DynamicTables Tutorial

This is a user guide to interacting with DynamicTable objects in MatNWB.
Table of Contents
MatNWB Setup Constructing a table with initialized columns Adding rows Adding columns Enumerated (categorical) data Warning Regarding EnumData Ragged array columns Adding ragged array rows Accessing row elements Accessing column elements Referencing rows of other tables Converting a DynamicTable to a MATLAB table Creating an expandable table Multidimensional Columns Constructing multidimensional columns Multidimensional ragged array columns Adding rows to multidimensional array columns Learn More! Python Tutorial

MatNWB Setup

Start by setting up your MATLAB workspace. The code below adds the directory containing the MatNWB package to the MATLAB search path. MatNWB works by automatically creating API classes based on a defined schema.
%{
path_to_matnwb = '~/Repositories/matnwb'; % change to your own path location
addpath(genpath(pwd));
%}

Constructing a table with initialized columns

The DynamicTable class represents a column-based table to which you can add custom columns. It consists of a a description, a list of columns , and a list of row IDs. You can create a DynamicTable by first defining the VectorData objects that will make up the columns of the table. Each VectorData object must contain the same number of rows. A list of rows IDs may be passed to the DynamicTable using the id argument. Row IDs are a useful way to access row information independent of row location index. The list of row IDs must be cast as an ElementIdentifiers object before being passed to the DynamicTable object. If no value is passed to id, an ElementIdentifiers object with 0-indexed row IDs will be created for you automatically.
MATLAB Syntax Note: Using column vectors is crucial to properly build vectors and tables. When defining individual values, make sure to use semi-colon (;) instead of instead of comma (,) when defining the data fields of these.
col1 = types.hdmf_common.VectorData( ...
'description', 'column #1', ...
'data', [1;2] ...
);
 
col2 = types.hdmf_common.VectorData( ...
'description', 'column #2', ...
'data', {'a';'b'} ...
);
 
my_table = types.hdmf_common.DynamicTable( ...
'description', 'an example table', ...
'colnames', {'col1', 'col2'}, ...
'col1', col1, ...
'col2', col2, ...
'id', types.hdmf_common.ElementIdentifiers('data', [0;1]) ... % 0-indexed, for compatibility with Python
);
my_table
my_table =
DynamicTable with properties: id: [1×1 types.hdmf_common.ElementIdentifiers] colnames: {'col1' 'col2'} description: 'an example table' vectordata: [2×1 types.untyped.Set]

Adding rows

You can add rows to an existing DynamicTable using the object's addRow method. One way of using this method is to pass in the names of columns as parameter names followed by the elements to append. The class of the elements of the column must match the elements to append.
my_table.addRow('col1', 3, 'col2', {'c'}, 'id', 2);

Adding columns

You can add new columns to an existing DynamicTable object using the addColumn method. One way of using this method is to pass in the names of each new column followed by the corresponding values for each new column. The height of the new columns must match the height of the table.
col3 = types.hdmf_common.VectorData('description', 'column #3', ...
'data', [100; 200; 300]);
col4 = types.hdmf_common.VectorData('description', 'column #4', ...
'data', {'a1'; 'b2'; 'c3'});
 
my_table.addColumn('col3', col3,'col4', col4);

Enumerated (categorical) data

EnumData is a special type of column for storing an enumerated data type. This way each unique value is stored once, and the data references those values by index. Using this method is more efficient than storing a single value many times, and has the advantage of communicating to downstream tools that the data is categorical in nature.

Warning Regarding EnumData

EnumData is currently an experimental feature and as such should not be used in a production environment.
CellTypeElements = types.hdmf_common.VectorData(...
'description', 'fixed set of elements referenced by cell_type' ...
, 'data', {'aa', 'bb', 'cc'} ... % the enumerated elements
);
CellType = types.hdmf_experimental.EnumData( ...
'description', 'this column holds categorical variables' ... % properties derived from VectorData
, 'data', [0, 1, 2, 1, 0] ... % zero-indexed offset to elements.
, 'elements', types.untyped.ObjectView(CellTypeElements) ...
);
 
