import traceback

from copy import deepcopy

import geopy
import sqlalchemy

from flask import Blueprint, render_template, flash, request, redirect, \

    url_for, abort, send_from_directory, Response

from os.path import join

from os import unlink

from flaskr.extensions import cache, basic_auth

from flaskr.forms import LoginForm, EstimateForm

from flaskr.models import db, User, Estimation, StatusEnum, ScenarioEnum

from flaskr.geocoder import CachedGeocoder

from flaskr.core import generate_unique_id, \
    get_emission_models, increment_hit_counter

from flaskr.content import content

from wtforms import validators

from yaml import safe_dump as yaml_dump

import csv
# from io import StringIO
from cStringIO import StringIO

import pandas
from pandas.compat import StringIO as PandasStringIO

main = Blueprint('main', __name__)

OUT_ENCODING = 'utf-8'

# -----------------------------------------------------------------------------

# -----------------------------------------------------------------------------

@main.route('/favicon.ico')

@cache.cached(timeout=10000)

def favicon():  # we want it served from the root, not from static/
    return send_from_directory(
        join(main.root_path, '..', 'static', 'img'),
        'favicon.ico', mimetype='image/vnd.microsoft.icon'
    )

@main.route('/')

@main.route('/home')
@main.route('/home.html')

# @cache.cached(timeout=1000)

def home():

    models = get_emission_models()

    models_dict = {}
    for model in models:
        models_dict[model.slug] = model.__dict__

    increment_hit_counter()

    return render_template(
        'home.html',

        models=models_dict,

        colors=[model.color for model in models],

        labels=[model.name for model in models],

    )

def gather_addresses(from_list, from_file):
    addresses = []
    if from_file:
        file_mimetype = from_file.mimetype
        file_contents = from_file.read()

        rows_dicts = None

        if 'text/csv' == file_mimetype:

            rows_dicts = pandas \
                .read_csv(PandasStringIO(file_contents)) \
                .rename(str.lower, axis='columns') \
                .to_dict(orient="row")

        # Here are just *some* of the mimetypes that Microsoft's
        # garbage spreadsheet files may have.
        # application/vnd.ms-excel (official)
        # application/msexcel
        # application/x-msexcel
        # application/x-ms-excel
        # application/x-excel
        # application/x-dos_ms_excel
        # application/xls
        # application/x-xls
        # application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
        # ... Let's check extension instead.

        elif from_file.filename.endswith('xls') \
                or from_file.filename.endswith('xlsx'):

            rows_dicts = pandas \
                .read_excel(PandasStringIO(file_contents)) \
                .rename(str.lower, axis='columns') \
                .to_dict(orient="row")

        # Python 3.7 only
        # elif from_file.filename.endswith('ods'):
        #
        #     rows_dicts = read_ods(PandasStringIO(file_contents), 1) \
        #         .rename(str.lower, axis='columns') \
        #         .to_dict(orient="row")

        if rows_dicts is not None:
            for row_dict in rows_dicts:
                if 'address' in row_dict:
                    addresses.append(row_dict['address'])
                    continue
                address = None
                if 'city' in row_dict:
                    address = row_dict['city']
                if 'country' in row_dict:
                    if address is None:
                        address = row_dict['country']
                    else:
                        address += "," + row_dict['country']
                if address is not None:
                    addresses.append(address)
                else:

                    raise validators.ValidationError(
                        "We could not find Address data in the spreadsheet."
                    )

        else:

            raise validators.ValidationError(
                "We could not find any data in the spreadsheet."
            )

    else:
        addresses = from_list.replace("\r", '').split("\n")

    clean_addresses = []
    for address in addresses:
        if not address:
            continue
        elif type(address).__name__ == 'str':
            clean_addresses.append(unicode(address, 'utf-8'))
        else:
            clean_addresses.append(address)
    addresses = clean_addresses

    # Remove empty lines (if any) and white characters
    addresses = [a.strip() for a in addresses if a]

    return "\n".join(addresses)

