def basemapper():
t = time.time()
layout_map = tiles.QuadtreeTiles()
fig = plt.figure(figsize=(12, 9))
# Create a GeoAxes in the tile's projection.
ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())
# Limit the extent of the map to a small longitude/latitude range.
ax.set_extent([bbox[0], bbox[2], bbox[1], bbox[3]], crs=ccrs.PlateCarree())
# Add the Stamen data at zoom level 8.
ax.add_image(layout_map, 10)
gl = ax.gridlines(crs=ccrs.PlateCarree(),
draw_labels=True,
linewidth=2,
color='gray',
alpha=1,
linestyle='--')
gl.xlabels_top = False
gl.ylabels_right = False
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
if str_switch:
print("Basemapper plotter elapsed time: %.2f s" % (time.time() - t))
return fig, ax
def test_web_tiles():
extent = [-15, 0.1, 50, 60]
target_domain = sgeom.Polygon([[extent[0], extent[1]],
[extent[2], extent[1]],
[extent[2], extent[3]],
[extent[0], extent[3]],
[extent[0], extent[1]]])
map_prj = cimgt.GoogleTiles().crs
ax = plt.subplot(3, 2, 1, projection=map_prj)
gt = cimgt.GoogleTiles()
gt._image_url = types.MethodType(ctest_tiles.GOOGLE_IMAGE_URL_REPLACEMENT,
gt)
img, extent, origin = gt.image_for_domain(target_domain, 1)
ax.imshow(np.array(img),
extent=extent,
transform=gt.crs,
interpolation='bilinear',
origin=origin)
ax.coastlines(color='white')
ax = plt.subplot(3, 2, 2, projection=map_prj)
qt = cimgt.QuadtreeTiles()
img, extent, origin = qt.image_for_domain(target_domain, 1)
ax.imshow(np.array(img),
extent=extent,
transform=qt.crs,
interpolation='bilinear',
origin=origin)
ax.coastlines(color='white')
ax = plt.subplot(3, 2, 3, projection=map_prj)
mq_osm = cimgt.MapQuestOSM()
img, extent, origin = mq_osm.image_for_domain(target_domain, 1)
ax.imshow(np.array(img),
extent=extent,
transform=mq_osm.crs,
interpolation='bilinear',
origin=origin)
ax.coastlines()
ax = plt.subplot(3, 2, 4, projection=map_prj)
mq_oa = cimgt.MapQuestOpenAerial()
img, extent, origin = mq_oa.image_for_domain(target_domain, 1)
ax.imshow(np.array(img),
extent=extent,
transform=mq_oa.crs,
interpolation='bilinear',
origin=origin)
ax.coastlines()
ax = plt.subplot(3, 2, 5, projection=map_prj)
osm = cimgt.OSM()
img, extent, origin = osm.image_for_domain(target_domain, 1)
ax.imshow(np.array(img),
extent=extent,
transform=osm.crs,
interpolation='bilinear',
origin=origin)
ax.coastlines()
def basemapper(crd, res):
bbox = g16.site_info(crd, res)
layout_map = tiles.QuadtreeTiles()
fig = plt.figure(figsize=(12, 9))
# Create a GeoAxes in the tile's projection.
ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())
# Limit the extent of the map to a small longitude/latitude range.
ax.set_extent([bbox[0], bbox[2], bbox[1], bbox[3]], crs=ccrs.PlateCarree())
