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GenUtils.cpp
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GenUtils.cpp
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/**
* GeoDa TM, Copyright (C) 2011-2015 by Luc Anselin - all rights reserved
*
* This file is part of GeoDa.
*
* GeoDa is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* GeoDa is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
#include <cfloat>
#include <iomanip>
#include <limits>
#include <math.h>
#include <sstream>
#include <boost/math/distributions/students_t.hpp>
#include <wx/dc.h>
#include <wx/msgdlg.h>
#include <wx/stdpaths.h>
#include "GdaConst.h"
#include "GenUtils.h"
using namespace std;
wxString GdaColorUtils::ToHexColorStr(const wxColour& c)
{
return c.GetAsString(wxC2S_HTML_SYNTAX);
}
wxColour GdaColorUtils::ChangeBrightness(const wxColour& input_col,
int brightness)
{
unsigned char r = input_col.Red();
unsigned char g = input_col.Green();
unsigned char b = input_col.Blue();
unsigned char alpha = input_col.Alpha();
wxColour::ChangeLightness(&r, &g, &b, brightness);
return wxColour(r,g,b,alpha);
}
uint64_t Gda::ThomasWangHashUInt64(uint64_t key) {
key = (~key) + (key << 21); // key = (key << 21) - key - 1;
key = key ^ (key >> 24);
key = (key + (key << 3)) + (key << 8); // key * 265
key = key ^ (key >> 14);
key = (key + (key << 2)) + (key << 4); // key * 21
key = key ^ (key >> 28);
key = key + (key << 31);
return key;
}
double Gda::ThomasWangHashDouble(uint64_t key) {
key = (~key) + (key << 21); // key = (key << 21) - key - 1;
key = key ^ (key >> 24);
key = (key + (key << 3)) + (key << 8); // key * 265
key = key ^ (key >> 14);
key = (key + (key << 2)) + (key << 4); // key * 21
key = key ^ (key >> 28);
key = key + (key << 31);
return 5.42101086242752217E-20 * key;
}
/** Use with std::sort for sorting in ascending order */
bool Gda::dbl_int_pair_cmp_less(const dbl_int_pair_type& ind1,
const dbl_int_pair_type& ind2)
{
return ind1.first < ind2.first;
}
/** Use with std::sort for sorting in descending order */
bool Gda::dbl_int_pair_cmp_greater(const dbl_int_pair_type& ind1,
const dbl_int_pair_type& ind2)
{
return ind1.first > ind2.first;
}
/** Use with std::sort for sorting in ascending order */
bool Gda::dbl_int_pair_cmp_second_less(const dbl_int_pair_type& ind1,
const dbl_int_pair_type& ind2)
{
return ind1.second < ind2.second;
}
/** Use with std::sort for sorting in descending order */
bool Gda::dbl_int_pair_cmp_second_greater(const dbl_int_pair_type& ind1,
const dbl_int_pair_type& ind2)
{
return ind1.second > ind2.second;
}
void
HingeStats::
CalculateHingeStats(const std::vector<Gda::dbl_int_pair_type>& data)
{
num_obs = data.size();
double N = num_obs;
is_even_num_obs = (num_obs % 2) == 0;
min_val = data[0].first;
max_val = data[num_obs-1].first;
Q2_ind = (N+1)/2.0 - 1;
if (is_even_num_obs) {
Q1_ind = (N+2)/4.0 - 1;
Q3_ind = (3*N+2)/4.0 - 1;
} else {
Q1_ind = (N+3)/4.0 - 1;
Q3_ind = (3*N+1)/4.0 - 1;
}
Q1 = (data[(int) floor(Q1_ind)].first +
data[(int) ceil(Q1_ind)].first)/2.0;
Q2 = (data[(int) floor(Q2_ind)].first +
