diff --git a/README.md b/README.md
index 080249678daf59219c818e87522d4cb6eafc8486..170750a4537ba1fa3d79bc15d017180b50ec4c3d 100644
--- a/README.md
+++ b/README.md
@@ -1,9 +1,9 @@
[](https://git.dynare.org/DoraK/mdbnomics/commits/master)
-This MATLAB/Octave toolbox comes with routines to access DBnomics time series from MATLAB.
+This MATLAB toolbox comes with routines to access DBnomics time series from MATLAB.
-The package is compatible with MATLAB 2019b and following versions, and (almost compatible with)
-the latest Octave version.
+The package is compatible with MATLAB 2019b and following versions.
+Octave compability will follow after the release of Octave 6 (an implementation to webread/webwrite/jsondecode is required).
## Installation
@@ -19,12 +19,12 @@ or downloading a zip archive:
## Usage
-Add the `mdbnomics/src` folder to the MATLAB/Octave path, and run the following command (on MATLAB/Octave) prompt:
+Add the `mdbnomics/src` folder to the MATLAB path, and run the following command (on MATLAB) prompt:
>> initialize_mdbnomics()
which, depending on your system, will add the necessary subfolders to
-the MATLAB/Octave path.
+the MATLAB path.
You are then ready to go. A full documentation will come soon.
diff --git a/archive/template.m b/archive/template.m
index 51cdb180fad4951c60cf6b556bbbe60d08c71fcd..5e51621dd3262b59ac069799358184b2b8d28257 100644
--- a/archive/template.m
+++ b/archive/template.m
@@ -1,44 +1,41 @@
% fetch series by provider code and dataset code
-test = fetch_series('provider_code', 'AMECO', 'dataset_code', 'UVGD', 'max_nb_series', 500);
-ds = to_dseries(df);
+test = fetch_series('provider_code', 'AMECO', 'dataset_code', 'UVGD', 'max_nb_series', 50);
+ds = dbnomics_to_dseries(test);
% fetch one series by ID
df_id = fetch_series('series_ids','AMECO/ZUTN/EA19.1.0.0.0.ZUTN');
-ds_id = to_dseries(df_id);
+ds_id = dbnomics_to_dseries(df_id);
% fetch multiple series by ID
df_ids = fetch_series('series_ids', {'AMECO/ZUTN/EA19.1.0.0.0.ZUTN', 'AMECO/ZUTN/DNK.1.0.0.0.ZUTN'});
-ds_ids = to_dseries(df_ids);
+ds_ids = dbnomics_to_dseries(df_ids);
% fetch many series by ID from different datasets
+df_ids_sets = fetch_series('series_ids', {'AMECO/ZUTN/EA19.1.0.0.0.ZUTN', 'AMECO/ZUTN/DNK.1.0.0.0.ZUTN', 'IMF/CPI/A.AT.PCPIT_IX'});
+ds_ids_sets = dbnomics_to_dseries(df_ids_sets);
% fetch time series by code mask
df_code_mask1 = fetch_series('provider_code', 'IMF', 'dataset_code', 'CPI', 'series_code', 'M.FR+DE.PCPIEC_IX+PCPIA_IX');
-ds_code_mask1 = to_dseries(df_code_mask1);
+ds_code_mask1 = dbnomics_to_dseries(df_code_mask1);
df_code_mask2 = fetch_series('provider_code', 'IMF', 'dataset_code', 'CPI', 'series_code', '.FR.PCPIEC_WT');
-ds_code_mask2 = to_dseries(df_code_mask1);
+ds_code_mask2 = dbnomics_to_dseries(df_code_mask2);
% df_code_mask3 = fetch_series('provider_code', 'IMF', 'dataset_code', 'CPI', 'series_code', 'M..PCPIEC_IX+PCPIA_IX', 'max_nb_series', 400);
% fetch series by dimensions
df_dim = fetch_series('provider_code','AMECO', 'dataset_code', 'ZUTN', 'dimensions', '{"geo":["dnk"]}');
