def main():
terrain = cimgt.MapboxTiles(
'pk.eyJ1IjoiZWVzYWhlIiwiYSI6ImNqdGFjMTI1ODA1ZGc0NHRmN28wcG5ybnEifQ.ZBSMcdbWkjfjRgDVQLjfnw',
'satellite')
mycrs = terrain.crs
go = ccrs.Mercator.GOOGLE
proj = ccrs.epsg(3857)
ax = plt.axes(projection=ccrs.PlateCarree())
deltax = 0.1
deltay = 0.05
ax.set_extent([
24.8359483 - deltax, 24.835948 + deltax, 60.187860 - deltay,
60.187860 + deltay
],
crs=go)
#fig_terrain, ax_terrain = plt.subplots(figsize=(10,10), subplot_kw=dict(projection=cartopy.crs.Mercator(), facecolor='#000000'))
#ax_terrain.set_extent([60.160964,60.180603,24.818373,24.852362], crs=ccrs.Geodetic())
#ax_terrain.add_image(terrain, 13)
#plt.savefig("m.png", transparent=True, bbox_inches='tight', pad_inches=0, frameon=None)
ax.add_image(terrain, 11)
#ax.coastlines('10m')
plt.show()
def test_mapbox_tiles():
token = 'foo'
map_id = 'bar'
tile = [0, 1, 2]
exp_url = 'https://api.tiles.mapbox.com/v4/bar/2/0/1.png?access_token=foo'
mapbox_sample = cimgt.MapboxTiles(token, map_id)
url_str = mapbox_sample._image_url(tile)
assert_equal(url_str, exp_url)
def test_mapbox_tiles_api_url():
token = 'foo'
map_name = 'bar'
tile = [0, 1, 2]
exp_url = ('https://api.mapbox.com/v4/mapbox.bar'
'/2/0/1.png?access_token=foo')
mapbox_sample = cimgt.MapboxTiles(token, map_name)
url_str = mapbox_sample._image_url(tile)
assert url_str == exp_url
def maparea(area_name, lat=None, lon=None):
clean_name = str(re.sub(r'[^a-zA-Z\d]', '', area_name))
nominatim = Nominatim()
if area_name.isdigit():
areaId = int(area_name)
else:
areaId = nominatim.query(area_name).areaId()
overpass = Overpass()
query = overpassQueryBuilder(area=areaId,
elementType='way',
selector='"landuse"="residential"',
out='geom')
residential_areas = overpass.query(query).ways() or []
query = overpassQueryBuilder(area=areaId,
elementType='way',
selector='"highway"="footway"',
out='geom')
roads_footway = overpass.query(query).ways() or []
query = overpassQueryBuilder(
area=areaId,
elementType='way',
selector=['"highway"="service"', '"service"!="parking_aisle"'],
out='geom')
roads_cycleway = overpass.query(query).ways() or []
query = overpassQueryBuilder(area=areaId,
elementType='way',
selector='"highway"="residential"',
out='geom')
roads_residential = overpass.query(query).ways() or []
query = overpassQueryBuilder(area=areaId,
elementType='way',
selector='"highway"="tertiary"',
out='geom')
roads_tertiary = overpass.query(query).ways() or []
query = overpassQueryBuilder(area=areaId,
elementType='way',
selector='"highway"="secondary"',
out='geom')
roads_secondary = overpass.query(query).ways() or []
query = overpassQueryBuilder(area=areaId,
elementType='way',
selector='"highway"="primary"',
out='geom')
roads_primary = overpass.query(query).ways() or []
query = overpassQueryBuilder(area=areaId,
elementType='way',
selector='"highway"="motorway"',
out='geom')
roads_motorway = overpass.query(query).ways() or []
