void muste_nterm(int argc, char *argv[])
{
int i;
/*
if (argc==1)
{
Rprintf("This program can be used as a SURVO 84C module only.");
return;
}
*/
s_init(argv[1]);
i=init_sequence();
if (i<0) { s_end(argv[1]); return; }
i=init_regressors();
if (i<0) return;
i=linear_regression(seq_n,10);
if (i<0) return;
data_close(&data);
edwrite(nterm_output_buffer,nterm_output_line,1);
s_end(argv[1]);
}
int main(int argc, char *argv[]) {
printf("Linear Regression\n");
printf("=================\n\n");
// Check for a filename
if (argc == 2) {
FILE *input = fopen(argv[1], "r");
if (input == NULL) {
perror(argv[1]);
exit(1);
} else {
DataSet theData = load_data(input);
fclose(input);
LinRegResult linReg = linear_regression(theData);
clean(theData);
printf("\nSlope \tm = %15.6e\n", DESCALE(linReg.m)); // print slope
printf("y-intercept\tb = %15.6e\n", DESCALE(linReg.b)); // print y-intercept
printf("Correlation\tr = %15.6e\n", DESCALE(linReg.r)); // print correlation
}
} else {
printf("ERROR: Must enter filename after ./linReg\n");
}
return 0;
}
void test_symplectic_central_force(void** state)
{
// We use the symplectic Crank-Nicolson method to integrate the trajectory
// of a particle under the influence of a central force. We test that its
// energy is conserved, and that the integration error converges to zero
// at second order accuracy.
ode_integrator_t* I = symplectic_central_force_integrator();
real_t rel_tol = 1e-4, abs_tol = 1e-6;
euler_ode_integrator_set_tolerances(I, rel_tol, abs_tol);
real_t R = 1.0; // radius of circular orbit.
real_t V = 1.0; // Linear velocity magnitude.
real_t T = 2.0 * M_PI * R / V; // period of rotation
int num_trials = 4;
int num_steps_array[4] = {16, 32, 64, 128};
real_t L2_array[num_trials];
for (int i = 0; i < num_trials; ++i)
{
int num_steps = num_steps_array[i];
real_t X[4] = {R, 0.0, 0.0, V}; // {x, y, vx, vy}
real_t dt = T / num_steps;
// Integrate the trajectory with the given time step.
real_t t = 0.0;
for (int j = 0; j < num_steps; ++j)
{
bool success = ode_integrator_step(I, dt, &t, X);
//printf("%g %g %g %g %g\n", t, X[0], X[1], X[2], X[3]);
assert_true(success);
}
// Check energy conservation.
real_t E = 0.5 * (X[2]*X[2] + X[3]*X[3]);
assert_true(fabs(E - 0.5) < 1e-3);
// Compute error norms.
real_t L2 = sqrt((X[0]-R)*(X[0]-R) + X[1]*X[1] + X[2]*X[2] + (X[3]-V)*(X[3]-V));
L2_array[i] = L2;
}
// Compute the convergence rate of the L2 error norm.
real_t log_n_ratios[num_trials-1], log_L2_ratios[num_trials-1];
for (int i = 0; i < num_trials-1; ++i)
{
log_n_ratios[i] = log(pow(2.0, i));
log_L2_ratios[i] = log(L2_array[i+1] / L2_array[0]);
}
real_t A, B, sigma;
linear_regression(log_n_ratios, log_L2_ratios, num_trials-1, &A, &B, &sigma);
real_t L2_conv_rate = -A;
log_urgent("symplectic central force L2 error conv rate = %g\n", L2_conv_rate);
assert_true(L2_conv_rate >= 1.9947);
// Clean up.
ode_integrator_free(I);
}
/* COMPUTE_SMOOTH_TIME_MAP
compute regression line and estimate point at i
Number of points in regression is smooth (an odd number). First
index to compute is (smooth-1)/2. Use that line for the first
(smooth+1)/2 points. The last index to compute is
(file0_frames - (smooth+1)/2). Use that line for the last
(smooth+1)/2 points.
*/
void Scorealign::compute_smooth_time_map()
{
int i;
int hw = (smooth - 1) / 2; // half width of smoothing window
// find the first point
for (i = 0; i < first_x; i++) {
smooth_time_map[i] = NOT_MAPPED;
}
// do the first points:
float a, b;
linear_regression(first_x + hw, smooth, a, b);
for (i = first_x; i <= first_x + hw; i++) {
smooth_time_map[i] = a + b * i;
}
// do the middle points:
for (i = first_x + hw + 1; i < last_x - hw; i++) {
linear_regression(i, smooth, a, b);
smooth_time_map[i] = a + b * i;
#if DEBUG_LOG
fprintf(dbf, "time_map[%d] = %g, smooth_time_map[%d] = %g\n",
i, time_map[i], i, a + b*i);
#endif
}
// do the last points
linear_regression(last_x - hw, smooth, a, b);
for (i = last_x - hw; i <= last_x; i++) {
smooth_time_map[i] = a + b * i;
}
// finally, fill with NOT_MAPPED
for (i = last_x + 1; i < file0_frames; i++)
smooth_time_map[i] = NOT_MAPPED;
}
void test_stiffly_accurate_kinetics(void** state)
{
// We use the symplectic Crank-Nicolson method to integrate the trajectory
// of a particle under the influence of a central force. We test that its
// energy is conserved, and that the integration error converges to zero
// at second order accuracy.
ode_integrator_t* I = stiffly_accurate_kinetics_integrator();
// Reference values.
real_t T = 400.0;
real_t X0 = 0.4505440, X1 = 3.223217e-06, X2 = 5.494528e-01;
int num_trials = 11;
int num_steps_array[11] = {1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024};
real_t L2_array[num_trials];
for (int i = 0; i < num_trials; ++i)
{
int num_steps = num_steps_array[i];
real_t X[3] = {1.0, 0.0, 0.0};
real_t dt = T / num_steps;
// Integrate with the given time step.
real_t t = 0.0;
for (int j = 0; j < num_steps; ++j)
{
bool success = ode_integrator_step(I, dt, &t, X);
assert_true(success);
}
// Compute error norms from reference values.