data[(int) ceil(Q2_ind)].first)/2.0;
Q3 = (data[(int) floor(Q3_ind)].first +
data[(int) ceil(Q3_ind)].first)/2.0;
IQR = Q3 - Q1;
extreme_lower_val_15 = Q1 - 1.5*IQR;
extreme_lower_val_30 = Q1 - 3.0*IQR;
extreme_upper_val_15 = Q3 + 1.5*IQR;
extreme_upper_val_30 = Q3 + 3.0*IQR;
min_IQR_ind = -1;
for (int i=0; i<num_obs; i++) {
if (data[i].first < Q1) min_IQR_ind = i;
else break;
}
if (min_IQR_ind < num_obs-1) min_IQR_ind++;
max_IQR_ind = num_obs;
for (int i=num_obs-1; i>=0; i--) {
if (data[i].first > Q3) max_IQR_ind = i;
else break;
}
if (max_IQR_ind > 0) max_IQR_ind--;
}
void
HingeStats::
CalculateHingeStats(const std::vector<Gda::dbl_int_pair_type>& data,
const std::vector<bool>& data_undef)
{
num_obs = data.size();
double N = 0.0;
std::vector<double> data_valid;
bool has_init = false;
for (size_t i =0; i<num_obs; i++) {
int obs_idx = data[i].second;
if (!data_undef[obs_idx]) {
double val = data[i].first;
data_valid.push_back(val); // sorted
if (!has_init) {
min_val = val;
max_val = val;
has_init = true;
}
if (val < min_val)
min_val = val;
if (val > max_val)
max_val = val;
}
}
N = data_valid.size();
is_even_num_obs = (data_valid.size() % 2) == 0;
Q2_ind = (N+1)/2.0 - 1;
if (is_even_num_obs) {
Q1_ind = (N+2)/4.0 - 1;
Q3_ind = (3*N+2)/4.0 - 1;
} else {
Q1_ind = (N+3)/4.0 - 1;
Q3_ind = (3*N+1)/4.0 - 1;
}
Q1 = (data_valid[(int) floor(Q1_ind)] + data_valid[(int) ceil(Q1_ind)])/2.0;
Q2 = (data_valid[(int) floor(Q2_ind)] + data_valid[(int) ceil(Q2_ind)])/2.0;
Q3 = (data_valid[(int) floor(Q3_ind)] + data_valid[(int) ceil(Q3_ind)])/2.0;
IQR = Q3 - Q1;
extreme_lower_val_15 = Q1 - 1.5*IQR;
extreme_lower_val_30 = Q1 - 3.0*IQR;
extreme_upper_val_15 = Q3 + 1.5*IQR;
extreme_upper_val_30 = Q3 + 3.0*IQR;
min_IQR_ind = -1;
for (int i=0; i<num_obs; i++) {
if (data[i].first < Q1) {
min_IQR_ind = i;
}
else
break;
}
if (min_IQR_ind < num_obs-1) {
min_IQR_ind++;
}
max_IQR_ind = num_obs;
for (int i=num_obs-1; i>=0; i--) {
if (data[i].first > Q3) {
max_IQR_ind = i;
}
else
break;
}
if (max_IQR_ind > 0)
max_IQR_ind--;
}
// Assume input v is sorted. If not, can sort
// with std::sort(v.begin(), v.end())
// Testing: for v = {15, 20, 35, 40, 50},
// percentile(1, v) = 15, percentile(10, v) = 15, percentile(11) = 15.25
// percentile(50, v) = 35, percentile(89, v) = 49.5,
// percentile(90, v) = 50, percentile(99, v) = 50
double Gda::percentile(double x, const std::vector<double>& v)
{
int N = v.size();
double Nd = (double) N;
double p_0 = (100.0/Nd) * (1.0-0.5);
double p_Nm1 = (100.0/Nd) * (Nd-0.5);
if (x <= p_0) return v[0];
if (x >= p_Nm1) return v[N-1];
for (int i=1; i<N; i++) {
double p_i = (100.0/Nd) * ((((double) i)+1.0)-0.5);
if (x == p_i) return v[i];
if (x < p_i) {
double p_im1 = (100.0/Nd) * ((((double) i))-0.5);
return v[i-1] + Nd*((x-p_im1)/100.0)*(v[i]-v[i-1]);
}
}
return v[N-1]; // execution should never get here
}
// Same assumptions as above
double Gda::percentile(double x, const Gda::dbl_int_pair_vec_type& v,
const std::vector<bool>& undefs)
{
std::vector<double> valid_data;
for (size_t i = 0; i<v.size(); i++ ) {
