def __init__(self, **proj_info):
mpl.use(MPL_BACKEND)
import matplotlib.pyplot as plt
if not proj_info:
proj_info = {
'central_latitude': 58,
'central_longitude': 16,
'satellite_height': 35785831,
'false_easting': 0,
'false_northing': 0,
'globe': None
}
fig = plt.figure(figsize=(8, 6))
self._ax = fig.add_subplot(
1, 1, 1, projection=ccrs.NearsidePerspective(**proj_info))
self._ax.add_feature(cfeature.OCEAN, zorder=0)
self._ax.add_feature(cfeature.LAND, zorder=0, edgecolor='black')
self._ax.add_feature(cfeature.BORDERS, zorder=0)
self._ax.set_global()
self._ax.gridlines()
def setUp(self):
self.latitude_of_projection_origin = 0.0
self.longitude_of_projection_origin = 0.0
self.perspective_point_height = 38204820000.0
self.false_easting = 0.0
self.false_northing = 0.0
self.semi_major_axis = 6377563.396
self.semi_minor_axis = 6356256.909
self.ellipsoid = GeogCS(self.semi_major_axis, self.semi_minor_axis)
self.globe = ccrs.Globe(semimajor_axis=self.semi_major_axis,
semiminor_axis=self.semi_minor_axis,
ellipse=None)
# Actual and expected coord system can be re-used for
# VerticalPerspective.test_crs_creation and test_projection_creation.
self.expected = ccrs.NearsidePerspective(
central_longitude=self.longitude_of_projection_origin,
central_latitude=self.latitude_of_projection_origin,
satellite_height=self.perspective_point_height,
false_easting=self.false_easting,
false_northing=self.false_northing,
globe=self.globe)
self.vp_cs = VerticalPerspective(self.latitude_of_projection_origin,
self.longitude_of_projection_origin,
self.perspective_point_height,
self.false_easting,
self.false_northing, self.ellipsoid)
def nearside_perspective(experiment: Experiment):
proj = ccrs.NearsidePerspective(experiment.grid.target_lon,
experiment.grid.target_lat)
ax = plt.subplot(1, 1, 1, projection=proj)
ax.set_global()
ax.coastlines(linewidth=0.8)
figax = FigureAxes(ax, proj)
return figax
def as_cartopy_crs(self):
globe = self._ellipsoid_to_globe(self.ellipsoid, ccrs.Globe())
return ccrs.NearsidePerspective(
central_latitude=self.latitude_of_projection_origin,
central_longitude=self.longitude_of_projection_origin,
satellite_height=self.perspective_point_height,
false_easting=self.false_easting,
false_northing=self.false_northing,
globe=globe)
def test_central_latitude():
# Check the effect of the added 'central_latitude' key.
geos = ccrs.NearsidePerspective(central_latitude=53.7)
other_args = {'a=6378137.0', 'h=35785831', 'lat_0=53.7', 'lon_0=0.0',
'units=m', 'x_0=0', 'y_0=0'}
check_proj_params('nsper', geos, other_args)
assert_almost_equal(geos.boundary.bounds,
(-5476336.098, -5476336.098,
5476336.098, 5476336.098),
decimal=3)
def plot_lon_zones(view, **kwargs):
if view == "full":
projection = ccrs.NearsidePerspective(central_latitude=60,
central_longitude=30,
satellite_height=8000000)
# projection = ccrs.Orthographic(central_latitude=50, central_longitude=5)
# projection = ccrs.Miller()
elif view == "zoom":
projection = ccrs.Stereographic(central_latitude=60,
central_longitude=20)
else:
raise RuntimeError("unknown view.")
bootstrap.fig(5, 5)
ax = plt.axes(projection=projection)
ax.add_feature(land)
lat_prev = 87
for NL in np.arange(2, 60):
lat = flat(NL)
ax.plot(
np.linspace(-180, 180, 1000),
lat * np.ones(1000),
transform=ccrs.Geodetic(),
color="gray",
lw=0.5,
)
dlon = 360 / NL
for lon in np.arange(0, 360, dlon):
ax.plot((lon, lon), (lat, lat_prev),
transform=ccrs.Geodetic(),
color="k",
lw=1)
dlon = 360 / (NL - 1)
for lon in np.arange(0, 360, dlon):
ax.plot(
(lon, lon),
(lat, lat_prev),
transform=ccrs.Geodetic(),
color="gray",
# ls=":",
lw=1,
)
lat_prev = lat
if view == "zoom":
ax.set_extent([-5, 60, 60, 90])
return plt
def test_default():
geos = ccrs.NearsidePerspective()
other_args = {'a=6378137.0', 'h=35785831', 'lat_0=0.0', 'lon_0=0.0',
'units=m', 'x_0=0', 'y_0=0'}
check_proj_params('nsper', geos, other_args)
assert_almost_equal(geos.boundary.bounds,
(-5476336.098, -5476336.098,
5476336.098, 5476336.098),
decimal=3)
def test_offset():
geos = ccrs.NearsidePerspective(false_easting=5000000,
false_northing=-123000,)
other_args = {'a=6378137.0', 'h=35785831', 'lat_0=0.0', 'lon_0=0.0',
'units=m', 'x_0=5000000', 'y_0=-123000'}
check_proj_params('nsper', geos, other_args)
assert_almost_equal(geos.boundary.bounds,
(-476336.098, -5599336.098,
10476336.098, 5353336.098),
decimal=4)
def test_smooth_boundary_NearsidePerspective(lon, lat, sat_height):
lon = -100
lat = -40
sat_height = 35785831
pr = ccrs.NearsidePerspective(central_longitude=lon,
central_latitude=lat,
satellite_height=sat_height)
# modify the projection with smooth boundary
pr_mod = _smooth_boundary_NearsidePerspective(pr)
assert pr.proj4_params == pr_mod.proj4_params
assert pr.globe == pr_mod.globe
def quick_reg_plot(cube,figdims=(15,10)):
proj=ccrs.NearsidePerspective(central_latitude=60,central_longitude=-20)
K=np.shape(cube)[0]
fig=plt.figure()
axes=[]
for i in range(K):
ax=plt.subplot(1,K,i+1,projection=proj)
quick_contourf(cube[i],ax=ax)
ax.set_title(f"Cluster {i+1}")
axes.append(ax)
fig.set_figwidth(figdims[0])
fig.set_figheight(figdims[1])
return fig,axes
def plot_30y_area(data = None, mask_res = None, years = 31, cmap= "Reds", save_name = False):
fig = plt.figure(figsize = (9,27))
gs = gridspec.GridSpec(3, 1)
ax = fig.add_subplot(gs[0,0],\
projection=ccrs.NearsidePerspective(
central_latitude=50.72,
central_longitude=10.53,
