class Map:
def __init__(self, definition_file, points):
# Open definition file and background image
definition = ElementTree.parse(open(definition_file)).getroot()
map_file = os.path.join(os.path.split(definition_file)[0], definition.attrib['bg'])
self.background = Image.open(map_file)
self.north = math.radians(float(definition.attrib['maxlat']))
self.south = math.radians(float(definition.attrib['minlat']))
self.west = math.radians(float(definition.attrib['minlon']))
self.east = math.radians(float(definition.attrib['maxlon']))
# Store ranges color and distance
self.ranges = [(float(r.attrib['distance']),
(int(r.attrib['red']), int(r.attrib['green']), int(r.attrib['blue'])))
for r in definition.getiterator("range")]
self.width, self.height = self.background.size
self.points = points
# Creating top quadtree element
self.grid = Quadtree(self.north, self.south, self.west, self.east, 6)
for point in points:
self.grid.add(point)
# Precompute latitude and longitude at each row and column
self.pixel_lons = [self.west + (self.east-self.west)*(float(i)/float(self.width)) for i in range(self.width)]
self.pixel_lats = [self.north - (self.north-self.south)*(float(j)/float(self.height)) for j in range(self.height)]
def generate(self):
"Generates the output image"
ranges_image = Image.new('RGB', self.background.size)
# Create a lists list with the distance from each pixel to its nearest point
colors = [[self.color(self.distance(i,j)) for i in range(self.width)] for j in range(self.height)]
# Flatten list
colors = [item for sublist in colors for item in sublist]
ranges_image.putdata(colors)
return Image.blend(ranges_image, self.background.convert('RGB'), 0.5)
def distance(self, i, j):
lon = self.pixel_lons[i]
lat = self.pixel_lats[j]
point = Point(lat, lon)
elements = PriorityQueue()
elements.put_nowait((self.grid.distance(point), self.grid))
# We iterate over the priority queue until the nearest element is a point. While it isn't we add its children to the queue.
while True:
(distance, elem) = elements.get_nowait()
#print "Iterating (%d, %d) distance: %f" % (i, j, distance)
if isinstance(elem, Point):
return distance
else:
for child in elem.children:
elements.put_nowait((child.distance(point), child))
def color(self, distance):
"Returns which color represents distance"
return [c for (d,c) in self.ranges if d>distance][0]
class Map:
def __init__(self, definition_file, points):
# Open definition file and background image
definition = ElementTree.parse(open(definition_file)).getroot()
map_file = os.path.join(
os.path.split(definition_file)[0], definition.attrib['bg'])
self.background = Image.open(map_file)
self.north = math.radians(float(definition.attrib['maxlat']))
self.south = math.radians(float(definition.attrib['minlat']))
self.west = math.radians(float(definition.attrib['minlon']))
self.east = math.radians(float(definition.attrib['maxlon']))
# Store ranges color and distance
self.ranges = [(float(r.attrib['distance']), (int(r.attrib['red']),
int(r.attrib['green']),
int(r.attrib['blue'])))
for r in definition.getiterator("range")]
self.width, self.height = self.background.size
self.points = points
# Creating top quadtree element
self.grid = Quadtree(self.north, self.south, self.west, self.east, 6)
for point in points:
self.grid.add(point)
# Precompute latitude and longitude at each row and column
self.pixel_lons = [
self.west + (self.east - self.west) *
(float(i) / float(self.width)) for i in range(self.width)
]
self.pixel_lats = [
self.north - (self.north - self.south) *
(float(j) / float(self.height)) for j in range(self.height)
]
def generate(self):
"Generates the output image"
ranges_image = Image.new('RGB', self.background.size)
# Create a lists list with the distance from each pixel to its nearest point
colors = [[self.color(self.distance(i, j)) for i in range(self.width)]
for j in range(self.height)]
# Flatten list
colors = [item for sublist in colors for item in sublist]
ranges_image.putdata(colors)
return Image.blend(ranges_image, self.background.convert('RGB'), 0.5)
def distance(self, i, j):
lon = self.pixel_lons[i]
lat = self.pixel_lats[j]
point = Point(lat, lon)
elements = PriorityQueue()
elements.put_nowait((self.grid.distance(point), self.grid))
# We iterate over the priority queue until the nearest element is a point. While it isn't we add its children to the queue.
while True:
(distance, elem) = elements.get_nowait()
#print "Iterating (%d, %d) distance: %f" % (i, j, distance)
if isinstance(elem, Point):
return distance
else:
for child in elem.children:
elements.put_nowait((child.distance(point), child))
def color(self, distance):
"Returns which color represents distance"
return [c for (d, c) in self.ranges if d > distance][0]