query = overpassQueryBuilder(area=areaId,
elementType='way',
selector='"highway"="trunk"',
out='geom')
roads_trunk = overpass.query(query).ways() or []
query = overpassQueryBuilder(area=areaId,
elementType='way',
selector='"highway"="trunk_link"',
out='geom')
roads_trunk_link = overpass.query(query).ways() or []
query = overpassQueryBuilder(area=areaId,
elementType='way',
selector='"highway"="unclassified"',
out='geom')
roads_unclassified = overpass.query(query).ways() or []
#print (result.countElements())
#ax = plt.axes(projection=ccrs.PlateCarree(globe=ccrs.Globe(datum='WGS84',
# ellipse='WGS84')))
#print(bbx)
request = cimgt.OSM()
terrain = cimgt.MapboxTiles(
'pk.eyJ1IjoiZWVzYWhlIiwiYSI6ImNqdGFjMTI1ODA1ZGc0NHRmN28wcG5ybnEifQ.ZBSMcdbWkjfjRgDVQLjfnw',
'terrain-rgb')
plt.style.use('dark_background')
fig, ax = plt.subplots(num=1,
figsize=(10, 10),
subplot_kw=dict(projection=request.crs,
facecolor='#000000'))
#print("trying to save debug terrain img")
#plt.savefig("terrain.png")
#print("saved")
#ax.set_extent([26.6561, 22.6589, 59.611, 60.8409])
#ax.add_image(request, 10, zorder=0)
roads = roads_residential + roads_tertiary + roads_secondary + roads_primary + roads_unclassified + roads_trunk + roads_trunk_link
def roads_in_bbox(bbox):
ret = []
for road in roads:
if road._json['bounds']['minlat'] < bbox['maxlat'] and road._json[
'bounds']['maxlat'] > bbox['minlat'] and road._json[
'bounds']['minlon'] < bbox['maxlon'] and road._json[
'bounds']['maxlon'] > bbox['minlon']:
ret.append(road)
return ret
# find areas completely enclosed inside a bounding box (not partially)
def residential_areas_enclosed_in_bbox(bbox):
ret = []
for area in residential_areas:
if area._json['bounds']['maxlat'] < bbox['maxlat'] and area._json[
'bounds']['minlat'] > bbox['minlat'] and area._json[
'bounds']['maxlon'] < bbox['maxlon'] and area._json[
'bounds']['minlon'] > bbox['minlon']:
ret.append(area)
return ret
cll = []
bbx = None
'''
processed = 0
roads = roads_residential + roads_tertiary + roads_secondary + roads_primary + roads_unclassified + roads_trunk + roads_trunk_link
print("processing total " + str(len(roads)) + " roads")
for area in (roads_residential + roads_tertiary + roads_secondary + roads_primary + roads_unclassified + roads_trunk + roads_trunk_link):
if(processed % 500 == 0):
print("processing number " + str(processed))
geometry = area._json['geometry']
lons = []
lats = []
for point in geometry:
lons.append(point['lon'])
lats.append(point['lat'])
#mycoords.append( (point['lat'], ) )
xs = np.array(lons)
ys = np.array(lats)
xynps=ax.projection.transform_points(ccrs.Geodetic(), xs, ys)
#print(xynps)
#break
ax.plot(xynps[:,0], xynps[:,1], "r", zorder=11, color='green', marker='o',linewidth=0.2, markersize=0, antialiased=False)
processed+=1
'''
print("Found " + str(len(residential_areas)) + " residential areas in " +
area_name)
