def main():
""" Main *boilerplate* function to start simulation """
# Now let's make use of logging
logger = logging.getLogger()
# Create folders for data and plots
folder = os.path.join(os.getcwd(), 'experiments', 'ca_patterns_pypet')
if not os.path.isdir(folder):
os.makedirs(folder)
filename = os.path.join(folder, 'all_patterns.hdf5')
# Create an environment
env = Environment(trajectory='cellular_automata',
multiproc=True,
ncores=4,
wrap_mode='QUEUE',
filename=filename,
overwrite_file=True)
# extract the trajectory
traj = env.traj
traj.par.ncells = Parameter('ncells', 400, 'Number of cells')
traj.par.steps = Parameter('steps', 250, 'Number of timesteps')
traj.par.rule_number = Parameter('rule_number', 30, 'The ca rule')
traj.par.initial_name = Parameter('initial_name', 'random',
'The type of initial state')
traj.par.seed = Parameter('seed', 100042, 'RNG Seed')
# Explore
exp_dict = {
'rule_number': [10, 30, 90, 110, 184],
'initial_name': ['single', 'random'],
}
# # You can uncomment the ``exp_dict`` below to see that changing the
# # exploration scheme is now really easy:
# exp_dict = {'rule_number' : [10, 30, 90, 110, 184],
# 'ncells' : [100, 200, 300],
# 'seed': [333444555, 123456]}
exp_dict = cartesian_product(exp_dict)
traj.f_explore(exp_dict)
# Run the simulation
logger.info('Starting Simulation')
env.run(wrap_automaton)
# Load all data
traj.f_load(load_data=2)
logger.info('Printing data')
for idx, run_name in enumerate(traj.f_iter_runs()):
# Plot all patterns
filename = os.path.join(folder, make_filename(traj))
plot_pattern(traj.crun.pattern, traj.rule_number, filename)
progressbar(idx, len(traj), logger=logger)
# Finally disable logging and close all log-files
env.disable_logging()
def test_max_depth_loading_and_storing(self):
filename = make_temp_dir('newassignment.hdf5')
traj = Trajectory(filename=filename, overwrite_file=True)
traj.par.d1 = Parameter('d1.d2.d3.d4.d5', 55)
traj.f_store(max_depth=4)
traj = load_trajectory(index=-1, filename=filename)
traj.f_load(load_data=2)
self.assertTrue('d3' in traj)
self.assertFalse('d4' in traj)
traj = load_trajectory(index=-1, filename=filename, max_depth=3)
self.assertTrue('d2' in traj)
self.assertFalse('d3' in traj)
traj.par.f_remove(recursive=True)
traj.dpar.d1 = Parameter('d1.d2.d3.d4.d5', 123)
traj.dpar.f_store_child('d1', recursive=True, max_depth=3)
traj.dpar.f_remove_child('d1', recursive=True)
self.assertTrue('d1' not in traj)
traj.dpar.f_load_child('d1', recursive=True)
self.assertTrue('d3' in traj)
self.assertTrue('d4' not in traj)
traj.dpar.f_remove_child('d1', recursive=True)
self.assertTrue('d1' not in traj)
traj.dpar.f_load_child('d1', recursive=True, max_depth=2)
self.assertTrue('d2' in traj)
self.assertTrue('d3' not in traj)
traj.dpar.l1 = Parameter('l1.l2.l3.l4.l5', 123)
traj.dpar.f_store(recursive=True, max_depth=0)
self.assertFalse(traj.dpar.l1._stored)
traj.dpar.f_store(recursive=True, max_depth=4)
traj.dpar.f_remove()
self.assertTrue('l1' not in traj)
traj.dpar.f_load(recursive=True)
self.assertTrue('l4' in traj)
self.assertTrue('l5' not in traj)
traj.dpar.f_remove()
self.assertTrue('l1' not in traj)
traj.dpar.f_load(recursive=True, max_depth=3)
self.assertTrue('l3' in traj)
self.assertTrue('l4' not in traj)
def test_loading_explored_parameters(self):
filename = make_temp_dir('load_explored.hdf5')
traj = Trajectory(filename=filename, overwrite_file=True, add_time=False)
traj.par.x = Parameter('x', 42, comment='answer')
traj.f_explore({'x':[1,2,3,4]})
traj.f_store()
name = traj.v_name
traj = Trajectory(filename=filename, add_time=False)
traj.f_load()
x = traj.f_get('x')
self.assertIs(x, traj._explored_parameters['parameters.x'])
def test_no_run_information_loading(self):
filename = make_temp_dir('testnoruninfo.hdf5')
traj = Trajectory(name='TestDelete',
filename=filename,
add_time=True)
length = 100000
traj.par.x = Parameter('', 42)
traj.f_explore({'x': range(length)})
traj.f_store()
traj = load_trajectory(index=-1, filename=filename, with_run_information=False)
self.assertEqual(len(traj), length)
self.assertEqual(len(traj._run_information), 1)
def test_full_store(self):
filename = make_temp_dir('full_store.hdf5')
with Environment(filename=filename,
log_config=get_log_config()) as env:
traj = env.v_trajectory
traj.par.x = Parameter('x', 3, 'jj')
traj.f_explore({'x': [1,2,3]})
env.f_run(add_one_particular_item, True)
traj = load_trajectory(index=-1, filename=filename)
self.assertTrue('hi' in traj)
def add_parameters(traj, params, use_balanced_sc=False):
traj.f_add_parameter_group('simulation', comment='simulation parameters')
traj.simulation.dt = Parameter(
'dt',
params['dt'],
comment=
'Size of simulation timestep [ms], choose so that integration is stable!'