real_t L2 = sqrt((X[0]-X0)*(X[0]-X0) + (X[1]-X1)*(X[1]-X1) + (X[2]-X2)*(X[2]-X2));
L2_array[i] = L2;
}
// Compute the convergence rate of the L2 error norm.
real_t log_n_ratios[num_trials-1], log_L2_ratios[num_trials-1];
for (int i = 0; i < num_trials-1; ++i)
{
log_n_ratios[i] = log(pow(2.0, i));
log_L2_ratios[i] = log(L2_array[i+1] / L2_array[0]);
}
real_t A, B, sigma;
linear_regression(log_n_ratios, log_L2_ratios, num_trials-1, &A, &B, &sigma);
real_t L2_conv_rate = -A;
log_urgent("stiffly accurate kinetics L2 error conv rate = %g\n", L2_conv_rate);
assert_true(L2_conv_rate >= 0.93);
// Clean up.
ode_integrator_free(I);
}
int main(int argc, char **argv)
{
extern char *optarg;
int optchar;
char *treefile_name = NULL, *genomefile_name = NULL, *outfile_name = NULL, *corrfile_name = NULL;
FILE *treefile, *genomefile, *outfile, *corrfile;
long genome_generation, tree_generation, i, j, num_trees;
double tree_d, gen_d, change_rate, m1, m_a, mutation_rate;
double *tree_dist = NULL, *m_app = NULL;
double grade, y0, r;
long num_data;
PHYLTREE phyltree;
POPULATION population;
int ret_code;
phyl_init_tree(&phyltree);
while ((optchar = getopt(argc, argv, "hg:t:o:c:m:")) != -1)
{
switch (optchar)
{
case 'g':
genomefile_name = optarg;
break;
case 't':
treefile_name = optarg;
break;
case 'c':
corrfile_name = optarg;
break;
case 'o':
outfile_name = optarg;
break;
case 'h':
printf("mutcheck -- analyze apparent mutation rates\n");
printf("\n");
printf("Command line usage:\n");
printf("-g <filename>: Specify genome file (mandatory)\n");
printf("-t <filename>: Specify tree file (mandatory)\n");
printf("-o <filename>: Specify name for individual generation's output file\n");
printf("-c <filename>: Specify file for treedist vs reconstructed dist correlations\n");
printf("-h: Print this help and exit\n");
exit (EXIT_SUCCESS);
}
}
if (treefile_name == NULL)
{
fprintf(stderr, "no tree file specified -- exit\n");
exit (EXIT_FAILURE);
}
if ((treefile = fopen(treefile_name, "r")) == NULL)
{
fprintf(stderr, "failed to open tree file \"%s\" -- exit\n", treefile_name);
exit (EXIT_FAILURE);
}
if (genomefile_name == NULL)
{
fprintf(stderr, "No genome file specified -- exit\n");
fclose(treefile);
exit (EXIT_FAILURE);
}
if ((genomefile = fopen(genomefile_name, "r")) == NULL)
{
fprintf(stderr, "Failed to open genome file \"%s\" -- exit\n", genomefile_name);
fclose(treefile);
exit (EXIT_FAILURE);
}
if (corrfile_name)
{
if ((corrfile = fopen(corrfile_name, "w")) == NULL)
{
fprintf(stderr, "Failed to open \"%s\" for correlation output -- exit\n", corrfile_name);
fclose(treefile);
fclose(genomefile);
exit (EXIT_FAILURE);
}
}
else
{
corrfile = stdout;
corrfile_name = "stdout";
}
if (outfile_name)
{
if ((outfile = fopen(outfile_name, "w")) == NULL)
{
fprintf(stderr, "mutcheck: Failed to open %s for output -- exit\n", outfile_name);
fclose(treefile);
fclose(genomefile);
if (corrfile_name)
fclose(corrfile);
exit (EXIT_FAILURE);
}
}
else
outfile = NULL;
while (!feof(genomefile) && !ferror(genomefile) && !feof(treefile) && !ferror(treefile))
{
fgets(buf, 256, treefile);
if ((buf[0] != 'g') || (buf[1] != ' '))
{
fprintf(stderr, "Treefile corrupt after generation %ld\n", tree_generation);
break;
}
tree_generation = strtol(buf + 2, NULL, 10);
fgets(buf, 256, genomefile);
if ((buf[0] != 'g') || (buf[1] != ' '))
{
fprintf(stderr, "Genome file corrupt after generation %ld\n", genome_generation);
break;
}
genome_generation = strtol(buf + 2, NULL, 10);
fgets(buf, 256, treefile);
num_trees = strtol(buf, (char **) NULL, 10);
if (num_trees != 1)
{
fprintf(stderr, "%ld trees in generation %ld -- skipping\n", num_trees, tree_generation);
for (i = 0; i < num_trees; i++)
{
phyl_read_tree(treefile, &phyltree);
phyl_free_tree(&phyltree);
}
}
else
phyl_read_tree(treefile, &phyltree);
read_genomes(genomefile, &population);
if ((num_trees == 1) && (tree_generation == genome_generation))
{
if ((tree_dist = (double *) malloc((population.size * population.size - 1) / 2 * sizeof(double))) == NULL)
fprintf(stderr, "Failed to allocate array of tree distances\n");
else
{
if ((m_app = (double *) malloc(population.size * (population.size - 1) / 2 * sizeof(double))) == NULL)
fprintf(stderr, "Failed to allocate array of reconstructed distances\n");
else
{
num_data = 0;
for (i = 0; i < population.size; i++)
{
for (j = 0; j < i; j++)
{
tree_d = phyl_leafdistance(&phyltree, population.seq[i].name, population.seq[j].name);
if (tree_d < 0)
{
fprintf(stderr, "Generation %ld: Trees and genomes inconsistent: error #%f\n", tree_generation, tree_d);
break;
}
