63 строки
1.9 KiB
Python
63 строки
1.9 KiB
Python
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#!/usr/bin/env python3
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import sys
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import pytest
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import numpy as np
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import pandas as pd
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# =========================== Validating fields common for both host-trap and stochastic CSV output
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def test_validate_pc_sampling_exec_mask_manipulation_csv(
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input_csv: pd.DataFrame, all_sampled: bool
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):
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from rocprofiler_sdk.pc_sampling.exec_mask_manipulation.csv import (
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exec_mask_manipulation_validate_csv,
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)
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exec_mask_manipulation_validate_csv(input_csv, all_sampled=all_sampled)
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# # ========================= Validating fields common for both host-trap and stochastic JSON output
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def test_validate_pc_sampling_exec_mask_manipulation_json(
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input_json, input_csv: pd.DataFrame, all_sampled: bool
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):
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data = input_json["rocprofiler-sdk-tool"]
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# The same amount of samples should be in both CSV and JSON files.
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assert len(input_csv) == len(data["buffer_records"]["pc_sample_stochastic"])
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# # validating JSON output
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from rocprofiler_sdk.pc_sampling.exec_mask_manipulation.json import (
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validate_json_exec_mask_manipulation,
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)
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validate_json_exec_mask_manipulation(
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data, pc_sampling_method="stochastic", all_sampled=all_sampled
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)
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# ======================== Validating fields specific for stochastic sampling
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def test_validate_pc_sampling_stochastic_specific_csv(input_csv: pd.DataFrame):
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from rocprofiler_sdk.pc_sampling.stochastic.csv.gfx9 import (
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validate_stochastic_samples_csv,
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)
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validate_stochastic_samples_csv(input_csv)
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def test_validate_pc_sampling_stochastic_specific_json(input_json):
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from rocprofiler_sdk.pc_sampling.stochastic.json.gfx9 import (
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validate_stochastic_samples_json,
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)
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validate_stochastic_samples_json(input_json["rocprofiler-sdk-tool"])
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if __name__ == "__main__":
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exit_code = pytest.main(["-x", __file__] + sys.argv[1:])
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sys.exit(exit_code)
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