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distributionPlot.m
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text/x-Algol68
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Mon, May 6, 15:55 (2 d)
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Attached To
rCAPTOOLBOX CAP_Toolbox
distributionPlot.m
View Options
function
handles
=
distributionPlot
(
varargin
)
%
DISTRIBUTIONPLOT
creates
violin
plots
for
convenient
visualization
of
multiple
distributions
%
%
SYNOPSIS:
handles
=
distributionPlot
(
data
,
propertyName
,
propertyValue
,...)
%
handles
=
distributionPlot
(
ah
,...)
%
%
INPUT
data
:
m
-
by
-
nData
array
of
values
,
or
vector
of
grouped
data
(
use
%
the
'
groups
'
property
to
specify
the
grouping
variable
),
or
%
cell
array
of
length
nData
.
%
The
cell
array
can
either
contain
vectors
with
values
,
or
%
m
-
by
-
2
arrays
with
[
bins
,
counts
]
if
you
want
to
determine
the
%
histograms
by
yourself
(
m
can
be
different
between
cell
%
elements
).
Note
that
arrays
inside
cells
with
any
%
other
shape
than
m
-
by
-
2
are
reshaped
to
vector
an
a
warning
is
%
thrown
(
DISTRIBUTIONPLOT:
AUTORESHAPE
).
%
%
DISTRIBUTIONPLOT
accepts
the
following
propertyName
/
propertyValue
%
pairs
(
all
are
optional
)
:
%
%
distWidth
:
width
of
distributions
;
ideally
between
0
and
1.
%
1
means
that
adjacent
distributions
might
touch
.
Default:
0.9
%
variableWidth
:
If
true
,
the
width
of
the
distribution
changes
,
%
reflecting
the
shape
of
the
histogram
of
the
data
.
If
false
,
%
the
distribution
is
only
encoded
by
color
levels
.
Default:
true
%
color
:
uniform
coloring
of
histograms
.
Supply
either
a
color
%
string
(
'r'
),
or
a
truecolor
vector
([
1
0
0
]).
Use
a
%
cell
array
of
length
nData
to
specify
one
color
per
%
distribution
.
Default:
'k'
%
If
variableWidth
is
set
to
false
,
a
colormap
is
generated
that
%
goes
from
white
to
the
chose
color
(
or
from
black
,
if
%
invert
==
true
).
%
If
both
'
color
'
,
and
'
colormap
'
are
specified
,
'
colormap
'
takes
%
precedence
.
%
colormap
:
colormap
used
to
describe
the
distribution
(
first
row
%
corresponds
to
bins
with
least
data
,
last
row
corresponds
to
%
bins
with
most
data
(
invert
the
grayscale
colormap
to
have
%
black
indicate
the
most
data
).
%
Supply
a
cell
array
of
length
nData
to
color
distributions
%
individually
.
Note
that
using
multiple
colormaps
means
that
%
the
colorbar
doesn
'
t
contain
much
useful
information
.
%
Default:
[]
%
Colormap
will
index
into
the
figure
colormap
,
which
will
be
%
modified
by
distributionPlot
.
This
is
done
to
allow
editing
the
%
distributions
in
e
.
g
.
Adobe
Illustrator
.
%
If
both
'
color
'
,
and
'
colormap
'
are
specified
,
'
colormap
'
takes
%
precedence
.
%
globalNorm
:
normalization
for
bin
width
(
x
-
direction
)
%
0
:
every
histogram
is
normalized
individually
so
that
the
%
maximum
bin
width
is
equal
to
distWidth
.
This
is
best
%
suited
to
comparing
distribution
shapes
.
Default
.
%
1
:
histograms
are
normalized
such
that
equal
bin
width
%
reports
equal
numbers
of
counts
per
bin
.
%
2
:
histograms
are
normalized
so
that
the
relative
areas
%
covered
by
the
histograms
reflect
the
relative
total
number
%
of
data
points
.
%
3
:
histograms
areas
are
normalized
so
that
relative
densities
%
are
the
same
across
histograms
.
Thus
,
if
%
data
=
{
rand
(
100
,
1
),
rand
(
500
,
1
)},
%
then
%
distributionPlot
(
data
,
'
globalNorm
'
,
2
,
'
histOpt
'
,
0
,
'
divFactor
'
,
10
)
%
shows
the
left
histogram
5
x
as
wide
as
the
right
,
while
%
distributionPlot
(
data
,
'
globalNorm
'
,
3
,
'
histOpt
'
,
0
,
'
divFactor
'
,
10
)
%
displays
both
histograms
equally
wide
,
since
each
bin
%
contains
~
10
%
of
the
data
.
%
Options
1
and
2
produce
similar
results
if
the
bins
are
spaced
%
equally
for
the
distributions
.
Options
0
and
3
produce
similar
%
results
if
the
data
are
drawn
from
the
same
distributions
.
%
Note
that
colormaps
currently
always
report
the
number
of
data
%
points
per
bin
;
'
globalNorm
'
only
applies
to
the
distribution
%
shape
.
%
%
groups
:
grouping
variable
for
grouped
data
.
Grouping
will
be
%
resolved
by
calling
grp2idx
,
and
unless
xNames
have
%
been
supplied
,
group
names
determine
the
x
-
labels
.
%
If
the
grouping
variable
is
numeric
,
group
labels
also
%
determine
x
-
values
,
unless
the
parameter
xValues
has
%
been
specified
.
%
histOpt
:
histogram
type
to
plot
%
0
:
use
hist
command
(
no
smoothing
,
fixed
number
of
%
bins
)
%
1
:
smoothened
histogram
using
ksdensity
with
%
Normal
kernel
.
Default
.
%
1.1
:
smoothened
histogram
using
ksdensity
where
the
%
kernel
is
robustly
estimated
via
histogram
.
m
.
%
Normal
kernel
.
%
2
:
histogram
command
(
no
smoothing
,
automatic
%
determination
of
thickness
(
y
-
direction
)
of
bins
)
%
divFactor
:
Parameter
dependent
on
histOpt
.
If
...
%
histOpt
==
0
:
divFactor
=
#
of
bins
.
