Hi,
thanks
for
finding
our
talk
today.
Hadley
and
I
are
going
to
be
talking
about
making
an
easy
button
for
data
access.
Now,
this
is
a
talk
that
we
had
given
previously
at
a
former
Discovery
Talk,
and
we're
going
to
be
talking
about
how
you
can
extend
this
capability
with
our
new
OSI
soft
PI
connector.
Hadley,
take
it
away.
Yes,
that
is
absolutely
right.
Before
I
move
into
what
we're
going
to
be
showing
you
today
and
what
you
can
use
yourself,
I'd
like
to
just
introduce
those
of
you
who
aren't
familiar
with
the
JMP
Analytic
Workflow.
E
veryone
watching
this
talk
likely
already
knows
that
JMP
contains
all
of
the
analytic
capabilities
necessary
to
take
any
data
that
you
have
in
any
raw
format
and
transform
it
into
insight
that
can
then
be
shared
throughout
an
organization.
What
we
are
going
to
be
focusing
on
today
is
the
data
access
and
the
data
blending
and
cleanup
aspects
of
the
analytic
workflow.
Why
are
these
important?
Well,
any
problem-
solving
effort
begins
by
collecting
and
compiling
the
data.
One
big
problem,
is
that
this
can
often
be
time-
consuming
and
tedious,
especially
for
scientists
and
engineers
who
have
background
in
this
stuff.
What
this
effectively
means
is
that
it's
often
not
done
or
not
done
in
a
timely
enough
manner.
So
problems
can
get
unnoticed
and
problems,
therefore,
aren't
solved.
The
other
problem
is
that
data
can
be
found
in
many
different
places,
and
it's
an
effort
to
grab
all
of
this
and
put
it
in
the
right
format,
compile
it
together. A
solution
is
an
easy
button
for
quick
access
to
data
wherever
it
is.
What
we
have
got
and
what
we
are
going
to
show
you
is
a
simple
interface
built
using
the
application
builder,
which
is
a
simplified
strip
down
option
allowing
people
to
press
a
button
and
get
data
from
exactly
where
they
need
it
in
the
format
that
they
need
to
be
able
to
solve
their
problems.
They
can
pick
a
data
source
and
filter
what
is
needed
if
necessary,
even
combining
multiple
sources
and
automating
this.
As
Pete
mentioned,
what
we're
going
to
be
doing
is
we're
going
to
be
building
on
a
tool
that
we
had
previously
shown
which
used
SQL
web
APIs
and
even
manual
entries
as
well
as
combining
data
from
other
sources.
Where
have
we
shown
this
before?
We've
shown
this
in
a
previous
Discovery
Talk. So t
hose
of
you
watching
this
can
look
at
the
past
Discovery
presentations
and
check
those
out
if
you
like.
What
we
are
going
to
do
now
is
take
it
one
step step
further
from
where
we
were
back
in
2022,
and
that
is
to
extend
it
to
data
contained
in
OSIsoft PI
servers.
We're
going
to
be
making
use
of
two
features
that
were
introduced
in
JMP
17.
There's
the
Connect
to
the
PI
Server
as
well
as
the
OSIsoft PI
Wizard.
With
that,
I'll
turn
things
over
to
Pete
to
demonstrate
that
functionality.
Thank
you,
Hadley.
Share
my
screen
here.
If
I'm
going
to
launch
that
PI
importer,
you'll
find
it
in
the
same
place
you'll
find
all
of
the
database
connectors.
Just
like
we
would
do
for
SQL,
you'll
go
under
file,
database,
and
import
from
OSIsoft PI.
You
enter
the
name
of
your
PI Server,
your
authentication
method.
Hit
okay.
Then
it
gives
you
this
nice
interface
here
and
you
can
browse
to
what
you're
interested
in
and
pick
out
a
couple
of
attributes
or
tags
that
you
want.
Let's
just
pick
one
for
now.
Then
I
can
select
what
my
start
time
is.
I'm
going
to
go
back
a
little
bit
in
time
and
shorten
this
query
a
bit
so
it
goes
a
little
quicker.
Once
you're
ready,
you
can
hit
Import.
This
is
a
big
improvement
over
what
you
had
to
do
before,
which
involved
a
fair
amount
of
scripting.
But
the
nice
thing
here
is
once
I've
imported
this,
everything
that
I
need
to
pull
that
up
again
is
captured
right
here
in
the
source
script.
So
if
I
hit
Edit,
you
can
see
all
that
was
needed
to
be
passed
into
that
PI
data
source
was
right
here.
Hadley
is
going
to
take
this
now
and
start
to
make
our
easy
button.
I'm
going
to
stop
sharing
and
pass
it
back
to
Hadley.
All
right,
thanks
for very m
uch,
Pete.
