(output['time'][i])
tmp['voltage'].append(output['voltage'][i])
tmp['current'].append(output['current'][i])
# reassign ordered set to `output` as nparrays
output['time'] = ar(tmp['time'])
output['voltage'] = ar(tmp['voltage'])
output['current'] = ar(tmp['current'])
# BELOW: first 20 seconds are identical time stamps;
# skip these to avoid errors from incorrect sorting
# REASON FOR ERROR: Dualfoil only prints time data as
# precice as minutes to three decimal places. So when
# the following is generated....
# Manual Run | Pycap Simulation
# (min) (V) (amp) | (min) (V) (amp)
# .001 4.52345 10.0 | .001 4.52345 10.0
# .001 4.52349 10.0 | .001 4.52349 10.0
# ... ...
# ...python's `sorted()` function has no way of
# distinguishing entries; it instead returns this:
# [
# (.001, 4.52345, 10.0),
# (.001, 4.52349, 10.0), <- these two should
# (.001, 4.52345, 10.0), <- be switched
# (.001, 4.52349, 10.0)
# ]
# SOLUTION: consistency test affirms that the exact same
# time step will produce same current and voltage, so
# skip ahead to first instance where time stamps will
# be out of alignment
i = 0
while output['time'][i] <= 0.4: # 24 seconds
i = i + 1
index_limit = len(output['time'])-1
# go through and affirm smoothness of curve
while i < index_limit:
# Check if time values are the same to 3 decimal places.
# If so, current and voltage are not guarunteed
# to also be exactly the same, but should be close
if output['time'][i] == output['time'][i-1]:
# affirm that current is virtually the same
self.assertAlmostEqual(output['current'][i],
output['current'][i-1])
# BELOW: delta is eased slightly
# REASON: `sorted()` can't tell which entry came
# first from same time-stamp if from different
# simulations; allow for this with error
error = 3e-5
self.assertAlmostEqual(output['voltage'][i],
output['voltage'][i-1],
delta=error)
else:
# Time values are different
# Check to affirm that the variable NOT being held
# constant is steadily increasing / decreasing
# First part happens in first 4 minutes
if output['time'][i] <= 5.0: # part 1, const currrent
# current should be equal
self.assertEqual(output['current'][i],
output['current'][i-1])
# voltage should not have decreased
self.assertTrue(output['voltage'][i],
output['voltage'][i-1])
else: # part 2, const voltage
# current should be getting less positive
self.assertTrue(output['current'][i] <=
output['current'][i-1],
msg=(output['current'][i-2:i+10],
output['time'][i-2:i+10]))
# voltage should decrease, then stay at 4.54
if output['voltage'][i-1] == 4.54:
self.assertEqual(output['voltage'][i],
output['voltage'][i-1])
else:
self.assertTrue(output['voltage'][i] <=
output['voltage'][i-1])
# update index
i = i + 1