@main.route("/estimate", methods=["GET", "POST"])

@main.route("/estimate.html", methods=["GET", "POST"])

def estimate():

    models = get_emission_models()

    form = EstimateForm()

    def show_form():
        return render_template("estimate.html", form=form, models=models)

    if form.validate_on_submit():

        estimation = Estimation()

        # estimation.email = form.email.data

        estimation.run_name = form.run_name.data

        estimation.first_name = form.first_name.data
        estimation.last_name = form.last_name.data

        estimation.institution = form.institution.data

        estimation.status = StatusEnum.pending

        try:
            estimation.origin_addresses = gather_addresses(
                form.origin_addresses.data,
                form.origin_addresses_file.data
            )
        except validators.ValidationError as e:
            form.origin_addresses_file.errors.append(e.message)
            return show_form()

        try:
            estimation.destination_addresses = gather_addresses(
                form.destination_addresses.data,
                form.destination_addresses_file.data
            )
        except validators.ValidationError as e:
            form.destination_addresses_file.errors.append(e.message)
            return show_form()

        estimation.use_train_below_km = form.use_train_below_km.data

        models_slugs = []
        for model in models:

            if getattr(form, 'use_model_%s' % model.slug).data:

                models_slugs.append(model.slug)

        estimation.models_slugs = u"\n".join(models_slugs)

        db.session.add(estimation)
        db.session.commit()

        flash("Estimation request submitted successfully.", "success")

        return redirect(url_for(
            endpoint=".consult_estimation",
            public_id=estimation.public_id,

            extension='html'

        ))

        # return render_template("estimate-debrief.html", form=form)

    return show_form()

@main.route("/invalidate")

@main.route("/invalidate.html")

def invalidate():
    stuck_estimations = Estimation.query \
        .filter_by(status=StatusEnum.working) \
        .all()

    for estimation in stuck_estimations:
        estimation.status = StatusEnum.failure
        estimation.errors = "Invalidated. Try again."
        db.session.commit()

    return "Estimations invalidated: %d" % len(stuck_estimations)


@main.route("/invalidate-geocache")
@main.route("/invalidate-geocache.html")
def invalidate_geocache():
    geocache = 'geocache.db'

    unlink(geocache)

    return "Geocache invalidated."

@main.route("/compute")

def compute():  # process the queue of estimation requests

    maximum_addresses_to_compute = 30000

    def _respond(_msg):
        return "<pre>%s</pre>" % _msg

    def _handle_failure(_estimation, _failure_message):

        _estimation.status = StatusEnum.failure

        _estimation.errors = _failure_message

        db.session.commit()

    def _handle_warning(_estimation, _warning_message):
        _estimation.warnings = _warning_message
        db.session.commit()

    try:
        response = ""

        count_working = Estimation.query \
            .filter_by(status=StatusEnum.working) \
            .count()

        if 0 < count_working:
            return _respond("Already working on estimation.")

        try:
            estimation = Estimation.query \
                .filter_by(status=StatusEnum.pending) \
                .order_by(Estimation.id.asc()) \
                .first()
        except sqlalchemy.orm.exc.NoResultFound:
            return _respond("No estimation in the queue.")
        except Exception as e:
            return _respond("Database error: %s" % (e,))

        if not estimation:
            return _respond("No estimation in the queue.")

        estimation.status = StatusEnum.working
        db.session.commit()

        response += u"Processing estimation `%s`...\n" % (
            estimation.public_id
        )

        # GEOCODE ADDRESSES ###################################################

        failed_addresses = []
        geocoder = CachedGeocoder()

        # GEOCODE ORIGINS #########################################################

        origins_addresses = estimation.origin_addresses.strip().split("\n")

        origins_addresses_count = len(origins_addresses)

        origins = []

        if origins_addresses_count > maximum_addresses_to_compute:

            errmsg = u"Too many origins. (%d > %d)  Please contact us for support of that many origins." % (origins_addresses_count, maximum_addresses_to_compute)