Default:
25.
%
Alternatively
,
pass
a
vector
which
will
be
%
interpreted
as
bin
centers
.
%
histOpt
==
1
:
divFactor
decides
by
how
much
the
default
%
kernel
-
width
is
multiplied
in
order
to
avoid
an
%
overly
smooth
histogram
.
Default:
1
/
2
%
histOpt
==
2
:
divFactor
decides
by
how
much
the
%
automatic
bin
width
is
multiplied
in
order
to
have
%
more
(
<
1
)
or
less
(
>
1
)
detail
.
Default:
1
%
addSpread
:
if
1
,
data
points
are
plotted
with
plotSpread
.
%
distWidth
is
ideally
set
to
0.95
%
This
option
is
not
available
if
the
data
is
supplied
as
%
histograms
.
%
Please
download
plotSpread
.
m
separately
from
the
File
%
Exchange
using
the
link
in
the
remarks
%
showMM
:
if
1
,
mean
and
median
are
shown
as
red
crosses
and
%
green
squares
,
respectively
.
This
is
the
default
%
2
:
only
mean
%
3
:
only
median
%
4
:
mean
+/-
standard
error
of
the
mean
(
no
median
)
%
5
:
mean
+/-
standard
deviation
(
no
median
)
%
6
:
draw
lines
at
the
25
,
50
,
75
percentiles
(
no
mean
)
%
0
:
plot
neither
mean
nor
median
%
xValues:
x
-
coordinate
where
the
data
should
be
plotted
.
%
If
xValues
are
given
,
"distWidth"
is
scaled
by
the
median
%
difference
between
adjacent
(
sorted
)
x
-
values
.
Note
that
%
this
may
lead
to
overlapping
distributions
.
Default:
%
1
:
nData
%
xNames
:
cell
array
of
length
nData
containing
x
-
tick
names
%
(
instead
of
the
default
'
1
,
2
,
3
'
)
%
xMode
:
if
'
auto
'
,
x
-
ticks
are
spaced
automatically
.
If
'
manual
'
,
%
there
is
a
tick
for
each
distribution
.
If
xNames
is
%
provided
as
input
,
xMode
is
forced
to
'
manual
'
.
Default:
%
'
manual
'
.
%
NOTE:
SPECIFYING
XNAMES
OR
XVALUES
OR
XMODE
WILL
ERASE
PREVIOUS
%
LABELS
IF
PLOTTING
INTO
EXISTING
AXES
%
yLabel
:
string
with
label
for
y
-
axis
.
Default
:
''
%
If
empty
and
data
is
histograms
,
ylabel
is
set
to
'
counts
'
%
invert
:
if
1
,
axes
color
is
changed
to
black
,
and
colormap
is
%
inverted
.
%
histOri:
Orientation
of
histogram
.
Either
'
center
'
,
'
left
'
,
or
%
'
right
'
.
With
'
left
'
or
'
right
'
,
the
left
or
right
half
of
%
the
standard
violin
plot
is
shown
.
Has
no
effect
if
%
variableWidth
is
false
.
Default:
center
%
xyOri
:
orientation
of
axes
.
Either
'
normal
'
(
=
default
),
or
%
'
flipped
'
.
If
'
flipped
'
,
the
x
-
and
y
-
axes
are
switched
,
so
%
that
violin
plots
are
horizontal
.
Consequently
,
%
axes
-
specific
properties
,
such
as
'
yLabel
'
are
applied
to
%
the
other
axis
.
%
widthDiv
:
1
-
by
-
2
array
with
[
numberOfDivisions
,
currentDivision
]
%
widthDiv
allows
cutting
the
stripe
dedicated
to
a
single
%
distribution
into
multible
bands
,
which
can
be
filled
with
%
sequential
calls
to
distributionPlot
.
This
is
one
way
%
to
compare
two
(
or
more
)
sequences
of
distributions
.
See
%
example
below
.
%
ah
:
axes
handle
to
plot
the
distributions
.
Default:
gca
%
%
OUTPUT
handles
:
1
-
by
-
4
cell
array
with
patch
-
handles
for
the
%
distributions
,
plot
handles
for
mean
/
median
,
the
%
axes
handle
,
and
the
plotSpread
-
points
handle
%
%
%
EXAMPLES
%
%--
Distributions
contain
more
information
than
boxplot
can
capture
%
r
=
rand
(
1000
,
1
);
%
rn
=
randn
(
1000
,
1
)
*
0.38
+
0.5
;
%
rn2
=
[
randn
(
500
,
1
)
*
0.1
+
0.27
;
randn
(
500
,
1
)
*
0.1
+
0.73
];
%
rn2
=
min
(
rn2
,
1
);
rn2
=
max
(
rn2
,
0
);
%
figure
%
ah
(
1
)
=
subplot
(
3
,
4
,
1
:
2
);
%
boxplot
([
r
,
rn
,
rn2
])
%
ah
(
2
)
=
subplot
(
3
,
4
,
3
:
4
);
%
distributionPlot
([
r
,
rn
,
rn2
],
'
histOpt
'
,
2
);
%
histOpt
=
2
works
better
for
uniform
distributions
than
the
default
%
set
(
ah
,
'
ylim
'
,[
-
1
2
])
%
%
%---
additional
options
%
%
data
=
[
randn
(
100
,
1
);
randn
(
50
,
1
)
+
4
;
randn
(
25
,
1
)
+
8
];
%
subplot
(
3
,
4
,
5
)
%
%
%---
defaults
%
distributionPlot
(
data
);
%
subplot
(
3
,
4
,
6
)
%
%
%---
show
density
via
custom
colormap
only
,
show
mean
/
std
,
%
distributionPlot
(
data
,
'
colormap
'
,
copper
,
'
showMM
'
,
5
,
'
variableWidth
'
,
false
)
%
subplot
(
3
,
4
,
7
:
8
)
%
%
%---
auto
-
binwidth
depends
on
#
of
datapoints
;
for
small
n
,
plotting
the
data
is
useful
%
%
note
that
this
option
requires
the
additional
installation
%
%
of
plotSpread
from
the
File
Exchange
(
link
below
)
%
distributionPlot
({
data
(
1
:
5
:
end
),
repmat
(
data
,
2
,
1
)},
'
addSpread
'
,
true
,
'
showMM
'
,
false
,
'
histOpt
'
,
2
)
%
%
%---
show
