show ()Įxample 2: Visualize benchmark results (extended) from crystaldiskmark_parser.parser import parse_df import matplotlib.pyplot as plt #%% # Read to one big DataFrame df = parse_df ( "./test/data/CrystalDiskMark_20210622162528 WD Blue 3D 1TB WDS100T2B0A.txt" ) df = df. set_title ( "Sequential Read Rate of SSDs" ) ax. subplots ( figsize = ( 10, 6 )) con = ax. loc = 'read' ) & ( df = "SEQ" ) & ( df = 8 ) & ( df = 1 ) & ( df = "MiB" )] # Extract relevant columns rate = data name = data # Plot fig, ax = plt. info ()) # Extract relevant rows data = df. Queues threads rate unit_rate iops unit_iops latency unit_latencyĠ 8 1 531.458 MB/s 506.8 IOPS 15726.77 usĢ 32 1 269.406 MB/s 65772.9 IOPS 470.58 usĤ 8 1 498.656 MB/s 475.6 IOPS 16750.30 usĦ 32 1 222.691 MB/s 54367.9 IOPS 583.02 usĮxample 1: Visualize benchmark results from crystaldiskmark_parser.parser import parse_df import matplotlib.pyplot as plt # Read to one big DataFrame df = parse_df ( "./test/data/CrystalDiskMark_20210622162528 WD Blue 3D 1TB WDS100T2B0A.txt" ) df = df. Mode profile comment read_write type blocksize unit_blocksize \Ġ Default WD Blue 3D 1TB read SEQ 1.0 MiBġ Default WD Blue 3D 1TB read SEQ 1.0 MiBĢ Default WD Blue 3D 1TB read RND 4.0 KiBģ Default WD Blue 3D 1TB read RND 4.0 KiBĤ Default WD Blue 3D 1TB write SEQ 1.0 MiBĥ Default WD Blue 3D 1TB write SEQ 1.0 MiBĦ Default WD Blue 3D 1TB write RND 4.0 KiBħ Default WD Blue 3D 1TB write RND 4.0 KiB parse_df(filepath): Returns parsed data as a pandas.DataFrame.parse(filepath): Returns parsed data as a crystaldiskmark_parser.BenchmarkResult. Install pip install crystaldiskmark-parser The following plot created with matplotlib is an example. This library was created to enable visualizing the benchmark results of txt files generated by CrystalDiskMark in Python.
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