satellite_height=10000000.0))
ax.coastlines(resolution='110m')
ax.gridlines()
plot_data = data.t2m.where(mask_res, other = np.nan).max("time")
pm, ct = area_plot(data= plot_data, ax = ax, \
levels = np.arange(28, 42, 0.5), \
contour_levels= np.arange(25,40,2), cmap = cmap,
landcolor = [0.4,0.4,0.4])
def make_plot2(arctic_circle_lat,origin_lon,origin_lat,does_this_route_pass_through_arctic,dest_airports,airport_code): import matplotlib.pyplot as plt, mpld3 fig, ax = plt.subplots() #ax = plt.plot([4,5,6],[6,4,2],'ok') ax = plt.axes(projection=ccrs.NearsidePerspective(central_latitude=80.0,central_longitude=origin_lon)) ax.stock_img() ax.text(0, 90, '+',horizontalalignment='right',transform=ccrs.Geodetic()) ax.plot(np.linspace(0,359,360), np.ones(360)*arctic_circle_lat,color='black', linewidth=2, ls='--',transform=ccrs.Geodetic()) ax.text(origin_lon, origin_lat, airport_code, transform=ccrs.Geodetic()) for i in range(len(dest_airports)): if does_this_route_pass_through_arctic[i]: ax.plot([origin_lon,np.float(dest_airports.loc[i].lon)],[origin_lat,np.float(dest_airports.loc[i].lat)],color=color_list[route_count],linewidth=2,marker='o',transform=ccrs.Geodetic()) ax.text(np.float(dest_airports.loc[i].lon),np.float(dest_airports.loc[i].lat),dest_airports.loc[i].code,transform=ccrs.Geodetic()) route_count += 1 html_text = mpld3.fig_to_html(fig) return html_text
def quick_contourf(cube,cmap=cm.balance,clevs=None,ax=None):
proj=ccrs.NearsidePerspective(central_latitude=60,central_longitude=-20)
if ax is None:
fig=plt.figure()
ax=plt.subplot(1,1,1,projection=proj)
if clevs is None:
clevs=np.linspace(-abs(cube.data).max(),abs(cube.data).max(),21)
ax.coastlines()
ax.set_global()
plot=iplt.contourf(cube, levels=clevs, cmap=cmap,extend="both",axes=ax)
cbar=plt.colorbar(orientation="horizontal",mappable=plot,ax=ax)
return plot,ax
def draw():
fig = plt.figure(figsize=(60, 30))
pixelData, lat, long = data()
north = fig.add_subplot(1, 3, 1, projection=ccrs.Orthographic(0, 90))
north.gridlines()
north.coastlines()
north.stock_img()
north.imshow(pixelData,
vmin=0,
vmax=100,
transform=ccrs.PlateCarree(),
extent=(-180, 180, -90, 90),
origin="lower",
cmap=colorMap())
all = fig.add_subplot(1,
3,
2,
projection=ccrs.NearsidePerspective(
long, lat, satellite_height=5000000, globe=None))
all.gridlines()
all.coastlines()
all.stock_img()
all.imshow(pixelData,
vmin=0,
vmax=100,
transform=ccrs.PlateCarree(),
extent=(-180, 180, 0, 90),
origin="lower",
cmap=colorMap())
south = fig.add_subplot(1, 3, 3, projection=ccrs.Orthographic(180, -90))
south.gridlines()
south.coastlines()
south.stock_img()
south.imshow(pixelData,
vmin=0,
vmax=100,
transform=ccrs.PlateCarree(),
extent=(-180, 180, -90, 0),
origin="lower",
cmap=colorMap())
plt.show()
def make_plot(arctic_circle_lat,origin_lon,origin_lat,does_this_route_pass_through_arctic,dest_airports,airport_code): fig=Figure() ax=fig.add_subplot(111) ax = plt.axes(projection=ccrs.NearsidePerspective(central_latitude=80.0,central_longitude=origin_lon)) ax.stock_img() plt.text(0, 90, '+',horizontalalignment='right',transform=ccrs.Geodetic()) plt.plot(np.linspace(0,359,360), np.ones(360)*arctic_circle_lat,color='black', linewidth=2, ls='--',transform=ccrs.Geodetic()) plt.text(origin_lon, origin_lat, airport_code, transform=ccrs.Geodetic()) N_routes = does_this_route_pass_through_arctic.sum() color_list = plt.cm.Set1(np.linspace(0, 1, N_routes)) route_count = 0 for i in range(len(dest_airports)): if does_this_route_pass_through_arctic[i]: plt.plot([origin_lon,np.float(dest_airports.loc[i].lon)],[origin_lat,np.float(dest_airports.loc[i].lat)],color=color_list[route_count],linewidth=2,marker='o',transform=ccrs.Geodetic()) plt.text(np.float(dest_airports.loc[i].lon),np.float(dest_airports.loc[i].lat),dest_airports.loc[i].code,transform=ccrs.Geodetic()) route_count += 1 img = io.BytesIO() # create the buffer plt.savefig(img, format='png') # save figure to the buffer img.seek(0) # rewind your buffer plot_data = urllib.parse.quote(base64.b64encode(img.read()).decode()) # base64 encode & URL-escape return plot_data
def heatmap(freq_array,
export=False,
dest_fn="wind_history_heatmap",
is_hu_only=False,
subtitle=None):
if subtitle == None:
subtitle = ""
freq_array_transparent = np.ma.masked_equal(freq_array, 0)
projection = ccrs.NearsidePerspective(central_longitude=-55,
central_latitude=30,
satellite_height=10000000)
lon = np.linspace(-180, 180, 361) # changed from 179
lat = np.linspace(-90, 90, 181) # changed from 89
Lon, Lat = np.meshgrid(lon, lat)
fig = plt.figure(figsize=(10, 10))
ax = plt.axes(projection=projection)
ax.set_global()
ax.coastlines()
mesh = ax.pcolormesh(Lon,
Lat,
freq_array_transparent,
transform=ccrs.PlateCarree())
plt.colorbar(mesh)
if is_hu_only:
plt.title(
f"Number of Cyclones Producing Hurricane-Force Winds /n {subtitle}",
fontsize=20)
else:
plt.title(f"Number of Cyclones Producing TS-Force Winds \n {subtitle}",
fontsize=20)
if export:
fig.savefig(f"../results/images/{dest_fn}.jpg")
return
def _smooth_boundary_NearsidePerspective(
central_longitude=0.0,
central_latitude=0.0,
satellite_height=35785831,
false_easting=0,
false_northing=0,
globe=None,
):
proj = ccrs.NearsidePerspective(
central_longitude=central_longitude,
central_latitude=central_latitude,
satellite_height=satellite_height,
false_easting=false_easting,
false_northing=false_northing,
globe=globe,
)
