#!/usr/bin/python
# -*- coding: utf-8 -*-
# \file supervisor.py
# \brief TODO
# \author Florent Guiotte <florent.guiotte@gmail.com>
# \version 0.1
# \date 25 juil. 2018
#
# TODO details

import yaml
import numpy as np
import importlib
import sys
import hashlib
from collections import OrderedDict
import time
import os
import datetime
from sklearn import metrics
from pathlib import Path
from operator import itemgetter

from sklearn.ensemble import RandomForestClassifier

#sys.path.append('.')
import Descriptors
from CrossValidationGenerator import APsCVG

sys.path.append('./triskele/python')
import triskele


### Keep yaml ordered
def setup_yaml():
  """ https://stackoverflow.com/a/8661021 """
  represent_dict_order = lambda self, data:  self.represent_mapping('tag:yaml.org,2002:map', data.items())
  yaml.add_representer(OrderedDict, represent_dict_order)    
setup_yaml()

enrichment_dir = Path('./Enrichment/')
test_dir = enrichment_dir / 'Tests'
staging_dir = enrichment_dir / 'Staging'
result_dir = enrichment_dir / 'Results'

def update_queue():
    tmp_queue = list()
    for child in test_dir.iterdir():
        if child.is_file() and child.suffix == '.yml':
            tmp_queue.append({'expe_file': child, 'priority': get_priority(child)})
            
    queue = sorted(tmp_queue, key=itemgetter('priority'))
    return queue


def get_priority(yml_file):
    with open(yml_file) as f:
        expe = OrderedDict(yaml.safe_load(f)['expe'])
    return expe['priority']
    

def run(expe_file):
    with open(expe_file) as f:
        expe = OrderedDict(yaml.safe_load(f)['expe'])
    
    ### Keep track of time 
    kronos = Kronos()
    
    ### Compute hashes
    expe_hashes = compute_hashes(expe)
    
    ### Create output names
    oname = '{}_{}'.format(expe_file.stem, expe_hashes['global'][:6])
    oname_yml = oname + '.yml'
    oname_tif = oname + '.tif'
    
    ### Create partial report
    expe_report = create_report(kronos)
    
    ### Stage expe
    write_expe_file(staging_dir / oname_yml, expe, expe_hashes, expe_report)

    ### Compute descriptors
    descriptors = compute_descriptors(expe)
    kronos.time('description')

    ### Compute classification
    classification = compute_classification(expe, descriptors)
    kronos.time('classification')
    
    ### Metrics  
    metrics = run_metrics(expe, classification)
    kronos.time('metrics')
    
    ### Create complete report
    expe_report = create_report(kronos)
    
    ### Name and write prediction
    triskele.write(result_dir / oname_tif, classification)

    ### Write report and results
    write_expe_file(result_dir / oname_yml, expe, expe_hashes, expe_report, oname_tif, metrics)
    
    
def write_expe_file(file, expe, hashes=None, report=None, classification=None, results=None):
    with open(file, 'w') as of:
        yaml.dump(OrderedDict({'expe': expe, 
                   'expe_hashes': hashes, 
                   'expe_report': report,
                   'expe_classification': classification,
                   'expe_results': results}),
                  of, default_flow_style=False, encoding=None, allow_unicode=True)
    

def compute_hashes(expe):
    glob = hashlib.sha1()

    expe_hashes = OrderedDict()

    for k in ['ground_truth', 'descriptors_script', 'cross_validation', 'classifier']:
        v = str(expe[k]).encode('utf-8')
        expe_hashes[k] = hashlib.sha1(v).hexdigest()
        glob.update(v)
    expe_hashes['global'] = glob.hexdigest()
    return expe_hashes


def compute_descriptors(expe):
    """Compute descriptors from a standard expe recipe"""
    script = expe['descriptors_script']
    desc = importlib.import_module(script['name'], package=Descriptors)
    #importlib.reload(Descriptors)
    att = desc.run(**script['parameters'])
    
    return att


def compute_classification(expe, descriptors):
    """Read a standard expe recipe and descriptors, return the result classification"""
    # Ground truth
    gt = triskele.read(expe['ground_truth'])


    # CrossVal and ML
    cv = expe['cross_validation']
    cl = expe['classifier']

    prediction = np.zeros_like(gt)

    for xt, xv, yt, yv, ti in APsCVG(gt, descriptors, **cv['parameters']):
        rfc = RandomForestClassifier(**cl['parameters'])
        rfc.fit(xt, yt)

        ypred = rfc.predict(xv)

        prediction[ti] = ypred
        
    return prediction


def compute_metrics(ground_truth, classification):
    """Return dict of metrics for ground_truth and classification prediction in parameters"""
    f = np.nonzero(classification)
    pred = classification[f].ravel()
    gt = ground_truth[f].ravel()
    
    results = OrderedDict() 
    results['overall_accuracy'] = float(metrics.accuracy_score(gt, pred))
    results['cohen_kappa'] = float(metrics.cohen_kappa_score(gt, pred))
    
    return results


def run_metrics(expe, classification):
    """Compute the metrics from a standard expe recipe and an given classification"""
    
    ### Extensible: meta-classes
    gt = triskele.read(expe['ground_truth'])
    return compute_metrics(gt, classification)


def create_report(kronos):
    expe_report = OrderedDict()

    expe_report['supervisor'] = os.uname()[1]

    for timev, datek in zip((kronos.get_start_date(), kronos.get_end_date()), ('start_date', 'end_date')):
        expe_report[datek] = datetime.datetime.fromtimestamp(timev).strftime('Le %d/%m/%Y à %H:%M:%S') if timev is not None else None

    ressources = kronos.get_times()
    ressources['ram'] = None

    expe_report['ressources'] = ressources
    return expe_report


class Kronos(object):
    def __init__(self):
        self._pt = time.process_time()
        self._times = OrderedDict()
        self._stime = time.time()
        self._etime = None
        
    def time(self, name):
        self._times[name + '_process_time'] = time.process_time() - self._pt
        self._pt = time.process_time()
        self._etime = time.time()
        
    def get_times(self):
        return self._times
    
    def get_start_date(self):
        return self._stime
    
    def get_end_date(self):
        return self._etime

    
def watch_folder():
    time.sleep(10)
    
    
def main():
    while(True):
        queue = update_queue()
        if not queue:
            watch_folder()
            continue
        run(queue.pop()['expe_file'])
    
if __name__ == '__main__':
    main()