def drawSST(ax, map, longSST, latSST, filledSST, myalpha):
# Input arrays has to be 2D
print "Drawing SST: min %s and max %s" % (filledSST.min(), filledSST.max())
x2, y2 = map(longSST, latSST)
levels = np.arange(-2, 18, 0.5)
#levels=np.arange(filledSST.min(), filledSST.max(),0.2)
if myalpha > 0.99:
CS2 = map.contourf(x2,
y2,
filledSST,
levels,
cmap=mpl_util.LevelColormap(levels,
cmap=cm.RdYlBu_r),
extend='both',
alpha=myalpha)
#CS2 = map.pcolor(x2,y2,filledSST)
plt.colorbar(CS2, orientation='vertical', extend='both', shrink=0.5)
else:
CS2 = map.contourf(x2,
y2,
filledSST,
levels,
cmap=mpl_util.LevelColormap(levels,
cmap=cm.RdYlBu_r),
extend='both',
alpha=myalpha)
def addBathymetry(self):
etopo1 = '/Users/trondkr/Dropbox/NIVA/ETOPO1/ETOPO1_Ice_g_gmt4.grd'
etopo = xr.open_dataset(etopo1)
lats = etopo['y'].values
lons = etopo['x'].values
res = self.findSubsetIndices(self.llcrnrlat - 5, self.urcrnrlat + 5,
self.llcrnrlon - 40, self.urcrnrlon + 10,
lats, lons)
res = [int(x) for x in res]
bathy = etopo["z"][int(res[2]):int(res[3]),
int(res[0]):int(res[1])].values
bathySmoothed = laplacefilter.laplace_filter(bathy, M=None)
lon, lat = np.meshgrid(lons[res[0]:res[1]], lats[res[2]:res[3]])
levels = [
-6000, -5000, -4000, -3500, -3000, -2500, -2000, -1750, -1500,
-1250, -1000, -750, -500, -400, -300, -250, -200, -150, -100, -50,
-10
]
x, y = self.mymap(lon, lat)
etopo_contour = self.mymap.contourf(x,
y,
bathySmoothed,
levels,
cmap=mpl_util.LevelColormap(
levels, cmap=cm.Blues_r),
extend='upper',
alpha=1.0,
origin='lower')
def plotMap(lon,
lat,
mydata,
period,
qctype,
season,
hotspotsLon=None,
hotspotsLat=None,
hostpotsVals=None):
plt.figure(figsize=(12, 12), frameon=False)
mymap = Basemap(projection='npstere', lon_0=0, boundinglat=50)
llat, llon = np.meshgrid(lat, lon)
x, y = mymap(llon, llat)
step = (np.max(mydata) - np.min(mydata)) / 10.
if step > 0:
print("Plotting season: {} for period {}".format(season, period))
levels = np.arange(np.min(mydata), np.max(mydata), step)
levels = np.arange(-1, 6, 0.2)
print("Number of masked points {}".format(np.sum(mydata.mask == True)))
CS1 = mymap.contourf(x,
y,
mydata,
levels,
cmap=mpl_util.LevelColormap(levels,
cmap=cm.RdBu_r))
mymap.drawparallels(np.arange(-90., 120., 15.),
labels=[1, 0, 0, 0]) # draw parallels
mymap.drawmeridians(np.arange(0., 420., 30.),
labels=[0, 1, 0, 1]) # draw meridians
mymap.drawcoastlines()
mymap.drawcountries()
if not (hotspotsLat is None and hotspotsLon is None):
hlon, hlat = mymap(hotspotsLon, hotspotsLat)
mymap.scatter(hlon,
hlat,
s=100,
alpha=0.5,
color='r',
edgecolors='k')
mymap.fillcontinents(color='grey', alpha=0.2)
plt.colorbar(CS1, shrink=0.5)
title('QC:' + str(period) + ' season:' + str(season))
CS1.axis = 'tight'
if not os.path.exists("Figures"):
os.mkdir("Figures/")
plotfile = 'Figures/map_qc_' + str(period) + '_season_' + str(
