# THIS SCRIPT:

# Tests repeated simulation for the ensemble of parameter sets. Does Timme inference, saves result. Times whole process.

## Varies: noise intensity

## saves mean and variance in relative error (excepting J11 - raw error since this parameter is always zero)

from datetime import datetime

from configure import NOISE_VALUE, NUMBER_OF_PARAMETERS, PARAMETER_FILE, DT, REPEATS_PER_PARAMETER_SET, NUMBER_OF_SAMPLES, NUMBER_OF_BINS

from linear_simulator import linear_simulator

from sampler import sampler

from timme_calculator import timme_calculator

import numpy as np

import os, sys

##################################

##configure:

PARAMETER_FILE = './parameter_log.txt'

REPEATS_PER_PARAMETER_SET = 10

DT = 0.0001

NUMBER_OF_SAMPLES = 10000

params = range(100)

printout = False

plot_dynamics = False

##################################

start_sim = datetime.now()

parameters = np.genfromtxt(PARAMETER_FILE, delimiter=',')

noise = range(100)

RESULTS = np.zeros((len(noise), 1 + (6 + 2)*2)) ## as below. *2 for mean and variance over repeats.

RESULTS[:,0] = noise

nid = 0

Nparams = len(params)

for ni in noise:

print "noise = ", ni

summary_array = np.zeros((Nparams * REPEATS_PER_PARAMETER_SET, 6 + 2)) # to store results for each simulation, used to calculate mean and variance of estimates

## a0 | J00 | J01 | a1 | J10 | J11 | err1 | err2

## relative error in all of the above, excepting J11, err1, err2

sa_id = 0

for pi in params:

for r in range(REPEATS_PER_PARAMETER_SET):

a = parameters[pi,1]

b = parameters[pi,2]

c = parameters[pi,3]

x00 = parameters[pi,13]

x10 = parameters[pi,14]

T2P = parameters[pi,12]

ls = linear_simulator(a, b, c, x00, x10, DT, T2P, ni, plot_dynamics)

ls.run()

D = ls.get_dynamics()

E_prey = np.asarray(ls.ext_prey)

E_pred = np.asarray(ls.ext_pred)

## now do inference:

S = sampler(NUMBER_OF_SAMPLES, D)

S.sample()

calc = timme_calculator(S, NUMBER_OF_BINS)

results, err = calc.calculate()

results = results[0:6]

results = np.append(results, err[0])

results = np.append(results, err[1])

## calculate relative error:

re = []

re.append(np.abs((results[0]-a)/a))

re.append(np.abs((results[1]+a)/a))

re.append(np.abs((results[2]+b)/b))

re.append(np.abs((results[3]+1)/1.0))

re.append(np.abs((results[4]-c)/c))

re.append(np.abs(results[5]))

re.append(np.abs(results[6]))

re.append(np.abs(results[7]))

summary_array[sa_id,:] = re ## saving relative errors

sa_id += 1

if printout==True:

print("prey extinctions = %d" %(len(E_prey)))

print("pred extinctions = %d" %(len(E_pred)))

print(np.shape(D))

print("\nRESULTS:\n")

print(results)

RESULTS[nid,1:] = np.append(np.mean(summary_array,0), np.var(summary_array,0))

nid += 1

np.savetxt('ensemble_params_vs_noise_nsamples_%d_dt_%f_reps_%d.results' %(NUMBER_OF_SAMPLES, DT, REPEATS_PER_PARAMETER_SET), RESULTS, delimiter=',')

stop_sim = datetime.now()

elapsed_sim = stop_sim-start_sim

print 'time for simulation' , elapsed_sim