            _handle_failure(estimation, errmsg)
            return _respond(errmsg)

        for i in range(origins_addresses_count):

            origin_address = origins_addresses[i].strip()
            if origin_address in failed_addresses:
                continue

            try:
                origin = geocoder.geocode(origin_address.encode('utf-8'))
            except geopy.exc.GeopyError as e:
                response += u"Failed to geocode origin `%s`.\n%s\n" % (
                    origin_address, e,
                )
                _handle_warning(estimation, response)
                failed_addresses.append(origin_address)
                continue

            if origin is None:
                response += u"Failed to geocode origin `%s`.\n" % (
                    origin_address,
                )
                _handle_warning(estimation, response)
                failed_addresses.append(origin_address)
                continue

            origins.append(origin)

            response += u"Origin: %s == %s (%f, %f)\n" % (
                origin_address, origin.address,
                origin.latitude, origin.longitude,

            )

        # GEOCODE DESTINATIONS ####################################################

        destinations_addresses = estimation.destination_addresses.strip().split("\n")

        destinations_addresses_count = len(destinations_addresses)

        destinations = []

        if destinations_addresses_count > maximum_addresses_to_compute:

            errmsg = u"Too many destinations. (%d > %d)  Please contact us for support of that many destinations." % (destinations_addresses_count, maximum_addresses_to_compute)

            _handle_failure(estimation, errmsg)
            return _respond(errmsg)

        for i in range(destinations_addresses_count):

            destination_address = destinations_addresses[i].strip()
            if destination_address in failed_addresses:
                continue

            try:
                destination = geocoder.geocode(destination_address.encode('utf-8'))
            except geopy.exc.GeopyError as e:
                response += u"Failed to geocode destination `%s`.\n%s\n" % (
                    destination_address, e,
                )
                _handle_warning(estimation, response)
                failed_addresses.append(destination_address)
                continue

            if destination is None:
                response += u"Failed to geocode destination `%s`.\n" % (
                    destination_address,
                )
                _handle_warning(estimation, response)
                failed_addresses.append(destination_address)
                continue

            # print(repr(destination.raw))

            destinations.append(destination)

            response += u"Destination: %s == %s (%f, %f)\n" % (
                destination_address, destination.address,
                destination.latitude, destination.longitude,

            )

        geocoder.close()

        # GTFO IF NO ORIGINS OR NO DESTINATIONS ###################################

        if 0 == len(origins):
            response += u"Failed to geocode all the origin(s).\n"
            _handle_failure(estimation, response)
            return _respond(response)
        if 0 == len(destinations):
            response += u"Failed to geocode all the destination(s).\n"
            _handle_failure(estimation, response)
            return _respond(response)

        # GRAB AND CONFIGURE THE EMISSION MODELS ##################################

        emission_models = estimation.get_models()
        # print(emission_models)

        extra_config = {
            'use_train_below_distance': estimation.use_train_below_km,
            # 'use_train_below_distance': 300,
        }

        # PREPARE RESULT DICTIONARY THAT WILL BE STORED ###########################

        results = {}

        # UTILITY PRIVATE FUNCTION(S) #############################################

        def get_city_key(_location):
            # Will this hack hold?  Suspense...
            return _location.address.split(',')[0]

            # _city_key = _location.address
            # # if 'address100' in _location.raw['address']:
            # #     _city_key = _location.raw['address']['address100']
            # if 'city' in _location.raw['address']:
            #     _city_key = _location.raw['address']['city']
            # elif 'state' in _location.raw['address']:
            #     _city_key = _location.raw['address']['state']
            # return _city_key

        def compute_one_to_many(
                _origin,
                _destinations,
                _extra_config=None
        ):
            _results = {}
            footprints = {}

            destinations_by_city_key = {}

            cities_sum_foot = {}
            cities_sum_dist = {}
            cities_dict_first_model = None
            for model in emission_models:
                cities_dict = {}
                for _destination in _destinations:
                    footprint = model.compute_travel_footprint(
                        origin_latitude=_origin.latitude,
                        origin_longitude=_origin.longitude,
                        destination_latitude=_destination.latitude,
                        destination_longitude=_destination.longitude,
                        extra_config=_extra_config,
                    )

