Grouping filenames by multiple categories into list of lists-CodePudding

Given a directory containing the following files:

pcasvm_dataset_window_blackman_nperseg_4096_distance_1_speed_25k
pcasvm_dataset_window_blackman_nperseg_4096_distance_2_speed_25k
pcasvm_dataset_window_blackman_nperseg_8192_distance_1_speed_100k
pcasvm_dataset_window_blackman_nperseg_16384_distance_1_speed_200k
pcasvm_dataset_window_hamming_nperseg_4096_distance_1_speed_25k
pcasvm_dataset_window_hamming_nperseg_8192_distance_5_speed_25k
pcasvm_dataset_window_hann_nperseg_4096_distance_1_speed_25k
...

I can read these in with the following comprehension: datasets = [d for d in os.listdir('path/to/dir')]

However, what I want to do is analyse these datasets in group, with the groups being:

window (i.e. blackman, hann) and nperseg (i.e. 8192, 4096, etc.)

The problem here is how to best achieve this fairly quickly given a large number of actual datasets. Would a dictionary be ideal? For example:

dict(
    blackman: dict(
        4096: [file1, file2, file3],
        8192: [..., ],
        ...
    ),
    ...
)

Thanks!

CodePudding user response：

If I understand you correctly, you can use re to parse filenames and dict.setdefault to group them:

import re

file_names = [
    "pcasvm_dataset_window_blackman_nperseg_4096_distance_1_speed_25k",
    "pcasvm_dataset_window_blackman_nperseg_4096_distance_2_speed_25k",
    "pcasvm_dataset_window_blackman_nperseg_8192_distance_1_speed_100k",
    "pcasvm_dataset_window_blackman_nperseg_16384_distance_1_speed_200k",
    "pcasvm_dataset_window_hamming_nperseg_4096_distance_1_speed_25k",
    "pcasvm_dataset_window_hamming_nperseg_8192_distance_5_speed_25k",
    "pcasvm_dataset_window_hann_nperseg_4096_distance_1_speed_25k",
]

pat = re.compile(r"window_([^_] )_nperseg_([^_] )")

out = {}
for name in file_names:
    m = pat.search(name)
    if m:
        out.setdefault(m.group(1), {}).setdefault(m.group(2), []).append(name)

print(out)

Prints:

{
    "blackman": {
        "4096": [
            "pcasvm_dataset_window_blackman_nperseg_4096_distance_1_speed_25k",
            "pcasvm_dataset_window_blackman_nperseg_4096_distance_2_speed_25k",
        ],
        "8192": [
            "pcasvm_dataset_window_blackman_nperseg_8192_distance_1_speed_100k"
        ],
        "16384": [
            "pcasvm_dataset_window_blackman_nperseg_16384_distance_1_speed_200k"
        ],
    },
    "hamming": {
        "4096": [
            "pcasvm_dataset_window_hamming_nperseg_4096_distance_1_speed_25k"
        ],
        "8192": [
            "pcasvm_dataset_window_hamming_nperseg_8192_distance_5_speed_25k"
        ],
    },
    "hann": {
        "4096": ["pcasvm_dataset_window_hann_nperseg_4096_distance_1_speed_25k"]
    },
}