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@sonium0 /main.py
Last active Apr 1, 2017

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Display logfile in realtime with bokeh
# Plot logtime in realtime using bokeh and tail -f
# Tested with python 3.5 and bokeh 0.12.4
# OSX/Linux only
# usage:
# 1. run 'bokeh serve'
# 2. run 'python3.5 main.py logfile.csv'
# assumes a logfile.csv with format:
# min_ask,1489758134.150000,1077.00,1076.78,0.45
# max_bid,1489758139.660000,1076.56,1076.76,0.41
# min_ask,1489758142.076000,1076.95,1076.76,0.40
import sys
import datetime
import asyncio
import asyncio.subprocess
from bokeh.models import ColumnDataSource, DatetimeTickFormatter
from bokeh.client import push_session
from bokeh.plotting import figure, curdoc
def push(timestamp, fair_price):
source.stream(dict(x=[timestamp], y=[fair_price]))
@asyncio.coroutine
def update():
create = asyncio.create_subprocess_exec('tail', '-f', '-n' , '+1', sys.argv[-1],
stdout=asyncio.subprocess.PIPE)
proc = yield from create
while True:
# Read one line of output
data = yield from proc.stdout.readline()
line = data.decode('ascii').rstrip()
line = line.split(', ')
# line format:
# label,unix-timestamp,fair_price,spread
if line[0] == 'max_bid' or line[0] == 'min_ask':
timestamp = datetime.datetime.fromtimestamp(float(line[1]))
fair_price = float(line[2])
push(timestamp, fair_price)
source = ColumnDataSource(data=dict(x=[], y=[]))
p = figure()
p.xaxis.formatter=DatetimeTickFormatter()
l = p.line(x='x', y='y', source=source)
# open a session to keep our local document in sync with server
session = push_session(curdoc(), session_id='main')
session.show(p) # open the document in a browser
loop = asyncio.get_event_loop()
loop.run_until_complete(update())
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