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balancer.py
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balancer.py
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"""
/***************************************************************************
DelimitationToolbox
delimitation.py A QGIS plugin
Various tools for electoral delimitation
-------------------
begin : 2014-07-06
git sha : $Format:%H$
copyright : (C) 2014 by Sean Lin
email : seanlinmt at gmail dot com
***************************************************************************/
/***************************************************************************
* *
* This program is free software; you can redistribute it and/or modify *
* it under the terms of the GNU General Public License as published by *
* the Free Software Foundation; either version 2 of the License, or *
* (at your option) any later version. *
* *
***************************************************************************/
"""
import math
from re import search
import colouring
from delimitation import LayerType
from enum import Enum
from helper.ui import QgisMessageBarProgress, isnull
from qgis.core import QgsVectorLayer, QgsFeature, QgsPoint, QgsGeometry
class EqualStatus(Enum):
TOOSMALL = -1
OK = 0
TOOBIG = 1
class Balancer(object):
def __init__(self, layer, voters_field, polling_field, state_field, par_field, delta,
par_average_target=None,
state_average_target=None,
par_count_limit=None,
state_count_limit=None,
state_prefix_format="%02d",
par_prefix_format="%03d"):
self.total_voters = 0
self.topology_polling = {}
self.topology_state = {}
self.topology_par = {}
self.map_par_state = {}
self.layer = layer
self.polling_field = polling_field
self.state_field = state_field
self.par_field = par_field
self.voters_field = voters_field
self.delta = delta
self.state_count_limit = state_count_limit
self.par_count_limit = par_count_limit
self.state_count = 0
self.par_count = 0
self.state_average = 0
self.state_average_target = state_average_target
self.statemax_target = None
self.statemax_actual = None
self.statemax_actual_precentage = 0.00
self.statemin_target = None
self.statemin_actual = None
self.statemin_actual_precentage = 0.00
self.par_average = 0
self.par_average_target = par_average_target
self.parmax_target = None
self.parmax_actual = None
self.parmax_actual_precentage = 0.00
self.parmin_target = None
self.parmin_actual = None
self.parmin_actual_precentage = 0.00
self.polling_prefix_format = "%02d"
self.state_prefix_format = state_prefix_format
self.par_prefix_format = par_prefix_format
# store list of DMs that fits into the normalized range
self.__successes = {}
# stores list of previous failed attempts
self.__failures = {}
# current attempt
self.__current_walk = []
# adjacency graph
self.__graphs_state = None
self.__graphs_par = None
self.__inprogress = False
self.load_topology()
self.colouring_par = colouring.Colouring()
self.colouring_state = colouring.Colouring()
self.init_colouring()
def adjlayer_make(self, name, layertype):
if layertype == LayerType.Parliament:
ig = self.colouring_par.id_graph
elif layertype == LayerType.State:
ig = self.colouring_state.id_graph
else:
raise Exception("Not implemented")
vl = QgsVectorLayer("LineString?crs={}&index=yes&field=label:string&field=from:string&field=to:string"
.format(self.layer.dataProvider().crs().authid()),
"{}-{}-adj".format(name, layertype.name),
"memory")
pr = vl.dataProvider()
info = self.__box_info(layertype)
fet = QgsFeature()
for (f, tlist) in ig.nodeEdge.iteritems():
for t in tlist:
# make a feature from id=f to id=t
centref, labelf = info[f]
ptf = QgsPoint(centref[0], centref[1])
centret, labelt = info[t]
ptt = QgsPoint(centret[0], centret[1])
lines = [ptf, ptt]
fet.setGeometry(QgsGeometry().fromPolyline(lines))
attributes = ["{}-{}".format(labelf, labelt), labelf, labelt]
fet.setAttributes(attributes)
pr.addFeatures([fet])
vl.updateExtents()
return vl
def __box_info(self, layertype):
info = {}
for k, v in self.__get_layertype_features(layertype).items():
bbox = v['geom'].boundingBox()
info[k] = ((bbox.xMinimum() + bbox.width() / 2.0, bbox.yMinimum() + bbox.height() / 2.0), k)
return info
def init_colouring(self):
self.colouring_par.init_colours(self.topology_par)
self.colouring_state.init_colours(self.topology_state)
def get_adjacency(self, layertype):
if layertype == LayerType.Parliament:
return self.adjlayer_make(self.colouring_par.id_graph, layertype)
elif layertype == LayerType.State:
return self.adjlayer_make(self.colouring_state.id_graph, layertype)
raise Exception("Not implemented")
def init_topology(self):
self.topology_polling.clear()
nfeatures = self.layer.featureCount()
nop = nfeatures
iop = 0
progress = QgisMessageBarProgress("Initialising topology ...")