)
traj.simulation.duration = Parameter('duration',
params['duration'],
comment='total simulation time [ms]')
traj.f_add_parameter_group('globalNetwork',
comment='global network parameters')
traj.globalNetwork.I = Parameter('I', params['I'])
traj.globalNetwork.K = Parameter('K', params['K'])
traj.globalNetwork.sigma = Parameter('sigma', params['sigma'])
traj.globalNetwork.c = Parameter('c', params['c'])
traj.globalNetwork.ou_mean = Parameter('ou_mean', params['ou_mean'])
traj.globalNetwork.ou_sigma = Parameter('ou_sigma', params['ou_sigma'])
traj.globalNetwork.ou_tau = Parameter('ou_tau', params['ou_tau'])
if use_balanced_sc:
traj.globalNetwork.SC = Parameter('SC', params['SC_lr'])
else:
traj.globalNetwork.SC = Parameter('SC', params['SC'])
traj.globalNetwork.DM = Parameter('DM', params['DM'])
traj.globalNetwork.N = Parameter('N', params['N'])
traj.globalNetwork.dif = Parameter('dif', params['dif'])
traj.f_add_parameter_group('localModel', comment='local model parameters')
traj.localModel.alpha = Parameter('alpha', params['alpha'])
traj.localModel.beta = Parameter('beta', params['beta'])
traj.localModel.gamma = Parameter('gamma', params['gamma'])
traj.localModel.delta = Parameter('delta', params['delta'])
traj.localModel.epsilon = Parameter('epsilon', params['epsilon'])
traj.localModel.tau = Parameter('tau', params['tau'])
traj.localModel.init = Parameter('init', params['init'])
traj.localModel.randominitstd = Parameter(
'randominitstd',
params['randominitstd'],
comment='add gaussian noise with std on initial values')
traj.f_add_parameter_group('evaluation',
comment='threshold/analysis parameter for eval')
traj.evaluation.u_thres = Parameter(
'u_thres',
params['u_thres'],
comment='threshold for HS/LS when no oscillation')
traj.evaluation.rms_thres = Parameter(
'rms_thres',
params['rms_thres'],
comment='threshold for the significance of a freq amplitude')
traj.evaluation.gausfilter = Parameter(
'gausfilter',
params['gausfilter'],
comment='if >0: filter us before analysing')
traj.f_add_result('runs.$.z', z, comment='Result of our simulation!')
# Create an environment that handles running
filename = os.path.join('hdf5', 'example_14.hdf5')
env = Environment(trajectory='Multiplication',
filename=filename,
file_title='Example_14_Links',
overwrite_file=True,
comment='How to use links')
# The environment has created a trajectory container for us
traj = env.trajectory
# Add both parameters
traj.par.x = Parameter('x', 1, 'I am the first dimension!')
traj.par.y = Parameter('y', 1, 'I am the second dimension!')