double val = v[i].first;
int ind = v[i].second;
if (undefs[ind])
continue;
valid_data.push_back(val);
}
return percentile(x, valid_data);
}
// Same assumptions as above
double Gda::percentile(double x, const Gda::dbl_int_pair_vec_type& v)
{
int N = v.size();
double Nd = (double) N;
double p_0 = (100.0/Nd) * (1.0-0.5);
double p_Nm1 = (100.0/Nd) * (Nd-0.5);
if (x <= p_0)
return v[0].first;
if (x >= p_Nm1)
return v[N-1].first;
for (int i=1; i<N; i++) {
double p_i = (100.0/Nd) * ((((double) i)+1.0)-0.5);
if (x == p_i)
return v[i].first;
if (x < p_i) {
double p_im1 = (100.0/Nd) * ((((double) i))-0.5);
return v[i-1].first + Nd*((x-p_im1)/100.0)*(v[i].first
-v[i-1].first);
}
}
return v[N-1].first; // execution should never get here
}
SampleStatistics::SampleStatistics(const std::vector<double>& data)
: sample_size(0), min(0), max(0), mean(0),
var_with_bessel(0), var_without_bessel(0),
sd_with_bessel(0), sd_without_bessel(0)
{
CalculateFromSample(data);
}
SampleStatistics::SampleStatistics(const std::vector<double>& data,
const std::vector<bool>& undefs)
: sample_size(0), min(0), max(0), mean(0),
var_with_bessel(0), var_without_bessel(0),
sd_with_bessel(0), sd_without_bessel(0)
{
std::vector<double> valid_data;
for (int i=0; i<data.size(); i++) {
if (undefs[i] == false)
valid_data.push_back(data[i]);
}
CalculateFromSample(valid_data);
}
SampleStatistics::SampleStatistics(const std::vector<double>& data,
const std::vector<bool>& undefs1,
const std::vector<bool>& undefs2)
: sample_size(0), min(0), max(0), mean(0),
var_with_bessel(0), var_without_bessel(0),
sd_with_bessel(0), sd_without_bessel(0)
{
std::vector<double> valid_data;
for (int i=0; i<data.size(); i++) {
if (undefs1[i] || undefs2[i])
continue;
valid_data.push_back(data[i]);
}
CalculateFromSample(valid_data);
}
void SampleStatistics::CalculateFromSample(const std::vector<double>& data,
const std::vector<bool>& undefs)
{
std::vector<double> valid_data;
for (int i=0; i<data.size(); i++) {
if (undefs[i] == false)
valid_data.push_back(data[i]);
}
CalculateFromSample(valid_data);
}
void SampleStatistics::CalculateFromSample(const std::vector<double>& data)
{
sample_size = data.size();
if (sample_size == 0) return;
CalcMinMax(data, min, max);
mean = CalcMean(data);
double n = sample_size;
double sum_squares = 0;
for (int i=0, iend = data.size(); i<iend; i++) {
sum_squares += data[i] * data[i];
}
var_without_bessel = (sum_squares/n) - (mean*mean);
sd_without_bessel = sqrt(var_without_bessel);
if (sample_size == 1) {
var_with_bessel = var_without_bessel;
sd_with_bessel = sd_without_bessel;
} else {
var_with_bessel = (n/(n-1)) * var_without_bessel;
sd_with_bessel = sqrt(var_with_bessel);
}
}
/** We assume that the data has been sorted in ascending order */
void
SampleStatistics::
CalculateFromSample(const std::vector<Gda::dbl_int_pair_type>& data_,
const std::vector<bool>& undefs)
{
std::vector<double> data;
for (int i=0, iend = data_.size(); i<iend; i++) {
int id = data_[i].second;
if (!undefs[id]) {
data.push_back(data_[i].first);
}
}
sample_size = data.size();
