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shape.py
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shape.py
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# _*_ coding: utf-8 _*_
import re
import math
import shapefile
import csv
import pandas as pd
from SVY21 import *
from coordinatescrape import *
"""
This file contains the classes related to opening shapefiles
- subzone shapefiles
- breeding habitat list
- dengue cases list
"""
class BasicExtractor(object):
"""Superclass containing utility methods for finding:
1. midpoint
2. area of shape
3. check if point is inside a larger area
4. collates repeated list, summing its components
"""
def __init__(self):
self.x = 10
def dist(self, A, B):
x = A[0] - B[0]
y = A[1] - B[1]
return math.hypot(x,y)
def get_midpoint(self, points):
"""Calculates longitude and latitude of midpoint from the middle of
max lon-lat and min lon-lat
Parameters
----------
points : list of list
each embedded list stores longitude and latitude as floats
Returns
-------
lon : int
longitude
lat : int
latitude
"""
lon = [point[0]for point in points]
lat = [point[1]for point in points]
lon = (max(lon) + min(lon)) / 2
lat = (max(lat) + min(lat)) / 2
if lon > 200:
cv = SVY21()
lat, lon = cv.computeLatLon(lat, lon)
return lon, lat
def get_area(self, points):
"""Calculates area of irregular object
# area of singapore is 719.1
# 1 degree = 111.2km
# 1 degree^2 = 12365.44km2
# implementation of Green's Theorem to calculate area in polygon
Returns
--------
area : float
"""
deg_to_km_constant = 12365.44
total = 0.0
N = len(points)
for i in range(N):
v1 = points[i]
v2 = points[(i+1) % N]
total += v1[0]*v2[1] - v1[1]*v2[0]
return abs(total/2) * deg_to_km_constant
def is_in_area(self, lon, lat, points):
"""Checks if point is within an irregular shape by two steps
1. if the sum of angle from point to all points is 2*pi
2. checks if point is within max-min of both lon and lat
Parameters
----------
lon : float
longitude of the point to be checked
lat : float
latitude of the point to be checked
points : list of tuples (floats)
boundary of the irregular shape
Returns
-------
is_in_area : boolean
"""
def get_cos_theta(a, b, c):
# method calculates the cosine-theta in the cosine rule equation
dist_c = self.dist(a, b)
dist_a = self.dist(a, c)
dist_b = self.dist(b, c)
return (dist_c**2 - dist_a**2 - dist_b**2) / (-2.0 * dist_a * dist_b)
def is_within():
pointslon = [point[0]for point in points]
pointslat = [point[1]for point in points]
if ( min(pointslon)< lon < max(pointslon)) and (min(pointslat)< lat < max(pointslat)):
return True
else:
return False
N = len(points)
C = (lon, lat)
total_angle = 0
for i in range(N):
v1 = points[i]
v2 = points[(i+1) % N]
cos_theta = get_cos_theta(v1, v2, C)
if cos_theta >= 1:
angle = math.acos(1)
else:
angle = math.acos(cos_theta)
total_angle += angle
if total_angle > 6.28 and is_within(): # extra layer of check, midpoint must be within boundary, pi is 6.28319:
return True
else:
return False
def collate(self, caselist):
"""Method sums up the number of the cases within each subzone as there may be repeats.
