Python Basemap.imshow方法代码示例

# 需要导入模块: from matplotlib.toolkits.basemap import Basemap [as 别名]
# 或者: from matplotlib.toolkits.basemap.Basemap import imshow [as 别名]
def draw_client_density():

    m = Basemap(llcrnrlon=-180.,llcrnrlat=-90,urcrnrlon=180.,urcrnrlat=90.,\
                resolution='c',projection='cyl')

    # plot them as filled circles on the map.
    # first, create a figure.
    dpi=100
    dimx=800/dpi
    dimy=400/dpi
    fig=figure(figsize=(dimx,dimy), dpi=dpi, frameon=False, facecolor='blue')
#    ax=fig.add_axes([0.1,0.1,0.7,0.7],axisbg='g')
    ax=fig.add_axes([0.0,0.0,1.0,1.0],axisbg='g')
    canvas = FigureCanvas(fig)
    results = lookup_client_locations()
    X,Y,Z = find_client_density(m,results)
#    s = random.sample(results, 40000)
#    for t in s:
#        lat=t[2]
#        lon=t[3]
#        # draw a red dot at the center.
#        xpt, ypt = m(lon, lat)
#        m.plot([xpt],[ypt],'ro', zorder=10)
    # draw coasts and fill continents.
    m.drawcoastlines(linewidth=0.5)
    m.drawcountries(linewidth=0.5)
    m.drawlsmask([100,100,100,0],[0,0,255,255])
#    m.fillcontinents(color='green')
    palette = cm.YlOrRd
    m.imshow(Z,palette,extent=(m.xmin,m.xmax,m.ymin,m.ymax),interpolation='gaussian',zorder=0)
#    l,b,w,h = ax.get_position()
#    cax = axes([l+w+0.075, b, 0.05, h])
#    colorbar(cax=cax) # draw colorbar

    canvas.print_figure(outdir+'/clientmap.png', dpi=100)

# 需要导入模块: from matplotlib.toolkits.basemap import Basemap [as 别名]
# 或者: from matplotlib.toolkits.basemap.Basemap import imshow [as 别名]
pilImage = Image.open('land_shallow_topo_2048.jpg')
rgba = pil_to_array(pilImage)
rgba = rgba.astype(P.Float32)/255. # convert to normalized floats.

# define lat/lon grid that image spans (projection='cyl').
nlons = rgba.shape[1]; nlats = rgba.shape[0]
delta = 360./float(nlons)
lons = P.arange(-180.+0.5*delta,180.,delta)
lats = P.arange(-90.+0.5*delta,90.,delta)

# create new figure
fig=P.figure()
# define cylindrical equidistant projection.
m = Basemap(projection='cyl',llcrnrlon=-180,llcrnrlat=-90,urcrnrlon=180,urcrnrlat=90,resolution='l')
# plot (unwarped) rgba image.
im = m.imshow(rgba)
# draw coastlines.
m.drawcoastlines(linewidth=0.5,color='0.5')
# draw lat/lon grid lines.
m.drawmeridians(P.arange(-180,180,60),labels=[0,0,0,1],color='0.5')
m.drawparallels(P.arange(-90,90,30),labels=[1,0,0,0],color='0.5')
P.title("Blue Marble image - native 'cyl' projection",fontsize=12)
print 'plot cylindrical map (no warping needed) ...'

# create new figure
fig=P.figure()
# define orthographic projection centered on North America.
m = Basemap(projection='ortho',lat_0=40,lon_0=40,resolution='l')
# transform to nx x ny regularly spaced native projection grid
# nx and ny chosen to have roughly the same horizontal res as original image.
dx = 2.*P.pi*m.rmajor/float(nlons)

# 需要导入模块: from matplotlib.toolkits.basemap import Basemap [as 别名]
# 或者: from matplotlib.toolkits.basemap.Basemap import imshow [as 别名]
# shift data so lons go from -180 to 180 instead of 20 to 380.
topoin,lons = shiftgrid(180.,topoin,lons,start=False)

print 'min/max etopo20 data:'
print min(ravel(topoin)),max(ravel(topoin))

