#!/usr/bin/python
# emacs: -*- coding: utf-8; mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-

from glob import glob
try:
    from json import load as jload
    def jsonload(f):
        return jload(f)
except ImportError:
    from json import read as jread
    def jsonload(f):
        return jread(f.read())
import sys, os
import pylab as pl
import numpy as np
import time

from common import entries_to_refresh
fresh_keys = [k
              for k, (regex, b) in
                    reduce(list.__add__,[x.items()
                                         for x in entries_to_refresh['sw_other_name'].values()],
                           [])
              if b]

# uniform colors for OS results
os_colors = ['#AA2029', '#D1942B', '#7FB142', '#69A7CE']
os_order = ['linux', 'mac', 'win', 'otheros']
time_order = ['notime', 'little', 'most', 'always']
time_colors = ['#FF0000', '#FF5500', '#FFAC00', '#FFFD08']
time_categories = {
        'notime': "don't use it",
        'little': "less than 50%",
        'most': "more than 50%",
        'always': "always"
        }
# resources
resource_categories = {
    'vendor': 'Vendor/Project website',
    'retailer': 'Retailer',
    'os': 'Operating system',
    'cpan': 'CPAN',
    'cran': 'CRAN',
    'epel': 'EPEL',
    'fink': 'Fink',
    'freebsdports': 'FreeBSD ports',
    'incf': 'INCF',
    'macports': 'Macports',
    'matlabcentral': 'Matlab Central',
    'neurodebian': 'NeuroDebian',
    'nitrc': 'NITRC',
    'pypi': 'PyPi',
    'pythonbundles': 'Python bundles',
    'sourceforge': 'Sourceforge',
    'otherres': 'Other resource'
    }
# software categories
sw_categories = {
        'general': 'General computing',
        'dc': 'Distributed computing',
        'img': 'Brain imaging',
        'datamanage': 'Data management',
        'neusys': 'Neural systems modeling',
        'electro': 'Electrophysiology, MEG/EEG',
        'bci': 'Brain-computer interface',
        'acq': 'Hardware interface/Data acquisition',
        'rt': 'Real-time solutions',
        'psychphys': 'Psychophysics/Experiment control'
        }

# some meaningful groups of OSes
redhat_family = ["rhel", "centos", "fedora", "scilinux"]
debian_family = ["debian", "ubuntu", "biolinux"]
suse_family = ["suse", "slel"]
other_linux_family = ["gentoo", "mandriva", "arch", "slackware", "otherlinux"]
other_family = ["starbsd", "unix", "qnx", "beos", "solaris", "other", "dontknow"]

os_cat_names = {
        'win': 'Windows',
        'mac': 'Mac OS',
        'linux': 'GNU/Linux',
        'otheros': 'Other OS'
        }

os_family = {
        'win': ["windows"],
        'mac': ["macosx"],
        'linux': redhat_family + debian_family + suse_family + other_linux_family,
        'otheros': other_family
        }
# end the reverse mapping
os_family_rev = {}
for ost in os_family:
    for os_ in os_family[ost]:
        os_family_rev[os_] = ost


def load_list2dict(name):
    d = {}
    lfile = open(name)
    for line in lfile:
        if line.strip() == "":
            continue
        kv = line.split(':')
        if kv[0] in d:
            raise RuntimeError(
                "Got a line %s with a duplicate key %s whenever value for it "
                "is known already to be %r" % (line, kv[0], d[kv[0]]))
        d[kv[0]] = kv[1].strip().strip('"')
    return d



class DB(dict):
    os_dict = load_list2dict('oslist.txt')
    datamod_dict = load_list2dict('datamodlist.txt')
    sw_dict = load_list2dict('swlist.txt')
    position_dict = load_list2dict('position-dd-list.txt')
    employer_dict = load_list2dict('employer-dd-list.txt')
    ratings_dict = load_list2dict('ratingslist.txt')
    vm_dict = load_list2dict('vmlist.txt')

