mirror of
https://github.com/OpenSpace/OpenSpace.git
synced 2026-01-03 01:59:35 -06:00
701 lines
24 KiB
C++
701 lines
24 KiB
C++
/*****************************************************************************************
|
|
* *
|
|
* OpenSpace *
|
|
* *
|
|
* Copyright (c) 2014-2015 *
|
|
* *
|
|
* Permission is hereby granted, free of charge, to any person obtaining a copy of this *
|
|
* software and associated documentation files (the "Software"), to deal in the Software *
|
|
* without restriction, including without limitation the rights to use, copy, modify, *
|
|
* merge, publish, distribute, sublicense, and/or sell copies of the Software, and to *
|
|
* permit persons to whom the Software is furnished to do so, subject to the following *
|
|
* conditions: *
|
|
* *
|
|
* The above copyright notice and this permission notice shall be included in all copies *
|
|
* or substantial portions of the Software. *
|
|
* *
|
|
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, *
|
|
* INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A *
|
|
* PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT *
|
|
* HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF *
|
|
* CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE *
|
|
* OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. *
|
|
****************************************************************************************/
|
|
#include <modules/iswa/util/dataprocessor.h>
|
|
#include <openspace/util/histogram.h>
|
|
|
|
#include <fstream>
|
|
#include <ghoul/io/texture/texturereader.h>
|
|
#include <ghoul/opengl/programobject.h>
|
|
#include <ghoul/opengl/textureunit.h>
|
|
#include <openspace/scene/scene.h>
|
|
#include <openspace/scene/scenegraphnode.h>
|
|
#include <openspace/engine/openspaceengine.h>
|
|
#include <openspace/rendering/renderengine.h>
|
|
#include <openspace/util/spicemanager.h>
|
|
#include <ghoul/filesystem/filesystem.h>
|
|
#include <modules/iswa/util/iswamanager.h>
|
|
#include <modules/iswa/ext/json/json.hpp>
|
|
|
|
namespace {
|
|
const std::string _loggerCat = "DataPlane";
|
|
using json = nlohmann::json;
|
|
}
|
|
|
|
namespace openspace {
|
|
DataProcessor::DataProcessor(bool useLog, bool useHistogram, glm::vec2 normValues)
|
|
:_useLog(useLog)
|
|
,_useHistogram(useHistogram)
|
|
,_normValues(normValues)
|
|
,_filterValues(glm::vec2(0))
|
|
,_numValues(0)
|
|
{
|
|
_coordinateVariables = {"x", "y", "z", "phi", "theta"};
|
|
};
|
|
DataProcessor::~DataProcessor(){};
|
|
|
|
std::vector<std::string> DataProcessor::readHeader(std::string& dataBuffer){
|
|
std::vector<std::string> options = std::vector<std::string>();
|
|
if(!dataBuffer.empty()){
|
|
std::stringstream memorystream(dataBuffer);
|
|
std::string line;
|
|
|
|
while(getline(memorystream,line)){
|
|
if(line.find("#") == 0){
|
|
if(line.find("# Output data:") == 0){
|
|
|
|
line = line.substr(26);
|
|
std::stringstream ss(line);
|
|
|
|
std::string token;
|
|
getline(ss, token, 'x');
|
|
int x = std::stoi(token);
|
|
|
|
getline(ss, token, '=');
|
|
int y = std::stoi(token);
|
|
|
|
_dimensions = glm::size3_t(x, y, 1);
|
|
|
|
getline(memorystream, line);
|
|
line = line.substr(1);
|
|
|
|
ss = std::stringstream(line);
|
|
std::string option;
|
|
while(ss >> option){
|
|
if(_coordinateVariables.find(option) == _coordinateVariables.end()){
|
|
options.push_back(option);
|
|
}
|
|
}
|
|
}
|
|
}else{
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
return options;
|
|
}
|
|
|
|
std::vector<std::string> DataProcessor::readJSONHeader(std::string& dataBuffer){
|
|
