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https://github.com/markqvist/Sideband.git
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450 lines
13 KiB
Python
450 lines
13 KiB
Python
# coding=utf-8
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"""
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Layer that support point clustering
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===================================
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"""
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from math import atan, exp, floor, log, pi, sin, sqrt
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from os.path import dirname, join
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from kivy.lang import Builder
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from kivy.metrics import dp
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from kivy.properties import (
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ListProperty,
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NumericProperty,
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ObjectProperty,
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StringProperty,
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)
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from mapview.view import MapLayer, MapMarker
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Builder.load_string(
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"""
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<ClusterMapMarker>:
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size_hint: None, None
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source: root.source
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size: list(map(dp, self.texture_size))
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allow_stretch: True
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Label:
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color: root.text_color
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pos: root.pos
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size: root.size
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text: "{}".format(root.num_points)
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font_size: dp(18)
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"""
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)
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# longitude/latitude to spherical mercator in [0..1] range
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def lngX(lng):
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return lng / 360.0 + 0.5
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def latY(lat):
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if lat == 90:
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return 0
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if lat == -90:
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return 1
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s = sin(lat * pi / 180.0)
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y = 0.5 - 0.25 * log((1 + s) / (1 - s)) / pi
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return min(1, max(0, y))
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# spherical mercator to longitude/latitude
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def xLng(x):
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return (x - 0.5) * 360
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def yLat(y):
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y2 = (180 - y * 360) * pi / 180
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return 360 * atan(exp(y2)) / pi - 90
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class KDBush:
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"""
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kdbush implementation from:
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https://github.com/mourner/kdbush/blob/master/src/kdbush.js
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"""
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def __init__(self, points, node_size=64):
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self.points = points
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self.node_size = node_size
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self.ids = ids = [0] * len(points)
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self.coords = coords = [0] * len(points) * 2
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for i, point in enumerate(points):
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ids[i] = i
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coords[2 * i] = point.x
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coords[2 * i + 1] = point.y
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self._sort(ids, coords, node_size, 0, len(ids) - 1, 0)
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def range(self, min_x, min_y, max_x, max_y):
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return self._range(
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self.ids, self.coords, min_x, min_y, max_x, max_y, self.node_size
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)
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def within(self, x, y, r):
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return self._within(self.ids, self.coords, x, y, r, self.node_size)
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def _sort(self, ids, coords, node_size, left, right, depth):
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if right - left <= node_size:
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return
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m = int(floor((left + right) / 2.0))
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self._select(ids, coords, m, left, right, depth % 2)
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self._sort(ids, coords, node_size, left, m - 1, depth + 1)
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self._sort(ids, coords, node_size, m + 1, right, depth + 1)
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def _select(self, ids, coords, k, left, right, inc):
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swap_item = self._swap_item
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while right > left:
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if (right - left) > 600:
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n = float(right - left + 1)
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m = k - left + 1
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z = log(n)
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s = 0.5 + exp(2 * z / 3.0)
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sd = 0.5 * sqrt(z * s * (n - s) / n) * (-1 if (m - n / 2.0) < 0 else 1)
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new_left = max(left, int(floor(k - m * s / n + sd)))
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new_right = min(right, int(floor(k + (n - m) * s / n + sd)))
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self._select(ids, coords, k, new_left, new_right, inc)
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t = coords[2 * k + inc]
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i = left
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j = right
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swap_item(ids, coords, left, k)
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if coords[2 * right + inc] > t:
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swap_item(ids, coords, left, right)
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while i < j:
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swap_item(ids, coords, i, j)
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i += 1
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j -= 1
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while coords[2 * i + inc] < t:
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i += 1
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while coords[2 * j + inc] > t:
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j -= 1
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if coords[2 * left + inc] == t:
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swap_item(ids, coords, left, j)
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else:
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j += 1
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swap_item(ids, coords, j, right)
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if j <= k:
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left = j + 1
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if k <= j:
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right = j - 1