    def __init__(self, srcdir):
        # eats the whole directory
        if srcdir is None:
            return
        datafilenames = glob('%s/*.json' % srcdir)
        for dfn in datafilenames:
            rawdata = jsonload(open(dfn))
            self[rawdata['timestamp']] = rawdata

    def get_unique(self, key):
        # return a set of all (unique) values for a field id
        uniq = set()
        for d in self.values():
            if key in d:
                el = d[key]
                if isinstance(el, list):
                    uniq = uniq.union(el)
                else:
                    uniq = uniq.union((el,))
        return uniq

    def get_not_none(self, key):
        # return a list of all values of a specific field id
        # the second return value is count of submission that did not have data
        # for this field id
        val = []
        missing = 0
        for d in self.values():
            if key in d:
                el = d[key]
                if isinstance(el, list):
                    val.extend(el)
                else:
                    if el == 'none':
                        missing += 1
                    else:
                        val.append(el)
            else:
                missing += 1
        return val, missing

    def get_counts(self, key, predef_keys=None):
        # return a dict with field values as keys and respective submission 
        # count as value
        vals = self.get_not_none(key)[0]
        uniq = np.unique(vals)
        counts = dict(zip(uniq, [vals.count(u) for u in uniq]))
        if not predef_keys is None:
            ret = dict(zip(predef_keys, [0] * len(predef_keys)))
        else:
            ret = {}
        ret.update(counts)
        return ret

    def select_match(self, key, values):
        # return a db with all submissions were a field id has one of the
        # supplied values
        match = DB(None)
        for k, v in self.items():
            if not key in v:
                continue
            el = v[key]
            if isinstance(el, list):
                if len(set(values).intersection(el)):
                    match[k] = v
            elif el in values:
                match[k] = v
        return match

    def select_match_exactly(self, key, values):
        # return a db with all submissions were a field id has value
        # equal to the supplied
        match = DB(None)
        set_values = set(values)
        for k, v in self.items():
            if not key in v:
                continue
            el = v[key]
            if isinstance(el, list):
                if set(el) == set_values:
                    match[k] = v
            elif set([el]) == set_values:
                match[k] = v
        return match

    def get_nice_name(self, id):
        srcs = [DB.os_dict, os_cat_names, DB.sw_dict, sw_categories,
                resource_categories, time_categories,
                DB.datamod_dict, DB.position_dict, DB.employer_dict,
                DB.vm_dict, DB.ratings_dict]
        suffix = u'$^†$' if id in fresh_keys else ''
        for src in srcs:
            if id in src:
                return src[id] + suffix
        # not found, nothing nicer
        return id + suffix


def mkpic_os_per_env(db, destdir):
    envs = ['pers_os', 'man_os', 'virt_host_os', 'virt_guest_os']
    env_names = ['Personal', 'Managed', 'Virt. Host', 'Virt. Guest']
    env_stats = {}
    for env in envs:
        counts = db.get_counts(env)
        stats = dict(zip(os_family.keys(), [0] * len(os_family)))
        for os in counts:
            stats[os_family_rev[os]] += counts[os]
        total_count = np.sum(stats.values())
        for osf in stats:
            if not total_count:
                stats[osf] = 0
            else:
                stats[osf] = float(stats[osf]) / total_count
        env_stats[env] = stats
    # make stacked barplot
    pl.figure(figsize=(7.5, 4))
    x = np.arange(len(envs))
    bottoms = np.zeros(len(envs))
    for i, os in enumerate(os_order):
        stat = [env_stats[e][os] for e in envs[::-1]]
        pl.barh(x, stat, left=bottoms, color=os_colors[i],
               label=db.get_nice_name(os), height=0.8)
        bottoms += stat
    pl.legend(loc='center left')
    pl.yticks(x + 0.4,  env_names[::-1])
    pl.ylim(-0.25, len(envs))
    pl.xlim(0,1)
    pl.title("Operating system preference by environment")
    pl.xlabel("Fraction of submissions")
    pl.subplots_adjust(left=0.15, right=0.97)
    pl.savefig('%s/ospref_by_env.png' % destdir, format='png', dpi=80)