quantiles
%
subplot
(
3
,
4
,
9
),
distributionPlot
(
randn
(
100
,
1
),
'
showMM
'
,
6
)
%
%
%---
horizontal
orientation
%
subplot
(
3
,
4
,
10
:
11
),
%
distributionPlot
({
chi2rnd
(
3
,
1000
,
1
),
chi2rnd
(
5
,
1000
,
1
)},
'
xyOri
','
flipped
','
histOri
','
right
','
showMM
'
,
0
),
%
xlim
([
-
3
13
])
%
%
%---
compare
distributions
side
-
by
-
side
(
see
also
example
below
)
%
%
plotting
into
specified
axes
will
throw
a
warning
that
you
can
%
%
turn
off
using
" warning off DISTRIBUTIONPLOT:ERASINGLABELS "
%
ah
=
subplot
(
3
,
4
,
12
);
%
subplot
(
3
,
4
,
12
),
distributionPlot
(
chi2rnd
(
3
,
1000
,
1
),
'
histOri
','
right
','
color
','
r
','
widthDiv
'
,[
2
2
],
'
showMM
'
,
0
)
%
subplot
(
3
,
4
,
12
),
distributionPlot
(
chi2rnd
(
5
,
1000
,
1
),
'
histOri
','
left
','
color
','
b
','
widthDiv
'
,[
2
1
],
'
showMM
'
,
0
)
%
%
%--
Use
globalNorm
to
generate
meaningful
colorbar
%
data
=
{
randn
(
100
,
1
),
randn
(
500
,
1
)};
%
figure
%
distributionPlot
(
data
,
'
globalNorm
'
,
true
,
'
colormap
'
,
1
-
gray
(
64
),
'
histOpt
'
,
0
,
'
divFactor
'
,[
-
5
:
0.5
:
5
])
%
colorbar
%
%
%--
Use
widthDiv
to
compare
two
series
of
distributions
%
data1
=
randn
(
500
,
5
);
%
data2
=
bsxfun
(
@
plus
,
randn
(
500
,
5
),
0
:
0.1
:
0.4
);
%
figure
%
distributionPlot
(
data1
,
'
widthDiv
'
,[
2
1
],
'
histOri
','
left
','
color
','
b
','
showMM
'
,
4
)
%
distributionPlot
(
gca
,
data2
,
'
widthDiv
'
,[
2
2
],
'
histOri
','
right
','
color
','
k
','
showMM
'
,
4
)
%
%
%--
Christmas
trees
!
%
x
=
meshgrid
(
1
:
10
,
1
:
10
);
%
xx
=
tril
(
x
);
%
xx
=
xx
(
xx
>
0
);
%
figure
%
hh
=
distributionPlot
({
xx
,
xx
,
xx
},
'
color
','
g
','
addSpread
'
,
1
,
'
histOpt
'
,
2
,
'
showMM
'
,
0
);
%
set
(
hh
{
4
}{
1
},
'
color
','
r
','
marker
','
o
'
)
%
END
%
%
REMARKS
To
show
distributions
as
clouds
of
points
(
~
beeswarm
plot
),
%
and
/
or
to
use
the
option
"addSpread"
,
please
download
the
%
additional
function
plotSpread
.
m
from
the
File
Exchange
%
http:
//www.mathworks.com/matlabcentral/fileexchange/37105-plot-spread-points-beeswarm-plot
%
%
I
used
to
run
ksdensity
with
the
Epanechnikov
kernel
.
However
,
%
for
integer
data
,
the
shape
of
the
kernel
can
produce
peaks
%
between
the
integers
,
which
is
not
ideal
(
use
histOpt
=
2
for
%
integer
valued
data
).
%
%
A
previous
iteration
of
distributionPlot
used
the
input
%
specifications
below
.
They
still
work
to
ensure
backward
%
compatibility
,
but
are
no
longer
supported
or
updated
.
%
handles
=
distributionPlot
(
data
,
distWidth
,
showMM
,
xNames
,
histOpt
,
divFactor
,
invert
,
addSpread
,
globalNorm
)
%
where
distWidth
of
1
means
that
the
maxima
%
of
two
adjacent
distributions
might
touch
.
Negative
numbers
%
indicate
that
the
distributions
should
have
constant
width
,
i
.
e
%
the
density
is
only
expressed
through
greylevels
.
%
Values
between
1
and
2
are
like
values
between
0
and
1
,
except
%
that
densities
are
not
expressed
via
graylevels
.
Default:
1.9
%
%
%
SEE
ALSO
histogram
,
ksdensity
,
plotSpread
,
boxplot
,
grp2idx
%
%
created
with
MATLAB
ver
.
:
7.6.0.324
(
R2008a
)
on
Windows_NT
%
%
created
by:
Jonas
Dorn
;
jonas
.
dorn
@
gmail
.
com
%
DATE:
08
-
Jul
-
2008
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%====================================
%%
TEST
INPUT
%====================================
%
set
defaults
def
.
xNames
=
[];
def
.
showMM
=
1
;
def
.
distWidth
=
0.9
;
def
.
histOpt
=
1
;
def
.
divFactor
=
[
25
,
2
,
1
];
def
.
invert
=
false
;
def
.
colormap
=
[];
def
.
color
=
'k'
;
def
.
addSpread
=
false
;
def
.
globalNorm
=
false
;
def
.
variableWidth
=
true
;
def
.
groups
=
[];
def
.
yLabel
=
''
;
def
.
xValues
=
''
;
def
.
xMode
=
'
manual
'
;
def
.
histOri
=
'
center
'
;
def
.
xyOri
=
'
normal
'
;
def
.
widthDiv
=
[
1
1
];
isHistogram
=
false
;
%
#
this
parameter
is
not
set
by
input
if
nargin
==
0
||
isempty
(
varargin
{
1
})
error
(
'
not
enough
input
arguments
'
)
end
%
check
for
axes
handle
if
~
iscell
(
varargin
{
1
})
&&
isscalar
(
varargin
{
1
})
==
1
&&
...
ishandle
(
varargin
{
1
})
&&
strcmp
(
get
(
varargin
{
1
},
'
Type
'
),
'
axes
'
)
ah
=
varargin
{
1
};
data
=
varargin
{
2
};
varargin
(
1
:
2
)
=
[];
newAx
=
false
;
else
ah
=
gca
;
data
=
varargin
{
1
};
varargin
(
1
)
=
[];
newAx
=
true
;
end
%
check
for
current
axes
limits
.
