def create_raster_worldfile(self, path, xy_range=None):
from globalmaptiles import GlobalMercator
x_y = xy_range or self.xy_range
im = Image.open(path)
gw_path = ''.join(os.path.split(path)[-1].split('.')[:-1])
world_file_path = os.path.join(
os.path.curdir, os.path.join(self.output_dir, "%s.jgw" % gw_path))
with open(world_file_path, 'w') as world:
min_y, min_x = num2deg(x_y['xMin'], x_y['yMax'] + 1, self.zoom)
max_y, max_x = num2deg(x_y['xMax'] + 1, x_y['yMin'], self.zoom)
gm = GlobalMercator()
min_x, min_y = gm.LatLonToMeters(min_y, min_x)
max_x, max_y = gm.LatLonToMeters(max_y, max_x)
x_pixel_size = (max_x - min_x) / im.size[0]
y_pixel_size = (max_y - min_y) / im.size[1]
world.write(b"%f\n" % x_pixel_size
) # pixel size in the x-direction in map units/pixel
world.write(b"%f\n" % 0) # rotation about y-axis
world.write(b"%f\n" % 0) # rotation about x-axis
world.write(
b"%f\n" % -(abs(y_pixel_size))
) # pixel size in the y-direction in map units. Always negative
world.write(
b"%f\n" %
min_x) # x-coordinate of the center of the upper left pixel
world.write(
b"%f\n" %
max_y) # y-coordinate of the center of the upper left pixel
def __init__(self, renderer, cache_dir):
super(CustomMapLayer, self).__init__(renderer)
self.cache_dir = cache_dir
self.mercator = GlobalMercator()
self.tileloader = None
if self.tiles is not None:
map_envelope = self.m.envelope()
# map_envelope is in mercator projection, convert it to
# long/lat projection
envelope = renderer.merc_to_lnglat(map_envelope)
min_lon = envelope.minx
min_lat = envelope.miny
max_lon = envelope.maxx
max_lat = envelope.maxy
width = self.m.width
indexing = self.tiles.get('indexing')
max_zoom = self.tiles.get('maxZoom')
if indexing == 'google':
self.tileloader = GoogleTileLoader(min_lat, min_lon, max_lat,
max_lon, width, max_zoom)
elif indexing == 'tms':
self.tileloader = TMSTileLoader(min_lat, min_lon, max_lat,
max_lon, width, max_zoom)
elif indexing == 'f':
self.tileloader = FTileLoader(min_lat, min_lon, max_lat,
max_lon, width, max_zoom)
def __init__(self,
bearing=0.0,
zoomlevel=16,
lat=decimal.Decimal('32.018300'),
lon=decimal.Decimal('34.898161'),
parent=None):
#set initial values
self.parent = parent
self.bearingSensitivity = decimal.Decimal('0.00001')
self.bearing = bearing
self.zoomlevel = zoomlevel
self.lat = lat
self.lon = lon
self.gx, self.gy = None, None
self.velocity = 0.0
self.sysPath = os.path.join(sys.path[0], "")
self.mapPath = self.sysPath
self.maxZoomLevel = 16
self.destlat = decimal.Decimal('32.776250')
self.destlon = decimal.Decimal('35.028946')
self.distance = 0
self.setBounds(parent.geometry().width(), parent.geometry().height())
self.halfboundx = math.ceil(self.boundx / 2)
self.halfboundy = math.ceil(self.boundy / 2)
#make GlobalMercator instance
self.mercator = GlobalMercator()
# create pathways
self.refresh()
def main(tiles_path, db_file, groups, zoom_levels):
merc = GlobalMercator()
# Set-up the output db
conn = sqlite3.connect(db_file)
c = conn.cursor()
for zoom in [zoom_levels]: #TODO zoom levels
results_set = c.execute("select x, y, quadkey, group_type from people_by_group order by quadkey asc, rand asc" )
use_ellipse, radius_rel, gamma, os_scale = STYLE[zoom]
radius = os_scale*radius_rel/4/2
quadkey = None
img = None
for i,r in enumerate(results_set):
if (i % 1000 == 0):
print i
x = float(r[0])
y = float(r[1])
next_quadkey = r[2][:zoom]
group = r[3]
if next_quadkey != quadkey:
#finish last tile
if img:
save_tile(img, tiles_path, zoom, gtx, gty)
quadkey = next_quadkey
tx, ty = merc.MetersToTile(x, y, zoom)
gtx, gty = merc.GoogleTile(tx,ty,zoom)
img = Image.new("RGB", (TILE_X*os_scale, TILE_Y*os_scale), "white")
draw = ImageDraw.Draw(img)
minx, miny, maxx, maxy = (c/A for c in merc.TileBounds(tx, ty, zoom))
xscale = (TILE_X*os_scale)/(maxx - minx)
yscale = (TILE_Y*os_scale)/(maxy - miny)
#print 'minx', minx, 'miny', miny, 'maxx', maxx, 'maxy', maxy
#print 'xscale',xscale,'yscale',yscale
#print 'x',x,'y',y,'tx',tx,'ty',ty
# Translate coordinates to tile-relative, google ready coordinates
rx = (x/A - minx)*xscale
ry = (maxy - y/A)*yscale
fill=ImageColor.getrgb(groups[group]['color'])
if use_ellipse:
draw.ellipse((rx-radius,ry-radius,rx+radius,ry+radius), fill=fill)
else:
draw.point((rx, ry), fill=fill)
#print "Draw at ", (rx-radius,ry-radius,rx+radius,ry+radius), ImageColor.getrgb(groups[group]['color'])
save_tile(img, tiles_path, zoom, gtx, gty)
save_defined_tiles(tiles_path)
def __init__(self, min_lat, min_lon, max_lat, max_lon, width, max_zoom=18):
self.tiles = []
self.min_lat = min_lat
self.min_lon = min_lon
self.max_lat = max_lat
self.max_lon = max_lon
self.mercator = GlobalMercator()
self.downloader = Downloader()
# count how many horizontal tiles we need
self.x_tiles_needed = math.ceil(width / self.TILE_WIDTH)
self.max_zoom = max_zoom
def __init__(self, client):
self.proj = GlobalMercator()
self.nodeRecords = []
self.wayRecords = []
