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https://github.com/xcat2/confluent.git
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e46b4ede6d
This directs CLI with image output to use a preferred protocol. This is retroactively applied to stats. Currently we prefer kitty, as it seems to be the most widely supported. Though some things only support iterm, so that's an option. And some only support sixel, but the user has to be the one to figure out adding pysixel dependency.
203 lines
6.7 KiB
Python
Executable File
203 lines
6.7 KiB
Python
Executable File
#!/usr/bin/python2
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# vim: tabstop=4 shiftwidth=4 softtabstop=4
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# Copyright 2019 Lenovo
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import base64
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import csv
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import io
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import numpy as np
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import sys
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try:
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import sixel
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class DumbWriter(sixel.SixelWriter):
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def restore_position(self, output):
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return
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except ImportError:
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pass
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def iterm_draw(data):
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databuf = data.getbuffer()
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datalen = len(databuf)
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data = base64.b64encode(databuf).decode('utf8')
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sys.stdout.write(
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'\x1b]1337;File=inline=1;size={}:'.format(datalen))
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sys.stdout.write(data)
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sys.stdout.write('\a')
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sys.stdout.write('\n')
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sys.stdout.flush()
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def kitty_draw(data):
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data = base64.b64encode(data.getbuffer())
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while data:
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chunk, data = data[:4096], data[4096:]
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m = 1 if data else 0
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sys.stdout.write('\x1b_Ga=T,f=100,m={};'.format(m))
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sys.stdout.write(chunk.decode('utf8'))
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sys.stdout.write('\x1b\\')
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sys.stdout.flush()
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sys.stdout.write('\n')
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def plot(gui, output, plotdata, bins, fmt):
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import matplotlib as mpl
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if gui and mpl.get_backend() == 'agg':
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sys.stderr.write('Error: No GUI backend available and -g specified!\n')
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if not gui:
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mpl.use('Agg')
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import matplotlib.pyplot as plt
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n, bins, patches = plt.hist(plotdata, bins)
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plt.show()
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if not gui:
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if output:
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tdata = output
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else:
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tdata = io.BytesIO()
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plt.savefig(tdata)
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if not gui and not output:
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if fmt == 'environment':
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fmt = os.environ.get('CONFLUENT_IMAGE_PROTOCOL', 'kitty')
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if fmt == 'sixel':
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writer = DumbWriter()
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writer.draw(tdata)
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elif fmt == 'kitty':
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kitty_draw(tdata)
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elif fmt == 'iterm':
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iterm_draw(tdata)
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return n, bins
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def textplot(plotdata, bins):
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n, bins = np.histogram(plotdata, bins)
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labels = []
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for bin in bins:
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labels.append('{0:0.1f}'.format(bin))
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width = 80
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# Since this will be primarily piped into, hard to get
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# terminal width
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labelwidth = 0
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for lab in labels:
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if len(lab) > labelwidth:
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labelwidth = len(lab)
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width -= (labelwidth) + 1
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labelfmt = '{{0:>{0}s}}|'.format(labelwidth)
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maxn = 0.0
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for lgth in n:
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if lgth > maxn:
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maxn = float(lgth)
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for i in range(len(n)):
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print(labelfmt.format(labels[i]) + '=' * int(np.round((n[i]/maxn) * width)))
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return n, bins
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histogram = False
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aparser = argparse.ArgumentParser(description='Quick access to common statistics')
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aparser.add_argument('-c', type=int, default=0, help='Column number to analyze (default is last column)')
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aparser.add_argument('-d', default=None, help='Value used to separate columns')
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aparser.add_argument('-x', default=False, action='store_true', help='Output histogram in graphical format')
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aparser.add_argument('-f', default='environment', help='Format for histogram output (sixel/iterm/kitty)')
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aparser.add_argument('-s', default=0, help='Number of header lines to skip before processing')
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aparser.add_argument('-g', default=False, action='store_true', help='Open histogram in separate graphical window')
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aparser.add_argument('-o', default=None, help='Output histogram to the specified filename in PNG format')
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aparser.add_argument('-t', default=False, action='store_true', help='Output a histogram in text format')
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aparser.add_argument('-v', default=False, action='store_true', help='Attempt to list nodes relevant to each histogram bar (requires -s, -o, or -t)')
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aparser.add_argument('-b', type=int, default=10, help='Number of bins to use in histogram (default is 10)')
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args = aparser.parse_args(sys.argv[1:])
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plotdata = []
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headlines = int(args.s)
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while headlines >= 0:
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data = sys.stdin.readline()
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headlines -= 1
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if args.d:
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delimiter = args.d
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else:
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if '\t' in data:
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delimiter = '\t'
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elif ' ' in data:
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delimiter = ' '
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elif ',' in data:
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delimiter = ','
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else:
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delimiter = ' ' # handle single column
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data = list(csv.reader([data], delimiter=delimiter))[0]
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nodebydatum = {}
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idx = args.c - 1
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autoidx = False
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while data:
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node = None
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if ':' in data[0]:
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node, data[0] = data[0].split(':', 1)
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else:
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node = data[0]
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if idx == -1 and not autoidx:
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while not autoidx:
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try:
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datum = float(data[idx])
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except ValueError:
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idx -= 1
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continue
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except IndexError:
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sys.stderr.write('Unable to identify a numerical column\n')
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sys.exit(1)
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autoidx = True
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else:
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datum = float(data[idx])
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if node:
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if datum in nodebydatum:
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nodebydatum[datum].add(node)
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else:
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nodebydatum[datum] = set([node])
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plotdata.append(datum)
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data = sys.stdin.readline()
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data = list(csv.reader([data], delimiter=delimiter))[0]
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n = None
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if args.g or args.o or args.x:
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n, bins = plot(args.g, args.o, plotdata, bins=args.b, fmt=args.f)
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if args.t:
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n, bins = textplot(plotdata, bins=args.b)
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print('Samples: {5} Min: {3} Median: {0} Mean: {1} Max: {4} StandardDeviation: {2} Sum: {6}'.format(np.median(plotdata), np.mean(plotdata), np.std(plotdata), np.min(plotdata), np.max(plotdata), len(plotdata), np.sum(plotdata)))
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if args.v and n is not None and nodebydatum:
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print('')
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currbin = bins[0]
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bins = bins[1:]
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currbinmembers = []
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for datum in sorted(nodebydatum):
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if datum > bins[0]:
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nextbin = None
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endbin = bins[0]
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while len(bins) and bins[0] < datum:
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nextbin = bins[0]
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bins = bins[1:]
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if not nextbin:
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nextbin = np.max(plotdata)
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print('Entries between {0} and {1}'.format(currbin, endbin))
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currbin = nextbin
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print('-' * 80)
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print(','.join(sorted(currbinmembers)))
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print('')
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print('')
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currbinmembers = []
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for node in nodebydatum[datum]:
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currbinmembers.append(node)
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if currbinmembers:
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print('Entries between {0} and {1}'.format(currbin, np.max(plotdata)))
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print('-' * 80)
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print(','.join(sorted(currbinmembers)))
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print('')
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print('')
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