def create_map(extent, xstep, ystep):
# 加载shp
shp_path_p = '/Users/wangyucheng/Desktop/kuihua/Province_9/'
shp_path_c = '/Users/wangyucheng/Desktop/kuihua/diqujie/'
# --创建画图空间
proj = ccrs.PlateCarree() # 创建坐标系
fig = plt.figure(figsize=(8, 10)) # 创建页面
ax = plt.axes(projection=ccrs.PlateCarree())
# --设置地图属性
reader_p = Reader(shp_path_p + 'Province_9.shp')
province = cfeat.ShapelyFeature(reader_p.geometries(),
proj,
edgecolor='k',
facecolor='none')
reader_c = Reader(shp_path_c + 'diquJie_polyline.shp')
city = cfeat.ShapelyFeature(reader_c.geometries(),
proj,
edgecolor='k',
facecolor='none')
# 加载省界线
ax.add_feature(province)
# 加载市界
ax.add_feature(city)
# 加载经纬范围
ax.set_extent(extent, crs=proj)
# --设置网格点属性
gl = ax.gridlines(crs=ccrs.PlateCarree(),
draw_labels=True,
linewidth=1.2,
color='k',
alpha=0.5,
linestyle='--')
gl.xlabels_top = False # 关闭顶端的经纬度标签
gl.ylabels_right = False # 关闭右侧的经纬度标签
gl.xformatter = LONGITUDE_FORMATTER # x轴设为经度的格式
gl.yformatter = LATITUDE_FORMATTER # y轴设为纬度的格式
gl.xlocator = mticker.FixedLocator(
np.arange(extent[0], extent[1] + xstep, xstep))
gl.ylocator = mticker.FixedLocator(
np.arange(extent[2], extent[3] + ystep, ystep))
return fig, ax
def plot_mineral_deposits(self, ax, facecolors='none'):
reader = Reader(mineral_output_basename)
ax.scatter([point.x for point in reader.geometries()],
[point.y for point in reader.geometries()],
facecolors=facecolors,
transform=ccrs.PlateCarree(),
edgecolors='black',
s=30,
zorder=99)
reader_au = Reader(mineral_AU_output_basename)
ax.scatter([point.x for point in reader_au.geometries()],
[point.y for point in reader_au.geometries()],
facecolors=facecolors,
edgecolors='gold',
transform=ccrs.PlateCarree(),
s=30,
zorder=99)
def plot(self, xar):
plt.close()
self.fig = plt.figure(**self.fig_kws)
self.savefig = self.fig.savefig
ax = plt.axes(projection=self.proj)
states_provinces = cfeature.NaturalEarthFeature(
category='cultural',
name='admin_1_states_provinces_lines',
scale='50m',
facecolor='none')
countries = cfeature.NaturalEarthFeature(
category='cultural',
name='admin_0_boundary_lines_land',
scale='50m',
facecolor='none')
rivers = cfeature.NaturalEarthFeature(scale='50m',
category='physical',
name='rivers_lake_centerlines',
edgecolor='blue',
facecolor='none')
ax.add_feature(countries, edgecolor='grey')
ax.coastlines('50m')
#ax.add_feature(states_provinces, edgecolor='gray')
ax.add_feature(rivers, edgecolor='blue')
if self.kwargs.get('drainage_baisins', True):
sf = Reader(
"../data/drainage_basins/Major_Basins_of_the_World.shp")
shape_feature = ShapelyFeature(sf.geometries(),
self.transform,
edgecolor='black')
ax.add_feature(shape_feature, facecolor='none', edgecolor='green')
xar.plot(transform=self.transform,
ax=ax,
subplot_kws={'projection': self.proj})
self.ax = ax
}
##############################################################################
'''
Set up the daily average salinity grid
'''
if 'mask' not in vars():
working_scenario = cb
avesalD_coords = coords.ix[avesalD_nodes]
lon = np.array(avesalD_coords['lon'])
lat = np.array(avesalD_coords['lat'])
loni = np.linspace(min(lon), max(lon), 100)
lati = np.linspace(min(lat), max(lat), 100)
glon, glat = np.meshgrid(loni, lati)
shp = Reader(shp_path)
geoms = list(shp.geometries())
max_area = 0
max_area_id = 0
for i in range(len(geoms)):
if geoms[i].area > max_area:
max_area = geoms[i].area
max_area_id = i
polygon = geoms[i]
mask = inpolygon(polygon, glon.ravel(), glat.ravel())
mask = mask.reshape(glon.shape)
##############################################################################
'''
Make the base map
'''
lats = [28.142815, 28.142622, 28.128572, 28.128588]
lons = [-97.057931, -97.041549, -97.041542, -97.057923]
def plot(self, xar, **kwargs):
"""Wraps xr.DataArray.plot.pcolormesh and formats the plot as configured
in the call to Map().