MyTable = types.hdmf_common.DynamicTable('description', 'an example table');
MyTable.vectordata.set('cell_type_elements', CellTypeElements); % the *_elements format is required for compatibility with pynwb
MyTable.addColumn('cell_type', CellType);

Ragged array columns

A table column with a different number of elements for each row is called a "ragged array column." To define a table with a ragged array column, pass both the VectorData and the corresponding VectorIndex as columns of the DynamicTable object. The VectorData columns will contain the data values. The VectorIndex column serves to indicate how to arrange the data across rows. By convention the VectorIndex object corresponding to a particular column must have have the same name with the addition of the '_index' suffix.
Below, the VectorIndex values indicate to place the 1st to 3rd (inclusive) elements of the VectorData into the first row and 4th element into the second row. The resulting table will have the cell {'1a'; '1b'; '1c'} in the first row and the cell {'2a'} in the second row.
 
col1 = types.hdmf_common.VectorData( ...
'description', 'column #1', ...
'data', {'1a'; '1b'; '1c'; '2a'} ...
);
 
col1_index = types.hdmf_common.VectorIndex( ...
'description', 'column #1 index', ...
'target',types.untyped.ObjectView(col1), ... % object view of target column
'data', [3; 4] ...
);
 
table_ragged_col = types.hdmf_common.DynamicTable( ...
'description', 'an example table', ...
'colnames', {'col1'}, ...
'col1', col1, ...
'col1_index', col1_index, ...
'id', types.hdmf_common.ElementIdentifiers('data', [0; 1]) ... % 0-indexed, for compatibility with Python
);

Adding ragged array rows

You can add a new row to the ragged array column. Under the hood, the addRow method will add the appropriate value to the VectorIndex column to maintain proper formatting.
table_ragged_col.addRow('col1', {'3a'; '3b'; '3c'}, 'id', 2);

Accessing row elements

You can access data from entire rows of a DynamicTable object by calling the getRow method for the corresponding object. You can supply either an individual row number or a list of row numbers.
my_table.getRow(1)
ans = 1×4 table
 col1col2col3col4
11'a'100'a1'
If you want to access values for just a subset of columns you can pass in the 'columns' arguement along with a cell array with the desired column names
my_table.getRow(1:3, 'columns', {'col1'})
ans = 3×1 table
 col1
11
22
33
You can also access specific rows by their corresponding row ID's, if they have been defined, by supplying a 'true' Boolean to the 'useId' parameter
my_table.getRow(1, 'useId', true)
ans = 1×4 table
 col1col2col3col4
12'b'200'b2'
For a ragged array columns, the getRow method will return a cell with different number of elements for each row
table_ragged_col.getRow(1:2)
ans = 2×1 table
 col1
1[{'1a'};{'1b'};{'1c'}]
21×1 cell

Accessing column elements

To acess all rows from a particular column use the .get method on the vectordata field of the DynamicTable object
 
my_table.vectordata.get('col2').data
ans = 3×1 cell
'a'
'b'
'c'

Referencing rows of other tables

You can create a column that references rows of other tables by adding a DynamicTableRegion object as a column of a DynamicTable. This is analogous to a foreign key in a relational database. The DynamicTableRegion class takes in an ObjectView object as arguement. ObjectView objects create links from one object type referencing another.
dtr_col = types.hdmf_common.DynamicTableRegion( ...
'description', 'references multiple rows of earlier table', ...
'data', [0; 1; 1; 0], ... # 0-indexed
'table',types.untyped.ObjectView(my_table) ... % object view of target table
);
 
data_col = types.hdmf_common.VectorData( ...
'description', 'data column', ...
'data', {'a'; 'b'; 'c'; 'd'} ...
);
 
dtr_table = types.hdmf_common.DynamicTable( ...
'description', 'test table with DynamicTableRegion', ...
'colnames', {'dtr_col','data_col'}, ...
'dtr_col', dtr_col, ...
'data_col',data_col, ...
'id',types.hdmf_common.ElementIdentifiers('data', [0; 1; 2; 3]) ...
);