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def _swap_item(self, ids, coords, i, j):
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swap = self._swap
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swap(ids, i, j)
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swap(coords, 2 * i, 2 * j)
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swap(coords, 2 * i + 1, 2 * j + 1)
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def _swap(self, arr, i, j):
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tmp = arr[i]
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arr[i] = arr[j]
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arr[j] = tmp
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def _range(self, ids, coords, min_x, min_y, max_x, max_y, node_size):
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stack = [0, len(ids) - 1, 0]
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result = []
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x = y = 0
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while stack:
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axis = stack.pop()
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right = stack.pop()
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left = stack.pop()
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if right - left <= node_size:
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for i in range(left, right + 1):
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x = coords[2 * i]
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y = coords[2 * i + 1]
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if x >= min_x and x <= max_x and y >= min_y and y <= max_y:
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result.append(ids[i])
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continue
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m = int(floor((left + right) / 2.0))
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x = coords[2 * m]
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y = coords[2 * m + 1]
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if x >= min_x and x <= max_x and y >= min_y and y <= max_y:
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result.append(ids[m])
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nextAxis = (axis + 1) % 2
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if min_x <= x if axis == 0 else min_y <= y:
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stack.append(left)
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stack.append(m - 1)
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stack.append(nextAxis)
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if max_x >= x if axis == 0 else max_y >= y:
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stack.append(m + 1)
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stack.append(right)
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stack.append(nextAxis)
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return result
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def _within(self, ids, coords, qx, qy, r, node_size):
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sq_dist = self._sq_dist
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stack = [0, len(ids) - 1, 0]
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result = []
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r2 = r * r
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while stack:
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axis = stack.pop()
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right = stack.pop()
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left = stack.pop()
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if right - left <= node_size:
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for i in range(left, right + 1):
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if sq_dist(coords[2 * i], coords[2 * i + 1], qx, qy) <= r2:
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result.append(ids[i])
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continue
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m = int(floor((left + right) / 2.0))
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x = coords[2 * m]
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y = coords[2 * m + 1]
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if sq_dist(x, y, qx, qy) <= r2:
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result.append(ids[m])
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nextAxis = (axis + 1) % 2
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if (qx - r <= x) if axis == 0 else (qy - r <= y):
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stack.append(left)
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stack.append(m - 1)
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stack.append(nextAxis)
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if (qx + r >= x) if axis == 0 else (qy + r >= y):
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stack.append(m + 1)
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stack.append(right)
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stack.append(nextAxis)
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return result
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def _sq_dist(self, ax, ay, bx, by):
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dx = ax - bx
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dy = ay - by
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return dx * dx + dy * dy
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class Cluster:
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def __init__(self, x, y, num_points, id, props):
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self.x = x
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self.y = y
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self.num_points = num_points
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self.zoom = float("inf")
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self.id = id
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self.props = props
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self.parent_id = None
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self.widget = None
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# preprocess lon/lat
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self.lon = xLng(x)
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self.lat = yLat(y)
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class Marker:
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def __init__(self, lon, lat, cls=MapMarker, options=None):
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self.lon = lon
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self.lat = lat
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self.cls = cls
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self.options = options
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# preprocess x/y from lon/lat
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self.x = lngX(lon)
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self.y = latY(lat)
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# cluster information
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self.id = None
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self.zoom = float("inf")
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self.parent_id = None
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self.widget = None
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def __repr__(self):
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return "<Marker lon={} lat={} source={}>".format(
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self.lon, self.lat, self.source
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)
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class SuperCluster:
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"""Port of supercluster from mapbox in pure python
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"""
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def __init__(self, min_zoom=0, max_zoom=16, radius=40, extent=512, node_size=64):
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self.min_zoom = min_zoom
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self.max_zoom = max_zoom
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self.radius = radius
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self.extent = extent
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self.node_size = node_size
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def load(self, points):
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"""Load an array of markers.
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Once loaded, the index is immutable.
