FTIO/examples/API/test_api.py at main · tuda-parallel/FTIO · GitHub

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"""
Author: Ahmad Tarraf
Copyright (c) 2026 TU Darmstadt, Germany
Version: v0.0.8

Licensed under the BSD 3-Clause License.
For more information, see the LICENSE file in the project root:
https://github.com/tuda-parallel/FTIO/blob/main/LICENSE
"""

import numpy as np

from ftio.cli.ftio_core import core
from ftio.parse.args import parse_args
from ftio.parse.bandwidth import overlap
from ftio.processing.print_output import display_prediction

ranks = 10
total_bytes = 100

# Set up data
## 1) overlap for rank level metrics
b_rank = [
    0.0,
    0.0,
    1000.0,
    1000.0,
    0.0,
    0.0,
    1000.0,
    1000.0,
    0.0,
    0.0,
    1000.0,
    1000.0,
    0.0,
    0.0,
]
t_rank_s = [
    0.5,
    0.0,
    10.5,
    10.0,
    20.5,
    20.0,
    30.5,
    30.0,
    40.5,
    40.0,
    50.5,
    50.0,
    60.5,
    60,
]
t_rank_e = [
    5.0,
    4.5,
    15.0,
    14.5,
    25.0,
    24.5,
    35.0,
    34.5,
    45.0,
    44.5,
    55.0,
    54.5,
    65.0,
    64.5,
]
b, t = overlap(b_rank, t_rank_s, t_rank_e)

## 2) or directly specify the app level metrics
# t = [10.0, 20.1, 30.0, 40.2, 50.3, 60, 70, 80.0,]
# b = [10, 0, 10, 0, 10, 0, 10, 0]


# command line arguments
argv = ["-e", "no"]  # ["-e", "mat"]

# set up data
data = {
    "time": np.array(t),
    "bandwidth": np.array(b),
    "total_bytes": total_bytes,
    "ranks": ranks,
}

# parse args
args = parse_args(argv, "ftio")

# perform prediction
prediction, analysis_figures = core(data, args)


# plot and print info
analysis_figures.show()
display_prediction(args, prediction)