I'm
going
to
go
ahead
and
share
my
screen
once
again
and
show
you
that
what
we
are
going
to
do
is
we're
going
to
take
that
script
that
Pete
just
generated
using
the
OSIsoft PI
Import
Wizard
in
JMP 17
and
turn
that
into
a
simple
add-
in
that
literally
anybody
could
use
to
select
whatever
tags
they
need
and
then
grab
that
data.
If
you
know
what
server
it's
coming
from,
you
know
what
the
configuration
is,
you're
always
grabbing
the
same
data
in
exactly
the
same
way,
the
only
thing
that
might
change
is
the
tags,
then
this
may
be
a
stripped
down
simplified
solution
that
anybody
could
use.
Of
course,
if
you
had
other
things
that
you
wanted
to
filter
on,
like
timelines
and
stuff,
that's
easy
to
include
as
well.
And
if
you
wanted
to
take
this
a
step
further
and
combine
these
data
sources
and
maybe
do
some
automation
on
them
or
automated
analysis.
That's
an
easy
step
from
there,
and
Pete will
show
you
how
to
do
that
a
little
bit
later.
But
what
I'm
going
to
do
is
I'm
going
to
take
the
source
script
that
was
used
to
generate
this
data.
I'm
going
to
copy
it
and
paste
it
into
a
JMP
script.
Now
when
I
run
the
script,
it
goes
back
and
collects
the
data
from
this
tag
IA
right
there
.
It
could
very
well
be
that
there
are
multiple
tags
that
you
would
like
rather
than
just
one.
Maybe
you
have
a
list
of
tags
that
you
need.
What
I'm
going
to
do
at
first
is
I'm
going
to
define
a
tag
list
which
may
contain
the
tag
IA
as
well
as
IB
and
IC
and
as
many
more
as
I
feel
like
including.
This
would
be
a
good
option,
is
if
you
were
always
getting
the
same
tags
every
time.
It
didn't
need
to
select
them
they're
always
the
same.
Here
they
are.
What
I'm
going
to
do
is
I'm
going
to
run
this
for
each
one
of
these
tags
in
this
tag
list.
To
do
that,
I'm
going
to
make
use
of
another
relatively
recent
addition
to
JMP.
Only
I
think
it
was
added
in
15
or
16,
I'm
not
quite
sure,
but
that's
the
For
Each
function.
For
each
tag
in
my
tag
list,
run
this.
My
tag
is
going
to
be
here. I
nstead
of
running
IA,
we're
going
to
just
concatenate
the
tags
and
then
go
ahead
and
run
that.
Excuse
me.
There
we
have
it.
It
really
is
just
that
simple.
It'll
take
a
few
seconds,
but
there
we've
got
our
text.
That's
a
good
solution
if
you
were
always
running
the
same
text.
But
if
you
wanted
to
take
this
functionality
and
extend
it
a
bit
to
allow
a
user
to
select
some
tags
using
these
configurations,
I'm
going
to
take
this
code
and
I'm
going
to
run
it
or
set
it
up
in
application
builder.
Now,
rather
than
hard
coding
a
list,
I'm
going
to
ask
the
user
to
select
the
list
from the
list
here.
We'll
just
add
a
few
tags
to
that.
Let's
add
tag
IA,
IB,
IC,
and
maybe
one
more
kilowatt
A.
There
they
are.
Now
we'll
just
add
a
button
that
the
user
can
press
to
grab
whatever
they've
selected
in
the
list
and
then
get
the
tags.
Button
1
is
a
good
variable
name,
but
we
need
a
bit
of
a
better
descriptor
so
the
user
know
what
to
do.
There
we
have
it.
When
we
press
this
button,
we
are
going
to
have
it
run
the
script
that
we
just
wrote.
Of
course,
instead
of
getting
our
tags
from
this
tag
list,
we
are
going
to
have
it
grab
whatever
a
user
selected
from
our
list
1
list
box.
Can
it
really
be
that
simple?
Yes,
it
can, and
yes,
it
is.
Of
course,
if
we
wanted
to
extend
this
functionality,
at
this
point
in
the
sky,
our
imagination
and
our
needs
are
the
limit.
At
this
point,
I
will
pass
things
back
to
Pete
to
show
you
how
you
can
go
ahead
and
do
that.
Wow,
Hadley,
that
really
does
look
easy.
Very
nicely
done.
Why
don't
I
share
where
we
went
from
here?
Actually,
let
me
share
this
screen
here.
All
I
did
was
take
what
Hadley
had
shown
and
add
a
few
more
tags.
The
next
thought
is,
"Hey,
that's
great
that
the
PI
importer
is
bringing
in
these
tables
individually,
but
what
happens
if
I
want
to
bring
them
together?" L
et
me
just
show
what
this
does
first.
I'll
do
that
data
poll.
This
is
what
Hadley
showed.