Converting a DynamicTable to a MATLAB table

You can convert a DynamicTable object to a MATLAB table by making use of the object's toTable method. This is a useful way to view the whole table in a human-readable format.
my_table.toTable()
ans = 3×5 table
 idcol1col2col3col4
101'a'100'a1'
212'b'200'b2'
323'c'300'c3'
When the DynamicTable object contains a column that references other tables, you can pass in a Boolean to indicate whether to include just the row indices of the referenced table. Passing in false will result in inclusion of the referenced rows as nested tables.
dtr_table.toTable(false)
ans = 4×3 table
 iddtr_coldata_col
101×4 table'a'
211×4 table'b'
321×4 table'c'
431×4 table'd'

Creating an expandable table

When using the default HDF5 backend, each column of these tables is an HDF5 Dataset, which by default are set to an unchangeable size. This means that once a file is written, it is not possible to add a new row. If you want to be able to save this file, load it, and add more rows to the table, you will need to set this up when you create the VectorData and ElementIdentifiers columns of a DynamicTable. Specifically, you must wrap the column data with a DataPipe object. The DataPipe class takes in maxSize and axis as arguments to indicate the maximum desired size for each axis and the axis to whcih to append to, respectively. For example, creating a DataPipe object with a maxSize value equal to [Inf, 1] indicates that the number of rows may increase indifinetely. In contrast, setting maxSize equal to [8, 1] would allow the column to grow to a maximum height of 8.
% create NwbFile object with required fields
file= NwbFile( ...
'session_start_time', '2021-01-01 00:00:00', ...
'identifier', 'ident1', ...
'session_description', 'ExpandableTableTutorial' ...
);
 
% create VectorData objects with DataPipe objects
start_time_exp = types.hdmf_common.VectorData( ...
'description', 'start times column', ...
'data', types.untyped.DataPipe( ...
'data', [1, 2], ... # data must be numerical
'maxSize', Inf ...
) ...
);
 
stop_time_exp = types.hdmf_common.VectorData( ...
'description', 'stop times column', ...
'data', types.untyped.DataPipe( ...
'data', [2, 3], ... #data must be numerical
'maxSize', Inf ...
) ...
);
 
random_exp = types.hdmf_common.VectorData( ...
'description', 'random data column', ...
'data', types.untyped.DataPipe( ...
'data', rand(5, 2), ... #data must be numerical
'maxSize', [5, Inf], ...
'axis', 2 ...
) ...
);
 
ids_exp = types.hdmf_common.ElementIdentifiers( ...
'data', types.untyped.DataPipe( ...
'data', int32([0; 1]), ... # data must be numerical
'maxSize', Inf ...
) ...
);
% create expandable table
colnames = {'start_time', 'stop_time', 'randomvalues'};
file.intervals_trials = types.core.TimeIntervals( ...
'description', 'test expdandable dynamic table', ...
'colnames', colnames, ...
'start_time', start_time_exp, ...
'stop_time', stop_time_exp, ...
'randomvalues', random_exp, ...
'id', ids_exp ...
);
% export file
nwbExport(file, 'expandableTableTestFile.nwb');
Now, you can read in the file, add more rows, and save again to file
readFile = nwbRead('expandableTableTestFile.nwb', 'ignorecache');
readFile.intervals_trials.addRow( ...
'start_time', 3, ...
'stop_time', 4, ...
'randomvalues', rand(5,1), ...
'id', 2 ...
)
nwbExport(readFile, 'expandableTableTestFile.nwb');
Note: DataPipe objects change how the dimension of the datasets for each column map onto the shape of HDF5 datasets. See README for more details.

Multidimensional Columns

The order of dimensions of multidimensional columns in MatNWB is reversed relative to the Python HDMF package (see README for detailed explanation). Therefore, the height of a multidimensional column belonging to a DynamicTable object is defined by the shape of its last dimension. A valid DynamicTable must have matched height across columns.

