Page Menu
Home
c4science
Search
Configure Global Search
Log In
Files
F77570921
plot.py
No One
Temporary
Actions
Download File
Edit File
Delete File
View Transforms
Subscribe
Mute Notifications
Award Token
Subscribers
None
File Metadata
Details
File Info
Storage
Attached
Created
Thu, Aug 15, 06:09
Size
3 KB
Mime Type
text/x-python
Expires
Sat, Aug 17, 06:09 (1 d, 23 h)
Engine
blob
Format
Raw Data
Handle
19889950
Attached To
rAKA akantu
plot.py
View Options
#!/usr/bin/env python3
import
os
import
pandas
as
pd
import
numpy
as
np
# needed to generate the plots on jed
import
matplotlib
# matplotlib.use("TKAgg")
import
matplotlib.pyplot
as
plt
# Same font as JOSS
plt
.
rcParams
[
"font.sans-serif"
]
=
"cmss10"
# Loading data
plots
=
{
"elastic gcc v5"
:
{
"prefix"
:
"timmings_"
,
"material"
:
"elastic"
,
"compiler"
:
"gcc"
,
"suffix"
:
"_jed_v5.0.4"
},
"cohesive gcc v5"
:
{
"prefix"
:
"timmings_"
,
"material"
:
"cohesive"
,
"compiler"
:
"gcc"
,
"suffix"
:
"_jed_v5.0.4"
},
"elastic gcc v4"
:
{
"prefix"
:
"timmings_"
,
"material"
:
"elastic"
,
"compiler"
:
"gcc"
,
"suffix"
:
"_jed_v4.0.1"
},
"cohesive gcc v4"
:
{
"prefix"
:
"timmings_"
,
"material"
:
"cohesive"
,
"compiler"
:
"gcc"
,
"suffix"
:
"_jed_v4.0.1"
},
}
fig
,
ax
=
plt
.
subplots
(
figsize
=
(
4.5
,
4
))
# fig, ax = plt.subplots(1, 1)
plotting
=
"TTS"
handles
=
[]
for
plot_name
,
data
in
plots
.
items
():
data
[
"df"
]
=
pd
.
read_csv
(
f
"""{data["prefix"]}{data["material"]}_{data["compiler"]}{data["suffix"]}.csv"""
,
sep
=
","
,
skipinitialspace
=
True
,
)
df
=
data
[
"df"
]
step
=
df
[
"solve_step"
]
*
df
[
"solve_step nb_rep"
]
if
data
[
"material"
]
==
"cohesive"
:
step
=
step
+
df
[
"check_cohesive_stress"
]
*
df
[
"check_cohesive_stress nb_rep"
]
df
[
"TTS"
]
=
step
/
1000
df
[
"speedup"
]
=
step
[
0
]
/
step
df
[
"mumps"
]
=
df
[
"static_solve"
]
*
df
[
"static_solve nb_rep"
]
def
plot_measure
(
ax
,
df
,
plotting
,
label
,
**
kwargs
):
"""Plot a given measure."""
grouped
=
df
.
groupby
(
"psize"
)
# compute stats grouped by number of procs
med
=
grouped
.
median
()
min
=
grouped
.
min
()
max
=
grouped
.
max
()
min_psize
=
df
[
"psize"
][
0
]
print
(
list
(
med
[
plotting
]))
(
l
,)
=
ax
.
plot
(
med
.
index
,
med
[
plotting
],
label
=
f
"{label} (median)"
,
**
kwargs
)
ax
.
fill_between
(
med
.
index
,
min
[
plotting
],
max
[
plotting
],
color
=
l
.
get_color
(),
alpha
=
0.2
)
ax
.
plot
(
med
.
index
,
min_psize
*
med
[
plotting
][
min_psize
]
/
med
.
index
,
ls
=
"--"
,
color
=
l
.
get_color
())
# ax.boxplot(
# data[plotting]["grouped"], positions=psize, widths=[0.1 * s for s in psize]
# )
plot_measure
(
ax
,
plots
[
"cohesive gcc v5"
][
"df"
],
plotting
,
"insertion"
,
marker
=
"o"
,
)
plot_measure
(
ax
,
plots
[
"elastic gcc v5"
][
"df"
],
plotting
,
"no insertion v5"
,
marker
=
"o"
,
)
# Selecting appropriate tick values
psize
=
np
.
array
(
np
.
unique
(
plots
[
list
(
plots
.
keys
())[
0
]][
"df"
][
"psize"
]))
labels
=
np
.
concatenate
(
[[
psize
[
0
]],
psize
[
1
:][
psize
[
1
:]
>=
2
*
psize
[:
-
1
]],
[
psize
[
-
1
]]]
)
# for name, ax in axes.items():
ax
.
set_xscale
(
"log"
,
base
=
2
)
ax
.
set_yscale
(
"log"
)
ylabel
=
plotting
if
plotting
!=
"TTS"
else
"Time to solution"
yunit
=
"s"
if
plotting
!=
"speedup"
else
"-"
ax
.
set_xlabel
(
"Nb Cores [-]"
)
ax
.
set_ylabel
(
f
"""{ylabel} [{yunit}]"""
)
ax
.
set_xticks
(
ticks
=
labels
,
labels
=
map
(
str
,
labels
))
# Constructing legend with min/max and ideal labels
handles
,
labels
=
ax
.
get_legend_handles_labels
()
handles
+=
[
matplotlib
.
lines
.
Line2D
([],
[],
linestyle
=
"--"
,
color
=
"k"
),
matplotlib
.
patches
.
Patch
(
color
=
"k"
,
alpha
=
0.2
),
]
labels
+=
[
"ideal"
,
"min/max"
]
ax
.
legend
(
handles
=
handles
,
labels
=
labels
)
fig
.
tight_layout
()
fig
.
savefig
(
f
"{plotting}.svg"
,
transparent
=
True
,
bbox_inches
=
"tight"
,
pad_inches
=
0.1
)
plt
.
show
()
Event Timeline
Log In to Comment