# # Add the satellite background data at zoom level 12.
ax.add_image(sat_img, 12)
gl = ax.gridlines(crs=ccrs.PlateCarree(),
draw_labels=True,
linewidth=0,
color='gray',
alpha=1,
linestyle='--')
gl.xlabels_top = False
gl.ylabels_right = False
# gl.xformatter = LONGITUDE_FORMATTER
# gl.yformatter = LATITUDE_FORMATTER
return fig, ax
def test_web_tiles():
extent = [-15, 0.1, 50, 60]
target_domain = shapely.geometry.Polygon([[extent[0], extent[1]],
[extent[2], extent[1]],
[extent[2], extent[3]],
[extent[0], extent[3]],
[extent[0], extent[1]]])
ax = plt.subplot(3, 2, 1, projection=ccrs.Mercator())
gt = cimgt.GoogleTiles()
img, extent, origin = gt.image_for_domain(target_domain, 1)
ax.imshow(np.array(img),
extent=extent,
transform=ccrs.Mercator(),
interpolation='bilinear',
origin=origin)
ax.coastlines(color='white')
ax = plt.subplot(3, 2, 2, projection=ccrs.Mercator())
qt = cimgt.QuadtreeTiles()
img, extent, origin = qt.image_for_domain(target_domain, 1)
ax.imshow(np.array(img),
extent=extent,
transform=ccrs.Mercator(),
interpolation='bilinear',
origin=origin)
ax.coastlines(color='white')
ax = plt.subplot(3, 2, 3, projection=ccrs.Mercator())
mq_osm = cimgt.MapQuestOSM()
img, extent, origin = mq_osm.image_for_domain(target_domain, 1)
ax.imshow(np.array(img),
extent=extent,
transform=ccrs.Mercator(),
interpolation='bilinear',
origin=origin)
ax.coastlines()
ax = plt.subplot(3, 2, 4, projection=ccrs.Mercator())
mq_oa = cimgt.MapQuestOpenAerial()
img, extent, origin = mq_oa.image_for_domain(target_domain, 1)
ax.imshow(np.array(img),
extent=extent,
transform=ccrs.Mercator(),
interpolation='bilinear',
origin=origin)
ax.coastlines()
ax = plt.subplot(3, 2, 5, projection=ccrs.Mercator())
osm = cimgt.OSM()
img, extent, origin = osm.image_for_domain(target_domain, 1)
ax.imshow(np.array(img),
extent=extent,
transform=ccrs.Mercator(),
interpolation='bilinear',
origin=origin)
ax.coastlines()
def makeMap(self):
pl.figure(figsize=(16.53, 11.69))
x = numpy.asarray([m[0] for m in self.coords if m])
y = numpy.asarray([m[1] for m in self.coords if m])
# request = cimgt.GoogleTiles(style='street')
# request = cimgt.Stamen('watercolor')
request = cimgt.QuadtreeTiles()
ax = pl.axes(projection=request.crs)
ax.add_image(request, self.tiledepth)
xynps = ax.projection.transform_points(ccrs.Geodetic(), x, y)
m = ax.hist2d(
xynps[:, 0],
xynps[:, 1],
bins=[self.xbins, self.ybins],
alpha=0.5,
cmap=pl.cm.jet,
cmin=1,
)
cbar = pl.colorbar(m[3], ax=ax, shrink=0.4, format="%.1f")
# pl.scatter(xynps[:,0], xynps[:,1], s=50, c="yellow", marker='o')
units = self.getUnits()
unps = ax.projection.transform_points(ccrs.Geodetic(),
units[:, 1].astype(float),
units[:, 0].astype(float))
pl.scatter(unps[:, 0], unps[:, 1], s=30, c=units[:, 2], marker='D')
# pl.scatter(unps[:, 0], unps[:, 1], s=100, c="purple", marker="D")
# ax.set_extent([51.526760, 51.454065, -2.698467, -2.564378])
pl.margins(0.5)
# pl.show()
pl.savefig(
"figure.pdf",
orientation="landscape",
# papertype="a3",
format="pdf",
dpi=600,
bbox_inches="tight",
)
def test_quadtree_wts():
qt = cimgt.QuadtreeTiles()
extent = [-15, 0.1, 50, 60]
target_domain = shapely.geometry.Polygon([[extent[0], extent[1]],
[extent[2], extent[1]],
