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#!/usr/bin/env python
import defapp

from plot import decode
from util import load_csv_file, write_csv_file

from math import ceil

from numpy import amin, amax, mean, median, std, histogram, zeros, arange

from os.path  import splitext, basename
from optparse import make_option as o

from gnuplot  import gnuplot, FORMATS, Plot, label, curve

options = [
    # output options
    o('-f', '--format', action='store', dest='format', type='choice',
      choices=FORMATS, help='output format'),
    o(None, '--save-script', action='store_true', dest='save_script'),
    o('-p', '--prefix', action='store', dest='prefix'),

    # formatting options
    # These may or may not be supported by a particular experiment plotter.
    o(None, '--smooth', action='store_true', dest='smooth'),
    o(None, '--hist', action='store_true', dest='histogram'),
    ]

defaults = {
    # output options
    'format' : 'pdf',
    'save_script' : False,
    'prefix' : '',

    # formatting options
    'histogram' : False,
    'smooth' : False,
    }

def get_stats_label(samples):
    avg = mean(samples)
    med = median(samples)
    dev = std(samples)
    max = amax(samples)
    min = amin(samples)
    return  "min=%.2f max=%.2f avg=%.2f  median=%.2f  std=%.2f" \
        % (min, max, avg, med, dev)

class NetsecPlotter(defapp.App):
    def __init__(self):
        defapp.App.__init__(self, options, defaults, no_std_opts=True)
        self.tmpfiles = []

    def make_plot(self, fname=None):
        p = Plot()
        p.output = "%s%s.%s" % (self.options.prefix, fname, self.options.format)
        p.format = self.options.format
        return p

    def setup_png(self, plot):
        # standard png options; usually correct; never tweaked for paper
        if self.options.format == 'png':
            plot.font_size = 'large'
            plot.size   = (1024, 768)
            plot.xticks = (0, 1)
            plot.yticks = (0, 0.1)
            plot.default_style = "linespoints"
            return True
        else:
            return False

    def write(self, data, name, ext='data'):
        if self.options.save_script:
            fname = "%s.%s" % (name, ext)
            write_csv_file(fname, data)
            return fname
        else:
            tmp = write_csv_file(None, data)
            # keep a reference so that it isn't deleted
            self.tmpfiles.append(tmp)
            return tmp.name

    def load(self, datafile):
        data = load_csv_file(datafile)
        print "loaded %d lines" % len(data)
        return data

    def write_histogram(self, samples, name):
        max = amax(samples)
        (hist, edges) = histogram(samples, bins=max,
                range=(0.5,max+.5))
        data = zeros((len(edges)-1, 3))
        cumulative = 0
        for i in xrange(len(hist)):
            data[i, 0]  = (edges[i] + edges[i + 1]) / 2.0
            data[i, 1]  = hist[i]
            cumulative += hist[i]
            data[i, 2]  = cumulative

        if len(hist) > 20:
            label_freq = 10
        else:
            label_freq = 1

        for_file = []
        for i, row in enumerate(data):
            label = '%d' % row[0] if row[0] % label_freq == 0 else ''
            for_file.append([row[0], row[1], row[2], label])

        return (data, self.write(for_file, name, ext='hist'))

    def render(self, p):
        if self.options.save_script:
            p.gnuplot_save(p.output + '.plot')
        else:
            p.gnuplot_exec()

    def plot_hc(self, datafile, name, conf):
        current_host_num = 0
        host_mapping = {}

        name += "_counts"

        data = self.load(datafile)
        for row in data:
            if row[0] not in host_mapping:
                current_host_num += 1
                host_mapping[row[0]] = current_host_num
            row[0] = host_mapping[row[0]]

        del host_mapping

        fname = self.write(data, name, ext='data')

        p = self.make_plot(name)

        p.labels = [label(0.5, 0.9, get_stats_label(data[:,1]),
                          coord=['graph', 'screen'], align='center')]

        # plot raw samples
        p.title = "hop counts by hosts"
        p.title += self.get_as_title(conf)
        p.ylabel = "hop count"
        p.xlabel = "host id"
        p.xrange = (0, current_host_num)
        #p.xticks = (0, 100)
        #p.yticks = (0, 1)
        p.yrange = (1, amax(data[:,1]) + 1)
        if amax(data[:,1]) > 100:
            p.ylog = True

        p.curves = [curve(fname=fname, xcol=1, ycol=2, title="hop count")]

        #### Styling.

        if not self.setup_png(p):
            p.rounded_caps = True
            p.font = 'Helvetica'

            p.font_size = '10'
            p.size = ('20cm', '10cm')
            p.monochrome   = False
            p.dashed_lines = False
            p.key = 'off'
            p.default_style = 'points lw 1'

        if self.options.smooth:
            p.default_style += " smooth bezier"

        self.render(p)

    def get_as_title(self, conf):
        if 'as-num' in conf:
            return " AS=%s, IP/mask=%s/%s MB=%s Unique Hosts=%s" % \
                    (conf['as-num'], conf['as-str'], conf['as-mask'], \
                    conf['megabytes'], conf['num-hosts'])
        return ""


    def plot_hchisto(self, datafile, name, conf):
        data  = self.load(datafile)

        #max_val = amax(data[:,1])

        if self.options.histogram:
            name += '_hist'

        p = self.make_plot(name)

        # place a label on the graph
        p.labels = [label(0.5, 0.9, get_stats_label(data[:,1]),
                          coord=['graph', 'screen'], align='center')]

        (data, fname) = self.write_histogram(data[:,1], name)
        p.xlabel = "hop count"

        p.ylabel = "number of sources"
        p.setup_histogram(gap=1, boxwidth=1.0)
        p.title = "hop counts;"

        if 'per-host' in conf:
            p.title += " HC per-host: %s" % conf['per-host']

        p.title += self.get_as_title(conf)

#            p.xrange = (0, ceil(max_cost))
        p.xticks = (0, 10)
#            p.yticks = (0, 1)
        #p.yrange = (0, (ceil(amax(data[:,1]) / 100) * 100))


        ymax = amax(data[:,1]) 
        p.curves = [curve(histogram=fname, col=2, labels_col=4)]
        p.yrange = (1, amax(data[:,1]) + 2)

        if ymax > 10000:
            p.ylog = True

        #### Styling.

        if not self.setup_png(p):
            p.rounded_caps = True
            p.font = 'Helvetica'

            p.font_size = '10'
            p.size = ('20cm', '10cm')
            p.monochrome   = False
            p.dashed_lines = False
            p.key = 'off'
            p.default_style = 'points lw 1'

        if self.options.smooth:
            p.default_style += " smooth bezier"

        self.render(p)


    def plot_file(self, datafile):
        bname     = basename(datafile)
        name, ext = splitext(bname)
        conf      = decode(name)

        self.plot_hc(datafile, name, conf)
        return

        if 'per-host' in conf or 'as-num' in conf:
            self.plot_hchisto(datafile, name, conf)

        #for plot_type in plotters:
        #    if plot_type in conf:
        #        try:
        #            plotters[plot_type](datafile, name, conf)
        #        except IOError as err:
        #            self.err("Skipped '%s' (%s)." % err)
        #        break
        #else:
        #    self.err("Skipped '%s'; unkown experiment type."
        #             % bname)

        # release all tmp files
        self.tmpfiles = []

    def default(self, _):
        for i, datafile in enumerate(self.args):
            self.out("[%d/%d] Processing %s ..." % (i + 1, len(self.args), datafile))
            self.plot_file(datafile)

if __name__ == "__main__":
    NetsecPlotter().launch()