gen_d = diffchar_distance(population.seq[i].genome.length,
population.seq[i].genome.g,
population.seq[j].genome.length, population.seq[j].genome.g);
change_rate = gen_d / (population.seq[i].genome.length);
m1 = 1.0 - 256.0 / 255.0 * change_rate;
if (m1 > 0.0)
{
errno = 0;
m_a = 1.0 - pow(m1, 1.0 / 2.0 / tree_d);
if (errno)
perror("mutcheck: error in pow()");
else
{
tree_dist[num_data] = tree_d;
m_app[num_data] = m_a;
num_data++;
if (outfile)
fprintf(outfile, "%f %f\n", tree_d, m_a);
}
}
else if (outfile)
fprintf(outfile, "# Negative base operand to exponentiation: %f)\n", m1);
}
if (j < i)
break;
}
if ((ret_code = linear_regression(num_data, tree_dist, m_app, &grade, &y0, &r)) < 0)
{
fprintf(stderr, "Error #%d in linear regression\n", ret_code);
}
else
{
mutation_rate = 1.0 - exp(grade);
if (corrfile)
fprintf(corrfile, "%ld %f %f\n", tree_generation, grade, r);
}
free(m_app);
}
free(tree_dist);
}
}
phyl_free_tree(&phyltree);
free_population(&population);
#ifdef MEMDEBUG
print_MemdebugStatistics();
#endif
}
if (corrfile != stdout)
fclose(corrfile);
if (outfile)
fclose(outfile);
fclose(treefile);
fclose(genomefile);
return (EXIT_SUCCESS);
}
int
main(int argc, char **argv)
{
int i,j,k; /* counters */
int ns=0; /* number of samples in input data */
int nwavelet=1024; /* number of samples in mother wavelet */
float base=0.0; /* base */
float first=0.0; /* first exponent */
float expinc=0.0; /* exponent increment */
float last=0.0; /* last exponent */
float exponent=0.0; /* each exponent */
float maxscale=0.0; /* maximum scale value */
float minscale=0.0; /* minimum scale value */
float x=0.0;
float dx=0.0; /* xvalues incr */
float xmin=0.0; /* last xvalues - first vval */
float xcenter=0.0; /* x value of center of wavelet */
float xmax=0.0; /* last xvalues - first vval */
float sigma=1.0; /* sharpening parameter */
float waveletinc=0.0; /* wavelet interval */
float fmin=0.0; /* min, max filt value (debug) */
float *xvalues=NULL; /* wavelet xvalues */
float **filt=NULL; /* filter used for each conv */
float *f=NULL; /* scratch for filter fliplr */
float *sucwt_buff=NULL; /* scratch for convolution */
float *scales=NULL; /* scales */
float *waveletsum=NULL; /* cumulative sum of wavelet */
float *rt=NULL; /* temp data storage */
float *qt=NULL; /* temp hilbert transformed data storage */
float **tmpdata=NULL; /* temp data storage */
int wtype=0; /* type of wavelet selected */
float *wavelet=NULL; /* pointer to data constituting the wavelet */
int verbose=0; /* verbose flag */
int *index=NULL; /* wavelet subscripts to use for filter */
int *nconv=NULL; /* length of each filter */
int nscales=0; /* number of scales */
int holder=0; /* =1 compute the Holder-Lipschitz regularity */
float divisor=1.0; /* divisor used in Holder exponent calculation*/
/* Initialize */
initargs(argc, argv);
requestdoc(1);
/* Get parameters */
if(!getparfloat("base",&base)) base = 10.;
if(!getparfloat("first",&first)) first = -1.0;
if(!getparfloat("expinc",&expinc)) expinc = 0.01;
if(!getparfloat("last",&last)) last = 1.5;
if(!getparint("wtype",&wtype)) wtype = 0;
if(!getparint("nwavelet",&nwavelet)) nwavelet = 1024;
if(!getparfloat("xmin",&xmin)) xmin = -20.0;
if(!getparfloat("xcenter",&xcenter)) xmin = 0.0;
if(!getparfloat("xmax",&xmax)) xmax = 20.0;
if(!getparfloat("sigma",&sigma)) sigma = 1.0;
if(!getparint("holder",&holder)) holder = 0;
if(!getparfloat("divisor",&divisor)) divisor = 1.0;
if(!getparint("verbose",&verbose)) verbose = 0;
if(verbose)
warn("base=%f, first=%f, expinc=%f, last=%f",base,first,expinc,last);
/* Allocate space */
xvalues = ealloc1float(nwavelet);
wavelet = ealloc1float(nwavelet);
memset((void *) xvalues, 0, nwavelet*FSIZE);
memset((void *) wavelet, 0, nwavelet*FSIZE);
/* Compute wavelet */
if (wtype == 0 ) { /* so far only Mex. Hat function */
MexicanHatFunction(nwavelet, xmin, xcenter,
xmax, sigma, wavelet);
} else {
err("%d type of wavelet not yet implemented",wtype);
}
/* wavelet increment */
waveletinc = (xmax - xmin)/(nwavelet - 1);
/* verbose warning */
if(verbose)
warn("xmin=%f, xmax=%f, nwavelet=%d, waveletinc=%f",
xmin,xmax,nwavelet,waveletinc);
/* form xvalues[] array */
for(i=0,x=xmin; i<nwavelet; ++i,x+=waveletinc) xvalues[i] = x;
xvalues[nwavelet-1] = xmax;
/* compute scales */
scales = ealloc1float(SHRT_MAX);
memset((void *) scales, 0, SHRT_MAX*FSIZE);
exponent = first;
x = 0;
nscales = 0;
minscale = pow(base,first);
maxscale = pow(base,last);
while(x <= maxscale) {
x = pow(base,exponent);
scales[nscales] = x;
exponent+=expinc;
++nscales;
if(nscales == SHRT_MAX)
err("Too many scales, change params and re-run\n");
