diff --git a/include/evalhyd/detail/determinist/evaluator.hpp b/include/evalhyd/detail/determinist/evaluator.hpp
index 6295a20130e5cc34ad993185203cc8a9a34597ef..bd2b7457ef6ef4f4e64fbe2a050730360622acfd 100644
--- a/include/evalhyd/detail/determinist/evaluator.hpp
+++ b/include/evalhyd/detail/determinist/evaluator.hpp
@@ -74,7 +74,7 @@ namespace evalhyd
t_msk = xt::ones<bool>({n_msk, n_srs, n_tim});
xt::view(t_msk, xt::all()) =
xt::view(msk, xt::all(), xt::newaxis(), xt::all());
- for (int m = 0; m < n_msk; m++) {
+ for (std::size_t m = 0; m < n_msk; m++) {
xt::view(t_msk, m) =
xt::where(obs_nan | prd_nan,
false, xt::view(t_msk, m));
@@ -116,13 +116,13 @@ namespace evalhyd
void Evaluator<D2, B2>::calc_mean_obs()
{
mean_obs = xt::zeros<double>(mean_dims);
- for (int m = 0; m < n_msk; m++) {
+ for (std::size_t m = 0; m < n_msk; m++) {
// apply the mask
// (using NaN workaround until reducers work on masked_view)
auto obs_masked = xt::where(xt::view(t_msk, m), q_obs, NAN);
// compute variable one sample at a time
- for (int e = 0; e < n_exp; e++) {
+ for (std::size_t e = 0; e < n_exp; e++) {
// apply the bootstrap sampling
auto obs = xt::view(obs_masked, xt::all(), b_exp[e]);
xt::view(mean_obs, m, e) =
@@ -143,13 +143,13 @@ namespace evalhyd
void Evaluator<D2, B2>::calc_mean_prd()
{
mean_prd = xt::zeros<double>(mean_dims);
- for (int m = 0; m < n_msk; m++) {
+ for (std::size_t m = 0; m < n_msk; m++) {
// apply the mask
// (using NaN workaround until reducers work on masked_view)
auto prd_masked = xt::where(xt::view(t_msk, m), q_prd, NAN);
// compute variable one sample at a time
- for (int e = 0; e < n_exp; e++) {
+ for (std::size_t e = 0; e < n_exp; e++) {
// apply the bootstrap sampling
auto prd = xt::view(prd_masked, xt::all(), b_exp[e]);
xt::view(mean_prd, m, e) =
@@ -190,12 +190,12 @@ namespace evalhyd
void Evaluator<D2, B2>::calc_quad_obs()
{
quad_obs = xt::zeros<double>(inter_dims);
- for (int m = 0; m < n_msk; m++) {
+ for (std::size_t m = 0; m < n_msk; m++) {
// apply the mask
// (using NaN workaround until reducers work on masked_view)
auto obs_masked = xt::where(xt::view(t_msk, m), q_obs, NAN);
- for (int e = 0; e < n_exp; e++) {
+ for (std::size_t e = 0; e < n_exp; e++) {
xt::view(quad_obs, m, e) = xt::square(
obs_masked - xt::view(mean_obs, m, e)
);
@@ -218,12 +218,12 @@ namespace evalhyd
void Evaluator<D2, B2>::calc_quad_prd()
{
quad_prd = xt::zeros<double>(inter_dims);
- for (int m = 0; m < n_msk; m++) {
+ for (std::size_t m = 0; m < n_msk; m++) {
// apply the mask
// (using NaN workaround until reducers work on masked_view)
auto prd_masked = xt::where(xt::view(t_msk, m), q_prd, NAN);
- for (int e = 0; e < n_exp; e++) {
+ for (std::size_t e = 0; e < n_exp; e++) {
xt::view(quad_prd, m, e) = xt::square(
prd_masked - xt::view(mean_prd, m, e)
);
@@ -260,14 +260,14 @@ namespace evalhyd
// calculate error in timing and dynamics $r_{pearson}$
// (Pearson's correlation coefficient)