E.g. A-5, B-1, C-2, A-4, D-2, B-2 will return A-9, B-3, C-2, D-2
Parameters
----------
caselist : list of tuples (string, float)
tuple containing subzones and the number of dengue cases
Returns
-------
caselist : list of tuples (string, float)
condensed input list, frequencies tabulated
"""
newlist = {}
for case, number in caselist:
newlist[case] = 0
for case, number in caselist:
newlist[case] += number
caselist = []
caselist = [(k, v) for k, v in newlist.iteritems()]
return caselist
def get_nearest(self, lon, lat, shape):
"""
Finds nearest subzone by calculating distance from given point to centroid
Parameters
----------
lon : float
longitude of the point to be checked
lat : float
latitude of the point to be checked
shape : shapefile object (shapeRecords)
contains list of shapes in
Returns
-------
nearest : string
name of nearest subzone
"""
nearest = ""
nearest_dist = 1000000 #arbitary huge number
for i in range(len(shape)):
zone_lon, zone_lat = self.get_midpoint(shape[i].shape.points)
dist = self.dist((lon, lat), (zone_lon, zone_lat))
if dist < nearest_dist:
nearest_dist = dist
nearest = shape[i].record[1]
return nearest
class SubzoneShapeExtractor(BasicExtractor):
"""This class opens a subzone shapefile and finds the following attributes
1. longitude
2. latitude
3. area
4. region it belongs to
5. planning area it belongs to
main public method returns list of tuples with attributes
Parameters
----------
subzone : string
file path of subzone shapefile
planning_area : string
file path of URA planning area shapefile
region : string
file path of URA region shapefile
"""
def __init__(self, subzone, planning_area, region):
self.subzone = subzone
self.list_ = self.open_shape(subzone, planning_area, region)
def get_list(self):
return self.list_
def open_shape(self, filename, planning_area_file, region_file): #
# only opens the subzone shapefile with lon-lat data included
sf = shapefile.Reader(filename)
shapeRec = sf.shapeRecords()
extract = []
for i in range(len(shapeRec)):
points = shapeRec[i].shape.points
lon, lat = super(SubzoneShapeExtractor,self).get_midpoint(points)
area = super(SubzoneShapeExtractor, self).get_area(points)
subzone_ID = shapeRec[i].record[1]
print subzone_ID
planning_area = self.__get_planning_area(lon, lat, planning_area_file)
region = self.__get_region(lon, lat, region_file)
print subzone_ID + "done"
extract.append((subzone_ID, float(lon), float(lat), area, planning_area, region))
return extract
def __get_planning_area(self, lon, lat, areafile):
"""This method checks which planning area the subzone/finer subzone belongs to
Parameters
----------
lon : float
longitude of the point to be checked
lat : float
latitude of the point to be checked
areafile : string
file name of area shapefile
Returns
-------
area_name : string
name of planning area which the subzone if in
"""
sf = shapefile.Reader(areafile)
area = sf.shapeRecords()
for i in range(len(area)):
if super(SubzoneShapeExtractor, self).is_in_area(lon, lat, area[i].shape.points):
#print "its in " + area[i].record[1]
return area[i].record[1]
return "None"
def __get_region(self, lon, lat, regionfile):
"""This method checks which region the subzone/finer subzone belongs to
Parameters
----------
lon : float
longitude of the point to be checked
lat : float
latitude of the point to be checked
regionfile : string
file name of region shapefile
Returns
-------
region_name : string
name of region which the subzone if in
"""
sf = shapefile.Reader(regionfile)
region = sf.shapeRecords()
for i in range(len(region)):
if super(SubzoneShapeExtractor, self).is_in_area(lon, lat, region[i].shape.points):
return region[i].record[1]
return "None"
class BreedingHabitatExtractor(BasicExtractor):
"""This class collates a list of breeding habitats in all 5 regions and collates
list of subzone with number of breeding habitat and coordinates.
Parameters
----------
breedinghabitat : string
filepath of breeding habitat shapefile
shape : string
file path of subzone shapefile
"""
def __init__(self, breedinghabitat, shape):
self.bh = breedinghabitat
self.loclist_, self.fulllist_ = self.load_breeding_habitats(breedinghabitat, shape)
def get_loclist(self):
return self.loclist_
def get_fulllist(self):
return self.fulllist_
def load_breeding_habitats(self, habfilelist, subzonefile):
"""This method opens list of breeding habitat file (5 zones) and performs 2 tasks
- get centroid coordinates of each BH
- finds the subzone it belongs to and generates a frequency list
Parameters
----------
habfilelist : list of string
list of file paths for breeding habitats in 5 areas in Singapore
subzonefile : string
file path for subzone shapefile
Returns
--------
extract : list of tuples (string, int)
list of tuples containing subzone names and number of breeding habitats
fulllist : list of tuples (float, float)
list of lon & lat
"""
sub = shapefile.Reader(subzonefile)
subzone = sub.shapeRecords()
extract = []
fulllist = []
for file_ in habfilelist: # number of files
sf = shapefile.Reader(file_)
bh = sf.shapeRecords()
for i in range(len(bh)): # number of bh
parent = ""
points = bh[i].shape.points
lon, lat = super(BreedingHabitatExtractor, self).get_midpoint(points)
for j in range(len(subzone)): #number of subzones (323 or 1877)
if super(BreedingHabitatExtractor, self).is_in_area(lon, lat, subzone[j].shape.points):
parent = subzone[j].record[1]
if not parent:
parent = super(BreedingHabitatExtractor, self).get_nearest(lon, lat, subzone)
extract.append((parent, 1)) # 1 is just a counter. DON'T CHANGE IT
fulllist.append((float(lon), float(lat)))
extract = super(BreedingHabitatExtractor, self).collate(extract)
return extract, fulllist
class DengueCaseExtractor(BasicExtractor):
'''This class takes in a list of case files + shapefile outputs
the subzone, with number of cases
Parameters
----------
cases : list of string
list of shapefile's filepath for dengue cases in 5 subzones
shape : string
subzone shapefile's filepath
'''
def __init__(self, cases, shape):
self.cases = cases
self.list_ = self.load_cases(cases, shape)
def get_list(self):
return self.list_
def load_cases(self, cases, shape):
"""This method extracts the case number for each subzone from shapefiles in cases.
