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Sun, Sep 1, 07:15
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image/svg+xml
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Tue, Sep 3, 07:15 (1 d, 21 h)
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diff --git a/joss/results/TTS.svg b/joss/results/TTS.svg
index 842a8d351..e7fa1f92c 100644
--- a/joss/results/TTS.svg
+++ b/joss/results/TTS.svg
@@ -1,2085 +1,1658 @@
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+ <g id="text_17">
+ <!-- no insertion (median) -->
+ <g transform="translate(79.9 165.213438)scale(0.1 -0.1)">
+ <use xlink:href="#Cmss10-6e"/>
+ <use xlink:href="#Cmss10-6f" x="51.611328"/>
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+ <use xlink:href="#Cmss10-6d" x="560.644531"/>
+ <use xlink:href="#Cmss10-65" x="640.039062"/>
+ <use xlink:href="#Cmss10-64" x="684.423828"/>
+ <use xlink:href="#Cmss10-69" x="736.035156"/>
+ <use xlink:href="#Cmss10-61" x="759.814453"/>
+ <use xlink:href="#Cmss10-6e" x="807.8125"/>
+ <use xlink:href="#Cmss10-29" x="859.423828"/>
+ </g>
+ </g>
+ <g id="line2d_50">
+ <path d="M 51.9 176.213438
+L 61.9 176.213438
+L 71.9 176.213438
+" style="fill: none; stroke-dasharray: 5.55,2.4; stroke-dashoffset: 0; stroke: #000000; stroke-width: 1.5"/>
+ </g>
+ <g id="text_18">
+ <!-- ideal -->
+ <g transform="translate(79.9 179.713438)scale(0.1 -0.1)">
+ <use xlink:href="#Cmss10-69"/>
+ <use xlink:href="#Cmss10-64" x="23.779297"/>
+ <use xlink:href="#Cmss10-65" x="75.390625"/>
+ <use xlink:href="#Cmss10-61" x="119.775391"/>
+ <use xlink:href="#Cmss10-6c" x="167.773438"/>
+ </g>
+ </g>
+ <g id="patch_8">
+ <path d="M 51.9 194.1525
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+" style="opacity: 0.2; stroke: #000000; stroke-linejoin: miter"/>
</g>
- <g id="text_23">
- <!-- Min/Max TTS. -->
- <g transform="translate(350.4584 48.892) scale(0.1 -0.1)">
+ <g id="text_19">
+ <!-- min/max -->
+ <g transform="translate(79.9 194.1525)scale(0.1 -0.1)">
<defs>
<path id="Cmss10-2f" d="M 359 -1472
Q 359 -1453 366 -1447
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- <g id="text_24">
- <!-- Ideal TTS. -->
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- </clipPath>
- <clipPath id="pfbe49e36a6">
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+ <clipPath id="p491ac97592">
+ <rect x="42.9" y="7.2" width="229.298031" height="198.4525"/>
</clipPath>
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diff --git a/joss/results/plot.py b/joss/results/plot.py
index 5d4d9e2de..9b9768f29 100755
--- a/joss/results/plot.py
+++ b/joss/results/plot.py
@@ -1,119 +1,68 @@
#!/usr/bin/env python3
import pandas as pd
import numpy as np
-import matplotlib
-
-matplotlib.use("TKAgg")
import matplotlib.pyplot as plt
+from matplotlib.lines import Line2D
+from matplotlib.patches import Patch
+
# Same font as JOSS
plt.rcParams["font.sans-serif"] = "cmss10"
-print("Using:", matplotlib.get_backend())
-plots = {
- "elastic_gcc": {"prefix": "timmings_", "suffix": "_jed"},
- "cohesive_gcc": {"prefix": "timmings_", "suffix": "_jed"},
-}
-
-
-axes = {}
-fig, (axes["elastic"], axes["cohesive"]) = plt.subplots(2, 1, sharex=True)
-handles = []
-for plot_name, data in plots.items():
- mat, compiler = plot_name.split("_")
- data["df"] = pd.read_csv(
- f"""{data["prefix"]}{plot_name}{data["suffix"]}.csv""",
- sep=",",
- skipinitialspace=True,
- )
-
-plotting = "TTS"
-
-psize = np.array(sorted(set(plots[list(plots.keys())[0]]["df"]["psize"])))
-
-for plot_name, data in plots.items():
- mat, compiler = plot_name.split("_")
- df = data["df"]
-
- measures = {}
- if mat == "elastic":
- step = df["solve_step"]
- elif mat == "cohesive":