r_pearson = xt::zeros<double>(inner_dims);
- for (int m = 0; m < n_msk; m++) {
+ for (std::size_t m = 0; m < n_msk; m++) {
// apply the mask
// (using NaN workaround until reducers work on masked_view)
auto prd_masked = xt::where(xt::view(t_msk, m), q_prd, NAN);
auto obs_masked = xt::where(xt::view(t_msk, m), q_obs, NAN);
// compute variable one sample at a time
- for (int e = 0; e < n_exp; e++) {
+ for (std::size_t e = 0; e < n_exp; e++) {
auto prd = xt::view(prd_masked, xt::all(), b_exp[e]);
auto obs = xt::view(obs_masked, xt::all(), b_exp[e]);
auto r_num = xt::nansum(
@@ -309,14 +309,14 @@ namespace evalhyd
{
// calculate error in spread of flow $alpha$
alpha = xt::zeros<double>(inner_dims);
- for (int m = 0; m < n_msk; m++) {
+ for (std::size_t m = 0; m < n_msk; m++) {
// apply the mask
// (using NaN workaround until reducers work on masked_view)
auto prd_masked = xt::where(xt::view(t_msk, m), q_prd, NAN);
auto obs_masked = xt::where(xt::view(t_msk, m), q_obs, NAN);
// compute variable one sample at a time
- for (int e = 0; e < n_exp; e++) {
+ for (std::size_t e = 0; e < n_exp; e++) {
auto prd = xt::view(prd_masked, xt::all(), b_exp[e]);
auto obs = xt::view(obs_masked, xt::all(), b_exp[e]);
xt::view(alpha, m, e) =
@@ -342,14 +342,14 @@ namespace evalhyd
{
// calculate $bias$
bias = xt::zeros<double>(inner_dims);
- for (int m = 0; m < n_msk; m++) {
+ for (std::size_t m = 0; m < n_msk; m++) {
// apply the mask
// (using NaN workaround until reducers work on masked_view)
auto prd_masked = xt::where(xt::view(t_msk, m), q_prd, NAN);
auto obs_masked = xt::where(xt::view(t_msk, m), q_obs, NAN);
// compute variable one sample at a time
- for (int e = 0; e < n_exp; e++) {
+ for (std::size_t e = 0; e < n_exp; e++) {
auto prd = xt::view(prd_masked, xt::all(), b_exp[e]);
auto obs = xt::view(obs_masked, xt::all(), b_exp[e]);
xt::view(bias, m, e) =
@@ -371,13 +371,13 @@ namespace evalhyd
{
// compute RMSE
RMSE = xt::zeros<double>(final_dims);
- for (int m = 0; m < n_msk; m++) {
+ for (std::size_t m = 0; m < n_msk; m++) {
// apply the mask
// (using NaN workaround until reducers work on masked_view)
auto quad_err_masked = xt::where(xt::view(t_msk, m), quad_err, NAN);
// compute variable one sample at a time
- for (int e = 0; e < n_exp; e++) {
+ for (std::size_t e = 0; e < n_exp; e++) {
auto err2 = xt::view(quad_err_masked, xt::all(), b_exp[e]);
xt::view(RMSE, xt::all(), m, e) = xt::sqrt(xt::nanmean(err2, -1));
}
@@ -399,13 +399,13 @@ namespace evalhyd
void Evaluator<D2, B2>::calc_NSE()
{
NSE = xt::zeros<double>(final_dims);
- for (int m = 0; m < n_msk; m++) {
+ for (std::size_t m = 0; m < n_msk; m++) {
// apply the mask
// (using NaN workaround until reducers work on masked_view)
auto quad_err_masked = xt::where(xt::view(t_msk, m), quad_err, NAN);
// compute variable one sample at a time
- for (int e = 0; e < n_exp; e++) {
+ for (std::size_t e = 0; e < n_exp; e++) {