# setup cylindrical equidistant map projection (global domain).
m = Basemap(-180.,-90,180.,90.,\
            resolution='c',area_thresh=10000.,projection='cyl')
# setup figure with same aspect ratio as map.
xsize = rcParams['figure.figsize'][0]
fig=figure(figsize=(xsize,m.aspect*xsize))
ax = fig.add_axes([0.1,0.1,0.75,0.75])
# plot image over map.
im = m.imshow(topoin,cm.jet)
cax = axes([0.875, 0.1, 0.05, 0.75]) # setup colorbar axes.
colorbar(tickfmt='%d', cax=cax) # draw colorbar
axes(ax)  # make the original axes current again
m.drawcoastlines()
#m.drawcountries()
#m.drawstates()
#m.fillcontinents()
# draw parallels
delat = 30.
circles = arange(0.,90.+delat,delat).tolist()+\
          arange(-delat,-90.-delat,-delat).tolist()
m.drawparallels(circles,labels=[1,0,0,1])
# draw meridians
delon = 60.
meridians = arange(-180,180,delon)

# 需要导入模块: from matplotlib.toolkits.basemap import Basemap [as 别名]
# 或者: from matplotlib.toolkits.basemap.Basemap import imshow [as 别名]
# get data from DEM file
array = gd.ReadAsArray()
# get lat/lon coordinates from DEM file.
coords = gd.GetGeoTransform()
llcrnrlon = coords[0]
urcrnrlon = llcrnrlon+(array.shape[1]-1)*coords[1]
urcrnrlat = coords[3]
llcrnrlat = urcrnrlat+(array.shape[0]-1)*coords[5]
# create Basemap instance.
m = Basemap(llcrnrlon=llcrnrlon,llcrnrlat=llcrnrlat,urcrnrlon=urcrnrlon,urcrnrlat=urcrnrlat,projection='cyl')
# create a figure, add an axes
# (leaving room for a colorbar).
fig = p.figure()
ax = fig.add_axes([0.1,0.1,0.75,0.75])
# plot image from DEM over map.
im = m.imshow(array,origin='upper')
# make a colorbar.
cax = p.axes([0.875, 0.1, 0.05, 0.75]) # setup colorbar axes.
p.colorbar(cax=cax) # draw colorbar
p.axes(ax)  # make the original axes current again
# draw meridians and parallels.
m.drawmeridians(p.linspace(llcrnrlon+0.1,urcrnrlon-0.1,5),labels=[0,0,0,1],fmt='%4.2f')
m.drawparallels(p.linspace(llcrnrlat+0.1,urcrnrlat-0.1,5),labels=[1,0,0,0],fmt='%4.2f')
# plot county boundaries from
# http://edcftp.cr.usgs.gov/pub/data/nationalatlas/countyp020.tar.gz
shp_info = m.readshapefile('countyp020','counties',drawbounds=True,linewidth=1.0)
# plot some cities.
lons = [-105.22,-105.513,-105.316,-105.47]; lats = [39.76,39.801,39.633,39.41]
names =  ['Golden','Central City','Evergreen','Bailey']
x,y = m(lons,lats)
m.plot(x,y,'ko')

# 需要导入模块: from matplotlib.toolkits.basemap import Basemap [as 别名]
# 或者: from matplotlib.toolkits.basemap.Basemap import imshow [as 别名]
            resolution='l',area_thresh=1000.,projection='lcc',\
            lat_1=50.,lon_0=-107.)
# transform to nx x ny regularly spaced native projection grid
nx = int((m.xmax-m.xmin)/40000.)+1; ny = int((m.ymax-m.ymin)/40000.)+1
topodat,x,y = m.transform_scalar(topoin,lonsin,latsin,nx,ny,returnxy=True)
# create the figure.
fig=figure(figsize=(8,8))
# add an axes, leaving room for colorbar on the right.
ax = fig.add_axes([0.1,0.1,0.7,0.7])
# make topodat a masked array, masking values lower than sea level.
topodat = where(topodat < 0.,1.e10,topodat)
topodatm = ma.masked_values(topodat, 1.e10)
palette = cm.YlOrRd
palette.set_bad('aqua', 1.0)
# plot image over map with imshow.
im = m.imshow(topodatm,palette,norm=colors.normalize(vmin=0.0,vmax=3000.0,clip=False))
# setup colorbar axes instance.
l,b,w,h = ax.get_position()
cax = axes([l+w+0.075, b, 0.05, h])
colorbar(cax=cax) # draw colorbar
axes(ax)  # make the original axes current again
# plot blue dot on boulder, colorado and label it as such.
xpt,ypt = m(-104.237,40.125) 
m.plot([xpt],[ypt],'bo') 
text(xpt+100000,ypt+100000,'Boulder')
# draw coastlines and political boundaries.
m.drawcoastlines()
m.drawcountries()
m.drawstates()
# draw parallels and meridians.
# label on left, right and bottom of map.

