#include <math.h>
#include <R.h>
#include <Rdefines.h>
#include <Rmath.h>
#include <Rinternals.h>
#include <assert.h>
#include <string.h>

int genotypeConfidence(const double *prob){
int K=1000;
if (*prob == 1.0) return(INT_MAX);
else return( (int) round(-K*log2(1- *prob)));
}

int genotypeConfidence2(double probability){
int K = 1000;
if (probability == 1.0) return(INT_MAX);
else return( (int) round(-K * log2(1 - probability)));
}

int intInSet(const int *x, const int *set, const int *n){
int i;
for (i=0; i < *n; i++)
if (*x == set[i]) return(1);
return(0);
}

int genotypeCall(const double *pAA, const double *pAB, const double *pBB){
if( *pAA >= *pAB && *pAA >= *pBB ) return(1);
else if (*pAB > *pAA && *pAB >= *pBB) return(2);
else return(3);
}

double sdCorrection(const int *n){
return(sqrt(1.0+1.0/fmax(1.0, (double) *n)));
}

int sort_double(const double *a1, const double *a2){
if (*a1 < *a2)
return (-1);
if (*a1 > *a2)
return (1);
return 0;
}

void trimmed_mean(double *datavec, int *classvec, int class, double trim, int cols, int rows, double *m1, double *m2, double *m3, int i_ext){
double sum=0, sum2=0;
int i, j=0, n_ignore, n=0;

for (i = 0; i < cols; i++)
if (classvec[i] == class)
n++;

double *buffer=Calloc(n, double);
for (i = 0; i < cols; i++)
if (classvec[i] == class){
buffer[j]=datavec[i];
j++;
}
qsort(buffer, n, sizeof(double), (int(*)(const void*, const void*))sort_double);
n_ignore= (int) floor((double) n * trim);
j=0;
for (i = n_ignore; i < (n-n_ignore); i++){
sum+=buffer[i];
sum2+=pow(buffer[i], 2);
j++;
}
sum/=j;
sum2-=(pow(sum, 2)*(double) j);
sum2/=(j-1);
sum2=sqrt(sum2);
m1[i_ext + (class-1) * rows]=sum;
m2[i_ext + (class-1) * rows]=sum2;
m3[i_ext + (class-1) * rows]=j;
Free(buffer);
}

void trimmed_stats(double *data, double *m1, double *m2, double *m3, int *class, int rows, int cols, double *trim){
int i, j, n1, n2, n3;
double *datvec=Calloc(cols,double);
int *classvec=Calloc(cols,int);

for (i=0; i < rows; i++){
n1=0;
n2=0;
n3=0;

for (j=0; j < cols; j++){
if (class[j*rows + i] == 1){
datvec[j]=data[j*rows + i];
++n1;
classvec[j] = 1;
} else if (class[j*rows + i] == 2){
datvec[j]=data[j*rows + i];
++n2;
classvec[j] = 2;
} else if (class[j*rows + i] == 3){
datvec[j]=data[j*rows + i];
++n3;
classvec[j] = 3;
} else {
// Should be the NA's
classvec[j] = class[j*rows + i];
}
}
trimmed_mean(datvec, classvec, 1, trim[0], cols, rows, m1, m2, m3, i);
trimmed_mean(datvec, classvec, 2, trim[0], cols, rows, m1, m2, m3, i);
trimmed_mean(datvec, classvec, 3, trim[0], cols, rows, m1, m2, m3, i);
}
Free(datvec);
Free(classvec);
}

double  median(double *x, int length){
int half;
double med;
double *buffer = Calloc(length,double);
memcpy(buffer,x,length*sizeof(double));
half = (length + 1)/2;
rPsort(buffer, length, half-1);
med = buffer[half-1];
if (length % 2 == 0){
rPsort(buffer, length, half);
med = (med + buffer[half])/2.0;
}
Free(buffer);
return med;
}

void mad_median(double *datavec, int *classvec, int class, double trim, int cols, int rows, double *m1, double *m2, double *m3, int i_ext){
/* trim is ignored for the moment - for compatibility */
int i, j=0;
int n=0;

for (i = 0; i < cols; i++)
if (classvec[i] == class)
n++;

double *buffer=Calloc(n, double);

for (i = 0; i < cols; i++)
if (classvec[i] == class){
buffer[j]=datavec[i];
j++;
}
m1[i_ext + (class-1) * rows] = median(buffer, n);
for (i = 0; i < n; i++)
buffer[i] = fabs(buffer[i]-m1[i_ext + (class-1) * rows]);
m2[i_ext + (class-1) * rows] = median(buffer, n);
m3[i_ext + (class-1) * rows] = n;
Free(buffer);
}