# Explore just two points
traj.f_explore({'x': [3, 4]})
# So far everything was as in the first example. However now we add links:
traj.f_add_link('mylink1', traj.f_get('x'))
# Note the `f_get` here to ensure to get the parameter instance, not the value 1
# This allows us now to access x differently:
print('x=' + str(traj.mylink1))
# We can try to avoid fast access as well, and recover the original parameter
print(str(traj.f_get('mylink1')))
# And also colon notation is allowed that creates new groups on the fly
traj.f_add_link('parameters.mynewgroup.mylink2', traj.f_get('y'))
def add_params(self, traj):
traj.par.p1 = Parameter('', 42, 'Hey')
traj.f_apar('g1.p2', 145, comment='Test')
def add_parameters(traj, params):
""" Adds parameters to existing Trajectory 'traj' using dict params
The added parameters are the one from the interareal network with the aLN cascade model for the local dynamic.
WARNING: pypet 3.0 doesn't support lazy loading anymore, this function will not work!
"""
### simulation parameters
traj.f_add_parameter_group(
'simulation', comment='Group containing simulation parameters')
traj.simulation.model = Parameter('model',
params['model'],
comment='Which model to run (aln/brian)')
traj.simulation.dt = Parameter('dt',
params['dt'],
comment='I am a useful comment!'
) # params['dt'], 'integration time step'
traj.simulation.duration = Parameter('duration',
params['duration'],
comment='total simulation time [ms]')
traj.simulation.warn = Parameter(
'warn',
params['warn'],
comment='warns if out of precalc limits in \
interpolation, 0 for faster computation'
)
traj.simulation.dosc_version = Parameter('dosc_version',
params['dosc_version'],
comment='use dosc_version (1/0)')
traj.simulation.fast_interp = Parameter(
'fast_interp',
params['fast_interp'],
comment='Interpolate the value from the look-up table \
instead of taking the closest value'
)
traj.simulation.distr_delay = Parameter(
'distr_delay',
params['distr_delay'],
comment='use distributed delay instead of fixed (1/0)')
traj.simulation.filter_sigma = Parameter(
'filter_sigma',
params['filter_sigma'],
comment='full filter used for sigmae/sigmai(1),\
else use dirac filter(0)'
)
traj.simulation.seed = Parameter('seed',
params['seed'],
comment='Random Seed for Noise and ICs')
# global coupling parameters
traj.f_add_parameter_group('globalNetwork',
comment='Group containing \
global network coupling parameters')
traj.globalNetwork.N = Parameter(
'N', params['N'],
'Number of nodes in SC (or for brian: number of neurons in one pop)')
traj.globalNetwork.density = Parameter('density', params['density'],\
comment='density of SC: cutoff weakest links, from 1 to 0')
traj.globalNetwork.global_delay = Parameter('global_delay', params['global_delay'],\
comment='use global interareal delays, 1 or 0')
#traj.globalNetwork.CmatFileName = Parameter('CmatFileName', params['CmatFileName'],\
#comment='File name for the connectivity matrix')
#traj.globalNetwork.DmatFileName = Parameter('DmatFileName', params['DmatFileName'],\
#comment='File name for the delay matrix')
traj.globalNetwork.Cmat = Parameter('Cmat',
params['Cmat'],
comment='relative coupling strengths, \
Cmat[i,j]: connection from jth to ith (values between 0 and 1)'
)
traj.globalNetwork.lengthMat = Parameter('lengthMat',
params['lengthMat'],
comment='fiber length matrix')
traj.globalNetwork.signalV = Parameter(
'signalV',
params['signalV'],
comment='[m/s] Signal velocity for the interareal connections')
traj.globalNetwork.c_gl = Parameter('c_gl',
params['c_gl'],
comment='global coupling strength \
between areas(unitless)')
traj.globalNetwork.Ke_gl = Parameter(
'Ke_gl',
params['Ke_gl'],
comment='number of incoming E connect-\
ions from each area')
# local network (area) parameters
traj.f_add_parameter_group('localNetwork',
comment='Group containing \
local network (area) parameters')
traj.localNetwork.load_point = Parameter(
'load_point',
params['load_point'],
comment='Specifies which parameter point to load (from Cakan 2019)')
# external input parameters:
traj.localNetwork.tau_ou = Parameter('tau_ou',
params['tau_ou'],
comment='[ms]')
traj.localNetwork.sigma_ou = Parameter('sigma_ou',
params['sigma_ou'],
comment='[mV/ms/sqrt(ms)]')
traj.localNetwork.mue_ext_mean = Parameter('mue_ext_mean',
params['mue_ext_mean'],
comment='[mV/ms] (OU process)')
traj.localNetwork.mui_ext_mean = Parameter('mui_ext_mean',
params['mui_ext_mean'],
comment='[mV/ms] (OU process)')
traj.localNetwork.sigmae_ext = Parameter('sigmae_ext',
params['sigmae_ext'],
comment='[mV/sqrt(ms)]')
traj.localNetwork.sigmai_ext = Parameter('sigmai_ext',
params['sigmai_ext'],
comment='[mV/sqrt(ms)]')
traj.localNetwork.ext_exc_rate = Parameter(
'ext_exc_rate',
params['ext_exc_rate'],
comment='external excitatory rate input [kHz]')
traj.localNetwork.ext_inh_rate = Parameter(
'ext_inh_rate',
params['ext_inh_rate'],
comment='external inhibitory rate input [kHz]')
traj.localNetwork.ext_inh_current = Parameter(
'ext_inh_current',
params['ext_inh_current'],
comment='external inhibitory field input [mV/ms?]')
