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Smart Units: Account for baseline comparison
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Signed-off-by: JoseSantosAMD <[email protected]>
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@JoseSantosAMD
JoseSantosAMD committed Aug 18, 2023
1 parent 9737531 commit c5aa3d3
Showing 1 changed file with 114 additions and 77 deletions.
191 changes: 114 additions & 77 deletions src/omniperf_analyze/utils/tty.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,6 +25,7 @@
import pandas as pd
from pathlib import Path
from tabulate import tabulate
import numpy as np

from omniperf_analyze.utils import schema, parser

Expand All @@ -37,82 +38,119 @@ def smartUnits(df):
for curr_metric in row:
curr_row = df[df["Metric"] == curr_metric]
if not curr_row.empty:
# fix values
if "Value" in curr_row:
if isinstance(
curr_row["Value"][0],
float,
):
if curr_row.Value[0] < 0.001:
df.loc[
(df["Metric"] == curr_metric),
"Unit",
] = "Kb/s"
df.loc[
(df["Metric"] == curr_metric),
"Value",
] = (
1000000 * curr_row.Value
)
elif curr_row.Value[0] < 1:
df.loc[
(df["Metric"] == curr_metric),
"Unit",
] = "Mb/s"
df.loc[
(df["Metric"] == curr_metric),
"Value",
] = (
1000 * curr_row.Value
)
vals = curr_row["Value"].values
new_units = []
percent_diff = 0

# if baseline
if isinstance(vals[0], np.ndarray):
val_1 = curr_row["Value"].values[0][0]
baseline = curr_row["Value"].values[0][1].split()
val_2 = float(baseline[0])
percent_diff = baseline[1]
vals = np.array([val_1, val_2])

# calculate units
for val in vals:
if isinstance(
val,
float,
):
if val < 0.001:
new_units.append("Kb/s")
elif val < 1:
new_units.append("Mb/s")
if len(new_units) == 2:
if new_units[0] == "Kb/s":
new_units[0] = "Mb/s"
else:
new_units.append("Gb/s")
if len(new_units) == 2:
new_units[0] = "Gb/s"
# Convert to new_units
if new_units[0] == "Mb/s":
vals = 1000 * vals
elif new_units[0] == "Kb/s":
vals = 1000000 * vals
vals = vals.tolist()
# if baseline
if len(new_units) == 2:
vals[1] = str(vals[1]) + " " + str(percent_diff)

df.loc[df["Metric"] == curr_metric, "Value"] = vals
df.loc[df["Metric"] == curr_metric, "Unit"] = new_units[0]

elif "Avg" in curr_row:
if isinstance(curr_row["Avg"][0], float):
if curr_row.Avg[0] < 0.001:
df.loc[
(df["Metric"] == curr_metric),
"Unit",
] = "Kb/s"
df.loc[
(df["Metric"] == curr_metric),
"Avg",
] = (
1000000 * curr_row.Avg
)
df.loc[
(df["Metric"] == curr_metric),
"Min",
] = (
1000000 * curr_row.Min
)
df.loc[
(df["Metric"] == curr_metric),
"Max",
] = (
1000000 * curr_row.Max
)
elif curr_row.Avg[0] < 1:
df.loc[
(df["Metric"] == curr_metric),
"Unit",
] = "Mb/s"
df.loc[
(df["Metric"] == curr_metric),
"Avg",
] = (
1000 * curr_row.Avg
)
df.loc[
(df["Metric"] == curr_metric),
"Min",
] = (
1000 * curr_row.Min
)
df.loc[
(df["Metric"] == curr_metric),
"Max",
] = (
1000 * curr_row.Max
)
return df
avg_vals = curr_row["Avg"].values
max_vals = curr_row["Max"].values
min_vals = curr_row["Min"].values
new_units = []
percent_diff = 0

# if baseline
if isinstance(avg_vals[0], np.ndarray):
avg_baseline = curr_row["Avg"].values[0][1].split()
avg_percent_diff = avg_baseline[1]
avg_val_1 = curr_row["Avg"].values[0][0]
avg_val_2 = float(avg_baseline[0])
avg_vals = np.array([avg_val_1, avg_val_2])

min_baseline = curr_row["Min"].values[0][1].split()
min_percent_diff = min_baseline[1]
min_val_1 = curr_row["Min"].values[0][0]
min_val_2 = float(min_baseline[0])
min_vals = np.array([min_val_1, min_val_2])

max_baseline = curr_row["Max"].values[0][1].split()
max_percent_diff = max_baseline[1]
max_val_1 = curr_row["Max"].values[0][0]
max_val_2 = float(max_baseline[0])
max_vals = np.array([max_val_1, max_val_2])

# calculate units
for val in avg_vals:
if isinstance(
val,
float,
):
if val < 0.001:
new_units.append("Kb/s")
elif val < 1:
new_units.append("Mb/s")
if len(new_units) == 2:
if new_units[0] == "Kb/s":
new_units[0] = "Mb/s"
else:
new_units.append("Gb/s")
if len(new_units) == 2:
new_units[0] = "Gb/s"

# Convert to new_units
if new_units[0] == "Mb/s":
avg_vals = 1000 * avg_vals
max_vals = 1000 * max_vals
min_vals = 1000 * min_vals
elif new_units[0] == "Kb/s":
avg_vals = 1000000 * avg_vals
max_vals = 1000000 * max_vals
min_vals = 1000000 * min_vals
avg_vals = avg_vals.tolist()
max_vals = max_vals.tolist()
min_vals = min_vals.tolist()

# if baseline
if len(new_units) == 2:
avg_vals[1] = str(avg_vals[1]) + " " + str(avg_percent_diff)
max_vals[1] = str(max_vals[1]) + " " + str(max_percent_diff)
min_vals[1] = str(min_vals[1]) + " " + str(min_percent_diff)

df.loc[df["Metric"] == curr_metric, "Avg"] = avg_vals
df.loc[df["Metric"] == curr_metric, "Max"] = max_vals
df.loc[df["Metric"] == curr_metric, "Min"] = min_vals
df.loc[df["Metric"] == curr_metric, "Unit"] = new_units[0]
return df


def string_multiple_lines(source, width, max_rows):
Expand Down Expand Up @@ -232,11 +270,10 @@ def show_all(args, runs, archConfigs, output):
else x
for x in base_df[header]
]
if "Unit" in cur_df:
cur_df = smartUnits(cur_df)
df = pd.concat([df, cur_df[header]], axis=1)

if not df.empty:
if "Unit" in df:
df = smartUnits(df)
# subtitle for each table in a panel if existing
table_id_str = (
str(table_config["id"] // 100)
Expand Down

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