// compute squared errors operands
auto err2 = xt::view(quad_err_masked, xt::all(), b_exp[e]);
xt::xtensor<double, 1> f_num =
@@ -438,8 +438,8 @@ namespace evalhyd
void Evaluator<D2, B2>::calc_KGE()
{
KGE = xt::zeros<double>(final_dims);
- for (int m = 0; m < n_msk; m++) {
- for (int e = 0; e < n_exp; e++) {
+ for (std::size_t m = 0; m < n_msk; m++) {
+ for (std::size_t e = 0; e < n_exp; e++) {
// compute KGE
xt::view(KGE, xt::all(), m, e) = 1 - xt::sqrt(
xt::square(xt::view(r_pearson, m, e) - 1)
@@ -474,8 +474,8 @@ namespace evalhyd
void Evaluator<D2, B2>::calc_KGEPRIME()
{
KGEPRIME = xt::zeros<double>(final_dims);
- for (int m = 0; m < n_msk; m++) {
- for (int e = 0; e < n_exp; e++) {
+ for (std::size_t m = 0; m < n_msk; m++) {
+ for (std::size_t e = 0; e < n_exp; e++) {
// calculate error in spread of flow $gamma$
auto gamma = xt::view(alpha, m, e)
* (xt::view(mean_obs, m, e, xt::all(), 0)
diff --git a/include/evalhyd/detail/probabilist/evaluator.hpp b/include/evalhyd/detail/probabilist/evaluator.hpp
index 07bd764574e4dbfc4937fc7c46c6fd85559883f9..7574b197d5d9098f034838b9c3e32b77d868b54c 100644
--- a/include/evalhyd/detail/probabilist/evaluator.hpp
+++ b/include/evalhyd/detail/probabilist/evaluator.hpp
@@ -24,7 +24,7 @@ namespace evalhyd
using view1d_xtensor2d_double_type = decltype(
xt::view(
std::declval<const D2&>(),
- std::declval<int>(),
+ std::declval<std::size_t>(),
xt::all()
)
);
@@ -32,8 +32,8 @@ namespace evalhyd
using view2d_xtensor4d_double_type = decltype(
xt::view(
std::declval<const D4&>(),
- std::declval<int>(),
- std::declval<int>(),
+ std::declval<std::size_t>(),
+ std::declval<std::size_t>(),
xt::all(),
xt::all()
)
@@ -42,8 +42,8 @@ namespace evalhyd
using view2d_xtensor4d_bool_type = decltype(
xt::view(
std::declval<const B4&>(),
- std::declval<int>(),
- std::declval<int>(),
+ std::declval<std::size_t>(),
+ std::declval<std::size_t>(),
xt::all(),
xt::all()
)
@@ -187,7 +187,7 @@ namespace evalhyd
// compute variable one sample at a time
bar_o = xt::zeros<double>({n_msk, n_exp, n_thr});
- for (int e = 0; e < n_exp; e++)
+ for (std::size_t e = 0; e < n_exp; e++)
{
// apply the bootstrap sampling
auto o_k_masked_sampled =
@@ -284,14 +284,14 @@ namespace evalhyd
BS = xt::zeros<double>({n_msk, n_exp, n_thr});
// compute variable one mask at a time to minimise memory imprint
- for (int m = 0; m < n_msk; m++)
+ for (std::size_t m = 0; m < n_msk; m++)
{
// apply the mask
// (using NaN workaround until reducers work on masked_view)
auto bs_masked = xt::where(xt::row(t_msk, m), bs, NAN);
// compute variable one sample at a time
- for (int e = 0; e < n_exp; e++)
+ for (std::size_t e = 0; e < n_exp; e++)
{
// apply the bootstrap sampling
auto bs_masked_sampled =
@@ -354,7 +354,7 @@ namespace evalhyd
BS_CRD = xt::zeros<double>({n_msk, n_exp, n_thr, std::size_t {3}});
// compute variable one mask at a time to minimise memory imprint
- for (int m = 0; m < n_msk; m++)