title is xar.long_name
cbar_label is xar.units
Parameters
----------
xar : xr.DataArray
two-dimensional data array
kwargs : dict
are passed on to xr.DataArray.plot.pcolormesh()
Returns
----------
fig : matplotlib.pyplot.figure
ax : matplotlib.pyplot.axis
"""
for dim in ['longitude', 'latitude']:
if dim not in xar.coords:
raise KeyError(dim+' not found in coordinates!')
plt.close()
fig = plt.figure(**self.fig_kws)
if not self.proj:
self.proj = choose_proj_from_xar(xar)
ax = plt.axes(projection=self.proj)
countries = cfeature.NaturalEarthFeature(
category='cultural',
name='admin_0_boundary_lines_land',
scale='50m',
facecolor='none')
rivers = cfeature.NaturalEarthFeature(scale='50m', category='physical',
name='rivers_lake_centerlines',
edgecolor='blue', facecolor='none')
ax.add_feature(countries, edgecolor='grey')
ax.coastlines('50m')
ax.add_feature(rivers, edgecolor='blue')
gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True)
gl.xlabels_top = False
if self.drainage_baisins:
sf = Reader("../data/drainage_basins/Major_Basins_of_the_World.shp")
shape_feature = ShapelyFeature(sf.geometries(),
self.transform, edgecolor='black')
ax.add_feature(shape_feature, facecolor='none', edgecolor='green')
# cbar_kwargs = kwargs.pop('cbar_kwargs', dict())
subplot_kws = kwargs.pop('subplot_kws', dict())
subplot_kws['projection'] = self.proj
# choose which colormap to use: pos and neg values => RdYlGn, else inferno
if ((xar.max()-xar.min()) > xar.max()):
cmap = 'RdYlGn'
else:
cmap = 'spring_r'
# colorbar preset to match height of plot
# if 'fraction' not in cbar_kwargs: cbar_kwargs['fraction'] = 0.015
xar.plot.pcolormesh(ax=ax, transform=self.transform,
subplot_kws=subplot_kws,
cmap=cmap,
**kwargs)
return fig, ax
import os
plt.clf()
#ax = geo_plots.add_map(bbox=(-89,-85,27.5,31))
ax = geo_plots.add_map(bbox=(-90, -84, 27.5, 31))
#land_10m = cfeature.NaturalEarthFeature('physical','land','50m',\
# edgecolor='face',facecolor='0.75')
##ax.add_feature(cfeature.LAND, facecolor='0.75')
#ax.add_feature(land_10m)
#if bbox not specified, this will use map bounds from bna
bathy_file = os.path.join('gom_bathy','Bathymetry.shp')
bathy = Reader(bathy_file)
for rec,geo in zip(bathy.records(),bathy.geometries()):
if rec.attributes['DEPTH_METR'] in ['100m','500m','1000m']:
shape_feature = ShapelyFeature(geo,ccrs.PlateCarree(), facecolor='none')
ax.add_feature(shape_feature)
#add particles at one time
t = datetime.datetime(2016, 9, 21, 1)
filename = 'gulf_tamoc.nc'
ax = geo_plots.contour_particles(ax,filename,t,levels=[0.1, 0.4, 0.6, 1])
#ax = geo_plots.plot_particles(ax,filename,t,depth=0,color='b')
#add initial location
else:
background_image['alpha'] = 1.0
#Draw geography
m.drawstates()
m.drawcoastlines()
m.drawcountries()
print("--> Plotted geographic & political boundaries")
#========================================================================================================
# Plot data
#========================================================================================================
#Iterate through all states
total_cases = 0
for record, state in zip(shp.records(), shp.geometries()):
#Reference state name as a separate variable
name = record.attributes['NAME']
#Get state's case data for this date
if name.lower() in cases.keys():
idx = cases[name.lower()]['date'].index(plot_start_date)
if plot_type not in [
'confirmed', 'confirmed_normalized', 'deaths', 'recovered',
'active', 'daily'
]:
plot_type = 'confirmed'
case_number = cases[name.lower()][plot_type][idx]
total_cases += case_number
else:
#create the salinity mask
if 'mask' not in vars():
print('Preparing salinity mask\n')
#get all nodes within the extent, and get their lons and lats
avesalD_coords = coords.loc[avesalD_nodes]
lon = np.array(avesalD_coords['lon'])
lat = np.array(avesalD_coords['lat'])
#create an array of lat and lon ranges for gridding
loni = np.linspace(min(lon), max(lon), 150)
lati = np.linspace(min(lat), max(lat), 150)