[extent[2], extent[3]],
[extent[0], extent[3]],
[extent[0], extent[1]]])
with assert_raises(ValueError):
list(qt.find_images(target_domain, 0))
assert_equal(qt.tms_to_quadkey((1, 1, 1)), '1')
assert_equal(qt.quadkey_to_tms('1'), (1, 1, 1))
assert_equal(qt.tms_to_quadkey((8, 9, 4)), '1220')
assert_equal(qt.quadkey_to_tms('1220'), (8, 9, 4))
assert_equal(tuple(qt.find_images(target_domain, 1)), ('0', '1'))
assert_equal(tuple(qt.find_images(target_domain, 2)), ('03', '12'))
assert_equal(list(qt.subtiles('0')), ['00', '01', '02', '03'])
assert_equal(list(qt.subtiles('11')), ['110', '111', '112', '113'])
with assert_raises(ValueError):
qt.tileextent('4')
assert_arr_almost(qt.tileextent(''),
(-180.0, 180.0, 179.02740096, -179.02740096))
assert_arr_almost(qt.tileextent(qt.tms_to_quadkey((2, 0, 2), google=True)),
(0.0, 90.0, 179.02740096, 89.51370048))
assert_arr_almost(qt.tileextent(qt.tms_to_quadkey((0, 2, 2), google=True)),
(-180.0, -90.0, 5.68434189e-14, -8.95137005e+01))
assert_arr_almost(qt.tileextent(qt.tms_to_quadkey((0, 1, 2), google=True)),
(-180.0, -90.0, 8.95137005e+01, 5.68434189e-14))
assert_arr_almost(qt.tileextent(qt.tms_to_quadkey((2, 2, 2), google=True)),
(0.0, 90.0, 5.68434189e-14, -8.95137005e+01))
assert_arr_almost(qt.tileextent(qt.tms_to_quadkey((8, 9, 4), google=True)),
(0.0, 22.5, -22.37842512, -44.75685024))
def plot_map(longitude_lim, latitude_lim, map_quality=11):
"""
Plot map.
Parameters
----------
longitude_lim : list(2)
latitude_lim : list(2)
map_quality : int
Map rendering quality.
6 is very bad, 10 is ok, 12 is good, 13 is very good, 14 excellent.
"""
# Map extent
eps = 0*0.01
extent = [longitude_lim[0] - eps, longitude_lim[1] + eps,
latitude_lim[0] - eps, latitude_lim[1] + eps]
# Plot map of Stockholm
map_design = 'StamenTerrain'
if map_design == 'GoogleTiles':
request = cimgt.GoogleTiles()
elif map_design == 'QuadtreeTiles':
request = cimgt.QuadtreeTiles()
elif map_design == 'StamenTerrain':
request = cimgt.Stamen('terrain-background')
else:
# Map designs 'OSM' and 'MapboxTiles' do not work
raise Exception('Untested map design')
ax = plt.axes(projection=request.crs)
gl = ax.gridlines(draw_labels=True, alpha=0., linewidth=0, linestyle='-')
gl.top_labels = False
gl.right_labels = False
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
gl.xlabel_style = {'size': 10, 'color': 'black'}
gl.ylabel_style = {'size': 10, 'color': 'black', 'weight': 'normal'}
ax.set_extent(extent)
ax.add_image(request, map_quality)
def test_quadtree_wts():
qt = cimgt.QuadtreeTiles()
extent = [-15, 00, 50, 60]
target_domain = shapely.geometry.Polygon([[extent[0], extent[1]],
[extent[2], extent[1]],
[extent[2], extent[3]],
[extent[0], extent[3]],
[extent[0], extent[1]]])
with assert_raises(ValueError):
list(qt.find_images(target_domain, 0))
assert_equal(qt.tms_to_quadkey((1, 1, 1)), '1')
assert_equal(qt.quadkey_to_tms('1'), (1, 1, 1))
assert_equal(qt.tms_to_quadkey((8, 9, 4)), '1220')
assert_equal(qt.quadkey_to_tms('1220'), (8, 9, 4))
assert_equal(tuple(qt.find_images(target_domain, 1)), ('0', '1'))
assert_equal(tuple(qt.find_images(target_domain, 2)), ('03', '12'))
assert_equal(list(qt.subtiles('0')), ['00', '01', '02', '03'])