# workaround for a smoother outer boundary
# (https://github.com/SciTools/cartopy/issues/613)
# Re-implement the cartopy code to figure out the boundary.
# This is just really a guess....
WGS84_SEMIMAJOR_AXIS = 6378137.0
# because I cannot import it above...this should be fixed upstream
# anyways...
a = proj.globe.semimajor_axis or WGS84_SEMIMAJOR_AXIS
h = np.float(satellite_height)
max_x = a * np.sqrt(h / (2 * a + h))
coords = ccrs._ellipse_boundary(max_x,
max_x,
false_easting,
false_northing,
n=361)
proj._boundary = sgeom.LinearRing(coords.T)
return proj
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
plt.figure(figsize=(3, 3))
ax = plt.axes(projection=ccrs.NearsidePerspective(central_latitude=50.72,
central_longitude=-3.53,
satellite_height=10000000.0))
ax.coastlines(resolution='110m')
ax.gridlines()
################
# begin
opj = os.path.join
dirL3 = "L3_summer"
# from oc_tool import utils as u
projet = '/local/AIX/tristan.harmel/project/ardyna/'
odirfig = opj(projet, 'fig/yearly')
# load image data
# ds = xr.open_mfdataset(files, concat_dim='time', preprocess=u.get_time, mask_and_scale=True)
# ds = ds.dropna('time', how='all')
crs = ccrs.NearsidePerspective(100, 71)
land_feat = cpy.feature.NaturalEarthFeature(
'physical',
'land',
'50m',
edgecolor='face',
facecolor=cpy.feature.COLORS['land'])
extent = [90, 180, 71, 78]
# file = files[14]
crs = ccrs.PlateCarree()
crs1 = ccrs.NorthPolarStereo()
# crs1 = ccrs.NearsidePerspective(100, 71)
params = ['chlor_a_mean', 'chlor_a_sigma', 'num_obs']
params = ['chlor_a_p50_mean_mean', 'chlor_a_p50_sigma', 'num_obs_mean']
if not os.path.exists(odirfig):
def trajPlot(root, grids=True, title=None, zscores=None):
fig = plt.figure(figsize=[10, 8]) # setup fig
ax = fig.add_subplot(projection=ccrs.NearsidePerspective(-38,
72)) # subplots
fig.subplots_adjust(
top=0.970, # adjust sizing
bottom=0.012,
left=.002,
right=.981,
hspace=0.27,
wspace=0.02)
ax.coastlines(zorder=3) # add coastlines
ax.stock_img() # stock background
if grids is True:
ax.gridlines() # gridlines
ax.set_title(title)
# plot summit
ax.plot(-38.4592,
72.5796,
marker='*',
color='orange',
markersize=10,
alpha=0.9,
transform=ccrs.Geodetic(),
label='Summit')
# create colormap
cmap = cm.winter # cmap
colors = cmap(np.arange(256)) # color linspace
minz = min(zscores['zscores'].values) # max and min z scores
maxz = max(zscores['zscores'].values)
zscorechart = np.linspace(minz, maxz,
num=256) # equal z score linspace to match
# plot trajectories
frame = []
for filename in os.listdir(root):
colnames = [
'recep', 'v2', 'yr', 'mo', 'dy', 'hr', 'na', 'na1', 'backhrs',
'lat', 'long', 'alt', 'pres'
]
data = pd.read_csv(
root + '\\' + filename,
header=None, # read trajectories
delim_whitespace=True,
error_bad_lines=False,
names=colnames)
data.dropna(axis=0, how='any', inplace=True) # drop NaNs
badcols = ['recep', 'v2', 'na', 'na1'] # drop poor columns
data.drop(badcols, axis=1, inplace=True)
data.reset_index(drop=True, inplace=True)
data['datetime'] = createDatetime(
data['yr'].values, # create datetimes
data['mo'].values,
data['dy'].values,
data['hr'].values)
data.drop(['yr', 'mo', 'dy', 'hr'], axis=1,
inplace=True) # drop old dates
merged = pd.merge_asof(
data.sort_values('datetime'),
zscores, # merge with zscores
on='datetime',
direction='nearest',
tolerance=pd.Timedelta('1 hours'))
frame.append(merged) # append to frame
track = sgeom.LineString(zip(data['long'].values,
data['lat'].values)) # create trajectory
currentz = np.nanmax(
merged['zscores'].values) # identify z value of trajectory
nearmatch = zscorechart.flat[np.abs(
zscorechart - currentz).argmin()] # identify nearest match
index = np.where(
zscorechart == nearmatch)[0][0] # identify index in zscorechart
z_color = tuple(colors[index].tolist()) # backsearch for color tuple
ax.add_geometries(
[track],
ccrs.PlateCarree(), # add to plot
facecolor='none',
edgecolor=z_color,
linewidth=0.5,
label='Trajectories')
traj = pd.concat(frame, axis=0, ignore_index=True) # concat plots
# plot fires
fire = fireData() # import fire data
matches = [] # matches preallocated
for row in fire.iterrows(): # loop thru each fire
decplace = 0 # num of decimal places to match to
# identify if fire is in a back trajectory path at same times
lats = np.in1d(np.around(traj['lat'].values, decplace),
np.around(row[1]['latitude'], decplace))
longs = np.in1d(np.around(traj['long'].values, decplace),
np.around(row[1]['longitude'], decplace))
combos = traj[lats & longs].values.tolist() # lat/long at same time
matches.append(combos) # append the combo
projection = ccrs.Geodetic() # fire projection to use
if combos:
ax.plot(
row[1]['longitude'],
row[1]['latitude'], # if combo, make purple
marker='.',
color='purple',
markersize=8,
alpha=0.7,
transform=projection)
else:
ax.plot(
row[1]['longitude'],
row[1]['latitude'], # else, make red
marker='.',
color='red',
markersize=4,
alpha=0.7,
transform=projection)
# create colorbar
cbar_ax = fig.add_axes([.88, 0.15, 0.05, 0.7]) # add fig axes
norm = mpl.colors.Normalize(vmin=minz, vmax=maxz) # normalize units
cb = mpl.colorbar.ColorbarBase(
cbar_ax,
cmap=cmap, # create the colorbar
orientation='vertical',
norm=norm)
cb.set_label('Z-Score Magnitude') # give title
legend_elements = [
mpatches.Rectangle((0, 0), 1, 1,
facecolor='orange'), # create legend elements
mpatches.Rectangle((0, 0), 1, 1, facecolor='red'),
mpatches.Rectangle((0, 0), 1, 1, facecolor='purple'),
mpatches.Rectangle((0, 0), 1, 1, facecolor=tuple(colors[0].tolist()))
]
labels = [
'Summit',
'VIIRS Fires',
'Intersected Fire', # label creation
'Trajectory'
]
ax.legend(
handles=legend_elements,
loc='lower left', # create legend
fancybox=True,
labels=labels,
bbox_to_anchor=(-0.1, 0.01))
matches = pd.DataFrame(matches) # dataframe matches
matches.dropna(axis=0, how='any', inplace=True) # drop nans
print(f'{len(matches.columns)} intersections were '
f'found between a back trajectory and a fire')
plt.show()
def GenerateBasemapImageAutomated(DataDirectory,
RasterFile,
FigWidthInches=4,
FigHeightInches=3,
FigFormat="png",
fig_dpi=500,
regional_extent_multiplier=5,
label_spacing_multiplier=0.5,
out_fname_prefix="",
is_orthographic=False):
"""
This makes the basemap image. Uses data from the raster to size the figure and locate the centrepoint
Args:
DataDirectory (str): The directory of the raster file
RasterFile (str): the name of the raster file (include extension)
FigWidthInches (flt): How wide you want the basemap
FigHeightInches (float): How high you want your basemap
FigFormat (str): Figure format, can be `png`, `svg`, `pdf`, etc.
fig_dpi (int): Dots per inch of your figure
regional_extent_multiplier (float): How much bigger you want the extent vs the size of the raster
label_spacing_multiplier (float): If the meridians and parallels are too close, increase this number. Default of 0.5
out_fname_prefix (str): The prefix of the image file. If blank uses the fname_prefix
Author: SMM
Date: 01/02/2018
"""
# Make sure data directory is in correct format
if not DataDirectory.endswith(os.sep):
print(
"You forgot the separator at the end of the directory, appending..."
)
DataDirectory = DataDirectory + os.sep
# Set up the figure. This is needed to both size the figure and get the axis handle for plotting polygons
fig = plt.figure(figsize=(FigWidthInches, FigHeightInches))
print("The size is: " + str(FigWidthInches) + ", " + str(FigHeightInches))
# get some filenames
RasterSplit = RasterFile.split(".")
Raster_prefix = RasterSplit[0]
Shape_name = DataDirectory + Raster_prefix + "_footprint.shp"
SName = "Shape"
# Get the name of the image
if len(out_fname_prefix) == 0:
FigFileName = DataDirectory + Base_file + "_basemap." + FigFormat
else:
FigFileName = DataDirectory + out_fname_prefix + "_basemap." + FigFormat
# Now we get the extents from the raster
centre_lat, centre_long, extent_lat, extent_long, xproj_extent, yproj_extent = LSDMGDAL.GetCentreAndExtentOfRaster(
DataDirectory, RasterFile)
# Calculate the aspect ratio
aspect_ratio = BasemapExtentSizer(FigWidthInches, FigHeightInches)
# Figure out the longest dimension
long_dimension = xproj_extent
if yproj_extent > long_dimension:
long_dimension = yproj_extent
print("The long dimension is: " + str(long_dimension))
# Get the full extent by mulitplying the longest extent by the multiplier
full_dimension = long_dimension * regional_extent_multiplier
# now get the two dimensions for the extent of the figure
print("The aspect ratio is: " + str(aspect_ratio))
if aspect_ratio > 1:
# This is when the figure is wider than tall
x_ext = full_dimension * aspect_ratio
y_ext = full_dimension
full_extent_long = extent_long * aspect_ratio * regional_extent_multiplier
full_extent_lat = extent_long * regional_extent_multiplier
else:
x_ext = full_dimension
y_ext = full_dimension * aspect_ratio
full_extent_long = extent_lat * regional_extent_multiplier
full_extent_lat = extent_lat * aspect_ratio * regional_extent_multiplier
extents = [
centre_long - 0.5 * full_extent_long,
centre_long + 0.5 * full_extent_long,
centre_lat - 0.5 * full_extent_lat, centre_lat + 0.5 * full_extent_lat
]
print("Extents are: ")
print(extents)
# Now we set up the extents and coordinate system
#if (is_orthographic):
# ax = fig.add_axes([0, 0, 1, 1], projection=ccrs.Orthographic(centre_lat, #centre_long))
#
# ax.add_feature(cfeature.LAND)
# ax.add_feature(cfeature.OCEAN, edgecolor='black')
# ax.set_global()
# ax.gridlines()
if (is_orthographic):
print("I am setting up an orthographic projection.")
