def progvec_plot(x_model,y_model,mlat,mlon,depth_i,stime1,etime1):
fig = plt.figure()
fig.canvas.set_window_title('progvec')
ax1 = fig.add_subplot(111)
ax1.plot(x_model,y_model,"-")
plt.xlabel('km')
plt.ylabel('km')
plt.title('The time from '+str(stime1)+' to '+str(etime1)
+'\n'+'latitude:'+str(mlat)+' longitude:'+str(mlon)+' depth:'+str(depth_i))
plt.figure()
lo,la=[],[]
for k in range(len(x_model)):
lo.append(mlon+x_model[k]/1.86/60/math.cos(mlat/180.*math.pi))
la.append(mlat+y_model[k]/1.86/60)
basemap_usgs(la,lo,True)
plt.plot(lo,la,'-')
np.save('fcast.lat',la)
np.save('fcast.lon',lo)
plt.show()
def progvec_plot(x_model, y_model, mlat, mlon, depth_i, stime1, etime1):
fig = plt.figure()
fig.canvas.set_window_title('progvec')
ax1 = fig.add_subplot(111)
ax1.plot(x_model, y_model, "-")
plt.xlabel('km')
plt.ylabel('km')
plt.title('The time from ' + str(stime1) + ' to ' + str(etime1) + '\n' +
'latitude:' + str(mlat) + ' longitude:' + str(mlon) + ' depth:' +
str(depth_i))
plt.figure()
lo, la = [], []
for k in range(len(x_model)):
lo.append(mlon +
x_model[k] / 1.86 / 60 / math.cos(mlat / 180. * math.pi))
la.append(mlat + y_model[k] / 1.86 / 60)
basemap_usgs(la, lo, True)
plt.plot(lo, la, '-')
np.save('fcast.lat', la)
np.save('fcast.lon', lo)
plt.show()
lon = nc.variables['lon'][:]
h = nc.variables['h'][:]
# read connectivity array
nv = nc.variables['nv'][:].T - 1
# create a triangulation object, specifying the triangle connectivity array
tri = Tri.Triangulation(lon,lat, triangles=nv)
# plot depth using tricontourf
fig=plt.figure(figsize=(6,8))
ax1=fig.add_subplot(211)
basemap_standard([latrng[0],latrng[1]],[lonrng[0],lonrng[1]],[0.2])
cont_range.reverse()
tricontourf(tri,h*-1,list(np.array(cont_range)*-1))#contour range from fvcom_depth_ctl.py file
colorbar()
ylim(latrng)
xlim(lonrng)
plt.title('FVCOM GOM3 Depth (m)')
plt.plot([-68.158,-67.327,-67.248],[44.127,44.485,44.487],'w*',markersize=10)#cases of bad depths in FVCOM
plt.show()
# now plot usgs depths for specified region
ax2=fig.add_subplot(212)
print 'Now running basemap_usgs'
basemap_usgs([latrng[0],latrng[1]],[lonrng[0],lonrng[1]],True,True,[0.2],list(np.array(cont_range)*-1),1)
plt.title('USGS GOM3_v1_0 Depth (m)')
plt.plot([-68.158,-67.327,-67.248],[44.127,44.485,44.487],'w*',markersize=10)#bad depths
plt.text(-68.32,44.21,'Cranberry Isles',style='italic')
plt.show()
plt.savefig('/net/nwebserver/epd/ocean/MainPage/modvsobs/figs/'+urlname+'fvcom_vs_usgs_'+case+'.png')
plt.savefig('/net/nwebserver/epd/ocean/MainPage/modvsobs/figs/'+urlname+'fvcom_vs_usgs_'+case+'.eps')
df['lon'][i]=cc+(dd/60)
latsize=[39.0,45.0] #40
lonsize=[-73.,-67.0] #-72
plt.figure(figsize=(7,6))
m = Basemap(projection='cyl',llcrnrlat=min(latsize)-0.01,urcrnrlat=max(latsize)+0.01,\
llcrnrlon=min(lonsize)-0.01,urcrnrlon=max(lonsize)+0.01,resolution='h')#,fix_aspect=False)
m.drawparallels(np.arange(int(min(latsize)),int(max(latsize))+1,1),labels=[1,0,0,0])
m.drawmeridians(np.arange(int(min(lonsize)),int(max(lonsize))+1,1),labels=[0,0,0,1])
m.drawcoastlines()
m.fillcontinents(color='grey')
m.drawmapboundary()
# Now draw the isobaths (-200m)
#basemap_usgs(lat,lon,bathy,draw_parallels,parallels_interval,cont_range,ss):
basemap_usgs(latsize,lonsize,True,False,2,[-200],10)
#x, y = m2(-df['lon'],df['lat'])
x=-df['lon']
y=df['lat']
for i in range(len(df)):
if df['mean'][i]>=np.float64(0):
#print x[i],y[i]
#m.scatter(x[i],y[i],50*df['mean'][i],marker='o',color='red')
plt.scatter(x[i],y[i],50*df['mean'][i],marker='o',color='red')
if 50*df['mean'][i]>=np.float64(70) and df.index[i]<>'TA15':
plt.annotate(df.index[i],xy=(x[i],y[i]),xytext=(x[i]+0.51,y[i]+0.35),arrowprops=dict(frac=0.3,facecolor='red', shrink=0.2))
if df.index[i]=='TA15':
plt.annotate(df.index[i],xy=(x[i],y[i]),xytext=(x[i]-0.31,y[i]+0.45),arrowprops=dict(frac=0.3,shrink=0.25,facecolor='red'))
else:
#print x[i],y[i]
(a,b)=divmod(float(df['LAT_DDMM'][i]),100)
aa=int(a)
bb=float(b)
lat.append(aa+bb/60)
(c,d)=divmod(float(df['LON_DDMM'][i]),100)