}
--nscales;
/* Allocate space */
nconv = ealloc1int(nscales);
index = ealloc1int(nwavelet);
waveletsum = ealloc1float(nwavelet);
filt = ealloc2float(nwavelet,nscales);
f = ealloc1float(nwavelet);
/* Zero out arrays */
memset((void *) nconv, 0, nscales*ISIZE);
memset((void *) index, 0, nwavelet*ISIZE);
memset((void *) waveletsum, 0, nwavelet*FSIZE);
memset((void *) filt[0], 0, nwavelet*nscales*FSIZE);
memset((void *) f, 0, nwavelet*FSIZE);
/* Form difference of xvalues */
for(i=nwavelet-1; i>=0; --i)
xvalues[i] = xvalues[i] - xvalues[0];
dx = xvalues[1];
xmax = xvalues[nwavelet-1];
/* verbose warning */
if(verbose) {
warn("first xvalues=%f, last xvalues=%f",
xvalues[0],xvalues[nwavelet-1]);
warn("dx=%f, xmax=%f",dx,xmax);
}
/* waveletsum is cumulative sum of wavelet multipled by dx */
fmin = 0;
for(i=0; i<nwavelet; ++i) {
fmin += wavelet[i];
waveletsum[i] = fmin * dx;
}
/* Build filters from summed wavelet */
for(i=0; i<nscales; ++i) {
nconv[i] = 1 + (int)(scales[i] * xmax);
for(j=0; j<nconv[i]; ++j)
index[j] = 1 + j / (scales[i] * dx);
for(j=0; j<nconv[i]; ++j)
f[j] = waveletsum[index[j]-1];
/* flip left right */
for(j=0,k=nconv[i]-1; j<nconv[i]; ++j,--k)
filt[i][j] = f[k];
}
/* Verbose warning */
if(verbose) {
warn("Convolution Lengths");
for(i=0; i<nscales; ++i) warn("%d ",nconv[i]);
}
if(verbose) warn("%d scales will be used for transforms",nscales);
/* Get information from first trace */
if(!gettr(&tr))
err("Cannot get first trace\n");
ns = tr.ns;
/* Allocate temporary storage space */
rt = ealloc1float(ns);
qt = ealloc1float(ns);
tmpdata = ealloc2float(nscales,ns);
/* Zero out rt and qt */
memset((void *) rt, 0, ns*FSIZE);
memset((void *) qt, 0, ns*FSIZE);
/* Alloc sucwt_buffer for longest convolution */
sucwt_buff = ealloc1float(ns+nconv[nscales-1]+1);
do { /* main loop over traces */
outtr.d2 = waveletinc;
outtr.f2 = minscale;
memcpy((void *)&outtr,(const void *)&tr,HDRBYTES);
/* Apply filters to produce wavelet transform */
for(i=0; i<nscales; ++i) { /* loop over scales */
for(j=0; j<ns+nconv[nscales-1]+1; ++j)
sucwt_buff[j] = 0;
/* convolve wavelet with data */
conv(ns,0,tr.data,nconv[i],0,
filt[i],ns,0,sucwt_buff);
for(j=0; j<ns; ++j)
rt[j] = sucwt_buff[j+nconv[i]/2-1];
for(j=ns-1; j>0; --j)
rt[j] = rt[j] - rt[j-1];
for(j=0; j<ns; ++j)
rt[j] = -sqrt(scales[i]) * rt[j];
/* form the hilbert transform of rt */
hilbert(ns,rt,qt);
/* If not holder, then output envelope */
if (!holder) {
for (j=0 ; j<ns; ++j) {
outtr.data[j] = sqrt(rt[j]*rt[j]
+ qt[j]*qt[j]);
}
outtr.cdpt = i + 1;
puttr(&outtr);
} else {
/* compute the modulus */
for (j=0 ; j<ns; ++j) {
tmpdata[j][i] = sqrt(rt[j]*rt[j] + qt[j]*qt[j]);
}
}
}
if (holder) { /* compute the Holder regularity traces */
float *x;
float *y;
float lrcoeff[4];
x = ealloc1float(nscales);
y = ealloc1float(nscales);
/* Compute an estimate of the Lipschitz (Holder)
* regularity. Following Mallat (1992)
*
* ln | Wf(x,s)| < ln C + alpha * ln|s|
*
* alpha here is the Holder or Lipschitz exponent
* s is the length scale, and Wf(x,s) is f in the
* wavelet basis.
*
* Here we merely fit a straight line
* through the log-log graph of the of our wavelet
* transformed data and return the slope as
* the regularity measure.
*
*/
for ( j =0 ; j< ns ; ++j ) {
int icount=0;
x[0]=0;
for ( i = 1 ; i < nscales ; ++i ) {
/* We stay away from values that will make */
/* NANs in the output */
if ((i>1) &&
(tmpdata[j][i-1] - tmpdata[j][1] > 0.0)) {
y[icount] = log(ABS(tmpdata[j][i]
- tmpdata[j][1]));
x[icount] = log(scales[i]-scales[1]);
++icount;
}
}
--icount;
/* straight line fit, return slope */
if ((icount> 10) && (divisor==1.0) ) {
linear_regression(y, x, icount, lrcoeff);
/* lrcoeff[0] is the slope of the line */
/* which is the Holder (Lipschitz) */
/* exponent */
outtr.data[j] = lrcoeff[0];
} else if ((icount> 10) && (divisor>1.0) ) {
float maxalpha=0.0;
float interval=icount/divisor;
for ( k = interval; k < icount; k+=interval){
linear_regression(y, x, k, lrcoeff);
maxalpha = MAX(lrcoeff[0],maxalpha);
}
outtr.data[j] = maxalpha;
} else if ((icount < 10) && (divisor>=1.0)) {
outtr.data[j] = 0.0;
} else if ( divisor < 1.0 ) {
err("divisor = %f < 1.0!", divisor);
}
}
puttr(&outtr); /* output holder regularity traces */
}
} while(gettr(&tr));
return(CWP_Exit());
}
void a1(void)
{
int N = 3,
M = 1;
data *dat = (data*) malloc(N*sizeof(data));
dat[0] = (data) { .val = 299793.0, .delta = 2.0 };
dat[1] = (data) { .val = 299792.0, .delta = 4.5 };
dat[2] = (data) { .val = 299782.0, .delta = 25.0 };
double avg = average_value(N, dat),
sig_i = sigma_square_intern(N, dat),