# 需要导入模块: from matplotlib.toolkits.basemap import Basemap [as 别名]
# 或者: from matplotlib.toolkits.basemap.Basemap import imshow [as 别名]
pylab.axes(ax)  # make the original axes current again
# plot title
pylab.title(plottitle+'- contourf',fontsize=10)

pylab.subplot(212)
ax = pylab.gca()
# draw coastlines, state and country boundaries, edge of map.
m.drawcoastlines()
m.drawstates()
m.drawcountries()
# draw parallels.
m.drawparallels(parallels,labels=[1,0,0,0],fontsize=10)
# draw meridians
m.drawmeridians(meridians,labels=[0,0,0,1],fontsize=10)
# draw image
im = m.imshow(data,cmap=cm.s3pcpn,interpolation='nearest',vmin=0,vmax=750)
# make a copy of the image object, change
# colormap to linear version of the precip colormap.
im2 = copy.copy(im)
im2.set_cmap(cm.s3pcpn_l)
# new axis for colorbar.
l,b,w,h=ax.get_position()
cax = pylab.axes([l+w+0.025, b, 0.025, h]) # setup colorbar axes
# using im2, not im (hack to prevent colors from being
# too compressed at the low end on the colorbar - results
# from highly nonuniform colormap)
pylab.colorbar(im2, cax, format='%d') # draw colorbar
pylab.axes(ax)  # make the original axes current again
# reset colorbar tick labels (hack to get
cax.set_yticks(pylab.linspace(0,1,len(clevs)))
cax.set_yticklabels(['%g' % clev for clev in clevs])

# 需要导入模块: from matplotlib.toolkits.basemap import Basemap [as 别名]
# 或者: from matplotlib.toolkits.basemap.Basemap import imshow [as 别名]
from matplotlib.toolkits.basemap import Basemap
import cPickle
from pylab import *
# read in topo data from pickle (on a regular lat/lon grid)
topodict = cPickle.load(open('etopo20.pickle','rb'))
etopo = topodict['data']; lons = topodict['lons']; lats = topodict['lats']
# create Basemap instance (global cylindrical equidistant is default)
m = Basemap(lons[0],lats[0],lons[-1],lats[-1])
# create figure with same aspect ratio as map.
xsize = rcParams['figure.figsize'][0]
fig=figure(figsize=(xsize,m.aspect*xsize))
fig.add_axes([0.1,0.1,0.8,0.8])
im = m.imshow(etopo)
# draw coastlines and fill continents.
m.drawcoastlines()
m.fillcontinents()
# draw parallels, label on bottom.
circles = arange(-90.,120.,30.)
m.drawparallels(circles,labels=[1,0,0,0])
# draw meridians, label on left.
meridians = arange(0.,390.,60.)
m.drawmeridians(meridians,labels=[0,0,0,1])
title('Cylindrical Equidistant')
show()

# 需要导入模块: from matplotlib.toolkits.basemap import Basemap [as 别名]
# 或者: from matplotlib.toolkits.basemap.Basemap import imshow [as 别名]
fig = figure(figsize=(8,8))

plots = ['contour','pcolor']
#plots = ['contour','imshow']

for np,plot in enumerate(plots):

    fig.add_subplot(1,2,np+1)
    ax = gca()

    # plot data.
    print plot+' plot ...'
    if plot == 'pcolor':
        m.pcolor(x,y,hgt,shading='flat')
    elif plot == 'imshow':
        im = m.imshow(hgt)
    elif plot == 'contour':
        levels, colls = m.contour(x,y,hgt,15,linewidths=0.5,colors='k')
        levels, colls = m.contourf(x,y,hgt,15,cmap=cm.jet,colors=None)

    # set size of plot to match aspect ratio of map.
    l,b,w,h = ax.get_position()
    b = 0.5 - 0.5*w*m.aspect; h = w*m.aspect
    ax.set_position([l,b,w,h])
    
    # draw map.
    m.drawcoastlines()
	
    # draw parallels
    delat = 30.
    delon = 90.