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https://github.com/kellyjonbrazil/jc.git
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134 lines
2.6 KiB
Python
134 lines
2.6 KiB
Python
"""jc - JSON Convert `/proc/swaps` file parser
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Usage (cli):
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$ cat /proc/swaps | jc --proc
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or
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$ jc /proc/swaps
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or
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$ cat /proc/swaps | jc --proc-swaps
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Usage (module):
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import jc
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result = jc.parse('proc', proc_swaps_file)
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or
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import jc
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result = jc.parse('proc_swaps', proc_swaps_file)
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Schema:
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[
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{
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"filename": string,
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"type": string,
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"size": integer,
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"used": integer,
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"priority": integer
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}
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]
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Examples:
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$ cat /proc/swaps | jc --proc -p
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[
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{
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"filename": "/swap.img",
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"type": "file",
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"size": 3996668,
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"used": 0,
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"priority": -2
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},
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...
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]
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$ cat /proc/swaps | jc --proc-swaps -p -r
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[
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{
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"filename": "/swap.img",
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"type": "file",
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"size": "3996668",
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"used": "0",
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"priority": "-2"
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},
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...
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]
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"""
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from typing import List, Dict
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import jc.utils
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from jc.parsers.universal import simple_table_parse
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class info():
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"""Provides parser metadata (version, author, etc.)"""
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version = '1.0'
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description = '`/proc/swaps` file parser'
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author = 'Kelly Brazil'
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author_email = 'kellyjonbrazil@gmail.com'
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compatible = ['linux']
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tags = ['file']
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hidden = True
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__version__ = info.version
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def _process(proc_data: List[Dict]) -> List[Dict]:
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"""
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Final processing to conform to the schema.
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Parameters:
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proc_data: (List of Dictionaries) raw structured data to process
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Returns:
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List of Dictionaries. Structured to conform to the schema.
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"""
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int_list = {'size', 'used', 'priority'}
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for entry in proc_data:
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for key in entry:
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if key in int_list:
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entry[key] = jc.utils.convert_to_int(entry[key])
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return proc_data
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def parse(
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data: str,
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raw: bool = False,
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quiet: bool = False
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) -> List[Dict]:
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"""
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Main text parsing function
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Parameters:
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data: (string) text data to parse
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raw: (boolean) unprocessed output if True
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quiet: (boolean) suppress warning messages if True
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Returns:
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List of Dictionaries. Raw or processed structured data.
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"""
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jc.utils.compatibility(__name__, info.compatible, quiet)
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jc.utils.input_type_check(data)
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raw_output: List = []
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if jc.utils.has_data(data):
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cleandata = list(filter(None, data.splitlines()))
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cleandata[0] = cleandata[0].lower()
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raw_output = simple_table_parse(cleandata)
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return raw_output if raw else _process(raw_output)
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