В таком случае я бы предложил позвонить gnuplot напрямую , используя subprocess.run. Когда run возвращается, gnuplot завершил работу.
Для примера :
#!/usr/bin/env python3
# file: histdata.py
# vim:fileencoding=utf-8:fdm=marker:ft=python
#
# Copyright © 2012-2018 R.F. Smith <rsmith@xs4all.nl>.
# SPDX-License-Identifier: MIT
# Created: 2012-07-23T01:18:29+02:00
# Last modified: 2019-07-27T13:50:29+0200
"""Make a histogram and calculate entropy of files."""
import math
import os.path
import subprocess as sp
import sys
def main(argv):
"""
Entry point for histdata.
Arguments:
argv: List of file names.
"""
if len(argv) < 1:
sys.exit(1)
for fn in argv:
hdata, size = readdata(fn)
e = entropy(hdata, size)
print(f"entropy of {fn} is {e:.4f} bits/byte")
histogram_gnuplot(hdata, size, fn)
def readdata(name):
"""
Read the data from a file and count it.
Arguments:
name: String containing the filename to open.
Returns:
A tuple (counts list, length of data).
"""
f = open(name, 'rb')
data = f.read()
f.close()
ba = bytearray(data)
del data
counts = [0] * 256
for b in ba:
counts[b] += 1
return (counts, float(len(ba)))
def entropy(counts, sz):
"""
Calculate the entropy of the data represented by the counts list.
Arguments:
counts: List of counts.
sz: Length of the data in bytes.
Returns:
Entropy value.
"""
ent = 0.0
for b in counts:
if b == 0:
continue
p = float(b) / sz
ent -= p * math.log(p, 256)
return ent * 8
def histogram_gnuplot(counts, sz, name):
"""
Use gnuplot to create a histogram from the data in the form of a PDF file.
Arguments
counts: List of counts.
sz: Length of the data in bytes.
name: Name of the output file.
"""
counts = [100 * c / sz for c in counts]
rnd = 1.0 / 256 * 100
pl = ['set terminal pdfcairo size 18 cm,10 cm']
pl += ["set style line 1 lc rgb '#E41A1C' pt 1 ps 1 lt 1 lw 4"]
pl += ["set style line 2 lc rgb '#377EB8' pt 6 ps 1 lt 1 lw 4"]
pl += ["set style line 3 lc rgb '#4DAF4A' pt 2 ps 1 lt 1 lw 4"]
pl += ["set style line 4 lc rgb '#984EA3' pt 3 ps 1 lt 1 lw 4"]
pl += ["set style line 5 lc rgb '#FF7F00' pt 4 ps 1 lt 1 lw 4"]
pl += ["set style line 6 lc rgb '#FFFF33' pt 5 ps 1 lt 1 lw 4"]
pl += ["set style line 7 lc rgb '#A65628' pt 7 ps 1 lt 1 lw 4"]
pl += ["set style line 8 lc rgb '#F781BF' pt 8 ps 1 lt 1 lw 4"]
pl += ["set palette maxcolors 8"]
pl += [
"set palette defined ( 0 '#E41A1C', 1 '#377EB8', 2 '#4DAF4A',"
" 3 '#984EA3',4 '#FF7F00', 5 '#FFFF33', 6 '#A65628', 7 '#F781BF' )"
]
pl += ["set style line 11 lc rgb '#808080' lt 1 lw 5"]
pl += ["set border 3 back ls 11"]
pl += ["set tics nomirror"]
pl += ["set style line 12 lc rgb '#808080' lt 0 lw 2"]
pl += ["set grid back ls 12"]
nm = os.path.basename(name)
pl += [f"set output 'hist-{nm}.pdf'"]
pl += ['set xrange[-1:256]']
pl += ['set yrange[0:*]']
pl += ['set key right top']
pl += ['set xlabel "byte value"']
pl += ['set ylabel "occurance [%]"']
pl += [f'rnd(x) = {rnd:.6f}']
pl += [f"plot '-' using 1:2 with points ls 1 title '{name}', "
f"rnd(x) with lines ls 2 title 'continuous uniform ({rnd:.6f}%)'"]
for n, v in enumerate(counts):
pl += [f'{n} {v}']
pl += ['e']
pt = '\n'.join(pl)
sp.run(['gnuplot'], input=pt.encode('utf-8'), check=True)
if __name__ == '__main__':
main(sys.argv[1:])
Редактировать: Как видите, приведенный выше код имеет значительную историю. Одна вещь, которую я обычно делаю по-другому, это использование встроенных данных (см. help inline внутри gnuplot) в виде документов здесь.
Это более гибко, чем использование файла '-'. Данные являются постоянными и могут использоваться на нескольких графиках.
Например:
pl += ['$data << EOD']
pl += [f'{n} {n**2}' for n in range(20)]
pl += ['EOD']