ax = fig.add_axes([0, 0, 1, 1],
projection=ccrs.NearsidePerspective(
central_latitude=centre_lat,
central_longitude=centre_long,
satellite_height=10000000.0))
ax.coastlines(resolution='110m', linewidth=0.5)
borders_110m = cfeature.NaturalEarthFeature(
'cultural',
'admin_0_boundary_lines_land',
'110m',
edgecolor='face',
facecolor=cfeature.COLORS['land'])
ax.add_feature(borders_110m,
edgecolor='black',
facecolor="none",
linewidth=0.5)
ax.gridlines()
else:
print("The projection is not orthographic.")
ax = fig.add_axes([0, 0, 1, 1], projection=ccrs.PlateCarree())
print("Setting extent.")
ax.set_extent(extents, ccrs.PlateCarree())
print("Finished setting extent.")
land_50m = cfeature.NaturalEarthFeature(
'physical',
'land',
'50m',
edgecolor='face',
facecolor=cfeature.COLORS['land'])
borders_50m = cfeature.NaturalEarthFeature(
'cultural',
'admin_0_boundary_lines_land',
'50m',
edgecolor='face',
facecolor=cfeature.COLORS['land'])
ax.add_feature(land_50m, edgecolor='black', linewidth=0.5)
ax.add_feature(borders_50m,
edgecolor='black',
facecolor="none",
linewidth=0.5)
#ax.add_feature(cfeature.LAND)
#ax.add_feature(cfeature.OCEAN)
#ax.add_feature(cfeature.COASTLINE)
#ax.add_feature(cfeature.BORDERS, linestyle='--')
#ax.add_feature(cfeature.LAKES, alpha=0.5)
#ax.add_feature(cfeature.RIVERS)
# create the shapefile
print("Making the shapefile.")
LSDMGDAL.CreateShapefileOfRasterFootprint(DataDirectory, RasterFile)
ax.add_geometries(shpreader.Reader(Shape_name).geometries(),
ccrs.PlateCarree(),
edgecolor='black',
facecolor='green',
alpha=0.5,
linewidth=0.5)
ax.gridlines(draw_labels=False, dms=True, x_inline=False, y_inline=False)
#plt.tight_layout()
plt.savefig(FigFileName, format=FigFormat, dpi=fig_dpi)
def rotating_globe(da,
fig,
timestamp,
framedim="time",
plotmethod=None,
plot_variable=None,
overlay_variables=None,
lon_start=-110,
lon_rotations=0.5,
lat_start=25,
lat_rotations=0,
land=False,
gridlines=False,
coastline=True,
style=None,
debug=False,
**kwargs):
# rotate lon_rotations times throughout movie and start at lon_start
lon = np.linspace(0, 360 * lon_rotations, len(da[framedim])) + lon_start
# Same for lat
lat = np.linspace(0, 360 * lat_rotations, len(da[framedim])) + lat_start
# proj = ccrs.Orthographic(lon[timestamp], lat[timestamp])
# proj = _smooth_boundary_globe(proj)
# This looks more like a 3D globe in my opinion
proj = ccrs.NearsidePerspective(central_longitude=lon[timestamp],
central_latitude=lat[timestamp])
proj = _smooth_boundary_NearsidePerspective(proj)
subplot_kw = dict(projection=proj)
# mapping style kwargs
map_style_kwargs = dict(transform=ccrs.PlateCarree())
kwargs.update(map_style_kwargs)
# create axis (TODO:this should be handled by the basic preset )
ax = fig.subplots(subplot_kw=subplot_kw)
data = _check_input(da, plot_variable)
pp = _base_plot(ax,
data,
timestamp,
framedim,
plotmethod=plotmethod,
**kwargs)
_set_style(fig, ax, pp, style=style)
ax.set_title("")
ax.set_global()
# set style (TODO: move this to the basic function including the set style)
if land:
_add_land(ax, style)
if coastline:
_add_coast(ax, style)
if gridlines:
gl = ax.gridlines()
# Increase gridline res
gl.n_steps = 500
# for now fixed locations
gl.xlocator = mticker.FixedLocator(range(-180, 181, 30))
gl.ylocator = mticker.FixedLocator(range(-90, 91, 30))
else:
gl = None
# i should output this to test the preset. Maybe a dict output for the pp and gl (and potentially others)?
# need a way to do that for the outline too
# possibly for future versions, but I need a way to increase results
# ax.outline_patch.set_visible(False)
return ax, pp
def get_map_projection(
proj_name,
central_longitude=0.0,
central_latitude=0.0,
false_easting=0.0,
false_northing=0.0,
globe=None,
standard_parallels=(20.0, 50.0),
scale_factor=None,
min_latitude=-80.0,
max_latitude=84.0,
true_scale_latitude=None,
latitude_true_scale=None, ### BOTH
secant_latitudes=None,
pole_longitude=0.0,
pole_latitude=90.0,
central_rotated_longitude=0.0,
sweep_axis='y',
satellite_height=35785831,
cutoff=-30,
approx=None,
southern_hemisphere=False,
zone=15): #### numeric UTM zone
proj_name = proj_name.lower()
if (proj_name == 'albersequalarea'):
proj = ccrs.AlbersEqualArea(central_longitude=central_longitude,
central_latitude=central_latitude,
false_easting=false_easting,
false_northing=false_northing,
globe=globe,
standard_parallels=standard_parallels)
elif (proj_name == 'azimuthalequidistant'):
proj = ccrs.AzimuthalEquidistant(central_longitude=central_longitude,
central_latitude=central_latitude,
false_easting=false_easting,
false_northing=false_northing,
globe=globe)
elif (proj_name == 'equidistantconic'):
proj = ccrs.EquidistantConic(central_longitude=central_longitude,
central_latitude=central_latitude,
false_easting=false_easting,
false_northing=false_northing,
globe=globe,
standard_parallels=standard_parallels)
elif (proj_name == 'lambertconformal'):
proj = ccrs.LambertConformal(
central_longitude=-96.0, ##########
central_latitude=39.0, ##########
false_easting=false_easting,
false_northing=false_northing,
globe=globe,
secant_latitudes=None,
standard_parallels=None, ## default: (33,45)
cutoff=cutoff)
elif (proj_name == 'lambertcylindrical'):
proj = ccrs.LambertCylindrical(central_longitude=central_longitude)
elif (proj_name == 'mercator'):
proj = ccrs.Mercator(central_longitude=central_longitude,
min_latitude=min_latitude,
max_latitude=max_latitude,
latitude_true_scale=latitude_true_scale,
false_easting=false_easting,
false_northing=false_northing,
globe=globe,
scale_factor=None) #########
elif (proj_name == 'miller'):
proj = ccrs.Miller(central_longitude=central_longitude, globe=globe)
elif (proj_name == 'mollweide'):
proj = ccrs.Mollweide(central_longitude=central_longitude,
false_easting=false_easting,
false_northing=false_northing,