Set
NaN
if
the
axes
have
no
children
%
yet
-
we
need
that
in
case
we
'
re
building
a
complicated
set
of
%
distributions
if
~
isempty
(
get
(
ah
,
'
children
'
))
xAxLim
=
xlim
;
yAxLim
=
ylim
;
else
[
xAxLim
,
yAxLim
]
=
deal
([
NaN
NaN
]);
end
fh
=
get
(
ah
,
'
Parent
'
);
%
check
data
.
If
not
cell
,
convert
if
~
iscell
(
data
)
[
nPoints
,
nData
]
=
size
(
data
);
data
=
mat2cell
(
data
,
nPoints
,
ones
(
nData
,
1
));
else
%
get
nData
data
=
data
(
:
);
nData
=
length
(
data
);
%
make
sure
all
are
vectors
badCol
=
~
cellfun
(
@
isvector
,
data
)
&
~
cellfun
(
@
isempty
,
data
);
if
any
(
badCol
)
nCols
=
cellfun
(
@
(
x
)(
size
(
x
,
2
)),
data
(
badCol
));
if
all
(
nCols
==
2
)
%
bins
,
counts
isHistogram
=
true
;
else
warning
(
'
DISTRIBUTIONPLOT:
AUTORESHAPE
'
,...
'
Elements
%
s
of
the
cell
array
are
not
vectors
.
They
will
be
reshaped
automatically
'
,...
num2str
(
find
(
badCol
)
'
));
data
(
badCol
)
=
cellfun
(
@
(
x
)(
x
(
:
)),
data
(
badCol
),
'
UniformOutput
'
,
false
);
end
end
end
parserObj
=
inputParser
;
parserObj
.
FunctionName
=
'
distributionPlot
'
;
stdWidth
=
1
;
%
scaling
parameter
for
variableWidth
with
uneven
x
-
values
%
check
whether
we
'
re
dealing
with
pN
/
pV
or
straight
arguments
if
~
isempty
(
varargin
)
&&
~
ischar
(
varargin
{
1
})
&&
~
isstruct
(
varargin
{
1
})
%
use
old
format
%
distWidth
,
showMM
,
xNames
,
histOpt
,
divFactor
,
invert
,
addSpread
,
globalNorm
def
.
distWidth
=
1.9
;
parserObj
.
addOptional
(
'
distWidth
'
,
def
.
distWidth
);
parserObj
.
addOptional
(
'
showMM
'
,
def
.
showMM
);
parserObj
.
addOptional
(
'
xNames
'
,
def
.
xNames
);
parserObj
.
addOptional
(
'
histOpt
'
,
def
.
histOpt
);
parserObj
.
addOptional
(
'
divFactor
'
,
def
.
divFactor
);
parserObj
.
addOptional
(
'
invert
'
,
def
.
invert
);
parserObj
.
addOptional
(
'
addSpread
'
,
def
.
addSpread
);
parserObj
.
addOptional
(
'
globalNorm
'
,
def
.
globalNorm
);
parserObj
.
addOptional
(
'
groups
'
,
def
.
groups
);
parserObj
.
addOptional
(
'
yLabel
'
,
def
.
yLabel
);
parserObj
.
addOptional
(
'
color
'
,
def
.
color
);
parserObj
.
parse
(
varargin
{
:
});
opt
=
parserObj
.
Results
;
%
fill
in
defaults
that
are
not
supported
in
the
old
version
of
the
%
code
opt
.
colormap
=
[];
opt
.
variableWidth
=
true
;
opt
.
histOri
=
'
center
'
;
opt
.
xValues
=
[];
opt
.
xMode
=
'
auto
'
;
opt
.
xyOri
=
'
normal
'
;
opt
.
widthDiv
=
[
1
1
];
%
overwrite
empties
with
defaults
-
inputParser
considers
empty
to
be
a
%
valid
input
.
fnList
=
fieldnames
(
opt
);
for
fn
=
fnList
'
if
isempty
(
opt
.(
fn
{
1
}))
opt
.(
fn
{
1
})
=
def
.(
fn
{
1
});
end
end
%
fix
a
few
parameters
if
opt
.
distWidth
>
1
opt
.
distWidth
=
opt
.
distWidth
-
1
;
else
opt
.
colormap
=
1
-
gray
(
128
);
end
if
opt
.
distWidth
<
0
opt
.
variableWidth
=
false
;
opt
.
distWidth
=
abs
(
opt
.
distWidth
);
end
if
~
isempty
(
opt
.
xNames
)
opt
.
xMode
=
'
manual
'
;
end
else
defNames
=
fieldnames
(
def
);
for
dn
=
defNames
(
:
)
'
parserObj
.
addParamValue
(
dn
{
1
},
def
.(
dn
{
1
}));
end
parserObj
.
parse
(
varargin
{
:
});
opt
=
parserObj
.
