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121 lines
2.8 KiB
Python
121 lines
2.8 KiB
Python
"""jc - JSON CLI output utility free Parser
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Usage:
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specify --free as the first argument if the piped input is coming from free
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Examples:
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$ free | jc --free -p
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[
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{
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"type": "Mem",
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"total": 3861340,
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"used": 220508,
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"free": 3381972,
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"shared": 11800,
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"buff_cache": 258860,
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"available": 3397784
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},
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{
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"type": "Swap",
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"total": 2097148,
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"used": 0,
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"free": 2097148
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}
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]
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$ free | jc --free -p -r
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[
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{
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"type": "Mem",
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"total": "2017300",
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"used": "213104",
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"free": "1148452",
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"shared": "1176",
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"buff_cache": "655744",
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"available": "1622204"
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},
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{
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"type": "Swap",
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"total": "2097148",
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"used": "0",
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"free": "2097148"
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}
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]
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"""
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import jc.utils
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def process(proc_data):
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"""
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schema:
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[
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{
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"type": string,
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"total": integer,
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"used": integer,
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"free": integer,
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"shared": integer,
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"buff_cache": integer,
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"available": integer
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}
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]
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"""
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for entry in proc_data:
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int_list = ['total', 'used', 'free', 'shared', 'buff_cache', 'available']
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for key in int_list:
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if key in entry:
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try:
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key_int = int(entry[key])
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entry[key] = key_int
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except (ValueError):
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entry[key] = None
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return proc_data
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def parse(data, raw=False, quiet=False):
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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) output preprocessed JSON if True
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quiet: (boolean) suppress warning messages if True
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Returns:
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dictionary raw or processed structured data
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"""
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# compatible options: linux, darwin, cygwin, win32, aix, freebsd
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compatible = ['linux']
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if not quiet:
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jc.utils.compatibility(__name__, compatible)
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# code adapted from Conor Heine at:
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# https://gist.github.com/cahna/43a1a3ff4d075bcd71f9d7120037a501
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cleandata = data.splitlines()
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headers = [h for h in ' '.join(cleandata[0].lower().strip().split()).split() if h]
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headers.insert(0, "type")
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# clean up 'buff/cache' header
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# even though forward slash in a key is valid json, it can make things difficult
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headers = ['buff_cache' if x == 'buff/cache' else x for x in headers]
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raw_data = map(lambda s: s.strip().split(None, len(headers) - 1), cleandata[1:])
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raw_output = [dict(zip(headers, r)) for r in raw_data]
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for entry in raw_output:
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entry['type'] = entry['type'].rstrip(':')
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if raw:
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return raw_output
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else:
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return process(raw_output)
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