#define _USE_MATH_DEFINES
#include "math.h"
#include <stdlib.h>
#include <stdio.h>
#include <locale.h>
#include <time.h>
#include <stdbool.h>

#define Neuron_count 800
#define Neuron_count_half Neuron_count / 2

#define Equations_per_neuron 4
#define Equations_count Neuron_count * Equations_per_neuron

double f[Equations_count];

double C_m = 1;

double g_K  = 36;
double g_Na = 120;
double g_L  = 0.3;

double E_K  = -77;
double E_Na =  55;
double E_L  = -54.4;

double k_syn = 0.2;

double I_app[Equations_count];

const double sigma_G  = 0.0;
const double sigma_GN = 0.0;
const double sigma_N  = 0.1;

double** A;
double** B;
double*  C;
double** sigma;

double V(int i)
{
	return f[i * 4];
}

double m(int i)
{
	return f[i * 4 + 1];
}

double n(int i)
{
	return f[i * 4 + 2];
}

double h(int i)
{
	return f[i * 4 + 3];
}

int RandomI(int min, int max)
{
	return ((double)rand() / (RAND_MAX - 1)) * (max - min) + min;
}

double RandomD(double min, double max)
{
	return ((double)rand() / RAND_MAX) * (max - min) + min;
}

double alpha_n(double* f, int i)
{
	return 0.01 * (V(i) + 55) / (1 - exp(-(V(i) + 55) / 10));
}

double beta_n(double* f, int i)
{
	return 0.125 * exp(-(V(i) + 65) / 80);
}

double alpha_m(double* f, int i)
{
	return 0.1 * (V(i) + 40) / (1 - exp(-(V(i) + 40) / 10));
}

double beta_m(double* f, int i)
{
	return 4 * exp(-(V(i) + 65) / 18);
}

double alpha_h(double* f, int i)
{
	return 0.07 * exp(-(V(i) + 65) / 20);
}

double beta_h(double* f, int i)
{
	return 1 / (exp(-(V(i) + 35) / 10) + 1);
}

double HodgkinHuxley(int i, double* f)
{
	int in = i / 4;
	int il = i % 4;

	switch (il)
	{
		case 0:
		{
		  double sum = 0;

		  /*for (int j = 0; j < Neuron_count; j++)
		  {
			  sum += sigma[in][j] * A[in][j] * (V(j) - V(in));
		  }*/

		  /*for (int j = 0; j < C[in]; j++)
		  {
			  sum += sigma[in][(int)B[in][j]] * (V((int)B[in][j]) - V(in));
      }*/

			for (int j = 0; j < C[in]; j++)
		  {
			  sum += sigma[in][(int)B[in][j]] * V((int)B[in][j]) / (1 + exp(-V(in) / k_syn));
      }

			return 1000 * ((g_Na * m(in) * m(in) * m(in) * h(in) * (E_Na - V(in)) + g_K * n(in) * n(in) * n(in) * n(in) * (E_K - V(in)) + g_L * (E_L - V(in)) + I_app[in] + sum) / C_m);
		}

		case 1:
		{
			return 1000 * (alpha_m(f, in) * (1 - m(in)) - beta_m(f, in) * m(in));
		}

		case 2:
		{
			return 1000 * (alpha_n(f, in) * (1 - n(in)) - beta_n(f, in) * n(in));
		}

		case 3:
		{
			return 1000 * (alpha_h(f, in) * (1 - h(in)) - beta_h(f, in) * h(in));
		}
	}

	return 0;
}

void RungeKutta(double dt, double* f, double* f_next)
{
	double k[Equations_count][4];

	// k1
	for (int i = 0; i < Equations_count; i++)
		k[i][0] = HodgkinHuxley(i, f) * dt;

	double phi_k1[Equations_count];
	for (int i = 0; i < Equations_count; i++)
		phi_k1[i] = f[i] + k[i][0] / 2;

	// k2
	for (int i = 0; i < Equations_count; i++)
		k[i][1] = HodgkinHuxley(i, phi_k1) * dt;

	double phi_k2[Equations_count];
	for (int i = 0; i < Equations_count; i++)
		phi_k2[i] = f[i] + k[i][1] / 2;

	// k3
	for (int i = 0; i < Equations_count; i++)
		k[i][2] = HodgkinHuxley(i, phi_k2) * dt;

	double phi_k3[Equations_count];
	for (int i = 0; i < Equations_count; i++)
		phi_k3[i] = f[i] + k[i][2] / 2;

	// k4
	for (int i = 0; i < Equations_count; i++)
		k[i][3] = HodgkinHuxley(i, phi_k3) * dt;

	for (int i = 0; i < Equations_count; i++)
		f_next[i] = f[i] + (k[i][0] + 2 * k[i][1] + 2 * k[i][2] + k[i][3]) / 6;
}

void CopyArray(double* source, double* target, int N)
{
	for (int i = 0; i < N; i++)
		target[i] = source[i];
}

bool Approximately(double a, double b)
{
	if (a < 0)
		a = -a;

	if (b < 0)
		b = -b;

	return a - b <= 0.000001;
}

void FillAMatrixZero()
{
	for (int i = 0; i < Neuron_count; i++)
	{
		for (int j = 0; j < Neuron_count; j++)
		{
			A[i][j] = 0;
		}
	}
}

void FillBCMatrix()
{
	for (int i = 0; i < Neuron_count; i++)
	{
		int bIndex = 0;
		C[i] = 0;
		for (int j = 0; j < Neuron_count; j++)
		{
			if (A[i][j] == 1)
			{
				B[i][bIndex] = j;
				bIndex++;
				C[i]++;
			}
		}
	}
}