cc=int(c)
dd=float(d)
lon.append(-1*(cc+dd/60))
latsize=[39.,45.0]
lonsize=[-73.,-66.0]
bathy=True
draw_parallels=True
parallels_interval=[2]
cont_range=[-200,-100]
ss=10
basemap_usgs(latsize,lonsize,bathy,draw_parallels,parallels_interval,cont_range,ss)
basemap_standard(latsize,lonsize,[2])
#plt.figure(figsize=(7,6))
#x, y = m(-lon.values,lat.values)
x, y = lon,lat
plt.scatter(x,y,20,marker='o',color='r')
for i in range(len(x)):
if df.index[i] in threesite:
#print df.index[i],lat[i],lon[i]
plt.scatter(x[i],y[i],20,marker='o',color='blue')
if df.index[i]=='BN01':
plt.annotate(df.index[i],xy=(x[i],y[i]),xytext=(x[i]+0.81,y[i]-0.35),arrowprops=dict(facecolor='blue', shrink=0.1,frac=0.3))#facecolor='black',));
else:
plt.annotate(df.index[i],xy=(x[i],y[i]),xytext=(x[i]-0.31,y[i]-0.65),arrowprops=dict(facecolor='blue', shrink=0.1,frac=0.3))#facecolor='black',));
#plt.annotate(df.index[i],xy=(x[i],y[i]),xytext=(x[i]-0.31,y[i]-0.75),arrowprops=dict(arrowstyle="-|>",shrink=0.1))#connectionstyle="arc3,rad=-0.2",fc="w"));
if df.index[i] in surfacesite:
polygon=[]
for j in range(len(deepest)):
polygon.append([distance[j],deepest[j]])
polygon.append([distance[-1],max(deepest)+1])
polygon.append([distance[0],max(deepest)+1]) #plot white when it is solid
pg=plt.Polygon(polygon,color='white')
ax.add_patch(pg)
depth_AB01=deepest[np.argmin(abs(distance_AB01-np.array(distance)))]
plt.scatter(distance_AB01,depth_AB01)
plt.annotate('AB01',xy=(distance_AB01,depth_AB01),xytext=(distance_AB01+1,depth_AB01+1),arrowprops=dict(frac=0.3,facecolor='red', shrink=0.2))
plt.ylim([max(deepest)+1,0])
plt.xlim([min(distance),max(distance)])
plt.title(str(time[q].date()),fontsize=20)
cbar=plt.colorbar(CS)
cbar.ax.tick_params(labelsize=15)
plt.show()
'''
latsize=[41.,42.5]
lonsize=[-71.,-70.1]
bathy=True
draw_parallels=True
parallels_interval=[0.5]
cont_range=[-200,-100]
ss=10
basemap_usgs(latsize,lonsize,bathy,draw_parallels,parallels_interval,cont_range,ss)
basemap_standard(latsize,lonsize,[2])
plt.scatter(lon,lat,20,marker='o',color='r')
plt.scatter(lon_AB01,lat_AB01,10,marker='o',color='b')
plt.annotate('AB01',xy=(lon_AB01,lat_AB01),xytext=(lon_AB01,lat_AB01+0.1),arrowprops=dict(frac=0.3,shrink=0.25,facecolor='blue'))
plt.show()
'''
"""
import getdata
from drifter import plot_latlon,calculate_speedcolors,plot_speedcolors
from basemap import basemap_usgs
import matplotlib.pyplot as plt
import pylab
#id=raw_input("please input id,(example: 115361221 ): ")
ids=[55201,55202,55203]
#linecol=['red','green','blue']
linecol=['green','red','blue']
ndays_label=10
#basemap_JiM([40,43],[-72,-67],False,False)
basemap_usgs([40.25,43],[-72,-67],True,True)
for k in range(len(ids)):
#get lat,lon,time use function "getdrift"
(lat,lon,datet,dep)=getdata.getdrift(ids[k])
#plot lat,lon, and add text of time in the figure
if ids[k]==55201:
daymth=['5/10','5/21','5/22','7/3','7/16']
elif ids[k]==55202:
daymth=['5/10','5/22','6/1','6/10']
elif ids[k]==55203:
daymth=['5/18','6/1','6/10']
plot_latlon(lat,lon,datet,ids[k],ndays_label,linecol[k],daymth)
# position figure window on the screen
thismanager1 = pylab.get_current_fig_manager()
thismanager1.window.SetPosition((0, 0))
plt.title("basemap_standard")
plt.show()
# use the ascii files of coastline already on disk
plt.figure(2, figsize=(5, 4))
bm.basemap_region("sne")
thismanager2 = pylab.get_current_fig_manager()
thismanager2.window.SetPosition((0, 500))
plt.title("basemap_region")
plt.show()
# extract coastline and bathymetry using pydap
plt.figure(3, figsize=(5, 4))
bm.basemap_usgs([42.0, 43.0], [-71.0, -70.0], True, True, [0.5], [-50, 0], 5)
thismanager3 = pylab.get_current_fig_manager()
thismanager3.window.SetPosition((500, 0))
plt.title("basemap_usgs")
plt.show()
# extract coastline and bathymetry using pydap
plt.figure(4, figsize=(5, 4))
bm.basemap_detail([40.0, 43.0], [-71.0, -65.0], True, True, 0.5)
thismanager4 = pylab.get_current_fig_manager()
thismanager4.window.SetPosition((500, 500))
plt.title("basemap_detail")
plt.show()