traj.localNetwork.ext_exc_current = Parameter(
'ext_exc_current',
params['ext_exc_current'],
comment='external excitatory field input [mV/ms?]')
# sinusodial input paramters
traj.localNetwork.A_sin = Parameter('A_sin', params['A_sin'])
traj.localNetwork.f_sin = Parameter('f_sin', params['f_sin'])
traj.localNetwork.ph_sin = Parameter('ph_sin', params['ph_sin'])
# recurrent coupling parameters
traj.localNetwork.Ke = Parameter('Ke',
params['Ke'],
comment='"EE = IE" assumed')
traj.localNetwork.Ki = Parameter('Ki',
params['Ki'],
comment='"EI = II" assumed')
traj.localNetwork.de = Parameter('de',
params['de'],
comment='[ms] local constant delay, \
"EE = IE"')
traj.localNetwork.di = Parameter('di',
params['di'],
comment='[ms] local constant delay, \
"EI = II"')
traj.localNetwork.tau_se = Parameter('tau_se',
params['tau_se'],
comment='[ms], "EE = IE"')
traj.localNetwork.tau_si = Parameter('tau_si',
params['tau_si'],
comment='[ms], "EI = II"')
traj.localNetwork.cee = Parameter('cee',
params['cee'],
comment='(unitless)')
traj.localNetwork.cei = Parameter('cei',
params['cei'],
comment='(unitless)')
traj.localNetwork.cii = Parameter('cii',
params['cii'],
comment='(unitless)')
traj.localNetwork.cie = Parameter('cie',
params['cie'],
comment='(unitless)')
traj.localNetwork.Jee_max = Parameter('Jee_max',
params['Jee_max'],
comment='[mV/ms]')
traj.localNetwork.Jie_max = Parameter('Jie_max',
params['Jie_max'],
comment='[mV/ms]')
traj.localNetwork.Jei_max = Parameter('Jei_max',
params['Jei_max'],
comment='[mV/ms]')
traj.localNetwork.Jii_max = Parameter('Jii_max',
params['Jii_max'],
comment='[mV/ms]')
traj.localNetwork.tau_di = Parameter(
'tau_di',
params['tau_di'],
comment=
'[ms] Distributed delay time constant, local inhibitory connection')
traj.localNetwork.tau_de = Parameter(
'tau_de',
params['tau_de'],
comment=
'[ms] Distributed delay time constant, local excitatory connection')
# neuron model parameters
traj.f_add_parameter_group('neuron',
comment='Group containing \
neuron model parameters')
traj.neuron.a = Parameter('a', params['a'], comment='nS')
traj.neuron.b = Parameter('b', params['b'], comment='pA')
traj.neuron.EA = Parameter('EA', params['EA'], comment='mV')
traj.neuron.tauA = Parameter('tauA', params['tauA'], comment='ms')
# if params below are changed, preprocessing required
traj.neuron.C = Parameter('C', params['C'], comment='pF')
traj.neuron.gL = Parameter('gL', params['gL'], comment='nS')
traj.neuron.EL = Parameter('EL', params['EL'], comment='mV')
traj.neuron.DeltaT = Parameter('DeltaT', params['DeltaT'], comment='mV')
traj.neuron.VT = Parameter('VT', params['VT'], comment='mV')
traj.neuron.Vr = Parameter('Vr', params['Vr'], comment='mV')
traj.neuron.Vs = Parameter('Vs', params['Vs'], comment='mV')
traj.neuron.Tref = Parameter('Tref', params['Tref'], comment='ms')
## initial parameters IC, randomly generated in 'loadDefaultParams'
traj.f_add_parameter_group('IC', comment='Group containing initial params')
traj.IC.mufe_init = Parameter(