if (sample_size == 0) return;
min = data[0];
max = data[sample_size-1];
mean = CalcMean(data);
double n = sample_size;
double sum_squares = 0;
for (int i=0, iend = data.size(); i<iend; i++) {
sum_squares += data[i] * data[i];
}
var_without_bessel = (sum_squares/n) - (mean*mean);
sd_without_bessel = sqrt(var_without_bessel);
if (sample_size == 1) {
var_with_bessel = var_without_bessel;
sd_with_bessel = sd_without_bessel;
} else {
var_with_bessel = (n/(n-1)) * var_without_bessel;
sd_with_bessel = sqrt(var_with_bessel);
}
}
string SampleStatistics::ToString()
{
ostringstream ss;
ss << "sample_size = " << sample_size << endl;
ss << "min = " << min << endl;
ss << "max = " << max << endl;
ss << "mean = " << mean << endl;
ss << "var_with_bessel = " << var_with_bessel << endl;
ss << "var_without_bessel = " << var_without_bessel << endl;
ss << "sd_with_bessel = " << sd_with_bessel << endl;
ss << "sd_without_bessel = " << sd_without_bessel << endl;
return ss.str();
}
double SampleStatistics::CalcMin(const std::vector<double>& data)
{
double min = std::numeric_limits<double>::max();
for (int i=0, iend=data.size(); i<iend; i++) {
if ( data[i] < min ) min = data[i];
}
return min;
}
double SampleStatistics::CalcMax(const std::vector<double>& data)
{
double max = -std::numeric_limits<double>::max();
for (int i=0, iend=data.size(); i<iend; i++) {
if ( data[i] > max ) max = data[i];
}
return max;
}
void SampleStatistics::CalcMinMax(const std::vector<double>& data,
double& min, double& max)
{
if (data.size() == 0) return;
min = data[0];
max = data[0];
for (int i=1, iend=data.size(); i<iend; i++) {
if ( data[i] < min ) {
min = data[i];
} else if ( data[i] > max ) {
max = data[i];
}
}
}
double SampleStatistics::CalcMean(const std::vector<double>& data)
{
if (data.size() == 0) return 0;
double total = 0;
for (int i=0, iend=data.size(); i<iend; i++) {
total += data[i];
}
return total / (double) data.size();
}
double SampleStatistics::CalcMean(
const std::vector<Gda::dbl_int_pair_type>& data)
{
if (data.size() == 0) return 0;
double total = 0;
for (int i=0, iend=data.size(); i<iend; i++) {
total += data[i].first;
}
return total / (double) data.size();
}
SimpleLinearRegression::SimpleLinearRegression(const std::vector<double>& X,
const std::vector<double>& Y,
double meanX, double meanY,
double varX, double varY)
: n(0), covariance(0), correlation(0), alpha(0), beta(0), r_squared(0),
std_err_of_estimate(0), std_err_of_beta(0), std_err_of_alpha(0),
t_score_alpha(0), t_score_beta(0), p_value_alpha(0), p_value_beta(0),
valid(false), valid_correlation(false), valid_std_err(false),
error_sum_squares(0)
{
CalculateRegression(X, Y, meanX, meanY, varX, varY);
}
SimpleLinearRegression::SimpleLinearRegression(const std::vector<double>& X,
const std::vector<double>& Y,
const std::vector<bool>& X_undef,
const std::vector<bool>& Y_undef,
double meanX, double meanY,
double varX, double varY)
: n(0), covariance(0), correlation(0), alpha(0), beta(0), r_squared(0),
std_err_of_estimate(0), std_err_of_beta(0), std_err_of_alpha(0),
t_score_alpha(0), t_score_beta(0), p_value_alpha(0), p_value_beta(0),