SEXP normalizeBAF(SEXP theta, SEXP cTheta){
/*
ARGUMENTS:
theta.: N x C matrix with estimated \theta
cTheta: N x 3 matrix with canonical \thetas (AA, AB, BB)

VALUE:
baf: N x C matrix with normalized \theta
*/

SEXP baf;
double *p2baf, *p2theta, *p2ctheta;
int i, j, idx, rowsT, rowsCT, colsT, colsCT;
rowsT = INTEGER(getAttrib(theta, R_DimSymbol))[0];
rowsCT = INTEGER(getAttrib(cTheta, R_DimSymbol))[0];
if (rowsT != rowsCT)
error("Number of rows of 'theta' must match number of rows of 'cTheta'\n");
colsCT = INTEGER(getAttrib(cTheta, R_DimSymbol))[1];
if (colsCT != 3)
error("'cTheta' must have 3 columns: AA, AB and BB\n");
colsT = INTEGER(getAttrib(theta, R_DimSymbol))[1];

PROTECT(baf = allocMatrix(REALSXP, rowsT, colsT));
p2baf = NUMERIC_POINTER(baf);
p2theta = NUMERIC_POINTER(theta);
p2ctheta = NUMERIC_POINTER(cTheta);
for (i=0; i < rowsT; i++){
for (j=0; j < colsT; j++){
idx = i + j*rowsT;
if (ISNA(p2theta[idx]) || ISNA(p2ctheta[i]) || ISNA(p2ctheta[i+rowsT]) || ISNA(p2ctheta[i+2*rowsT])){
p2baf[idx] = NA_REAL;
}else if (p2theta[idx] < p2ctheta[i]){
p2baf[idx] = 0;
}else if ((p2theta[idx] >= p2ctheta[i]) & (p2theta[idx] < p2ctheta[i + rowsT])){
p2baf[idx] = .5*(p2theta[idx]-p2ctheta[i])/(p2ctheta[i+rowsT]-p2ctheta[i]);
}else if((p2theta[idx] >= p2ctheta[i+rowsT]) & (p2theta[idx] < p2ctheta[i + 2*rowsT])){
p2baf[idx] = .5+.5*(p2theta[idx]-p2ctheta[i+rowsT])/(p2ctheta[i+2*rowsT]-p2ctheta[i+rowsT]);
}else{
p2baf[idx] = 1;
}
}
}

UNPROTECT(1);
return(baf);
}

/* Pieces below are for testing */

static void mad_stats(double *data, double *m1, double *m2, double *m3, int *class, int rows, int cols, double *trim){
int i, j, n1, n2, n3;
double *datvec=Calloc(cols,double);
int *classvec=Calloc(cols,int);

for (i=0; i < rows; i++){
n1=0;
n2=0;
n3=0;

for (j=0; j < cols; j++){
if (class[j*rows + i] == 1){
datvec[j]=data[j*rows + i];
++n1;
classvec[j] = 1;
} else if (class[j*rows + i] == 2){
datvec[j]=data[j*rows + i];
++n2;
classvec[j] = 2;
} else if (class[j*rows + i] == 3){
datvec[j]=data[j*rows + i];
++n3;
classvec[j] = 3;
} else {
// Should be the NA's
classvec[j] = class[j*rows + i];
}
}
mad_median(datvec, classvec, 1, trim[0], cols, rows, m1, m2, m3, i);
mad_median(datvec, classvec, 2, trim[0], cols, rows, m1, m2, m3, i);
mad_median(datvec, classvec, 3, trim[0], cols, rows, m1, m2, m3, i);
}
Free(datvec);
Free(classvec);
}

