#!/usr/bin/env python

import sys
import optparse

from plot import Plot
from gnuplot import curve

COLS = {'beta-mean':            0,
        'rel-rt-max-b':         1,
        'rel-rt-mean-b':        2,
        'misses-b':             3,
        'new-misses-b':         4,
        'rel-rt-max-c':         5,
        'rel-rt-mean-c':        6,
        'misses-c':             7,
        'new-misses-c':         8,
        'mean-misses-c':        9,
        'miss-ratio-b':         10,
        'miss-ratio-c':         11}


def parse_args():
    p = optparse.OptionParser(description='make graphs')
    p.add_option('--paper', dest='paper', action='store_true', default=False)
    return p.parse_args()


def col(key):
    """Get the column in the data set."""
    return COLS[key]


def g_col(key):
    """Gnuplot is 1-indexed."""
    return 1 + col(key)


def get_y_label(metric):
    LABELS = {'rel-rt-max': 'relative response time maximum',
              'rel-rt-mean': 'relative response time mean',
              'miss-ratio': 'deadline miss ratio'}
    return LABELS[metric]


def set_plot_opts(p):
    p.rounded_caps = True
    p.font = 'Helvetica'
    p.font_size = '5pt'
    p.size = ('8.5cm', '5.25cm')
    p.default_style = 'linespoints lw 2.5'
    p.default_style += ' smooth bezier'
    p.monochrome = False
    p.ylog = True
    p.key = 'off'

    p.line_styles = [(1, "lw 1.5 ps 0.4"),
                     (2, "lw 1.5 ps 0.4"),
                     (3, "lw 1.5 ps 0.4"),
                     (4, "lw 1.5 ps 0.4")]
    for i, c in enumerate(p.curves):
        c.style = "linespoints ls %d" % (i + 1)


def plot_metric(opts, dat_dir, metric, coin_prob, xr=None):
    """Plot metric for each level, enforcement pair at the specified
    probability.
    
    ."""
    # each coin value gets a plot
    p = Plot()
    p.output = '{0}/beta-exp_metric={1}_prob={2}'.format(dat_dir, metric, coin_prob)
    if xr is not None:
        p.output = '{0}_zoom=1'.format(p.output)
    p.output = '{0}.pdf'.format(p.output)
        
    p.format = 'pdf'

    for (level, enforcement) in [(l, e) for l in ('b', 'c') for e in (0, 1)]:
        fname = '{0}/beta_be={1}_prob={2}.csv'.format(dat_dir, enforcement, coin_prob)
        ycol = g_col('{0}-{1}'.format(metric, level))
        if enforcement == 1:
            ti ='{0} {1}'.format(level.upper(), 'enforcement')
        else:
            ti ='{0} {1}'.format(level.upper(), 'no enforcement')
        c = curve(fname=fname, xcol=g_col('beta-mean'), ycol=ycol, title=ti)
        p.curves += [c]
    p.xlabel = 'beta mean with probability {0}'.format(coin_prob)
    p.ylabel = get_y_label(metric)
    set_plot_opts(p)
    if xr is not None:
        p.xrange = xr

    # special formatting cases
    if -1 != metric.find('ratio'):
        p.ylog = False
        p.yrange = (-0.01, 1.01)
    if metric == 'miss-ratio' and coin_prob == 0.1:
        p.key = 'top right'
    p.gnuplot_exec()

def main():

    opts, files = parse_args()

    if len(files) < 1:
        print >>sys.stderr, "missing directory"
        sys.exit(1)

    data_dir = files[0]

    COIN_VALS = [0.1, 0.5]
    for coin_val in COIN_VALS:
        plot_metric(opts, data_dir, 'rel-rt-mean', coin_val)
        plot_metric(opts, data_dir, 'rel-rt-mean', coin_val, xr=(0.02, 0.11))
        plot_metric(opts, data_dir, 'miss-ratio', coin_val)
        # not in paper
        plot_metric(opts, data_dir, 'rel-rt-max', coin_val)

if __name__ == '__main__':
    main()