assert_equal(list(qt.subtiles('11')), ['110', '111', '112', '113'])
with assert_raises(ValueError):
qt.tileextent('4')
assert_equal(qt.tileextent(''),
(-180.0, 180.0, 179.4103506767748, -179.41035067677487))
assert_equal(qt.tileextent(qt.tms_to_quadkey((2, 0, 2), google=True)),
(0.0, 90.0, 179.41035067677481, 89.705175338387392))
assert_equal(qt.tileextent(qt.tms_to_quadkey((0, 2, 2), google=True)),
(-180.0, -90.0, -2.8421709430404007e-14, -89.70517533838742))
assert_equal(qt.tileextent(qt.tms_to_quadkey((2, 2, 2), google=True)),
(0.0, 90.0, -2.8421709430404007e-14, -89.70517533838742))
assert_equal(qt.tileextent(qt.tms_to_quadkey((8, 9, 4), google=True)),
(0.0, 22.5, -22.426293834596891, -44.852587669193753))
def test_quadtree_wts():
qt = cimgt.QuadtreeTiles()
ll_target_domain = sgeom.box(-15, 50, 0, 60)
multi_poly = qt.crs.project_geometry(ll_target_domain, ccrs.PlateCarree())
target_domain = multi_poly.geoms[0]
with pytest.raises(ValueError):
list(qt.find_images(target_domain, 0))
assert qt.tms_to_quadkey((1, 1, 1)) == '1'
assert qt.quadkey_to_tms('1') == (1, 1, 1)
assert qt.tms_to_quadkey((8, 9, 4)) == '1220'
assert qt.quadkey_to_tms('1220') == (8, 9, 4)
assert tuple(qt.find_images(target_domain, 1)) == ('0', '1')
assert tuple(qt.find_images(target_domain, 2)) == ('03', '12')
assert list(qt.subtiles('0')) == ['00', '01', '02', '03']
assert list(qt.subtiles('11')) == ['110', '111', '112', '113']
with pytest.raises(ValueError):
qt.tileextent('4')
assert_arr_almost(qt.tileextent(''), KNOWN_EXTENTS[(0, 0, 0)])
assert_arr_almost(qt.tileextent(qt.tms_to_quadkey((2, 0, 2), google=True)),
KNOWN_EXTENTS[(2, 0, 2)])
assert_arr_almost(qt.tileextent(qt.tms_to_quadkey((0, 2, 2), google=True)),
KNOWN_EXTENTS[(0, 2, 2)])
assert_arr_almost(qt.tileextent(qt.tms_to_quadkey((2, 0, 2), google=True)),
KNOWN_EXTENTS[(2, 0, 2)])
assert_arr_almost(qt.tileextent(qt.tms_to_quadkey((2, 2, 2), google=True)),
KNOWN_EXTENTS[(2, 2, 2)])
assert_arr_almost(qt.tileextent(qt.tms_to_quadkey((8, 9, 4), google=True)),
KNOWN_EXTENTS[(8, 9, 4)])
plt.show()
if title:
plt.savefig(title + '.png')
for provider in [
{
'tiler': cimgt.OSM(),
'title': 'OSM'
},
{
'tiler': cimgt.GoogleTiles(),
'title': 'GoogleTiles'
},
{
'tiler': cimgt.MapQuestOpenAerial(),
'title': 'MapQuestOpenAerial'
},
#this last one raises an errot which is managed
{
'tiler': cimgt.Stamen(),
'title': 'Stamen'
},
{
'tiler': cimgt.QuadtreeTiles(),
'title': 'QuadtreeTiles'
},
]:
print(provider['title'])
plot_track_with_tiles(provider['tiler'], title=provider['title'], lw=3)
def plot_point_dataset(netcdf_point_utils,
variable_to_map,
utm_bbox=None,
plot_title=None,
colour_scheme='binary',
point_size=10,
point_step=1):
'''
Function to plot data points on a map
@param netcdf_point_utils: NetCDFPointUtils object wrapping a netCDF dataset
@param variable_to_map: String specifying variable name for colour map
@param utm_bbox: UTM Bounding box of form [xmin, ymin, xmax, ymax] or None for all points. Default=None
@param plot_title: String to prefix before dataset title. Default=None for dataset title or dataset basename
@param colour_scheme: String specifying colour scheme for data points. Default='binary'
@param point_size: Point size for data points. Default=10