# What are we exactly doing here??
'''
for area in residential_areas:
#print(vars(area))
geometry = area._json['geometry']
bbx = area._json['bounds']
lons = []
lats = []
for point in geometry:
lons.append(point['lon'])
lats.append(point['lat'])
#mycoords.append( (point['lat'], ) )
xs = np.array(lons)
ys = np.array(lats)
xynps=ax.projection.transform_points(ccrs.Geodetic(), xs, ys)
#print(xynps)
#break
plt.figure(1)
ax.fill(xynps[:,0], xynps[:,1], "b", zorder=10, antialiased=False)
cll.append( (lons,lats) )
#print(len(cll))
#ax.axis('off')
'''
counter = 0
for area in residential_areas:
#if(lat is not None):
# bbx['minlon'] = lon
# bbx['maxlon'] = lon
# bbx['minlat'] = lat
# bbx['maxlat'] = lat
# width = 0
# height = 0
#else:
bbx = area._json['bounds']
width = bbx['maxlon'] - bbx['minlon']
height = bbx['maxlat'] - bbx['minlat']
if (width < 0.0008 or height < 0.0008):
print("area too small, skipping")
continue
#print(width)
#print(height)
zoom = 0.7
conv_factor = (2.0 * math.pi) / 360.0
lat = bbx['minlat']
lat = lat * conv_factor
m1 = 111132.92
# latitude calculation term 1
m2 = -559.82
# latitude calculation term 2
m3 = 1.175
# latitude calculation term 3
m4 = -0.0023
# latitude calculation term 4
p1 = 111412.84
# longitude calculation term 1
p2 = -93.5
# longitude calculation term 2
p3 = 0.118
# longitude calculation term 3
# Calculate the length of a degree of latitude and longitude in meters
latlen = m1 + (m2 * math.cos(2 * lat)) + (m3 * math.cos(4 * lat)) + \
(m4 * math.cos(6 * lat))
longlen = (p1 * math.cos(lat)) + (p2 * math.cos(3 * lat)) + \
(p3 * math.cos(5 * lat))
#print("lat len " + str(latlen))
#print("lon len " + str(longlen))
targetWidth = 2500
targetHeight = 2500
currentWidth = longlen * width
currentHeight = latlen * height
offset_w_meters = (targetWidth - currentWidth) / 2
offset_h_meters = (targetHeight - currentHeight) / 2
offset_w_angles = offset_w_meters / longlen
offset_h_angles = offset_h_meters / latlen
#print("currentWidth " + str(currentWidth))
#print("currentHeight " + str(currentHeight))
#print("offsetAngleW " + str(offset_w_angles))
#print("offsetAngleH " + str(offset_h_angles))
offsetW = offset_w_angles
offsetH = offset_h_angles
#my_cos=math.cos(rad)
#print("my cos " + str(my_cos))
#test = 0.1 * abs(math.cos(abs(bbx['minlat'])))
#print("test " + str(test))
#print("trying to make it " + str(zoom*test) + " degrees wide")
#print("testOffsetW" + str(testOffsetW))
#offsetW = ((zoom*0.051122172576223) - width) / 2
#print("realoffsetW" + str(offsetW))
#offsetH = ((zoom*0.038) - height) / 2
#print("offsetH" + str(offsetH))
if offsetW < 0 or offsetH < 0:
print("area too big, skipping")
continue
test_savename = outdir + "/" + clean_name + "_" + str(counter) + ".png"
# continue if we already created this file
if os.path.isfile(test_savename):
counter += 1
continue
#print(bbx)
new_bbx = bbx.copy()
try:
new_bbx['minlon'] = bbx['minlon'] - offsetW
new_bbx['maxlon'] = bbx['maxlon'] + offsetW
new_bbx['minlat'] = bbx['minlat'] - offsetH
new_bbx['maxlat'] = bbx['maxlat'] + offsetH
except:
print("FAILED, BBX: " + str(bbx))
pprint(area._json)
# get population density
ring = [[new_bbx['minlon'], new_bbx['minlat']],
