
#include "abf_data.h"
// This file is part of the Collective Variables module (Colvars).
// The original version of Colvars and its updates are located at:
// https://github.com/colvars/colvars
// Please update all Colvars source files before making any changes.
// If you wish to distribute your changes, please submit them to the
// Colvars repository at GitHub.

#include <fstream>
#include <string>
#include <cstring>
#include <cstdlib>
#include <ctime>

/// Construct gradient field object from an ABF-saved file
ABFdata::ABFdata(const char *gradFileName)
{

    std::ifstream gradFile;
    std::ifstream countFile;
    char hash;
    double xi;
    char *countFileName;

    countFileName = new char[strlen(gradFileName) + 2];
    strcpy(countFileName, gradFileName);
    countFileName[strlen(gradFileName) - 4] = '\0';
    int len = strlen(countFileName);
    if (len >= 5 && !strcmp(countFileName + len - 5, "czar.")) { // this is a CZAR gradient
      countFileName[len - 5] = '\0';
      strcat(countFileName, "zcount");
    } else {
      strcat(countFileName, "count");
    }

    std::cout << "Opening file " << gradFileName << " for reading\n";
    gradFile.open(gradFileName);
    if (!gradFile.good()) {
        std::cerr << "Cannot read from file " << gradFileName << ", aborting\n";
        exit(1);
    }

    gradFile >> hash;
    if (hash != '#') {
        std::cerr << "Missing \'#\' sign in gradient file\n";
        exit(1);
    }
    gradFile >> Nvars;

    std::cout << "Number of variables: " << Nvars << "\n";

    sizes = new int[Nvars];
    blocksizes = new int[Nvars];
    PBC = new int[Nvars];
    widths = new double[Nvars];
    mins = new double[Nvars];

    scalar_dim = 1;             // total is (n1 * n2 * ... * n_Nvars )

    for (int i = 0; i < Nvars; i++) {
        gradFile >> hash;
        if (hash != '#') {
            std::cerr << "Missing \'#\' sign in gradient file\n";
            exit(1);
        }
        // format is: xiMin dxi Nbins PBCflag
        gradFile >> mins[i] >> widths[i] >> sizes[i] >> PBC[i];
        std::cout << "min = " << mins[i] << " width = " << widths[i]
            << " n = " << sizes[i] << " PBC: " << (PBC[i]?"yes":"no") << "\n";

        if (sizes[i] == 0) {
            std::cout << "ERROR: size should not be zero!\n";
            exit(1);
        }
        scalar_dim *= sizes[i];
    }

    // block sizes, smallest for the last dimension
    blocksizes[Nvars - 1] = 1;
    for (int i = Nvars - 2; i >= 0; i--) {
        blocksizes[i] = blocksizes[i + 1] * sizes[i + 1];
    }

    vec_dim = scalar_dim * Nvars;
    //std::cout << "Gradient field has length " << vec_dim << "\n";

    gradients = new double[vec_dim];
    estimate = new double[vec_dim];
    deviation = new double[vec_dim];
    count = new unsigned int[scalar_dim];

    int *pos = new int[Nvars];
    for (int i = 0; i < Nvars; i++)
        pos[i] = 0;

    for (unsigned int i = 0; i < scalar_dim; i++) {
        // Here we do the Euclidian division iteratively
        for (int k = Nvars - 1; k > 0; k--) {
            if (pos[k] == sizes[k]) {
                pos[k] = 0;
                pos[k - 1]++;
            }
        }
        for (int j = 0; j < Nvars; j++) {
            // Read values of the collective variables only to check for consistency with grid
            gradFile >> xi;

            double rel_diff = (mins[j] + widths[j] * (pos[j] + 0.5) - xi) / widths[j];
            if ( rel_diff * rel_diff > 1e-12 )  {
                std::cout << "\nERROR: wrong coordinates in gradient file\n";
                std::cout << "Expected " << mins[j] + widths[j] * (pos[j] + 0.5) << ", got " <<  xi << std::endl;
                exit(1);
            }
        }
        for (int j = 0; j < Nvars; j++) {
            // Read and store gradients
            if ( ! (gradFile >> gradients[i * Nvars + j]) ) {
                std::cout << "\nERROR: could not read gradient data\n";
                exit(1);
            }
        }
        pos[Nvars - 1]++;       // move on to next position
    }
    // check for end of file
    if ( gradFile >> xi ) {
        std::cout << "\nERROR: extraneous data at end of gradient file\n";
        exit(1);
    }
    gradFile.close();


    std::cout << "Opening file " << countFileName << " for reading\n";
    countFile.open(countFileName);

    if (!countFile.good()) {
        std::cerr << "Cannot read from file " << countFileName << ", aborting\n";
        exit(1);
    }

    countFile >> hash;
    if (hash != '#') {
        std::cerr << "Missing \'#\' sign in count file\n";
        exit(1);
    }
    countFile >> Nvars;

    for (int i = 0; i < Nvars; i++) {
        countFile >> hash;
        if (hash != '#') {
            std::cerr << "Missing \'#\' sign in gradient file\n";
            exit(1);
        }
        countFile >> mins[i] >> widths[i] >> sizes[i] >> PBC[i];
    }

    for (unsigned int i = 0; i < scalar_dim; i++) {
        for (int j = 0; j < Nvars; j++) {
            // Read and ignore values of the collective variables
            countFile >> xi;
        }
        // Read and store counts
        countFile >> count[i];
    }
    // Could check for end-of-file string here
    countFile.close();
    delete[] countFileName;
    delete[] pos;

