#define MICHLIB_NOSOURCE #include "actiongrad.h" MString GradMethods::NCFileW::Create(const MString& name, const MString& history, const std::vector& vnames, const std::vector& lnames, const std::vector& lons, const std::vector& lats, int compress) { const float node_offset = 0.0; auto nx = lons.size(); auto ny = lats.size(); const auto fill = static_cast(std::numeric_limits::max()); // Creating Open(name); if(!*this) return "Can't create netcdf file " + name + ": " + ErrMessage(); AddAtt("history", history); AddAtt("node_offset", node_offset); AddDim("longitude", nx); AddDim("latitude", ny); AddVar("longitude", NC_FLOAT, "longitude"); AddVar("latitude", NC_FLOAT, "latitude"); SetComp("longitude", compress); SetComp("latitude", compress); AddAtt("longitude", "standard_name", "longitude"); AddAtt("longitude", "long_name", "Longitude"); AddAtt("latitude", "standard_name", "latitude"); AddAtt("latitude", "long_name", "Latitude"); // Variables for(size_t i = 0; i < vnames.size(); i++) { AddVar(vnames[i], NC_FLOAT, "latitude", "longitude"); SetComp(vnames[i], compress); if(lnames[i].Exist()) AddAtt(vnames[i], "long_name", lnames[i]); AddAtt(vnames[i], "_FillValue", fill); } // End definitions EndDef(); // Writing lon, lat WriteVar("longitude", lons.data()); WriteVar("latitude", lats.data()); if(!*this) return "Can't set grid in the netcdf file " + name + ": " + ErrMessage(); return ""; } MString GradMethods::NCFileW::WriteVariable(const MString& name, const GradMethods::Matrix& data) { WriteVar(name, data.Data().data()); if(!*this) return "Can't write variable " + name + ": " + ErrMessage(); return ""; } GradMethods::Matrix::Matrix(const std::vector& in, size_t nx_, size_t ny_, struct GradMethods::MinMax minmax): nx(nx_), ny(ny_), data(nx_ * ny_) { if(minmax.automin || minmax.automax) { DataType min = in[0]; DataType max = in[0]; for(size_t i = 1; i < in.size(); i++) if(in[i] != minmax.fill) { min = std::min(min, in[i]); max = std::max(max, in[i]); } if(minmax.automin) minmax.min = min; if(minmax.automax) minmax.max = max; } if(minmax.log) { minmax.min = michlib_internal::RealType::Log(minmax.min); minmax.max = michlib_internal::RealType::Log(minmax.max); } DataType a = (std::numeric_limits::max() - 1) / (minmax.max - minmax.min); for(size_t i = 1; i < in.size(); i++) { DataType v = minmax.log ? michlib_internal::RealType::Log(in[i]) : in[i]; if(in[i] == minmax.fill) data[i] = std::numeric_limits::max(); else if(v <= minmax.min) data[i] = 0; else if(v >= minmax.max) data[i] = std::numeric_limits::max() - 1; else data[i] = static_cast(michlib_internal::RealType::Round(a * (v - minmax.min))); } } void GradMethods::Matrix::Grad() { std::vector out(data.size()); const auto bad = std::numeric_limits::max(); for(size_t iy = 0; iy < ny; iy++) for(size_t ix = 0; ix < nx; ix++) { if(iy < 1 || ix < 1 || iy > ny - 2 || ix > nx - 2) out[iy * nx + ix] = bad; else if(V(ix - 1, iy - 1) == bad || V(ix, iy - 1) == bad || V(ix + 1, iy - 1) == bad || V(ix - 1, iy) == bad || V(ix, iy) == bad || V(ix + 1, iy) == bad || V(ix - 1, iy + 1) == bad || V(ix, iy + 1) == bad || V(ix + 1, iy + 1) == bad) out[iy * nx + ix] = bad; else { using IT = michlib::int4; // Possible but unlikely overflow const IT m1 = -1; const IT m2 = -2; const IT p1 = 1; const IT p2 = 2; IT gx = m1 * V(ix - 1, iy + 1) + p1 * V(ix + 1, iy + 1) + m2 * V(ix - 1, iy) + p2 * V(ix + 1, iy) + m1 * V(ix - 1, iy - 1) + p1 * V(ix + 1, iy - 1); IT gy = m1 * V(ix - 1, iy - 1) + p1 * V(ix - 1, iy + 1) + m2 * V(ix, iy - 1) + p2 * V(ix, iy + 1) + m1 * V(ix + 1, iy - 1) + p1 * V(ix + 1, iy + 1); auto sq = static_cast(michlib::Round(michlib::Hypot(gx, gy))); if(sq >= bad) sq = bad - 1; out[iy * nx + ix] = static_cast(sq); } } data = std::move(out); }