void ABFdata::write_bias()

in Documents/NamdSample/namd210b/lib/abf_integrate/abf_data.cpp [215:270]

• 45 lines of code
• 15 McCabe index (conditional complexity)

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;
}