[new_bbx['minlon'], new_bbx['maxlat']],
[new_bbx['maxlon'], new_bbx['maxlat']],
[new_bbx['maxlon'], new_bbx['minlat']],
[new_bbx['minlon'], new_bbx['minlat']]]
ring_string = json.dumps(ring)
if (lon is None):
r = requests.post("http://sedac.ciesin.columbia.edu/arcgis/rest/services/sedac/geoprocessing/GPServer/pes-v1/execute", \
data={'Input_Feature_Set': '{ "geometryType": "esriGeometryPolygon","fields": [{"name": "id","type": "esriFieldTypeInteger"}],"spatialReference": {"wkid": 4326},"features": [{"geometry": {"rings": [ \
' + ring_string + ' \
],"spatialReference": {"wkid": 4326}},"attributes": {"id": 1}}]}' , 'f': 'json'})
json_result = json.loads(r.text)
attributes = json_result['results'][0]['value']['features'][0][
'attributes']
pop = attributes['POPULATION05']
area = attributes['LANDAREA']
density = pop / area
if (density < 1000):
print("density too small")
continue
#print(new_bbx)
#exit()
#print("bbx height " + str(new_bbx['maxlat'] - new_bbx['minlat']))
#red_fill = ax.fill(xynps[:,0], xynps[:,1], "r", zorder=10, antialiased=False)
plt.figure("terrain")
fig_terrain, ax_terrain = plt.subplots(figsize=(10, 10),
subplot_kw=dict(
projection=request.crs,
facecolor='#000000'))
try:
ax_terrain.set_extent([
new_bbx['minlon'], new_bbx['maxlon'], new_bbx['minlat'],
new_bbx['maxlat']
])
except Exception:
print(traceback.format_exc())
print(sys.exc_info()[0])
continue
ax_terrain.add_image(terrain, 13)
savename = "osm/" + str(re.sub(r'[^a-zA-Z\d]', '',
area_name)) + str(counter) + "_t.png"
plt.savefig(savename,
dpi=_dpi,
transparent=True,
bbox_inches='tight',
pad_inches=0,
frameon=None)
terrain_raster_261 = cv2.imread(savename)
terrain_raster_256_b = terrain_raster_261[3:259, 3:259, 0]
terrain_raster_256_g = terrain_raster_261[3:259, 3:259, 1]
terrain_raster_256_r = terrain_raster_261[3:259, 3:259, 2]
plt.figure(2)
fig2, ax2 = plt.subplots(figsize=(10, 10),
subplot_kw=dict(projection=request.crs,
facecolor='#000000'))
xynps = ax.projection.transform_points(
ccrs.Geodetic(), np.asarray([new_bbx['minlon'],
new_bbx['maxlon']]),
np.asarray([new_bbx['minlat'], new_bbx['maxlat']]))
#max_lonlat=ax.projection.transform_points(ccrs.Geodetic(), new_bbx['maxlon'], new_bbx['maxlat'])
#ax2.set_extent([new_bbx['minlon'], new_bbx['maxlon'], new_bbx['minlat'], new_bbx['maxlat']])
ax2.set_extent([
new_bbx['minlon'], new_bbx['maxlon'], new_bbx['minlat'],
new_bbx['maxlat']
],
crs=ccrs.Geodetic())
roads_current = roads_in_bbox(new_bbx)
all_areas = residential_areas_enclosed_in_bbox(new_bbx)
#print(str(len(all_areas)) + " areas in bbox")
red_fills = []
for current_residential_area in (all_areas):
bbx = current_residential_area._json['bounds']
width = bbx['maxlon'] - bbx['minlon']
height = bbx['maxlat'] - bbx['minlat']
if (width < 0.001 or height < 0.001):
#print("area too small, skipping")
continue
geometry = current_residential_area._json['geometry']
lons = []
lats = []
for point in geometry:
lons.append(point['lon'])
lats.append(point['lat'])
#mycoords.append( (point['lat'], ) )
xs = np.array(lons)
ys = np.array(lats)
xynps = ax.projection.transform_points(ccrs.Geodetic(), xs, ys)
#print("area " + str(current_residential_area))