globe=globe)
elif (proj_name == 'orthographic'):
proj = ccrs.Orthographic(central_longitude=central_longitude,
central_latitude=central_latitude,
globe=globe)
elif (proj_name == 'robinson'):
proj = ccrs.Robinson(central_longitude=central_longitude,
false_easting=false_easting,
false_northing=false_northing,
globe=globe)
elif (proj_name == 'sinusoidal'):
proj = ccrs.Sinusoidal(central_longitude=central_longitude,
false_easting=false_easting,
false_northing=false_northing,
globe=globe)
elif (proj_name == 'stereographic'):
proj = ccrs.Stereographic(central_latitude=central_latitude,
central_longitude=central_longitude,
false_easting=false_easting,
false_northing=false_northing,
globe=globe,
true_scale_latitude=true_scale_latitude,
scale_factor=scale_factor)
elif (proj_name == 'transversemercator'):
proj = ccrs.TransverseMercator(
central_longitude=central_longitude,
central_latitude=central_latitude,
false_easting=false_easting,
false_northing=false_northing,
globe=globe,
scale_factor=1.0, ##########
approx=approx)
elif (proj_name == 'utm'):
proj = ccrs.UTM(zone,
southern_hemisphere=southern_hemisphere,
globe=globe)
elif (proj_name == 'interruptedgoodehomolosine'):
proj = ccrs.InterruptedGoodeHomolosine(
central_longitude=central_longitude, globe=globe)
elif (proj_name == 'rotatedpole'):
proj = ccrs.RotatedPole(
pole_longitude=pole_longitude,
pole_latitude=pole_latitude,
globe=globe,
central_rotated_longitude=central_rotated_longitude)
elif (proj_name == 'osgb'):
proj = ccrs.OSGB(approx=approx)
elif (proj_name == 'europp'):
proj = ccrs.EuroPP
elif (proj_name == 'geostationary'):
proj = ccrs.Geostationary(central_longitude=central_longitude,
satellite_height=satellite_height,
false_easting=false_easting,
false_northing=false_northing,
globe=globe,
sweep_axis=sweep_axis)
elif (proj_name == 'nearsideperspective'):
proj = ccrs.NearsidePerspective(central_longitude=central_longitude,
central_latitude=central_latitude,
satellite_height=satellite_height,
false_easting=false_easting,
false_northing=false_northing,
globe=globe)
elif (proj_name == 'eckerti'):
proj = ccrs.EckertI(central_longitude=central_longitude,
false_easting=false_easting,
false_northing=false_northing,
globe=globe)
elif (proj_name == 'eckertii'):
proj = ccrs.EckertII(central_longitude=central_longitude,
false_easting=false_easting,
false_northing=false_northing,
globe=globe)
elif (proj_name == 'eckertiii'):
proj = ccrs.EckertIII(central_longitude=central_longitude,
false_easting=false_easting,
false_northing=false_northing,
globe=globe)
elif (proj_name == 'eckertiv'):
proj = ccrs.EckertIV(central_longitude=central_longitude,
false_easting=false_easting,
false_northing=false_northing,
globe=globe)
elif (proj_name == 'eckertv'):
proj = ccrs.EckertV(central_longitude=central_longitude,
false_easting=false_easting,
false_northing=false_northing,
globe=globe)
elif (proj_name == 'eckertvi'):
proj = ccrs.EckertVI(central_longitude=central_longitude,
false_easting=false_easting,
false_northing=false_northing,
globe=globe)
elif (proj_name == 'equalearth'):
proj = ccrs.EqualEarth(central_longitude=central_longitude,
false_easting=false_easting,
false_northing=false_northing,
globe=globe)
elif (proj_name == 'gnomonic'):
proj = ccrs.Gnomonic(central_latitude=central_latitude,
central_longitude=central_longitude,
globe=globe)
elif (proj_name == 'lambertazimuthalequalarea'):
proj = ccrs.LambertAzimuthalEqualArea(
central_longitude=central_longitude,
central_latitude=central_latitude,
globe=globe,
false_easting=false_easting,
false_northing=false_northing)
elif (proj_name == 'northpolarstereo'):
proj = ccrs.NorthPolarStereo(central_longitude=central_longitude,
true_scale_latitude=true_scale_latitude,
globe=globe)
elif (proj_name == 'osni'):
proj = ccrs.OSNI(approx=approx)
elif (proj_name == 'southpolarstereo'):
proj = ccrs.SouthPolarStereo(central_longitude=central_longitude,
true_scale_latitude=true_scale_latitude,
globe=globe)
else:
# This is same as "Geographic coordinates"
proj = ccrs.PlateCarree(central_longitude=central_longitude,
globe=globe)
return proj
lat_mars = nc["y"][y0:y1]
z_mars = nc["z"][y0:y1, x0:x1]
## plot data
# vmin vmax for pcolormesh
# use percentiles to ignore outliers
lev_moon = (np.percentile(z_moon, 2), np.percentile(z_moon, 99.8))
lev_mars = (np.percentile(z_mars, 1), np.percentile(z_mars, 99.9))
# figure
fig = plt.figure(figsize=(8, 4))
# map projection
moonproj = ccrs.NearsidePerspective(central_latitude=0.,
central_longitude=250,
satellite_height=40000000.0)
marsproj = ccrs.NearsidePerspective(central_latitude=0.,
central_longitude=270,
satellite_height=40000000.0)
contourfproj = ccrs.RotatedPole(pole_longitude=180.)
ax_moon = fig.add_subplot(1, 2, 1, projection=moonproj)
ax_mars = fig.add_subplot(1, 2, 2, projection=marsproj)
plt.tight_layout(rect=[-.2, .12, 1.2, 0.95])
fig.subplots_adjust(wspace=-0.4, hspace=0.)
cax_moon = fig.add_axes([0.06, 0.11, 0.38, 0.03])
cax_mars = fig.add_axes([0.56, 0.11, 0.38, 0.03])
ax_moon.set_global()
def set_proj(projection='Robinson', proj_default=True):
""" Set the projection for Cartopy.
Parameters
----------
projection : string
the map projection. Available projections:
'Robinson' (default), 'PlateCarree', 'AlbertsEqualArea',
'AzimuthalEquidistant','EquidistantConic','LambertConformal',
'LambertCylindrical','Mercator','Miller','Mollweide','Orthographic',
'Sinusoidal','Stereographic','TransverseMercator','UTM',
'InterruptedGoodeHomolosine','RotatedPole','OSGB','EuroPP',
'Geostationary','NearsidePerspective','EckertI','EckertII',
'EckertIII','EckertIV','EckertV','EckertVI','EqualEarth','Gnomonic',
'LambertAzimuthalEqualArea','NorthPolarStereo','OSNI','SouthPolarStereo'
proj_default : bool
If True, uses the standard projection attributes from Cartopy.