self.relationRecords = []
self.record = {}
self.nodeLocations = {}
self.client = client
self.stats = {'nodes': 0, 'ways': 0, 'relations': 0}
self.lastStatString = ""
self.statsCount = 0
def process_vectors_in_dir(self, rootdir):
self.gm = GlobalMercator()
num_images = self.count_rasters_in_dir(rootdir) * pow(
self.tile_size / self.thumb_size, 2)
print("num_images is {} in {}".format(num_images, rootdir))
labels = None
if self.train_vector_tiles_dir == rootdir:
self.train_labels = numpy.zeros(num_images * 2,
dtype=numpy.float32)
self.train_labels = self.train_labels.reshape(num_images, 2)
labels = self.train_labels
else:
self.test_labels = numpy.zeros(num_images * 2, dtype=numpy.float32)
self.test_labels = self.test_labels.reshape(num_images, 2)
labels = self.test_labels
index = 0
for folder, subs, files in os.walk(rootdir):
for filename in files:
if not filename.endswith('.json'):
continue
has_ways = False
with open(os.path.join(folder, filename), 'r') as src:
linestrings = self.linestrings_for_vector_tile(src)
tile_matrix = self.empty_tile_matrix()
tile = self.tile_for_folder_and_filename(
folder, filename, rootdir)
for linestring in linestrings:
# check if tile has any linestrings to set it's one-hot
tile_matrix = self.add_linestring_to_matrix(
linestring, tile, tile_matrix)
# self.print_matrix(tile_matrix)
# print '\n\n\n'
# Now set the one_hot value for this label
for y in range(int(self.tile_size / self.thumb_size)):
for x in range(int(self.tile_size / self.thumb_size)):
for tmy in range(self.thumb_size):
for tmx in range(self.thumb_size):
if tile_matrix[tmx][tmy] == 1:
has_ways = True
if has_ways:
labels[index][0] = 1
else:
labels[index][1] = 1
index += 1
def __init__(self, mapdir, minzoom, maxzoom):
self.mercator = GlobalMercator(256)
self.minzoom = minzoom
self.maxzoom = maxzoom
self.TopRightLat = None
self.TopRightLon = None
self.BottomLeftLat = None
self.BottomLeftLon = None
self.mminx = None
self.mminy = None
self.mmaxx = None
self.mmaxy = None
self.mapdir = mapdir
self.jobs = Queue.Queue()
def GetGridID(Coord):
lat=Coord[0]/1000
lon=Coord[1]/1000
tz=8
mercator = GlobalMercator()
mx, my = mercator.LatLonToMeters( Coord[0]/1000.0, Coord[1]/1000.0 )
tx, ty = mercator.MetersToTile( mx, my, tz )
gx, gy = mercator.GoogleTile(tx, ty, tz)
#print "\tGoogle:", gx, gy
#print tx, ty
return ("%03d" % gx)+("%03d" % gy)
def main():
merc = GlobalMercator()
file = open('pts1990.csv', 'rb')
reader = csv.DictReader(file, delimiter=',')
print "x,y,quad,category"
for row in reader:
lat = float(row['lat'])
long = float(row['long'])
x, y = merc.LatLonToMeters(lat, long)
tx, ty = merc.MetersToTile(x, y, 21)
# Create a unique quadkey for each point object
quadkey = merc.QuadTree(tx, ty, 21)
# Create categorical variable for the race category
# Export data to the database file
print "{},{},{},{}".format(x, y, quadkey, row['group'])
def ImageryRequest(tileStr):
tile = tileRequest(tileStr)
z = tile.zoom
x = tile.tx
y = tile.ty
downloadedTileList = os.listdir('DownloadedTiles/')
tileFileName = str(z)+'.'+str(y)+'.'+str(x)+'.png'
print(x,y,z)
tilesize = 256
tx = tile.tx
ty =tile.ty
zoom = tile.zoom
px = tx*tilesize
py = ty*tilesize
gm = GlobalMercator()
mx1,my1 = gm.PixelsToMeters(px, py, zoom)
mx2,my2 = gm.PixelsToMeters(px+tilesize, py+tilesize, zoom)
print(mx1,-my2,mx2,-my1)
os.system('rm Subset.TIF')
os.system('gdalwarp -q -t_srs epsg:3857 -te '+str(mx1)+' '+str(-my2)+' '+str(mx2)+' '+str(-my1)+' -r Lanczos -ts 256 256 Warped.TIF Subset.TIF')
#Open the image
tileImage = Image.open('Subset.TIF')
#Turn the image into a string
buffer_image = StringIO()
tileImage.save(buffer_image, 'png')
buffer_image.seek(0)
#Send the string
return(send_file(buffer_image, mimetype='image/png'))
parser.add_option(
'-f',
'--format',
action='store',
dest='format',
default='',
help='tile image format',
)
(options, args) = parser.parse_args()
#parse the bounds
boundsarr = options.bounds.split(';')
lonarr = sorted([float(boundsarr[0]), float(boundsarr[2])])
latarr = sorted([float(boundsarr[1]), float(boundsarr[3])])
z = int(options.zoom)
gm = GlobalMercator()
#Convert bounds to meters
mx0, my0 = gm.LatLonToMeters(latarr[0], lonarr[0])
mx1, my1 = gm.LatLonToMeters(latarr[1], lonarr[1])
#get TMS tile address range
tx0, ty0 = gm.MetersToTile(mx0, my0, z)
tx1, ty1 = gm.MetersToTile(mx1, my1, z)
#sort the tile addresses low to high
xarr = sorted([tx0, tx1])
yarr = sorted([ty0, ty1])
#figure out relevant extensions
extension = "." + options.format #getExtension(options.template)
wf = getWorldFileExtension(extension)
#create the destination location using the z value
root = options.destination + '/' + str(z)
try:
def pdfer(data, page_size=PAGE_SIZES['letter'], output='pdf'):
shape_overlays = data.get('shape_overlays')
point_overlays = data.get('point_overlays')
grid = {'zoom': data.get('zoom')}
center_lon, center_lat = data['center']
