def test_bounds2raster():
w, s, e, n = (-106.6495132446289, 25.845197677612305, -93.50721740722656,
36.49387741088867)
_ = ctx.bounds2raster(w, s, e, n, 'test.tif', zoom=4, ll=True)
rtr = rio.open('test.tif')
img = np.array([band for band in rtr.read()]).transpose(1, 2, 0)
solu = (-12528334.684053527, 2509580.5126589066, -10023646.141204873,
5014269.05550756)
for i, j in zip(rtr.bounds, solu):
assert round(i - j, TOL) == 0
assert img[100, 100, :].tolist() == [230, 229, 188]
assert img[100, 200, :].tolist() == [156, 180, 131]
assert img[200, 100, :].tolist() == [230, 225, 189]
assert img.sum() == 36926856
assert_array_almost_equal(img.mean(), 187.8197021484375)
# multiple tiles for which result is not square
w, s, e, n = (2.5135730322461427, 49.529483547557504, 6.15665815595878,
51.47502370869813)
img, ext = ctx.bounds2raster(w, s, e, n, 'test2.tif', zoom=7, ll=True)
rtr = rio.open('test2.tif')
rimg = np.array([band for band in rtr.read()]).transpose(1, 2, 0)
assert rimg.shape == img.shape
assert rimg.sum() == img.sum()
assert_array_almost_equal(rimg.mean(), img.mean())
assert_array_almost_equal(
ext, (0.0, 939258.2035682457, 6261721.35712164, 6887893.492833804))
rtr_bounds = [
-611.49622628141, 6262332.853347922, 938646.7073419644,
6888504.989060086
]
assert_array_almost_equal(list(rtr.bounds), rtr_bounds)
def download_map_bg(self):
if self.config.bg_img is None:
raise Exception("Variable Drawer.background_img_path is not defined")
print(f"Downloading map's background image to {self.config.bg_img}")
img, ext = cx.bounds2raster(
self.config.map_xlim[0],
self.config.map_ylim[0],
self.config.map_xlim[1],
self.config.map_ylim[1],
self.config.bg_img,
ll=True,
# source=cx.providers.CartoDB.Positron,
# source='https://a.tile.openstreetmap.fr/hot/{z}/{x}/{y}.png'
source='http://www.google.cn/maps/vt?lyrs=s@189&gl=cn&x={x}&y={y}&z={z}'
)
def rgb2gray(rgb):
r, g, b = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
return gray
plt.close()
plt.imshow(rgb2gray(img),
extent=ext,
cmap=plt.get_cmap('gray'),
vmin=0,
vmax=255)
plt.show()
exit()
def test_ll2wdw():
w, s, e, n = (-106.6495132446289, 25.845197677612305, -93.50721740722656,
36.49387741088867)
hou = (-10676650.69219051, 3441477.046670125, -10576977.7804825,
3523606.146650609)
_ = ctx.bounds2raster(w, s, e, n, 'test.tif', zoom=4, ll=True)
rtr = rio.open('test.tif')
wdw = ctx.tile.bb2wdw(hou, rtr)
assert wdw == ((152, 161), (189, 199))
def test_bounds2raster():
w, s, e, n = (-106.6495132446289, 25.845197677612305, -93.50721740722656,
36.49387741088867)
_ = ctx.bounds2raster(w, s, e, n, 4, 'test.tif', ll=True)
rtr = rio.open('test.tif')
img = np.array([band for band in rtr.read()]).transpose(1, 2, 0)
solu = (-12528334.684053527, 2509580.5126589066, -10023646.141204873,
5014269.05550756)
for i, j in zip(rtr.bounds, solu):
assert round(i - j, TOL) == 0
assert img[100, 100, :].tolist() == [230, 229, 188]
assert img[100, 200, :].tolist() == [156, 180, 131]
assert img[200, 100, :].tolist() == [230, 225, 189]
# multiple tiles for which result is not square
w, s, e, n = (2.5135730322461427, 49.529483547557504, 6.15665815595878,
51.47502370869813)
raster, _ = ctx.bounds2raster(w, s, e, n, 7, 'test.tif', ll=True)
rtr = rio.open('test.tif')
img = np.array([band for band in rtr.read()]).transpose(1, 2, 0)
assert raster.shape == img.shape
def download_bg(self, save_path):
print(f"Downloading map's background image to {save_path}")
img, ext = cx.bounds2raster(
self.xlim[0],
self.ylim[0],
self.xlim[1],
self.ylim[1],
save_path,
ll=True,
# source=cx.providers.CartoDB.Positron,
# source='https://a.tile.openstreetmap.fr/hot/{z}/{x}/{y}.png'
source=
'http://www.google.cn/maps/vt?lyrs=s@189&gl=cn&x={x}&y={y}&z={z}')
def test_warp_img_transform():
w, s, e, n = ext = (
-106.6495132446289,
25.845197677612305,
-93.50721740722656,
36.49387741088867,
)
_ = ctx.bounds2raster(w, s, e, n, "test.tif", zoom=4, ll=True)
rtr = rio.open("test.tif")
img = np.array([band for band in rtr.read()])
wimg, wext = ctx.warp_img_transform(img, rtr.transform, rtr.crs,
{"init": "epsg:4326"})
assert wimg[:, 100, 100].tolist() == [228, 221, 184]
assert wimg[:, 100, 200].tolist() == [213, 219, 177]
assert wimg[:, 200, 100].tolist() == [133, 130, 109]
def write_district_osm_tile(district_gdf: gpd.geodataframe.GeoDataFrame,
filename: str) -> bool:
"""Writes the map tile corresponding to a district geo dataframe to a geotiff.
