diff --git a/README.md b/README.md
index 170750a4537ba1fa3d79bc15d017180b50ec4c3d..b7ba485c8a1992adc466b3d6b4a9bcb305a88f6e 100644
--- a/README.md
+++ b/README.md
@@ -2,8 +2,8 @@
This MATLAB toolbox comes with routines to access DBnomics time series from MATLAB.
-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).
+The package is compatible with MATLAB 2015a and following versions.
+Octave compatibility will follow after the release of Octave 6 (an implementation to webread/webwrite/jsondecode is required).
## Installation
@@ -47,17 +47,17 @@ The returned data is stored in the `df_id` variable. Its type is a cell array. T
>> tab_id = cell2table(df_id(2:end,:), 'VariableNames', df_id(1,:));
>> tab_id(1:3,:)
-
- 3×16 table
+
+ 3�16 table
x_frequency provider_code dataset_code dataset_name series_code series_name original_period period original_value value freq unit geo Frequency Unit Country
___________ _____________ ____________ __________________________________________________________________ _____________________ ____________________________________________________________ _______________ ______________ ______________ _____ _____ ___________________________________ ________ ____________ _____________________________________ _____________
- {'annual'} {'AMECO'} {'ZUTN'} {'Unemployment rate: total :- Member States: definition EUROSTAT'} {'EA19.1.0.0.0.ZUTN'} {'Annually – (Percentage of active population) – Euro area'} {'1960'} {'1960-01-01'} {'NA'} NaN {'a'} {'percentage-of-active-population'} {'ea19'} {'Annually'} {'(Percentage of active population)'} {'Euro area'}
- {'annual'} {'AMECO'} {'ZUTN'} {'Unemployment rate: total :- Member States: definition EUROSTAT'} {'EA19.1.0.0.0.ZUTN'} {'Annually – (Percentage of active population) – Euro area'} {'1961'} {'1961-01-01'} {'NA'} NaN {'a'} {'percentage-of-active-population'} {'ea19'} {'Annually'} {'(Percentage of active population)'} {'Euro area'}
- {'annual'} {'AMECO'} {'ZUTN'} {'Unemployment rate: total :- Member States: definition EUROSTAT'} {'EA19.1.0.0.0.ZUTN'} {'Annually – (Percentage of active population) – Euro area'} {'1962'} {'1962-01-01'} {'NA'} NaN {'a'} {'percentage-of-active-population'} {'ea19'} {'Annually'} {'(Percentage of active population)'} {'Euro area'}
-
+ {'annual'} {'AMECO'} {'ZUTN'} {'Unemployment rate: total :- Member States: definition EUROSTAT'} {'EA19.1.0.0.0.ZUTN'} {'Annually � (Percentage of active population) � Euro area'} {'1960'} {'1960-01-01'} {'NA'} NaN {'a'} {'percentage-of-active-population'} {'ea19'} {'Annually'} {'(Percentage of active population)'} {'Euro area'}
+ {'annual'} {'AMECO'} {'ZUTN'} {'Unemployment rate: total :- Member States: definition EUROSTAT'} {'EA19.1.0.0.0.ZUTN'} {'Annually � (Percentage of active population) � Euro area'} {'1961'} {'1961-01-01'} {'NA'} NaN {'a'} {'percentage-of-active-population'} {'ea19'} {'Annually'} {'(Percentage of active population)'} {'Euro area'}
+ {'annual'} {'AMECO'} {'ZUTN'} {'Unemployment rate: total :- Member States: definition EUROSTAT'} {'EA19.1.0.0.0.ZUTN'} {'Annually � (Percentage of active population) � Euro area'} {'1962'} {'1962-01-01'} {'NA'} NaN {'a'} {'percentage-of-active-population'} {'ea19'} {'Annually'} {'(Percentage of active population)'} {'Euro area'}
+
>>
-
+
In such cell array, you will always find at least those columns:
* `x_frequency`: (harmonized frequency generated by DBnomics)
diff --git a/archive/template.m b/archive/template.m
deleted file mode 100644
index 5e51621dd3262b59ac069799358184b2b8d28257..0000000000000000000000000000000000000000
--- a/archive/template.m
+++ /dev/null
@@ -1,54 +0,0 @@
-% fetch series by provider code and dataset code
-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 = 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 = 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 = 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 = 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 = 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 = 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 = 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 = dbnomics_to_dseries(df_cart);
-
-% fetch multi frequency series
-df_multi_freq = fetch_series('series_ids', {'BEA/NIUnderlyingDetail-U001BC/S315-A',...
- 'BEA/NIUnderlyingDetail-U001BC/S315-Q',...
- 'BEA/NIUnderlyingDetail-U001BC/S315-M'});
-
-% fetch one series and apply interpolation filter
-filters_ = '[{"code": "interpolate", "parameters": {"frequency": "monthly", "method": "spline"}}]';
-df_filter = fetch_series('series_ids', 'AMECO/ZUTN/EA19.1.0.0.0.ZUTN',...
- 'dbnomics_filters', filters_);
-
-% fetch multiple series and apply interpolation filter
-df_filters = fetch_series('series_ids', {'AMECO/ZUTN/EA19.1.0.0.0.ZUTN', 'AMECO/ZUTN/DNK.1.0.0.0.ZUTN'},...
- 'dbnomics_filters', filters_);
-
-
\ No newline at end of file
diff --git a/src/fetch_series_by_api_link.m b/src/fetch_series_by_api_link.m
index 88c3f24d1fa936104885b4e3374ad737e62224b1..d87f3f53c18effac35c0b09eec1b7624e53aec22 100644
--- a/src/fetch_series_by_api_link.m
+++ b/src/fetch_series_by_api_link.m
@@ -86,7 +86,6 @@ else
series_list = [series_list, filtered_series_list];
end
- %%%%%%% OPTIMIZE SIZE PRE-ALLOCATION %%%%%%%
rows_ = 0;
for s = 1:length(series_list)
rows_ = rows_ + length(series_list{s}.value);