center_tile_x, center_tile_y = tileXY(float(center_lat), float(center_lon),
int(data['zoom']))
dim_across, dim_up = data['dimensions']
if dim_across > dim_up:
page_height, page_width, tiles_up, tiles_across = page_size
else:
page_width, page_height, tiles_across, tiles_up = page_size
min_tile_x = center_tile_x - (tiles_across / 2)
min_tile_y = center_tile_y - (tiles_up / 2)
max_tile_x = min_tile_x + tiles_across
max_tile_y = min_tile_y + tiles_up
# Get base layer tiles
base_pattern = 'http://d.tile.stamen.com/toner/{z}/{x}/{y}.png'
if data.get('base_tiles'):
base_pattern = data['base_tiles']
base_links = generateLinks(base_pattern, grid['zoom'], min_tile_x,
min_tile_y, max_tile_x, max_tile_y)
base_names = dl_write_all(base_links, 'base')
# Get overlay tiles
overlay_pattern = None
if data.get('overlay_tiles'):
overlay_pattern = data['overlay_tiles']
overlay_links = generateLinks(overlay_pattern, grid['zoom'],
min_tile_x, min_tile_y, max_tile_x,
max_tile_y)
overlay_names = dl_write_all(overlay_links, 'overlay')
now = datetime.now()
date_string = datetime.strftime(now, '%Y-%m-%d_%H-%M-%S')
outp_name = os.path.join('/tmp', '{0}.png'.format(date_string))
base_image_names = ['-'.join(l.split('/')[-3:]) for l in base_names]
base_image_names = sorted([i.split('-')[-3:] for i in base_image_names],
key=itemgetter(1))
for parts in base_image_names:
z, x, y = parts
y = y.rstrip('.png').rstrip('.jpg')
z = z.rsplit('_', 1)[1]
key = '-'.join([z, x, y])
grid[key] = {'bbox': tileEdges(float(x), float(y), int(z))}
keys = sorted(grid.keys())
mercator = GlobalMercator()
bb_poly = None
bmin_rx = None
bmin_ry = None
if shape_overlays or point_overlays:
polys = []
for k, v in grid.items():
try:
one, two, three, four = grid[k]['bbox']
polys.append(box(two, one, four, three))
except TypeError:
pass
mpoly = MultiPolygon(polys)
bb_poly = box(*mpoly.bounds)
min_key = keys[0]
max_key = keys[-2]
bminx, bminy = grid[min_key]['bbox'][0], grid[min_key]['bbox'][1]
bmaxx, bmaxy = grid[max_key]['bbox'][2], grid[max_key]['bbox'][3]
bmin_mx, bmin_my = mercator.LatLonToMeters(bminx, bminy)
bmax_mx, bmax_my = mercator.LatLonToMeters(bmaxx, bmaxy)
bmin_px, bmin_py = mercator.MetersToPixels(bmin_mx, bmin_my,
float(grid['zoom']))
bmax_px, bmax_py = mercator.MetersToPixels(bmax_mx, bmax_my,
float(grid['zoom']))
bmin_rx, bmin_ry = mercator.PixelsToRaster(bmin_px, bmin_py,
int(grid['zoom']))
if shape_overlays:
all_polys = []
for shape_overlay in shape_overlays:
shape_overlay = json.loads(shape_overlay)
if shape_overlay.get('geometry'):
shape_overlay = shape_overlay['geometry']
coords = shape_overlay['coordinates'][0]
all_polys.append(Polygon(coords))
mpoly = MultiPolygon(all_polys)
one, two, three, four, five = list(
box(*mpoly.bounds).exterior.coords)
left, right = LineString([one, two]), LineString([three, four])
top, bottom = LineString([two, three]), LineString([four, five])
left_to_right = left.distance(right)
top_to_bottom = top.distance(bottom)
if left_to_right > top_to_bottom:
page_height, page_width, _, _ = page_size
else:
page_width, page_height, _, _ = page_size
center_lon, center_lat = list(mpoly.centroid.coords)[0]
if point_overlays:
all_points = []
for point_overlay in point_overlays:
point_overlay = json.loads(point_overlay)
for p in point_overlay['points']:
if p[0] and p[1]:
all_points.append(p)
mpoint = MultiPoint(all_points)
center_lon, center_lat = list(mpoint.centroid.coords)[0]
one, two, three, four, five = list(
box(*mpoint.bounds).exterior.coords)
left, right = LineString([one, two]), LineString([three, four])
top, bottom = LineString([two, three]), LineString([four, five])
left_to_right = left.distance(right)
top_to_bottom = top.distance(bottom)
if left_to_right > top_to_bottom:
page_height, page_width, _, _ = page_size
else:
page_width, page_height, _, _ = page_size
center_lon, center_lat = list(mpoint.centroid.coords)[0]
print(center_lon, center_lat)
arrays = []
for k, g in groupby(base_image_names, key=itemgetter(1)):
images = list(g)
fnames = ['/tmp/%s' % ('-'.join(f)) for f in images]
array = []
for img in fnames:
i = cv2.imread(img, -1)
if isinstance(i, type(None)):
i = np.zeros((256, 256, 4), np.uint8)
elif i.shape[2] != 4:
i = cv2.cvtColor(cv2.imread(img), cv2.COLOR_BGR2BGRA)
array.append(i)
arrays.append(np.vstack(array))
outp = np.hstack(arrays)
cv2.imwrite(outp_name, outp)
if overlay_pattern:
overlay_outp_name = os.path.join('/tmp',
'overlay_{0}.png'.format(date_string))
overlay_image_names = [
'-'.join(l.split('/')[-3:]) for l in overlay_names
]
overlay_image_names = sorted(
[i.split('-')[-3:] for i in overlay_image_names],
key=itemgetter(1))
arrays = []
for k, g in groupby(overlay_image_names, key=itemgetter(1)):
images = list(g)
fnames = ['/tmp/%s' % ('-'.join(f)) for f in images]
array = []
for img in fnames:
i = cv2.imread(img, -1)
if isinstance(i, type(None)):
i = np.zeros((256, 256, 4), np.uint8)
elif i.shape[2] != 4:
i = cv2.cvtColor(cv2.imread(img), cv2.COLOR_BGR2BGRA)