@param point_step: Point step between plotted points - used to skip points in dense datasets. Default=1
'''
def rescale_array(input_np_array, new_range_min=0, new_range_max=1):
old_min = input_np_array.min()
old_range = input_np_array.max() - old_min
new_range = new_range_max - new_range_min
scaled_np_array = (
(input_np_array - old_min) / old_range * new_range) + new_range_min
return scaled_np_array
if plot_title is None:
if hasattr(netcdf_point_utils.netcdf_dataset, 'title'):
plot_title = netcdf_point_utils.netcdf_dataset.title
else:
plot_title = netcdf_point_utils.netcdf_dataset.filepath()
utm_wkt, utm_coords = netcdf_point_utils.utm_coords(
netcdf_point_utils.xycoords)
utm_zone = get_spatial_ref_from_wkt(
utm_wkt).GetUTMZone() # -ve for Southern Hemisphere
southern_hemisphere = (utm_zone < 0)
utm_zone = abs(utm_zone)
projection = ccrs.UTM(zone=utm_zone,
southern_hemisphere=southern_hemisphere)
print('utm_zone = {}'.format(utm_zone))
#print(utm_coords)
variable = netcdf_point_utils.netcdf_dataset.variables[variable_to_map]
# Set geographic range of plot
if utm_bbox is None:
utm_bbox = [
np.min(utm_coords[:, 0]),
np.min(utm_coords[:, 1]),
np.max(utm_coords[:, 0]),
np.max(utm_coords[:, 1])
]
spatial_mask = np.ones(shape=variable.shape, dtype='Bool')
else:
spatial_mask = np.logical_and(
np.logical_and((utm_bbox[0] <= utm_coords[:, 0]),
(utm_coords[:, 0] <= utm_bbox[2])),
np.logical_and((utm_bbox[1] <= utm_coords[:, 1]),
(utm_coords[:, 1] <= utm_bbox[3])))
utm_coords = utm_coords[spatial_mask]
print('{} points in UTM bounding box: {}'.format(
np.count_nonzero(spatial_mask), utm_bbox))
#print(utm_coords)
colour_array = rescale_array(variable[spatial_mask], 0, 1)
fig = plt.figure(figsize=(30, 30))
ax = fig.add_subplot(1, 1, 1, projection=projection)
ax.set_title(plot_title)
#map_image = cimgt.OSM() # https://www.openstreetmap.org/about
#map_image = cimgt.StamenTerrain() # http://maps.stamen.com/
map_image = cimgt.QuadtreeTiles()
#print(map_image.__dict__)
ax.add_image(map_image, 10)
# Compute and set regular tick spacing
range_x = utm_bbox[2] - utm_bbox[0]
range_y = utm_bbox[3] - utm_bbox[1]
x_increment = pow(10.0, floor(log10(range_x))) / 2
y_increment = pow(10.0, floor(log10(range_y))) / 2
x_ticks = np.arange((utm_bbox[0] // x_increment + 1) * x_increment,
utm_bbox[2], x_increment)
y_ticks = np.arange((utm_bbox[1] // y_increment + 1) * y_increment,
utm_bbox[3], y_increment)
plt.xticks(x_ticks, rotation=45)
plt.yticks(y_ticks)
# set the x and y axis labels
plt.xlabel("Eastings (m)", rotation=0, labelpad=20)
plt.ylabel("Northings (m)", rotation=90, labelpad=20)
# See link for possible colourmap schemes: https://matplotlib.org/examples/color/colormaps_reference.html
cm = plt.cm.get_cmap(colour_scheme)
# build a scatter plot of the specified data, define marker, spatial reference system, and the chosen colour map type
sc = ax.scatter(utm_coords[::point_step, 0],
utm_coords[::point_step, 1],
marker='o',
c=colour_array[::point_step],
s=point_size,
alpha=0.9,
transform=projection,
cmap=cm)
# set the colour bar ticks and labels
try: # not all variables have units. These will fail on the try and produce the map without tick labels.