Then
I'm
going
to
do
the
next
step,
which
is
a
data
compile.
Now,
this
takes
advantage
of
the
workflow
builder,
and
we'll
go
ahead
and
walk
through
and
actually
write
this since
it's
really
easy
to
do.
I'm
going
to
just
pick
a
couple
of
things
to
compile
here.
There
you
go.
I'll
show
you
how
all
of
this
was
done.
Okay,
so
basically,
what
JMP
has
done
is
it
went
through
and
it
grabbed
a
bunch
of
those
data
tables,
it
concatenated
them,
then
it
split
them
apart,
and
all
of
these
steps
are
here.
So
it
concatenated
those
data
tables,
it
split
them
apart,
then
it
recoded
those
column
names,
and
finally,
it
made
just
a
simple
report.
So
let's
walk
through
how
we
would
do
this
inside
of
the
workflow
builder.
I'm
going
to
close
out
of
these.
I'll
minimize
this,
and
I'll
just
start
with
those
three
tables
that
were
pulled
from
the
data.
Here
I
have
IA,
IB,
and
IC
metrics
that
I'm
looking
at.
To
start
a
workflow,
you'll
find
it
under
File,
New,
and
Workflow.
A
ll
this
is
doing
is
it's
grabbing
stuff
out
of
the
log
when
I
tell
it
to.
If
I
hit
record,
it
will
capture
all
the
steps
that
I
do
to
any
table
manipulations,
any
joining
or
splitting
of
tables,
any
renaming
or
recoding
of
variables.
All
of
that
will
be
captured
in
here.
Let's
start
with
that.
The
first
thing
I'm
going
to
do
is
concatenate
these.
U
nder
the
Tables
menu,
Concatenate.
I
have
A
there.
I
want
to
add
B
and
C.
I'll
give
it
a
name
here.
We'll
just
call
this
Stacked
Data
and
hit
okay.
There
you
can
see
that
this
was
stacked
and
it's
captured
here
as
well.
Everything
I
needed
to
do
there
was
captured.
I
want
to
back
up
here.
You
can
see
anything,
while
that
recording
is
going
on,
is
captured.
Let
me
back
up,
start
over,
show
this
one
more
time.
With
that
off,
it
won't
record
anything.
With
that
on,
it
will, so
here,
we'll
do
that.
Tables,
Concatenate
again.
I
forgot
to
click
one
button
there.
I
want
to
add
a
source
name,
so
we'll
do
this
again.
Again,
call
this
Stacked
Data
and
hit
okay.
T
hat
was
my
first
step.
Now,
the
next
thing
I'm
going
to
do
is
split
this
apart
because
I
actually
don't
want
it
stacked.
I
want
them
together
in
the
same
table,
but
I
want
to
split
it
now.
So
we're
going
to
go
to
Tables
and
Split.
I
want
to
Split
by
that
source
column,
which
is
why
I
didn't
have
that
in
the
first
time
I
did
it.
Here
we
can
see
this.
I'm
splitting
by
source
column.
This
is
also
a
new
feature
here.
It
gives
us
a
nice
preview.
Now,
this
was
something
that
I
don't
know
about
everyone
else
in
here,
but
I
used
to
struggle
with
this.
I
wasn't
quite
sure
what
I
was
going
to
get,
especially
with
things
like
transpose
and
split,
join
some
of
the
more
complex
table
formulas.
Here
I
have
all
of
my
500
rows
of
data
for
each
of
these
different
metrics,
but
what
I'm
missing
is
a
time
stamp.
Without
having
that
before,
I
might
have
done
this
wrong,
but
now
I
want
to
group
this
by
time
stamp.
All
of
these
now
have
a
time
stamp
associated
with
that
particular
metric.
Now
I'm
going
to
just
call
this
Split
Data
and
hit
okay.
There,
back
to
my
workflow,
you
can
see
I've
concatenated,
I've
split,
but
now
I
have
this
big
ugly
column
name
that
I
don't
want.
T
here's
a
nice
feature
inside
of
JMP
to
recode
these
column
names.
I
f
I
go
to
Columns
and
Column
Names,
there's
a
Recode
Column
Name,
and
this
works
just
like
recode
for
your
normal
data.
I'm
going
to
do
a
little
advanced
extract
segment
here.
I
want
to
pull
out
a
portion
that
just
looks
at
the
very
end,
and
that
looks
like
the
right
values
and
I
hit
okay.
Then
I'm
going
to
hit
recode,
and
there
we
go.
Then
the
last
thing
you'll
notice,
again,
this
is
all
captured.
The
last
thing
I
want
to
do
here
is
make
that
graph.
I'm
going
to
just
go
graph
and
I'm
going
to
grab
those
metrics,
A,
B,
and
C,
and
then
map
them
out
by
timestamp.
Now,
you'll
notice
one
thing.