'mufe_init',
params['mufe_init'],
comment='[mV/ms], dosc_version: complex variable')
traj.IC.IA_init = Parameter('IA_init', params['IA_init'], comment='[pA]')
traj.IC.mufi_init = Parameter(
'mufi_init',
params['mufi_init'],
comment='[mV/ms], dosc_version: complex variable')
traj.IC.seem_init = Parameter('seem_init',
params['seem_init'],
comment='COMMENT')
traj.IC.seim_init = Parameter('seim_init',
params['seim_init'],
comment='COMMENT')
traj.IC.seev_init = Parameter('seev_init',
params['seev_init'],
comment='COMMENT')
traj.IC.seiv_init = Parameter('seiv_init',
params['seiv_init'],
comment='COMMENT')
traj.IC.siim_init = Parameter('siim_init',
params['siim_init'],
comment='COMMENT')
traj.IC.siem_init = Parameter('siem_init',
params['siem_init'],
comment='COMMENT')
traj.IC.siiv_init = Parameter('siiv_init',
params['siiv_init'],
comment='COMMENT')
traj.IC.siev_init = Parameter('siev_init',
params['siev_init'],
comment='COMMENT')
traj.IC.rates_exc_init = Parameter('rates_exc_init',
params['rates_exc_init'],
comment='COMMENT')
traj.IC.rates_inh_init = Parameter('rates_inh_init',
params['rates_inh_init'],
comment='COMMENT')
# Look up tables used to integrate the local network dynamics
traj.f_add_parameter_group(
'LT',
comment='Look-up tables containing the parameter of the aLN model')
#traj.LT.Z_dosc = Parameter('Z_dosc', params['Z_dosc'], comment='Look up table - dosc version')
#traj.LT.Ze_dosc = Parameter('Ze_dosc', params['Ze_dosc'], comment='Look up table - dosc version')
#traj.LT.Zi_dosc = Parameter('Zi_dosc', params['Zi_dosc'], comment='Look up table - dosc version')
traj.LT.precalc_r = Parameter('precalc_r',
params['precalc_r'],
comment='Look up table')
traj.LT.precalc_V = Parameter('precalc_V',
params['precalc_V'],
comment='Look up table')
traj.LT.precalc_tau_mu = Parameter('precalc_tau_mu',
params['precalc_tau_mu'],
comment='Look up table')
traj.LT.precalc_tau_sigma = Parameter('precalc_tau_sigma',
params['precalc_tau_sigma'],
comment='Look up table')
#traj.LT.Zi = Parameter('Zi', params['Zi'], comment='Look up table')
traj.LT.Irange = Parameter(
'Irange',
params['Irange'],
comment='Range of the mean input for the look up tables')
traj.LT.sigmarange = Parameter(
'sigmarange',
params['sigmarange'],
comment='Range of the input variance for the look up tables')
traj.LT.dI = Parameter(
'dI',
params['dI'],
comment='Step size of the mean input for the look up tables')
traj.LT.ds = Parameter(
'ds',
params['ds'],
comment='Step size of the input variance for the look up tables')
__author__ = 'Robert Meyer'
from pypet import Trajectory, Result, Parameter
traj = Trajectory()
# There are more ways to add data,
# 1st the standard way:
traj.f_add_parameter('x', 1, comment='I am the first dimension!')
# 2nd by providing a new parameter/result instance, be aware that the data is added where
# you specify it. There are no such things as shortcuts for parameter creation:
traj.parameters.y = Parameter('y', 1, comment='I am the second dimension!')