Args:
district_gdf (gpd.geodataframe.GeoDataFrame): District for which map tile needs to be extracted
filename (str): geotiff file name to which the map tile is written to.
Returns:
bool: Returns True if the map tile is not empty.
"""
(w, s, e, n) = district_extents(district_gdf)
img, ex = ctx.bounds2raster(w,
s,
e,
n,
ll=True,
path=os.environ.get("OSM_DIR") + filename,
source=ctx.providers.CartoDB.Positron)
if img.size != 0:
return True
else:
return False
# 多个行政区域多个多边形的情形，total_bounds对了，用total_bounds
gdzh["geometry"].bounds
gdzh["geometry"].total_bounds
we, se, ee, ne = gdzh["geometry"].total_bounds
# 计算视野需要的zoom参数 -6? 不对
zoom = ctx.tile._calculate_zoom(we, se, ee, ne)
# 计算要下载的瓦片数量，zoom=10:15，11:40, zoom = -6时只需1块 tile
_ = ctx.howmany(we, se, ee, ne, 11, ll=False)
# 下载瓦片图并画图，不接受负值的zoom，ll=True, 火星坐标等，ll=False, 墨卡托坐标,前面已转换成该坐标
# zoom=11是最合适的，就是上面ctx.add_basemap(ax,source=gaodeurl)自动选定的zoom
img, ext = ctx.bounds2img(we, se, ee, ne, 11, ll=False, source=gaodeurl)
fig, ax = plt.subplots(1, figsize=(16, 16))
plt.imshow(img, extent=ext)
# 另存成tif图像文件，程序挂起，？
_ = ctx.bounds2raster(we, se, ee, ne, 'zh.tif', ll=False)
# Orange Geo的离线地图，除美国外都是到省一级
gdf7 = gpd.read_file("D:/Anaconda3/Lib/site-packages/orangecontrib/geo/geojson/admin1-CHN.json",\
encoding='utf-8')
gdf7.plot(figsize=(16, 12))
plt.show()
# 美国的地图边界到郡一级
gdf8 = gpd.read_file("D:/Anaconda3/Lib/site-packages/orangecontrib/geo/geojson/admin2-USA.json",\
encoding='utf-8')
gdf8.plot(figsize=(128, 80))
plt.show()
# 手工建立映射表
provs3 = ["甘肃","青海","广西","贵州","重庆","北京","福建","安徽","广东","西藏","新疆","海南",\
"宁夏","陕西","山西","湖北","湖南","四川","云南","河北","河南","辽宁","山东","天津",\
def test_add_basemap():
# Plot boulder bbox as in test_place
x1, x2, y1, y2 = [
-11740727.544603072,
-11701591.786121061,
4852834.0517692715,
4891969.810251278,
]
# Test web basemap
fig, ax = matplotlib.pyplot.subplots(1)
ax.set_xlim(x1, x2)
ax.set_ylim(y1, y2)
ctx.add_basemap(ax, zoom=10)
# ensure add_basemap did not change the axis limits of ax
ax_extent = (x1, x2, y1, y2)
assert ax.axis() == ax_extent
assert ax.images[0].get_array().sum() == 51551927
assert ax.images[0].get_array().shape == (256, 256, 4)
assert_array_almost_equal(ax.images[0].get_array()[:,:,:3].mean(), 177.20665995279947)
assert_array_almost_equal(ax.images[0].get_array().mean(), 196.654995)
# Test local source
## Windowed read
subset = (
-11730803.981631357,
-11711668.223149346,
4862910.488797557,
4882046.247279563,
)
f, ax = matplotlib.pyplot.subplots(1)
ax.set_xlim(subset[0], subset[1])
ax.set_ylim(subset[2], subset[3])
loc = ctx.Place(SEARCH, path="./test2.tif", zoom_adjust=ADJUST)
ctx.add_basemap(ax, source="./test2.tif", reset_extent=True)
assert_array_almost_equal(subset, ax.images[0].get_extent())
assert ax.images[0].get_array().sum() == 3187219
assert ax.images[0].get_array()[:,:,:3].sum() == 2175124
assert ax.images[0].get_array().shape == (64, 64, 4)
assert_array_almost_equal(ax.images[0].get_array()[:,:,:3].mean(), 177.01204427083334)
assert_array_almost_equal(ax.images[0].get_array().mean(), 194.53240966796875)