array.append(i)
arrays.append(np.vstack(array))
nuked = [os.remove(f) for f in fnames]
outp = np.hstack(arrays)
cv2.imwrite(overlay_outp_name, outp)
base = cv2.imread(outp_name, -1)
overlay = cv2.imread(overlay_outp_name, -1)
overlay_g = cv2.cvtColor(overlay, cv2.COLOR_BGR2GRAY)
ret, mask = cv2.threshold(overlay_g, 10, 255, cv2.THRESH_BINARY)
inverted = cv2.bitwise_not(mask)
overlay = cv2.bitwise_not(overlay, overlay, mask=inverted)
base_alpha = 0.55
overlay_alpha = 1
for channel in range(3):
x, y, d = overlay.shape
base[:,:,channel] = (base[:,:,channel] * base_alpha + \
overlay[:,:,channel] * overlay_alpha * \
(1 - base_alpha)) / \
(base_alpha + overlay_alpha * (1 - base_alpha))
cv2.imwrite(outp_name, base)
###########################################################################
# Code below here is for drawing vector layers within the PDF #
# Leaving it in just because it was a pain to come up with the first time #
###########################################################################
if shape_overlays or point_overlays:
im = cairo.ImageSurface.create_from_png(outp_name)
ctx = cairo.Context(im)
if shape_overlays:
for shape_overlay in shape_overlays:
shape_overlay = json.loads(shape_overlay)
if shape_overlay.get('geometry'):
shape_overlay = shape_overlay['geometry']
color = hex_to_rgb('#f06eaa')
coords = shape_overlay['coordinates'][0]
x, y = get_pixel_coords(coords[0], grid['zoom'], bmin_rx,
bmin_ry)
ctx.move_to(x, y)
ctx.set_line_width(4.0)
red, green, blue = [float(c) for c in color]
ctx.set_source_rgba(red / 255, green / 255, blue / 255, 0.3)
for p in coords[1:]:
x, y = get_pixel_coords(p, grid['zoom'], bmin_rx, bmin_ry)
ctx.line_to(x, y)
ctx.close_path()
ctx.fill()
ctx.set_source_rgba(red / 255, green / 255, blue / 255, 0.5)
for p in coords[1:]:
x, y = get_pixel_coords(p, grid['zoom'], bmin_rx, bmin_ry)
ctx.line_to(x, y)
ctx.close_path()
ctx.stroke()
ctx.set_line_width(2.0)
if point_overlays:
for point_overlay in point_overlays:
point_overlay = json.loads(point_overlay)
color = hex_to_rgb(point_overlay['color'])
for p in point_overlay['points']:
if p[0] and p[1]:
pt = Point((float(p[0]), float(p[1])))
if bb_poly.contains(pt):
nx, ny = get_pixel_coords(p, grid['zoom'], bmin_rx,
bmin_ry)
red, green, blue = [float(c) for c in color]
ctx.set_source_rgba(red / 255, green / 255,
blue / 255, 0.6)
ctx.arc(
nx, ny, 5.0, 0,
50) # args: center-x, center-y, radius, ?, ?
ctx.fill()
ctx.arc(nx, ny, 5.0, 0, 50)
ctx.stroke()
im.write_to_png(outp_name)
scale = 1
# Crop image from center
center_point_x, center_point_y = latlon2xy(float(center_lat),
float(center_lon),
float(data['zoom']))
offset_x = (center_point_x - float(center_tile_x)) + 50
offset_y = (center_point_y - float(center_tile_y)) - 50
outp_image = cv2.imread(outp_name, -1)
pixels_up, pixels_across, channels = outp_image.shape
center_x, center_y = (pixels_across / 2) + offset_x, (pixels_up /
2) + offset_y
start_y, end_y = center_y - (page_height / 2), center_y + (page_height / 2)
start_x, end_x = center_x - (page_width / 2), center_x + (page_width / 2)
cv2.imwrite(outp_name, outp_image[start_y:end_y, start_x:end_x])
if output == 'pdf':
outp_file_name = outp_name.rstrip('.png') + '.pdf'
pdf = cairo.PDFSurface(outp_file_name, page_width, page_height)
ctx = cairo.Context(pdf)
image = cairo.ImageSurface.create_from_png(outp_name)
ctx.set_source_surface(image)
ctx.paint()
pdf.finish()
elif output == 'jpeg':
outp_file_name = outp_name.rstrip('.png') + '.jpg'
jpeg = cv2.cvtColor(cv2.imread(outp_name, -1), cv2.COLOR_RGBA2RGB)
cv2.imwrite(outp_file_name, jpeg)
return outp_file_name
def main(shapes_file_list, db_file, groups):
field_ids = {}
# Create a GlobalMercator object for later conversions
merc = GlobalMercator()
# Set-up the output db
conn = sqlite3.connect(db_file)
c = conn.cursor()
#c.execute("drop table if exists people_by_group")
c.execute(
"create table if not exists people_by_group (x real, y real, quadkey text, rand real, group_type text)"
)
c.execute("drop index if exists i_quadkey")
# Open the shapefiles
for input_filename in shapes_file_list:
print "Processing file {0}".format(input_filename)
ds = ogr.Open(input_filename)
if ds is None:
print "Open failed.\n"
sys.exit(1)
# Obtain the first (and only) layer in the shapefile
lyr = ds.GetLayerByIndex(0)
lyr.ResetReading()
# Obtain the field definitions in the shapefile layer
feat_defn = lyr.GetLayerDefn()
field_defns = [
feat_defn.GetFieldDefn(i) for i in range(feat_defn.GetFieldCount())
]
# Set up a coordinate transformation to latlon
wgs84 = osr.SpatialReference()
wgs84.SetWellKnownGeogCS("WGS84")
sr = lyr.GetSpatialRef()
xformer = osr.CoordinateTransformation(sr, wgs84)
# Obtain the index of the group fields
for i, defn in enumerate(field_defns):
if defn.GetName() in groups:
field_ids[defn.GetName()] = i
# Obtain the number of features (Census Blocks) in the layer
n_features = len(lyr)