sig_e = sigma_square_extern(M, N, dat);
printf("c_avg = %f\n", avg);
printf("sigma_i = %f\n", sqrt(sig_i));
printf("sigma_e = %f\n", sqrt(sig_e));
}
void a2(void)
{
int N;
measurement *m = read_measurements("dat/a2.txt", &N);
double a, b, sig_a, sig_b, chi;
printf("\nLinear regression I = b*U:\n");
linear_regression(N, m, NULL, &b, NULL, &sig_b);
chi = chi_square_(N, m, 0, b);
printf("b = %f +/- %f, chi = %f\n", b, sqrt(sig_b)/2, sqrt(chi));
printf("\nLinear regression I = a + b*U:\n");
linear_regression(N, m, &a, &b, &sig_a, &sig_b);
chi = chi_square_(N, m, a, b);
printf("a = %f +/- %f, b = %f +/- %f, chi = %f\n", a, sqrt(sig_a)/2, b, sqrt(sig_b)/2, sqrt(chi));
}
void a3(void)
{
// order of polynoms + 1
int N[5] = {3, 5, 9, 13, 17};
// output function (%i = current N)
char *output_p_gp = "results/a3/poly-%i.gp",
*output_p_txt = "results/a3/poly-%i.txt",
*output_l_dat = "results/a3/legendre-%i.dat",
*output_l_txt = "results/a3/legendre-%i.txt";
// read data
matrix dat = read_data("dat/a3.txt");
int i;
for (i = 0; i < sizeof(N)/sizeof(int); i++)
{
//
// use polynoms
//
matrix F = init_F(dat, N[i], &polynom);
vector b = init_b(dat, N[i], &polynom);
// solve system of linear equations
vector x = solve_gauss(F,b);
char fp[100];
// parameter output
snprintf(fp, sizeof(fp), output_p_txt, N[i]-1);
printf("Opening file %s...\n", fp);
FILE *file = fopen(fp, "w+");
vector_fprint(file, x);
fclose(file);
// gnuplot output
snprintf(fp, sizeof(fp), output_p_gp, N[i]-1);
printf("Opening file %s...\n", fp);
file = fopen(fp, "w+");
fprintf(file, "plot ");
int k;
for (k = 0; k < x.N; k++)
{
fprintf(file, "%e * x**%i", VectorGET(x,k), k);
if (k < x.N-1)
fprintf(file, " + ");
}
fprintf(file, "\n");
fclose(file);
//
// Use legendre polynoms
//
F = init_F(dat, N[i], &legendre_polynom);
b = init_b(dat, N[i], &legendre_polynom);
// solve system of linear equations
x = solve_gauss(F,b);
// parameter output
snprintf(fp, sizeof(fp), output_l_txt, N[i]-1);
printf("Opening file %s...\n", fp);
file = fopen(fp, "w+");
vector_fprint(file, x);
// "plot" data for legendre polynoms
snprintf(fp, sizeof(fp), output_l_dat, N[i]-1);
printf("Opening file %s...\n", fp);
file = fopen(fp, "w+");
double r = -1.0,
dr = 0.01;
while (r <= 1.0)
{
double sum = 0;
for (k = 0; k < x.N; k++)
{
sum += VectorGET(x,k) * legendre_polynom(k,r);
}
fprintf(file, "%f %e\n", r, sum);
r += dr;
}
fclose(file);
file = NULL;
}
}
int main()
{
//a1();
//a2();
a3();
return 0;
}
int main(int argc, char **argv)
{
extern char *optarg;
int optchar;
char *treefile_name = NULL, *genomefile_name = NULL, *outfile_name = NULL, *corrfile_name = NULL, *mutfile_name = NULL;
FILE *treefile, *genomefile, *outfile, *corrfile, *mutfile;
long genome_generation, tree_generation, i, j, num_trees;
double tree_d, rec_d, rec_d1, change_rate, mutation_rate;
double *tree_dist = NULL, *reconst_dist = NULL;
double lambda, y0, r;
long num_data;
PHYLTREE phyltree;
POPULATION population;
int ret_code;
phyl_init_tree(&phyltree);
while ((optchar = getopt(argc, argv, "hg:t:o:c:m:")) != -1)
{
switch (optchar)
{
case 'g':
genomefile_name = optarg;
break;
case 't':
treefile_name = optarg;
break;
case 'c':
corrfile_name = optarg;
break;
case 'm':
mutfile_name = optarg;
break;
case 'o':
outfile_name = optarg;
break;
case 'h':
printf("treecorr -- check correlation between corrected\n");
printf(" edit or hamming diatance and true tree distance\n");
printf("\n");
printf("Command line usage:\n");
printf("-g <filename>: Specify genome file (mandatory)\n");
printf("-t <filename>: Specify tree file (mandatory)\n");
printf("-o <filename>: Specify base name for individual generation's output file\n");
printf("-c <filename>: Specify file for treedist vs reconstructed dist correlations\n");
printf("-m <filename>: Specify file for reconstructed mutation rates\n");
printf("-h: Print this help and exit\n");
exit (EXIT_SUCCESS);
}
}
if (treefile_name == NULL)
{
fprintf(stderr, "no tree file specified -- exit\n");
exit (EXIT_FAILURE);
}
if ((treefile = fopen(treefile_name, "r")) == NULL)
{
fprintf(stderr, "failed to open tree file \"%s\" -- exit\n", treefile_name);
exit (EXIT_FAILURE);
}
if (genomefile_name == NULL)
{
fprintf(stderr, "No genome file specified -- exit\n");
fclose(treefile);
exit (EXIT_FAILURE);
}
if ((genomefile = fopen(genomefile_name, "r")) == NULL)
{
fprintf(stderr, "Failed to open genome file \"%s\" -- exit\n", genomefile_name);
fclose(treefile);
exit (EXIT_FAILURE);
}
if (corrfile_name)
{
if ((corrfile = fopen(corrfile_name, "w")) == NULL)
{
fprintf(stderr, "Failed to open \"%s\" for correlation output -- exit\n", corrfile_name);