## Full read
f, ax = matplotlib.pyplot.subplots(1)
ax.set_xlim(x1, x2)
ax.set_ylim(y1, y2)
loc = ctx.Place(SEARCH, path="./test2.tif", zoom_adjust=ADJUST)
ctx.add_basemap(ax, source="./test2.tif", reset_extent=False)
raster_extent = (
-11740880.418659642,
-11662608.901695622,
4774715.408861821,
4931258.442789858,
)
assert_array_almost_equal(raster_extent, ax.images[0].get_extent())
assert ax.images[0].get_array()[:,:,:3].sum() == 76248416
assert ax.images[0].get_array().sum() == 109671776
assert ax.images[0].get_array().shape == (512, 256, 4)
assert_array_almost_equal(ax.images[0].get_array()[:,:,:3].mean(), 193.90974934895834)
assert_array_almost_equal(ax.images[0].get_array().mean(), 209.18231201171875)
# Test with auto-zoom
f, ax = matplotlib.pyplot.subplots(1)
ax.set_xlim(x1, x2)
ax.set_ylim(y1, y2)
ctx.add_basemap(ax, zoom="auto")
ax_extent = (
-11740727.544603072,
-11701591.786121061,
4852834.051769271,
4891969.810251278,
)
assert_array_almost_equal(ax_extent, ax.images[0].get_extent())
assert ax.images[0].get_array()[:,:,:3].sum() == 563185119
assert ax.images[0].get_array().sum() == 830571999
assert ax.images[0].get_array().shape == (1024, 1024, 4)
assert_array_almost_equal(ax.images[0].get_array()[:,:,:3].mean(), 179.03172779083252)
assert_array_almost_equal(ax.images[0].get_array().mean(), 198.023796)
# Test on-th-fly warping
x1, x2 = -105.5, -105.00
y1, y2 = 39.56, 40.13
f, ax = matplotlib.pyplot.subplots(1)
ax.set_xlim(x1, x2)
ax.set_ylim(y1, y2)
ctx.add_basemap(ax, crs={"init": "epsg:4326"}, attribution=None)
assert ax.get_xlim() == (x1, x2)
assert ax.get_ylim() == (y1, y2)
assert ax.images[0].get_array()[:,:,:3].sum() == 724238693
assert ax.images[0].get_array().shape == (1135, 1183, 4)
assert_array_almost_equal(ax.images[0].get_array()[:,:,:3].mean(), 179.79593258881636)
assert_array_almost_equal(ax.images[0].get_array().mean(), 198.596949)
# Test local source warping
_ = ctx.bounds2raster(x1, y1, x2, y2, "./test2.tif", ll=True)
f, ax = matplotlib.pyplot.subplots(1)
ax.set_xlim(x1, x2)
ax.set_ylim(y1, y2)
ctx.add_basemap(
ax, source="./test2.tif", crs={"init": "epsg:4326"}, attribution=None
)
assert ax.get_xlim() == (x1, x2)
assert ax.get_ylim() == (y1, y2)
assert ax.images[0].get_array()[:,:,:3].sum() == 464536503
assert ax.images[0].get_array().shape == (980, 862, 4)
assert_array_almost_equal(ax.images[0].get_array()[:,:,:3].mean(), 183.301175)
assert ax.images[0].get_array().sum() == 678981558
assert_array_almost_equal(ax.images[0].get_array().mean(), 200.939189)
x1, x2, y1, y2 = [
-11740727.544603072,
-11701591.786121061,
4852834.0517692715,
4891969.810251278,
]
def test_add_basemap():
# Plot boulder bbox as in test_place
x1, x2, y1, y2 = [
-11740727.544603072,
-11701591.786121061,
4852834.0517692715,
4891969.810251278,
]
# Test web basemap
fig, ax = matplotlib.pyplot.subplots(1)
ax.set_xlim(x1, x2)
ax.set_ylim(y1, y2)
ax = ctx.add_basemap(ax, zoom=10)
# ensure add_basemap did not change the axis limits of ax
ax_extent = (x1, x2, y1, y2)
