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Commit 0dd1a42f authored by Ludovic Courtès's avatar Ludovic Courtès
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core: Split 'load_data_from_text' into several functions.

parent 65d45902
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Showing with 131 additions and 97 deletions
sources/core/data_storage.cpp
View file @ 0dd1a42f
......@@ -23,6 +23,126 @@
//using namespace alta;
static bool cosine_correction(vec& v, unsigned int dimX, unsigned int dimY,
alta::params::input in_param)
{
using namespace alta;
// Correction of the data by 1/cosine(theta_L)
double factor = 1.0;
double cart[6];
params::convert(&v[0], in_param, params::CARTESIAN, cart);
if(cart[5] > 0.0 && cart[2] > 0.0)
{
factor = 1.0/cart[5]*cart[2];
for(unsigned int i = 0; i < dimY; ++i)
{
v[i + dimX] /= factor;
}
return true;
}
else return false;
}
// Return true if V is between MIN and MAX.
static bool within_bounds(const vec& v, unsigned int dimX,
const vec& xmin, const vec& xmax,
unsigned int dimY, const vec& ymin,
const vec& ymax)
{
bool is_in = true;
for(unsigned int i=0; i<dimX; ++i)
{
if(v[i] < xmin[i] || v[i] > xmax[i])
{
is_in = false ;
}
}
for(unsigned int i=0; i<dimY; ++i)
{
if(v[dimX+i] < ymin[i] || v[dimX+i] > ymax[i])
{
is_in = false ;
}
}
return is_in;
}
// The type of confidence interval.
enum ci_kind
{
NO_CONFIDENCE_INTERVAL = 0,
SYMMETRICAL_CONFIDENCE_INTERVAL,
ASYMMETRICAL_CONFIDENCE_INTERVAL
};
// Read a confidence interval on the output parameters from INPUT into V.
static void read_confidence_interval(std::istream& input,
vec& v, ci_kind kind,
unsigned int dimX,
unsigned int dimY,
const alta::arguments& args)
{
for(int i=0; i<dimY; ++i)
{
double min_dt = 0.0, max_dt = 0.0;
if(i == 0 && kind == ASYMMETRICAL_CONFIDENCE_INTERVAL)
{
input >> min_dt ;
input >> max_dt ;
min_dt = min_dt-v[dimX+i];
max_dt = max_dt-v[dimX+i];
}
else if(i == 0 && kind == SYMMETRICAL_CONFIDENCE_INTERVAL)
{
double dt ;
input >> dt ;
min_dt = -dt;
max_dt = dt;
}
else
{
// Confidence interval data not provided in INPUT.
double dt = args.get_float("dt", 0.1f);
min_dt = -dt;
max_dt = dt;
}
if(args.is_defined("dt-relative"))
{
v[dimX + dimY+i] = v[dimX + i] * (1.0 + min_dt) ;
v[dimX + 2*dimY+i] = v[dimX + i] * (1.0 + max_dt) ;
}
else if(args.is_defined("dt-max"))
{
v[dimX + dimY+i] = v[dimX + i] + std::max(v[dimX + i] * min_dt, min_dt);
v[dimX + 2*dimY+i] = v[dimX + i] + std::max(v[dimX + i] * max_dt, max_dt);
}
else
{
v[dimX + dimY+i] = v[dimX + i] + min_dt ;
v[dimX + 2*dimY+i] = v[dimX + i] + max_dt ;
}
// You can enforce the vertical segment to stay in the positive
// region using the --data-positive command line argument. Note
// that the data point is also clamped to zero if negative.