fclose(treefile);
fclose(genomefile);
exit (EXIT_FAILURE);
}
}
else
{
corrfile = stdout;
corrfile_name = "stdout";
}
if (mutfile_name)
{
if ((mutfile = fopen(mutfile_name, "w")) == NULL)
{
fprintf(stderr, "Failed to open \"%s\" for mutation output -- exit\n", mutfile_name);
fclose(treefile);
fclose(genomefile);
if (corrfile != stdout)
fclose(corrfile);
exit (EXIT_FAILURE);
}
}
else
mutfile = NULL;
while (!feof(genomefile) && !ferror(genomefile) && !feof(treefile) && !ferror(treefile))
{
fgets(buf, 256, treefile);
if ((buf[0] != 'g') || (buf[1] != ' '))
{
fprintf(stderr, "Treefile corrupt after generation %ld\n", tree_generation);
break;
}
tree_generation = strtol(buf + 2, NULL, 10);
fgets(buf, 256, genomefile);
if ((buf[0] != 'g') || (buf[1] != ' '))
{
fprintf(stderr, "Genome file corrupt after generation %ld\n", genome_generation);
break;
}
genome_generation = strtol(buf + 2, NULL, 10);
fgets(buf, 256, treefile);
num_trees = strtol(buf, (char **) NULL, 10);
if (num_trees != 1)
{
fprintf(stderr, "%ld trees in generation %ld -- skipping\n", num_trees, tree_generation);
for (i = 0; i < num_trees; i++)
{
phyl_read_tree(treefile, &phyltree);
phyl_free_tree(&phyltree);
}
}
else
phyl_read_tree(treefile, &phyltree);
read_genomes(genomefile, &population);
if ((num_trees == 1) && (tree_generation == genome_generation))
{
if ((tree_dist = (double *) malloc((population.size * population.size - 1) / 2 * sizeof(double))) == NULL)
fprintf(stderr, "Failed to allocate array of tree distances\n");
else
{
if ((reconst_dist = (double *) malloc(population.size * (population.size - 1) / 2 * sizeof(double))) == NULL)
fprintf(stderr, "Failed to allocate array of reconstructed distances\n");
else
{
if (outfile_name)
{
sprintf(buf, "%s-%05ld.gpd", outfile_name, tree_generation);
outfile = fopen(buf, "w");
fprintf(outfile, "# generation %ld\n", tree_generation);
}
num_data = 0;
for (i = 0; i < population.size; i++)
{
for (j = 0; j < i; j++)
{
tree_d = phyl_leafdistance(&phyltree, population.seq[i].name, population.seq[j].name);
if (tree_d < 0)
{
fprintf(stderr, "Generation %ld: Trees and genomes inconsistent: error #%f\n", tree_generation, tree_d);
break;
}
rec_d = diffchar_distance(population.seq[i].genome.length,
population.seq[i].genome.g,
population.seq[j].genome.length, population.seq[j].genome.g);
change_rate = rec_d / (population.seq[i].genome.length);
rec_d1 = 1.0 - 256.0 / 255.0 * change_rate;
if (rec_d1 > 0.0)
{
rec_d = log(rec_d1);
tree_dist[num_data] = tree_d;
reconst_dist[num_data] = rec_d;
num_data++;
if (outfile)
fprintf(outfile, "%f %f\n", tree_d, rec_d);
}
else if (outfile)
fprintf(outfile, "# Infinite distance (log operand: %f)\n", rec_d1);
}
if (j < i)
break;
}
if (outfile_name && outfile)
fclose(outfile);
if ((ret_code = linear_regression(num_data, tree_dist, reconst_dist, &lambda, &y0, &r)) < 0)
{
fprintf(stderr, "Error #%d in linear regression\n", ret_code);
}
else
{
mutation_rate = 1.0 - exp(lambda);
if (corrfile)
fprintf(corrfile, "%ld %f\n", tree_generation, r);
if (mutfile)
fprintf(mutfile, "%ld %f\n", tree_generation, mutation_rate);
}
free(reconst_dist);
}
free(tree_dist);
}
}
phyl_free_tree(&phyltree);
free_population(&population);
#ifdef MEMDEBUG
print_MemdebugStatistics();
#endif
}
if (mutfile)
fclose(mutfile);
if (corrfile != stdout)
fclose(corrfile);
fclose(treefile);
fclose(genomefile);
return (EXIT_SUCCESS);
}
void ICharacter::calculateStats ()
{
boost::unique_lock<boost::mutex> statsLock(m_stats_mutex);
boost::mutex::scoped_lock property_lock(m_property_mutex);
/// SET BASE STATS AND RECALCULATE
m_MA = std::make_pair(0,0);
m_PA = std::make_pair(0,0);
m_PAbs = 0;
m_MAbs = 0;
m_PD = m_property->PhysicalDefense;
m_MD = m_property->MagicalDefense;
m_BR = m_property->BlockRatio;
m_CR = 0;
m_PR = m_property->ParryRatio;
m_HR = m_property->HitRatio;
m_PBalance = 0;
m_MBalance = 0;
m_PRate = 0;
m_MRate = 0;
m_AD = 0;
float wlkspeed = m_property->WalkSpeed;
float runspeed = m_property->RunSpeed;
float bskspeed = m_property->BersekSpeed;
property_lock.unlock();
bool hasWall = false;
uint32_t curWallHP = m_Effects[STAT_WALL_HP];
m_Effects.clear();
short speed_percent = 0;
short hp_percent = 0;
short mp_percent = 0;
short pr_percent = 0;
short hr_percent = 0;
short pd_percent = 0;
short md_percent = 0;
std::vector<std::pair<CHAR_STATS,int32_t> > bonus;
calculatePreStats(bonus);
boost::mutex::scoped_lock buff_lock(m_buff_mutex);
for ( std::map<uint32_t,Buff>::const_iterator k = m_buff_list.begin(); k != m_buff_list.end(); ++k)
{
