61 lines
1.8 KiB
Python
61 lines
1.8 KiB
Python
from datetime import datetime, timedelta
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from decimal import Decimal
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from fn.func import curried
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import pandas as pd
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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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# ??? dont for-loop to apply exo_procs, use exo_proc struct
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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 bound_norm_random(rng, low, high):
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# Add RNG Seed
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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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# accept timedelta instead of timedelta params
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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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# accept timedelta instead of timedelta params
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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 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: # inside f body to reduce performance costs
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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()} |