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Commit 80fa8bc6 authored by Cornelius Oberon Pätzold's avatar Cornelius Oberon Pätzold
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Upload grouped barplot

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%% Cell type:code id:b74e6051-2004-4713-b0b7-015febb66ffb tags:
``` python
import math
import pandas as pd
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.patheffects as pe
import numpy as np
```
%% Cell type:code id:c26c5978-54e3-488b-82f5-012857bb9f58 tags:
``` python
#Read csv and create a dataframe
df = pd.read_csv('stencil_2.csv')
# Calculate slowdown, and tool slowdown
#df["slowdown"] = [row["avg_t(s)"]/df.loc[(df['compile'] == "base") & (df['measurement'] == "base") & (df['tasks'] == df['tasks'].min())]["avg_t(s)"].iloc[0] for index, row in df.iterrows()]
df["tool slowdown"] = [row["avg_t(s)"]/df.loc[(df['compile'] == "base") & (df['measurement'] == "base") & (df['tasks'] == row['tasks'])]["avg_t(s)"].iloc[0] for index, row in df.iterrows()]
# Create the new feature "compile:measurement" that is the combination of the "compile" and "measurement" features.
# We use this later for the "hue" arguement in the seaborn plotting functions, which creates a color encoding based
#on the values of the "compile:measurement" feature. e.g. the data points for "base:base" and "tsan:must" will have different colors
df["compile:measurement"] = df["compile"] + ":" + df["measurement"]
display(df)
# Drop "base:must" because it is not of interest
df = df.drop(df[df["compile:measurement"] == "base:must"].index)
```
%% Output
%% Cell type:code id:8b1abc69-f247-4ab6-8df0-78b07ea405e4 tags:
``` python
custom = {"grid.linestyle": "solid", "grid.color": "white"}
sns.set_style("darkgrid", rc = custom)
#Slowdown
plt.clf()
ax = sns.barplot(x="tasks", y="tool slowdown", data=df, hue="compile:measurement", palette=["#00549f", "#8ebae5", "#fabe50"])
ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.12), fancybox=True, ncol=3)
#Show values
for i in ax.containers:
texts = ax.bar_label(i, fmt='%.3g', rotation=90)
for text in texts:
text.set(y=5, zorder=2000)
plt.show()
#Runtime
plt.clf()
ax = sns.barplot(x="tasks", y="avg_t(s)", data=df, hue="compile:measurement", palette=["#00549f", "#8ebae5", "#fabe50"])
ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.12), fancybox=True, ncol=3)
#Show values
for i in ax.containers:
texts = ax.bar_label(i, fmt='%.3g', rotation=90)
for text in texts:
text.set(y=5, zorder=2000)
plt.show()
```
%% Output
%% Cell type:code id:c5642ce4-036d-4745-80bc-e3a0d30d332b tags:
``` python
```
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