red_fill2 = ax2.fill(xynps[:, 0],
xynps[:, 1],
"k",
zorder=11,
antialiased=False)
red_fills.append(red_fill2)
road_refs = []
savename = "osm/" + str(re.sub(r'[^a-zA-Z\d]', '',
area_name)) + str(counter) + "_2.png"
#print("trying to save file called " +savename)
plt.savefig(savename,
dpi=_dpi,
facecolor='0.0',
edgecolor='0.0',
transparent=True,
bbox_inches='tight',
pad_inches=0,
frameon=None)
#exit()
mask_261 = cv2.imread(savename, 0)
mask_256 = mask_261[3:259, 3:259]
#print("shape:" + str(mask.shape))
#exit()
mask_256 = cv2.threshold(mask_256, 127, 255,
cv2.THRESH_BINARY_INV)[1] # ensure binary
#kernel = np.ones((3,3), np.uint8)
kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3))
mask_dilated_256 = cv2.dilate(mask_256, kernel, iterations=2)
savename = "osm/" + str(re.sub(r'[^a-zA-Z\d]', '',
area_name)) + str(counter) + "_3.png"
cv2.imwrite(savename, mask_256)
#for road_ref in (road_refs):
# l = road_ref.pop(0)
# wl = weakref.ref(l)
# l.remove()
# del l
plt.figure(1)
ax.set_extent([
new_bbx['minlon'], new_bbx['maxlon'], new_bbx['minlat'],
new_bbx['maxlat']
])
#print("fetch roads")
#print("got " + str(len(roads))+ " roads ")
#exit()
#query = overpassQueryBuilder(bbox=[new_bbx['minlat'], new_bbx['minlon'], new_bbx['maxlat'], new_bbx['maxlon']], elementType='way', selector=['"highway"="residential"', '"highway"="tertiary"', '"highway"="secondary"', '"highway"="primary"', '"highway"="unclassified"', '"highway"="trunk"', '"highway"="trunk_link"'], out='geom')
#roads_current = overpass.query(query).ways()
#print("got num roads:")
if len(roads_current) > 0:
#print(len(roads_current))
processed = 0
for area in (roads_current):
_type = area._json['tags']['highway']
#print(_type)
if _type not in ['primary', 'secondary', 'highway', 'trunk']:
continue
geometry = area._json['geometry']
lons = []
lats = []
for point in geometry:
lons.append(point['lon'])
lats.append(point['lat'])
#mycoords.append( (point['lat'], ) )
xs = np.array(lons)
ys = np.array(lats)
xynps = ax.projection.transform_points(ccrs.Geodetic(), xs, ys)
#print(xynps)
#break
plt.figure(1)
road_ref = ax.plot(xynps[:, 0],
xynps[:, 1],
zorder=11,
color='#000000',
marker='o',
linewidth=0.05,
markersize=0,
antialiased=False)
road_refs.append(road_ref)
processed += 1
savename = "osm/" + str(re.sub(r'[^a-zA-Z\d]', '',
area_name)) + str(counter) + "_0.png"
plt.savefig(savename,
dpi=_dpi,
facecolor='0.0',
edgecolor='1.0',
transparent=True,
bbox_inches='tight',
pad_inches=0,
frameon=None)
roads_primary_secondary_raster_261 = cv2.imread(savename, 0)
roads_primary_secondary_raster_256 = roads_primary_secondary_raster_261[
3:259, 3:259]
roads_primary_secondary_raster_256 = cv2.threshold(
roads_primary_secondary_raster_256, 127, 255,
cv2.THRESH_BINARY_INV)[1]
if len(roads_current) > 0:
#print(len(roads_current))
processed = 0
for area in (roads_current):
#if(processed % 500 == 0):
#print("processing number " + str(processed))
geometry = area._json['geometry']
lons = []
lats = []
for point in geometry:
lons.append(point['lon'])
lats.append(point['lat'])
#mycoords.append( (point['lat'], ) )
xs = np.array(lons)
ys = np.array(lats)
xynps = ax.projection.transform_points(ccrs.Geodetic(), xs, ys)