def mkpic_time_per_env(db, destdir):
    envs = ['pers_time', 'man_time', 'virt_time']
    env_names = ['Personal', 'Managed', 'Virtual']
    env_stats = {}
    for env in envs:
        counts = dict(zip(time_order, [0] * len(time_order)))
        counts.update(db.get_counts(env))
        total_count = np.sum(counts.values())
        for c in counts:
            counts[c] = float(counts[c]) / total_count
        env_stats[env] = counts
    # make stacked barplot
    pl.figure(figsize=(7.5, 4))
    x = np.arange(len(envs))
    bottoms = np.zeros(len(envs))
    for i, t in enumerate(time_order):
        stat = [env_stats[e][t] for e in envs[::-1]]
        pl.barh(x, stat, left=bottoms, color=time_colors[i],
               label=db.get_nice_name(t), height=.6)
        bottoms += stat
    pl.legend(loc='lower left')
    pl.yticks(x + 0.2,  env_names[::-1])
    pl.ylim(-0.4, len(envs))
    pl.title("Research activity time by environment")
    pl.xlabel("Fraction of submissions")
    pl.subplots_adjust(right=0.97)
    pl.savefig('%s/time_by_env.png' % destdir, format='png', dpi=80)


def mkpic_submissions_per_key(db, destdir, key, title, sortby='name',
                            multiple=False):
    counts = db.get_counts(key)
    pl.figure(figsize=(6.4, (len(counts)-2) * 0.4 + 2))
    if not len(counts): tmargin = 0.4
    else: tmargin = .8/len(counts)
    if tmargin > 0.3: tmargin = 0.3
    pl.subplots_adjust(left=0.03, right=0.97, top=1-tmargin, bottom=tmargin)
    pl.title(title)
    if not len(counts):
        pl.text(.5, .5, "[Insufficient data for this figure]",
                horizontalalignment='center')
        pl.axis('off')
    else:
        # sort by name
        if sortby == 'name':
            stats = sorted(counts.items(), cmp=lambda x, y: cmp(x[0], y[0]))
        elif sortby == 'count':
            stats = sorted(counts.items(), cmp=lambda x, y: cmp(x[1], y[1]))[::-1]
        else:
            raise ValueError("Specify either name or count for sortby")
        x = np.arange(len(stats))
        pl.barh(x + (1./8), [s[1] for s in stats[::-1]], height=0.75, color = '#008200')
        pl.yticks(x + 0.5,  ['' for s in stats])
        text_offset = pl.gca().get_xlim()[1] / 30.
        for i, s in enumerate(stats[::-1]):
            pl.text(text_offset, i+.5, db.get_nice_name(s[0]) + " [%d]" % (s[1],),
                    horizontalalignment='left',
                    verticalalignment='center',
                    bbox=dict(facecolor='white', alpha=0.8, edgecolor='white'))
        pl.ylim(0, len(stats))
        yl = "Number of submissions"
        if multiple:
            yl += "\n(multiple choices per submission possible)"
        pl.xlabel(yl)
    pl.savefig('%s/submissions_per_%s.png' % (destdir, key), format='png', dpi=80)


def mkpic_software(db, destdir):
    for typ in sw_categories.keys():
        mkpic_submissions_per_key(
            db, destdir, 'sw_%s' % typ,
            title="Software popularity: %s" % db.get_nice_name(typ),
            sortby='count')

def mkpic_rating_by_os(db, env, items, destdir, title):
    from mvpa.misc.plot.base import plot_bars

    pl.figure(figsize=(6.4, 4.8))
    for i, os in enumerate(os_order):
        ratings = [db.select_match(env,
                        os_family[os]).get_not_none('%s' % (it,))[0]
                            for it in items]
        plot_bars(ratings, offset=((i+1)*0.2)-0.1, color=os_colors[i],
                  title=title, ylabel="Mean rating", label=db.get_nice_name(os))
    pl.ylim((0,3))
    pl.xlim((0,len(items)))
    pl.yticks((0, 3), ['Disagree', 'Agree'], rotation=90)
    pl.xticks(np.arange(len(items))+0.5, [i[-2:] for i in items],
              horizontalalignment='center')
    pl.legend(loc='lower right')
    pl.savefig('%s/ratings_%s.png' % (destdir, env), format='png', dpi=80)