Parameters
----------
cases : list of string
list of shapefile's filepath for dengue cases in 5 subzones
shape : string
subzone shapefile's filepath
Returns
-------
extract : list of tuple (string, int)
tuples contain subzone id and number of cases
"""
casenumber = 0
sub = shapefile.Reader(shape)
subzone = sub.shapeRecords()
extract = []
for file_ in cases: # files
sf = shapefile.Reader(file_)
bh = sf.shapeRecords()
for i in range(len(bh)): # number of bh
parent = ""
points = bh[i].shape.points
lon, lat = super(DengueCaseExtractor, self).get_midpoint(points)
for j in range(len(subzone)): #number of subzones
if super(DengueCaseExtractor, self).is_in_area(lon, lat, subzone[j].shape.points):
parent = subzone[j].record[1]
#in the event that BH fall in gaps between finer resolution subzones
if not parent:
parent = super(DengueCaseExtractor, self).get_nearest(lon, lat, subzone)
case_number = self.__get_case_number(bh[i].record[1])
extract.append((parent, case_number))
extract = super(DengueCaseExtractor, self).collate(extract)
return extract
def __get_case_number(self, string):
# extracts case number from string
# returns number as int
try:
number = re.sub('[^0-9]' ,'', string)
except:
number = string
return int(number)
def load_cluster_data(self, filename, subzonefile): # UNUSED METHOD
'''
Opens ONE cluster shapefile and calculates list of cluster with their case number
@returns list of subzone with # of cases
'''
sf = shapefile.Reader(filename)
shapeRec = sf.shapeRecords()
sub = shapefile.Reader(subzonefile)
subzoneshapes = sub.shapeRecords()
extract = []
for i in range(len(shapeRec)):
parent = ""
points = shapeRec[i].shape.points
cases = shapeRec[i].record[2] #tentative number
lon, lat = super(DengueCaseExtractor, self).get_midpoint(points)
for j in range(len(subzoneshapes)):
if super(DengueCaseExtractor, self).is_in_area(lon, lat, subzone[j].shape.points):
parent = subzoneshapes[j].record[1]
break
#print parent + " " + str(cases)
extract.append((parent, cases))
extract = super(DengueCaseExtractor, self).collate(extract)
return extract
if __name__ == '__main__':
cluster = "DailyData/240516/dengue-clusters/DENGUE_CLUSTER.shp"
bhlist = ['DailyData/230516/breedinghabitat-central-area/BreedingHabitat_Central_Area.shp',\
'DailyData/230516/breedinghabitat-northeast-area/BreedingHabitat_Northeast_Area.shp',\
'DailyData/230516/breedinghabitat-northwest-area/BreedingHabitat_Northwest_Area.shp',\
'DailyData/230516/breedinghabitat-southeast-area/BreedingHabitat_Southeast_Area.shp',\
'DailyData/230516/breedinghabitat-southwest-area/BreedingHabitat_Southwest_Area.shp',]
caselist = ['DailyData/230516/breedinghabitat-central-area/BreedingHabitat_Central_Area.shp',\
'DailyData/230516/breedinghabitat-northeast-area/BreedingHabitat_Northeast_Area.shp',\
'DailyData/230516/breedinghabitat-northwest-area/BreedingHabitat_Northwest_Area.shp',\
'DailyData/230516/breedinghabitat-southeast-area/BreedingHabitat_Southeast_Area.shp',\
'DailyData/230516/breedinghabitat-southwest-area/BreedingHabitat_Southwest_Area.shp']
shape = "shape/subzone.shp"
"""
def dist(A, B):
x = A[0] - B[0]
y = A[1] - B[1]
