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save figures to PNG files

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Youngjoon Lee 2024-09-11 07:56:10 +09:00
parent ac3c5c2e59
commit 655be33d66
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GPG Key ID: 167546E2D1712F8C
3 changed files with 19 additions and 10 deletions
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1
mixnet-rs/analysis/.gitignore vendored
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@ -1 +1,2 @@
.venv/
*.png

14
mixnet-rs/analysis/coeff.py
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@ -6,13 +6,13 @@ import pandas as pd
from common import MARKERS, X_FIELDS
def analyze(path: str):
def analyze(path: str, outdir: str):
data = pd.read_csv(path)
for x_field in X_FIELDS:
analyze_versus(data, x_field)
analyze_versus(data, x_field, outdir)
def analyze_versus(data: pd.DataFrame, x_field: str):
def analyze_versus(data: pd.DataFrame, x_field: str, outdir: str):
# Group by both x_field and queue_type, then select the row with the largest paramset for each group
max_paramset_data = data.loc[
data.groupby([x_field, "queue_type"])["paramset"].idxmax()
@ -55,6 +55,7 @@ def analyze_versus(data: pd.DataFrame, x_field: str):
ax[ax_row][ax_col].set_ylim(-10, max_y * 1.05)
plt.tight_layout()
fig.savefig(f"{outdir}/coeff_vs_{x_field}.png")
# Display the table of values
# Create a table with x_field, queue_type, and coefficients
@ -84,7 +85,9 @@ def analyze_versus(data: pd.DataFrame, x_field: str):
for i in range(len(table_data.columns)):
table.auto_set_column_width(i)
plt.show()
fig_table.savefig(f"{outdir}/coeff_vs_{x_field}_table.png")
plt.draw()
if __name__ == "__main__":
@ -92,5 +95,6 @@ if __name__ == "__main__":
description="Aggregate the results of all paramsets of an experiment"
)
parser.add_argument("path", type=str, help="dir path")
parser.add_argument("outdir", type=str, help="output dir path")
args = parser.parse_args()
analyze(args.path)
analyze(args.path, args.outdir)

14
mixnet-rs/analysis/latency.py
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@ -6,13 +6,13 @@ import pandas as pd
from common import MARKERS, X_FIELDS
def analyze(path: str):
def analyze(path: str, outdir: str):
data = pd.read_csv(path)
for x_field in X_FIELDS:
analyze_versus(data, x_field)
analyze_versus(data, x_field, outdir)
def analyze_versus(data: pd.DataFrame, x_field: str):
def analyze_versus(data: pd.DataFrame, x_field: str, outdir: str):
# Group by both x_field and queue_type, then select the row with the largest paramset for each group
max_paramset_data = data.loc[
data.groupby([x_field, "queue_type"])["paramset"].idxmax()
@ -45,6 +45,7 @@ def analyze_versus(data: pd.DataFrame, x_field: str):
ax[ax_col].set_ylim(bottom=0)
plt.tight_layout()
fig.savefig(f"{outdir}/latency_vs_{x_field}.png")
# Display the table of values
# Create a table with x_field, queue_type, and coefficients
@ -69,7 +70,9 @@ def analyze_versus(data: pd.DataFrame, x_field: str):
for i in range(len(table_data.columns)):
table.auto_set_column_width(i)
plt.show()
fig_table.savefig(f"{outdir}/latency_vs_{x_field}_table.png")
plt.draw()
if __name__ == "__main__":
@ -77,5 +80,6 @@ if __name__ == "__main__":
description="Aggregate the results of all paramsets of an experiment"
)
parser.add_argument("path", type=str, help="dir path")
parser.add_argument("outdir", type=str, help="output dir path")
args = parser.parse_args()
analyze(args.path)
analyze(args.path, args.outdir)