Enter new attributes in a dictionary to change them. Lists of attributes
can be found in the Cartopy documentation:
https://scitools.org.uk/cartopy/docs/latest/crs/projections.html#eckertiv
Returns
-------
proj : the Cartopy projection object
See Also
--------
pyleoclim.utils.mapping.map_all : mapping function making use of the projection
"""
if proj_default is not True and type(proj_default) is not dict:
raise TypeError(
'The default for the projections should either be provided' +
' as a dictionary or set to True')
# Set the projection
if projection == 'Robinson':
if proj_default is True:
proj = ccrs.Robinson()
else:
proj = ccrs.Robinson(**proj_default)
elif projection == 'PlateCarree':
if proj_default is True:
proj = ccrs.PlateCarree()
else:
proj = ccrs.PlateCarree(**proj_default)
elif projection == 'AlbersEqualArea':
if proj_default is True:
proj = ccrs.AlbersEqualArea()
else:
proj = ccrs.AlbersEqualArea(**proj_default)
elif projection == 'AzimuthalEquidistant':
if proj_default is True:
proj = ccrs.AzimuthalEquidistant()
else:
proj = ccrs.AzimuthalEquidistant(**proj_default)
elif projection == 'EquidistantConic':
if proj_default is True:
proj = ccrs.EquidistantConic()
else:
proj = ccrs.EquidistantConic(**proj_default)
elif projection == 'LambertConformal':
if proj_default is True:
proj = ccrs.LambertConformal()
else:
proj = ccrs.LambertConformal(**proj_default)
elif projection == 'LambertCylindrical':
if proj_default is True:
proj = ccrs.LambertCylindrical()
else:
proj = ccrs.LambertCylindrical(**proj_default)
elif projection == 'Mercator':
if proj_default is True:
proj = ccrs.Mercator()
else:
proj = ccrs.Mercator(**proj_default)
elif projection == 'Miller':
if proj_default is True:
proj = ccrs.Miller()
else:
proj = ccrs.Miller(**proj_default)
elif projection == 'Mollweide':
if proj_default is True:
proj = ccrs.Mollweide()
else:
proj = ccrs.Mollweide(**proj_default)
elif projection == 'Orthographic':
if proj_default is True:
proj = ccrs.Orthographic()
else:
proj = ccrs.Orthographic(**proj_default)
elif projection == 'Sinusoidal':
if proj_default is True:
proj = ccrs.Sinusoidal()
else:
proj = ccrs.Sinusoidal(**proj_default)
elif projection == 'Stereographic':
if proj_default is True:
proj = ccrs.Stereographic()
else:
proj = ccrs.Stereographic(**proj_default)
elif projection == 'TransverseMercator':
if proj_default is True:
proj = ccrs.TransverseMercator()
else:
proj = ccrs.TransverseMercator(**proj_default)
elif projection == 'TransverseMercator':
if proj_default is True:
proj = ccrs.TransverseMercator()
else:
proj = ccrs.TransverseMercator(**proj_default)
elif projection == 'UTM':
if proj_default is True:
proj = ccrs.UTM()
else:
proj = ccrs.UTM(**proj_default)
elif projection == 'UTM':
if proj_default is True:
proj = ccrs.UTM()
else:
proj = ccrs.UTM(**proj_default)
elif projection == 'InterruptedGoodeHomolosine':
if proj_default is True:
proj = ccrs.InterruptedGoodeHomolosine()
else:
proj = ccrs.InterruptedGoodeHomolosine(**proj_default)
elif projection == 'RotatedPole':
if proj_default is True:
proj = ccrs.RotatedPole()
else:
proj = ccrs.RotatedPole(**proj_default)
elif projection == 'OSGB':
if proj_default is True:
proj = ccrs.OSGB()
else:
proj = ccrs.OSGB(**proj_default)
elif projection == 'EuroPP':
if proj_default is True:
proj = ccrs.EuroPP()
else:
proj = ccrs.EuroPP(**proj_default)
elif projection == 'Geostationary':
if proj_default is True:
proj = ccrs.Geostationary()
else:
proj = ccrs.Geostationary(**proj_default)
elif projection == 'NearsidePerspective':
if proj_default is True:
proj = ccrs.NearsidePerspective()
else:
proj = ccrs.NearsidePerspective(**proj_default)
elif projection == 'EckertI':
if proj_default is True:
proj = ccrs.EckertI()
else:
proj = ccrs.EckertI(**proj_default)
elif projection == 'EckertII':
if proj_default is True:
proj = ccrs.EckertII()
else:
proj = ccrs.EckertII(**proj_default)
elif projection == 'EckertIII':
if proj_default is True:
proj = ccrs.EckertIII()
else:
proj = ccrs.EckertIII(**proj_default)
elif projection == 'EckertIV':
if proj_default is True:
proj = ccrs.EckertIV()
else:
proj = ccrs.EckertIV(**proj_default)
elif projection == 'EckertV':
if proj_default is True:
proj = ccrs.EckertV()
else:
proj = ccrs.EckertV(**proj_default)
elif projection == 'EckertVI':
if proj_default is True:
proj = ccrs.EckertVI()
else:
proj = ccrs.EckertVI(**proj_default)
elif projection == 'EqualEarth':
if proj_default is True:
proj = ccrs.EqualEarth()
else:
proj = ccrs.EqualEarth(**proj_default)
elif projection == 'Gnomonic':
if proj_default is True:
proj = ccrs.Gnomonic()
else:
proj = ccrs.Gnomonic(**proj_default)
elif projection == 'LambertAzimuthalEqualArea':
if proj_default is True:
proj = ccrs.LambertAzimuthalEqualArea()
else:
proj = ccrs.LambertAzimuthalEqualArea(**proj_default)
elif projection == 'NorthPolarStereo':
if proj_default is True:
proj = ccrs.NorthPolarStereo()
else:
proj = ccrs.NorthPolarStereo(**proj_default)
elif projection == 'OSNI':
if proj_default is True:
proj = ccrs.OSNI()
else:
proj = ccrs.OSNI(**proj_default)
elif projection == 'OSNI':
if proj_default is True:
proj = ccrs.SouthPolarStereo()
else:
proj = ccrs.SouthPolarStereo(**proj_default)
else:
raise ValueError('Invalid projection type')
return proj
def CICE_XMXL_plots(aice, XMXL, lons, lats, MASK, run, i):
""""""
year = int(XMXL.time.item() / 365)
month = int((XMXL.time.item() % 365) / 30)
filename = f'{path_results}/CICE/CICE_XMXL_video/CICE_XMXL_{run}_{year}_{month}.png'
if os.path.exists(filename):
print(f'{filename} exists already')
return
f = plt.figure(figsize=(8, 8))
ax1 = plt.subplot(2,
2,
1,
projection=ccrs.NearsidePerspective(central_latitude=90))
ax2 = plt.subplot(
2, 2, 2, projection=ccrs.NearsidePerspective(central_latitude=-90))
ax3 = plt.subplot(2,
2,
3,
projection=ccrs.NearsidePerspective(satellite_height=1e7,
central_latitude=90))
ax4 = plt.subplot(2,
2,
4,
projection=ccrs.NearsidePerspective(
satellite_height=1e7, central_latitude=-90))
for ax in [ax1, ax2]: # MXL
im = ax.pcolormesh(XMXL.TLONG,
XMXL.TLAT,
XMXL.where(MASK > 0) / 100,
cmap=cmocean.cm.amp,
vmin=0,
vmax=500,
transform=ccrs.PlateCarree())
cbar_ax1 = f.add_axes([0.48, 0.57, 0.02, 0.35])
f.colorbar(im, cax=cbar_ax1, extend='max')
for ax in [ax3, ax4]: # ICE
im = ax.pcolormesh(lons,
lats,
aice,
cmap=cmocean.cm.ice,
vmin=0,
vmax=100,
transform=ccrs.PlateCarree())
cbar_ax2 = f.add_axes([0.48, 0.08, 0.02, 0.35])
f.colorbar(im, cax=cbar_ax2)
cbar_ax1.text(.5,
1.08,
f'{year} - {month:2d}',
ha='center',
transform=cbar_ax1.transAxes,
fontsize=18)
cbar_ax1.text(.5,
-.16,
'maximum mixed layer depth [m]',
ha='center',
transform=cbar_ax1.transAxes,
fontsize=14)
cbar_ax2.text(.5,
1.06,
'sea ice cover [%]',
ha='center',
transform=cbar_ax2.transAxes,
fontsize=14)
for ax in [ax1, ax2, ax3, ax4]:
ax.add_feature(cartopy.feature.LAND,
zorder=2,
edgecolor='black',
facecolor='lightgrey')
# ax.coastlines(resolution='110m')
ax.gridlines()
plt.subplots_adjust(left=0.02, right=0.98, top=0.95, bottom=0.05)
f.savefig(filename)
copyfile(
filename,
f'{path_results}/CICE/CICE_XMXL_video/CICE_XMXL_{run}_no_{i:04d}.png')
plt.close('all')
return