#print(xynps)
#break
plt.figure(1)
road_ref = ax.plot(xynps[:, 0],
xynps[:, 1],
zorder=11,
color='#000000',
marker='o',
linewidth=0.05,
markersize=0,
antialiased=False)
road_refs.append(road_ref)
processed += 1
savename = "osm/" + str(re.sub(r'[^a-zA-Z\d]', '',
area_name)) + str(counter) + "_1.png"
plt.savefig(savename,
dpi=_dpi,
facecolor='0.0',
edgecolor='1.0',
transparent=True,
bbox_inches='tight',
pad_inches=0,
frameon=None)
# FINISHED ALL MATPLOTLIB EXPORTS AT THIS POINT
roads_raster_261 = cv2.imread(savename, 0)
roads_raster_256 = roads_raster_261[3:259, 3:259]
#b,g,r = cv2.split(masked_roads)''
roads_raster_256 = cv2.threshold(
roads_raster_256, 127, 255,
cv2.THRESH_BINARY_INV)[1] # ensure binary
# This one will only display the roads that are behind residential areas for the SECOND image
masked_roads_256 = cv2.bitwise_and(roads_raster_256,
roads_raster_256,
mask=mask_dilated_256)
#masked_roads = cv2.threshold(mask, 127, 255, cv2.THRESH_BINARY_INV)[1] # ensure binary
#savename = outdir + str(re.sub(r'[^a-zA-Z\d]', '', area_name)) + str(counter) + "_4.png"
#cv2.imwrite(savename, masked_roads)
# This one will only show the roads behind residential areas
roads_raster_256 = cv2.bitwise_and(roads_raster_256,
roads_raster_256,
mask=(255 - mask_256))
roads_raster_256 = cv2.bitwise_or(roads_raster_256,
roads_primary_secondary_raster_256)
__width = mask_256.shape[1]
__height = mask_256.shape[0]
empty = np.zeros((__height, __width), dtype=np.uint8)
#print(str(roads_raster.shape))
#print(str(mask.shape))
#print(str(empty.shape))
out = cv2.merge((roads_raster_256, empty, mask_256))
height = out.shape[0]
width = out.shape[1]
#print("width")
#print(width)
if width > 256:
out = out[0:height, 0:256]
masked_roads = masked_roads[0:height, 0:256]
width = 256
if height > 256:
out = out[0:256, 0:width]
masked_roads = masked_roads[0:256, 0:width]
height = 256
if width < 256 or height < 256:
width_diff = 256 - width
height_diff = 256 - height
out = cv2.copyMakeBorder(out,
height_diff,
0,
width_diff,
0,
cv2.BORDER_CONSTANT,
value=[0, 0, 0])
masked_roads = cv2.copyMakeBorder(masked_roads,
height_diff,
0,
width_diff,
0,
cv2.BORDER_CONSTANT,
value=[0, 0, 0])
height = out.shape[0]
width = out.shape[1]
#savename = outdir + str(re.sub(r'[^a-zA-Z\d]', '', area_name)) + str(counter) + "_5.png"
#cv2.imwrite(savename, out)
combined_r = np.zeros((256, 512), dtype=np.uint8)
combined_r[0:256, 0:256] = out[:, :, 0]
combined_r[0:256, 256:512] = masked_roads_256
combined_g = np.zeros((256, 512), dtype=np.uint8)
#combined_g[0:256, 0:256] = terrain_combined_256
#combined_g[0:256, 256:512] = masked_roads
combined_b = np.zeros((256, 512), dtype=np.uint8)
combined_b[0:256, 0:256] = out[:, :, 2]
#combined_b[0:256, 256:512] = masked_roads
#b,g,r = cv2.split(terrain_raster)
terrain_combined_256 = -10000 + (
(terrain_raster_256_r * 256 * 256 + terrain_raster_256_g * 256 +
terrain_raster_256_b) * 0.1)
#water = cv2.inRange(terrain_combined_256, 0, 24467)
smallest = terrain_combined_256.min() #terrain_combined_256.min()
biggest = smallest + 100 # terrain_combined_256.max()
#biggest = terrain_combined_256.max()