    // for metadynamics
    bias = new double[scalar_dim];
    histogram = new unsigned int[scalar_dim];
    for (unsigned int i = 0; i < scalar_dim; i++) {
        histogram[i] = 0;
        bias[i] = 0.0;
    }
}

ABFdata::~ABFdata()
{
    delete[] sizes;
    delete[] blocksizes;
    delete[] PBC;
    delete[] widths;
    delete[] mins;
    delete[] gradients;
    delete[] estimate;
    delete[] deviation;
    delete[] count;
    delete[] bias;
    delete[] histogram;
}

unsigned int ABFdata::offset(const int *pos)
{
    unsigned int index = 0;

    for (int i = 0; i < Nvars; i++) {
        // Check for out-of bounds indices here
        if (pos[i] < 0 || pos[i] >= sizes[i]) {
            std::cerr << "Out-of-range index: " << pos[i] << " for rank " << i << "\n";
            exit(1);
        }
        index += blocksizes[i] * pos[i];
    }
    // we leave the multiplication below for the caller to do
    // we just give the offset for scalar fields
    // index *= Nvars; // Nb of gradient vectors -> nb of array elts
    return index;
}

void ABFdata::write_histogram(const char *fileName)
{

    std::ofstream os;
    unsigned int index;
    int *pos, i;

    os.open(fileName);
    if (!os.good()) {
        std::cerr << "Cannot write to file " << fileName << ", aborting\n";
        exit(1);
    }
    pos = new int[Nvars];
    for (i = 0; i < Nvars; i++)
        pos[i] = 0;

    for (index = 0; index < scalar_dim; index++) {
        // Here we do the Euclidian division iteratively
        for (i = Nvars - 1; i > 0; i--) {
            if (pos[i] == sizes[i]) {
                pos[i] = 0;
                pos[i - 1]++;
                os << "\n";
            }
        }
        // Now a stupid check:
        if (index != offset(pos)) {
            std::cerr << "Wrong position vector at index " << index << "\n";
            exit(1);
        }

        for (i = 0; i < Nvars; i++) {
            os << mins[i] + widths[i] * (pos[i] + 0.5) << " ";
        }
        os << histogram[index] << "\n";
        pos[Nvars - 1]++;       // move on to next position
    }
    os.close();
    delete[]pos;
}


void ABFdata::write_bias(const char *fileName)
{
// write the opposite of the bias, with global minimum set to 0

    std::ofstream os;
    unsigned int index;
    int *pos, i;
    double minbias, maxbias;

    os.open(fileName);
    if (!os.good()) {
        std::cerr << "Cannot write to file " << fileName << ", aborting\n";
        exit(1);
    }
    pos = new int[Nvars];
    for (i = 0; i < Nvars; i++)
        pos[i] = 0;

    // Set the minimum value to 0 by subtracting each value from the max
    maxbias = bias[0];
    for (index = 0; index < scalar_dim; index++) {
        if (bias[index] > maxbias)
            maxbias = bias[index];
    }

    // Set the maximum value to that of the lowest nonzero bias
    minbias = bias[0];
    for (index = 0; index < scalar_dim; index++) {
        if (minbias == 0.0 || (bias[index] > 0.0 && bias[index] < minbias))
            minbias = bias[index];
    }

    for (index = 0; index < scalar_dim; index++) {
        // Here we do the Euclidian division iteratively
        for (i = Nvars - 1; i > 0; i--) {
            if (pos[i] == sizes[i]) {
                pos[i] = 0;
                pos[i - 1]++;
                os << "\n";
            }
        }
        // Now a stupid check:
        if (index != offset(pos)) {
            std::cerr << "Wrong position vector at index " << index << "\n";
            exit(1);
        }

        for (i = 0; i < Nvars; i++) {
            os << mins[i] + widths[i] * (pos[i] + 0.5) << " ";
        }
        os << maxbias - (bias[index] > 0.0 ? bias[index] : minbias) << "\n";
        pos[Nvars - 1]++;       // move on to next position
    }
    os.close();
    delete[]pos;
}


void ABFdata::write_field(double *field, const char *fileName)
{
    std::ofstream os;
    unsigned int index;
    int *pos, i;
    double *f;

    os.open(fileName);
    if (!os.good()) {
        std::cerr << "Cannot write to file " << fileName << ", aborting\n";
        exit(1);
    }
    pos = new int[Nvars];
    for (i = 0; i < Nvars; i++)
        pos[i] = 0;

    // start at beginning of array
    f = field;

    for (index = 0; index < scalar_dim; index++) {
        // Here we do the Euclidian division iteratively
        for (i = Nvars - 1; i > 0; i--) {
            if (pos[i] == sizes[i]) {
                pos[i] = 0;
                pos[i - 1]++;
                os << "\n";
            }
        }

        for (i = 0; i < Nvars; i++) {
            os << mins[i] + widths[i] * (pos[i] + 0.5) << " ";
        }
        for (i = 0; i < Nvars; i++) {
            os << f[i] << " ";;
        }
        os << "\n";

        pos[Nvars - 1]++;       // move on to next position...
        f += Nvars;             // ...also in the array
    }
    os.close();
    delete[]pos;
}