return math.hypot(x,y)
def get_midpoint(points):
#get max and min of both lat and long
lon = [point[0]for point in points]
lat = [point[1]for point in points]
lon = (max(lon) + min(lon)) / 2
lat = (max(lat) + min(lat)) / 2
if lon > 200:
cv = SVY21()
lat, lon = cv.computeLatLon(lat, lon)
return lon, lat
def get_area(points):
# area of singapore is 719.1
# 1 degree = 111.2km
# 1 degree^2 = 12365.44km2
# implementation of Green's Theorem to calculate area in polygon
deg_to_km_constant = 12365.44
total = 0.0
N = len(points)
for i in range(N):
v1 = points[i]
v2 = points[(i+1) % N]
total += v1[0]*v2[1] - v1[1]*v2[0]
return abs(total/2) * deg_to_km_constant
def open_shape(filename): #
# only opens the subzone shapefile with lon-lat data included
sf = shapefile.Reader(filename)
shapeRec = sf.shapeRecords()
extract = []
for i in range(len(shapeRec)):
points = shapeRec[i].shape.points
lon, lat = get_midpoint(points)
area = get_area(points)
subzone_ID = shapeRec[i].record[1]
extract.append((subzone_ID, float(lon), float(lat), area))
return extract
def open_scrap(filename): # google scraper
# used to extract coordinates by querying address on map api
sf = shapefile.Reader(filename)
shapeRec = sf.shapeRecords()
extract = []
for i in range(len(shapeRec)):
locality = shapeRec[i].record[1]
if '(' in locality:
locality = locality[:locality.index('(')].strip()
elif '/' in locality:
locality = locality[:locality.index('/')].strip()
lon, lat = scrap_coordinate(locality)
case_number = shapeRec[i].record[2]
idnum = shapeRec[i].record[1]
extract.append((idnum, lon, lat, case_number))
return extractex
#####################
# Data.gov shp files#
#####################
def load_breeding_habitats(habfilelist, subzonefile):
'''
Opens list of breeding habitat file (5 zones) and does 2 tasks
- get centroid coordinates of each BH
- finds the subzone it belongs to and makes frequency list
@returns list of subzone + # breeding habitats
'''
sub = shapefile.Reader(subzonefile)
subzone = sub.shapeRecords()
extract = []
for file in habfilelist: # files
sf = shapefile.Reader(file)
bh = sf.shapeRecords()
for i in range(len(bh)): # number of bh
parent = ""
points = bh[i].shape.points
lon, lat = get_midpoint(points)
for j in range(len(subzone)): #number of subzones
if is_in_area(lon, lat, subzone[j].shape.points):
parent = subzone[j].record[1]
extract.append((parent, 1))
return collate_cases(extract)
def load_cluster_data(filename, subzonefile):
'''
Opens ONE cluster shapefile and calculates list of cluster with their case number
@returns list of subzone with # of cases
'''
sf = shapefile.Reader(filename)
sub = shapefile.Reader(subzonefile)
subzoneshapes = sub.shapeRecords()
shapeRec = sf.shapeRecords()
extract = []
for i in range(len(shapeRec)):
parent = ""
points = shapeRec[i].shape.points
cases = shapeRec[i].record[2] #tentative number
lon, lat = get_midpoint(points)
for j in range(len(subzoneshapes)):
if is_in_area(lon, lat, subzoneshapes[j].shape.points):
parent = subzoneshapes[j].record[1]
break
print parent + " " + str(cases)
extract.append((parent, cases))
return collate_cases(extract) #list (use numpy instead?)