assert ax.axis() == ax_extent
assert ax.images[0].get_array().sum() == 75853866
assert ax.images[0].get_array().shape == (256, 512, 3)
assert_array_almost_equal(ax.images[0].get_array().mean(),
192.90635681152344)
# Test local source
f, ax = matplotlib.pyplot.subplots(1)
ax.set_xlim(x1, x2)
ax.set_ylim(y1, y2)
loc = ctx.Place(SEARCH, path="./test2.tif", zoom_adjust=ADJUST)
ax = ctx.add_basemap(ax, url="./test2.tif")
raster_extent = (
-11740803.981631357,
-11701668.223149346,
4852910.488797557,
4892046.247279563,
)
assert_array_almost_equal(raster_extent, ax.images[0].get_extent())
assert ax.images[0].get_array().sum() == 34840247
assert ax.images[0].get_array().shape == (256, 256, 3)
assert_array_almost_equal(ax.images[0].get_array().mean(),
177.20665995279947)
# Test with auto-zoom
f, ax = matplotlib.pyplot.subplots(1)
ax.set_xlim(x1, x2)
ax.set_ylim(y1, y2)
ax = ctx.add_basemap(ax, zoom="auto")
ax_extent = (
-11740727.544603072,
-11691807.846500559,
4852834.0517692715,
4891969.810251278,
)
assert_array_almost_equal(ax_extent, ax.images[0].get_extent())
assert ax.images[0].get_array().sum() == 723918764
assert ax.images[0].get_array().shape == (1024, 1280, 3)
assert_array_almost_equal(ax.images[0].get_array().mean(),
184.10206197102863)
# Test on-th-fly warping
x1, x2 = -105.5, -105.00
y1, y2 = 39.56, 40.13
f, ax = matplotlib.pyplot.subplots(1)
ax.set_xlim(x1, x2)
ax.set_ylim(y1, y2)
ax = ctx.add_basemap(ax, crs={"init": "epsg:4326"}, attribution=None)
assert ax.get_xlim() == (x1, x2)
assert ax.get_ylim() == (y1, y2)
assert ax.images[0].get_array().sum() == 724238693
assert ax.images[0].get_array().shape == (1135, 1183, 3)
assert_array_almost_equal(ax.images[0].get_array().mean(),
179.79593258881636)
# Test local source warping
_ = ctx.bounds2raster(x1, y1, x2, y2, "./test2.tif", ll=True)
f, ax = matplotlib.pyplot.subplots(1)
ax.set_xlim(x1, x2)
ax.set_ylim(y1, y2)
ax = ctx.add_basemap(ax,
url="./test2.tif",
crs={"init": "epsg:4326"},
attribution=None)
assert ax.get_xlim() == (x1, x2)
assert ax.get_ylim() == (y1, y2)
assert ax.images[0].get_array().sum() == 724238693
assert ax.images[0].get_array().shape == (1135, 1183, 3)
assert_array_almost_equal(ax.images[0].get_array().mean(),
179.79593258881636)
df_well_tops[::5].apply(lambda x:
ax.annotate(
s=x.wlbWellboreName,
xy=x.geometry.centroid.coords[0],
ha='left',
c='white'
), axis=1);
# -
west, south, east, north = bbox = df_well_tops.total_bounds
img, ext = ctx.bounds2raster(west,
south,
east,
north,
"world_watercolor.tif",
source=ctx.providers.Stamen.Watercolor,
ll=True,
zoom=8
)
west, south, east, north = bbox = df_well_tops.total_bounds
img, ext = ctx.bounds2raster(west,
south,
east,
north,
"oceanesri.tif",
source=ctx.providers.Esri.OceanBasemap,
def test_add_basemap():
# Plot boulder bbox as in test_place
x1, x2, y1, y2 = [
-11740727.544603072,
-11701591.786121061,
4852834.0517692715,
4891969.810251278,
]
# Test web basemap
fig, ax = matplotlib.pyplot.subplots(1)
ax.set_xlim(x1, x2)
ax.set_ylim(y1, y2)
ctx.add_basemap(ax, zoom=10)