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"""
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from time import time
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self.trees = {}
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self.points = points
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for index, point in enumerate(points):
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point.id = index
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clusters = points
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for z in range(self.max_zoom, self.min_zoom - 1, -1):
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start = time()
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print("build tree", z)
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self.trees[z + 1] = KDBush(clusters, self.node_size)
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print("kdbush", (time() - start) * 1000)
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start = time()
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clusters = self._cluster(clusters, z)
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print(len(clusters))
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print("clustering", (time() - start) * 1000)
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self.trees[self.min_zoom] = KDBush(clusters, self.node_size)
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def get_clusters(self, bbox, zoom):
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"""For the given bbox [westLng, southLat, eastLng, northLat], and
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integer zoom, returns an array of clusters and markers
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"""
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tree = self.trees[self._limit_zoom(zoom)]
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ids = tree.range(lngX(bbox[0]), latY(bbox[3]), lngX(bbox[2]), latY(bbox[1]))
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clusters = []
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for i in range(len(ids)):
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c = tree.points[ids[i]]
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if isinstance(c, Cluster):
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clusters.append(c)
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else:
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clusters.append(self.points[c.id])
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return clusters
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def _limit_zoom(self, z):
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return max(self.min_zoom, min(self.max_zoom + 1, z))
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def _cluster(self, points, zoom):
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clusters = []
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c_append = clusters.append
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trees = self.trees
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r = self.radius / float(self.extent * pow(2, zoom))
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# loop through each point
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for i in range(len(points)):
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p = points[i]
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# if we've already visited the point at this zoom level, skip it
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if p.zoom <= zoom:
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continue
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p.zoom = zoom
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# find all nearby points
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tree = trees[zoom + 1]
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neighbor_ids = tree.within(p.x, p.y, r)
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num_points = 1
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if isinstance(p, Cluster):
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num_points = p.num_points
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wx = p.x * num_points
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wy = p.y * num_points
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props = None
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for j in range(len(neighbor_ids)):
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b = tree.points[neighbor_ids[j]]
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# filter out neighbors that are too far or already processed
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if zoom < b.zoom:
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num_points2 = 1
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if isinstance(b, Cluster):
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num_points2 = b.num_points
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# save the zoom (so it doesn't get processed twice)
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b.zoom = zoom
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# accumulate coordinates for calculating weighted center
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wx += b.x * num_points2
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wy += b.y * num_points2
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num_points += num_points2
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b.parent_id = i
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if num_points == 1:
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c_append(p)
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else:
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p.parent_id = i
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c_append(
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Cluster(wx / num_points, wy / num_points, num_points, i, props)
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)
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return clusters
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class ClusterMapMarker(MapMarker):
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source = StringProperty(join(dirname(__file__), "icons", "cluster.png"))
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cluster = ObjectProperty()
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num_points = NumericProperty()
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text_color = ListProperty([0.1, 0.1, 0.1, 1])
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def on_cluster(self, instance, cluster):
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self.num_points = cluster.num_points
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def on_touch_down(self, touch):
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return False
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class ClusteredMarkerLayer(MapLayer):
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cluster_cls = ObjectProperty(ClusterMapMarker)
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cluster_min_zoom = NumericProperty(0)
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cluster_max_zoom = NumericProperty(16)
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cluster_radius = NumericProperty("40dp")
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cluster_extent = NumericProperty(512)
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cluster_node_size = NumericProperty(64)
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def __init__(self, **kwargs):
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self.cluster = None
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self.cluster_markers = []
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super().__init__(**kwargs)
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def add_marker(self, lon, lat, cls=MapMarker, options=None):
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if options is None:
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options = {}
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marker = Marker(lon, lat, cls, options)
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self.cluster_markers.append(marker)
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return marker
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def remove_marker(self, marker):
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self.cluster_markers.remove(marker)
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def reposition(self):
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if self.cluster is None:
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self.build_cluster()
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margin = dp(48)
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mapview = self.parent
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set_marker_position = self.set_marker_position
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bbox = mapview.get_bbox(margin)
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bbox = (bbox[1], bbox[0], bbox[3], bbox[2])
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self.clear_widgets()
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for point in self.cluster.get_clusters(bbox, mapview.zoom):
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widget = point.widget
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if widget is None:
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widget = self.create_widget_for(point)
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set_marker_position(mapview, widget)
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self.add_widget(widget)
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def build_cluster(self):
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self.cluster = SuperCluster(
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min_zoom=self.cluster_min_zoom,
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max_zoom=self.cluster_max_zoom,
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radius=self.cluster_radius,
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extent=self.cluster_extent,
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node_size=self.cluster_node_size,
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)
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self.cluster.load(self.cluster_markers)
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def create_widget_for(self, point):
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if isinstance(point, Marker):
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point.widget = point.cls(lon=point.lon, lat=point.lat, **point.options)
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elif isinstance(point, Cluster):
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point.widget = self.cluster_cls(lon=point.lon, lat=point.lat, cluster=point)
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return point.widget
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def set_marker_position(self, mapview, marker):
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x, y = mapview.get_window_xy_from(marker.lat, marker.lon, mapview.zoom)
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marker.x = int(x - marker.width * marker.anchor_x)
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marker.y = int(y - marker.height * marker.anchor_y)
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