                    _key = get_city_key(_destination)

                    destinations_by_city_key[_key] = _destination

                    if _key not in cities_dict:
                        cities_dict[_key] = {
                            'city': _key,
                            'address': _destination.address,
                            'footprint': 0.0,
                            'distance': 0.0,
                            'train_trips': 0,
                            'plane_trips': 0,
                        }
                    cities_dict[_key]['footprint'] += footprint['co2eq_kg']
                    cities_dict[_key]['distance'] += footprint['distance_km']
                    cities_dict[_key]['train_trips'] += footprint['train_trips']
                    cities_dict[_key]['plane_trips'] += footprint['plane_trips']
                    if _key not in cities_sum_foot:
                        cities_sum_foot[_key] = 0.0
                    cities_sum_foot[_key] += footprint['co2eq_kg']
                    if _key not in cities_sum_dist:
                        cities_sum_dist[_key] = 0.0
                    cities_sum_dist[_key] += footprint['distance_km']

                cities = sorted(cities_dict.values(), key=lambda c: c['footprint'])

                footprints[model.slug] = {
                    'cities': cities,
                }

                if cities_dict_first_model is None:
                    cities_dict_first_model = deepcopy(cities_dict)

            _results['footprints'] = footprints

            total_foot = 0.0
            total_dist = 0.0
            total_train_trips = 0
            total_plane_trips = 0

            cities_mean_dict = {}
            for city in cities_sum_foot.keys():
                city_mean_foot = 1.0 * cities_sum_foot[city] / len(emission_models)
                city_mean_dist = 1.0 * cities_sum_dist[city] / len(emission_models)
                city_train_trips = cities_dict_first_model[city]['train_trips']
                city_plane_trips = cities_dict_first_model[city]['plane_trips']
                cities_mean_dict[city] = {
                    'address': destinations_by_city_key[city].address,
                    'city': city,
                    'footprint': city_mean_foot,
                    'distance': city_mean_dist,
                    'train_trips': city_train_trips,
                    'plane_trips': city_plane_trips,
                }
                total_foot += city_mean_foot
                total_dist += city_mean_dist
                total_train_trips += city_train_trips
                total_plane_trips += city_plane_trips

            cities_mean = [cities_mean_dict[k] for k in cities_mean_dict.keys()]
            cities_mean = sorted(cities_mean, key=lambda c: c['footprint'])

            _results['mean_footprint'] = {  # DEPRECATED?
                'cities': cities_mean

            }

            _results['cities'] = cities_mean

            _results['total'] = total_foot  # DEPRECATED
            _results['footprint'] = total_foot

            _results['distance'] = total_dist
            _results['train_trips'] = total_train_trips
            _results['plane_trips'] = total_plane_trips

            return _results

        # SCENARIO A : One Origin, At Least One Destination #######################
        #
        # In this scenario, we compute the sum of each of the travels' footprint,
        # for each of the Emission Models, and present a mean of all Models.
        #
        if 1 == len(origins):
            estimation.scenario = ScenarioEnum.one_to_many
            results = compute_one_to_many(
                _origin=origins[0],
                _destinations=destinations,
                _extra_config=extra_config,
            )

        # SCENARIO B : At Least One Origin, One Destination #######################
        #
        # Same as A for now.
        #
        elif 1 == len(destinations):
            estimation.scenario = ScenarioEnum.many_to_one
            results = compute_one_to_many(
                _origin=destinations[0],

                _destinations=origins,

                _extra_config=extra_config,

            )

