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Gregory Ashton
PyFstat
Commits
38fbf98e
Commit
38fbf98e
authored
Sep 27, 2016
by
Gregory Ashton
Browse files
Add context to plot_corner to allow the user to customise the plot
parent
b26d1c74
Changes
1
Hide whitespace changes
Inline
Side-by-side
pyfstat.py
View file @
38fbf98e
...
...
@@ -643,64 +643,66 @@ class MCMCSearch(BaseSearchClass):
self
.
save_data
(
sampler
,
samples
,
lnprobs
,
lnlikes
)
def
plot_corner
(
self
,
figsize
=
(
7
,
7
),
tglitch_ratio
=
False
,
add_prior
=
False
,
nstds
=
None
,
label_offset
=
0.4
,
**
kwargs
):
fig
,
axes
=
plt
.
subplots
(
self
.
ndim
,
self
.
ndim
,
figsize
=
figsize
)
samples_plt
=
copy
.
copy
(
self
.
samples
)
theta_symbols_plt
=
copy
.
copy
(
self
.
theta_symbols
)
theta_symbols_plt
=
[
s
.
replace
(
'_{glitch}'
,
r
'_\textrm{glitch}'
)
for
s
in
theta_symbols_plt
]
if
tglitch_ratio
:
for
j
,
k
in
enumerate
(
self
.
theta_keys
):
if
k
==
'tglitch'
:
s
=
samples_plt
[:,
j
]
samples_plt
[:,
j
]
=
(
s
-
self
.
tstart
)
/
(
self
.
tend
-
self
.
tstart
)
theta_symbols_plt
[
j
]
=
r
'$R_{\textrm{glitch}}$'
if
type
(
nstds
)
is
int
and
'range'
not
in
kwargs
:
_range
=
[]
for
j
,
s
in
enumerate
(
samples_plt
.
T
):
median
=
np
.
median
(
s
)
std
=
np
.
std
(
s
)
_range
.
append
((
median
-
nstds
*
std
,
median
+
nstds
*
std
))
else
:
_range
=
None
fig_triangle
=
corner
.
corner
(
samples_plt
,
labels
=
theta_symbols_plt
,
fig
=
fig
,
bins
=
50
,
max_n_ticks
=
4
,
plot_contours
=
True
,
plot_datapoints
=
True
,
label_kwargs
=
{
'fontsize'
:
8
},
data_kwargs
=
{
'alpha'
:
0.1
,
'ms'
:
0.5
},
range
=
_range
,
**
kwargs
)
axes_list
=
fig_triangle
.
get_axes
()
axes
=
np
.
array
(
axes_list
).
reshape
(
self
.
ndim
,
self
.
ndim
)
plt
.
draw
()
for
ax
in
axes
[:,
0
]:
ax
.
yaxis
.
set_label_coords
(
-
label_offset
,
0.5
)
for
ax
in
axes
[
-
1
,
:]:
ax
.
xaxis
.
set_label_coords
(
0.5
,
-
label_offset
)
for
ax
in
axes_list
:
ax
.
set_rasterized
(
True
)
ax
.
set_rasterization_zorder
(
-
10
)
plt
.
tight_layout
(
h_pad
=
0.0
,
w_pad
=
0.0
)
fig
.
subplots_adjust
(
hspace
=
0.05
,
wspace
=
0.05
)
if
add_prior
:
self
.
add_prior_to_corner
(
axes
,
samples_plt
)
fig_triangle
.
savefig
(
'{}/{}_corner.png'
.
format
(
self
.
outdir
,
self
.
label
))
add_prior
=
False
,
nstds
=
None
,
label_offset
=
0.4
,
dpi
=
300
,
rc_context
=
{},
**
kwargs
):
with
plt
.
rc_context
(
rc_context
):
fig
,
axes
=
plt
.
subplots
(
self
.
ndim
,
self
.
ndim
,
figsize
=
figsize
)
samples_plt
=
copy
.
copy
(
self
.
samples
)
theta_symbols_plt
=
copy
.
copy
(
self
.
theta_symbols
)
theta_symbols_plt
=
[
s
.
replace
(
'_{glitch}'
,
r
'_\textrm{glitch}'
)
for
s
in
theta_symbols_plt
]
if
tglitch_ratio
:
for
j
,
k
in
enumerate
(
self
.
theta_keys
):
if
k
==
'tglitch'
:
s
=
samples_plt
[:,
j
]
samples_plt
[:,
j
]
=
(
s
-
self
.
tstart
)
/
(
self
.
tend
-
self
.
tstart
)
theta_symbols_plt
[
j
]
=
r
'$R_{\textrm{glitch}}$'
if
type
(
nstds
)
is
int
and
'range'
not
in
kwargs
:
_range
=
[]
for
j
,
s
in
enumerate
(
samples_plt
.
