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Let’s examine the select DAT.
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The select DAT chooses a subset
of rows and columns from the
input table DAT based on
specific search criteria.
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The select DAT has several
options for defining that search
or extraction criteria.
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In this example we will select
the range of rows based on the
name of the row.
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The first entry in a row is
considered its name.
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—
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In this example we will select
our rows by name, and we will
select our column subset by
index.
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The index is the numerical
identification value of the row
or column, starting with 0.
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If we extract a range of columns
starting with column index 1, we
will effectively delete column
0.
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The columns in “table2” are
indexed in the numerical range 0
to 5.
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In the parameters panel, we can
enter values by typing them in
directly, or by using the
slider.
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We can also use Python to create
expressions that will generate
numerical values.
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The Python expression will
automatically be evaluated, and
in this case, produce a
numerical result.
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In this example, “me” is the DAT
named “select2”, “inputs[0]” is
the DAT named “table2”, and
“numCols” is the number of
columns in “table2”.
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Even though we use a base 0
numbering system to index the
number of columns or rows, there
are 6 total columns, not 5.
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We subtract 1 from the total row
count to generate our desired
index.
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—
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We can click the “edit” button
on the parameters panel of a
text DAT to launch our default
text editor.
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Any changes we make and save in
the text editor will
automatically be updated in
Touch Designer.
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We can also press the “viewer
active” button on the bottom
right of the operator, to type
changes directly into the
operator.
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The text editor will launch with
a temporary file, so it’s
important to keep track of
changes made in either the text
editor, or directly in the
operator.
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In this example we convert our
text DAT to a table, by using
the convert DAT.
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The “split cells” option defines
which character Touch Designer
will use create new cells from
the incoming text DAT.
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As with any operator, we can
middle mouse click on the
operator to display information
about that node.
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—
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We’ve converted our text to a
table, now we feed that into a
select DAT.
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—
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We’ve chosen to extract our rows
by selected values.
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The first entry in each row is
the value or string we will need
to search for.
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—
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We can separate string entries
by a “space” character to search
for multiple patterns.
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The select DAT will extract each
row from the incoming table that
matches the search pattern.
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Now we will extract our columns
by selected values.
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Again, the first value in each
column is the value or string we
will need to search for.
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—
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In this example we will use
Python expressions to create our
search and extraction criteria.
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I’ve included some links so you
can learn more about Python
regular expressions.
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We start with a text DAT.
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As you can see, I’ve capitalized
each occurrence of the letter
“M”.
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We wire the text DAT to a
convert DAT, to convert the text
into a table.
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We use the “space” character as
our split cell value.
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—
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We wire the convert DAT to our
select DAT, and we choose to
extract our rows by selected
condition.
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—
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We use a Python regular
expression to create our search
criteria, in this case the
capitalized letter M.
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—
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We use the “str.search” method
which will search for every
occurrence of the capitalized M
in every cell of our input table
DAT.
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We’ve extracted every row that
contains the string capital M.
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—
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In its default settings, the
selected DAT extracts only the
rows whose first cell contains
the letter M.
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We can easily change this
default setting, by adjusting
the “from column” parameter.
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We can search a specific column
to find cells that contain our
search string, then extract that
entire row.
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—
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In this example we search for
the value “5” in our input table
DAT.
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We search column 0, and for each
cell that contains the character
5, we extract that entire row.
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Now let’s extract some specific
columns.
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Let’s take a look at the
“str.match” method.
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The “match” method is different
than the “search” method.
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The “match” method only
evaluates the first character of
the input cell.
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With the match method, the input
cell “34” will evaluate FALSE.
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The search method evaluates all
the characters of the input
cell.
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When we search for the 3
character, we find two
conditions that evaluate TRUE.
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When we match for the 3
character, we also find two
conditions that evaluate TRUE.
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