void FillSigmaMatrix()
{
	for (int i = 0; i < Neuron_count; i++)
	{
		for (int j = 0; j < Neuron_count; j++)
		{
			if ((i >= 0 && i < Neuron_count_half) && (j >= 0 || j < Neuron_count_half))
			{
				sigma[i][j] = sigma_G;
			}
			if ((((i >= Neuron_count_half && i < Neuron_count) && (j >= 0 && j < Neuron_count_half)) || ((i >= 0 && i < Neuron_count_half) && (j >= Neuron_count_half && j < Neuron_count))))
			{
				sigma[i][j] = sigma_GN;
			}
			if ((i >= Neuron_count_half && i < Neuron_count) && (j >= Neuron_count_half && j < Neuron_count))
			{
				sigma[i][j] = sigma_N;
			}
		}
	}
}

void FillRandomMatrix()
{
	for (int k = 0; k < 2 * Neuron_count_half; k++)
	{
		int i, j;

		do
		{
			i = RandomI(Neuron_count_half, Neuron_count);
			j = RandomI(Neuron_count_half, Neuron_count);
		} while ((i == j) || (A[i][j] == 1));

		A[i][j] = 1;
		A[j][i] = 1;
	}
}

int main(int argc, char *argv[])
{
	FILE *fp0;
	srand(time(NULL));

	A = malloc(Neuron_count * sizeof(double));
	for (int i = 0; i < Neuron_count; i++)
		A[i] = malloc(Neuron_count * sizeof(double));

	B = malloc(Neuron_count * sizeof(double));
	for (int i = 0; i < Neuron_count; i++)
		B[i] = malloc(Neuron_count * sizeof(double));

	C = malloc(Neuron_count * sizeof(double));

	sigma = malloc(Neuron_count * sizeof(double));
	for (int i = 0; i < Neuron_count; i++)
		sigma[i] = malloc(Neuron_count * sizeof(double));

	FillAMatrixZero();
	FillRandomMatrix();
	FillBCMatrix();
	FillSigmaMatrix();

  fp0 = fopen("A.txt", "w+");
  for (int i = 0; i < Neuron_count; i++)
  {
    for (int j = 0; j < Neuron_count; j++)
    {
      fprintf(fp0, "%d\t", (int)A[i][j]);
    }
    fprintf(fp0, "\n");
  }
  fclose(fp0);

  //Пишем в файл число связей у каждого осциллятора в четвертом квадранте
  fp0 = fopen("links.txt", "w+");
  for (int i = Neuron_count_half; i < Neuron_count; i++)
  {
    int links_count = 0;
    for (int j = Neuron_count_half; j < Neuron_count; j++)
    {
      if (A[i][j] == 1)
      {
        links_count++;
      }
    }
    fprintf(fp0, "%d\n", (int)links_count);

  }
  fclose(fp0);

  fp0 = fopen("B.txt", "w+");
	for (int i = 0; i < Neuron_count; i++)
	{
		for (int j = 0; j < C[i]; j++)
		{
			fprintf(fp0, "%d\t", (int)B[i][j]);
		}
		fprintf(fp0, "\n");
	}
	fclose(fp0);

	fp0 = fopen("C.txt", "w+");
	for (int i = 0; i < Neuron_count; i++)
	{
		fprintf(fp0, "%d\n", (int)C[i]);
	}
	fclose(fp0);

  fp0 = fopen("sigma.txt", "w+");
  for (int i = 0; i < Neuron_count; i++)
	{
		for (int j = 0; j < Neuron_count; j++)
		{
			fprintf(fp0, "%f\t", sigma[i][j]);
		}
		fprintf(fp0, "\n");
	}
	fclose(fp0);

  // Initial values
	for (int i = 0; i < Equations_count; i++)
  {
		f[i] = 0;
  }

  for (int i = 0; i < Equations_count; i++)
  {
		I_app[i] = RandomD(9, 40);
  }

	const double t_start = 0;
	const double t_max = 1; // 100 msec = 0.1 sec
	const double dt = 0.00001; // 0.01 msec = 0.00001 sec

	double t = t_start;

	fp0 = fopen("results.txt", "w+");
	//setlocale(LC_NUMERIC, "French_Canada.1252");

	clock_t start_rk4, end_rk4;
	start_rk4 = clock();
	int lastPercent = -1;

	while (t < t_max || Approximately(t, t_max))
	{
		fprintf(fp0, "%f\t", t);

		for (int i = 0; i < Equations_count; i += 4)
			fprintf(fp0, i == Equations_count - 1 ? "%f" : "%f\t", f[i]);

		fprintf(fp0, "\n");

		double f_next[Equations_count];

		RungeKutta(dt, f, f_next);
		CopyArray(f_next, f, Equations_count);

		t += dt;

		int percent = (int)(100 * (t - t_start) / (t_max - t_start));
		if (percent != lastPercent)
		{
			printf("Progress: %d%%\n", percent);
			lastPercent = percent;
		}
	}

  fclose(fp0);

	end_rk4 = clock();
	double extime_rk4 = (double)(end_rk4 - start_rk4) / CLOCKS_PER_SEC;
	int minutes = (int)extime_rk4 / 60;
	int seconds = (int)extime_rk4 % 60;
	printf("\nExecution time is: %d minutes %d seconds\n ", minutes, seconds);

	fp0 = fopen("time_exec.txt", "w+");
	fprintf(fp0, "%f\n", extime_rk4);
	fclose(fp0);

  for (int i = 0; i < Neuron_count; i++)
      free(A[i]);
  free(A);

	for (int i = 0; i < Neuron_count; i++)
			free(B[i]);
	free(B);

	for (int i = 0; i < Neuron_count; i++)
			free(sigma[i]);
	free(sigma);

	free(C);
}