valid(false), valid_correlation(false), valid_std_err(false),
error_sum_squares(0)
{
std::vector<double> X_valid;
std::vector<double> Y_valid;
for (int i=0; i<X.size(); i++) {
if (X_undef[i] || Y_undef[i])
continue;
X_valid.push_back(X[i]);
Y_valid.push_back(Y[i]);
}
CalculateRegression(X_valid, Y_valid, meanX, meanY, varX, varY);
}
void SimpleLinearRegression::CalculateRegression(const std::vector<double>& X,
const std::vector<double>& Y,
double meanX, double meanY,
double varX, double varY)
{
n = X.size();
if (X.size() != Y.size() || X.size() < 2 )
return;
double expectXY = 0;
for (int i=0, iend=X.size(); i<iend; i++) {
expectXY += X[i]*Y[i];
}
expectXY /= (double) X.size();
covariance = expectXY - meanX * meanY;
if (varX > 4*DBL_MIN) {
beta = covariance / varX;
alpha = meanY - beta * meanX;
valid = true;
}
double SS_tot = varY*Y.size();
error_sum_squares = 0; // error_sum_squares = SS_err
double err=0;
for (int i=0, iend=Y.size(); i<iend; i++) {
err = Y[i] - (alpha + beta * X[i]);
error_sum_squares += err * err;
}
if (error_sum_squares < 16*DBL_MIN) {
r_squared = 1;
} else {
r_squared = 1 - error_sum_squares / SS_tot;
}
if (Y.size()>2 && varX > 4*DBL_MIN) {
// error_sum_squares/(n-k-1), k=1
std_err_of_estimate = error_sum_squares/(Y.size()-2);
std_err_of_estimate = sqrt(std_err_of_estimate);
std_err_of_beta = std_err_of_estimate/sqrt(X.size()*varX);
double sum_x_squared = 0;
for (int i=0, iend=X.size(); i<iend; i++) {
sum_x_squared += X[i] * X[i];
}
std_err_of_alpha = std_err_of_beta * sqrt(sum_x_squared / X.size());
if (std_err_of_alpha >= 16*DBL_MIN) {
t_score_alpha = alpha / std_err_of_alpha;
} else {
t_score_alpha = 100;
}
if (std_err_of_beta >= 16*DBL_MIN) {
t_score_beta = beta / std_err_of_beta;
} else {
t_score_beta = 100;
}
p_value_alpha = TScoreTo2SidedPValue(t_score_alpha, X.size()-2);
p_value_beta = TScoreTo2SidedPValue(t_score_beta, X.size()-2);
valid_std_err = true;
}
double d = sqrt(varX)*sqrt(varY);
if (d > 4*DBL_MIN) {
correlation = covariance / d;
valid_correlation = true;
}
}
double SimpleLinearRegression::TScoreTo2SidedPValue(double tscore, int df)
{
using namespace boost::math;
students_t dist(df);
// Cumulative Distribution Function evaluated at tscore
if ( tscore >= 0) {
return 2*(1.0-cdf(dist, tscore));
} else {
return 2*cdf(dist,tscore);
}
}
string SimpleLinearRegression::ToString()
{
ostringstream ss;
ss << "covariance = " << covariance << endl;
ss << "correlation = " << correlation << endl;
ss << "alpha = " << alpha << endl;
ss << "beta = " << beta << endl;
ss << "r_squared = " << r_squared << endl;
ss << "valid = " << (valid ? "true" : "false") << endl;
ss << "valid_correlation = " << (valid_correlation ? "true" : "false")
<< endl;
ss << "error_sum_squares = " << error_sum_squares << endl;
return ss.str();
}
AxisScale::AxisScale(double data_min_s, double data_max_s, int ticks_s, int lbl_precision_s)
: data_min(0), data_max(0), scale_min(0), scale_max(0),
scale_range(0), tic_inc(0), p(0), ticks(ticks_s), lbl_precision(lbl_precision_s)
{