# 3rd as before, but if our new leaf has NO name it will be renamed accordingly:
traj.parameters.t = Parameter('', 1, comment='Third dimension')
# See:
print('t=' + str(traj.t))
# This also works for adding groups on the fly and with the well known *dot* notation:
traj.parameters.subgroup = Parameter('subgroup.subsubgroup.w', 2)
# See
print('w=' + str(traj.par.subgroup.subsubgroup.w))
# Finally, there's one more thing. Using this notation we can also add links.
# Simply use the `=` assignment with objects that already exist in your trajectory:
traj.mylink = traj.f_get('x')
# now `mylink` links to parameter `x`, also fast access works:
print('Linking to x gives: ' + str(traj.mylink))
def test_new_assignment_method(self):
filename = make_temp_dir('newassignment.hdf5')
traj = Trajectory(filename=filename, add_time=True)
comment = 'A number'
traj.par.x = Parameter('', 44, comment)
self.assertTrue(traj.f_get('x').v_comment == comment)
traj.x = 45
self.assertTrue(traj.par.f_get('x').f_get() == 45)
self.assertTrue(isinstance(traj.f_get('x'), Parameter))
with self.assertRaises(AttributeError):
traj.f = Parameter('lll', 444, 'lll')
traj.f = Parameter('', 444, 'lll')
self.assertTrue(traj.f_get('f').v_name == 'f')
conf = traj.conf
with self.assertRaises(AttributeError):
conf = traj.conf.jjjj
traj.f_set_properties(fast_access=True)
traj.crun = Result('', k=43, m='JJJ')
self.assertTrue(traj.run_A['k'] == 43)
with self.assertRaises(AttributeError):
traj.f_set_properties(j=7)
with self.assertRaises(AttributeError):
traj.f_set_properties(depth=7)
traj.hui = Result('hui', ('444', 'kkkk',), 'l')
self.assertTrue(traj.f_get('hui')[1] == 'l')
traj.f_get('hui').f_set(('445', 'kkkk',))
self.assertTrue(traj.f_get('hui')[1] == 'l')
self.assertTrue(traj.hui[0] == ('445', 'kkkk',))
traj.f_add_link('klkikju', traj.par) # for shizzle
traj.meee = Result('meee.h', 43, hui = 3213, comment='du')
self.assertTrue(traj.meee.h.h == 43)
with self.assertRaises(TypeError):
traj.par.jj = NNGroupNode('jj', comment='mi')
with self.assertRaises(TypeError):
traj.res.jj = NNGroupNode('jj', comment='mi')
with self.assertRaises(TypeError):
traj.conf.jj = NNGroupNode('jj', comment='mi')
with self.assertRaises(TypeError):
traj.dpar.jj = NNGroupNode('jj', comment='mi')
with self.assertRaises(TypeError):
traj.par.jj = ResultGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.dpar.jj = ResultGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.conf.jj = ResultGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.jj = ResultGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.par.jj = ConfigGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.dpar.jj = ConfigGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.res.jj = ConfigGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.jj = ConfigGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.par.jj = DerivedParameterGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.conf.jj = DerivedParameterGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.res.jj = DerivedParameterGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.jj = DerivedParameterGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.dpar.jj = ParameterGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.res.jj = ParameterGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.conf.jj = ParameterGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.jj = ParameterGroup('jj', comment='mi')
traj.par.jj = ParameterGroup('jj', comment='mi')
traj.res.jj = ResultGroup('jj', comment='mi')
traj.jj = NNGroupNode('jj')
cg = ConfigGroup('a.g')
traj.conf.a = cg
self.assertTrue(traj.f_get('conf.a.g', shortcuts=False) is cg)
dg = DerivedParameterGroup('ttt')
traj.dpar.ttt = dg
self.assertTrue(traj.f_get('dpar.ttt', shortcuts=False) is dg)
traj.mylink = traj.par
self.assertTrue(traj.mylink is traj.par)
traj.vvv = NNGroupNode('', comment='kkk')
self.assertTrue(traj.vvv.v_full_name == 'vvv')
self.assertTrue(traj.par.jj.v_name == 'jj')
traj.ff = MyParamGroup('ff')
traj.par.g = MyParamGroup('')
pg = traj.f_add_parameter_group(comment='gg', full_name='me')
self.assertTrue(traj.par.me is pg)
traj.f_store()
traj = load_trajectory(index=-1, filename=filename, dynamic_imports=MyParamGroup)
self.assertTrue(isinstance(traj.ff, MyParamGroup))
self.assertTrue(isinstance(traj.par.g, MyParamGroup))
traj.par.hiho = Parameter('hiho', 42, comment='you')
traj.par.g1 = Parameter('g1.g2.g3.g4.g5', 43)
self.assertTrue(traj.hiho == 42)
self.assertTrue(isinstance(traj.par.g1, ParameterGroup ))
self.assertTrue(isinstance(traj.par.g3, ParameterGroup ))
self.assertTrue(traj.g3.g5 == 43)