Results
;
%
if
groups:
deal
with
data
if
~
isempty
(
opt
.
groups
)
[
idx
,
labels
,
vals
]
=
grp2idx
(
opt
.
groups
);
%
convert
data
to
cell
array
data
=
accumarray
(
idx
,
data
{
1
},[],
@
(
x
){
x
});
nData
=
length
(
data
);
%
if
not
otherwise
provided
,
use
group
labels
for
xnames
if
isempty
(
opt
.
xNames
)
opt
.
xNames
=
labels
;
if
~
iscell
(
opt
.
xNames
)
opt
.
xNames
=
num2cell
(
opt
.
xNames
);
end
end
if
isnumeric
(
vals
)
&&
isempty
(
opt
.
xValues
)
opt
.
xValues
=
vals
;
end
end
if
~
ischar
(
opt
.
xyOri
)
||
~
any
(
ismember
(
opt
.
xyOri
,{
'
normal
','
flipped
'
}))
error
(
'
option
xyOri
must
be
either
''
normal
''
or
''
flipped
''
(
is
''
%
s
'
')'
,
opt
.
xyOri
);
end
end
%
common
checks
%
default
x
-
values:
1
:
n
if
isempty
(
opt
.
xValues
)
opt
.
xValues
=
1
:
nData
;
elseif
length
(
opt
.
xValues
)
~=
nData
error
(
'
please
supply
as
many
x
-
data
values
as
there
are
data
entries
'
)
elseif
length
(
opt
.
xValues
)
>
1
%
only
check
for
scale
if
more
than
1
value
%
scale
width
stdWidth
=
median
(
diff
(
sort
(
opt
.
xValues
)));
opt
.
distWidth
=
opt
.
distWidth
*
stdWidth
;
end
if
~
isscalar
(
opt
.
divFactor
)
&&
length
(
opt
.
divFactor
)
==
3
&&
all
(
opt
.
divFactor
==
def
.
divFactor
)
opt
.
divFactor
=
opt
.
divFactor
(
floor
(
opt
.
histOpt
)
+
1
);
end
if
isHistogram
opt
.
histOpt
=
99
;
if
isempty
(
opt
.
yLabel
)
opt
.
yLabel
=
'
counts
'
;
end
end
%
check
colors
/
colormaps:
do
we
need
to
expand
colormap
?
if
~
iscell
(
opt
.
colormap
)
opt
.
colormap
=
{
opt
.
colormap
};
end
if
~
iscell
(
opt
.
color
)
opt
.
color
=
{
opt
.
color
};
end
for
iColor
=
1
:
length
(
opt
.
color
)
if
ischar
(
opt
.
color
{
iColor
})
opt
.
color
{
iColor
}
=
colorCode2rgb
(
opt
.
color
{
iColor
});
end
end
%
expand
-
if
only
single
colormap
specified
,
we
expand
only
once
if
~
opt
.
variableWidth
missingColormaps
=
find
(
cellfun
(
@
isempty
,
opt
.
colormap
));
for
iMissing
=
missingColormaps
(
:
)
'
endColor
=
opt
.
color
{
max
(
iMissing
,
length
(
opt
.
color
))};
%
normally
,
we
go
from
white
to
color
cmap
=
zeros
(
128
,
3
);
for
rgb
=
1
:
3
cmap
(
:
,
rgb
)
=
linspace
(
1
,
endColor
(
rgb
),
128
);
end
opt
.
colormap
{
iMissing
}
=
cmap
;
end
end
%
if
we
have
colormaps
,
we
need
to
create
a
master
which
we
add
to
the
%
figure
.
Invert
if
necessary
,
and
expand
the
cell
array
to
nData
colormapLength
=
cellfun
(
@
(
x
)
size
(
x
,
1
),
opt
.
colormap
);
if
any
(
colormapLength
>
0
)
colormap
=
cat
(
1
,
opt
.
colormap
{
:
});
if
opt
.
invert
colormap
=
1
-
colormap
;
end
set
(
fh
,
'
Colormap
'
,
colormap
)
if
length
(
opt
.
colormap
)
==
1
opt
.
colormap
=
repmat
(
opt
.
colormap
,
nData
,
1
);
colormapLength
=
repmat
(
colormapLength
,
nData
,
1
);
colormapOffset
=
zeros
(
nData
,
1
);
singleMap
=
true
;
else
colormapOffset
=
[
0
;
cumsum
(
colormapLength
(
1
:
end
-
1
))];
singleMap
=
false
;
end
else
colormapLength
=
zeros
(
nData
,
1
);
if
length
(
opt
.
color
)
==
1
opt
.
color
=
repmat
(
opt
.
color
,
nData
,
1
);
end
if
opt
.
invert
opt
.
color
=
cellfun
(
@
(
x
)
1
-
x
,
opt
.
color
,
'
uniformOutput
'
,
false
);
end
end
%
set
hold
on
holdState
=
get
(
ah
,
'
NextPlot
'
);
set
(
ah
,
'
NextPlot
','
add
'
);
%
if
new
axes:
invert
if
newAx
&&
opt
.
invert
set
(
ah
,
'
Color
','
k
'
)
end
%===================================
%===================================
%%
PLOT
DISTRIBUTIONS
%===================================
%
assign
output
hh
=
NaN
(
nData
,
1
);
[
m
,
md
,
sem
,
sd
]
=
deal
(
nan
(
nData
,
1
));
if
opt
.
showMM
==
6
md
=
nan
(
nData
,
3
,
3
);
%
md
/
q1
/
q3
,
third
dim
is
y
/
xmin
/
xmax
end
%
get
base
x
-
array
%
widthDiv
is
a
1
-
by
-
2
array
with
%
#
ofDivs
,
whichDiv
%
The
full
width
(
distWidth
)
is
split
into
%
#
ofDivs
;
whichDiv
says
which
"stripe"
is
active
xWidth
=
opt
.
distWidth
/
opt
.
widthDiv
(
1
);
xMin
=
-
opt
.
distWidth
/
2
;
xLow
=
xMin
+
xWidth
*
(
opt
.
widthDiv
(
2
)
-
1
);
xBase
=
[
-
xWidth
;
xWidth
;
xWidth
;
-
xWidth
]
/
2
;
xOffset
=
xLow
+
xWidth
/
2
;
%
b
/
c
of
global
norm:
loop
twice
plotData
=
cell
(
nData
,
2
);
%
loop
through
data
.
Prepare
patch
input
,
then
draw
patch
into
gca
for
iData
=
1
:
nData
currentData
=
data
{
iData
};
%
only
plot
if
there
is
some
finite
data
if
~
isempty
(
currentData
(
:
))
&&
any
(
isfinite
(
currentData
(
:
)))
switch
floor
(
opt
.
histOpt
)
case
0
%
use
hist
[
xHist
,
yHist
]
=
hist
(
currentData
,
opt
.
divFactor
);
case
1
%
use
ksdensity
if
opt
.
histOpt
==
1.1
%
use
histogram
to
estimate
kernel
[
dummy
,
x
]
=
histogram
(
currentData
);
%
#
ok
<
ASGLU
>
if
length
(
x
)
==
1
%
only
one
value
.