std::vector<std::string> options = std::vector<std::string>();
|
|
if(!dataBuffer.empty()){
|
|
json j = json::parse(dataBuffer);
|
|
json var = j["variables"];
|
|
for (json::iterator it = var.begin(); it != var.end(); ++it) {
|
|
std::string option = it.key();
|
|
if(option == "x"){
|
|
json lon = it.value();
|
|
json lat = lon.at(0);
|
|
|
|
_dimensions = glm::size3_t(lat.size(), lon.size(), 1);
|
|
}
|
|
if(_coordinateVariables.find(option) == _coordinateVariables.end()){
|
|
options.push_back(option);
|
|
}
|
|
}
|
|
}
|
|
return options;
|
|
}
|
|
|
|
void DataProcessor::addValues(std::string& dataBuffer, properties::SelectionProperty dataOptions){
|
|
int numOptions = dataOptions.options().size();
|
|
|
|
if(_min.empty()) _min = std::vector<float>(numOptions, std::numeric_limits<float>::max());
|
|
if(_max.empty()) _max = std::vector<float>(numOptions, std::numeric_limits<float>::min());
|
|
if(_sum.empty()) _sum = std::vector<float>(numOptions, 0.0f);
|
|
if(_sd.empty()) _sd = std::vector<float>(numOptions, 0.0f);
|
|
if(_numValues.empty()) _numValues= std::vector<float>(numOptions, 0.0f);
|
|
if(_histograms.empty())_histograms = std::vector<std::shared_ptr<Histogram>>(numOptions, nullptr);
|
|
|
|
if(!dataBuffer.empty()){
|
|
|
|
std::stringstream memorystream(dataBuffer);
|
|
std::string line;
|
|
std::vector<float> sum(numOptions, 0.0f);
|
|
std::vector<std::vector<float>> values(numOptions, std::vector<float>());
|
|
|
|
int numValues = 0;
|
|
while(getline(memorystream, line)){
|
|
if(line.find("#") == 0) continue;
|
|
|
|
std::stringstream ss(line);
|
|
std::vector<float> value;
|
|
float v;
|
|
while(ss >> v){
|
|
value.push_back(v);
|
|
}
|
|
|
|
if(value.size()){
|
|
for(int i=0; i<numOptions; i++){
|
|
float v = value[i+3];
|
|
|
|
values[i].push_back(v);
|
|
_min[i] = std::min(_min[i], v);
|
|
_max[i] = std::max(_max[i], v);
|
|
sum[i] += v;
|
|
}
|
|
numValues++;
|
|
}
|
|
}
|
|
|
|
for(int i=0; i<numOptions; i++){
|
|
float mean = (1.0/numValues)*sum[i];
|
|
float var = 0;
|
|
for(int j=0; j<numValues; j++){
|
|
var += pow(values[i][j] - mean, 2);
|
|
}
|
|
float sd = sqrt(var / numValues);
|
|
|
|
_sum[i] += sum[i];
|
|
_sd[i] = sqrt(pow(_sd[i],2) + pow(sd, 2));
|
|
_numValues[i] += numValues;
|
|
std::cout << i << " " << _numValues[i] << " " << _sum[i] << " " << _sd[i] << std::endl;
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
|
|
std::vector<float*> DataProcessor::readData(std::string& dataBuffer, properties::SelectionProperty dataOptions){
|
|
if(!dataBuffer.empty()){
|
|
std::stringstream memorystream(dataBuffer);
|
|
std::string line;
|
|
|
|
std::vector<int> selectedOptions = dataOptions.value();
|
|
|
|
int numSelected = selectedOptions.size();
|
|
|
|
std::vector<float> min(numSelected, std::numeric_limits<float>::max());
|
|
std::vector<float> max(numSelected, std::numeric_limits<float>::min());
|
|
|
|
std::vector<float> sum(numSelected, 0.0f);
|
|
std::vector<std::vector<float>> optionValues(numSelected, std::vector<float>());
|
|
|
|
std::vector<float*> data(dataOptions.options().size(), nullptr);
|
|
for(int option : selectedOptions){
|
|
data[option] = new float[_dimensions.x*_dimensions.y]{0.0f};
|
|
}
|
|
|
|
int numValues = 0;
|
|
while(getline(memorystream, line)){
|
|
if(line.find("#") == 0){ //part of the header
|
|
continue;
|
|
}
|
|
|
|
std::stringstream ss(line);
|
|
std::vector<float> value;
|
|
float v;
|
|
while(ss >> v){
|
|
value.push_back(v);
|
|
}
|
|
|
|
if(value.size()){
|
|
for(int i=0; i<numSelected; i++){
|
|
|
|
float v = value[selectedOptions[i]+3]; //+3 because "options" x, y and z.