# Iterate through every feature (Census Block Ploygon) in the layer,
# obtain the population counts, and create a point for each person within
# that feature.
start_time = time.time()
for j, feat in enumerate(lyr):
# Print a progress read-out for every 1000 features and export to hard disk
if j % 1000 == 0:
conn.commit()
perc_complete = (j + 1) / float(n_features)
time_left = (1 - perc_complete) * (
(time.time() - start_time) / perc_complete)
print "%s/%s (%0.2f%%) est. time remaining %0.2f mins" % (
j + 1, n_features, 100 * perc_complete, time_left / 60)
# Obtain total population, racial counts, and state fips code of the individual census block
counts = {}
for f in field_ids:
val = feat.GetField(field_ids[f])
if val:
counts[f] = int(val)
else:
counts[f] = 0
# Obtain the OGR polygon object from the feature
geom = feat.GetGeometryRef()
if geom is None:
continue
# Convert the OGR Polygon into a Shapely Polygon
poly = loads(geom.ExportToWkb())
if poly is None:
continue
# Obtain the "boundary box" of extreme points of the polygon
bbox = poly.bounds
if not bbox:
continue
leftmost, bottommost, rightmost, topmost = bbox
# Generate a point object within the census block for every person by race
for f in field_ids:
for i in range(counts[f]):
# Choose a random longitude and latitude within the boundary box
# and within the orginial ploygon of the census block
while True:
samplepoint = Point(uniform(leftmost, rightmost),
uniform(bottommost, topmost))
if samplepoint is None:
break
if poly.contains(samplepoint):
break
# Convert the longitude and latitude coordinates to meters and
# a tile reference
try:
# In general we don't know the coordinate system of input data
# so transform it to latlon
lon, lat, z = xformer.TransformPoint(
samplepoint.x, samplepoint.y)
x, y = merc.LatLonToMeters(lat, lon)
except:
print "Failed to convert ", lat, lon
sys.exit(-1)
tx, ty = merc.MetersToTile(x, y, 21)
# Create a unique quadkey for each point object
quadkey = merc.QuadTree(tx, ty, 21)
# Create categorical variable for the race category
group_type = f
# Export data to the database file
try:
c.execute(
"insert into people_by_group values (?,?,?,random(),?)",
(x, y, quadkey, group_type))
except:
print "Failed to insert ", x, y, tx, ty, group_type
sys.exit(-1)
c.execute(
"create index if not exists i_quadkey on people_by_group(x, y, quadkey, rand, group_type)"
)
conn.commit()
if google_image_folder is None or output_jpeg_file is None or map_type is None or format is None or tz is None or lon is None or lat is None or radius is None or bottom_crop is None or KEY is None or image_size is None or scale is None or resume is None or debug is None or tif_output is None:
print("invalid parameter exists!")
exit()
actual_tile_size = image_size * scale
debug_print("actual tile size %d" % actual_tile_size)
if not resume:
if os.path.exists(google_image_folder):
shutil.rmtree(google_image_folder)
if os.path.exists(output_jpeg_file):
os.unlink(output_jpeg_file)
if not os.path.exists(google_image_folder):
os.makedirs(google_image_folder)
mercator = GlobalMercator()
cx, cy = mercator.LatLonToMeters(lat, lon)
minx = cx - radius
maxx = cx + radius
miny = cy - radius
maxy = cy + radius
debug_print('minx = %f, miny = %f, maxx = %f, maxy = %f\n' %
(minx, miny, maxx, maxy))
tminx, tminy = mercator.MetersToTile(minx, miny, tz)
tmaxx, tmaxy = mercator.MetersToTile(maxx, maxy, tz)
total_tiles = (tmaxx - tminx + 1) * (tmaxy - tminy + 1)
debug_print('count = %d' % total_tiles)
# progress bar
def __init__(self):
self.client = Connection()
self.proj = GlobalMercator()
def main(input_filename, wac_filename, output_filename):
wac = pd.io.parsers.read_csv(wac_filename)
wac.set_index(wac['w_geocode'],inplace = True)
#Create columns for four megasectors
wac['makers'] = wac['CNS01']+wac['CNS02']+wac['CNS03']+wac['CNS04']+wac['CNS05']+wac['CNS06']+wac['CNS08']
wac['services'] = wac['CNS07']+wac['CNS14'] + wac['CNS17'] + wac['CNS18']
wac['professions'] = wac['CNS09'] + wac['CNS10'] + wac['CNS11'] + wac['CNS12'] + wac['CNS13']
wac['support'] = wac['CNS15'] + wac['CNS16'] + wac['CNS19'] + wac['CNS20']
assert sum(wac['C000'] -(wac['makers']+wac['services']+wac['professions']+wac['support'])) == 0 or rw[1]['abbrev'] == 'ny'
#In NY there's one block in Brooklyn with 177000 jobs. It appears to be rounding entries > 100k, which is making the assertion fail.
#This is the Brooklyn Post Office + Brooklyn Law School + Borough Hall. So maybe weirdness around post office?