cb = plt.colorbar(sc, ticks=[0, 1])
cb.ax.set_yticklabels([
str(np.min(variable[spatial_mask])),
str(np.max(variable[spatial_mask]))
]) # vertically oriented colorbar
cb.set_label("{} {}".format(variable.long_name, variable.units))
except:
pass
plt.show()
def plot_survey_points(
netcdf_point_utils, # NetCDFPointUtils object wrapping a netCDF dataset
variable_to_map, # String specifying variable name for colour map
utm_bbox=None, # utm_bbox is of form [xmin, ymin, xmax, ymax]
colour_scheme='binary', # Colour map
point_size=10 # Size of point in plot
):
utm_wkt, utm_coords = netcdf_point_utils.utm_coords(
netcdf_point_utils.xycoords[:])
utm_zone = get_spatial_ref_from_wkt(
utm_wkt).GetUTMZone() # -ve for Southern Hemisphere
southern_hemisphere = (utm_zone < 0)
utm_zone = abs(utm_zone)
projection = ccrs.UTM(zone=utm_zone,
southern_hemisphere=southern_hemisphere)
print('utm_zone = {}'.format(utm_zone))
# print(utm_coords)
variable = netcdf_point_utils.netcdf_dataset.variables[variable_to_map]
# Set geographic range of plot
if utm_bbox is None:
utm_bbox = [
np.min(utm_coords[:, 0]),
np.min(utm_coords[:, 1]),
np.max(utm_coords[:, 0]),
np.max(utm_coords[:, 1])
]
spatial_mask = np.ones(shape=variable.shape, dtype='Bool')
else:
spatial_mask = np.logical_and(
np.logical_and((utm_bbox[0] <= utm_coords[:, 0]),
(utm_coords[:, 0] <= utm_bbox[2])),
np.logical_and((utm_bbox[1] <= utm_coords[:, 1]),
(utm_coords[:, 1] <= utm_bbox[3])))
utm_coords = utm_coords[spatial_mask]
print('UTM bounding box: {}'.format(utm_bbox))
# print(utm_coords)
print(netcdf_point_utils.dimensions['point'])
colour_array = rescale_array(variable[spatial_mask], 0, 1)
# map_image = cimgt.OSM() # https://www.openstreetmap.org/about
# map_image = cimgt.StamenTerrain() # http://maps.stamen.com/
map_image = cimgt.QuadtreeTiles()
fig = plt.figure(figsize=(30, 30))
ax = fig.add_subplot(1, 1, 1, projection=projection)
ax.set_title("Point Gravity Survey - " +
str(netcdf_point_utils.netcdf_dataset.getncattr('title')))
ax.add_image(map_image, 10)
print(utm_bbox)
# Compute and set regular tick spacing
range_x = utm_bbox[2] - utm_bbox[0]
range_y = utm_bbox[3] - utm_bbox[1]
print("range_x: ".format(range_x))
x_increment = pow(10.0, floor(log10(range_x))) / 2
y_increment = pow(10.0, floor(log10(range_y))) / 2
x_ticks = np.arange((utm_bbox[0] // x_increment + 1) * x_increment,
utm_bbox[2], x_increment)
y_ticks = np.arange((utm_bbox[1] // y_increment + 1) * y_increment,
utm_bbox[3], y_increment)
ax.set_xticks(x_ticks)
ax.set_yticks(y_ticks)
# set the x and y axis labels
ax.set_xlabel("Eastings (m)", rotation=0, labelpad=20)
ax.set_ylabel("Northings (m)", rotation=90, labelpad=20)
# See link for possible colourmap schemes: https://matplotlib.org/examples/color/colormaps_reference.html
cm = plt.cm.get_cmap(colour_scheme)
# build a scatter plot of the specified data, define marker, spatial reference system, and the chosen colour map type
sc = ax.scatter(utm_coords[:, 0],
utm_coords[:, 1],
marker='o',
c=colour_array,
s=point_size,
alpha=0.9,
transform=projection,
cmap=cm)
# set the colour bar ticks and labels
cb = plt.colorbar(sc, ticks=[0, 1])
cb.ax.set_yticklabels([str(np.min(variable)),
str(np.max(variable))
]) # vertically oriented colorbar
cb.set_label("{} {}".format(variable.long_name, variable.units))
#plt.show()
plt.savefig("C:\\Users\\u62231\\Desktop\\GravityWork\\maps\\{}".format(
netcdf_point_utils.netcdf_dataset.getncattr('title')))