season) + '.png'
plt.savefig(plotfile, dpi=100)
plt.clf()
plt.close()
def plotMap(lon, lat, mydata, modelName, scenario, mydate, mytype):
plt.figure(figsize=(12, 12), frameon=False)
mymap = Basemap(projection='npstere', lon_0=0, boundinglat=65)
x, y = mymap(lon, lat)
if mytype == "anomalies":
levels = np.arange(np.min(mydata), np.max(mydata), 1)
#levels=np.arange(-2,5,0.1)
else:
levels = np.arange(np.min(mydata), np.max(mydata), 5)
#levels=np.arange(-2,15,0.5)
CS1 = mymap.contourf(x,
y,
mydata,
levels,
cmap=mpl_util.LevelColormap(levels, cmap=cm.RdBu_r),
extend='max',
antialiased=False)
mymap.drawparallels(np.arange(-90., 120., 15.),
labels=[1, 0, 0, 0]) # draw parallels
mymap.drawmeridians(np.arange(0., 420., 30.), labels=[0, 1, 0,
1]) # draw meridians
mymap.drawcoastlines()
mymap.drawcountries()
mymap.fillcontinents(color='grey')
plt.colorbar(CS1, shrink=0.5)
if (mytype == "anomalies"):
title('Model:' + str(modelName) + ' Year:' + str(mydate.year) +
' Month:' + str(mydate.month))
if (mytype == "regular"):
title('Model:' + str(modelName) + ' Year:' + str(mydate.year) +
' Month:' + str(mydate.month))
#CS1.axis='tight'
#plt.show()
if not os.path.exists("Figures"):
os.mkdir("Figures/")
if (mytype == "anomalies"):
plotfile = 'figures/map_anomalies_' + str(modelName) + '_' + str(
mydate.year) + '_' + str(mydate.month) + '.png'
else:
plotfile = 'figures/map_' + str(modelName) + '_' + str(
mydate.year) + '_' + str(mydate.month) + '.png'
plt.savefig(plotfile, dpi=300)
plt.clf()
plt.close()
def plotMap(lon, lat, mydata, period, qctype, season):
plt.figure(figsize=(12, 12), frameon=False)
mymap = Basemap(projection='npstere', lon_0=0, boundinglat=50)
llat, llon = np.meshgrid(lat, lon)
print("Plotting season: {} for period {}".format(season, period))
x, y = mymap(llon, llat)
print(np.min(mydata), np.max(mydata))
levels = np.arange(np.min(mydata), np.max(mydata), 0.01)
# levels = [0, 1]
CS1 = mymap.contourf(x,
y,
mydata,
levels,
cmap=mpl_util.LevelColormap(levels, cmap=cm.RdBu_r),
extend='max')
mymap.drawparallels(np.arange(-90., 120., 15.),
labels=[1, 0, 0, 0]) # draw parallels
mymap.drawmeridians(np.arange(0., 420., 30.), labels=[0, 1, 0,
1]) # draw meridians
mymap.drawcoastlines()
mymap.drawcountries()
mymap.fillcontinents(color='grey', alpha=0.2)
plt.colorbar(CS1, shrink=0.5)
title('QC:' + str(period) + ' season:' + str(season))
CS1.axis = 'tight'
if not os.path.exists("Figures"):
os.mkdir("Figures/")
plotfile = 'figures/map_qc_' + str(period) + '_season_' + str(
season) + '.png'
# plt.show()
plt.savefig(plotfile, dpi=100, bbox_inches='tight')
plt.clf()
plt.close()
def createNiceMap(grd, h):
fig = plt.figure()
levels = [10, 25, 50, 100, 250, 500, 1000, 2500, 5000]
mymap = Basemap(llcrnrlon=-18.0,
llcrnrlat=46.0,
urcrnrlon=25.5,
urcrnrlat=67.5,
resolution='i',
projection='tmerc',
lon_0=0,
lat_0=50,
area_thresh=50.)