Make
fixed
distribution
dx
=
0.1
;
yHist
=
x
;
xHist
=
sum
(
isfinite
(
currentData
));
else
dx
=
x
(
2
)
-
x
(
1
);
%
make
sure
we
sample
frequently
enough
x
=
min
(
x
)
-
dx:
dx
/
3
:
max
(
x
)
+
dx
;
[
xHist
,
yHist
]
=
ksdensity
(
currentData
,
x
,
'
kernel
','
normal
','
width
'
,
dx
/
(
1.5
*
opt
.
divFactor
));
end
else
%
x
,
y
are
switched
relative
to
normal
histogram
[
xHist
,
yHist
,
u
]
=
ksdensity
(
currentData
,
'
kernel
','
normal
'
);
%
take
smaller
kernel
to
avoid
over
-
smoothing
if
opt
.
divFactor
~=
1
[
xHist
,
yHist
]
=
ksdensity
(
currentData
,
'
kernel
','
normal
','
width
'
,
u
/
opt
.
divFactor
);
end
end
%
modify
histogram
such
that
the
sum
of
bins
(
not
the
%
integral
under
the
curve
!
)
equals
the
total
number
of
%
observations
,
in
order
to
be
comparable
to
hist
xHist
=
xHist
/
sum
(
xHist
)
*
sum
(
isfinite
(
currentData
));
case
2
%
use
histogram
-
bar
heights
are
counts
as
in
hist
[
xHist
,
yHist
]
=
histogram
(
currentData
,
opt
.
divFactor
,
0
);
case
99
%
bins
,
counts
already
supplied
xHist
=
currentData
(
:
,
2
)
'
;
yHist
=
currentData
(
:
,
1
)
'
;
end
plotData
{
iData
,
1
}
=
xHist
;
plotData
{
iData
,
2
}
=
yHist
;
end
end
goodData
=
find
(
~
cellfun
(
@
isempty
,
plotData
(
:
,
1
)));
%
get
norm
switch
opt
.
globalNorm
case
3
%
#
3
normalizes
relative
densities
xNorm
(
goodData
)
=
cellfun
(
@
(
x
)
min
(
diff
(
x
)),
plotData
(
goodData
,
2
));
xNorm
(
goodData
)
=
xNorm
(
goodData
)
.
*
cellfun
(
@
sum
,
plotData
(
goodData
,
1
))
'
;
maxNorm
(
goodData
)
=
cellfun
(
@
max
,
plotData
(
goodData
,
1
));
xNorm
(
goodData
)
=
xNorm
(
goodData
)
*
max
(
maxNorm
(
goodData
).
/
xNorm
(
goodData
));
case
2
%
#
2
should
normalize
so
that
the
integral
of
the
%
different
histograms
(
i
.
e
.
area
covered
)
scale
with
the
%
respective
sum
of
counts
across
all
bins
.
Requires
evenly
spaced
%
histograms
at
the
moment
xNorm
(
goodData
)
=
cellfun
(
@
(
x
)
min
(
diff
(
x
)),
plotData
(
goodData
,
2
));
maxNorm
(
goodData
)
=
cellfun
(
@
max
,
plotData
(
goodData
,
1
));
xNorm
(
goodData
)
=
xNorm
(
goodData
)
*
max
(
maxNorm
(
goodData
).
/
xNorm
(
goodData
));
case
1
xNorm
(
goodData
)
=
max
(
cat
(
2
,
plotData
{
:
,
1
}));
case
0
xNorm
(
goodData
)
=
cellfun
(
@
max
,
plotData
(
goodData
,
1
));
end
for
iData
=
goodData
'
%
find
current
data
again
currentData
=
data
{
iData
};
xHist
=
plotData
{
iData
,
1
};
yHist
=
plotData
{
iData
,
2
};
%
find
y
-
step
dy
=
min
(
diff
(
yHist
));
if
isempty
(
dy
)
dy
=
0.1
;
end
%
create
x
,
y
arrays
nPoints
=
length
(
xHist
);
xArray
=
repmat
(
xBase
,
1
,
nPoints
);
yArray
=
repmat
([
-
0.5
;
-
0.5
;
0.5
;
0.5
],
1
,
nPoints
);
%
x
is
iData
+/-
almost
0.5
,
multiplied
with
the
height
of
the
%
histogram
if
opt
.
variableWidth
tmp
=
xArray
.
*
repmat
(
xHist
,
4
,
1
).
/
xNorm
(
iData
);
switch
opt
.
histOri
case
'
center
'
%
we
can
simply
use
xArray
xArray
=
tmp
;
case
'
right
'
%
shift
everything
to
the
left
delta
=
tmp
(
1
,
:
)
-
xArray
(
1
,
:
);
xArray
=
bsxfun
(
@
minus
,
tmp
,
delta
);
case
'
left
'
%
shift
everything
to
the
right
delta
=
tmp
(
1
,
:
)
-
xArray
(
1
,
:
);
xArray
=
bsxfun
(
@
plus
,
tmp
,
delta
);
end
xArray
=
xArray
+
opt
.
xValues
(
iData
);
else
xArray
=
xArray
+
iData
;
end
%
add
offset
(
in
case
we
have
multiple
widthDiv
)
xArray
=
xArray
+
xOffset
;
%
yData
is
simply
the
bin
locations
yArray
=
repmat
(
yHist
,
4
,
1
)
+
dy
*
yArray
;
%
add
patch
vertices
=
[
xArray
(
:
),
yArray
(
:
)];
faces
=
reshape
(
1
:
numel
(
yArray
),
4
,[])
'
;
if
colormapLength
(
iData
)
==
0
colorOpt
=
{
'
FaceColor
'
,
opt
.
color
{
iData
}};
else
%
calculate
index
into
colormap
if
singleMap
%
use
scaled
mapping
so
that
colorbar
is
meaningful
if
opt
.