#Set up outfile as csv
outf = open(output_filename,'w')
outf.write('x,y,sect,inctype,quadkey\n')
# Create a GlobalMercator object for later conversions
merc = GlobalMercator()
# Open the shapefile
ds = ogr.Open(input_filename)
if ds is None:
print "Open failed.\n"
sys.exit( 1 )
# Obtain the first (and only) layer in the shapefile
lyr = ds.GetLayerByIndex(0)
lyr.ResetReading()
# Obtain the field definitions in the shapefile layer
feat_defn = lyr.GetLayerDefn()
field_defns = [feat_defn.GetFieldDefn(i) for i in range(feat_defn.GetFieldCount())]
# Obtain the index of the field for the count for whites, blacks, Asians,
# Others, and Hispanics.
for i, defn in enumerate(field_defns):
print defn.GetName()
#GEOID is what we want to merge on
if defn.GetName() == "GEOID10":
fips = i
# Set-up the output file
#conn = sqlite3.connect( output_filename )
#c = conn.cursor()
#c.execute( "create table if not exists people_by_race (statefips text, x text, y text, quadkey text, race_type text)" )
# Obtain the number of features (Census Blocks) in the layer
n_features = len(lyr)
# Iterate through every feature (Census Block Ploygon) in the layer,
# obtain the population counts, and create a point for each person within
# that feature.
for j, feat in enumerate( lyr ):
# Print a progress read-out for every 1000 features and export to hard disk
if j % 1000 == 0:
#conn.commit()
print "%s/%s (%0.2f%%)"%(j+1,n_features,100*((j+1)/float(n_features)))
# Obtain total population, racial counts, and state fips code of the individual census block
blkfips = int(feat.GetField(fips))
try:
jobs = {'m':wac.loc[blkfips,'makers'],'s':wac.loc[blkfips,'services'],'p':wac.loc[blkfips,'professions'],'t':wac.loc[blkfips,'support']}
except KeyError:
#print "no"
# missing.append(blkfips) #Missing just means no jobs there. Lots of blocks have this.
continue
income = {'l':wac.loc[blkfips,'CE01'],'m':wac.loc[blkfips,'CE02'],'h':wac.loc[blkfips,'CE03']}
# Obtain the OGR polygon object from the feature
geom = feat.GetGeometryRef()
if geom is None:
continue
# Convert the OGR Polygon into a Shapely Polygon
poly = loads(geom.ExportToWkb())
if poly is None:
continue
# Obtain the "boundary box" of extreme points of the polygon
bbox = poly.bounds
if not bbox:
continue
leftmost,bottommost,rightmost,topmost = bbox
# Generate a point object within the census block for every person by race
inccnt = 0
incord = ['l','m','h']
shuffle(incord)
for sect in ['m','s','p','t']:
for i in range(int(jobs[sect])):
# Choose a random longitude and latitude within the boundary box
# and within the orginial ploygon of the census block
while True:
samplepoint = Point(uniform(leftmost, rightmost),uniform(bottommost, topmost))
if samplepoint is None:
break
if poly.contains(samplepoint):
break
x, y = merc.LatLonToMeters(samplepoint.y,samplepoint.x)
tx,ty = merc.MetersToTile(x, y, 21)
#Determine the right income
inccnt += 1
inctype = ''
assert inccnt <= income[incord[0]] + income[incord[1]] + income[incord[2]] or rw[1]['abbrev'] == 'ny'
if inccnt <= income[incord[0]]:
inctype = incord[0]
elif inccnt <= income[incord[0]] + income[incord[1]]:
inctype = incord[1]
elif inccnt <= income[incord[0]] + income[incord[1]] + income[incord[2]]:
inctype = incord[2]
# Create a unique quadkey for each point object
quadkey = merc.QuadTree(tx, ty, 21)
outf.write("%s,%s,%s,%s,%s\n" %(x,y,sect,inctype,quadkey))
# Convert the longitude and latitude coordinates to meters and
# a tile reference
outf.close()
def __init__(self):
self.client = Connection(host="mongomaster")
self.proj = GlobalMercator()
def getTile(self, zoomlevel):
mercator = GlobalMercator()
mx, my = mercator.LatLonToMeters(self.lat, self.lon)
tminx, tminy = mercator.MetersToTile(mx, my, zoomlevel)
gx, gy = mercator.GoogleTile(tminx, tminy, zoomlevel) #+1?
return gx, gy, zoomlevel
def main(input_filename, output_filename):
# Create a GlobalMercator object for later conversions
merc = GlobalMercator()
# Open the shapefile
ds = ogr.Open(input_filename)
if ds is None:
print "Open failed.\n"
sys.exit(1)
# Obtain the first (and only) layer in the shapefile
lyr = ds.GetLayerByIndex(0)
lyr.ResetReading()
# Obtain the field definitions in the shapefile layer
feat_defn = lyr.GetLayerDefn()
field_defns = [
feat_defn.GetFieldDefn(i) for i in range(feat_defn.GetFieldCount())
]
# Obtain the index of the field for the count for whites, blacks, Asians,
# Others, and Hispanics.
for i, defn in enumerate(field_defns):
if defn.GetName() == "POP10":
pop_field = i
if defn.GetName() == "nh_white_n":
white_field = i
if defn.GetName() == "nh_black_n":
black_field = i
if defn.GetName() == "nh_asian_n":
asian_field = i
if defn.GetName() == "hispanic_n":
hispanic_field = i
if defn.GetName() == "NH_Other_n":
other_field = i
if defn.GetName() == "STATEFP10":
statefips_field = i
# Set-up the output file
conn = sqlite3.connect(output_filename)
c = conn.cursor()
c.execute(
"create table if not exists people_by_race (statefips text, x text, y text, quadkey text, race_type text)"
)
# Obtain the number of features (Census Blocks) in the layer
n_features = len(lyr)