#create mesh grid
glon, glat = np.meshgrid(loni, lati)
#read the shapefile
shp = Reader(shp_path)
#get the geometries from the shapefile
geoms = list(shp.geometries())
max_area = 0
max_area_id = 0
#find the largest polygon in the shapefile, the inside of which will be the bay
for i in range(len(geoms)):
if geoms[i].area > max_area:
max_area = geoms[i].area
max_area_id = i
polygon = geoms[i]
#create the mask based on which points are inside the largest polygon
mask = inpolygon(polygon, glon.ravel(), glat.ravel())
#reshape the mask to the same size as the meshgrid
mask = mask.reshape(glon.shape)
else:
print('Salinity mask already created\n')
for i in range(len(cn_multipoly)):
cn_geom, = geos_to_path(cn_multipoly.geoms[i])
paths.append(cn_geom)
path = Path.make_compound_path(*paths)
for collection in contourf.collections:
collection.set_clip_path(path, proj._as_mpl_transform(ax))
# 白化刻度
# contour = ax.contour(lon,lat,u1_avg,lev,cmap='Blues',transform=proj,extend='both')
# cn_label = plt.clabel(contour,inline=1,fontsize=10,fmt="%.fr")
# if cn_label:
# clip_map_shapely = Polygon(path.vertices)
# for text_object in cn_label:
# if not clip_map_shapely.contains(Point(text_object.get_position())):
# text_object.set_visible(False)
ax.add_geometries(cn_multipoly, crs=proj, edgecolor='black', linewidths=1, facecolor='none', zorder=10, alpha=0.8)
#ax.gridlines(color="black", linestyle="--")
ax.gridlines(color='gray', linestyle = 'dotted', xlocs = np.arange(70,140+20,20), ylocs = np.arange(15,65+10,10))
fig.colorbar(contourf, format='%.1f',shrink=0.8)
sub_ax = fig.add_axes([0.60, 0.20, 0.17, 0.2],projection=proj)
# Add ocean, land, rivers and lakes
# Set figure extent
sub_ax.set_extent([105, 125, 0, 25],crs=proj)
sub_ax.add_geometries(shp.geometries(),crs=proj,edgecolor='black', linewidths=1,facecolor='none',zorder=10,alpha=0.8)
fig.savefig('*.png',bbox_inches = 'tight')
#facecolor=feature.COLORS['land'],
facecolor='lightgray',
name='land',
)
fname = 'files/ne_10m_populated_places.shp'
reader = Reader(fname)
#city names
city = []
for i, record in enumerate(reader.records()):
name = record.attributes['name_es']
geometry = record.geometry
x, y = geometry.centroid.x, geometry.centroid.y
if all([lon_min < x < lon_max, lat_min < y < lat_max]):
city.append([x, y, name])
#city points
points = [list(point.coords) for point in reader.geometries()]
xp = [
point[0][0] for point in points if all(
[lon_min < point[0][0] < lon_max, lat_min < point[0][1] < lat_max])
]
yp = [
point[0][1] for point in points if all(
[lon_min < point[0][0] < lon_max, lat_min < point[0][1] < lat_max])
]
#features=[land,coastline,states,reader]
#ocean= feature.ShapelyFeature(
# Reader('ne_10m_ocean.shp').geometries(),
# ccrs.PlateCarree(),
# )
import os
plt.clf()
#ax = geo_plots.add_map(bbox=(-89,-85,27.5,31))
ax = geo_plots.add_map(bbox=(-90, -84, 27.5, 31))
#land_10m = cfeature.NaturalEarthFeature('physical','land','50m',\
# edgecolor='face',facecolor='0.75')
##ax.add_feature(cfeature.LAND, facecolor='0.75')
#ax.add_feature(land_10m)
#if bbox not specified, this will use map bounds from bna
bathy_file = os.path.join('gom_bathy', 'Bathymetry.shp')
bathy = Reader(bathy_file)
for rec, geo in zip(bathy.records(), bathy.geometries()):
if rec.attributes['DEPTH_METR'] in ['100m', '500m', '1000m']:
shape_feature = ShapelyFeature(geo,
ccrs.PlateCarree(),
facecolor='none')
ax.add_feature(shape_feature)
#add particles at one time
t = datetime.datetime(2016, 9, 21, 1)
filename = 'gulf_tamoc.nc'
ax = geo_plots.contour_particles(ax, filename, t, levels=[0.1, 0.4, 0.6, 1])
#ax = geo_plots.plot_particles(ax,filename,t,depth=0,color='b')
#add initial location
filename=r'F:/Rpython/lp28/data/XJ1224.xlsx'
df=pd.read_excel(filename)#读取文件
lon=df['lon']#读取站点经度
lat=df['lat']#读取站点纬度
tem=df['h']#读取站点气温
# 创建画布
fig = plt.figure(figsize=(12,10),dpi=600)
olon=np.linspace(70,100,90)
olat=np.linspace(30,55,75)
olon,olat=np.meshgrid(olon,olat)#网格化