for (Skill::buff_const_iterator j = k->second.skill->buff_begin(); j != k->second.skill->buff_end(); ++j)
{
switch(j->ID)
{
case BUFF_CRITICAL_INC:
m_CR += j->Arg[0];
break;
case BUFF_SPEED_INC:
case BUFF_SPEED_INC2:
case BUFF_SPEED_INC3:
speed_percent += j->Arg[0];
break;
case BUFF_PARRY_INC:
m_PR += j->Arg[0];
pr_percent += j->Arg[1];
break;
case BUFF_PARRY_DEC:
m_PR -= j->Arg[0];
break;
case BUFF_HIT_INC:
m_HR += j->Arg[0];
hr_percent += j->Arg[1];
break;
case BUFF_HIT_DEC:
m_HR -= j->Arg[0];
break;
case BUFF_HP_INC:
m_MaxHP += j->Arg[0];
hp_percent += j->Arg[1];
break;
case BUFF_MP_INC:
m_MaxMP += j->Arg[0];
mp_percent += j->Arg[1];
break;
case BUFF_RANGE_INC:
m_AD += j->Arg[0];
break;
case BUFF_DAMAGE_INC:
m_PRate += j->Arg[0];
m_MRate += j->Arg[0];
break;
case BUFF_INT_INC:
case BUFF_STR_INC:
case BUFF_SHIELD_PWR_4_DAMAGE:
calculateBuffEffects(bonus,*j);
break;
case BUFF_REINCARNATION:
m_Effects[STAT_HP_RGN] += j->Arg[0];
m_Effects[STAT_MP_RGN] += j->Arg[1];
break;
case BUFF_IGNORE_DEFENSE:
m_Effects[STAT_IGD] += j->Arg[0];
break;
case BUFF_CRIT_PARRY_INC:
m_Effects[STAT_CPR] += j->Arg[0];
break;
case BUFF_GOLD_DROP_INC:
m_Effects[STAT_GOLD_DROP] += j->Arg[0];
break;
case BUFF_MAGICOPTION_LUCK_INC:
m_Effects[STAT_MOL] += j->Arg[0];
break;
case BUFF_ENCHANT_LUCK_INC:
m_Effects[STAT_ETL] += j->Arg[0];
break;
case BUFF_MP_ABSORB:
m_Effects[STAT_MP_ABSORB] += j->Arg[0];
break;
case BUFF_DAMAGE_PWR_INC:
m_PA.first += j->Arg[0];
m_PA.second += j->Arg[0];
m_MA.first += j->Arg[1];
m_MA.second += j->Arg[1];
break;
case BUFF_DETECT:
case BUFF_DETECT_V2:
//(5 Stealth) (6 Invisible) (7 All)
//Max level
break;
case BUFF_HEAL_INC:
m_Effects[STAT_HP_HEAL] += j->Arg[0];
m_Effects[STAT_MP_HEAL] += j->Arg[1];
break;
case BUFF_ABSORB_WALL:
hasWall = true;
m_Effects[STAT_WALL] = j->Arg[0] == WALL_TYPE_PHYSICAL;
m_Effects[STAT_WALL_MAX] = j->Arg[1];
m_Effects[STAT_WALL_RATE] = j->Arg[3];
m_Effects[STAT_WALL_ID] = k->first;
break;
case BUFF_BLOCK_INC:
//BLOCK TYPE +0 (15 ALL) (11 Magic %) (7 Physical %)
//BLOCK RATE +1
m_BR += j->Arg[1];
break;
case BUFF_DAMAGE_REFLECT:
//PROBABILITY [%]
//PHY DAMAGE RETURNED [%]
//MAG DAMAGE RETURNED [%]
//RETURN RANGE
break;
case BUFF_ABSORB:
//TYPE (3 - Physical Absorb) (12 & 15 - Absorb all) (4 Physical) (8 Magical) (6 Physical ranged)
//Amount (%)
//0
break;
case BUFF_DEFENSE_INC:
m_PD += j->Arg[0];
m_MD += j->Arg[1];
//Caster Range
break;
case BUFF_DEFENSE_DEC:
pd_percent -= j->Arg[0];
md_percent -= j->Arg[1];
break;
case BUFF_DAMAGE_DEC:
m_PRate -= j->Arg[0];
m_MRate -= j->Arg[1];
break;
case BUFF_HP_DEC:
//Duration
//0
hp_percent -= j->Arg[2];
//2
break;
default:
break;
}
}
}
if (hasWall)
{
uint32_t wallMax = m_Effects[STAT_WALL_MAX];
if (curWallHP > wallMax)
m_Effects[STAT_WALL_HP] = wallMax;
}
buff_lock.unlock();
for (std::vector<std::pair<CHAR_STATS,int32_t> >::const_iterator it = bonus.begin(); it != bonus.end(); ++it)
{
switch(it->first)
{
case STAT_HP_PERCENT:
hp_percent += it->second;
break;
case STAT_HR_PERCENT:
hr_percent += it->second;
break;
case STAT_MP_PERCENT:
mp_percent += it->second;
break;
case STAT_PR_PERCENT:
pr_percent += it->second;
break;
default:
break;
}
}
calculatePostStats();
if (hp_percent)
m_MaxHP = linear_regression(m_MaxHP,hp_percent);
if (m_HP > m_MaxHP)
m_HP = m_MaxHP;
if (mp_percent)
m_MaxMP = linear_regression(m_MaxMP,mp_percent);
if (m_MP > m_MaxMP)
m_MP = m_MaxMP;
if (pr_percent)
m_PR = linear_regression(m_PR,pr_percent);
if (hr_percent)
m_HR = linear_regression(m_HR,hr_percent);
if (pd_percent)
m_PD = linear_regression(m_PD,pd_percent);
if (md_percent)
m_MD = linear_regression(m_MD,md_percent);
if (speed_percent)
{
wlkspeed = linear_regression(wlkspeed,speed_percent);
runspeed = linear_regression(runspeed,speed_percent);
bskspeed = linear_regression(bskspeed,speed_percent);
}
if (IsBerserk())
{
wlkspeed *=2;
runspeed *=2;
bskspeed *=2;
m_PA.first *= 2;
m_PA.second *= 2;
m_MA.first *= 2;
m_MA.second *= 2;
}
m_WalkSpeed = wlkspeed;
m_RunSpeed = runspeed;
m_BerserkSpeed = bskspeed;
calculateStatus();
statsLock.unlock();
if (!signal_stats.empty())
signal_stats();
if (!signal_speed.empty())
signal_speed(m_WalkSpeed,m_RunSpeed);
}
/* main routine */
int main(int argc, char *argv[]){
FILELog::ReportingLevel() = logINFO;
VARIABLES vars;
vars = commandline_input(argc, argv);
clock_t init, final;
int number_of_particles = vars.num;
double timestep = vars.dt;
bool use_T2 = vars.use_T2; // = false (no T2 decay), = true (T2 decay)
int num_of_repeat = vars.num_of_repeat; //Number of times to repeat simulation. For every repeat all data is flushed and we start the simulation again.