'''
target_lon, target_lat = get_midpoint(subzoneshapes[j].shape.points)
distance = dist((lon, lat), (target_lon, target_lat))
if not j:
nearest = 1000
(distance < nearest) and
nearest = distance
'''
def collate_cases(caselist):
newlist = {}
for case, number in caselist:
newlist[case] = 0
for case, number in caselist:
newlist[case] += number
caselist = []
caselist = [(k, v) for k, v in newlist.iteritems()]
return caselist
def is_in_area(lon, lat, points):
#check sum of angles == 360
def dist(A, B):
x = A[0] - B[0]
y = A[1] - B[1]
return math.hypot(x,y)
def get_cos_theta(a, b, c):
dist_c = dist(a, b)
dist_a = dist(a, c)
dist_b = dist(b, c)
return (dist_c**2 - dist_a**2 - dist_b**2) / (-2.0 * dist_a * dist_b)
N = len(points)
C = (lon, lat)
total_angle = 0
for i in range(N):
v1 = points[i]
v2 = points[(i+1) % N]
cos_theta = get_cos_theta(v1, v2, C)
if cos_theta >= 1:
angle = math.acos(1)
else:
angle = math.acos(cos_theta)
total_angle += angle
if total_angle > 6.28: # 6.28319:
return True
else:
return False
def load_cluster_data(filename, subzonefile):
'''
Opens ONE cluster shapefile and calculates list of cluster with their case number
@returns list of subzone with # of cases
'''
sf = shapefile.Reader(filename)
sub = shapefile.Reader(subzonefile)
subzoneshapes = sub.shapeRecords()
shapeRec = sf.shapeRecords()
extract = []
for i in range(len(shapeRec)):
parent = ""
points = shapeRec[i].shape.points
cases = shapeRec[i].record[2] #tentative number
lon, lat = get_midpoint(points)
for j in range(len(subzoneshapes)):
if is_in_area(lon, lat, subzoneshapes[j].shape.points):
parent = subzoneshapes[j].record[1]
break
print parent + " " + str(cases)
extract.append((parent, cases))
return collate_cases(extract) #list (use numpy instead?)
'''
target_lon, target_lat = get_midpoint(subzoneshapes[j].shape.points)
distance = dist((lon, lat), (target_lon, target_lat))
if not j:
nearest = 1000
(distance < nearest) and
nearest = distance
'''
def collate_cases(caselist):
newlist = {}
for case, number in caselist:
newlist[case] = 0
for case, number in caselist:
newlist[case] += number
caselist = []
caselist = [(k, v) for k, v in newlist.iteritems()]
return caselist
def dist(A, B):
x = A[0] - B[0]
y = A[1] - B[1]
return math.hypot(x,y)
def get_midpoint(points):
#get max and min of both lat and long
lon = [point[0]for point in points]
lat = [point[1]for point in points]
lon = (max(lon) + min(lon)) / 2
lat = (max(lat) + min(lat)) / 2
if lon > 200:
cv = SVY21()
lat, lon = cv.computeLatLon(lat, lon)
return lon, lat
def get_area(points):
# area of singapore is 719.1
# 1 degree = 111.2km
# 1 degree^2 = 12365.44km2
# implementation of Green's Theorem to calculate area in polygon
deg_to_km_constant = 12365.44
total = 0.0
N = len(points)
for i in range(N):
v1 = points[i]
v2 = points[(i+1) % N]
total += v1[0]*v2[1] - v1[1]*v2[0]
return abs(total/2) * deg_to_km_constant
def is_in_area(lon, lat, points):
#check sum of angles == 360
def dist(A, B):
x = A[0] - B[0]
y = A[1] - B[1]
return math.hypot(x,y)
def get_cos_theta(a, b, c):
dist_c = dist(a, b)
dist_a = dist(a, c)
dist_b = dist(b, c)
return (dist_c**2 - dist_a**2 - dist_b**2) / (-2.0 * dist_a * dist_b)
N = len(points)
C = (lon, lat)
total_angle = 0
for i in range(N):
v1 = points[i]
v2 = points[(i+1) % N]
cos_theta = get_cos_theta(v1, v2, C)
if cos_theta >= 1:
angle = math.acos(1)
else:
angle = math.acos(cos_theta)
total_angle += angle
if total_angle > 6.28: # 6.28319:
return True
else:
return False
"""