# mymap.drawcoastlines()
# mymap.drawcountries()
# mymap.fillcontinents(color='grey')
mymap.drawmeridians(np.arange(grd.hgrid.lon_rho.min(),
grd.hgrid.lon_rho.max(), 10),
labels=[0, 0, 0, 1])
mymap.drawparallels(np.arange(grd.hgrid.lat_rho.min(),
grd.hgrid.lat_rho.max(), 4),
labels=[1, 0, 0, 0])
x, y = mymap(grd.hgrid.lon_rho, grd.hgrid.lat_rho)
CS1 = mymap.contourf(x,
y,
h,
levels,
cmap=mpl_util.LevelColormap(levels, cmap=cm.Blues),
extend='upper',
alpha=1.0,
origin='lower',
rasterized=True)
CS1.axis = 'tight'
plotfile = 'figures/map_KINO.pdf'
plt.savefig(plotfile, dpi='200')
def addEtopo1Bathymetry(ax, map):
"""Get the etopo1 data"""
etopo1name = '/Users/trond/Projects/arcwarm/maps/ETOPO1_Ice_g_gmt4.grd'
etopo1 = Dataset(etopo1name, 'r')
lons = etopo1.variables["lon"][:]
lats = etopo1.variables["lat"][:]
res = findSubsetIndices(latStart - 5, latEnd + 5, lonStart - 40,
lonEnd + 10, lats, lons)
lon, lat = np.meshgrid(lons[res[0]:res[1]], lats[res[2]:res[3]])
print "Extracted data for area %s : (%s,%s) to (%s,%s)" % (
name, lon.min(), lat.min(), lon.max(), lat.max())
bathy = etopo1.variables["z"][int(res[2]):int(res[3]),
int(res[0]):int(res[1])]
bathySmoothed = laplaceFilter.laplace_filter(bathy, M=None)
levels = [
-6000, -5000, -3000, -2000, -1500, -1000, -500, -400, -300, -250, -200,
-150, -100, -75, -65, -50, -35, -25, -15, -10, -5, 0
]
x, y = map(lon, lat)
CS1 = map.contourf(
x,
y,
bathySmoothed,
levels,
cmap=mpl_util.LevelColormap(levels, cmap=cm.Blues_r),
#cmap=cm.get_cmap('Greys_r',len(levels)),
extend='upper',
alpha=1.0,
origin='lower',
rasterized=True)
CS1.axis = 'tight'
def contourMap(myCDF, bathymetry,lonrho,latrho,myvar,survey,Nobs,obs,first):
print "Plotting values for index %s to %s"%(sum(Nobs[0:survey]),sum(Nobs[0:survey])+obs)
"""Get the apporporiate grid"""
lat = myCDF.variables["obs_lat"][sum(Nobs[0:survey]):sum(Nobs[0:survey])+obs]
lon = myCDF.variables["obs_lon"][sum(Nobs[0:survey]):sum(Nobs[0:survey])+obs]
xgrid = myCDF.variables["obs_Xgrid"][sum(Nobs[0:survey]):sum(Nobs[0:survey])+obs]
ygrid = myCDF.variables["obs_Ygrid"][sum(Nobs[0:survey]):sum(Nobs[0:survey])+obs]
obs_time = int(np.squeeze(myCDF.variables["obs_time"][sum(Nobs[0:survey]):sum(Nobs[0:survey])+1]))
mydata = myCDF.variables[myvar][sum(Nobs[0:survey]):sum(Nobs[0:survey])+obs]
if sum(np.isnan(mydata)) > 0:
print "DATA contains NAN!!!!!"
if first is True:
print "Max and min latitude: %s - %s"%(lat.max(), lat.min())
print "Max and min longitude: %s - %s\n"%(lon.max(), lon.min())
first=False
""" Current time:"""
refDate=datetime.datetime(1948,1,1,0,0,0)
currentDate=refDate + datetime.timedelta(days=obs_time)
print "Plotting timestep: %s"%(currentDate)
"""Start creating figure"""
plt.figure(figsize=(10,10), frameon=False)
map = Basemap(llcrnrlon=-18.0,
llcrnrlat=46.0,
urcrnrlon=25.5,
urcrnrlat=67.5,
resolution='i',projection='tmerc',lon_0=0,lat_0=50,area_thresh=50.)