|
|
|
|
if(_useLog){
|
|
int sign = (v>0)? 1:-1;
|
|
v = sign*log(fabs(v) + 1);
|
|
}
|
|
|
|
optionValues[i].push_back(v);
|
|
|
|
min[i] = std::min(min[i], v);
|
|
max[i] = std::max(max[i], v);
|
|
|
|
sum[i] += v;
|
|
}
|
|
numValues++;
|
|
}
|
|
}
|
|
// std::cout << "Actual size: " << numValues << " Expected: " << _dimensions.x*_dimensions.y << std::endl;
|
|
if(numValues != _dimensions.x*_dimensions.y){
|
|
LWARNING("Number of values read and expected are not the same");
|
|
return std::vector<float*>();
|
|
}
|
|
|
|
// FOR TESTING
|
|
// ===========
|
|
// std::chrono::time_point<std::chrono::system_clock> start, end;
|
|
// start = std::chrono::system_clock::now();
|
|
// ===========
|
|
|
|
for(int i=0; i<numSelected; i++){
|
|
processData(data[ selectedOptions[i] ], optionValues[i], min[i], max[i], sum[i]);
|
|
}
|
|
|
|
// FOR TESTING
|
|
// ===========
|
|
// end = std::chrono::system_clock::now();
|
|
// _numOfBenchmarks++;
|
|
// std::chrono::duration<double> elapsed_seconds = end-start;
|
|
// _avgBenchmarkTime = ( (_avgBenchmarkTime * (_numOfBenchmarks-1)) + elapsed_seconds.count() ) / _numOfBenchmarks;
|
|
// std::cout << " readData():" << std::endl;
|
|
// std::cout << "avg elapsed time: " << _avgBenchmarkTime << "s\n";
|
|
// std::cout << "num Benchmarks: " << _numOfBenchmarks << "\n";
|
|
// ===========
|
|
|
|
return data;
|
|
|
|
}
|
|
else {
|
|
// LWARNING("Nothing in memory buffer, are you connected to the information super highway?");
|
|
return std::vector<float*>();
|
|
}
|
|
}
|
|
|
|
|
|
std::vector<float*> DataProcessor::readData2(std::string& dataBuffer, properties::SelectionProperty dataOptions){
|
|
if(!dataBuffer.empty()){
|
|
std::stringstream memorystream(dataBuffer);
|
|
std::string line;
|
|
|
|
std::vector<int> selectedOptions = dataOptions.value();
|
|
int numSelected = selectedOptions.size();
|
|
|
|
std::vector<std::vector<float>> values(selectedOptions.size(), std::vector<float>());
|
|
std::vector<float*> data(dataOptions.options().size(), nullptr);
|
|
|
|
for(int option : selectedOptions){
|
|
data[option] = new float[_dimensions.x*_dimensions.y]{0.0f};
|
|
}
|
|
|
|
int numValues = 0;
|
|
while(getline(memorystream, line)){
|
|
if(line.find("#") == 0){ //part of the header
|
|
continue;
|
|
}
|
|
|
|
std::stringstream ss(line);
|
|
std::vector<float> value;
|
|
float v;
|
|
while(ss >> v){
|
|
value.push_back(v);
|
|
}
|
|
|
|
if(value.size()){
|
|
for(int option : selectedOptions){
|
|
float v = value[option+3]; //+3 because "options" x, y and z.