def mkpic_rating_by_os_hor_joined(db, env, items, destdir='.', title=None,
                                  intro_sentence="I agree with the statements",
                                  suffix='', max_rating=4):
    per_os_width = 1
    #pl.figure(figsize=(6.4, 4.8))
    if max_rating is None:
        assert(len(items) == 1)
        # We need to query for it
        field = items[0]
        max_rating = np.max([db[k][field] for k in db if field in db[k]]) + 1
        print "max ", max_rating
    nos = len(os_order)
    rst = open('figures/ratings_%s%s.rst' % (env, suffix), 'w')
    rst.write("""

%s
%s

%s

.. raw:: html

   <table>
    """ % (title, '=' * len(title), intro_sentence))
    for k, it in enumerate(items):
        if k == 0:
            pl.figure(figsize=(3.2, 0.75))
        else:
            pl.figure(figsize=(3.2, 0.5))
        it_nice = db.get_nice_name(it)#.lstrip('.').lstrip(' ')
        it_nice = it_nice[0].upper() + it_nice[1:]
        for i, os in enumerate(os_order):
            ratings = np.array(db.select_match(env,
                                               os_family[os]).get_not_none('%s' % (it,))[0])
            #if len(ratings):
            #    assert(max(ratings) < max_rating)
            # Complement with errorbar
            if len(ratings):
                scaling = float(per_os_width)/(max_rating-1)
                meanstat = np.mean(ratings)
                meanstat_point = scaling * meanstat
                # standard error of estimate
                errstat_point = len(ratings) > 1 and scaling*np.std(ratings)/np.sqrt(len(ratings)-1) or 0
                #print ratings, meanstat, meanstat_point, errstat_point
                if True:
                    # Beautiful piece not yet appreciated by the audience
                    total = len(ratings)
                    bottom = 0
                    max_rating_max = max(max_rating, max(ratings)+1)
                    for r in sorted(set(ratings)):#range(max_rating_max):
                        stat = np.sum(ratings == r) * meanstat_point / float(total)
                        #print r, it, os, stat, total
                        #if it == "pers_r8" and os == "linux" and r == 3:
                        #    import pydb; pydb.debugger()
                        kwargs = dict(label=None)
                        if stat:
                            pl.barh(1.0/nos * (nos - 1 - i), stat, left=bottom, color=os_colors[i],
                                    height=.25, alpha=1./max_rating_max + r/float(max_rating_max),
                                    label=None,
                                    edgecolor=os_colors[i])
                        bottom += stat
                else:
                    pl.barh(1.0/nos * (nos - 1 - i), meanstat_point, left=0,
                            color=os_colors[i], height=.25, alpha=1.0, label=None)
                pl.errorbar([meanstat_point],
                            [1.0/nos * (nos - 0.5 - i)],
                            xerr=[errstat_point], fmt='o', color=os_colors[i], ecolor='k')
        pl.xlim((0, per_os_width))
        if k == 0 and max_rating == 4:  # ad-hoc: only for those disagree/agree
            pl.text(0, 1.1, "Disagree", horizontalalignment='left')
            pl.text(per_os_width, 1.1, "Agree", horizontalalignment='right')
            pl.ylim((0, 1.5))
        else:
            pl.ylim((0, 1))
        pl.axis('off')
        pl.subplots_adjust(left=0.00, right=1., bottom=0.0, top=1,
                       wspace=0.05, hspace=0.05)
        fname = '%s/ratings_%s_%s%s.png' % (destdir, env, it, suffix)
        pl.savefig(fname, format='png', dpi=80)
        pl.close()
        oddrow_s = k % 2 == 0 and ' class="oddrow"' or ''
        rst.write("""
        <tr%(oddrow_s)s>
        <td>%(it_nice)s</td>
        <td><img border="0" alt="%(fname)s" src="%(fname)s" /></td> </tr>"""
                  % (locals()))