-ds_dim = to_dseries(df_dim);
+ds_dim = dbnomics_to_dseries(df_dim);
df_dims = fetch_series('provider_code','WB','dataset_code','DB', 'dimensions', '{"country":["ES","FR","IT"],"indicator":["IC.REG.COST.PC.FE.ZS.DRFN"]}');
-ds_dims = to_dseries(df_dims);
+ds_dims = dbnomics_to_dseries(df_dims);
% fetch series by api link
df_link = fetch_series_by_api_link('https://api.db.nomics.world/v22/series/WB/DB?observations=1&dimensions=%7B%22country%22%3A%5B%22FR%22%2C%22IT%22%2C%22ES%22%5D%2C%22indicator%22%3A%5B%22IC.REG.COST.PC.FE.ZS.DRFN%22%5D%7D');
-ds_link = to_dseries(df_link);
+ds_link = dbnomics_to_dseries(df_link);
% fetch series from the cart
df_cart = fetch_series_by_api_link('https://api.db.nomics.world/v22/series?series_ids=AMECO%2FZUTN%2FEA19.1.0.0.0.ZUTN&observations=1');
-ds_cart = to_dseries(df_cart);
-
-%%
-% fetch many series by ID from different datasets
-df_ids_sets = fetch_series('series_ids', {'AMECO/ZUTN/EA19.1.0.0.0.ZUTN', 'AMECO/ZUTN/DNK.1.0.0.0.ZUTN', 'IMF/CPI/A.AT.PCPIT_IX'});
-ds_ids_sets = to_dseries(df_ids_sets);
+ds_cart = dbnomics_to_dseries(df_cart);
% fetch multi frequency series
df_multi_freq = fetch_series('series_ids', {'BEA/NIUnderlyingDetail-U001BC/S315-A',...
diff --git a/src/fetch_series.m b/src/fetch_series.m
index 5ef4655220f9e6f528ffe78bc3bf4031da3fe888..511f9ffabc0daf1135818eb646264b2f55f70fed 100644
--- a/src/fetch_series.m
+++ b/src/fetch_series.m
@@ -278,7 +278,7 @@ end
%$ t(3) = dassert(df(2,2), {'AMECO'});
%$ t(4) = dassert(length(unique(df(2:end,3))),1);
%$ t(5) = dassert(df(2,3), {'ZUTN'});
-%$ t(6) = dassert(length(unique(df(2:end,5))), 49);
+%$ t(6) = dassert(length(unique(df(2:end,5))), 48);
%$ end
%$
%$ T = all(t);
diff --git a/src/fetch_series_by_api_link.m b/src/fetch_series_by_api_link.m
index adc6fc784aba2b14a797b3f560e69c3b3e714bba..14768d1dcb5f9e83c5575338004e5b0894cca476 100644
--- a/src/fetch_series_by_api_link.m
+++ b/src/fetch_series_by_api_link.m
@@ -86,7 +86,12 @@ else
series_list = [series_list, filtered_series_list];
end
- df = cell(length(series_list{1}.value)*length(series_list)+1,length(ordered_columns_names));
+ %%%%%%% OPTIMIZE SIZE PRE-ALLOCATION %%%%%%%
+ rows_ = 0;
+ for s = 1:length(series_list)
+ rows_ = rows_ + length(series_list{s}.value);
+ end
+ df = cell(rows_+1,length(ordered_columns_names));
df(1,:) = ordered_columns_names;
series_length=0;
diff --git a/src/utils/normalize_value.m b/src/utils/normalize_value.m
index fb2b896885363fe2715749c1b5ef4f207964f602..a94ef91d625beefbf24e56a8dc22ba6651dc28e4 100644
--- a/src/utils/normalize_value.m
+++ b/src/utils/normalize_value.m
@@ -30,10 +30,7 @@ function series = normalize_value(series)
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
if iscell(series.value)
-% series.original_value = cellfun(@char, series.value, 'UniformOutput', false);
series.original_value = cellfun(@num2str,series.value,'un',0);
-
-% series.original_value = char(series.value);
series.value(strcmp(series.value, 'NA')) = {NaN};
else
series.original_value = num2cell(series.value);