|
|
data[option][numValues] = processDataPoint(v, option);
|
|
}
|
|
}
|
|
numValues++;
|
|
}
|
|
|
|
if(numValues != _dimensions.x*_dimensions.y){
|
|
LWARNING("Number of values read and expected are not the same");
|
|
return std::vector<float*>();
|
|
}
|
|
|
|
return data;
|
|
|
|
}else{
|
|
return std::vector<float*>();
|
|
}
|
|
}
|
|
|
|
std::vector<float*> DataProcessor::readJSONData(std::string& dataBuffer, properties::SelectionProperty dataOptions){
|
|
if(!dataBuffer.empty()){
|
|
json j = json::parse(dataBuffer);
|
|
json var = j["variables"];
|
|
|
|
std::vector<int> selectedOptions = dataOptions.value();
|
|
int numSelected = selectedOptions.size();
|
|
|
|
std::vector<float> min(numSelected, std::numeric_limits<float>::max());
|
|
std::vector<float> max(numSelected, std::numeric_limits<float>::min());
|
|
|
|
std::vector<float> sum(numSelected, 0.0f);
|
|
std::vector<std::vector<float>> optionValues(numSelected, std::vector<float>());
|
|
|
|
auto options = dataOptions.options();
|
|
|
|
std::vector<float*> data(options.size(), nullptr);
|
|
int i = 0;
|
|
for(int option : selectedOptions){
|
|
|
|
data[option] = new float[_dimensions.x*_dimensions.y]{0.0f};
|
|
|
|
std::stringstream memorystream(options[option].description);
|
|
std::string optionName;
|
|
getline(memorystream, optionName, '/');
|
|
getline(memorystream, optionName, '/');
|
|
|
|
json valueArray = var[optionName];
|
|
int ySize = valueArray.size();
|
|
|
|
for(int y=0; y<valueArray.size(); y++){
|
|
json values = valueArray.at(y);
|
|
for(int x=0; x<values.size(); x++){
|
|
float v = values.at(x);
|
|
if(_useLog){
|
|
int sign = (v>0)? 1:-1;
|
|
if(v != 0){
|
|
v = sign*log(fabs(v));
|
|
}
|
|
}
|
|
|
|
optionValues[i].push_back(v);
|
|
|
|
min[i] = std::min(min[i], v);
|
|
max[i] = std::max(max[i], v);
|
|
|
|
sum[i] += v;
|
|
}
|
|
}
|
|
i++;
|
|
}
|
|
|
|
for(int i=0; i<numSelected; i++){
|
|
processData(data[ selectedOptions[i] ], optionValues[i], min[i], max[i], sum[i]);
|
|
}
|
|
|
|
return data;
|
|
}
|
|
else {
|
|
// LWARNING("Nothing in memory buffer, are you connected to the information super highway?");
|
|
return std::vector<float*>();
|
|
}
|
|
}
|
|
|
|
void DataProcessor::addValuesFromJSON(std::string& dataBuffer, properties::SelectionProperty dataOptions){
|
|
int numOptions = dataOptions.options().size();
|
|
|
|
if(_min.empty()) _min = std::vector<float>(numOptions, std::numeric_limits<float>::max());
|
|
if(_max.empty()) _max = std::vector<float>(numOptions, std::numeric_limits<float>::min());
|
|
if(_sum.empty()) _sum = std::vector<float>(numOptions, 0.0f);
|
|
if(_sd.empty()) _sd = std::vector<float>(numOptions, 0.0f);
|
|
if(_numValues.empty()) _numValues= std::vector<float>(numOptions, 0.0f);
|
|
if(_histograms.empty())_histograms = std::vector<std::shared_ptr<Histogram>>(numOptions, nullptr);
|
|
|
|
|
|
if(!dataBuffer.empty()){
|
|
json j = json::parse(dataBuffer);
|
|
json var = j["variables"];
|
|
|
|
std::vector<int> selectedOptions = dataOptions.value();
|
|
int numSelected = selectedOptions.size();
|
|