# Iterate through every feature (Census Block Ploygon) in the layer,
# obtain the population counts, and create a point for each person within
# that feature.
for j, feat in enumerate(lyr):
# Print a progress read-out for every 1000 features and export to hard disk
if j % 1000 == 0:
conn.commit()
print "%s/%s (%0.2f%%)" % (j + 1, n_features, 100 *
((j + 1) / float(n_features)))
# Obtain total population, racial counts, and state fips code of the individual census block
pop = int(feat.GetField(pop_field))
white = int(feat.GetField(white_field))
black = int(feat.GetField(black_field))
asian = int(feat.GetField(asian_field))
hispanic = int(feat.GetField(hispanic_field))
other = int(feat.GetField(other_field))
statefips = feat.GetField(statefips_field)
# Obtain the OGR polygon object from the feature
geom = feat.GetGeometryRef()
if geom is None:
continue
# Convert the OGR Polygon into a Shapely Polygon
poly = loads(geom.ExportToWkb())
if poly is None:
continue
# Obtain the "boundary box" of extreme points of the polygon
bbox = poly.bounds
if not bbox:
continue
leftmost, bottommost, rightmost, topmost = bbox
# Generate a point object within the census block for every person by race
for i in range(white):
# Choose a random longitude and latitude within the boundary box
# and within the orginial ploygon of the census block
while True:
samplepoint = Point(uniform(leftmost, rightmost),
uniform(bottommost, topmost))
if samplepoint is None:
break
if poly.contains(samplepoint):
break
# Convert the longitude and latitude coordinates to meters and
# a tile reference
x, y = merc.LatLonToMeters(samplepoint.y, samplepoint.x)
tx, ty = merc.MetersToTile(x, y, 21)
# Create a unique quadkey for each point object
quadkey = merc.QuadTree(tx, ty, 21)
# Create categorical variable for the race category
race_type = 'w'
# Export data to the database file
c.execute("insert into people_by_race values (?,?,?,?,?)",
(statefips, x, y, quadkey, race_type))
for i in range(black):
# Choose a random longitude and latitude within the boundary box
# points and within the orginial ploygon of the census block
while True:
samplepoint = Point(uniform(leftmost, rightmost),
uniform(bottommost, topmost))
if samplepoint is None:
break
if poly.contains(samplepoint):
break
# Convert the longitude and latitude coordinates to meters and
# a tile reference
x, y = merc.LatLonToMeters(samplepoint.y, samplepoint.x)
tx, ty = merc.MetersToTile(x, y, 21)
# Create a unique quadkey for each point object
quadkey = merc.QuadTree(tx, ty, 21)
# Create categorical variable for the race category
race_type = 'b'
# Export data to the database file
c.execute("insert into people_by_race values (?,?,?,?,?)",
(statefips, x, y, quadkey, race_type))
for i in range(asian):
# Choose a random longitude and latitude within the boundary box
# points and within the orginial ploygon of the census block
while True:
samplepoint = Point(uniform(leftmost, rightmost),
uniform(bottommost, topmost))
if samplepoint is None:
break
if poly.contains(samplepoint):
break
# Convert the longitude and latitude coordinates to meters and
# a tile reference
x, y = merc.LatLonToMeters(samplepoint.y, samplepoint.x)
tx, ty = merc.MetersToTile(x, y, 21)
# Create a unique quadkey for each point object
quadkey = merc.QuadTree(tx, ty, 21)
# Create categorical variable for the race category
race_type = 'a'
# Export data to the database file
c.execute("insert into people_by_race values (?,?,?,?,?)",
(statefips, x, y, quadkey, race_type))
for i in range(hispanic):
# Choose a random longitude and latitude within the boundary box
# points and within the orginial ploygon of the census block
while True:
samplepoint = Point(uniform(leftmost, rightmost),
uniform(bottommost, topmost))
if samplepoint is None:
break
if poly.contains(samplepoint):
break
# Convert the longitude and latitude coordinates to meters and
# a tile reference
x, y = merc.LatLonToMeters(samplepoint.y, samplepoint.x)
tx, ty = merc.MetersToTile(x, y, 21)
# Create a unique quadkey for each point object
quadkey = merc.QuadTree(tx, ty, 21)
# Create categorical variable for the race category
race_type = 'h'
# Export data to the database file
c.execute("insert into people_by_race values (?,?,?,?,?)",
(statefips, x, y, quadkey, race_type))
for i in range(other):
# Choose a random longitude and latitude within the boundary box
# points and within the orginial ploygon of the census block
while True:
samplepoint = Point(uniform(leftmost, rightmost),
uniform(bottommost, topmost))
if samplepoint is None:
break
if poly.contains(samplepoint):
break
# Convert the longitude and latitude coordinates to meters and
# a tile reference
x, y = merc.LatLonToMeters(samplepoint.y, samplepoint.x)
tx, ty = merc.MetersToTile(x, y, 21)
# Create a unique quadkey for each point object
quadkey = merc.QuadTree(tx, ty, 21)
# Create categorical variable for the race category
race_type = 'o'
# Export data to the database file
c.execute("insert into people_by_race values (?,?,?,?,?)",
(statefips, x, y, quadkey, race_type))
conn.commit()
def main(input_filename, output_filename):
print "Processing: %s - Ctrl-Z to cancel"%input_filename
merc = GlobalMercator()
# open the shapefile
ds = ogr.Open( input_filename )
if ds is None:
print "Open failed.\n"
sys.exit( 1 )
lyr = ds.GetLayerByIndex( 0 )
lyr.ResetReading()
feat_defn = lyr.GetLayerDefn()
field_defns = [feat_defn.GetFieldDefn(i) for i in range(feat_defn.GetFieldCount())]
# look up the index of the field we're interested in
for i, defn in enumerate( field_defns ):
if defn.GetName()=="POP10":
pop_field = i
# set up the output file
# if it already exists, ask for confirmation to delete and remake it
if os.path.isfile(output_filename):
if not confirm(" Database %s exists, overwrite?"%output_filename, False):
return False
else:
os.system("rm %s"%output_filename)
# if file removal failed, the file may be locked:
# ask for confirmation to unlock it
if os.path.isfile(output_filename):
if not confirm(" Attempt to unlock database %s?"%output_filename, False):
return False
else:
unlock(output_filename)
# if it's still there, there's a problem, bail
if os.path.isfile(output_filename):
print "Trouble - exiting."
sys.exit()
else:
print "Success - continuing:"
conn = sqlite3.connect( output_filename )
c = conn.cursor()
c.execute( "create table if not exists people (x real, y real, quadkey text)" )
n_features = len(lyr)
for j, feat in enumerate( lyr ):
if j%1000==0:
conn.commit()
if j%10000==0:
print " %s/%s (%0.2f%%)"%(j+1,n_features,100*((j+1)/float(n_features)))
else:
sys.stdout.write(".")