_range = biggest - smallest
#print ("biggest " + str(biggest))
#print ("range " + str(_range))
terrain_combined_256 = terrain_combined_256 - smallest
print("smallest " + str(terrain_combined_256.min()))
print("biggest " + str(terrain_combined_256.max()))
terrain_combined_256 = (terrain_combined_256 / _range) * 255
#ret,terrain_combined_256 = cv2.threshold(terrain_combined_256,2,255,cv2.THRESH_BINARY)
#terrain_combined_256 = cv2.bitwise_not(terrain_combined_256,terrain_combined_256, mask = water)
combined_g[0:256, 0:256] = terrain_combined_256
combined_g[0:256, 0:256] = np.clip(terrain_combined_256,
a_min=0,
a_max=255)
#terrain_debug_savename = outdir + "/" + clean_name + "_" + str(counter) + "_" + str(int(density)) + "_tdbg.png"
#cv2.imwrite(terrain_debug_savename,terrain_new)
savename = outdir + "/" + clean_name + "_" + str(counter) + "_" + str(
int(density)) + ".png"
out = cv2.merge((combined_r, combined_g, combined_b))
print("writing out file " + savename)
cv2.imwrite(savename, out)
for cur_red_fill in (red_fills):
l = cur_red_fill.pop(0)
wl = weakref.ref(l)
l.remove()
del l
#red_fill2 = ax.fill(xynps[:,0], xynps[:,1], "r", zorder=15, antialiased=False)
#savename = "osm/" + str(re.sub(r'[^a-zA-Z\d]', '', area_name)) + str(counter) + "_2.png"
#plt.savefig(savename, dpi=128, facecolor='0.0', edgecolor='0.0', transparent=False, bbox_inches='tight', pad_inches=0, frameon=None)
counter += 1
#l = red_fill.pop(0)
#wl = weakref.ref(l)
#l.remove()
#del l
#counter +=1
#l = red_fill2.pop(0)
#wl = weakref.ref(l)
#l.remove()
#del l
for road_ref in (road_refs):
l = road_ref.pop(0)
wl = weakref.ref(l)
l.remove()
del l
#ax.fill(xynps[:,0], xynps[:,1], "b", zorder=10, antialiased=False)
#ax.set_extent([bbx['minlat'], bbx['maxlat'], bbx['minlon'], bbx['maxlon']])
'''
def map_background(
bbox,
pad_pct=0.3,
image=None,
bbox_image=None,
zoom_level=9,
fig=None,
ax=None,
coastlines=False,
show_ticks=True,
tickside="left",
**imshow_kwargs,
):
"""Plot the raster `img` on top of background tiles
Inputs:
img (ndarray): raster iamge
bbox (tuple[float]): (left, bottom, right, top)
fig (matplotlib.figure): optional, existing figure to use
"""
import cartopy.crs as ccrs
from cartopy.io import img_tiles
tiler = img_tiles.Stamen("terrain-background")
tiler = img_tiles.GoogleTiles(style="satellite")
mykey = "pk.eyJ1Ijoic2NvdHRzdGFuaWUiLCJhIjoiY2s3Nno3bmE5MDJlbDNmcGNpanV0ZzJ3MCJ9.PyaQ_iwKFcFcRr-EveCObA"
tiler = img_tiles.MapboxTiles(mykey, "satellite")
crs = tiler.crs
# if fig is None and ax is None:
# print('ADDED FIG')
if ax is None:
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, projection=crs)
else:
fig = ax.figure
# matplotlib wants extent different than gdal/rasterio convention
pad_pct = pad_pct or 0.0
extent = _padded_extent(bbox, pad_pct)
ax.set_extent(extent, crs=ccrs.PlateCarree())
ax.add_image(tiler, zoom_level)
if image is not None:
if bbox_image is None:
bbox_image = _get_rio_bbox(image)
extent_img = _padded_extent(bbox_image, 0.0)
ax.imshow(
image,
transform=ccrs.PlateCarree(),
extent=extent_img,
origin="upper",
zorder=3,
**imshow_kwargs,
)
if show_ticks:
add_ticks(ax, side=tickside)
if coastlines:
ax.coastlines("10m")
return fig, ax