std::vector<float> sum(numOptions, 0.0f);
|
|
|
|
std::vector<std::vector<float>> values(numOptions, std::vector<float>());
|
|
auto options = dataOptions.options();
|
|
std::vector<float*> data(options.size(), nullptr);
|
|
int i = 0;
|
|
|
|
for(int i=0; i<numOptions; i++){
|
|
std::stringstream memorystream(options[i].description);
|
|
std::string optionName;
|
|
getline(memorystream, optionName, '/');
|
|
getline(memorystream, optionName, '/');
|
|
|
|
json valueArray = var[optionName];
|
|
int ySize = valueArray.size();
|
|
|
|
for(int y=0; y<valueArray.size(); y++){
|
|
json value = valueArray.at(y);
|
|
for(int x=0; x<value.size(); x++){
|
|
|
|
float v = value.at(x);
|
|
values[i].push_back(v);
|
|
|
|
_min[i] = std::min(_min[i],v);
|
|
_max[i] = std::min(_max[i],v);
|
|
sum[i] += v;
|
|
}
|
|
}
|
|
}
|
|
|
|
for(int i=0; i<numOptions; i++){
|
|
int numValues = values[i].size();
|
|
float mean = (1.0/numValues)*sum[i];
|
|
|
|
float var = 0;
|
|
for(int j=0; j<numValues; j++){
|
|
var += pow(values[i][j] - mean, 2);
|
|
}
|
|
float sd = sqrt(var / numValues);
|
|
|
|
_sum[i] += sum[i];
|
|
_sd[i] = sqrt(pow(_sd[i],2) + pow(sd, 2));
|
|
_numValues[i] += numValues;
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
std::vector<float*> DataProcessor::readJSONData2(std::string& dataBuffer, properties::SelectionProperty dataOptions){
|
|
if(!dataBuffer.empty()){
|
|
json j = json::parse(dataBuffer);
|
|
json var = j["variables"];
|
|
|
|
std::vector<int> selectedOptions = dataOptions.value();
|
|
int numSelected = selectedOptions.size();
|
|
|
|
std::vector<float> sum(numSelected, 0.0f);
|
|
std::vector<std::vector<float>> values(numSelected, std::vector<float>());
|
|
auto options = dataOptions.options();
|
|
|
|
std::vector<float*> data(options.size(), nullptr);
|
|
for(int option : selectedOptions){
|
|
|
|
data[option] = new float[_dimensions.x*_dimensions.y]{0.0f};
|
|
|
|
std::stringstream memorystream(options[option].description);
|
|
std::string optionName;
|
|
getline(memorystream, optionName, '/');
|
|
getline(memorystream, optionName, '/');
|
|
|
|
json yArray = var[optionName];
|
|
for(int y=0; y<yArray.size(); y++){
|
|
json xArray = yArray.at(y);
|
|
for(int x=0; x<xArray.size(); x++){
|
|
|
|
int i = x + y*xArray.size();
|
|
// std::cout << _dimensions.x*_dimensions.y << " " << i << std::endl;
|
|
float v = xArray.at(x);
|
|
data[option][i] = processDataPoint(v, option);
|
|
|
|
}
|
|
}
|
|
}
|
|
|
|
return data;
|
|
}
|
|
else {
|
|
// LWARNING("Nothing in memory buffer, are you connected to the information super highway?");
|
|
return std::vector<float*>();
|
|
}
|
|
}
|
|
|
|
|
|
void DataProcessor::addValuesFromKameleonData(float* kdata, glm::size3_t dimensions, int numOptions, int option){
|
|
if(_min.empty()) _min = std::vector<float>(numOptions, std::numeric_limits<float>::max());
|
|
if(_max.empty()) _max = std::vector<float>(numOptions, std::numeric_limits<float>::min());
|
|
if(_sum.empty()) _sum= std::vector<float>(numOptions, 0.0f);
|
|
if(_sd.empty()) _sd= std::vector<float>(numOptions, 0.0f);
|
|