for f in self.layer.getFeatures():
self.topology_polling.update({f.id(): {
self.polling_field: None if isnull(f[self.polling_field]) else f[self.polling_field],
self.par_field: None if isnull(f[self.par_field]) else int(
search(r'\d+', f[self.par_field]).group()).__str__(),
self.state_field: None if isnull(f[self.state_field]) else int(
search(r'\d+', f[self.state_field]).group()).__str__(),
self.voters_field: int(f[self.voters_field]),
"geom": f.geometryAndOwnership()
}})
iop += 1
progress.setPercentage(int(100 * iop / nop))
progress.close()
def update_topology(self, dict_values):
for k, v in dict_values.items():
for k2, v2 in v.items():
self.layer.changeAttributeValue(k, self.layer.fieldNameIndex(k2), v2)
self.topology_polling[k][k2] = int(search(r'\d+', v2).group())
def load_topology(self):
self.topology_state.clear()
self.topology_par.clear()
self.init_topology()
for k, v in self.topology_polling.iteritems():
voters_value = v[self.voters_field]
geom_value = v["geom"]
key_state = v[self.state_field]
key_par = v[self.par_field]
if key_state:
self.topology_state.setdefault(key_state, {'voters': 0, 'geom': QgsGeometry(geom_value)})
if key_state in self.topology_state:
self.topology_state[key_state]['geom'] = QgsGeometry(
self.topology_state[key_state]['geom'].combine(geom_value))
self.topology_state[key_state]['voters'] += voters_value
if key_par:
self.topology_par.setdefault(key_par, {'voters': 0, 'geom': QgsGeometry(geom_value)})
if key_par in self.topology_par:
self.topology_par[key_par]['geom'] = QgsGeometry(
self.topology_par[key_par]['geom'].combine(geom_value))
self.topology_par[key_par]['voters'] += voters_value
self.calculate_limits(self.delta)
self.init_par_state_map()
def get_colour_by_state(self, attr_value, colour_index):
value = self.topology_state.get(attr_value)
if not value:
return None
if value['voters'] > self.statemax_target:
return self.colouring_state.colours_red[colour_index - 1]
if value['voters'] < self.statemin_target:
return self.colouring_state.colours_blue[colour_index - 1]
return self.colouring_state.colours_grey[colour_index - 1]
def get_colour_by_parliament(self, attr_value, colour_index):
value = self.topology_par.get(attr_value)
if not value:
return None
if value['voters'] > self.parmax_target:
return self.colouring_par.colours_red[colour_index - 1]
if value['voters'] < self.parmin_target:
return self.colouring_par.colours_blue[colour_index - 1]
return self.colouring_par.colours_grey[colour_index - 1]
def init_par_state_map(self):
# {par_key : {voters:voters, d: "-+",
# states: { state_key: d} }}
self.map_par_state.clear()
for v in self.topology_polling.values():
par_key = v[self.par_field]
state_key = v[self.state_field]
if not par_key:
continue
self.map_par_state \
.setdefault(par_key, {"d": "{:.2f}%".format(self.get_par_deviation(par_key)),
"states": {},
"voters": self.get_par_voters(par_key)})
if not state_key:
continue
if state_key not in self.map_par_state[par_key]['states']:
self.map_par_state[par_key]['states'] \
.update({state_key: "{:.2f}%".format(self.get_state_deviation(state_key))})
def get_par_voters(self, par_name):
if not par_name:
return 0
return self.topology_par[par_name]['voters']
def get_par_deviation(self, par_name):
if not par_name:
return 0.0
return (self.topology_par[par_name]['voters'] - self.par_average) * 100 / self.par_average
def get_par_code_sequence(self):
if self.topology_par.keys().__len__():
startval = int(min(self.topology_par.keys()))
else:
startval = 1
return [p for p in range(startval, startval + self.par_count_limit)]
def get_state_code_sequence(self):
return [s for s in range(1, self.state_count_limit + 1)]
def get_recommendation(self):
# get min/max for number of state seats in a par
seats_state_min = math.floor(1.0 * self.state_count_limit / self.par_count_limit)
seats_state_max = math.ceil(1.0 * self.state_count_limit / self.par_count_limit)
seats_extras = self.state_count_limit % self.par_count_limit
# recommended size (voters)
# recommended_par
def get_state_deviation(self, state_name):
if not state_name:
return 0.0
return (self.topology_state[state_name]['voters'] - self.state_average) * 100 / self.state_average
def calculate_limits(self, delta):
self.delta = delta
self.total_voters = sum([v['voters'] for k, v in self.topology_par.iteritems()])
self.state_count = self.topology_state.keys().__len__()
self.par_count = self.topology_par.keys().__len__()
if not self.state_count:
self.state_average = float(self.total_voters) / self.state_count_limit
else:
self.state_average = float(self.total_voters) / self.state_count
# for old topo
if not self.state_count_limit:
self.state_count_limit = self.state_count
if self.state_average_target:
self.state_average = self.state_average_target
self.statemax_target = self.state_average + self.delta * self.state_average