Constructing multidimensional columns

% Define 1D column
simple_col = types.hdmf_common.VectorData( ...
'description', '1D column',...
'data', rand(10,1) ...
);
% Define ND column
multi_col = types.hdmf_common.VectorData( ...
'description', 'multidimensional column',...
'data', rand(3,2,10) ...
);
% construct table
multi_dim_table = types.hdmf_common.DynamicTable( ...
'description','test table', ...
'colnames', {'simple','multi'}, ...
'simple', simple_col, ...
'multi', multi_col, ...
'id', types.hdmf_common.ElementIdentifiers('data', (0:9)') ... % 0-indexed, for compatibility with Python
);
 

Multidimensional ragged array columns

DynamicTable objects with multidimensional ragged array columns can be constructed by passing in the corresponding VectorIndex column
% Define column with data
multi_ragged_col = types.hdmf_common.VectorData( ...
'description', 'multidimensional ragged array column',...
'data', rand(2,3,5) ...
);
% Define column with VectorIndex
multi_ragged_index = types.hdmf_common.VectorIndex( ...
'description', 'index to multi_ragged_col', ...
'target', types.untyped.ObjectView(multi_ragged_col),'data', [2; 3; 5] ...
);
 
multi_ragged_table = types.hdmf_common.DynamicTable( ...
'description','test table', ...
'colnames', {'multi_ragged'}, ...
'multi_ragged', multi_ragged_col, ...
'multi_ragged_index', multi_ragged_index, ...
'id', types.hdmf_common.ElementIdentifiers('data', [0; 1; 2]) ... % 0-indexed, for compatibility with Python
);

Adding rows to multidimensional array columns

DynamicTable objects with multidimensional array columns can also be constructed by adding a single row at a time. This method makes use of DataPipe objects due to the fact that MATLAB doesn't support singleton dimensions for arrays with more than 2 dimensions. The code block below demonstates how to build a DynamicTable object with a mutidimensional raaged array column in this manner.
% Create file
file= NwbFile( ...
'session_start_time', '2021-01-01 00:00:00', ...
'identifier', 'ident1', ...
'session_description', 'test_file' ...
);
 
% Define Vector Data Objects with first row of table
start_time_exp = types.hdmf_common.VectorData( ...
'description', 'start times column', ...
'data', types.untyped.DataPipe( ...
'data', 1, ...
'maxSize', Inf ...
) ...
);
stop_time_exp = types.hdmf_common.VectorData( ...
'description', 'stop times column', ...
'data', types.untyped.DataPipe( ...
'data', 10, ...
'maxSize', Inf ...
) ...
);
random_exp = types.hdmf_common.VectorData( ...
'description', 'random data column', ...
'data', types.untyped.DataPipe( ...
'data', rand(3,2,5), ... #random data
'maxSize', [3, 2, Inf], ...
'axis', 3 ...
) ...
);
random_exp_index = types.hdmf_common.VectorIndex( ...
'description', 'index to random data column', ...
'target',types.untyped.ObjectView(random_exp), ...
'data', types.untyped.DataPipe( ...
'data', uint64(5), ...
'maxSize', [Inf, 1], ...
'axis', 1 ...
) ...
);
ids_exp = types.hdmf_common.ElementIdentifiers( ...
'data', types.untyped.DataPipe( ...
'data', int64(0), ... # data must be numerical
'maxSize', Inf ...
) ...
);
% Create expandable table
colnames = {'start_time', 'stop_time', 'randomvalues'};
file.intervals_trials = types.core.TimeIntervals( ...
'description', 'test expdandable dynamic table', ...
'colnames', colnames, ...
'start_time', start_time_exp, ...
'stop_time', stop_time_exp, ...
'randomvalues', random_exp, ...
'randomvalues_index', random_exp_index, ...
'id', ids_exp ...
);
% Export file
nwbExport(file, 'multiRaggedExpandableTableTest.nwb');
% Read in file
read_file = nwbRead('multiRaggedExpandableTableTest.nwb', 'ignorecache');
% add individual rows
read_file.intervals_trials.addRow( ...
'start_time', 2, ...
'stop_time', 20, ...
'randomvalues', rand(3,2,6), ...
'id', 1 ...
);
read_file.intervals_trials.addRow( ...
'start_time', 3, ...
'stop_time', 30, ...
'randomvalues', rand(3,2,3), ...
'id', 2 ...
);
read_file.intervals_trials.addRow( ...
'start_time', 4, ...
'stop_time', 40, ...
'randomvalues', rand(3,2,8), ...
'id', 3 ...
);
 

Learn More!

Python Tutorial

DynamicTable Tutorial