# ensure add_basemap did not change the axis limits of ax
ax_extent = (x1, x2, y1, y2)
assert ax.axis() == ax_extent
assert ax.images[0].get_array().sum() == 34840247
assert ax.images[0].get_array().shape == (256, 256, 3)
assert_array_almost_equal(ax.images[0].get_array().mean(), 177.20665995279947)
# Test local source
## Windowed read
subset = (
-11730803.981631357,
-11711668.223149346,
4862910.488797557,
4882046.247279563,
)
f, ax = matplotlib.pyplot.subplots(1)
ax.set_xlim(subset[0], subset[1])
ax.set_ylim(subset[2], subset[3])
loc = ctx.Place(SEARCH, path="./test2.tif", zoom_adjust=ADJUST)
ctx.add_basemap(ax, url="./test2.tif", reset_extent=True)
raster_extent = (
-11740803.981631357,
-11701668.223149346,
4852910.488797556,
4892046.247279563,
)
assert_array_almost_equal(raster_extent, ax.images[0].get_extent())
assert ax.images[0].get_array().sum() == 8440966
assert ax.images[0].get_array().shape == (126, 126, 3)
assert_array_almost_equal(ax.images[0].get_array().mean(), 177.22696733014195)
## Full read
f, ax = matplotlib.pyplot.subplots(1)
ax.set_xlim(x1, x2)
ax.set_ylim(y1, y2)
loc = ctx.Place(SEARCH, path="./test2.tif", zoom_adjust=ADJUST)
ctx.add_basemap(ax, source="./test2.tif", reset_extent=False)
raster_extent = (
-11740803.981631357,
-11701668.223149346,
4852910.488797557,
4892046.247279563,
)
assert_array_almost_equal(raster_extent, ax.images[0].get_extent())
assert ax.images[0].get_array().sum() == 34840247
assert ax.images[0].get_array().shape == (256, 256, 3)
assert_array_almost_equal(ax.images[0].get_array().mean(), 177.20665995279947)
# Test with auto-zoom
f, ax = matplotlib.pyplot.subplots(1)
ax.set_xlim(x1, x2)
ax.set_ylim(y1, y2)
ctx.add_basemap(ax, zoom="auto")
ax_extent = (
-11740727.544603072,
-11701591.786121061,
4852834.051769271,
4891969.810251278,
)
assert_array_almost_equal(ax_extent, ax.images[0].get_extent())
assert ax.images[0].get_array().sum() == 563185119
assert ax.images[0].get_array().shape == (1024, 1024, 3)
assert_array_almost_equal(ax.images[0].get_array().mean(), 179.03172779083252)
# Test on-th-fly warping
x1, x2 = -105.5, -105.00
y1, y2 = 39.56, 40.13
f, ax = matplotlib.pyplot.subplots(1)
ax.set_xlim(x1, x2)
ax.set_ylim(y1, y2)
ctx.add_basemap(ax, crs={"init": "epsg:4326"}, attribution=None)
assert ax.get_xlim() == (x1, x2)
assert ax.get_ylim() == (y1, y2)
assert ax.images[0].get_array().sum() == 724238693
assert ax.images[0].get_array().shape == (1135, 1183, 3)
assert_array_almost_equal(ax.images[0].get_array().mean(), 179.79593258881636)
# Test local source warping
_ = ctx.bounds2raster(x1, y1, x2, y2, "./test2.tif", ll=True)
f, ax = matplotlib.pyplot.subplots(1)
ax.set_xlim(x1, x2)
ax.set_ylim(y1, y2)
ctx.add_basemap(
ax, source="./test2.tif", crs={"init": "epsg:4326"}, attribution=None
)
assert ax.get_xlim() == (x1, x2)
assert ax.get_ylim() == (y1, y2)
assert ax.images[0].get_array().sum() == 464751694
assert ax.images[0].get_array().shape == (980, 862, 3)
assert_array_almost_equal(ax.images[0].get_array().mean(), 183.38608756727749)