globalNorm
>
0
colorOpt
=
{
'
FaceVertexCData
'
,
xHist
','
CDataMapping
','
scaled
','
FaceColor
','
flat
'
};
else
colorOpt
=
{
'
FaceVertexCData
'
,
xHist
'
/
xNorm
(
iData
),
'
CDataMapping
','
scaled
','
FaceColor
','
flat
'
};
end
else
idx
=
round
((
xHist
/
xNorm
(
iData
))
*
(
colormapLength
(
iData
)
-
1
))
+
1
;
colorOpt
=
{
'
FaceVertexCData
'
,
idx
'
+
colormapOffset
(
iData
),
'
CDataMapping
','
direct
','
FaceColor
','
flat
'
};
end
end
switch
opt
.
xyOri
case
'
normal
'
hh
(
iData
)
=
patch
(
'
Vertices
'
,
vertices
,
'
Faces
'
,
faces
,
'
Parent
'
,
ah
,
colorOpt
{
:
},
'
EdgeColor
','
none
'
);
case
'
flipped
'
hh
(
iData
)
=
patch
(
'
Vertices
'
,
vertices
(
:
,[
2
,
1
]),
'
Faces
'
,
faces
,
'
Parent
'
,
ah
,
colorOpt
{
:
},
'
EdgeColor
','
none
'
);
end
if
opt
.
showMM
>
0
if
isHistogram
[
m
(
iData
),
sem
(
iData
)]
=
weightedStats
(
currentData
(
:
,
1
),
currentData
(
:
,
2
),
'w'
);
sd
(
iData
)
=
sem
(
iData
)
*
sqrt
(
sum
(
currentData
(
:
,
2
)));
%
weighted
median:
where
we
'
re
at
middle
weight
%
may
need
some
tweaking
goodCurrentData
=
sortrows
(
currentData
(
all
(
isfinite
(
currentData
),
2
),
:
),
1
);
weightList
=
cumsum
(
goodCurrentData
(
:
,
2
));
weightList
=
weightList
/
weightList
(
end
);
md
(
iData
)
=
goodCurrentData
(
find
(
weightList
>
0.5
,
1
,
'
first
'
),
1
);
else
m
(
iData
)
=
nanmean
(
currentData
);
md
(
iData
)
=
nanmedian
(
currentData
);
sd
(
iData
)
=
nanstd
(
currentData
);
sem
(
iData
)
=
sd
(
iData
)
/
sqrt
(
sum
(
isfinite
(
currentData
)));
end
if
opt
.
showMM
==
6
%
read
quantiles
-
"y"
-
value
,
plus
x
-
start
-
stop
%
re
-
use
md
array
which
allows
using
a
loop
below
instead
of
%
lots
of
copy
-
paste
%
md
array
is
md
/
q1
/
q3
,
with
third
dimension
y
/
xmin
/
xmax
md
(
iData
,
2
,
1
)
=
prctile
(
currentData
,
25
);
md
(
iData
,
3
,
1
)
=
prctile
(
currentData
,
75
);
for
qq
=
1
:
3
%
find
corresponding
y
-
bin
yLoc
=
repmat
(...
any
(
yArray
>
md
(
iData
,
qq
,
1
),
1
)
&
any
(
yArray
<=
md
(
iData
,
qq
,
1
),
1
),...
[
4
1
]);
%
look
up
corresponding
x
-
values
.
Note
that
there
is
a
bit
%
of
a
risk
that
the
line
will
be
exactly
between
two
very
%
different
bins
-
but
if
we
make
the
line
longer
,
it
will
%
be
ugly
almost
all
the
time
md
(
iData
,
qq
,
2
)
=
min
(
xArray
(
yLoc
)
);
md
(
iData
,
qq
,
3
)
=
max
(
xArray
(
yLoc
)
);
end
end
end
end
%
loop
sh
=
[];
if
opt
.
addSpread
if
isHistogram
disp
(
'
Option
addSpread
is
unavailable
if
data
is
supplied
as
histograms
.
Call
plotSpread
separately
'
)
else
%
add
spread
try
sh
=
plotSpread
(
ah
,
data
,
'
xValues
'
,
opt
.
xValues
,
'
xyOri
'
,
opt
.
xyOri
);
set
(
sh
{
1
},
'
color
'
,[
0
,
128
,
255
]
/
255
);
catch
me
if
strcmp
(
me
.
identifier
,
'
MATLAB:
UndefinedFunction
'
)
error
(
'
plotSpread
not
found
.
Please
download
it
from
the
Matlab
File
Exchange
'
)
else
rethrow
(
me
)
end
end
end
end
mh
=
[];
mdh
=
[];
if
opt
.
showMM
%
plot
mean
,
median
.
Mean
is
filled
red
circle
,
median
is
green
square
%
I
don
'
t
know
of
a
very
clever
way
to
flip
xy
and
keep
everything
%
readable
,
thus
it
'
ll
be
copy
-
paste
switch
opt
.
xyOri
case
'
normal
'
if
any
(
opt
.
showMM
==
[
1
,
2
])
mh
=
plot
(
ah
,
opt
.
xValues
+
xOffset
,
m
,
'
+
r
','
Color
','
r
','
MarkerSize
'
,
12
);
end
if
any
(
opt
.
showMM
==
[
1
,
3
])
mdh
=
plot
(
ah
,
opt
.
xValues
+
xOffset
,
md
,
'
sg
','
MarkerSize
'
,
12
);
end
if
opt
.
showMM
==
4
mh
=
plot
(
ah
,
opt
.
xValues
+
xOffset
,
m
,
'
+
r
','
Color
','
r
','
MarkerSize
'
,
12
);
mdh
=
myErrorbar
(
ah
,
opt
.
xValues
+
xOffset
,
m
,
sem
);
end
if
opt
.
showMM
==
5
mh
=
plot
(
ah
,
opt
.
xValues
+
xOffset
,
m
,
'
+
r
','
Color
','
r
','
MarkerSize
'
,
12
);
mdh
=
myErrorbar
(
ah
,
opt
.
