142 lines
4.2 KiB
Python
142 lines
4.2 KiB
Python
from datetime import datetime, timedelta
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from decimal import Decimal
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from copy import deepcopy
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from fn.func import curried
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import pandas as pd
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from pathos.threading import ThreadPool
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from SimCAD.utils import groupByKey, dict_filter, contains_type
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from SimCAD.utils import flatMap
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class TensorFieldReport:
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def __init__(self, config_proc):
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self.config_proc = config_proc
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def create_tensor_field(self, mechanisms, exo_proc, keys=['behaviors', 'states']):
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dfs = [self.config_proc.create_matrix_field(mechanisms, k) for k in keys]
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df = pd.concat(dfs, axis=1)
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for es, i in zip(exo_proc, range(len(exo_proc))):
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df['es' + str(i + 1)] = es
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df['m'] = df.index + 1
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return df
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# def s_update(y, x):
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# return lambda step, sL, s, _input: (y, x)
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#
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#
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def state_update(y, x):
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return lambda step, sL, s, _input: (y, x)
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def bound_norm_random(rng, low, high):
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res = rng.normal((high+low)/2,(high-low)/6)
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if (res<low or res>high):
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res = bound_norm_random(rng, low, high)
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return Decimal(res)
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@curried
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def proc_trigger(trigger_step, update_f, step):
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if step == trigger_step:
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return update_f
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else:
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return lambda x: x
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t_delta = timedelta(days=0, minutes=0, seconds=30)
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def time_step(dt_str, dt_format='%Y-%m-%d %H:%M:%S', _timedelta = t_delta):
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dt = datetime.strptime(dt_str, dt_format)
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t = dt + _timedelta
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return t.strftime(dt_format)
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t_delta = timedelta(days=0, minutes=0, seconds=1)
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def ep_time_step(s, dt_str, fromat_str='%Y-%m-%d %H:%M:%S', _timedelta = t_delta):
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if s['mech_step'] == 0:
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return time_step(dt_str, fromat_str, _timedelta)
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else:
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return dt_str
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def mech_sweep_filter(mech_field, mechanisms):
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mech_dict = dict([(k, v[mech_field]) for k, v in mechanisms.items()])
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return dict([
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(k, dict_filter(v, lambda v: isinstance(v, list))) for k, v in mech_dict.items()
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if contains_type(list(v.values()), list)
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])
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def state_sweep_filter(raw_exogenous_states):
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return dict([(k, v) for k, v in raw_exogenous_states.items() if isinstance(v, list)])
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@curried
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def sweep_mechs(_type, in_config):
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configs = []
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filtered_mech_states = mech_sweep_filter(_type, in_config.mechanisms)
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if len(filtered_mech_states) > 0:
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for mech, state_dict in filtered_mech_states.items():
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for state, state_funcs in state_dict.items():
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for f in state_funcs:
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config = deepcopy(in_config)
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config.mechanisms[mech][_type][state] = f
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configs.append(config)
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del config
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else:
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configs = [in_config]
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return configs
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@curried
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def sweep_states(state_type, states, in_config):
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configs = []
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filtered_states = state_sweep_filter(states)
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if len(filtered_states) > 0:
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for state, state_funcs in filtered_states.items():
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for f in state_funcs:
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config = deepcopy(in_config)
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exploded_states = deepcopy(states)
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exploded_states[state] = f
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if state_type == 'exogenous':
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config.exogenous_states = exploded_states
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elif state_type == 'environmental':
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config.env_processes = exploded_states
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configs.append(config)
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del config, exploded_states
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else:
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configs = [in_config]
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return configs
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def param_sweep(config, raw_exogenous_states):
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return flatMap(
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sweep_states('environmental', config.env_processes),
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flatMap(
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sweep_states('exogenous', raw_exogenous_states),
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flatMap(
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sweep_mechs('states'),
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sweep_mechs('behaviors', config)
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)
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)
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)
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def exo_update_per_ts(ep):
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@curried
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def ep_decorator(f, y, step, sL, s, _input):
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if s['mech_step'] + 1 == 1:
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return f(step, sL, s, _input)
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else:
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return (y, s[y])
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return {es: ep_decorator(f, es) for es, f in ep.items()}
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# def ep_decorator(f, y, step, sL, s, _input):
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# if s['mech_step'] + 1 == 1:
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# return f(step, sL, s, _input)
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# else:
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# return (y, s[y])
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# return {es: ep_decorator(f, es) for es, f in ep.items()}
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