int number_of_timesteps; number_of_timesteps = (int)ceil(vars.gs/timestep);
double permeability = 0.0;
double radius = .00535;
double d = sqrt( 2.0*PI*radius*radius/( sqrt(3.0)*.79 ) );
//double lattice_size = 0.00601922026995422789215053328651;
double grad_duration = 4.5;
double D_extra = 2.5E-6;
double D_intra = 1.0E-6;
double T2_e = 200;
double T2_i = 200;
FILE_LOG(logINFO) << "f = " << 2.0*PI*radius*radius/(sqrt(3.0)*d*d) << std::endl;
Vector3 xhat(1.0,0.0,0.0);
Vector3 yhat(0.0,1.0,0.0);
Vector3 zhat(0.0,0.0,1.0);
Lattice<Cylinder_XY> lattice(D_extra, T2_e, permeability);
lattice.setLatticeVectors(d,2.0*d*0.86602540378443864676372317075294,d,xhat,yhat,zhat);
lattice.addBasis(Cylinder_XY(d/2.0, 0.0, radius, T2_i, D_intra, 1));
lattice.addBasis(Cylinder_XY(0.0, d*0.86602540378443864676372317075294, radius, T2_i, D_intra, 2));
lattice.addBasis(Cylinder_XY(d/2.0, 2.0*d*0.86602540378443864676372317075294, radius, T2_i, D_intra, 3));
lattice.addBasis(Cylinder_XY(d, d*0.86602540378443864676372317075294, radius, T2_i, D_intra, 4));
double gspacings [] = { 8.0 , 10.5 , 14.0 , 18.5 , 24.5 , 32.5 , 42.5 , 56.5 };
double bvals [] = {108780, 154720, 219040, 301730, 411980, 558990, 742420, 1000000 };
double G [9];
for (int kk = 0; kk < num_of_repeat; kk++) {
vector<PGSE> measurements_x;
vector<PGSE> measurements_y;
vector<PGSE> measurements_z;
for (int i = 0; i < 8; i++){
double echo_time = 2.0*grad_duration + gspacings[i];
G[0] = 0.0;
G[i+1] = sqrt(bvals[i]/(GAMMA*GAMMA*grad_duration*grad_duration*(grad_duration + gspacings[i] - (grad_duration/3.0))));
measurements_x.push_back(PGSE(grad_duration,gspacings[i], timestep, G[0], echo_time, number_of_particles, xhat));
measurements_y.push_back(PGSE(grad_duration,gspacings[i], timestep, G[0], echo_time, number_of_particles, yhat));
measurements_z.push_back(PGSE(grad_duration,gspacings[i], timestep, G[0], echo_time, number_of_particles, zhat));
measurements_x.push_back(PGSE(grad_duration,gspacings[i], timestep, G[i+1], echo_time, number_of_particles, xhat));
measurements_y.push_back(PGSE(grad_duration,gspacings[i], timestep, G[i+1], echo_time, number_of_particles, yhat));
measurements_z.push_back(PGSE(grad_duration,gspacings[i], timestep, G[i+1], echo_time, number_of_particles, zhat));
}
vector<double> lnsignal(2);
vector<double> b(2);
cout << " trial = " << kk << endl;
Particles ensemble(number_of_particles,timestep, use_T2);
lattice.initializeUniformly(ensemble.getGenerator() , ensemble.getEnsemble() );
for (int k = 0; k < measurements_x.size();k++){
measurements_x[k].updatePhase(ensemble.getEnsemble(), 0.0);
measurements_y[k].updatePhase(ensemble.getEnsemble(), 0.0);
measurements_z[k].updatePhase(ensemble.getEnsemble(), 0.0);
}
for (int i = 1; i <= number_of_timesteps; i++){
ensemble.updateposition(lattice);
for (int k = 0; k < measurements_x.size();k++){
measurements_x[k].updatePhase(ensemble.getEnsemble(), i*timestep);
measurements_y[k].updatePhase(ensemble.getEnsemble(), i*timestep);
measurements_z[k].updatePhase(ensemble.getEnsemble(), i*timestep);
}
}
for (int i = 0; i < 8*2; i+=2){
double ADCx, ADCy, ADCz, diff_time;
lnsignal[0] = log(measurements_x[i].get_signal());
b[0] = measurements_x[i].get_b();
lnsignal[1] = log(measurements_x[i+1].get_signal());
b[1] = measurements_x[i+1].get_b();
ADCx = -1.0*linear_regression(lnsignal,b);
//std::cout << b[0] << " " << lnsignal[0] << " " << b[1] << " " << lnsignal[1] << " ";
lnsignal[0] = log(measurements_y[i].get_signal());
b[0] = measurements_y[i].get_b();
lnsignal[1] = log(measurements_y[i+1].get_signal());
b[1] = measurements_y[i+1].get_b();
ADCy = -1.0*linear_regression(lnsignal,b);
//std::cout << b[0] << " " << lnsignal[0] << " " << b[1] << " " << lnsignal[1] << " ";
lnsignal[0] = log(measurements_z[i].get_signal());
b[0] = measurements_z[i].get_b();
lnsignal[1] = log(measurements_z[i+1].get_signal());
b[1] = measurements_z[i+1].get_b();
ADCz = -1.0*linear_regression(lnsignal,b);
//std::cout << b[0] << " " << lnsignal[0] << " " << b[1] << " " << lnsignal[1] << std::endl;
diff_time = measurements_x[i].get_DT();
std::cout << i << " " << diff_time << " " << ADCx << " " << ADCy << " " << ADCz << " " << b[1] << " " << G[1] << std::endl;
}
}
final= clock()-init; //final time - intial time
