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#!/usr/bin/env python3
#
# Use pandas + bokeh to create graphs/charts/plots for stats CSV (to_csv.py).
#
import os
import pandas as pd
from bokeh.io import curdoc
from bokeh.models import ColumnDataSource, HoverTool
from bokeh.plotting import figure
from bokeh.palettes import Dark2_5 as palette
from bokeh.models.formatters import DatetimeTickFormatter
import pandas_bokeh
import argparse
import itertools
from fnmatch import fnmatch
datecolumn = '0time'
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Graph CSV files')
parser.add_argument('infiles', nargs='+', type=str,
help='CSV files to plot.')
parser.add_argument('--cols', type=str,
help='Columns to plot (CSV list)')
parser.add_argument('--skip', type=str,
help='Columns to skip (CSV list)')
parser.add_argument('--group-by', type=str,
help='Group data series by field')
parser.add_argument('--chart-cols', type=int, default=3,
help='Number of chart columns')
parser.add_argument('--plot-width', type=int, default=400,
help='Per-plot width')
parser.add_argument('--plot-height', type=int, default=300,
help='Per-plot height')
parser.add_argument('--out', type=str, default='out.html',
help='Output file (HTML)')
args = parser.parse_args()
outpath = args.out
if args.cols is None:
cols = None
else:
cols = args.cols.split(',')
cols.append(datecolumn)
if args.skip is None:
assert cols is None, "--cols and --skip are mutually exclusive"
skip = None
else:
skip = args.skip.split(',')
group_by = args.group_by
pandas_bokeh.output_file(outpath)
curdoc().theme = 'dark_minimal'
figs = {}
plots = []
for infile in args.infiles:
colors = itertools.cycle(palette)
cols_to_use = cols
if skip is not None:
# First read available fields
avail_cols = list(pd.read_csv(infile, nrows=1))
cols_to_use = [c for c in avail_cols
if len([x for x in skip if fnmatch(c, x)]) == 0]
df = pd.read_csv(infile,
parse_dates=[datecolumn],
index_col=datecolumn,
usecols=cols_to_use)
title = os.path.basename(infile)
print(f"{infile}:")
if group_by is not None:
grp = df.groupby([group_by])
# Make one plot per column, skipping the index and group_by cols.
for col in df.keys():
if col in (datecolumn, group_by):
continue
print("col: ", col)
for _, dg in grp:
print(col, " dg:\n", dg.head())
figtitle = f"{title}: {col}"
p = figs.get(figtitle, None)
if p is None:
p = figure(title=f"{title}: {col}",
plot_width=args.plot_width,
plot_height=args.plot_height,
x_axis_type='datetime',
tools="hover,box_zoom,wheel_zoom," +
"reset,pan,poly_select,tap,save")
figs[figtitle] = p
plots.append(p)
p.add_tools(HoverTool(
tooltips=[
("index", "$index"),
("time", "@0time{%F}"),
("y", "$y"),
("desc", "$name"),
],
formatters={
"@0time": "datetime",
},
mode='vline'))
p.xaxis.formatter = DatetimeTickFormatter(
minutes=['%H:%M'],
seconds=['%H:%M:%S'])
source = ColumnDataSource(dg)
val = dg[group_by][0]
for k in dg:
if k != col:
continue
p.line(x=datecolumn, y=k, source=source,
legend_label=f"{k}[{val}]",
name=f"{k}[{val}]",
color=next(colors))
continue
else:
p = df.plot_bokeh(title=title,
kind='line', show_figure=False)
plots.append(p)
for p in plots:
p.legend.click_policy = "hide"
grid = []
for i in range(0, len(plots), args.chart_cols):
grid.append(plots[i:i + args.chart_cols])
pandas_bokeh.plot_grid(grid)
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