x, y = map(lon,lat)
x2, y2 = map(lonrho,latrho)
ax1=plt.subplot(1,1,1)
map.drawcoastlines()
map.fillcontinents(color='grey')
map.drawcountries()
map.drawmapboundary()
print "Min %s and max %s of data"%(np.ma.min(mydata),np.ma.max(mydata))
levels=np.arange(-2,18,0.5)
map.scatter(x,y,s=4,c=mydata,cmap=mpl_util.LevelColormap(levels,cmap=cm.RdYlBu_r), edgecolor='none',vmin=-2, vmax=18)
c = plt.colorbar(orientation='horizontal')
c.set_label("Obs. SST")
# h=bathymetry.flatten()
#
# ax1=plt.subplot(2,2,2)
#
# map.scatter(x,y,s=4,c=bathymetry/np.ma.max(bathymetry),cmap=cm.get_cmap('jet'), edgecolor='none')
## c = plt.colorbar(orientation='horizontal')
# c.set_label("Bathymetry")
#
# ax2=plt.subplot(2,2,3)
# scatter(xgrid,ygrid,s=4,c=mydata/np.ma.max(mydata),cmap=cm.get_cmap('jet'), edgecolor='none')
# ax2.axis('tight')
#
# ax2=plt.subplot(2,2,4)
# scatter(xgrid,ygrid,s=4,c=bathymetry/np.ma.max(bathymetry),cmap=cm.get_cmap('jet'), edgecolor='none')
#
# ax2.axis('tight')
plotfile='figures/obs_'+str(myvar)+'_'+str(currentDate)+'.jpeg'
plt.savefig(plotfile)
def plotHistDistribution(shapefile, hist, xii, yii, polygon, experiment,
startdate, enddate, plotType):
plt.clf()
# plt.figure(figsize=(10,10), frameon=False)
ax = plt.subplot(111)
mymap = Basemap(llcrnrlon=17.0,
llcrnrlat=69.4,
urcrnrlon=19.0,
urcrnrlat=70.0,
resolution='h',
projection='merc',
lon_0=np.mean(np.array(xii)),
lat_0=np.mean(np.array(yii)),
area_thresh=0.)
xiii, yiii = mymap(xii, yii)
levels = np.arange(np.min(hist), np.max(hist), 1)
# levels = np.arange(np.min(hist), 200, 1)
levels = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30]
CS1 = mymap.contourf(xiii,
yiii,
np.fliplr(np.rot90(hist, 3)),
levels,
cmap=mpl_util.LevelColormap(levels,
cmap=cmocean.cm.haline),
origin='lower',
extend='both',
alpha=1.0)
plt.colorbar(CS1, orientation='vertical', extend='both', shrink=0.5)
mymap.drawcoastlines()
mymap.fillcontinents(color='grey', zorder=2)
mymap.drawcountries()
mymap.drawmapboundary()
mypatches = createPathsForPolygons(shapefile, mymap)
# colors = 100 * np.random.rand(len(mypatches))
# p = PatchCollection(mypatches, alpha=0.9, zorder=10)
# p.set_array(np.array(colors))
# ax.add_collection(p)
p = PatchCollection(mypatches,
alpha=0.8,
facecolor='none',
lw=0.5,
edgecolor='red',
zorder=10)
ax.add_collection(p)
if plotType == "individual":
p = PatchCollection([mypatches[polygon]],
alpha=0.9,
facecolor='darkorange',
lw=0.5,
edgecolor='darkorange',
zorder=10)
ax.add_collection(p)
if plotType == "all":
p = PatchCollection(mypatches,
alpha=0.9,
facecolor='darkorange',
lw=0.5,
edgecolor='darkorange',
zorder=10)