if(_numValues.empty()) _numValues= std::vector<float>(numOptions, 0.0f);
|
|
if(_histograms.empty())_histograms = std::vector<std::shared_ptr<Histogram>>(numOptions, nullptr);
|
|
|
|
|
|
int numValues = dimensions.x*dimensions.y*dimensions.z;
|
|
float sum = 0;
|
|
|
|
for(int i=0; i<numValues; i++){
|
|
float v = kdata[i];
|
|
_min[option] = std::min(_min[option],v);
|
|
_max[option] = std::max(_max[option],v);
|
|
sum += v;
|
|
}
|
|
|
|
float mean = (1.0 / numValues) * sum;
|
|
float var = 0;
|
|
for(int i=0; i<numValues; i++){
|
|
var += pow(kdata[i] - mean, 2);
|
|
}
|
|
float sd = sqrt ( var / numValues );
|
|
|
|
_sum[option] += sum;
|
|
_sd[option] = sqrt(pow(_sd[option],2)+ pow(sd,2));
|
|
_numValues[option] += numValues;
|
|
}
|
|
|
|
std::vector<float*> DataProcessor::processKameleonData2(std::vector<float*> kdata, glm::size3_t dimensions, properties::SelectionProperty dataOptions){
|
|
std::vector<int> selectedOptions = dataOptions.value();
|
|
int numSelected = selectedOptions.size();
|
|
|
|
std::vector<std::vector<float>> values(selectedOptions.size(), std::vector<float>());
|
|
std::vector<float*> data(dataOptions.options().size(), nullptr);
|
|
int numValues = dimensions.x*dimensions.y*dimensions.z;
|
|
|
|
for(int option : selectedOptions){
|
|
data[option] = new float[numValues]{0.0f};
|
|
|
|
for(int i=0; i<numValues; i++){
|
|
float v = kdata[option][i];
|
|
data[option][i] = processDataPoint(v, option);
|
|
}
|
|
}
|
|
|
|
return data;
|
|
}
|
|
|
|
std::vector<float*> DataProcessor::processKameleonData(std::vector<float*> kdata, glm::size3_t dimensions, properties::SelectionProperty dataOptions){
|
|
std::vector<int> selectedOptions = dataOptions.value();
|
|
int numSelected = selectedOptions.size();
|
|
auto options = dataOptions.options();
|
|
int numOptions = options.size();
|
|
|
|
if(_min.empty()){
|
|
_min = std::vector<float>(numOptions, std::numeric_limits<float>::max());
|
|
}
|
|
|
|
if(_max.empty()){
|
|
_max = std::vector<float>(numOptions, std::numeric_limits<float>::min());
|
|
}
|
|
|
|
if(_sum.empty()){
|
|
_sum= std::vector<float>(numOptions, 0.0f);
|
|
}
|
|
|
|
if(_sd.empty()){
|
|
_sd= std::vector<float>(numOptions, 0.0f);
|
|
}
|
|
|
|
if(_histograms.empty()){
|
|
_histograms = std::vector<std::shared_ptr<Histogram>>(numOptions, nullptr);
|
|
}
|
|
|
|
|
|
std::vector<float> min(numSelected, std::numeric_limits<float>::max());
|
|
std::vector<float> max(numSelected, std::numeric_limits<float>::min());
|
|
|
|
std::vector<float> sum(numSelected, 0.0f);
|
|
std::vector<std::vector<float>> optionValues(numSelected, std::vector<float>());
|
|
|
|
|
|
std::vector<float*> data(options.size(), nullptr);
|
|
int numValues = dimensions.x*dimensions.y*dimensions.z;
|
|
int i = 0;
|
|
|
|
for(int option : selectedOptions){
|
|
bool calculateMin = (_min[option] == std::numeric_limits<float>::max());
|
|
bool calculateMax = (_max[option] == std::numeric_limits<float>::min());
|
|
bool claculateSum = (_sum[option] == 0.0f);
|
|
|
|
data[option] = new float[numValues]{0.0f};
|
|
|
|
for(int j=0; j<numValues; j++){