if(args.is_defined("dt-positive"))
{
v[dimX + i] = std::max(v[dimX + i], 0.0);
v[dimX + dimY+i] = std::max(v[dimX + dimY+i], 0.0);
v[dimX + 2*dimY+i] = std::max(v[dimX + 2*dimY+i], 0.0);
}
#ifdef DEBUG
std::cout << "<<DEBUG>> vs = [" << v[dimX + dimY+i] << ", " << v[dimX + 2*dimY+i] << "]" << std::endl;
#endif
}
}
alta::data* alta::load_data_from_text(std::istream& input,
const alta::arguments& header)
{
......@@ -57,6 +177,10 @@ alta::data* alta::load_data_from_text(std::istream& input,
#endif
int vs_value = header.get_int("VS");
ci_kind kind =
vs_value == 2 ? ASYMMETRICAL_CONFIDENCE_INTERVAL
: (vs_value == 1 ? SYMMETRICAL_CONFIDENCE_INTERVAL
: NO_CONFIDENCE_INTERVAL);
std::vector<vec> content;
......@@ -82,108 +206,18 @@ alta::data* alta::load_data_from_text(std::istream& input,
}
// If data is not in the interval of fit
bool is_in = true ;
for(int i=0; i<dim.first; ++i)
{
if(v[i] < min[i] || v[i] > max[i])
{
is_in = false ;
}
}
for(int i=0; i<dim.second; ++i)
{
if(v[dim.first+i] < ymin[i] || v[dim.first+i] > ymax[i])
{
is_in = false ;
}
}
if(!is_in)
{
continue ;
}
if (!within_bounds(v, dim.first, min, max, dim.second, ymin, ymax))
continue;
// /*
// Correction of the data by 1/cosine(theta_L)
double factor = 1.0;
if(args.is_defined("data-correct-cosine"))
{
double cart[6];
params::convert(&v[0], in_param, params::CARTESIAN, cart);
if(cart[5] > 0.0 && cart[2] > 0.0)
{
factor = 1.0/cart[5]*cart[2];
for(int i=0; i<dim.second; ++i)
{
v[i + dim.first] /= factor;
}
}
else
{
continue;
}
if (!cosine_correction(v, dim.first, dim.second, in_param))
continue;
}
// End of correction
// */
// Check if the data containt a vertical segment around the mean
// value.
for(int i=0; i<dim.second; ++i)
{
double min_dt = 0.0;
double max_dt = 0.0;
if(i == 0 && vs_value == 2)
{
linestream >> min_dt ;
linestream >> max_dt ;
min_dt = min_dt-v[dim.first+i];
max_dt = max_dt-v[dim.first+i];
}
else if(i == 0 && vs_value == 1)
{
double dt ;
linestream >> dt ;
min_dt = -dt;
max_dt = dt;
}
else
{
double dt = args.get_float("dt", 0.1f);
min_dt = -dt;
max_dt = dt;
}
if(args.is_defined("dt-relative"))
{
v[dim.first + dim.second+i] = v[dim.first + i] * (1.0 + min_dt) ;
v[dim.first + 2*dim.second+i] = v[dim.first + i] * (1.0 + max_dt) ;
}
else if(args.is_defined("dt-max"))
{
v[dim.first + dim.second+i] = v[dim.first + i] + std::max(v[dim.first + i] * min_dt, min_dt);
v[dim.first + 2*dim.second+i] = v[dim.first + i] + std::max(v[dim.first + i] * max_dt, max_dt);
}
else
{
v[dim.first + dim.second+i] = v[dim.first + i] + min_dt ;
v[dim.first + 2*dim.second+i] = v[dim.first + i] + max_dt ;
}
// You can enforce the vertical segment to stay in the positive
// region using the --data-positive command line argument. Note
// that the data point is also clamped to zero if negative.
if(args.is_defined("dt-positive"))
{
v[dim.first + i] = std::max(v[dim.first + i], 0.0);
v[dim.first + dim.second+i] = std::max(v[dim.first + dim.second+i], 0.0);
v[dim.first + 2*dim.second+i] = std::max(v[dim.first + 2*dim.second+i], 0.0);
}
#ifdef DEBUG
std::cout << "<<DEBUG>> vs = [" << v[dim.first + dim.second+i] << ", " << v[dim.first + 2*dim.second+i] << "]" << std::endl;
#endif
}
// Read the confidence interval data if available.
read_confidence_interval(linestream, v, kind,
dim.first, dim.second, args);
content.push_back(std::move(v));
}
......
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