- step = df["check_cohesive_stress"] + df["solve_step"]
- measures["TTS"] = step * df["solve_step nb_rep"]
- measures["speedup"] = step[0] / step
- measures["mumps"] = df["static_solve"]
-
- for name, measure in measures.items():
- data[name] = {
- "values": measure,
- "median": [],
- "mean": [],
- "std": [],
- "max": [],
- "min": [],
- "grouped": [],
- "nb_measure": [],
- }
- for s in psize:
- values = measure[df["psize"] == s]
- data[name]["std"].append(values.std())
- data[name]["mean"].append(values.mean())
- data[name]["median"].append(values.median())
- data[name]["max"].append(values.max())
- data[name]["min"].append(values.min())
- data[name]["grouped"].append(values)
- data[name]["nb_measure"].append(len(values))
-
- # axes[mat].boxplot(
- # data[plotting]["grouped"], positions=psize, widths=[0.1 * s for s in psize]
- # )
-
- print(f"""{mat}: {data[plotting]["nb_measure"]}\n {psize}""")
- axes[mat].plot(
- psize,
- data[plotting]["median"],
- "-o",
- label=f"""Median {plotting}.""",
- )
-
- axes[mat].fill_between(
- psize,
- data[plotting]["min"],
- data[plotting]["max"],
- alpha=0.2,
- label=f"Min/Max {plotting}.",
- )
-
- axes[mat].plot(
- psize,
- data[plotting]["median"][0] / psize if plotting == "TTS" else psize,
- "-",
- label=f"Ideal {plotting}.",
- )
-
-labels = [psize[0]]
-for label in psize:
- if label >= labels[-1] * 2:
- labels.append(label)
-
-del labels[-1]
-labels.append(psize[-1])
-print(labels)
-
-for name, ax in axes.items():
- ax.set_xscale("log", base=2)
- ax.set_yscale("log")
- ax.set_ylabel(f"""{plotting} [{"s" if plotting == "TTS" else "-"}]""")
- ax.set_xticks(ticks=labels, labels=[str(label) for label in labels])
-
-axes["cohesive"].set_title("b) With cohesive insertion")
-axes["elastic"].set_title("a) Without cohesive insertion")
-
-plt.xlabel("Nb Cores [-]")
-plt.legend(bbox_to_anchor=(1.04, 2), loc="center left", borderaxespad=0)
-
-plt.subplots_adjust(right=0.7)
-plt.savefig(f"{plotting}.svg", bbox_inches="tight")
+# Loading data
+elastic = pd.read_csv("timmings_elastic_gcc_jed.csv", sep=",",
+ skipinitialspace=True)
+cohesive = pd.read_csv("timmings_cohesive_gcc_jed.csv", sep=",",
+ skipinitialspace=True)
+
+# Solve time
+elastic["step"] = elastic["solve_step"]
+cohesive["step"] = cohesive["check_cohesive_stress"] + cohesive["solve_step"]
+
+fig, ax = plt.subplots(figsize=(4, 3.5))
+
+
+def plot_tts(ax, df, **kwargs):
+ g = df.groupby("psize") # compute stats grouped by number of procs
+ med = g.median()
+ min = g.min()
+ max = g.max()
+ l, = ax.plot(med.index, med["step"], **kwargs)
+ ax.fill_between(med.index,
+ min["step"],
+ max["step"],
+ color=l.get_color(),
+ alpha=.2)
+ ax.plot(med.index, med["step"][1] / med.index, ls='--', color=l.get_color())
+
+
+plot_tts(ax, cohesive, marker="s", label="insertion (median)")
+plot_tts(ax, elastic, marker="o", label="no insertion (median)")
+
+ax.set_xscale("log", base=2)
+ax.set_yscale("log")
+
+ax.set_xlabel("Nb Cores [-]")
+ax.set_ylabel("Time to Solution [s]")
+
+# Selecting appropriate tick values
+psize = np.sort(np.unique(elastic["psize"]))
+psize = np.concatenate([[psize[0]],
+ psize[1:][psize[1:] >= 2 * psize[:-1]],
+ [psize[-1]]])
+ax.set_xticks(ticks=psize, labels=map(str, psize))
+
+# Constructing legend with min/max and ideal labels
+handles, labels = ax.get_legend_handles_labels()
+handles += [
+ Line2D([], [], linestyle='--', color='k'),
+ Patch(color='k', alpha=.2),
+]
+labels += ["ideal", "min/max"]
+ax.legend(handles=handles, labels=labels)
+
+fig.tight_layout()
+fig.savefig("TTS.svg", transparent=True, bbox_inches="tight", pad_inches=0.1)
+
plt.show()
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