ax.add_collection(p)
if plotType == "all":
x = xii.flatten()
y = yii.flatten()
hh = np.fliplr(np.rot90(hist, 3))
z = hh.flatten()
np.savetxt("allDensityXYZ.csv", (x, y, z))
# plt.title('Tare sedimentering')
print "Adding %s release polygons to map" % (len(mypatches))
# plt.show()
if plotType == "individual":
plotfilename = 'distributionFigures/Kelp_polygon_%s_experiment_%s_%s_to_%s.png' % (
polygon + 1, experiment, startdate, enddate)
if plotType == "all":
plotfilename = 'distributionFigures/Kelp_allPolygons_experiment_%s_%s_to_%s.png' % (
experiment, startdate, enddate)
print "=> Creating plot %s" % plotfilename
plt.savefig(plotfilename, dpi=300)
def makeMapnewArctic(lonStart, lonEnd, latStart, latEnd, lat1, lon1, foutPath,
ax):
# fig= plt.figure(figsize=(10,8))
"""Get the etopo2 data"""
# etopo1name='ETOPO1_Ice_g_gmt4.grd'
# etopo1name='/home/xuj/work/project/topo/ETOPO1/ETOPO1_Bed_c_gmt4.grd'
# etopo1name='/home/xuj/work/project/topo/ETOPO1/barrowStrait.nc'
# etopo1name='/home/xuj/work/project/topo/ETOPO1/testnew.nc'
etopo1name = '/home/xuj/work/project/whaleDectionRange/bathymetry/topo15_eastNew.nc'
etopo1 = Dataset(etopo1name, 'r')
lons = etopo1.variables["lon"][:]
lats = etopo1.variables["lat"][:]
# lons = etopo1.variables["x"][:]
# lats = etopo1.variables["y"][:]
print "lons:", max(lons), min(lons)
print lons
print "lats:", max(lats), min(lats)
print lats
print "lon,lat,start, end:", lonStart, lonEnd, latStart, latEnd
print " lat lon start end:", latStart - 2, latEnd + 5, lonStart - 5, lonEnd + 5
res = findSubsetIndices(latStart - 1, latEnd + 1, lonStart - 1, lonEnd + 1,
lats, lons)
print "res:::", res
lon, lat = np.meshgrid(lons[res[0]:res[1]], lats[res[2]:res[3]])
print "shape of lon:", np.shape(lon)
print "shape of lat:", np.shape(lat)
# exit(1)
# print "Extracted data for area %s : (%s,%s) to (%s,%s)"%(name,lon.min(),lat.min(),lon.max(),lat.max())
bathy = etopo1.variables["z"][int(res[2]):int(res[3]),
int(res[0]):int(res[1])]
etopo1.close()
# z0 = etopo1.variables["z"][int(res[0]):int(res[1]),int(res[2]):int(res[3])]
# print ma.shape(z0)
# z = ma.array().flatten()
bathySmoothed = laplaceFilter.laplace_filter(bathy, M=None)
y = lons[res[0]:res[1]]
x = lats[res[2]:res[3]]
# print ma.shape(z),ma.shape(x)
# zi = matplotlib.mlab.griddata(x,y,z,lat, lon)
# levels=[-5500,-5000,-2500, -2000, -1500, -1000,-500, -400, -300, -250, -200, -150, -100, -75, -65, -50, -35, -25, -15, -10, -5, 0]
levels = [
-5000, -4000, -3000, -1000, -500, -400, -300, -250, -200, -150, -100,
-50, -20, -5, -1
]
if lonStart < 0 and lonEnd < 0:
lon_0 = -(abs(lonEnd) + abs(lonStart)) / 2.0