T
):
median
=
np
.
median
(
s
)
std
=
np
.
std
(
s
)
_range
.
append
((
median
-
nstds
*
std
,
median
+
nstds
*
std
))
else
:
_range
=
None
fig_triangle
=
corner
.
corner
(
samples_plt
,
labels
=
theta_symbols_plt
,
fig
=
fig
,
bins
=
50
,
max_n_ticks
=
4
,
plot_contours
=
True
,
plot_datapoints
=
True
,
label_kwargs
=
{
'fontsize'
:
8
},
data_kwargs
=
{
'alpha'
:
0.1
,
'ms'
:
0.5
},
range
=
_range
,
**
kwargs
)
axes_list
=
fig_triangle
.
get_axes
()
axes
=
np
.
array
(
axes_list
).
reshape
(
self
.
ndim
,
self
.
ndim
)
plt
.
draw
()
for
ax
in
axes
[:,
0
]:
ax
.
yaxis
.
set_label_coords
(
-
label_offset
,
0.5
)
for
ax
in
axes
[
-
1
,
:]:
ax
.
xaxis
.
set_label_coords
(
0.5
,
-
label_offset
)
for
ax
in
axes_list
:
ax
.
set_rasterized
(
True
)
ax
.
set_rasterization_zorder
(
-
10
)
plt
.
tight_layout
(
h_pad
=
0.0
,
w_pad
=
0.0
)
fig
.
subplots_adjust
(
hspace
=
0.05
,
wspace
=
0.05
)
if
add_prior
:
self
.
add_prior_to_corner
(
axes
,
samples_plt
)
fig_triangle
.
savefig
(
'{}/{}_corner.png'
.
format
(
self
.
outdir
,
self
.
label
),
dpi
=
dpi
)
def
add_prior_to_corner
(
self
,
axes
,
samples
):
for
i
,
key
in
enumerate
(
self
.
theta_keys
):
...
...
@@ -757,6 +759,8 @@ class MCMCSearch(BaseSearchClass):
return
lambda
x
:
logunif
(
x
,
kwargs
[
'lower'
],
kwargs
[
'upper'
])
elif
kwargs
[
'type'
]
==
'halfnorm'
:
return
lambda
x
:
halfnorm
(
x
,
kwargs
[
'loc'
],
kwargs
[
'scale'
])
elif
kwargs
[
'type'
]
==
'neghalfnorm'
:
return
lambda
x
:
halfnorm
(
-
x
,
kwargs
[
'loc'
],
kwargs
[
'scale'
])
elif
kwargs
[
'type'
]
==
'norm'
:
return
lambda
x
:
-
0.5
*
((
x
-
kwargs
[
'loc'
])
**
2
/
kwargs
[
'scale'
]
**
2
+
np
.
log
(
2
*
np
.
pi
*
kwargs
[
'scale'
]
**
2
))
...
...
@@ -773,6 +777,9 @@ class MCMCSearch(BaseSearchClass):
if
dist_type
==
"halfnorm"
:
return
np
.
abs
(
np
.
random
.
normal
(
loc
=
kwargs
[
'loc'
],
scale
=
kwargs
[
'scale'
]))
if
dist_type
==
"neghalfnorm"
:
return
-
1
*
np
.
abs
(
np
.
random
.
normal
(
loc
=
kwargs
[
'loc'
],
scale
=
kwargs
[
'scale'
]))
if
dist_type
==
"lognorm"
:
return
np
.
random
.
lognormal
(
mean
=
kwargs
[
'loc'
],
sigma
=
kwargs
[
'scale'
])
...
...
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