        # SCENARIO C : At Least One Origin, At Least One Destination ##############
        #
        # Run Scenario A for each Destination, and expose optimum Destination.
        #
        else:
            estimation.scenario = ScenarioEnum.many_to_many
            unique_city_keys = []
            result_cities = []
            for destination in destinations:
                city_key = get_city_key(destination)

                if city_key in unique_city_keys:
                    continue
                else:
                    unique_city_keys.append(city_key)

                city_results = compute_one_to_many(
                    _origin=destination,
                    _destinations=origins,
                    _extra_config=extra_config,
                )
                city_results['city'] = city_key
                city_results['address'] = destination.address
                result_cities.append(city_results)

            result_cities = sorted(result_cities, key=lambda c: int(c['footprint']))
            results = {
                'cities': result_cities,
            }

        # WRITE RESULTS INTO THE DATABASE #########################################

        estimation.status = StatusEnum.success

        estimation.output_yaml = u"%s" % yaml_dump(results)

        db.session.commit()

        # FINALLY, RESPOND ########################################################

        response += yaml_dump(results) + "\n"

        return _respond(response)

    except Exception as e:

        errmsg = u"Computation failed : %s" % (e,)

        # errmsg = u"%s\n\n%s" % (errmsg, traceback.format_exc())

        if estimation:
            _handle_failure(estimation, errmsg)
        return _respond(errmsg)

@main.route("/estimation/<public_id>.<extension>")
def consult_estimation(public_id, extension):

    try:
        estimation = Estimation.query \
            .filter_by(public_id=public_id) \
            .one()
    except sqlalchemy.orm.exc.NoResultFound:
        return abort(404)
    except Exception as e:

        # TODO: log?

        return abort(500)

    # allowed_formats = ['html']
    # if format not in allowed_formats:
    #     abort(404)

    unavailable_statuses = [StatusEnum.pending, StatusEnum.working]

    if extension in ['xhtml', 'html', 'htm']:

        if estimation.status in unavailable_statuses:

            return render_template(
                "estimation-queue-wait.html",
                estimation=estimation
            )
        else:

            estimation_output = estimation.get_output_dict()
            estimation_sum = 0

            if estimation_output:
                for city in estimation_output['cities']:
                    estimation_sum += city['footprint']

            return render_template(
                "estimation.html",

                estimation=estimation,

                estimation_output=estimation_output,
                estimation_sum=estimation_sum,

            )

    elif extension in ['yaml', 'yml']:

        if estimation.status in unavailable_statuses:
            abort(404)

        return estimation.output_yaml

    elif 'csv' == extension:

        if estimation.status in unavailable_statuses:
            abort(404)

        si = StringIO()
        cw = csv.writer(si, quoting=csv.QUOTE_ALL)

        cw.writerow([
            u"city", u"address",
            u"co2 (kg)", u"distance (km)",
            u"plane trips", u'train trips',
        ])

        results = estimation.get_output_dict()

        for city in results['cities']:
            cw.writerow([
                city['city'].encode(OUT_ENCODING),
                city['address'].encode(OUT_ENCODING),
                round(city['footprint'], 3),
                round(city['distance'], 3),

                city['plane_trips'],
                city['train_trips'],

            ])

        # return si.getvalue().strip('\r\n')
        return Response(
            response=si.getvalue().strip('\r\n'),
            headers={
                'Content-type': 'text/csv',
                'Content-disposition': "attachment; filename=%s.csv"%public_id,
            },
        )

    else:
        abort(404)

@main.route("/scaling_laws.csv")
def get_scaling_laws_csv():

    distances = content.laws_plot.distances

    models = get_emission_models()

    si = StringIO()
    cw = csv.writer(si, quoting=csv.QUOTE_ALL)

    header = ['distance'] + [model.slug for model in models]
    cw.writerow(header)

    for distance in distances:
        row = [distance]
        for model in models:
            row.append(model.compute_airplane_distance_footprint(distance))
        cw.writerow(row)

    return Response(
        response=si.getvalue().strip('\r\n'),
        headers={
            'Content-type': 'text/csv',
            'Content-disposition': 'attachment; filename=scaling_laws.csv',
        },
    )

@main.route("/test")
@basic_auth.required
def dev_test():
    import os

    return os.getenv('ADMIN_USERNAME')