func=Rbf(lon,lat,tem,function='cubic')#定义径向基函数插值
tem_new=func(olon,olat)#获得插值后的网格气温
ax = fig.subplots(1, 1, subplot_kw={'projection': proj}) # 创建子图
extent=[73,97,34,50]#限定绘图范围
reader = Reader(shp_path)
enshicity = cfeat.ShapelyFeature(reader.geometries(), proj, edgecolor='k', facecolor='none')
ax.add_feature(enshicity, linewidth=0.7)#添加市界细节
ax.set_extent(extent,crs=proj)
ax.set_xticks(np.arange(extent[0],extent[1]+1,3),crs=proj)
ax.set_yticks(np.arange(extent[-2],extent[-1]+1,2),crs=proj)
ax.xaxis.set_major_formatter(LongitudeFormatter())
ax.yaxis.set_major_formatter(LatitudeFormatter())
cs1= ax.contourf(olon,olat,tem_new,levels=np.arange(0,2000,200),cmap='gist_rainbow',extend='both')#画图cmap='Spectral_r',
cs2= ax.contour(olon,olat,tem_new,colors='red',linewidths=0.6)#画图
b=plt.colorbar(cs1,shrink=0.65,orientation='vertical',extend='both',pad=0.035,aspect=20) #orientation='horizontal'
clip1=maskout2.shp2clip(cs1,ax,r'F:/Rpython/lp17/data/xinjiang0819.shp') #白化1
clip2=maskout2.shp2clip(cs2,ax,r'F:/Rpython/lp17/data/xinjiang0819.shp') #白化2
font3={'family':'SimHei','size':8,'color':'k'}
plt.scatter(df['lon'].values,df['lat'].values,marker='o',s=6,color ="k")
for i, j, k in list(zip(df['lon'].values, df['lat'].values, df['name'].values)):
plt.text(i-0.2,j-0.3,k,fontdict=font3)
south_china_sea = 3415
crs = ccrs.Mercator()
crs2 = ccrs.epsg(3415)
crs = ccrs.Mercator()
fig = plt.figure(figsize=(20, 20))
ax = fig.add_subplot(1, 1, 1, projection=crs)
#ax = plt.axes(projection=crs)
#ax.set_extent([140, 80, -2, 55], crs=crs)
ax.set_extent([136, 72, 3, 55], crs=crs)
world = Reader(path_join('Basemap', 'world'))
nation = Reader(path_join('Basemap', 'nation'))
province = Reader(path_join('Basemap', 'province'))
river = Reader(path_join('Basemap', 'river'))
lake = Reader(path_join('Basemap', 'lake'))
ax.add_geometries(world.geometries(),
crs=crs,
edgecolor='#555555',
facecolor='none',
linewidth=1.5)
ax.add_geometries(nation.geometries(),
crs=crs,
edgecolor='#000000',
facecolor='none',
linewidth=2)
ax.add_geometries(province.geometries(),
crs=crs,
edgecolor='#222222',
facecolor='none',
linewidth=1)
ax.add_geometries(river.geometries(),
new_dpco2_cube.standard_name = dpco2_cube.standard_name
new_d14c_cube.units = d14c_cube.units
new_dpco2_cube.units = dpco2_cube.units
new_d14c_cube.var_name = d14c_cube.var_name
new_dpco2_cube.var_name = dpco2_cube.var_name
shpfilename = natural_earth(resolution='110m', category='physical', name='land')
reader = Reader(shpfilename)
continents = reader.records()
new_d14c_cube.coord('latitude').guess_bounds()
new_d14c_cube.coord('longitude').guess_bounds()
new_dpco2_cube.coord('latitude').guess_bounds()
new_dpco2_cube.coord('longitude').guess_bounds()
continent_geometries = reader.geometries() # NB. Switched from using records()
all_continents_geometry = cascaded_union(list(continent_geometries))
area_weights = geometry_area_weights(new_d14c_cube, all_continents_geometry)
land_mask = np.where(area_weights > 0, True, False)
new_d14c_cube.data = np.ma.array(new_d14c_cube.data, mask=land_mask)
area_weights = geometry_area_weights(new_dpco2_cube, all_continents_geometry)
land_mask = np.where(area_weights > 0, True, False)
new_dpco2_cube.data = np.ma.array(new_dpco2_cube.data, mask=land_mask)
iris.fileformats.netcdf.save(new_d14c_cube, output_directory+model+'_d14c_hist.nc')
iris.fileformats.netcdf.save(new_dpco2_cube, output_directory+model+'_dpco2_hist.nc')
subprocess.call('rm '+temporary_file_space+temp_file3, shell=True)
else:
print 'No variable input files for this model'
else:
# %matplotlib inline
import cartopy.crs as ccrs
from cartopy.io.shapereader import Reader
from cartopy.feature import ShapelyFeature
ax = plt.axes(projection=ccrs.LambertConformal(
central_latitude=39, central_longitude=-96, standard_parallels=(33, 45)))
ax.set_extent([-125, -66.5, 20, 50])
# Read in county-level shapefiles
fname = "/data301/data/cb_2017_us_county_5m/cb_2017_us_county_5m"
shp = Reader(fname)