CalculateScale(data_min_s, data_max_s, ticks_s);
}
AxisScale::AxisScale(const AxisScale& s)
: data_min(s.data_min), data_max(s.data_max),
scale_min(s.scale_min), scale_max(s.scale_max),
scale_range(s.scale_range), tic_inc(s.tic_inc), p(s.p),
tics(s.tics), tics_str(s.tics_str), tics_str_show(s.tics_str_show),
ticks(s.ticks)
{
}
AxisScale& AxisScale::operator=(const AxisScale& s)
{
data_min = s.data_min;
data_max = s.data_max;
scale_min = s.scale_min;
scale_max = s.scale_max;
scale_range = s.scale_range;
tic_inc = s.tic_inc;
p = s.p;
tics = s.tics;
tics_str = s.tics_str;
tics_str_show = s.tics_str_show;
ticks = s.ticks;
return *this;
}
void AxisScale::CalculateScale(double data_min_s, double data_max_s,
const int ticks)
{
if (data_min_s <= data_max_s) {
data_min = data_min_s;
data_max = data_max_s;
} else {
data_min = data_max_s;
data_max = data_min_s;
}
double data_range = data_max - data_min;
if ( data_range <= 2*DBL_MIN ) {
scale_max = ceil((data_max + 0.05)*10)/10;
scale_min = floor((data_min - 0.05)*10)/10;
scale_range = scale_max - scale_min;
p = 1;
tic_inc = scale_range/2;
tics.resize(3);
tics_str.resize(3);
tics[0] = scale_min;
tics[1] = scale_min + tic_inc;
tics[2] = scale_max;
} else {
p = floor(log10(data_range))-1;
scale_max = ceil(data_max / pow((double)10,p)) * pow((double)10,p);
scale_min = floor(data_min / pow((double)10,p)) * pow((double)10,p);
scale_range = scale_max - scale_min;
tic_inc = floor((scale_range / pow((double)10,p))/ticks)
* pow((double)10,p);
if (scale_min + tic_inc*(ticks+1) <= scale_max + 2*DBL_MIN) {
tics.resize(ticks+2);
tics_str.resize(ticks+2);
} else {
tics.resize(ticks+1);
tics_str.resize(ticks+1);
}
for (int i=0, iend=tics.size(); i<iend; i++) {
tics[i] = scale_min + i*tic_inc;
}
}
tics_str_show.resize(tics_str.size());
for (int i=0, iend=tics.size(); i<iend; i++) {
tics_str[i] = GenUtils::DblToStr(tics[i], lbl_precision);
tics_str_show[i] = true;
}
}
/** only display every other tic value */
void AxisScale::SkipEvenTics()
{
for (int i=0; i<tics_str_show.size(); i++) tics_str_show[i] = (i%2 == 0);
}
void AxisScale::ShowAllTics()
{
for (int i=0; i<tics_str_show.size(); i++) tics_str_show[i] = true;
}
string AxisScale::ToString()
{
ostringstream ss;
ss << "data_min = " << data_min << endl;
ss << "data_max = " << data_max << endl;
ss << "scale_min = " << scale_min << endl;
ss << "scale_max = " << scale_max << endl;
ss << "scale_range = " << scale_range << endl;
ss << "p = " << p << endl;
ss << "tic_inc = " << tic_inc << endl;
for (int i=0, iend=tics.size(); i<iend; i++) {
ss << "tics[" << i << "] = " << tics[i];
ss << ", tics_str[" << i << "] = " << tics_str[i] << endl;
}
ss << "Exiting AxisScale::CalculateScale" << endl;
return ss.str();
}
wxString GenUtils::BoolToStr(bool b)
{
return b ? "true" : "false";
}
bool GenUtils::StrToBool(const wxString& s)
{
if (s.CmpNoCase("1") == 0) return true;
if (s.CmpNoCase("true") == 0) return true;
return false;
}
/** If input string has length < width, then prepends (or appends
if pad_left=false) string with spaces so that total length is now width.