    rst.write("""
    </table>

    """)
    rst.close()
    return


def main(srcdir, destdir):
    db = DB(srcdir)
    if False:
        ## Plot maintenance time per each group
        # assess what would be our range
        pmts, _ = db.get_not_none('pers_maint_time')
        max_rating = int(np.mean(pmts) + np.std(pmts))
        for pos in db.get_unique('bg_position'):
            print pos
            mkpic_rating_by_os_hor_joined(db.select_match('bg_position', pos),
                                          'pers_os', ['pers_maint_time'], destdir,
                                          "Personal environment", "", suffix='_maint_time_%s' % pos,
                                          max_rating=max_rating)

    ## db2 = db
    # custom selection for people dealing more with hardware
    # any electrophys
    ## db = db2.select_match('bg_datamod', (('ephys'),))
    # or only selected ones (so no fmri/pet etc)
    ## db = db2.select_match_exactly('bg_datamod', (('ephys'), ('behav'),))
    ## db.update(db2.select_match_exactly('bg_datamod', (('ephys'),)))
    ## db.update(db2.select_match_exactly('bg_datamod', (('ephys'), ('genetic'),)))
    ## db.update(db2.select_match_exactly('bg_datamod', (('ephys'), ('simulation'),)))
    ## db.update(db2.select_match_exactly('bg_datamod', (('ephys'), ('meeg'),)))

    if not os.path.exists(destdir):
        os.makedirs(destdir)

    mkpic_submissions_per_key(
        db, destdir, 'virt_prod', sortby='count',
        title='Virtualization product popularity')

    mkpic_submissions_per_key(
        db, destdir, 'bg_datamod', sortby='count',
        title='Submissions per data modality')

    mkpic_submissions_per_key(
        db, destdir, 'bg_position', title='Submissions per position', sortby='count')

    mkpic_submissions_per_key(
        db, destdir, 'bg_country', title='Submissions per country', sortby='count')

    mkpic_submissions_per_key(
        db, destdir, 'bg_employer', title='Submissions per venue', sortby='count')

    mkpic_submissions_per_key(
        db, destdir, 'software_resource', title='Software resource popularity', sortby='count')

    for pic in [mkpic_os_per_env, mkpic_software, mkpic_time_per_env]:
        pic(db, destdir)


    mkpic_rating_by_os_hor_joined(db, 'pers_os', ['pers_r%i' % i for i in range(1, 9)], destdir,
                       "Personal environment", "I prefer this particular scientific software environment because ...")
    mkpic_rating_by_os_hor_joined(db, 'man_os', ['man_r%i' % i for i in range(1, 6)], destdir,
                       "Managed environment")
    mkpic_rating_by_os_hor_joined(db, 'virt_host_os', ['virt_r%i' % i for i in range(1, 5)], destdir,
                       "Virtual environment (by host OS)")
    mkpic_rating_by_os_hor_joined(db, 'virt_guest_os', ['virt_r%i' % i for i in range(1, 5)], destdir,
                       "Virtual environment (by guest OS)")

    ## mkpic_rating_by_os_hor_joined(db.select_match('virt_prod', (('vmware'),)),
    ##                               'virt_host_os', ['virt_r%i' % i for i in range(1, 5)], destdir,
    ##                               "Virtualbox virtual environment (by host OS)")

    # submission stats: this is RST
    statsfile = open('%s/stats.txt' % destdir, 'w')
    statsfile.write('::\n\n  Number of submissions: %i\n' % len(db))
    statsfile.write('  Statistics last updated: %s\n\n' \
            % time.strftime('%A, %B %d %Y, %H:%M:%S UTC', time.gmtime()))
    statsfile.close()

if __name__ == '__main__':
    main(sys.argv[1], sys.argv[2])
    #main('dataout', 'figures')