xValues
+
xOffset
,
m
,
sd
);
end
if
opt
.
showMM
==
6
mdh
(
1
,
:
)
=
plot
(
ah
,
squeeze
(
md
(
:
,
1
,
2
:
3
))
'
,
repmat
(
md
(
:
,
1
,
1
)
'
,
2
,
1
),
'
color
','
r
','
lineWidth
'
,
2
);
%
,
'
lineStyle
','
--
'
);
mdh
(
2
,
:
)
=
plot
(
ah
,
squeeze
(
md
(
:
,
2
,
2
:
3
))
'
,
repmat
(
md
(
:
,
2
,
1
)
'
,
2
,
1
),
'
color
','
r
','
lineWidth
'
,
1
);
%
,
'
lineStyle
','
--
'
);
mdh
(
3
,
:
)
=
plot
(
ah
,
squeeze
(
md
(
:
,
3
,
2
:
3
))
'
,
repmat
(
md
(
:
,
3
,
1
)
'
,
2
,
1
),
'
color
','
r
','
lineWidth
'
,
1
);
%
,
'
lineStyle
','
--
'
);
end
case
'
flipped
'
if
any
(
opt
.
showMM
==
[
1
,
2
])
mh
=
plot
(
ah
,
m
,
opt
.
xValues
+
xOffset
,
'
+
r
','
Color
','
r
','
MarkerSize
'
,
12
);
end
if
any
(
opt
.
showMM
==
[
1
,
3
])
mdh
=
plot
(
ah
,
md
,
opt
.
xValues
+
xOffset
,
'
sg
','
MarkerSize
'
,
12
);
end
if
opt
.
showMM
==
4
mh
=
plot
(
ah
,
m
,
opt
.
xValues
+
xOffset
,
'
+
r
','
Color
','
r
','
MarkerSize
'
,
12
);
mdh
=
myErrorbar
(
ah
,
m
,
opt
.
xValues
+
xOffset
,[
sem
,
NaN
(
size
(
sem
))]);
end
if
opt
.
showMM
==
5
mh
=
plot
(
ah
,
m
,
opt
.
xValues
+
xOffset
,
'
+
r
','
Color
','
r
','
MarkerSize
'
,
12
);
mdh
=
myErrorbar
(
ah
,
m
,
opt
.
xValues
+
xOffset
,[
sd
,
NaN
(
size
(
sd
))]);
end
if
opt
.
showMM
==
6
mdh
(
1
,
:
)
=
plot
(
ah
,
repmat
(
md
(
:
,
1
,
1
)
'
,
2
,
1
),
squeeze
(
md
(
:
,
1
,
2
:
3
))
','
color
','
r
','
lineWidth
'
,
2
);
%
,
'
lineStyle
','
--
'
);
mdh
(
2
,
:
)
=
plot
(
ah
,
repmat
(
md
(
:
,
2
,
1
)
'
,
2
,
1
),
squeeze
(
md
(
:
,
2
,
2
:
3
))
','
color
','
r
','
lineWidth
'
,
1
);
%
,
'
lineStyle
','
--
'
);
mdh
(
3
,
:
)
=
plot
(
ah
,
repmat
(
md
(
:
,
3
,
1
)
'
,
2
,
1
),
squeeze
(
md
(
:
,
3
,
2
:
3
))
','
color
','
r
','
lineWidth
'
,
1
);
%
,
'
lineStyle
','
--
'
);
end
end
end
%
find
extents
of
x
-
axis
(
or
y
-
axis
,
if
flipped
)
minX
=
min
(
opt
.
xValues
)
-
stdWidth
;
maxX
=
max
(
opt
.
xValues
)
+
stdWidth
;
if
~
isnan
(
xAxLim
(
1
))
%
we
have
previous
limits
switch
opt
.
xyOri
case
'
normal
'
minX
=
min
(
minX
,
xAxLim
(
1
));
maxX
=
max
(
maxX
,
xAxLim
(
2
));
case
'
flipped
'
minX
=
min
(
minX
,
yAxLim
(
1
));
maxX
=
max
(
maxX
,
yAxLim
(
2
));
end
end
%
if
~
empty
,
use
xNames
switch
opt
.
xyOri
case
'
normal
'
switch
opt
.
xMode
case
'
manual
'
if
newAx
==
false
warning
(
'
DISTRIBUTIONPLOT:
ERASINGLABELS
','
Plotting
into
an
existing
axes
and
specifying
labels
will
erase
previous
labels
'
)
end
set
(
ah
,
'
XTick
'
,
opt
.
xValues
);
if
~
isempty
(
opt
.
xNames
)
set
(
ah
,
'
XTickLabel
'
,
opt
.
xNames
)
end
case
'
auto
'
%
no
need
to
do
anything
end
if
~
isempty
(
opt
.
yLabel
)
ylabel
(
ah
,
opt
.
yLabel
);
end
%
have
plot
start
/
end
properly
xlim
(
ah
,[
minX
,
maxX
])
case
'
flipped
'
switch
opt
.
xMode
case
'
manual
'
if
newAx
==
false
warning
(
'
DISTRIBUTIONPLOT:
ERASINGLABELS
','
Plotting
into
an
existing
axes
and
specifying
labels
will
erase
previous
labels
'
)
end
set
(
ah
,
'
YTick
'
,
opt
.
xValues
);
if
~
isempty
(
opt
.
xNames
)
set
(
ah
,
'
YTickLabel
'
,
opt
.
xNames
)
end
case
'
auto
'
%
no
need
to
do
anything
end
if
~
isempty
(
opt
.
yLabel
)
xlabel
(
ah
,
opt
.
yLabel
);
end
%
have
plot
start
/
end
properly
ylim
(
ah
,[
minX
,
maxX
])
end
%==========================
%==========================
%%
CLEANUP
&
ASSIGN
OUTPUT
%==========================
if
nargout
>
0
handles
{
1
}
=
hh
;
handles
{
2
}
=
[
mh
;
mdh
];
handles
{
3
}
=
ah
;
handles
{
4
}
=
sh
;
end
set
(
ah
,
'
NextPlot
'
,
holdState
);
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