sys.stdout.flush()
pop = feat.GetField(pop_field)
geom = feat.GetGeometryRef()
if geom is None:
continue
bbox = get_bbox( geom )
if not bbox:
continue
ll,bb,rr,tt = bbox
# generate a sample within the geometry for every person
for i in range(pop):
while True:
samplepoint = make_ogr_point( uniform(ll,rr), uniform(bb,tt) )
if geom.Intersects( samplepoint ):
break
x, y = merc.LatLonToMeters( samplepoint.GetY(), samplepoint.GetX() )
tx,ty = merc.MetersToTile( x, y, 21)
quadkey = merc.QuadTree( tx, ty, 21 )
c.execute( "insert into people values (?,?,?)", (x, y, quadkey) )
conn.commit()
print "Finished processing %s"%output_filename
def main():
# Code to open an image, and apply a transform
# open the image
# image = Image.open("Ally.jpg")
# w = image.width
# h = image.height
# print((w, h))
# Create a transformation to apply to the image
# shift = Shift(-w/2, -h/2)
# rotate = Rotation(math.pi/2)
# scale = Scale(2)
# shift2 = Shift(h/2, w/2)
# combined = PositionTransform()
# combined = combined.combine(shift)
# combined = combined.combine(rotate)
# combined = combined.combine(scale)
# combined = combined.combine(shift2)
# inverse the transformation (to apply it)
# t = combined.inverse()
# Image.transform(size, method, data=None, resample=0, fill=1, fillcolor=None)
# img2 = image.transform((h, w), Image.AFFINE, _get_image_transform(t))
# img2.save("Test.jpg")
# Code to create a mosaic 4x4 tile world map at level 2
# tm = TileMosaic(OSMTileRequester(), 2, 0, 3, 0, 3)
# tm.save("world2.png")
# Sample coordinates to avoid caring about the transformation just now
bng_coords = [(300000, 600000), (300000, 601000), (301000, 601000),
(301000, 600000)]
gwm_coords = [(-398075.709110655, 7417169.44503078),
(-398115.346383602, 7418925.37709793),
(-396363.034574031, 7418964.91393662),
(-396323.792660911, 7417208.95976453)]
bng_x = [bng[0] for bng in bng_coords]
bng_y = [bng[1] for bng in bng_coords]
bng_box = (min(bng_x), min(bng_y), max(bng_x), max(bng_y))
gwm_x = [gwm[0] for gwm in gwm_coords]
gwm_y = [gwm[1] for gwm in gwm_coords]
gwm_box = (min(gwm_x), min(gwm_y), max(gwm_x), max(gwm_y))
# If the coords above relate to a 400x400 map, calculate the resolution
bng_map_size = 600
bng_res = (bng_box[2] - bng_box[0]) / bng_map_size
print(bng_res)
# Use the GlobalMercator class to calculate the optimal zoom level to use
gwm = GlobalMercator()
gwm_zoom = gwm.ZoomForPixelSize(bng_res)
print(gwm_zoom)
# Calculate the min/max tile x and y for the given area at the calculates zoom level
tiles_x = []
tiles_y = []
for coord in gwm_coords:
tx, ty = gwm.MetersToTile(coord[0], coord[1], gwm_zoom)
tiles_x.append(tx)
tiles_y.append(ty)
print(f"{gwm_zoom} {tx} {ty}")
# print(OSMTileRequester().request_tile(gwm_zoom, tx, ty))
# Create a mosaic image from these tiles
start_x = min(tiles_x)
end_x = max(tiles_x)
start_y = min(tiles_y)
end_y = max(tiles_y)
gwm_mosaic = TileMosaic(OSMTileRequester(), gwm_zoom, start_x, end_x,
start_y, end_y)
gwm_mosaic.save("mosaic.png")
# Get the bbox for these tiles
gwm_mosaic_box_tl = gwm.TileBounds(start_x, start_y, gwm_zoom)
gwm_mosaic_box_tr = gwm.TileBounds(end_x, start_y, gwm_zoom)
gwm_mosaic_box_bl = gwm.TileBounds(start_x, end_y, gwm_zoom)
gwm_mosaic_box_br = gwm.TileBounds(end_x, end_y, gwm_zoom)
gwm_mosaic_box = (min(gwm_mosaic_box_tl[0], gwm_mosaic_box_bl[0]),
min(gwm_mosaic_box_bl[3], gwm_mosaic_box_br[3]),
max(gwm_mosaic_box_tr[2], gwm_mosaic_box_br[2]),
max(gwm_mosaic_box_tl[1], gwm_mosaic_box_tr[1]))
print(gwm_mosaic_box)
test = [(0, 0), (400, 0), (400, 400), (0, 400)]
# Create a transformation to convert pixels of the target BNG image to the GWM mosaic image
bng_img_to_gwm_image = PositionTransform()
# Translate/scale image px to BNG
bng_img_to_gwm_image = bng_img_to_gwm_image.combine(HorizontalFlip())
bng_img_to_gwm_image = bng_img_to_gwm_image.combine(Scale(bng_res))
bng_img_to_gwm_image = bng_img_to_gwm_image.combine(
Shift(bng_box[0], bng_box[3]))
test_coords(test, bng_img_to_gwm_image)
# Transform BNG to GWM coords
bng_gwm_transform = SamplePointTransform(bng_coords[0], bng_coords[1],
bng_coords[2], gwm_coords[0],
gwm_coords[1], gwm_coords[2])
bng_img_to_gwm_image = bng_img_to_gwm_image.combine(bng_gwm_transform)
test_coords(test, bng_img_to_gwm_image)
# Translate/scale GWM coords to GWM mosaic image coords
bng_img_to_gwm_image = bng_img_to_gwm_image.combine(
Shift(-gwm_mosaic_box[0], -gwm_mosaic_box[3]))
bng_img_to_gwm_image = bng_img_to_gwm_image.combine(
Scale(1 / gwm.Resolution(gwm_zoom)))
bng_img_to_gwm_image = bng_img_to_gwm_image.combine(HorizontalFlip())
test_coords(test, bng_img_to_gwm_image)
bng_result = gwm_mosaic.image.transform(
(bng_map_size, bng_map_size), Image.AFFINE,
_get_image_transform(bng_img_to_gwm_image))
bng_result.save("BNG.jpg")