def plot(self, figname=None, zoom=1, scale=500, U=20):
lats_bnds = self.polygon.lats
lons_bnds = self.polygon.lons
centroid_lat, centroid_lon = self.centroid[:2]
fig = plt.figure(dpi=200)
ax = fig.add_subplot(1,
1,
1,
projection=ccrs.NearsidePerspective(
centroid_lon,
centroid_lat,
satellite_height=6.5e7 / zoom))
ax.set_global()
# set the gridlines
ax.gridlines(color='gray',
linestyle='--',
xlocs=np.arange(-180, 180, 30),
ylocs=np.linspace(-80, 80, 9))
ax.add_feature(cfeature.OCEAN)
ax.add_feature(cfeature.LAND, zorder=0)
ax.add_feature(cfeature.COASTLINE)
ax.add_feature(cfeature.RIVERS, zorder=1)
ax.add_feature(cfeature.LAKES, zorder=2)
ax.plot(lons_bnds,
lats_bnds,
color='m',
transform=ccrs.Geodetic(),
linewidth=1)
ax.fill(lons_bnds,
lats_bnds,
color='coral',
transform=ccrs.Geodetic(),
alpha=0.4)
if 'omega_cartesian' in self.info:
lons_bnds_min, lons_bnds_max = lons_bnds.min(), lons_bnds.max()
lats_bnds_min, lats_bnds_max = lats_bnds.min(), lats_bnds.max()
if np.abs(lons_bnds_min - lons_bnds_max) > 355:
if self.contains_points([89, 0]): # North pole
lats_bnds_max = 85
elif self.contains_points([-89, 0]): # South pole
lats_bnds_min = -85
lats_fill = np.linspace(lats_bnds_min, lats_bnds_max, 10)
lons_fill = np.linspace(lons_bnds_min, lons_bnds_max, 10)
lons_mesh, lats_mesh = np.meshgrid(lons_fill, lats_fill)
lons_flat = lons_mesh.flatten()
lats_flat = lats_mesh.flatten()
inplate_flag = self.contains_points(
np.array([lats_flat, lons_flat]).T)
lons_inplate = lons_flat[inplate_flag]
lats_inplate = lats_flat[inplate_flag]
h_inplate = np.zeros_like(lats_inplate) * u.m
locations = [lats_inplate * u.deg, lons_inplate * u.deg, h_inplate]
inplate_vel_en = self.velocity_at(locations, 'geodetic').en
inplate_ve, inplate_vn = inplate_vel_en[:, 0], inplate_vel_en[:, 1]
font0 = FontProperties()
font0.set_size('small')
q = ax.quiver(lons_inplate,
lats_inplate,
inplate_ve,
inplate_vn,
transform=ccrs.PlateCarree(),
scale=scale,
width=0.004,
color='g',
regrid_shape=20)
ax.quiverkey(q,
X=0.65,
Y=0.85,
U=U,
label=r'$20 \, mm/yr$',
labelpos='E',
coordinates='figure',
fontproperties=font0)
if figname is None:
plt.show()
else:
plt.savefig(figname)
def GenerateBasemapImageAutomated(DataDirectory, RasterFile, FigWidthInches = 4, FigHeightInches = 3, FigFormat = "png", fig_dpi = 500, regional_extent_multiplier = 5, label_spacing_multiplier = 0.5, out_fname_prefix = "", is_orthographic = False):
"""
This makes the basemap image. Uses data from the raster to size the figure and locate the centrepoint
Args:
DataDirectory (str): The directory of the raster file
RasterFile (str): the name of the raster file (include extension)
FigWidthInches (flt): How wide you want the basemap
FigHeightInches (float): How high you want your basemap
FigFormat (str): Figure format, can be `png`, `svg`, `pdf`, etc.
fig_dpi (int): Dots per inch of your figure
regional_extent_multiplier (float): How much bigger you want the extent vs the size of the raster
label_spacing_multiplier (float): If the meridians and parallels are too close, increase this number. Default of 0.5
out_fname_prefix (str): The prefix of the image file. If blank uses the fname_prefix
Author: SMM
Date: 01/02/2018
"""
# Make sure data directory is in correct format
if not DataDirectory.endswith(os.sep):
print("You forgot the separator at the end of the directory, appending...")
DataDirectory = DataDirectory+os.sep
# Set up the figure. This is needed to both size the figure and get the axis handle for plotting polygons
fig = plt.figure(figsize=(FigWidthInches, FigHeightInches))
print("The size is: " +str(FigWidthInches)+", "+str(FigHeightInches))
# get some filenames
RasterSplit = RasterFile.split(".")
Raster_prefix = RasterSplit[0]
Shape_name = DataDirectory+Raster_prefix+"_footprint.shp"
SName = "Shape"
# Get the name of the image
if len(out_fname_prefix) == 0:
FigFileName = DataDirectory+Base_file+"_basemap."+FigFormat
else:
FigFileName = DataDirectory+out_fname_prefix+"_basemap."+FigFormat
# Now we get the extents from the raster
centre_lat, centre_long, extent_lat, extent_long, xproj_extent, yproj_extent = LSDMGDAL.GetCentreAndExtentOfRaster(DataDirectory, RasterFile)
# Calculate the aspect ratio
aspect_ratio = BasemapExtentSizer(FigWidthInches, FigHeightInches)
# Figure out the longest dimension
long_dimension = xproj_extent
if yproj_extent > long_dimension:
long_dimension = yproj_extent
print("The long dimension is: "+str(long_dimension))
# Get the full extent by mulitplying the longest extent by the multiplier
full_dimension = long_dimension*regional_extent_multiplier
# now get the two dimensions for the extent of the figure
print("The aspect ratio is: "+str(aspect_ratio))
if aspect_ratio > 1:
# This is when the figure is wider than tall
x_ext = full_dimension*aspect_ratio
y_ext = full_dimension
full_extent_long = extent_long*aspect_ratio*regional_extent_multiplier
full_extent_lat = extent_long*regional_extent_multiplier
else:
x_ext = full_dimension
y_ext = full_dimension*aspect_ratio
full_extent_long = extent_lat*regional_extent_multiplier
full_extent_lat = extent_lat*aspect_ratio*regional_extent_multiplier
extents = [centre_long-0.5*full_extent_long,
centre_long+0.5*full_extent_long,
centre_lat-0.5*full_extent_lat,
centre_lat+0.5*full_extent_lat]
print("Extents are: ")
print(extents)
# Now we set up the extents and coordinate system
#if (is_orthographic):
# ax = fig.add_axes([0, 0, 1, 1], projection=ccrs.Orthographic(centre_lat, #centre_long))
#
# ax.add_feature(cfeature.LAND)
# ax.add_feature(cfeature.OCEAN, edgecolor='black')
# ax.set_global()
# ax.gridlines()
if(is_orthographic):
ax = fig.add_axes([0, 0, 1, 1], projection=ccrs.NearsidePerspective(
central_latitude=centre_lat,
central_longitude=centre_long,
satellite_height=10000000.0))
ax.coastlines(resolution='110m',linewidth=0.5)
borders_110m = cfeature.NaturalEarthFeature('cultural', 'admin_0_boundary_lines_land', '110m',edgecolor='face', facecolor=cfeature.COLORS['land'])
ax.add_feature(borders_110m, edgecolor='black', facecolor = "none",linewidth=0.5)
ax.gridlines()
else:
ax = fig.add_axes([0, 0, 1, 1], projection=ccrs.PlateCarree())
ax.set_extent([centre_long-0.5*full_extent_long,
centre_long+0.5*full_extent_long,
centre_lat-0.5*full_extent_lat,
centre_lat+0.5*full_extent_lat], ccrs.PlateCarree())
land_50m = cfeature.NaturalEarthFeature('physical', 'land', '50m',
edgecolor='face',
facecolor=cfeature.COLORS['land'])
borders_50m = cfeature.NaturalEarthFeature('cultural', 'admin_0_boundary_lines_land', '50m',
edgecolor='face',
facecolor=cfeature.COLORS['land'])
ax.add_feature(land_50m, edgecolor='black', linewidth=0.5)
ax.add_feature(borders_50m, edgecolor='black', facecolor = "none",linewidth=0.5)
#ax.add_feature(cfeature.LAND)
#ax.add_feature(cfeature.OCEAN)
#ax.add_feature(cfeature.COASTLINE)
#ax.add_feature(cfeature.BORDERS, linestyle='--')
#ax.add_feature(cfeature.LAKES, alpha=0.5)
#ax.add_feature(cfeature.RIVERS)
# create the shapefile
LSDMGDAL.CreateShapefileOfRasterFootprint(DataDirectory, RasterFile)
ax.add_geometries(
shpreader.Reader(Shape_name).geometries(),
ccrs.PlateCarree(),edgecolor='black', facecolor='green', alpha=0.5, linewidth=0.5)