+ for (std::size_t m = 0; m < n_msk; m++)
{
// apply the mask
// (using NaN workaround until reducers work on masked_view)
@@ -362,7 +362,7 @@ namespace evalhyd
auto y_k_masked = xt::where(xt::row(t_msk, m), y_k, NAN);
// compute variable one sample at a time
- for (int e = 0; e < n_exp; e++)
+ for (std::size_t e = 0; e < n_exp; e++)
{
// apply the bootstrap sampling
auto o_k_masked_sampled =
@@ -479,7 +479,7 @@ namespace evalhyd
BS_LBD = xt::zeros<double>({n_msk, n_exp, n_thr, std::size_t {3}});
// compute variable one mask at a time to minimise memory imprint
- for (int m = 0; m < n_msk; m++)
+ for (std::size_t m = 0; m < n_msk; m++)
{
// apply the mask
// (using NaN workaround until reducers work on masked_view)
@@ -487,7 +487,7 @@ namespace evalhyd
auto y_k_masked = xt::where(xt::row(t_msk, m), y_k, NAN);
// compute variable one sample at a time
- for (int e = 0; e < n_exp; e++)
+ for (std::size_t e = 0; e < n_exp; e++)
{
// apply the bootstrap sampling
auto o_k_masked_sampled =
@@ -601,7 +601,7 @@ namespace evalhyd
BSS = xt::zeros<double>({n_msk, n_exp, n_thr});
// compute variable one mask at a time to minimise memory imprint
- for (int m = 0; m < n_msk; m++)
+ for (std::size_t m = 0; m < n_msk; m++)
{
// apply the mask
// (using NaN workaround until reducers work on masked_view)
@@ -609,7 +609,7 @@ namespace evalhyd
auto bs_masked = xt::where(xt::row(t_msk, m), bs, NAN);
// compute variable one sample at a time
- for (int e = 0; e < n_exp; e++)
+ for (std::size_t e = 0; e < n_exp; e++)
{
// apply the bootstrap sampling
auto o_k_masked_sampled =
@@ -707,14 +707,14 @@ namespace evalhyd
QS = xt::zeros<double>({n_msk, n_exp, n_mbr});
// compute variable one mask at a time to minimise memory imprint
- for (int m = 0; m < n_msk; m++)
+ for (std::size_t m = 0; m < n_msk; m++)
{
// apply the mask
// (using NaN workaround until reducers work on masked_view)
auto qs_masked = xt::where(xt::row(t_msk, m), qs, NAN);
// compute variable one sample at a time
- for (int e = 0; e < n_exp; e++)
+ for (std::size_t e = 0; e < n_exp; e++)
{
// apply the bootstrap sampling
auto qs_masked_sampled =
@@ -773,14 +773,14 @@ namespace evalhyd
CRPS = xt::zeros<double>({n_msk, n_exp});
// compute variable one mask at a time to minimise memory imprint
- for (int m = 0; m < n_msk; m++)
+ for (std::size_t m = 0; m < n_msk; m++)
{
// apply the mask
// (using NaN workaround until reducers work on masked_view)
auto crps_masked = xt::where(xt::row(t_msk, m), crps, NAN);
// compute variable one sample at a time
- for (int e = 0; e < n_exp; e++)
+ for (std::size_t e = 0; e < n_exp; e++)
{
// apply the bootstrap sampling
auto crps_masked_sampled =
diff --git a/include/evalhyd/detail/uncertainty.hpp b/include/evalhyd/detail/uncertainty.hpp
index fd26b45e17c868d9ad49b0498862a283522a5aa1..e55d04638e6d8aa42021b3e36bedad7cddf7c973 100644
--- a/include/evalhyd/detail/uncertainty.hpp
+++ b/include/evalhyd/detail/uncertainty.hpp