# Add each county to the data set.
ax.add_geometries(shp.geometries(),
ccrs.PlateCarree(),
facecolor="None",
edgecolor='black')
# -
# To make a choropleth, we simply need to set the `facecolor` of each geometry. First, let's read in some county-level data that we can plot.
import pandas as pd
election_df = pd.read_csv(
"https://raw.githubusercontent.com/dlsun/data-science-book/"
"master/data/election2016.csv")
election_df
# We need to merge this data with the shapefile that we just loaded. We can create a `DataFrame` out of the `records` of a shapefile.
working_dir = 'lon-%d~%d_lat-%d~%d_time-%s~%s' % (
extent[2], extent[3], extent[0], extent[1], from_date, to_date)
if not os.path.exists(working_dir):
os.makedirs(working_dir)
f = open('full.data', 'rb')
storedlist = pickle.load(f)
# Map GHI
storedlist = storedlist[0]
reader = Reader(os.path.join('shape', 'Province_9.shp'))
proj = ccrs.PlateCarree()
provinces = cfeat.ShapelyFeature(reader.geometries(),
proj,
edgecolor='k',
facecolor='none')
for i in range(total_frame):
print("%f%%\r" % (i / total_frame * 100))
time = int(i / total_frame * time_range)
plt.figure(figsize=(10, 10))
ax = plt.axes(projection=proj)
ax.add_feature(provinces, linewidth=0.5)
ax.set_extent(extent, crs=proj)
ax.stock_img()
gl = ax.gridlines(crs=ccrs.PlateCarree(),
from cartopy.io.shapereader import Reader
#------------------------------------------------------------------------------
# files_cli = sorted(glob.glob(os.path.join('/home/alley/work/Dong/mongo/seasonal_analysis/data/data/download_from_mongo/cli', '*.grb')))
# file_cur = "/home/alley/work/Dong/mongo/seasonal_analysis/data/data/download_from_mongo/cur/gh_500_201911.grb"
# f_cli = xr.concat([xr.load_dataarray(file, engine="cfgrib") for file in files_cli], dim="time")
# c_cur = xr.load_dataarray(file_cur, engine="cfgrib")
# f_cli = xr.open_mfdataset(filenames_cli, engine="cfgrib", concat_dim=["time"])
#f_cur = Nio.open_file(filenames_cur)
# print(f_cli.dims)
#------------------------------------------------------------------------------
shp_path = '/home/alley/work/data/Cartopy_shapefiles/ne_50m_coastline.shp'
reader = Reader(shp_path)
proj = ccrs.PlateCarree(central_longitude=140)
coastlines = cfeature.ShapelyFeature(reader.geometries(),
proj,
edgecolor='k',
facecolor='none')
fig = plt.figure(figsize=(4, 4), dpi=600)
ax = fig.subplots(1, 1, subplot_kw={'projection': proj})
ax.add_feature(coastlines, linewidth=0.7)
# ax.coastlines()
# ax.add_feature(cfeature.LAND)
# ax.add_feature(cfeature.COASTLINE, lw=0.3)
# ax.add_feature(cfeature.RIVERS, lw=0.3)
# ax.add_feature(cfeature.LAKES)
# ax.add_feature(cfeature.OCEAN)
extent = [100, 180, 0, 60]
ax.set_extent(extent)
gl = ax.gridlines(crs=ccrs.PlateCarree(),