|
|
float v = kdata[option][j];
|
|
|
|
if(_useLog){
|
|
int sign = (v>0)? 1:-1;
|
|
if(v != 0){
|
|
v = sign*log(fabs(v));
|
|
}
|
|
}
|
|
|
|
optionValues[i].push_back(v);
|
|
|
|
min[i] = std::min(min[i], v);
|
|
max[i] = std::max(max[i], v);
|
|
|
|
sum[i] += v;
|
|
|
|
if(calculateMin)
|
|
_min[option] = std::min(_min[option],v);
|
|
if(calculateMax)
|
|
_max[option] = std::max(_max[option],v);
|
|
if(claculateSum)
|
|
_sum[option] += v;
|
|
}
|
|
i++;
|
|
// if(calculateMin)
|
|
// std::cout << _min[option] << std::endl;
|
|
}
|
|
|
|
for(int i=0; i<numSelected; i++){
|
|
int selected = selectedOptions[i];
|
|
processData(data[ selected ], optionValues[i], _min[selected], _max[selected], _sum[selected], selected);
|
|
}
|
|
|
|
return data;
|
|
}
|
|
|
|
void DataProcessor::processData(float* outputData, std::vector<float>& inputData, float min, float max,float sum, int selected){
|
|
const int numValues = inputData.size();
|
|
Histogram histogram(min, max, 512);
|
|
|
|
//Calculate the mean
|
|
float mean = (1.0 / numValues) * sum;
|
|
|
|
//Calculate the Standard Deviation
|
|
float var = 0;
|
|
for(auto dataValue : inputData){
|
|
var += pow(dataValue - mean, 2);
|
|
}
|
|
float standardDeviation = sqrt ( var / numValues );
|
|
|
|
// Histogram functionality
|
|
if(_useHistogram){
|
|
for(auto dataValue : inputData){
|
|
histogram.add(dataValue, 1);
|
|
}
|
|
histogram.generateEqualizer();
|
|
standardDeviation = histogram.equalize(standardDeviation);
|
|
mean = histogram.equalize(mean);
|
|
}
|
|
|
|
// Normalize and equalize
|
|
for(int i=0; i < numValues; i++){
|
|
float v = inputData[i];
|
|
if(_useHistogram){
|
|
v = histogram.equalize(v);
|
|
}
|
|
v = normalizeWithStandardScore(v, mean, standardDeviation);
|
|
outputData[i] += v;
|
|
}
|
|
|
|
if(_useHistogram){
|
|
float val = histogram.highestBinValue(_useHistogram);
|
|
val = normalizeWithStandardScore(val, mean, standardDeviation);
|
|
float width = normalizeWithStandardScore(1, mean, standardDeviation);
|
|
_filterValues = glm::vec2( val, width);
|
|
}
|
|
|
|
// Histogram equalized = histogram.equalize();
|
|
// histogram.print();
|
|
// equalized.print();
|
|
}
|
|
|
|
float DataProcessor::processDataPoint(float value, int option){
|
|
if(_numValues.empty()) return 0.0f;
|
|
|
|
float mean = (1.0 / _numValues[option]) * _sum[option];
|
|
float v = normalizeWithStandardScore(value, mean, _sd[option]);
|
|
return v;
|
|
}
|
|
|
|
float DataProcessor::normalizeWithStandardScore(float value, float mean, float sd){
|
|
|
|
float zScoreMin = _normValues.x;
|
|
float zScoreMax = _normValues.y;
|
|
float standardScore = ( value - mean ) / sd;
|
|
// Clamp intresting values
|
|
standardScore = glm::clamp(standardScore, -zScoreMin, zScoreMax);
|
|
//return and normalize
|
|
return ( standardScore + zScoreMin )/(zScoreMin + zScoreMax );
|
|
}
|
|
|
|
glm::vec2 DataProcessor::filterValues(){
|
|
return _filterValues;
|
|
}
|
|
|
|
void DataProcessor::clear(){
|
|
_min.clear();
|
|
_max.clear();
|
|
_sum.clear();
|
|
_sd.clear();
|
|
_histograms.clear();
|
|
}
|
|
|
|
} |