Cosmetics

README.md

 [![pipeline status](https://git.dynare.org/DoraK/mdbnomics/badges/master/pipeline.svg)](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.
 

archive/template.m

 % 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',...

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);

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;

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);