self.statemin_target = self.state_average - self.delta * self.state_average
if self.state_count:
self.statemax_actual = max(v['voters'] for v in self.topology_state.values())
self.statemax_actual_precentage = (self.statemax_actual - self.state_average) * 100 / self.state_average
self.statemin_actual = min(v['voters'] for v in self.topology_state.values())
self.statemin_actual_precentage = (self.statemin_actual - self.state_average) * 100 / self.state_average
if not self.par_count:
self.par_average = float(self.total_voters) / self.par_count_limit
else:
self.par_average = float(self.total_voters) / self.par_count
# for old topo
if not self.par_count_limit:
self.par_count_limit = self.par_count
if self.par_average_target:
self.par_average = self.par_average_target
self.parmax_target = self.par_average + self.delta * self.par_average
self.parmin_target = self.par_average - self.delta * self.par_average
if self.par_count:
self.parmax_actual = max(v['voters'] for v in self.topology_par.values())
self.parmax_actual_precentage = (self.parmax_actual - self.par_average) * 100 / self.par_average
self.parmin_actual = min(v['voters'] for v in self.topology_par.values())
self.parmin_actual_precentage = (self.parmin_actual - self.par_average) * 100 / self.par_average
def get_best_deviation(self):
"""assumming fixed number of seats"""
state = [self.state_count, self.state_count_limit][bool(self.state_count_limit)]
par = [self.par_count, self.par_count_limit][bool(self.par_count_limit)]
mean = 100.0 * state / par
min = math.floor(mean) - mean
max = math.ceil(mean) - mean
return min, max
def calculate_live_totals(self, current_par, current_state, selected_ids):
voters_state = 0
voters_par = 0
voters_selected = 0
if current_par:
current_par = search(r'\d+', str(current_par)).group()
if current_state:
current_state = search(r'\d+', str(current_state)).group()
for k, v in self.topology_polling.items():
voters = v[self.voters_field]
if k in selected_ids:
voters_state += voters
voters_par += voters
voters_selected += voters
else:
if v[self.state_field] == current_state:
voters_state += voters
if v[self.par_field] == current_par:
voters_par += voters
return tuple((voters_par, voters_state, voters_selected))
def __get_layertype_features(self, layertype):
if layertype == LayerType.State:
return self.topology_state
elif layertype == LayerType.Parliament:
return self.topology_par
else:
return self.topology_polling
def is_balanced(self):
return (self.parmin_actual >= self.parmin_target and
self.parmax_actual <= self.parmax_target and
self.statemin_actual >= self.statemin_target and
self.statemax_actual <= self.statemax_target)
def get_features_total(self):
return tuple((self.par_count, self.state_count, self.topology_polling.keys().__len__()))
def get_unused(self):
pars = [str(p) for p in self.get_par_code_sequence()]
states = [str(s) for s in self.get_state_code_sequence()]
pars_left = set(pars) \
.difference([str(v[self.par_field]) for v in self.topology_polling.values()])
states_left = set(states) \
.difference([str(v[self.state_field]) for v in self.topology_polling.values()])
return tuple((pars_left, states_left))
# todo par_new_prefix unused
def resequence(self, par_new_prefix):
ordered = sorted(self.topology_polling.items(),
key=lambda x: (x[1][self.par_field], x[1][self.state_field]))
par_renumber = 0
state_renumber = 0
polling_renumber = 1
par_current = None
state_current = None
for o in ordered:
if state_current != o[1][self.state_field]:
state_renumber += 1
polling_renumber = 1 # restart polling area renumbering
state_current = o[1][self.state_field]
if par_current != o[1][self.par_field]:
par_renumber += 1
par_current = o[1][self.par_field]
self.update_topology(
{o[0]: {self.state_field: self.state_prefix_format % state_renumber,
self.polling_field: self.polling_prefix_format % polling_renumber,
self.par_field: self.par_prefix_format % par_renumber}})
polling_renumber += 1
class NodePOLL(object):
def __init__(self, pollid, neighbours, voters, state):
self.id = pollid
self.adjacent_states = neighbours
self.voters = voters
self.state_prev = None
self.state_current = state
class NodeSTATE(object):
def __init__(self, stateid, neighbours, states, parid):
self.id = stateid
self.adjacent_states = neighbours
self.states = states
self.par_prev = None
self.par_current = parid
# get all adjacent POLLs by excluding POLLs in our own STATE
self.adjacent_states = dict(
[(poll, [adj for adj in poll.nodeEdge if adj not in self.states]) for poll in self.states])
# (poll, voters) : sorted[(poll_adj, voters)]
self.adjacent_voters = {}
for k, v in self.adjacent_states:
self.adjacent_voters.update(
{(k, k.voters): [(poll, poll.voters) for poll in v].sort(key=lambda x: x.voters)})
return sum(poll.voters for poll in self.states)