else:
lon_0 = (abs(lonEnd) + abs(lonStart)) / 2.0
print 'Center longitude ', lon_0
map = Basemap(llcrnrlat=latStart,urcrnrlat=latEnd,\
llcrnrlon=lonStart,urcrnrlon=lonEnd,\
rsphere=(6378137.00,6356752.3142),\
resolution='h',area_thresh=1000.,projection='lcc',\
lat_1=latStart,lon_0=lon_0,ax=ax)
x, y = map(lon, lat)
map.drawcoastlines(linewidth=0.5)
map.drawcountries()
map.fillcontinents(color='grey')
map.drawmeridians(np.arange(int(lons.min()), int(lons.max()), 2),
labels=[0, 0, 0, 1],
fontsize=10)
map.drawparallels(np.arange(int(lats.min()), int(lats.max()), 1),
labels=[1, 0, 0, 0],
fontsize=10)
#map.bluemarble()
# CS1 = map.contourf(x,y,bathySmoothed,levels,
# cmap=mpl_util.LevelColormap(levels,cmap=cm.Blues_r),
# extend='upper',
# alpha=1.0,
# origin='lower')
# CS2 = map.contour(x,y,zi,levels1,linewidths=0.5,colors='k',animated=True)
# CS1 = map.contourf(x,y,zi,levels,cmap=mpl_util.LevelColormap(levels,cmap=cm.Blues_r),alpha=1.0)
levels1 = [-100]
CS2 = map.contour(x,
y,
bathySmoothed,
levels1,
linewidths=0.5,
colors='y',
linestyles='solid',
animated=True)
plt.clabel(CS2, fontsize=4, inline=True)
levels1 = [-200]
CS2 = map.contour(x,
y,
bathySmoothed,
levels1,
linewidths=0.5,
colors='g',
linestyles='solid',
animated=True)
plt.clabel(CS2, fontsize=4, inline=True)
# levels1=[-400]
# CS2 = map.contour(x,y,bathySmoothed,levels1,linewidths=0.5,colors='r',linestyles='solid',animated=True)
# plt.clabel(CS2,fontsize=4,inline=True)
#
# levels1=[-350]
# CS2 = map.contour(x,y,bathySmoothed,levels1,linewidths=0.5,colors='k',linestyles='solid',animated=True)
# plt.clabel(CS2,fontsize=4,inline=True)
levels1 = [-300]
CS2 = map.contour(x,
y,
bathySmoothed,
levels1,
linewidths=0.5,
colors='c',
linestyles='solid',
animated=True)
plt.clabel(CS2, fontsize=4, inline=True)
CS1 = map.contourf(x,
y,
bathySmoothed,
levels,
cmap=mpl_util.LevelColormap(levels, cmap=cm.Blues_r),
alpha=1.0)
# CS1 = map.contourf(x,y,bathySmoothed,levels,cmap=mpl_util.LevelColormap(levels,cmap=cm.ocean),alpha=1.0)
# CS1 = map.contourf(x,y,bathySmoothed,levels,cmap=mpl_util.LevelColormap(levels,cmap=cm.jet),alpha=1.0)
#
# CS1.axis='tight'
"""Plot the station as a position dot on the map"""
# cbar= fig.colorbar(CS1,orientation='horizontal')
cb = map.colorbar(CS1, "right", size="2.5%", pad='2%')
cb.set_label('Depth (m)', fontsize=10)
# map.plot(lon1,lat1,'r*', markersize=6,latlon='true')
print type(lon1), type(lat1), np.shape(lon1)
# for lon2,lat2 in zip(lon1,lat1):
# print lon2,lat2
# print type(lon2)
for lon2, lat2 in zip(lon1, lat1):
map.plot(float(lon2), float(lat2), 'r.', markersize=2, latlon='true')