SEXP test_mad_median(SEXP X, SEXP Y, SEXP trim){
SEXP dim1;
SEXP estimates1, estimates2, estimates3, output;
double *Xptr, *Mptr1, *Mptr2, *Mptr3, *Tptr;
int *Yptr;
int rows, cols;

PROTECT(dim1 = getAttrib(X,R_DimSymbol));
rows = INTEGER(dim1)[0];
cols = INTEGER(dim1)[1];

Xptr = NUMERIC_POINTER(AS_NUMERIC(X));
Yptr = INTEGER_POINTER(AS_INTEGER(Y));
Tptr = NUMERIC_POINTER(AS_NUMERIC(trim));

PROTECT(estimates1 = allocMatrix(REALSXP, rows, 3));
PROTECT(estimates2 = allocMatrix(REALSXP, rows, 3));
PROTECT(estimates3 = allocMatrix(REALSXP, rows, 3));

Mptr1 = NUMERIC_POINTER(estimates1);
Mptr2 = NUMERIC_POINTER(estimates2);
Mptr3 = NUMERIC_POINTER(estimates3);

mad_stats(Xptr, Mptr1, Mptr2, Mptr3, Yptr, rows, cols, Tptr);

PROTECT(output = allocVector(VECSXP,3));
SET_VECTOR_ELT(output, 0, estimates1);
SET_VECTOR_ELT(output, 1, estimates2);
SET_VECTOR_ELT(output, 2, estimates3);

UNPROTECT(5);

return output;
}

/* Converts row-col notation into base-zero vector notation, based on a column-wise conversion*/
long Cmatrix(int row, int col, int totrow){
return( (row)-1 + ((col)-1)*(totrow)); // num_SNP is number of rows
}

SEXP countFileLines(SEXP filename) {
FILE *fInput = fopen(CHAR(STRING_ELT(filename, 0)), "r");
char * line = NULL;
char *readline;
size_t len = 1000;
line = (char *)malloc(sizeof(char) * (len + 1));
if(fInput == NULL){
fclose(fInput);
return 0;
}
long line_count = 0;
while ((readline = fgets(line, len, fInput)) != NULL){
line_count++;
}
fclose(fInput);
free(line);

SEXP Rcount;
PROTECT(Rcount = allocVector(INTSXP, 1));
int *ptr;
ptr = INTEGER_POINTER(Rcount);
ptr[0] = line_count;
UNPROTECT(1);
return Rcount;
}

void
DoReadGenCallOutput(SEXP filename, int numSNP, int numsample, int SNPIDIndex, int sampleIDIndex, int XValueIndex, int YValueIndex, int *XValuesPt, int *YValuesPt, int delimiter, SEXP SNPNames, SEXP sampleNames){
FILE *fGenCall;
fGenCall = fopen(CHAR(STRING_ELT(filename, 0)), "r");

// ignore the first 10 lines, which contains heading information
char* token;
char * line = NULL;
size_t len = 1000;
char *readline;
line = (char *)malloc(len+1);

int i, j;
for (i = 0; i < 10; i++){
readline = fgets(line, len, fGenCall);
}

long index;
int pos;
int aXValue, aYValue;
aXValue = 0;
aYValue = 0;

for (j = 1; j <= numsample; j++){
for (i = 1; i <= numSNP; i++){
readline = fgets(line, len, fGenCall);
if (readline == NULL){
Rprintf("Error reading from file");
}
if (delimiter == 1){
token = strtok(line, ",");
} else {
token = strtok(line, "\t");
}
index = Cmatrix(i, j, numSNP);
pos = 0;
while (token != NULL) {
if (pos == SNPIDIndex){
if (j == 1){
SET_STRING_ELT(SNPNames, (i - 1), mkChar(token));
}
}
if (pos == sampleIDIndex){
if (i == 1){
SET_STRING_ELT(sampleNames, (j - 1), mkChar(token));
}
}
if (pos == XValueIndex){
aXValue = atoi(token);
}
if (pos == YValueIndex){
aYValue = atoi(token);
}
if (delimiter == 1){
token = strtok(NULL, ",");
} else {
token = strtok(NULL, "\t");
}
pos++;
}
XValuesPt[index] = aXValue;
YValuesPt[index] = aYValue;
}
}