This
is
not
added
to
the
workflow
yet,
so
I
can
hit
done
and
it's
still
not
added.
The
reason
that
is,
is
I
could
still
be
making
changes
to
this.
Maybe
I
don't
like
this
name.
I
might
want
to
call
it
metric
versus
time.
Maybe
I
don't
like
the
format
of
these
time
stamps,
so
I
can
change
that.
But
I'm
doing
all
of
these
changes
and
the
workflow
doesn't
capture
it
until
I
close
this.
When
I
hit
Close,
there
you
go.
I'm
going
to
stop
recording
now
and
I
will
go
ahead
and
close
these
two
new
tables
that
were
made
and
show
you
that
this
works.
There
we
go.
Okay,
so
now
you
may
be
asking,
"Well,
why
would
I
use
a
workflow?
Why
not
just
use
a
script?
What's
the
advantage
of
that?" So l
et's
close
out
of
this
and
show
you
why.
I
gave
you
an
accidental
preview
of
this
earlier,
but
we'll
show
you
here.
We're
back
to
our
application
here.
I
went
through
and
I
pulled
these
three
tags,
and
then
when
I
concatenated
them,
it
was
looking
for
those
three
data
tables
with
those
three
names.
If
I
pulled
different
tags,
so
let's
say
I
just
want
all
of
this
data,
so
everything
with
an
A
at
the
end
here,
I'm
going
to
do
a
data
poll
on.
If
I
was
using
a
script
and
I
was
looking
for
those
specific
data
file
names,
the
workflow or
a
script
wouldn't
work,
but
the
workflow
has
this
generalizability.
If
I
look
here
in
this
concatenate
tables,
it's
looking
for
three
tables,
IA,
IB,
and
I
C,
and
I
don't
have
those
tables
open.
I
have
AI
open,
but
I
have
two
other
tables
open. L
et's
see
what
happens
when
I
run
this.
It
prompts
me.
It
says,
"Hey,
what
data
do
you
actually
want
to
compile?
You
have
different
data
sources
here."
I
actually
want
to
compile
these
three
that
I
have
open.
Now
it
says,
"Oh,
wait,
I
couldn't
find
the
column
names."
Again,
when
I
went
through
and
I
recoded
columns,
if
I
was
running
a
script,
it
would
potentially
just
wouldn't
work
because
it
didn't
find
that
column
name.
But
here
it
says,
"Hey,
I
can't
find
this
column
IB.
Which
one
is
that?"
Let's
just
use
a
replacement
column.
There
we
go,
and
it
worked.
What
this
is
doing
is
it
has
the
ability
to
be
generalizable
with
this
reference.
By
default,
a
workflow
has
this
ability
to
have
a
replacement
reference,
and
I
can
manage
this.
Here
you
can
see
here
are
the
tables,
and
I
can
prompt
you
to
pick
those
tables.
Then
here
are
the
columns
that
are
referenced,
s
o
I
can
have
substitutes
there.
Unlike
a
script,
this
will
prompt
you
if
it
doesn't
find
what
it's
looking
for.
So
it's
very
nice
in
that
aspect.
T
hat
was
basically
how
to
build
a
workflow
and
then
use
that
to
compile
data
and
have
it
be
generalizable.
I'm
going
to
pass
it
back
to
Hadley
here
for
some
closing
thoughts.
Thanks
very
much.
Let
me
just
share
my
screen.
In
summary,
making
an
easy
button
for
data
access
solves
some
problems.
It
makes
a
lot
of
things
easier.
What
it
does
is
it
addresses
difficulties
in
assessing
data
because
problems
persist
longer
than
needed.
That's
what
happens
when
you
don't
have
access
to
the
data.
Getting
the
data
in
the
right
format
is
really
80
%
of
a
solution.
Once
you've
gotten
the
data
collected and
formatted,
compiled
it,
cleaned
it,
and
then
doing
the
rest
of
it
is
really
the
fun
and
easy
part.
Creating
these
buttons
allows
data
to
be
quickly
and
easily
imported.
It's
possible
to
add
filters,
not
something
that
we
showed
today,
but
if
you
go
back
and
look
at
our...
Well,
I
guess
the
selecting
from
the
list
was
one
of
the
filter
options.
Of
course,
you
can
always
add
others.
You
can
see
that
from
the
previous
presentation
that
we
did
back
in
2022,
as
well
as
extending
this
to
SQL,
web
API,
and
any
other
place
that
your
data
may
be.
There
are
two
add-
ins
on
the
community
that
we'd
like
to
mention.
There's
the
OSIsoft PI Importer
as
well
as
the PI
Concatenator,
you
can
find
those
things
here.
If
you
just
Google
these
or
look
on
our
community, JMP. C
ommunity.j
mp.c
om.
Thank
you
very
much.