#==========================================
# draw parallels and meridians.
# Calculate the spacing of the meridians and parallels
# THIS IS ALL FROM BASEMAP AND NOT USED BY CARTOPY
max_latlong_ext = extent_lat
if extent_long < max_latlong_ext:
max_latlong_ext = extent_long
max_latlong_ext = max_latlong_ext*regional_extent_multiplier
print("The maximum extent is:"+str(max_latlong_ext))
latlong_label_spacing = int(label_spacing_multiplier*max_latlong_ext+0.5)
print("And the label spacing is: "+str(latlong_label_spacing))
start_lat = int(centre_lat - max_latlong_ext*2 - 0.5)
end_lat = int(centre_lat + max_latlong_ext*2+0.5)
start_long = int(centre_long - max_latlong_ext*2 -0.5)
end_long = int(centre_long + max_latlong_ext*2+0.5)
# label parallels on right and top
# meridians on bottom and left
parallels = np.arange(start_lat,end_lat,latlong_label_spacing)
# labels = [left,right,top,bottom]
#m.drawparallels(parallels,labels=[False,True,True,False])
meridians = np.arange(start_long,end_long,latlong_label_spacing)
#m.drawmeridians(meridians,labels=[True,False,False,True])
#==========================================
#==========================================
# THIS IS ALL FROM BASEMAP AND NOT USED BY CARTOPY
# Make a patch from the shapefile
# All this stuff from:
# http://www.datadependence.com/2016/06/creating-map-visualisations-in-python/
#df_poly = pd.DataFrame({
# 'shapes': [Polygon(np.array(shape), True) for shape in m.footprint]})
#df_poly = df_poly.merge(new_areas, on='area', how='left')
#cmap = plt.get_cmap('Oranges')
#pc = PatchCollection(df_poly.shapes, zorder=2, alpha = 0.5)
#pc.set_facecolor("crimson")
#ax.add_collection(pc)
#==========================================
plt.savefig(FigFileName,format=FigFormat,dpi=fig_dpi)
def projection(self):
if self.proj is None:
return ccrs.PlateCarree()
proj_dict = ast.literal_eval(self.proj)
user_proj = proj_dict.pop("proj")
if user_proj == 'PlateCarree':
self.xylim_supported = True
return ccrs.PlateCarree(**proj_dict)
elif user_proj == 'AlbersEqualArea':
return ccrs.AlbersEqualArea(**proj_dict)
elif user_proj == 'AzimuthalEquidistant':
return ccrs.AzimuthalEquidistant(**proj_dict)
elif user_proj == 'EquidistantConic':
return ccrs.EquidistantConic(**proj_dict)
elif user_proj == 'LambertConformal':
return ccrs.LambertConformal(**proj_dict)
elif user_proj == 'LambertCylindrical':
return ccrs.LambertCylindrical(**proj_dict)
elif user_proj == 'Mercator':
return ccrs.Mercator(**proj_dict)
elif user_proj == 'Miller':
return ccrs.Miller(**proj_dict)
elif user_proj == 'Mollweide':
return ccrs.Mollweide(**proj_dict)
elif user_proj == 'Orthographic':
return ccrs.Orthographic(**proj_dict)
elif user_proj == 'Robinson':
return ccrs.Robinson(**proj_dict)
elif user_proj == 'Sinusoidal':
return ccrs.Sinusoidal(**proj_dict)
elif user_proj == 'Stereographic':
return ccrs.Stereographic(**proj_dict)
elif user_proj == 'TransverseMercator':
return ccrs.TransverseMercator(**proj_dict)
elif user_proj == 'UTM':
return ccrs.UTM(**proj_dict)
elif user_proj == 'InterruptedGoodeHomolosine':
return ccrs.InterruptedGoodeHomolosine(**proj_dict)
elif user_proj == 'RotatedPole':
return ccrs.RotatedPole(**proj_dict)
elif user_proj == 'OSGB':
self.xylim_supported = False
return ccrs.OSGB(**proj_dict)
elif user_proj == 'EuroPP':
self.xylim_supported = False
return ccrs.EuroPP(**proj_dict)
elif user_proj == 'Geostationary':
return ccrs.Geostationary(**proj_dict)
elif user_proj == 'NearsidePerspective':
return ccrs.NearsidePerspective(**proj_dict)
elif user_proj == 'EckertI':
return ccrs.EckertI(**proj_dict)
elif user_proj == 'EckertII':
return ccrs.EckertII(**proj_dict)
elif user_proj == 'EckertIII':
return ccrs.EckertIII(**proj_dict)
elif user_proj == 'EckertIV':
return ccrs.EckertIV(**proj_dict)
elif user_proj == 'EckertV':
return ccrs.EckertV(**proj_dict)
elif user_proj == 'EckertVI':
return ccrs.EckertVI(**proj_dict)
elif user_proj == 'EqualEarth':
return ccrs.EqualEarth(**proj_dict)
elif user_proj == 'Gnomonic':
return ccrs.Gnomonic(**proj_dict)
elif user_proj == 'LambertAzimuthalEqualArea':
return ccrs.LambertAzimuthalEqualArea(**proj_dict)
elif user_proj == 'NorthPolarStereo':
return ccrs.NorthPolarStereo(**proj_dict)
elif user_proj == 'OSNI':
return ccrs.OSNI(**proj_dict)
elif user_proj == 'SouthPolarStereo':
return ccrs.SouthPolarStereo(**proj_dict)
def plot_map_and_stations(fig,
gridspec,
station_locations,
station_names,
station_plot_dict,
region_corners=None,
ss_location=None,
date=None):
map_ax = fig.add_subplot(gridspec,
projection=ccrs.NearsidePerspective(
-95, 57, 15000000))
map_ax.stock_img()
# Plot the border of region
if region_corners is not None:
map_ax.plot(np.linspace(region_corners[0][0], region_corners[1][0],
10),
np.ones(10) * region_corners[0][1],
'--',
color='gray',
transform=ccrs.PlateCarree())
map_ax.plot(np.ones(10) * region_corners[1][0],
np.linspace(region_corners[0][1], region_corners[1][1],
10),
'--',
color='gray',
transform=ccrs.PlateCarree())
map_ax.plot(np.linspace(region_corners[0][0], region_corners[1][0],
10),
np.ones(10) * region_corners[1][1],
'--',
color='gray',
transform=ccrs.PlateCarree())
map_ax.plot(np.ones(10) * region_corners[0][0],
np.linspace(region_corners[0][1], region_corners[1][1],
10),
'--',
color='gray',
transform=ccrs.PlateCarree())
# plot all stations
map_ax.plot(station_locations[:, 0],
station_locations[:, 1],
'.',
color='black',
transform=ccrs.PlateCarree())
# plot special stations
for entry in station_plot_dict:
map_ax.plot(station_locations[entry['indices'], 0],
station_locations[entry['indices'], 1],
'.',
color=entry['color'],
transform=ccrs.PlateCarree(),
label=entry['name'])
if entry['station names']:
for j in entry['indices']:
map_ax.text(station_locations[j, 0] - 1,
station_locations[j, 1] - 1,
station_names[j],
horizontalalignment='right',
transform=ccrs.PlateCarree())
map_ax.legend()
if ss_location is not None:
map_ax.plot(ss_location[0],
ss_location[1],
'rx',
transform=ccrs.PlateCarree())
if date is not None:
map_ax.add_feature(Nightshade(date, alpha=0.2))