free(line);
fclose(fGenCall);
}

SEXP readGenCallOutputCFunc(SEXP genCallOutputfile, SEXP num_SNP, SEXP num_Sample, SEXP SNPID_Index, SEXP sampleID_Index, SEXP XValue_Index, SEXP YValue_Index, SEXP delimiter_Index){
int numSNP;
int numsample;
numSNP = INTEGER_VALUE(num_SNP);
numsample = INTEGER_VALUE(num_Sample);

int SNPIDIndex, sampleIDIndex, XIndex, YIndex, delimiterIndex;
SNPIDIndex = INTEGER_VALUE(SNPID_Index);
sampleIDIndex = INTEGER_VALUE(sampleID_Index);
XIndex = INTEGER_VALUE(XValue_Index);
YIndex = INTEGER_VALUE(YValue_Index);
delimiterIndex = INTEGER_VALUE(delimiter_Index);

SEXP Xvalues, Yvalues, ret, ret_names, rownames, colnames, dimnames;

char *names[2] = {"Xvalues", "Yvalues"};

/* protect R objects in C. */
PROTECT(Xvalues = allocMatrix(INTSXP, numSNP, numsample));
PROTECT(Yvalues = allocMatrix(INTSXP, numSNP, numsample));

PROTECT(ret = allocVector(VECSXP, 2));
PROTECT(ret_names = allocVector(STRSXP, 2));

/* set a list object for R. */
SET_VECTOR_ELT(ret, 0, Xvalues);
SET_VECTOR_ELT(ret, 1, Yvalues);

PROTECT(rownames = allocVector(STRSXP, numSNP));
PROTECT(colnames = allocVector(STRSXP, numsample));

PROTECT(dimnames = allocVector(VECSXP, 2));

SET_VECTOR_ELT(dimnames, 0, rownames);
SET_VECTOR_ELT(dimnames, 1, colnames);

setAttrib(Xvalues, R_DimNamesSymbol, dimnames);
setAttrib(Yvalues, R_DimNamesSymbol, dimnames);

/* set list's names for R. */
SET_STRING_ELT(ret_names, 0, mkChar(names[0]));
SET_STRING_ELT(ret_names, 1, mkChar(names[1]));
setAttrib(ret, R_NamesSymbol, ret_names);

int *Xptr, *Yptr;
/* assign points to R objects. */
Xptr = INTEGER_POINTER(Xvalues);
Yptr = INTEGER_POINTER(Yvalues);

// C is 0-based and XValueIndex and YVlaueIndex are 1-based
DoReadGenCallOutput(genCallOutputfile, numSNP, numsample, (SNPIDIndex-1), (sampleIDIndex-1), (XIndex-1), (YIndex-1), Xptr, Yptr, delimiterIndex, rownames, colnames);

UNPROTECT(7);
return(ret);
}

SEXP krlmmHardyweinberg(SEXP clustering){
int counts[3];
int N;
int num;
num = length(clustering);

int *clusterPtr;
clusterPtr = INTEGER_POINTER(AS_INTEGER(clustering));

int aSample, temp;
counts[0] = 0;
counts[1] = 0;
counts[2] = 0;
for (aSample = 0; aSample < num; aSample++) {
counts[(clusterPtr[aSample] - 1)]++;
}
N = counts[0] + counts[1] + counts[2];
if (N != num) {
error("the count from all three doesn't equal to num_sample");
}

SEXP Rans;
PROTECT(Rans = NEW_NUMERIC(1));

double *ansPtr;
ansPtr = NUMERIC_POINTER(Rans);

double p;
double exp[3];
double ans;

if (counts[0] < counts[2]) {
temp = counts[0];
counts[0] = counts[2];
counts[2] = temp;
}
p = 1.0 * (2 * counts[0] + counts[1]) / ( 2 * N);
if (p == 1)
ans = R_NaReal;
else {
exp[0] = N * pow(p, 2);
exp[1] = N * 2 * p * (1-p);
exp[2] = N * pow((1 - p), 2);
ans = 0;
ans += pow((counts[0] - exp[0]), 2) / exp[0];
ans += pow((counts[1] - exp[1]), 2) / exp[1];
ans += pow((counts[2] - exp[2]), 2) / exp[2];
}
ansPtr[0] = ans;

UNPROTECT(1);
return(Rans);
}