# lat2 = Lat2[60]
# lon2 = Lon2[60]
# lat2 = Lat2[-1]
# lon2 = Lon2[-1]
# map.plot(lon2,lat2,'y*', markersize=1,latlon='true')
# g = geodesic.Geodesic.WGS84.Inverse(lat1, lon1, lat2, lon2);
# Compute midpoint starting at 1
# l=geodesic.Geodesic.WGS84.Line(g['lat1'],g['lon1'],g['azi1'])
# num=50
# print np.shape(lon)
# print np.shape(lat)
# print np.shape(bathySmoothed)
#
## idx,idy = np.where(lon<-87 & lon<-95) # & lat>73 & lat<75 & bitwise operator
# idx1,idy1 = np.where((lon<-55) & (lon>-60))
# idx2,idy2 = np.where((lat > 55) & (lat < 58)) # & lat>73 & lat<75
#
## print np.shape(idx1)
## print np.shape(idy1)
#
# print min(idx2),max(idx2),min(idy1),max(idy1)
# print min(idx1),max(idx1),min(idy2),max(idy2)
#
# xn,yn = np.shape(lon)
#
# idx1,idx2 = min(idx2),max(idx2)
# idy1,idy2 = min(idy1),max(idy1)
# idx1,idx2 = 251,551
## idy1,idy2 = 1201,2201
#
# bathySmoothedN = bathySmoothed[idx1:idx2,idy1:idy2]
# lonN = lon[idx1:idx2,idy1:idy2]
# latN = lat[idx1:idx2,idy1:idy2]
#
# print np.shape(lonN)
# print np.shape(latN)
# print np.shape(bathySmoothedN)
#
# if False:
# interpFile = foutPath+'linearInterp.pik'
# if os.path.isfile(interpFile):
# print "interpolation file is ready, unpacking it...."
# with open(interpFile,'rb') as fpik:
# finterp=pickle.load(fpik)
# f = finterp
# else:
# # exit(1)
# # f = interpolate.interp2d(lonN, latN, bathySmoothedN, kind='cubic')
# print "interpolation file is not ready, working on it...."
# f = interpolate.interp2d(lonN, latN, bathySmoothedN, kind='linear')
# with open(interpFile,'w') as fpik:
# pickle.dump(f,fpik)
# f = interpolate.griddata(lonN, latN, bathySmoothedN, method='linear')
# print "interpolation function is done"
# exit(1)
# f = interpolate.RectBivariateSpline(lon, lat, bathySmoothed, kx=2, ky=2, s=0)
# f = interpolate.Rbf(lonN, latN, bathySmoothedN)
# for i in range(num+1):
# b=l.Position(i*g['s12']/num)
## print(b['lat2'],b['lon2'])
#
# if i ==0:
# pass
# else:
# map.plot(b['lon2'],b['lat2'],'ko', markersize=0.5,latlon='true')
#
# lati = b['lat2']
# loni = b['lon2']
# zi = f(loni, lati)
# zi = interpolate.griddata((lonN, latN), bathySmoothedN, (loni, lati),method='linear')
# print loni,lati,zi
# map.plot(lon2,lat2,'r*', markersize=1,latlon='true')
# h1 = geodesic.Geodesic.WGS84.Direct(lat1, lon1, g['azi1'], g['s12']/2);
# print(h1['lat2'],h1['lon2']);
# Alternatively, compute midpoint starting at 2
# h2 = geodesic.Geodesic.WGS84.Direct(lat2, lon2, g['azi2'], -g['s12']/2);
# print(h2['lat2'],h2['lon2']);
# p=Geodesic.WGS84.Inverse(40.6, -73.8, 1.4, 104);
# for i in ma.arange(len(name)):
#
# print i , name[i]
# xpt,ypt = map(stLon[i*2],stLat[i*2])
# xpt1,ypt1 = map(stLon[i*2+1],stLat[i*2+1])
#
# map.plot([xpt],[ypt],'r.', markersize=5)
# map.plot([xpt1],[ypt1],'r.', markersize=5)
#
# plt.text(xpt+10000,ypt+10000,name[i], fontsize=7)
# font = {'fontname':'Arial','weight':'bold','size':8}
# matplotlib.rc('font', **font)
# plt.title('Area %s'%(name))
# plotfile='/home/xuj/work/project/noise/map_nls_north.jpg'
# plotfile='/home/xuj/work/codes/barrowStraitSmall.jpg'
# plt.show()
# plt.close()
return map
def createNiceMap(grd, h, polygon_data_KINO, polygon_data_NS8KM,
plotSelectedStations):
fig = plt.figure(figsize=(12, 12))
ax = fig.add_subplot(111)
levels = [10, 25, 50, 100, 250, 500, 1000, 2500, 5000]
mymap = Basemap(llcrnrlon=-18.0,
llcrnrlat=46.0,
urcrnrlon=25.5,
urcrnrlat=67.5,
resolution='i',
projection='tmerc',
lon_0=0,
lat_0=50,
area_thresh=50.)
mymap.drawcoastlines()
mymap.drawcountries()
mymap.fillcontinents(color='grey')
mymap.drawmeridians(np.arange(grd.hgrid.lon_rho.min(),
grd.hgrid.lon_rho.max(), 10),
labels=[0, 0, 0, 1])
mymap.drawparallels(np.arange(grd.hgrid.lat_rho.min(),
grd.hgrid.lat_rho.max(), 4),
labels=[1, 0, 0, 0])
x, y = mymap(grd.hgrid.lon_rho, grd.hgrid.lat_rho)
print np.min(grd.hgrid.lon_rho), np.max(grd.hgrid.lon_rho)
print np.min(grd.hgrid.lat_rho), np.max(grd.hgrid.lat_rho)
CS1 = mymap.contourf(x,
y,
h,
levels,
cmap=mpl_util.LevelColormap(levels, cmap=cm.Blues),
extend='upper',
alpha=1.0,
origin='lower',
rasterized=True)
CS1.axis = 'tight'
"""Draw grid boundaries"""
drawGridBoundaries(ax, mymap, polygon_data_NS8KM, mycolor="red")
drawGridBoundaries(ax, mymap, polygon_data_KINO, mycolor="magenta")
if (plotSelectedStations):
xpos, ypos = getSelectedStations()
xloc, yloc = mymap(grd.hgrid.lon_rho[ypos, xpos],
grd.hgrid.lat_rho[ypos, xpos])
mymap.plot(xloc,
yloc,
marker="o",
color="red",
markersize=10,
linewidth=0)
plotfile = 'figures/map_NS8KM_and_KINO1600M.pdf'
plt.savefig(plotfile, dpi='200')
plt.show()