If string length >= width, then returns original input string. */
wxString GenUtils::Pad(const wxString& s, int width, bool pad_left)
{
if (s.length() >= width) return s;
int pad_len = width - s.length();
wxString output;
if (!pad_left) output << s;
for (int i=0; i<pad_len; i++) output << " ";
if (pad_left) output << s;
return output;
}
wxString GenUtils::PadTrim(const wxString& s, int width, bool pad_left)
{
if (s.length() > width) {
int trim_w = width - 2; //"xxx..xxx"
int second_w = trim_w / 2;
int first_w = trim_w - second_w;
wxString tmp = s.SubString(0, first_w-2);
tmp << ".." << s.SubString(s.length() - second_w -1, s.length()-1);
return tmp;
}
int pad_len = width - s.length();
wxString output;
if (!pad_left) output << s;
for (int i=0; i<pad_len; i++) output << " ";
if (pad_left) output << s;
return output;
}
wxString GenUtils::DblToStr(double x, int precision)
{
std::stringstream ss;
if (x < 10000000) {
ss << std::fixed;
}
ss << std::setprecision(precision);
ss << x;
return wxString(ss.str().c_str(), wxConvUTF8);
}
wxString GenUtils::PtToStr(const wxPoint& p)
{
std::stringstream ss;
ss << "(" << p.x << "," << p.y << ")";
return wxString(ss.str().c_str(), wxConvUTF8);
}
wxString GenUtils::PtToStr(const wxRealPoint& p)
{
std::stringstream ss;
ss << std::setprecision(5);
ss << "(" << p.x << "," << p.y << ")";
return wxString(ss.str().c_str(), wxConvUTF8);
}
// NOTE: should take into account undefined values.
void GenUtils::DeviationFromMean(int nObs, double* data)
{
if (nObs == 0) return;
double sum = 0.0;
for (int i=0, iend=nObs; i<iend; i++) sum += data[i];
const double mean = sum / (double) nObs;
for (int i=0, iend=nObs; i<iend; i++) data[i] -= mean;
}
void GenUtils::DeviationFromMean(int nObs, double* data, std::vector<bool>& undef)
{
if (nObs == 0) return;
int nValid = 0;
double sum = 0.0;
for (int i=0, iend=nObs; i<iend; i++) {
if (undef[i])
continue;
sum += data[i];
nValid += 1;
}
const double mean = sum / (double) nValid;
for (int i=0, iend=nObs; i<iend; i++) {
data[i] -= mean;
}
}
void GenUtils::DeviationFromMean(std::vector<double>& data)
{
if (data.size() == 0) return;
double sum = 0.0;
for (int i=0, iend=data.size(); i<iend; i++) sum += data[i];
const double mean = sum / (double) data.size();
for (int i=0, iend=data.size(); i<iend; i++) data[i] -= mean;
}
bool GenUtils::StandardizeData(int nObs, double* data)
{
if (nObs <= 1) return false;
GenUtils::DeviationFromMean(nObs, data);
double ssum = 0.0;
for (int i=0, iend=nObs; i<iend; i++) ssum += data[i] * data[i];
const double sd = sqrt(ssum / (double) (nObs-1.0));
if (sd == 0) return false;
for (int i=0, iend=nObs; i<iend; i++) data[i] /= sd;
return true;
}
bool GenUtils::StandardizeData(int nObs, double* data, std::vector<bool>& undef)
{
if (nObs <= 1) return false;
int nValid = 0;
for (int i=0; i<undef.size(); i++) {
if (!undef[i])
nValid += 1;
}
GenUtils::DeviationFromMean(nObs, data, undef);
double ssum = 0.0;
for (int i=0, iend=nObs; i<iend; i++) {
if (undef[i])
continue;
ssum += data[i] * data[i];
}
const double sd = sqrt(ssum / (double) (nValid-1.0));
if (sd == 0)
return false;
for (int i=0, iend=nObs; i<iend; i++) {
data[i] /= sd;
}
return true;
}
bool GenUtils::StandardizeData(std::vector<double>& data)
{
if (data.size() <= 1) return false;
GenUtils::DeviationFromMean(data);
double ssum = 0.0;
for (int i=0, iend=data.size(); i<iend; i++) ssum += data[i] * data[i];