@@ -58,7 +58,7 @@ namespace evalhyd
// check constant time interval
auto ti = x_timepoints[1] - x_timepoints[0];
- for (int t = 1; t < x_timepoints.size() - 1; t++)
+ for (std::size_t t = 1; t < x_timepoints.size() - 1; t++)
if (x_timepoints[t + 1] - x_timepoints[t] != ti) {
throw std::runtime_error(
"time interval not constant across datetimes"
@@ -128,7 +128,7 @@ namespace evalhyd
);
xt::xtensor<int, 1> idx = xt::concatenate(xt::xtuple(i0, i1), 0);
- for (int p = 2; p < exp.size(); p++) {
+ for (std::size_t p = 2; p < exp.size(); p++) {
auto i = xt::flatten_indices(
xt::argwhere(xt::equal(year_blocks, exp(p)))
);
diff --git a/include/evalhyd/evald.hpp b/include/evalhyd/evald.hpp
index 32e9c161506d9b0bfb69d633eb21470bc3381aba..625ae232477215c3a62fc58e56463f11ba58a4f9 100644
--- a/include/evalhyd/evald.hpp
+++ b/include/evalhyd/evald.hpp
@@ -213,12 +213,12 @@ namespace evalhyd
// if t_msk provided, it takes priority
if (t_msk_.size() > 0)
return t_msk_;
- // else if m_cdt provided, use them to generate t_msk
+ // else if m_cdt provided, use them to generate t_msk
else if (m_cdt.size() > 0)
{
B2 c_msk = xt::zeros<bool>({n_msk, n_tim});
- for (int m = 0; m < n_msk; m++)
+ for (std::size_t m = 0; m < n_msk; m++)
xt::view(c_msk, m) =
masks::generate_mask_from_conditions(
m_cdt[0], xt::view(q_obs_, 0), q_prd_
@@ -226,7 +226,7 @@ namespace evalhyd
return c_msk;
}
- // if neither t_msk nor m_cdt provided, generate dummy mask
+ // if neither t_msk nor m_cdt provided, generate dummy mask
else
return B2({xt::ones<bool>({std::size_t{1}, n_tim})});
};
diff --git a/include/evalhyd/evalp.hpp b/include/evalhyd/evalp.hpp
index 0103ab3c01e3349b49a55e7b8534ead22d178f1e..7a044c3eccc50dc431c07d353dfedb2755eddcfc 100644
--- a/include/evalhyd/evalp.hpp
+++ b/include/evalhyd/evalp.hpp
@@ -249,9 +249,9 @@ namespace evalhyd
auto gen_msk = [&]() {
B4 c_msk = xt::zeros<bool>({n_sit, n_ltm, n_msk, n_tim});
if (m_cdt.size() > 0)
- for (int s = 0; s < n_sit; s++)
- for (int l = 0; l < n_ltm; l++)
- for (int m = 0; m < n_msk; m++)
+ for (std::size_t s = 0; s < n_sit; s++)
+ for (std::size_t l = 0; l < n_ltm; l++)
+ for (std::size_t m = 0; m < n_msk; m++)
xt::view(c_msk, s, l, m) =
masks::generate_mask_from_conditions(
xt::view(m_cdt, s, m),
@@ -291,9 +291,9 @@ namespace evalhyd
auto summary = bootstrap.find("summary")->second;
// compute variables one site at a time to minimise memory imprint
- for (int s = 0; s < n_sit; s++)
+ for (std::size_t s = 0; s < n_sit; s++)
// compute variables one lead time at a time to minimise memory imprint
- for (int l = 0; l < n_ltm; l++)
+ for (std::size_t l = 0; l < n_ltm; l++)
{
// instantiate probabilist evaluator
const auto q_obs_v = xt::view(q_obs_, s, xt::all());
@@ -335,7 +335,7 @@ namespace evalhyd
}
// retrieve or compute requested metrics
- for (int m = 0; m < metrics.size(); m++)
+ for (std::size_t m = 0; m < metrics.size(); m++)
{
const auto& metric = metrics[m];