const double sd = sqrt(ssum / (double) (data.size()-1.0));
if (sd == 0) return false;
for (int i=0, iend=data.size(); i<iend; i++) data[i] /= sd;
return true;
}
wxString GenUtils::swapExtension(const wxString& fname, const wxString& ext)
{
if (ext.IsEmpty()) return fname;
wxString prefix = fname.BeforeLast('.');
if (prefix.IsEmpty()) return fname + "." + ext;
return prefix + "." + ext;
}
wxString GenUtils::GetFileDirectory(const wxString& path)
{
int pos = path.Find('/',true);
if (pos >= 0)
return path.Left(pos) + '/';
pos = path.Find('\\',true);
if (pos >= 0)
return path.Left(pos) + '\\';
return wxEmptyString;
}
wxString GenUtils::GetFileName(const wxString& path)
{
int pos = path.Find('/',true);
if (pos >= 0)
return path.Right(path.length() - pos - 1);
pos = path.Find('\\',true);
if (pos >= 0)
return path.Right(path.length() - pos - 1);
return wxEmptyString;
}
wxString GenUtils::GetFileNameNoExt(const wxString& path)
{
wxString fname = GetFileName(path);
int pos = fname.Find('.');
if (pos >=0)
return fname.SubString(0, pos-1);
return fname;
}
wxString GenUtils::GetFileExt(const wxString& path)
{
int pos = path.Find('.',true);
if (pos >= 0)
return path.Right(path.length() - pos - 1);
return wxEmptyString;
}
wxString GenUtils::RestorePath(const wxString& proj_path, const wxString& path)
{
wxFileName path_fn(path);
if (path_fn.IsAbsolute()) return path;
if (!path_fn.IsOk()) return path;
wxFileName wd;
wxFileName prj_path_fn(proj_path);
if (prj_path_fn.GetExt().IsEmpty()) {
wd.AssignDir(proj_path);
} else {
wd.AssignDir(prj_path_fn.GetPath());
}
if (!wd.IsOk() || !wd.IsDir() || !wd.IsAbsolute()) return path;
if (path_fn.MakeAbsolute(wd.GetPath())) {
if (path_fn.GetExt().IsEmpty()) {
return path_fn.GetPath();
}
return path_fn.GetFullPath();
}
return path;
}
wxString GenUtils::SimplifyPath(const wxString& proj_path, const wxString& path)
{
wxFileName wd;
wxFileName proj_path_fn(proj_path);
if (proj_path_fn.GetExt().IsEmpty()) {
wd.AssignDir(proj_path);
} else {
wd.AssignDir(proj_path_fn.GetPath());
}
return GenUtils::SimplifyPath(wd, path);
}
wxString GenUtils::SimplifyPath(const wxFileName& wd, const wxString& path)
{
wxFileName path_fn(path);
if (!wd.IsOk() || !wd.IsDir() || !wd.IsAbsolute() ||
path_fn.IsRelative()) return path;
wxFileName p;
if (wxDirExists(path)) {
p.AssignDir(path);
} else {
p.Assign(path);
}
if (p.GetVolume() != wd.GetVolume()) return path;
wxArrayString p_dirs = p.GetDirs();
wxArrayString wd_dirs = wd.GetDirs();
if (p_dirs.size() < wd_dirs.size()) return path;
for (int i=0, sz=wd_dirs.size(); i<sz; ++i) {
if (p_dirs[i] != wd_dirs[i]) return path;
}
if (p.MakeRelativeTo(wd.GetPath())) {
if (p.IsDir()) {
return p.GetPath();
}
return p.GetFullPath();
}
return path;
}
void GenUtils::SplitLongPath(const wxString& path,
std::vector<wxString>& parts,
wxString& html_formatted,
int max_chars_per_part)
{
if (max_chars_per_part < 15) max_chars_per_part = 15;
parts.clear();
html_formatted = "";
if (path.size() <= max_chars_per_part) {
parts.push_back(path);
html_formatted = path;
return;
}
wxFileName fn(path);
wxArrayString dirs(fn.GetDirs());
if (dirs.size() <= 1) {
parts.push_back(path);
html_formatted = path;
return;
}
wxString sep = wxFileName::GetPathSeparator();
// Note: always add sep char after dir added
if (path.SubString(0,0) == sep) {
parts.push_back(sep);
} else if (fn.HasVolume()) {